DOCTORAL THESIS ECOLOGY OF WOODY PLANTS IN COLOMBIAN DRY FORESTS Roy González-M. 1 Doctoral Thesis – Roy González-M. ECOLOGY OF WOODY PLANTS IN COLOMBIAN DRY FORESTS Roy González-M. 2 Ecology of woody plants in Colombian dry forests Thesis committee Supervisor Prof. Dr. Juan Manuel Posada Hostettler Biology Department, Faculty of Natural Sciences, Universidad del Rosario Bogotá, Colombia Co-supervisor Prof. Dr. Beatriz Salgado-Negret Departamento de Biología, Universidad Nacional de Colombia Bogotá, Colombia Other members Prof. Dr. Carlos Pérez Carmona, University of Tartu, Tartu, Estonia Prof. Dr. Camila Pizano, Universidad ICESI. Cali, Colombia Dr. Natalia Norden, Instituto de Investigación de Recursos Biológicos Alexander von Humboldt. Bogotá, Colombia Evaluators Prof. Dr. Jennifer Powers, Universidad de Minnesota. Minnesota, United States of America Dr. Juan Manuel Dupuy, Centro de Investigación Científica de Yucatán. Yucatán, México Prof. Dr. James Richardson, Universidad del Rosario. Bogotá, Colombia This research was conducted under the auspice of the Colombian tropical dry forests research and monitoring agenda from the Instituto de Investigación de Recursos Biológicos Alexander von Humboldt. 3 Doctoral Thesis – Roy González-M. Ecology of woody plants in Colombian dry forests Importance of environmental harshness and plant community attributes on ecosystem processes Roy González-M. Programa Ciencias Básicas de la Biodiversidad Instituto de Investigación de Recursos Biológicos Alexander von Humboldt Facultad de Ciencias Naturales Universidad del Rosario © 2020 Thesis submitted in fulfillment of the requirements for the degree of doctor at Universidad del Rosario 4 Ecology of woody plants in Colombian dry forests Table of contents Chapter 1 General introduction Chapter 2 Disentangling the environmental heterogeneity, floristic distinctiveness and current threats of tropical dry forests in Colombia Published in Environmental Research Letters (2018) 13:045007, doi:10.1088/1748-9326/aaad74 Chapter 3 Climate severity and land-cover transformation determine plant community attributes in Colombian dry forests Published in Biotropica (2019) 51(6): 826-837, doi:10.1111/btp.12715 Chapter 4 Importance of species abundances representativeness and trait variability for a functional trait community characterization in tropical dry forests Prepared for submission in Functional Ecology Chapter 5 Diverging functional strategies but high sensitivity to an extreme drought in tropical dry forests Submitted for publication in Ecology Letters Chapter 6 El Bosque Seco Tropical en Colombia: Reportes de estado y tendencias de la biodiversidad Publicados en Reporte de Estado y Tendencias de la Biodiversidad Continental de Colombia Chapter 7 General discussion Acknowledgements References 5 Doctoral Thesis – Roy González-M. Chapter 1 General introduction Roy González-M. 6 Ecology of woody plants in Colombian dry forests Tropical dry forests: a general context Tropical dry forests (TDF) are widely distributed around the world, representing 42% of all tropical forests (Brown & Lugo 1982), with 54.2% of them located in South America (Miles et al. 2006). However, this ecosystem has been historical exposed to strong human transformation (Rodríguez et al. 2008; Portillo- Quintero & Sánchez-Azofeifa 2010; Linares-Palomino et al. 2011), to the point of being listed as a critically endangered ecosystem in South America, and under a high probability of collapse in the next 50 years (Rowland et al. 2019). In South America, TDF are typically characterized by a mean annual rainfall lower than 2000 mm and a marked rainfall seasonality given by 3-6 months with precipitation below 100 mm·month-1, when potential evapotranspiration exceeds precipitation (Murphy & Lugo 1986; Mooney et al. 1995; Murphy & Lugo 1995; Dirzo et al. 2011). Across the continent, TDF occur in the lowlands and are distributed in 11 floristic groups (DRYFLOR et al. 2016) where rainfall seasonality and biogeography are recognized, at least for at a regional scale, as the main determinants of their floristic relationships, diversity and ecology (Pennington et al. 2009; Linares-Palomino et al. 2011; Hulshof et al. 2014). However, there are different studies highlighting the needs for evaluating the role of other environmental factors such as temperature, isothermality, soil nutrient and water storage, among others, in driving diversity, structure and ecological processes of TDF (Ferreira-Nunes et al. 2014; Neves et al. 2015). Likewise, recent studies called the attention about considering the legacy of the human footprint and the consequences of climatic anomalies on changes in the diversity and functioning of TDF (Portillo-Quintero & Sánchez-Azofeifa 2010; Allen et al. 2015, 2017a) that will improve conservation strategies in South America (Sánchez-Azofeifa et al. 2005b; García & González-M 2019). Colombian TDF provide a unique opportunity to address these gaps of knowledge and advance research guidelines for their conservation. First, the geographic location of Colombian TDF represents the three main forests types of this ecosystem in South America (i.e., forests in the Caribbean lowlands region, forests in the Inter Andean region, and forests in dry Savannas, Figure 1; Pennington et al. 2006, 2009; DRYFLOR et al. 2016) representing important floristic distinctiveness within the country but high floristic representativeness across the region. Second, Colombian TDF are under the influence of the intertropical convergence zone (Asmerom et al. 2020) and biogeographically isolated by the three central Andean mountains of Northern South America (Figure 1, Pizano & García 2014) with strong variations in the climatic and soil conditions. Therefore, differences in environmental controls on diversity, structure and ecology can be expected. Third, Colombian TDF are not an exception in the global context and have suffered intense conversion to pastures and agriculture (Bianchi & Haig 2013; Pizano & García 2014). In fact, the human footprint has caused that less than 10% of their original extent remain (García et al. 2014), with more than 60% of the forested areas corresponding to secondary and young forests (Andrade-Erazo et al. 2019); this may drive differences in plant diversity, forests structure and ecological dynamics. Four, Colombian TDF are located in the area of cyclic occurrence of El Niño Southern Oscillation that promotes recurrent drought anomalies and should drive ecological changes in this ecosystem (Figure 1, Holmgren et al. 2001; Kogan & Guo 2017). Despite its ecological value, as well as the relatively recent interest of ecologists and conservationists (Pizano et al. 2017), Colombian TDF remain widely understudied. This overarching objective of this thesis was to generate a deeper understanding of the ecology of TDF, which will favor its conservation. 7 Doctoral Thesis – Roy González-M. Caribbean lowlands Northern summer Central Andean Mountains Dry Savannas Intertropical convergence zone Inter Andean Dry ENSO Southern summer Figure 1. Tropical dry forests formations in Northern South America corresponding to the Caribbean lowlands region (orange), the Inter Andean region (brown), and dry Savannas (yellow area). Adapted from Pennington et al. (2009) and DRYFLOR et al. (2016). Intertropical convergence zone for the Northern summer (hatched area in red) and Southern summer (hatched area in blue). Adapted from Asmerom et al. (2020). ‘El Niño’ dry phenomenon in Northern South America (hatched area in yellow). Adapted from (Holmgren et al. 2001). Environmental harshness in TDF Environmental harshness is considered as the abiotic factors, or combinations of them, filtering species from communities and driving patters of diversity and structure across gradients (Marks, Muller-Landau & Tilman, 2016; Whittaker, 1965). Overall, rainfall seasonality is considered the main abiotic factors determining harsh conditions in TDF (Murphy & Lugo 1986; Allen et al. 2015, 2017a), and a wide number of researches have demonstrated that the frequency, timing and intensity of precipitations strongly control floristic turnover, diversity and forest structure of TDF (Murphy & Lugo 1986; Gillespie et al. 2000; Neves 8 Ecology of woody plants in Colombian dry forests et al. 2015; Le Bagousse-Pinguet et al. 2017). The underlying assumption is that water limitations impose restrictions on species that are non-adapted to cope with those limitations (Chaves et al. 2002). For instance, variation in annual precipitations exert a control on floristic composition across the South American Dry Diagonal (Neves et al. 2015), TDF species richness decreases with reductions in precipitation in Perú (Muenchow et al. 2013), and the richness of deciduous species increases with the severity of the dry season in Central Africa (Ouédraogo et al. 2016). Nevertheless, plant species do not only respond to water constraints in TDF (Peña-Claros et al. 2012; Ouédraogo et al. 2016) and variables such as temperature, isothermality, solar radiation, soil fertility, among others, have been found to exert important controls on species composition, diversity and forests structure of this ecosystem (Gillespie et al. 2000; Peña-Claros et al. 2012; Muenchow et al. 2013; Neves et al. 2015). For instance, TDF of Mexico that had low soil water retention capacity and high intensities of solar radiation had low density of stems and an increase in small diameter trees species (Galicia et al. 1999). These results reflect the importance of the climate-soils interactions in shaping harsh environments in TDF. Likewise, soil fertility as a source of nutrients supporting plant energy, physiological responses and performance (Kreft & Jetz 2007; Laliberté et al. 2013) is an important control on forest communities (García-Palacios et al. 2012). For example, in Peruvian TDFs the content of potassium and sodium also explained species turnover along a precipitation gradient (Muenchow et al. 2013), in TDF of central Africa, Western India and South America as an increase in nutrient availability increased the presence of species with high fertility requirements and the size of individuals trees (Nirmal Kumar et al. 2011; Peña-Claros et al. 2012; Ouédraogo et al. 2016). Thus, the concept of environmental harshness needs to be revisited and consider other harsh conditions such as low soil fertility. In addition, recent studies have suggested that changes in the land-cover have important consequences in determining plant community attributes due to their relation with the loss of habitat provisioning and raise barriers for dispersal processes (Lindborg & Eriksson 2004; Evelin et al. 2009; Stein et al. 2014). Factors such as the fraction of forest and secondary vegetation in the area, patch diversity, (e.g. number of cover types) and patchiness (e.g. density, mean patch size) are generally considered important drivers of changes in species composition, diversity and forest structure (McGarigal & Marks 1995; Zhi- yun et al. 1999; Lindborg & Eriksson 2004; Baynes et al. 2016). For instance, land cover transformation may increase habitat heterogeneity and promote local species richness (Stein et al. 2014), while forest cover fragmentation has found to reduce tree density in tropical forest as the result of habitat loss and isolation (Baynes et al. 2016). It is well known that TDF typically occur as small fragments due to historical land- cover transformation (Portillo-Quintero & Sánchez-Azofeifa 2010), which should have effects on forest diversity and structure (Muenchow et al. 2013). Surprisingly, despite the high levels of transformation there are not studies that have explored the effects of land-cover on vegetation attributes in Colombian TDF. In this context, it is critical to deepen our understanding of the role that land-cover transformation plays as part of the factors determining environmental harshness for TDF in the Anthropocene. From patterns to a mechanistic understanding of the ecology of TDF Diversity and forests structure show directional shifts with environmental harshness. Globally, plant diversity have been found to decrease with climate severity (Kreft & Jetz 2007), soil nutrient limitations (Laliberté et al. 2014; Wan et al. 2018) and land-cover transformation (Gerstner et al. 2014); less is known of forests structural changes. However, understanding the mechanisms behind these patterns of changes is a challenging task that needs to consider the autecology of species and its relationships with the 9 Doctoral Thesis – Roy González-M. environmental harshness. Trait-based ecology provides a conceptual framework that could identify mechanisms, under the assumption that traits confer different abilities to cope with the certain environmental conditions; e.g., species with low water-stress tolerance may be filtered out of a community (Cornwell & Ackerly 2009; Kraft et al. 2015). Therefore, shifts in species composition along environmental harshness gradients in dry ecosystems could be explained by a filtering of species with traits that are not adapted to tolerate stress (Bernard-Verdier et al. 2012; Wigley et al. 2016; Le Bagousse-Pinguet et al. 2017). In TDF filtering effects are mainly studied in the context of water constraints, with an emphasis on hydraulic traits (Markesteijn et al. 2011b; Méndez-Alonzo et al. 2012; Pineda-García et al. 2013). Water- stressed environments are expected to favor the dominance of species with conservative traits related to high hydraulic safety and, therefore, lower drought-induce mortality (e.g. high wood density or narrow vessels; Markesteijn et al. 2011; Méndez-Alonzo et al. 2012; Pineda-García et al. 2015). Yet, these environments could also favor species with acquisitive traits that avoid water constraints (e.g. deciduous species; Pineda-García et al. 2015; Méndez-Toribio et al. 2017). Less is known about the effects of harsh soil conditions on TDF trait variation. However, some studies suggest that infertile soils decreases the dominance of species with acquisitive traits (e.g., deciduous species), because these species have high leaf turnover rates and high leaf nutrient concentrations (Peña-Claros et al. 2012; Ouédraogo et al. 2016). In addition, there is no certainty in how other climatic factors, or their interaction with soil fertility, determine differences in traits across environmental harshness gradients in TDF. Moreover, despite the recognized importance of land-cover transformation as an axis of environmental harshness in TDF, to the best of our knowledge no studies have focused on the trait / land- cover transformation in these forests. However, there are many possible effects of land-cover transformation on species traits, and consequently on the diversity and structure of TDF. For instance, species with seed traits related to vertebrate-dispersion tend to decreases with fragmentation, as the result of a loss of dispersal vectors in small fragments (Cordeiro & Howe 2001). Likewise, there is evidence that land-cover transformation causes changes in the local environmental conditions for species establishment and their persistence. For example, high fragmentation levels decrease the continuity and size of forests causing higher wind exposure and temperature, inducing high mortality of species that are not adapted to more stressful conditions (Cagnolo et al. 2006; Laurance & Curran 2008). Therefore, it is important to develop conceptual frameworks that deepen our understanding of the ecological mechanisms that explain patterns of species diversity and forest structure under environmental harshness as the combined effects of climate severity, soil fertility limitations and land-cover transformation. Xylem anatomy traits as descriptors of hydraulic vulnerability in TDF Plants exposed to harsh conditions, in particular those causing water-stress, can be adapted to maintain their function and avoid mortality (McDowell 2011; Feng et al. 2016). Hydraulic vulnerability, understood as the risk of failure in the water distribution system in a plant due to cavitation, is one of the most commonly cited mechanisms that explains plant mortality under intense water-stress because it reduces water supply to the leaves for photosynthesis (McDowell et al. 2008; Bartlett et al. 2012; Venturas et al. 2016). Of the different traits that can be used to evaluate hydraulic vulnerability of species the most commonly used is hydraulic conductivity (Bartletta et al. 2016). This trait measures water transport capacity, with important consequences for water stress tolerance, cavitation risks and xylem damage (Brodribb et al. 2003; Santiago et al. 2004b; Lenz et al. 2006; Bartlett et al. 2012). A large body of literature 10 Ecology of woody plants in Colombian dry forests on hydraulic conductivity has shown that there is a tradeoff between hydraulic safety and hydraulic efficiency (Méndez-Alonzo et al. 2012; Meinzer & McCulloh 2013; Bartletta et al. 2016; Gleason et al. 2016). This hypothesis states that species with high hydraulic safety, e.g., more negative water potential values before loss of conductivity, at the expenses of low water transport capacity, have low risk of suffering embolisms and cavitation, and consequently, low risk of dying under water-stress (Bartletta et al. 2016; Gleason et al. 2016). In contrast, species with high hydraulic efficiency have a higher risk of dying under water-stress conditions due higher risks of embolisms formation and loss of conductivity (Chaves et al. 2002; Markesteijn 2010). It has been suggested that both extremes of this continuum correlate with the evergreen–deciduous leaf habit in TDF species, where evergreen species appear to be on the high hydraulic safety end while deciduous species should be on the high hydraulic efficiency end (Méndez-Alonzo et al. 2012; Pineda- García et al. 2015). Although it is generally expected that this continuum influences TDF demography processes (Markesteijn 2010; Markesteijn et al. 2011a, b), it is recognized that plants have multiple combinations of traits which may cause a decoupling from the expected hydraulic safety-efficiency trade- off or leaf phenology habits (Fortunel et al. 2012; Bartletta et al. 2016; Gleason et al. 2016). The xylem anatomy traits are considered important descriptors of these possible combinations, with the capacity of disentangling decoupling in the hydraulic safety-efficiency trade-offs and differences in the demography of plants in TDF (e.g., biohydraulics designs and biomechanics resistance, Sobrado 1997; Jacobsen et al. 2012; Beeckman 2016). For instance, water-stress tolerance is suggested to be associated with bio-hydraulic designs, where differences in size and density of vessels producing differences in hydraulic safety may be countered with high fractions of fiber and rays (Sobrado 1997; Jacobsen et al. 2012; Beeckman 2016). Thus, species having larger vessels, also expected to have high cavitation risks, may reinforced these vessels with high investments in cellular wall thickness (i.e., fiber and ray fractions; Hacke et al. 2001; Méndez- Alonzo et al. 2012; Beeckman 2016). However, there are no studies evaluating the hydraulic safety- efficiency trade-offs based on the conceptual framework of coordination with xylem anatomical traits, which may be important for advancing a comprehensive understanding of hydraulic vulnerability in TDF, as well as, its effect on species dynamics and ecosystem processes (e.g. net primary productivity). Influence of El Niño–Southern Oscillation in TDF Despite of the expected effects of environmental harshness on TDF, extreme dry conditions have also important controls on their functioning (Allen et al. 2010, 2015, 2017a). However, the study of extreme drought events and the functional responses of species are still poorly understood for this ecosystem (Powers et al. 2020). El Niño Southern Oscillation (El Niño) is considered one of most influencing climatic events affecting the dynamic and function of ecosystems in the tropics (Holmgren et al. 2001). During El Niño events, severe droughts have occurred in Northern Latin America (Holmgren et al. 2001; Allen et al. 2010), which have been associated to increase tree mortality rates (Sheil & Phillips 1995; Williamson et al. 2000; Nepstad et al. 2007). The 2015-6 El Niño (ENSO2015) was recognized as the stronger event of the past 36 years (Anyamba et al. 2019; Powers et al. 2020), and was even stronger than the event of 1997– 1998 (Kogan & Guo 2017). According to the National Oceanic and Atmospheric Administration (NOAA, http://www.cpc.ncep.noaa.gov/), warm episodes associated to ENSO2015 started on October 2014 and lasted until June 2016. In Colombia, according to the Institute of Hydrology, Meteorology and Environmental Studies (IDEAM, http://www.ideam.gov.co/), ENSO2015 caused an extreme drought event for a whole year, from April 2015 to March 2016, with peaks in May 2015 and January 2016. However, there are no known 11 Doctoral Thesis – Roy González-M. studies that have explored how this extreme drought event could have affected tree dynamics TDF in Colombia. Although plants in TDF have different strategies to survive the frequent water constraints due to recurring annual drought periods (Markesteijn et al. 2011a; Méndez-Alonzo et al. 2012), extremely dry events such as El Niño could induce abnormal mortality rates (Williamson et al. 2000; Chazdon et al. 2005; Nepstad et al. 2007) particularly for hydraulically vulnerable species (Powers et al. 2020). Yet, there are relatively few studies exploring this issue (Allen et al. 2017a). In Ecuador the drought event “La Niña 2011” was related to an absence of radial increments in deciduous trees (Spannl et al. 2016). In Costa Rica, species with low hydraulic safety margins exhibited high mortality rates under ENSO2015 with respect to those with high hydraulic safety margins (Powers et al. 2020). However, and in contrast to this evidence, species with high hydraulic safety margins showed high mortality rates during California’s drought of 2014, while species with low hydraulic safety margins but with deep roots were less affected (Venturas et al. 2016). These contrasting results, reinforce the need for more explorations of the trait-ENSO relationships for a predictive approach of the magnitude of this phenomenon in TDF and recommendation for future climatic change scenarios. In terms of ecosystem dynamics and processes, it is well known that strong climatic disturbances increase mortality (Condit et al. 1995; Venturas et al. 2016) and reduce biomass storage (Rolim et al. 2005; Allen et al. 2010). In dry ecosystems the most important factor explaining variation in biomass is precipitation (Holmgren et al. 2001; Ogaya & Peñuelas 2007; Becknell et al. 2012; Spannl et al. 2016). Thus, an extreme drought event such as ENSO2015, should cause important reductions in above-ground standing biomass and in net above-ground biomass gain (i.e., stem growth) due to higher tree mortality (Venturas et al. 2016; Powers et al. 2020), low tree growth rates (Rodríguez et al. 2005; Spannl et al. 2016) and low tree recruitment (Nepstad et al. 2007). However, I am not aware of studies that have evaluated the effects of an El Niño event in tree mortality, growth and net biomass change in Colombian TDF. Aim of this thesis Our understanding of the abiotic factors determining environmental harshness and its influence on ecological patterns is still limited in TDF. Therefore, the focus of my study was to determine what environmental conditions are driving species composition and diversity, forest structure, functional trait composition and dynamics of ecosystem processes of dry forest tree communities in Colombia (Figure 2). The specific objectives of the present thesis were: (1) To evaluate how climate, soil, and land-cover factors vary among Colombian TDF, characterizing the ‘environmental harshness’ of this ecosystem. (2) To explore how plant community attributes (e.g., species composition and diversity, forests structure, and trait community composition) vary along gradients of environmental harshness in Colombian TDF. (3) To evaluate the functional strategies of TDF tree species in response to gradients of environmental harshness and the extreme drought ENSO2015 in Colombia. 12 Ecology of woody plants in Colombian dry forests (4) To explore how standing biomass and biomass demographic changes are related to the extreme drought ENSO2015 in Colombia. (5) To translate the ecological comprehension of Colombian TDF to the information sources for decision- makers in conservation. Figure 2. Conceptual framework delineating the principal aim and specific objectives of this thesis. Thesis outline This thesis consists of 7 chapters: a general introduction (Chapter 1), four research chapters (Chapter 2-5), three infographic offprints (Chapter 6) and the general discussion (Chapter 7), showing how climatic and soil conditions, and land-cover transformation determine differences in floristic composition, species diversity, forests structure, trait community composition, standing biomass and biomass demographic changes in TDF. Chapter 2 evaluates how climate and soil conditions vary across six geographic regions of TDF in Colombia, based on an extensive field work covering all 571 forests floristic surveys across the country. Additionally, this chapter describes the status of land-cover transformation and successional stages of TDFs across these regions, and the main anthropogenic pressures impacting their conservation. Chapter 3 analyzes the effects of environmental harshness on TDF plant community attributes based on a network of 1 ha permanent plots installed across the country. First, this chapter evaluates how environmental harshness is reflected into climate severity, differences in soil fertility and land-cover transformation and if these axes of environmental harshness determine changes in species diversity and forests structure in TDF. Second, the chapter explores if functional groups with different strategies to 13 Doctoral Thesis – Roy González-M. overcome stressful conditions (e.g., deciduous and legumes species) exhibited different patterns of changes across the environmental harshness. Chapter 4 explores the trait-environment and trait-biomass productivity relationships in Colombian TDF based on the evaluation on different organization ecological scales (e.g., individuals, populations, species and communities). This chapter compares the ability of different sampling methods (varying the level of species and abundances representativeness and trait variability) to correctly detect these relationships. Chapter 5 aim is to identify the functional mechanisms that can explain species sensitivity to the extreme ENSO2015 in TDF of Colombia. To do that, this chapter first explores the functional trait space of TDF tree species based on an extensive sampling of 15 leaf and hydraulic functional traits in 338 species recorded in 11 1-ha permanent plots. Second, it evaluates how this space is related to the hydraulic safety-efficiency trade-off and tissue investment for this ecosystem. Third, it analyzes if the species occupation across this space is related to biomass demographic changes under the extreme drought. Chapter 6 consists of three infographic offprints with (i) the distribution and conservation status of TDF in Colombia, (ii) the permanent plots monitoring network of TDF in Colombia, and (iii) the functional trait data sampled for this ecosystem in the country. These offprints are part of the annual reports for the biodiversity status and trends in Colombia, transmitting the principal information in biodiversity to decision-makers in conservation. Chapter 7 presents the general discussion and summary of this thesis. 14 Ecology of woody plants in Colombian dry forests Chapter 2 Disentangling the environmental heterogeneity, floristic distinctiveness and current threats of tropical dry forests in Colombia Roy González-M., Hernando García, Paola Isaacs, Hermes Cuadros, René López- Camacho, Nelly Rodríguez, Karen Pérez, Francisco Mijares, Alejandro Castaño- Naranjo, Rubén Jurado, Álvaro Idárraga-Piedrahíta, Alicia Rojas, Hernando Vergara and Camila Pizano Published in Environmental Research Letters (2018), 13, 045007, doi:10.1088/1748-9326/aaad74 15 Doctoral Thesis – Roy González-M. Abstract Tropical dry forests (TDFs) have been defined as a single biome occurring mostly in the lowlands where there is a marked period of drought during the year. In the Neotropics, dry forests occur across contrasting biogeographical regions that contain high beta diversity and endemism, but also strong anthropogenic pressures that threaten their biodiversity and ecological integrity. In Colombia, TDFs occur across six regions with contrasting soils, climate, and anthropogenic pressures, therefore being ideal for studying how these variables relate to dry forest species composition, successional stage and conservation status. Here, we explore the variation in climate and soil conditions, floristic composition, forest fragment size and shape, successional stage and anthropogenic pressures in 571 dry forest fragments across Colombia. We found that TDFs should not be classified solely on rainfall seasonality, as high variation in precipitation and temperature were correlated with soil characteristics. In fact, based on environmental factors and floristic composition, the dry forests of Colombia are clustered in three distinctive groups, with high species turnover across and within regions, as reported for other TDF regions of the Neotropics. Widely distributed TDF species were found to be generalists favored by forest disturbance and the early successional stages of dry forests. On the other hand, TDF fragments were not only small in size, but highly irregular in shape in all regions, and comprising mostly early and intermediate successional stages, with very little mature forest left at the national level. At all sites, we detected at least seven anthropogenic disturbances with agriculture, cattle ranching and human infrastructure being the most pressing disturbances throughout the country. Thus, although environmental factors and floristic composition of dry forests vary across regions at the national level, dry forests are equally threatened by deforestation, degradation and anthropogenic pressures all over the country, making TDFs a top priority for conservation in Colombia. Key words: anthropogenic pressures, climate, floristic composition, forest fragments, soils, successional stages, tropical dry forest. Introduction Tropical dry forests (TDFs) occur in America, Asia and Africa, where mean annual temperature is greater than 17 °C, annual rainfall ranges from 250–2000 mm and potential evapotranspiration is higher than precipitation (Holdridge 1967; Murphy & Lugo 1986; Kalacska et al. 2004; Dirzo et al. 2011). However, climatic limits of dry ecosystems are still unclear, as the dry biome occurs across different rainfall regimes (e.g. dry savannas can have up to 2500 mm rainfall·year-1, Lehmann et al. 2011) and vary dramatically in soil conditions (Rundel & Boonpragob 1995; Sampaio 1995) and elevation. Therefore, TDFs are generally defined by their seasonality, with 3–6 dry months (precipitation < 100 mm·month-1, Portillo-Quintero & Sánchez-Azofeifa 2010), which determines the deciduous phenology of many woody plants, and the biological cycles of these forests as a whole (Pennington et al. 2009; Dirzo et al. 2011). In terms of floristic composition, TDFs strongly differ between South America, Africa and Asia (Dexter et al. 2015), and have a high plant species turnover across the Neotropics, where species of different floristic groups are commonly restricted to a single region (DRYFLOR et al. 2016). Although TDFs are used to represent 42% of all the worlds' tropical forests (Brown & Lugo 1982), only 1000 000 km2 are left worldwide (Miles et al. 2006; Portillo-Quintero & Sánchez-Azofeifa 2010; Powers et al. 2011), with more than 50% left in South America (Miles et al. 2006). These forests have been recognized as highly endangered ecosystems (Murphy & Lugo 1986; Janzen 1988b). However, research in 16 Ecology of woody plants in Colombian dry forests the tropics has been concentrated on more humid forests (Powers et al. 2011; Sánchez-Azofeifa & Portillo- Quintero 2011). This imbalance in knowledge has also been reflected in a general absence of studies that assess the different environmental conditions under which dry forests occur, and their degree of degradation and fragmentation across Latin America (Sánchez-Azofeifa et al. 2005a; Portillo-Quintero & Sánchez- Azofeifa 2010; Sánchez-Azofeifa & Portillo-Quintero 2011). For instance, recent studies showed high floristic turnover among different regions in the Neotropics (DRYFLOR et al. 2016), but little is known on how differences in species composition may be related to climate and soil factors. Accurate measurements of TDF extent and successional status are key tools for the conservation and landscape planning for these forests (Hesketh & Sánchez-Azofeifa 2014), and are necessary for addressing their ecological importance and as providers of ecosystem services (Calvo-Rodriguez et al. 2017). The only analysis of TDF cover at the global scale revealed that deforestation was six times higher in Latin America (12%) compared to Asia and Africa (2%) between 1980 and 2000 (Miles et al. 2006). Similarly, Olson et al. (2001) and Portillo-Quintero & Sánchez-Azofeifa (2010) showed that 66% of dry forest in Latin America has been lost due to deforestation, and only 4.5% is subject to protection. At the regional level, similar efforts to map the distribution and loss of TDFs have been published for Mexico (Trejo & Dirzo 2000; Sánchez-Azofeifa et al. 2009), Puerto Rico (Martinuzzi et al. 2013), Venezuela (Fajardo et al. 2005) and the Antilles (Helmer et al. 2008). However, few studies have evaluated the successional status and anthropogenic pressures of dry forests in the field (e.g. Larkin et al. 2012), which is key information for addressing their real conservation status. Furthermore, few studies have explored how in addition to fragmentation and successional status of dry forests, environmental conditions and species composition vary across different regions, which is crucial for implementing more effective conservation and management plans for TDFs. In Colombia, TDFs originally covered 8'882 854 ha, but around 90% of its cover was replaced by pastures, agricultural fields, and urbanization by the end of the 20th century (Etter et al. 2008; García et al. 2014). In fact, only 8% (720 000 ha) of TDF original cover is left in land mosaics in which successional forest covers at least 30% of the territory (384 416 ha) (García et al. 2014). This means that less than 4% of the original TDFs remain as mature forests. Moreover, only 5% of what is left is preserved in protected areas (García et al. 2014). Given this critical situation and the lack of information on the conservation status of this ecosystem in Colombia (Fernández-Méndez et al. 2013; Pizano et al. 2014a), the purpose of this study was to evaluate the variation of environmental conditions, floristic composition and conservation status of TDFs at the national level by doing extensive field surveys. Specifically, we intended to answer the following three questions: (1) How do environmental conditions and floristic composition of TDFs vary across six geographic regions? (2) What are the land-cover status and successional stages of TDFs across these regions? (3) Which are the main anthropogenic pressures impacting dry forests? This information will not only contribute to our understanding of the abiotic, biotic and anthropogenic factors that shape dry forests in Colombia, but can support better founded conservation and management strategies for this highly endangered ecosystem. Methods Study area In Colombia, TDFs occur across altitudinal and climatic gradients and in transitions from humid forests to savannas (Pizano et al. 2014a). Therefore, we used the broad definition of TDFs being lowland to mid- elevation (up to 1200 masl) forests that experience at least three months of drought (<300 mm total rainfall, 17 Doctoral Thesis – Roy González-M. ~100 mm·month-1) (Mooney et al. 1995). We used the 1:100 000 scaled national map of TDFs (Corzo & Delgado 2012) to randomly select 571 existing forest fragments (sites) within TDF landscapes in six geographic regions of Colombia suggested by (Pizano et al. 2014b) (Figure 1, Supporting Information Table S1). We excluded areas that appeared as dry forests in the national map, but were confirmed as not being TDFs by local experts. The number of sample sites was proportional to the extent of TDFs for each region, and it was validated by a field team of botanists, ecologists and spatial analysts. (a) (b) N 5000 150 Km 2500 Caribbean Cauca valley 0 Magdalena valley North Andean Tropical Dry Forest Orinoquia Patia -77 -75 -73 -71 -69 -77 -75 -73 -71 -69 Longitude ºW Longitude ºW Figure 1. Current extent and distribution (a), and field sample sites across six regions of TDFs in Colombia (b), as defined by (Pizano et al. 2014b). Environmental variables Climatic variables for TDF regions were estimated using the national climatic model developed by the Instituto de Hidrología, Meteorología y Estudios Ambientales and Instituto Humboldt of Colombia, based on 2046 weather stations around the country (monthly data in a resolution of 90 m Selected climatic variables included mean annual temperature (MAT ºC), total annual precipitation (TAP mm), total precipitation in the driest period (rainfall £ 300 mm in three continuous months (~100 mm·month-1); TPdriest mm), number of dry seasons when precipitation is < 300 mm (drySeason: 1 or 2 periods·year-1) and number of dry months where precipitation is < 100 mm (dryMonths: 1 to 12 months·year-1) (Table S1). Soil variables included pH (in H2O), soil organic carbon content (OCarbon g·kg-1), sand content (% particles > 50-2000 µm), silt content (% particles 2-50 µm) and clay content (% particles < 2 µm, bulk density (BulkDens kg·m-3), cation exchange capacity (CEC cmolc·kg-1), and absolute depth to bedrock (Bedrock cm). Soil variables where obtained from the global soil information system (SoilGrids 1–km, Hengl et al. 2014) (Table S1). 18 Latitude ºN 0 2 4 6 8 Elevation (m.a.s.l) Latitude ºN Ecology of woody plants in Colombian dry forests Field sampling data Field sampling was done between August 2013 and October 2014. Field teams collected the following information at each site: geographic coordinates (Lat./Long. decimal), altitude (m), presence of vascular plant species, successional stage of forest fragments, and the anthropogenic pressures present inside the forest fragment as well as in the surrounding matrix. For plant species data collection, field teams ran a linear transect inside each forest fragment sampled, in which plant species were sampled, photographed and identified by local botanists who also collected reference specimens. All plants of height ≥ 1.3m were sampled including palms, shrubs, lianas and cacti. For plants with dubious identity, 1–3 specimens were collected for taxonomic identification (Table S2). All specimens were processed in a local herbarium (Table S2) and homologated based on duplicates in the Federico Médem Herbarium in Bogotá using the APG III classification system (Haston et al. 2009). Quantifying land-cover metrics Forest fragment size and shape were quantified based on dry forest patches interpreted from a Landsat 8 Mosaic 2014 of TDF distribution published by the Humboldt Institute (15 × 15 m resolution) and developed following global models and protocols for image processing using remote sensing techniques (Xu & Becker 2012). Remote sensing resolution was improved using Google EarthPro© images from 2014–2015 (Yu & Gong 2012). Each fragment was mapped by visual interpretation, keeping a fixed digitalization height of 1500 m. This fixed scale assured fragment size and shape was correctly compared between and within regions. All 571 sample sites were re-interpreted using this method for land-cover metric evaluation during the field-sampling period. 77 sites were excluded from the analyses due to cloudiness in the images. Successional stages and anthropogenic pressures Botanists classified TDF successional stages in the field in four categories: no-forest (in some areas forest fragments had a different size or shape to those in the map due to difference in scale), early, intermediate, and late, based on the physiognomy and structural data including visually estimated canopy height and stem density, and the presence of pioneer and late successional species (Kalacska et al. 2004; García Millán et al. 2014). Early successional forests were characterized by low stem density, open vegetation, dominance of pioneer species, and a canopy height of 10 m. Intermediate forests were defined as more dense vegetation in which intermediate-successional species were common, there was a second layer of young trees, a dense understory, and mature trees up to 15 m in height. Finally, late forests were distinguished by a multi-layer and heterogeneous canopy of more than 15 m in height with emergent trees, the presence of late- successional species, and a more open understory (Kalacska et al. 2004; García Millán et al. 2014). At each sample site, anthropogenic pressures were recorded and categorized according to their impact level from the lowest to the highest as follows: ecotourism (1), hunting (2), non-timber forest product extraction (3), selective logging (4), cattle herding inside the forest (5), intensive logging (6), agriculture (7), cattle ranching (8), human infrastructure (9), hydrocarbons (10), fire (11), clear-cut mining (12) and erosion (13). Cattle herding inside the forest was classified as a different pressure to cattle ranching because herding means cattle browse in the understory of TDFs (particularly during the dry season), while forests are clear- cut for the establishment of cattle ranches. Categories 1–5 and fire were recorded based on interviews with local people, while all other pressures were visually assessed. 19 Doctoral Thesis – Roy González-M. Data analyses We ran a principal component analysis (PCA) to analyze environmental heterogeneity of TDFs across Colombia, reduce climate-soil dimensionality, and identify the principal axes of variation across regions. We also used the unweighted pair-group Simpson dissimilarity index (DSimpson) to evaluate plant species turnover across TDF field sites, as other authors have suggested this is an effective measure of geographical regionalization (Kreft & Jetz 2010) and floristic clustering of TDFs at different geographic scales (Dexter et al. 2015; DRYFLOR et al. 2016). DSimpson ranges between 0 and 1, where values close to the unit indicate maximum floristic dissimilarity. We then used the DSimpson distance matrix as the basis for ordination of TDFs in regions using non-metric multidimensional scaling (NMDS, Borcard et al. 2011). To test if TDF regions had significantly different mean DSimpson, we used the analysis of similarity test (ANOSIM, Clarke 1993). Finally, we computed a redundancy analysis (RDA, with Hellinger transformation) to address how differences in species composition may be related to soil and environmental conditions, for which R2 and adjusted R2 were calculated to identify the percentage of the explained variance (Borcard et al. 2011). The significance of the canonical axes in RDA was tested by a one-way analysis of variance (ANOVA) following (Legendre et al. 2011). Total fragment size (area in hectares) was estimated based on land-cover data for each TDF region, as a key metric for estimating patch occupancy and conservation status in the landscape (McGarigal & Marks 1995; Hill & Curran 2003). We also used land-cover data to calculate the shape index as the perimeter/area ratio. This index is defined as the fragment narrowing shape by which a theoretical zero value indicates an infinitely large perimeter around an infinitesimally small area (Moser et al. 2002; Berry 2007). Hence, a lower value in the index indicates a more irregularly shaped form of forest fragments resulting from land-cover transformations. A one-way ANOVA and a post-hoc Tukey test were performed to compare forest fragment size (area) and shape index (perimeter/area ratio) across the six regions. Both metrics were log10-transformed to fit the assumption of normality. Forest successional status relative frequency (%) was estimated as the sum of sites (s) in which we reported each successional category (C) divided by the total number of sites measured for each region (R) multiplied by 100: ! = 100 × ∑* *)+, ()⁄∑)+, -), a descriptive summary of the successional status of TDF fragments in each region. We also estimated the relative frequency (%) of each anthropogenic pressure inside forest fragments and in the surrounding matrix across regions (anthropogenic pressures: 1-13; see before). To evaluate the impact of pressures and differences across the six regions, we performed a non- parametric one-way ANOVA (Kruskal–Wallis; Sokal & Rohlf 1995; McDonal 2014), and a multiple pair comparisons test (Posthoc Kruskal-Nemenyi test; Dunn 1964; Pohlert 2016), using the mean ranks of pressures per region as the level of impact. In both analyses, values higher than 6.5 indicate a high level of anthropogenic pressures for a TDF region. All statistical analyses were performed using the statistical program R (R Development Core Team 2005, version 3.2.2). We used the package ‘vegan’ (Oksanen et al. 2007) for estimating the Simpson dissimilarity index, the NMDS and RDA, and 'PMCMR' package for calculating the Posthoc Kruskal- Nemenyi test (Pohlert 2016). For information on how many forest sampling sites were used for each analysis, please see Table S3. Results TDFs in Colombia occur in areas with high environmental and soil variation (Figure 2a), with a mean annual temperature of 26.5 ± 1.6 °C, mean annual precipitation of 1575.1 ± 596.9 mm, and one to two annual dry 20 Ecology of woody plants in Colombian dry forests seasons with a total precipitation of 115.3 ± 65.4 mm (~3 months continuous < 100 mm month-1) (Table S1). Soils varied from low fertility (pH < 5.5, CEC = 11.3 ± 4.1 cmolc kg-1), high sand content (> 40%) and low organic carbon content (< 13 g kg-1), to fertile due to high cation exchange capacity (> 20 cmol ·kg-1c ), and higher content of finer textures (clay content > 30%) and organic matter (>20 cmol -1c kg ) (Table S1). Dry forest vegetation clustered in six different groups (P < 0.001), with a clear overlap between inter- Andean valley regions (Figure 2b). Sites across the six TDF regions showed high floristic dissimilarity (mean DSipmson = 0.89, median DSipmson = 0.92), as 73.3% of plant species were found only in one region, 13.8% of species were shared between two regions, and 1.3%–4.3% species were found in 3–5 regions (Table S2). In fact, only three species were found in all six regions (Guazuma ulmifolia, Ceiba pentandra and Ochroma pyramidale, Malvaceae), and the most frequently detected species varied in each region (Table S2). Correspondingly, we found a high floristic dissimilarity within regions, which ranged from 0.67 in Patía to 0.88 in the North Andean region in the median of DSipmson. 6 (a) pH CEC (b) 0.4 4 dryMonths Silt Clay 0.2 2 BulkDens drySeason Altitude 0 MAT 0.0 Bedrock OCarbon -2 TPdriests -0.2 Caribbean -4 Sand Cauca valley Magdalena valley North Andean -0.4 stress = 0.18 TAP Orinoquia R = 0.56 p= 0.001 -6 Patia -6 -4 -2 0 2 4 6 -0.4 -0.2 0.0 0.2 0.4 PCA Axis 1 (31.3%) NMSD1 0.6 (c) Group I MAT 0.4 pH BulkDens Bedrock dryMonths 0.2 Silt Group II CEC Sand 0.0 TAP -0.2 Clay drySeason OCarbon -0.4 TPdriests Group III R2 = 0.13 Altitude adj. R2 = 0.10 -0.6 F = 4.60 p= 0.001 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 RDA Axis 1 (29.8%) Figure 2. Variation in TDF environmental and soil conditions (a), plant species composition (b), and their correlation (c) across six TDF regions in Colombia. (a) Ordination space of environmental conditions (PCA, soil-climate variables, N = 558); (b) ordination space of TDF floristic composition (NMDS, N = 464) using the Simpson 21 PCA Axis 2 (22.3%) RDA Axis 2 (25.8%) NMSD2 Doctoral Thesis – Roy González-M. dissimilarity index as distance between pair-sites; and (c) RDA fitted for the floristic composition and climate-soil conditions (RDA, N = 456). Ellipses represent 95% confidence intervals for all analyses TDFs clustered in three main floristic groups associated to climate-soil conditions (P = 0.001, Figure 2c): (1) the Caribbean, with high soil fertility (mean pH > 6.3, CEC > 20 cmolc kg-1), the longest dry season (5.0 ± 1.7 months per dry period, TPdriest ~1–155 mm) and high aridity due to high temperatures (MAT = 27.3 ± 0.9 °C) (Figure 2c, Table S1); (2) the Orinoquía, with total annual precipitation above 2367 mm, but dry season (3.9 ± 0.8 months) precipitation of only 40–231 mm, and soils with the highest sand content (40.4 ± 7.4%) and the lowest fertility (pH < 5.3, CEC 11.3 ± 4.1 cmolc kg-1) (Figure 2, Table S1, and S3) the inter-Andean valleys with high soil fertility (CEC > 19.6 cmol -1c kg , clay content > 36.5%) and the highest total precipitation during the driest period (45–298 mm), with two annual dry seasons (Figure 2, Table S1), and valleys with different altitudes (Cauca Valley = 375–1211 m, Magdalena Valley = 227–906 m, North Andean = 188–1154 m and Patía = 555–953 m; Figures 2a-c and Table S1). Environmental and soil factors explained 13% (R2) and 10% (adjusted R2) of the variation in species composition (Figure 2c), with a large proportion of unexplained variance. 20000 (a) 15000 F = 25.9 p < 0.001 10000 5000 a 0 b bc cd bcd d Caribbean Cauca Magdalena North Orinoquia Patia valley valley Andean (b) 0.08 0.06 F = 37.7 p < 0.001 0.04 0.02 0.00 b b b c a b Caribbean Cauca Mln a[Pgedrimaleetenra/Area rNatoior t(hm−1)] Orinoquia Patia valley valley Andean Figure 3. Fragment size in hectares (a) and shape index (perimeter/area) (b) for six regions of TDFs in Colombia. Different letters indicate statistically significant differences between groups (P < 0.05). The upper-right boxes show the forest fragment size and shape index without log transformation. 22 ln[Perimeter/Area ratio (m−1)] ln[Area (ha)] -8 -6 -4 -2 0 2 0 5 10 15 m−1) Area (ha)Perimeter/Area ratio ( Ecology of woody plants in Colombian dry forests A total of 332 342 ha of TDFs were mapped and validated in the field (494 forest fragments). Forest fragment size had a median of 115.2 ha and a perimeter/area ratio index of 0.008 at the national level (Figure 3a). The largest forest fragments where found in the Caribbean, where fragment size showed high variation (mean A = 1530.4 ± 2840.4 ha), followed by the Cauca and Magdalena Valleys, North Andean and Orinoquía (Figure 3a). The smallest forest fragments were found in the Patía Valley, where average fragment size was 56.9 ± 63.9 ha (Figure 3a). In general, all forest fragments across the six regions had high narrowing values as a result of large perimeters around low area per patch (median shape index 0.004– 0.025, Figure 3b). In the Caribbean, although fragments were the biggest, they also had the highest narrowing, indicating high levels of transformation (median shape index 0.004, Figures 3a and 3b). In contrast, the Patía region had the lowest fragment narrowing (median shape index 0.023, Figures 3a and 3b) as a result of low forest patch size and regularly shaped fragment shapes. In terms of successional stages, except for the Orinoquía region, the relative frequency of late successional forests (mature forests) was lower than 7% for all regions (Figure 4). An extreme case was the Patía region, where no mature forests could be found (Figure 4). Correspondingly, dry forests in all regions were at either early (~31%–50%) or intermediate succession (~21%–63%), but in Orinoquía, where no early successional forests were found, and TDFs were either intermediate or mature (Figure 4). Early succession Intermediate succession Late succession Caribbean Cauca valley Magdalena valley North Andean Orinoquia Patia 0 20 40 60 80 100 Relative Frequency (%) Figure 4. Relative frequency of three successional stages of dry forest fragments registered at each field sample site across six regions in Colombia Anthropogenic pressure mean rank was higher than 6.5 inside the forest (7.2) and in the surrounding matrix (8.2) across all regions, except inside the forests in the Caribbean and Orinoquía regions (Figure 5). In order of importance, the most frequent pressures for all regions inside the forests were: selective logging (reported presence 175, total represented percentage = 30.6%), herding (160, 28.0%), human infrastructure (150, 26.3%) and hunting (110, 19.3%). In contrast, the most frequently reported pressures in the surrounding matrix were: cattle-ranching (327, 57.3%), human infrastructure (314, 54.9%), agriculture (148, 25.9%) and fire (92, 16.1%). The Magdalena Valley, North Andean and Patía regions were the most threatened by high-impact levels inside the forest (Mean rank > 8.1, Figure 5a), while high impact levels were present in the surrounding matrix of all regions (Mean rank > 8.2, Figure 5b), with the highest in the Patía region. 23 Doctoral Thesis – Roy González-M. LOW HIGH (a) Ec Hu NTFP SL He IL Ag CR HI Hy Fi Mi Er H = 298.45 p < 0.001 a Caribbean ab Cauca valley c Magdalena valley bc North Andean d Orinoquia Patia bc 0 20 40 60 80 100 4 6 8 10 Relative Frequency [inside forest] (%) Impact level LOW HIGH (b) Ec Hu NTFP SL He IL Ag CR HI Hy Fi Mi Er H = 43.86 p < 0.001 a Caribbean abc Cauca valley ab Magdalena valley abc North Andean c Orinoquia Patia bc 0 20 40 60 80 100 4 6 8 10 Relative Frequency [surrounding matrix] (%) Impact level Figure 5. Frequency of anthropogenic pressures and their impact inside dry forest fragments (a) and in the surrounding matrix (b) in six regions of Colombia (N = 457). The arrows indicate the level of impact for each pressure from low (ecotourism (Ec), hunting (Hu), extraction of non-timber forest products (NTFP), selective logging (SL), herding inside the forest (He)) to high (intensive logging (IL), agriculture (Ag), cattle ranching (CR), human infrastructure (HI), hydrocarbons (Hy), fire (Fi), clear-cut mining (Mi), and erosion (Er)). Values higher than 6.5 (dashed line) in the plots indicate the presence of a high number of pressures in a given region based on the mean ranks (Kruskal– Wallis test, H). Different letters indicate statistically significant differences between groups (P < 0.05). Discussion Floristic distinctiveness matches environmental heterogeneity of TDF in Colombia TDFs are commonly defined as a single biome characterized by a strong seasonality in precipitation (Pennington et al. 2009; Portillo-Quintero & Sánchez-Azofeifa 2010). However, these forests vary significantly in rainfall seasonality (Murphy & Lugo 1986, 1995; Gentry 1995; Pennington et al. 2009), soil nutrients, soil texture (Peña-Claros et al. 2012), soil water storage (Neves et al. 2015), frost (Pennington et al. 2006b) and altitude (Gentry 1995). Correspondingly, TDF plants have been modeled as metacommunities historically adapted to dry conditions (Pennington et al. 2009) with high species turnover 24 Ecology of woody plants in Colombian dry forests and endemism as a result of historical fragmentation, dispersal limitation (Linares-Palomino et al. 2011; Neves et al. 2015) and environmental controls (Neves et al. 2015; Williams et al. 2017). We found that TDFs in Colombia cannot be defined solely on rainfall parameters such as total annual precipitation (as defined in methods), mean annual temperature (as defined in methods), TAP or MAT/TAP ratio (Murphy & Lugo 1986; Gentry 1995; Pennington et al. 2009). Taken together, all environmental variables measured across six TDF regions (Table S1) grouped dry forests in three clusters: the Caribbean, the Orinoquía, and the inter-Andean valleys (Figure 2a). The Caribbean experiences the longest and harshest dry season with high MAT and low precipitation, similar to TDFs in Venezuela (Fajardo et al. 2005) and Central America (Murphy & Lugo 1986; Gentry 1995), but contain mostly fertile soils, as a result of low nutrient leaching (Fajardo et al. 2005). In contrast, the Orinoquía had a high TAP and low soil fertility as the result of high nutrient leaching (Malagón-Castro 2003) and high sand content, resulting in low soil water storage during the dry season (Medina & Silva 1990; Dezzeo et al. 2008), an important determinant of dry forests across the Neotropics (Dezzeo et al. 2008; Peña-Claros et al. 2012; Neves et al. 2015). Finally, the inter-Andean valleys had the highest soil fertility and a high variation in rainfall during the dry season (Table S1), with two annual dry seasons determined by Colombia’s mountainous geography (Fernández-Méndez et al. 2013). In addition to these marked differences in climate and soils, these three regions differed in altitude (Table S1). Matching the variation in environmental conditions, we found that 73.3% of TDF plant species were only found in one region, and that the floristic composition of dry forests in Colombia is clustered in the same three groups: the Caribbean and the inter-Andean valleys (Patía, Cauca and Magdalena Valleys and North Andean), as suggested by (DRYFLOR et al. 2016), and the Orinoquía (P < 0.001), a region where TDFs have been poorly studied, but has been suggested as a separate floristic entity by Espinal & Montenegro (1977) and Pizano et al. (2014b) (Figures 2b-c). In fact, environmental and soil factors explained 13% of the variation in plant species composition (Figure 2c). Similar to other dry forests in the Neotropics, we found high species turnover for both across and within regions (Linares-Palomino et al. 2011; Neves et al. 2015; DRYFLOR et al. 2016; Williams et al. 2017), as well as high levels of endemism across TDF regions. Floristic composition in the Caribbean appears to be correlated with soils with a high pH and high bulk density, high mean annual temperature, and the longest dry season (Figure 2c). Meanwhile, the presence of plants in the inter-Andean valleys was correlated with soils with high clay and organic carbon content, two dry seasons, and the highest altitudes (Figure 2c). For example, Trichilia carinata and Trichilia oligofoliolata are restricted to the Magdalena Valley, although locally abundant (González-M et al 2016) (Table S2). Finally, dry forests of the Orinoquía, with the most unique flora, are characterized by sandy soils and the highest precipitation (Figure 2c). This supports the hypothesis that TDFs in northern South America were isolated from other dry areas due to geography barriers such as rainy formations (Amazonia and Chocó), and the Andes (Gentry 1982; Pennington et al. 2009). A caveat of our study is that we only sampled plant species of ≥ 1.3 m in height, therefore excluding species important for dry forests such as epiphytes and herbs (Linares-Palomino et al. 2009; Pizano et al. 2014b). On the other hand, although the correlation between environmental and soil conditions and floristic distinctiveness was clear, we failed to explained a large fraction (87%) of the variation in dry forest species composition (Figure 2c). However, as reported before, given the many factors that determine plant species composition, this is a usual result of studies on floristic composition over similar spatial scales with species presence-absence data (Guisan et al. 2011; Neves et al. 2015). We also found that the most widespread dry forest species were generalists that are favored by forest disturbance and early successional stages (Table S2), as reported by previous studies (Uribe et al. 25 Doctoral Thesis – Roy González-M. 2001; López-Camacho et al. 2007; Castellanos-Castro & Newton 2015; Williams et al. 2017) for other TDFs (Newbold et al 2014), indicating the incipient successional status of dry forests at the regional level (Derroire et al. 2016). Furthermore, we found an introduced invasive species (V. farnesiana) among the most common species of TDFs throughout the country, suggesting that TDFs are also highly susceptible to invasion (Pizano et al. 2014a). This shows the importance of taking into account human land-cover disturbances as determinants of floristic composition and species turnover of TDFs (Larkin et al. 2012). Successional status and current threats of TDF in Colombia Previous studies using remotely sensed data have shown that TDFs are highly fragmented in the Neotropics (Fajardo et al. 2005; Miles et al. 2006; Rodríguez et al. 2008; Portillo-Quintero & Sánchez-Azofeifa 2010). However, field-collected information on forest fragment shape and size, successional stage, species composition and forest conservation status, is rare. Anthropogenic pressures in TDFs vary from hunting, selective logging and local clearing with fire (for agriculture and cattle ranching) to complete deforestation and soil desertification (Janzen 1988b, a; García et al. 2014), but are still fairly unexplored in the Neotropics. In particular, methods such as satellite image analysis are unable to detect subtle changes in the forest due to hunting, non-timber forest harvesting, selective logging, invasion of exotic species and understory thinning due to cattle herding (Peres et al. 2006). In an extensive and unique field survey at the national level, we found that TDFs in Colombia are highly fragmented, narrowly shaped, and comprise mostly early and intermediate successional stages, with very little mature forest (Figures 3 and 4). At the national level, dry forest comprises very small and highly irregularly shaped forest fragments, with larger remnants only found in the Caribbean and the Magdalena Valley (Figure 3). Furthermore, of the 332 342 ha of TDFs mapped across six regions, between 31%–50% of the fragments contained early, ~21%–63% intermediate, and less than 7% mature forest (except for Orinoquía) (Figure 4). On the other hand, high-impact disturbances such as human infrastructure and hunting were common inside dry forest fragments in all regions but Orinoquía, where lower-impact disturbances (cattle herding, hunting, non-timber forest product extraction, and ecotourism) were more important (Figure 5a). Similarly, pressures in the surrounding matrix included higher-impact disturbances such as cattle ranching, human infrastructure, agriculture and fire in all regions (Figure 5b). Orinoquía, an extensive area (285 440 km2), had the least pressures inside the forest (Figure 5a), and was the only region where mature TDFs still exist (Figure 4). However, this is the new agricultural frontier as declared by the Colombian government, and therefore presented high-impact anthropogenic pressures in the surrounding matrix. Thus, in contrast to a tendency towards conservation, deforestation and degradation of TDFs, at the national level will probably tend to increase as a result of high-impact pressures detected in all regions. In particular, previous studies have shown that both high- and low-impact pressures may further degrade TDFs. For example, in the dry forests of Sonebhadra (India), an increase in human population led to increased illegal tree felling, extraction of non-timber resources and cattle herding, which led to significant declines in 52% of the population of all 65 forest plant species (Sagar & Singh 2004). Furthermore, degradation of TDFs may substantially increase carbon emissions, negatively impacting payment schemes such as the 'Reduced Emissions from Forest Deforestation and Degradation’ (REDD), one of the most advocated conservation strategies for TDFs (Portillo-Quintero et al. 2015). However, more extensive field work and satellite and remote sensing methods (Peres et al. 2006; García Millán et al. 2014; Li et al. 2017), as well as studies on the response of species to disturbance (Newbold et al. 2014), need to be done to better estimate the extent and impact of anthropogenic pressures on TDFs. 26 Ecology of woody plants in Colombian dry forests Conclusions Although considered a biome, TDFs have been shown to differ both environmentally and floristically at the regional scale (Murphy & Lugo 1986; Gentry 1995; Murphy & Lugo 1995; Pennington et al. 2006b, 2009; Peña-Claros et al. 2012; Neves et al. 2015). In this extensive field survey in Colombia, we found that both environmental and floristic characteristics of TDFs varied significantly across regions, and grouped dry forests in three separate entities: the Caribbean, the inter-Andean valleys, and the Orinoquía region. In fact, we found a high species turnover across and within regions (Linares-Palomino et al. 2011; Neves et al. 2015; DRYFLOR et al. 2016), and high levels of regional endemism. At the same time, the most common dry forest tree species were generalists that are favored by forest disturbance and are common in early successional stages. Thus, disturbance is a key determinant of plant community composition in the dry forests of Colombia. In addition to differences in environmental conditions and plant species across dry forest regions, our broad field study allowed us to verify that TDFs are highly fragmented, consisting of irregularly shaped forest fragments, and of mostly early and intermediate successional forests across the national level (Portillo-Quintero & Sánchez-Azofeifa 2010). Furthermore, anthropogenic pressures inside forest fragments and in the surrounding matrix were equally high across all regions of dry forest, with high-impact disturbance such as human infrastructure, fire, cattle ranching and agricultural plantations dominating TDFs across the country. Thus, the protection of TDF should be a priority in Colombia, where environmental, biotic, successional, and human dimensions need to be considered for assuring more effective management and conservation strategies of these unique forests. Acknowledgments We thank the owners and administrators of all TDF areas that we visited for their hospitality and assistance. The Federico Médem Herbarium allowed the use of their facilities for processing and identifying plant vouchers, also local herbaria: DUGAND (Universidad del Atlántico), TULV-INCIVA, HUA (Universidad del Antioquia), UDBC (Universidad Distrital Francisco José de Caldas), CDMB (Jardín Botánico de Bucaramanga), CAUP (Universidad del Cauca). Financial support was provided by Ministerio de Ambiente y Desarrollo Sostenible of Colombia and the Interamerican Development Bank Technical Cooperation # ATN/BD-15408-CO. We gratefully acknowledge the Colombian TDF Network students and research assistants for their invaluable field assistance. Statistical recommendations by Dr Marius Bottin and comments by three anonymous reviewers greatly improved this paper. 27 Doctoral Thesis – Roy González-M. Supporting information Table S1. Environmental conditions (climate and soils variables) for 571 dry forest fragments in six regions of TDF in Colombia Geographic region Caribbean Cauca valley Magdalena North valley Andean Orinoquia Patia Field samples (N) 202 88 80 25 146 30 Latitude (Lat. maximum | minimum, dec.) 12.24|8.31 7.1|2.04 5.2|2.58 7.99|6.69 7.05|4.48 2.05|1.43 Longitude (Long. maximum | minimum, dec.) -71.21|-75.51 -75.39|-77.08 -74.49|-75.45 -72.47|-73.28 -67.43|-73.1 -77.01|-77.49 Altitude (Alt. maximum | mean | minimum, masl) 11|173|844 375|865|1211 227|412|906 188|623|1154 47|217|743 555|718|953 Mean annual temperature (MAT ± SD, ºC) 27.3 ± 0.9 24.2 ± 1.5 27.6 ± 1.1 25.3 ± 1.5 26.8 ± 1 24.7 ± 0.7 Total annual of precipitation (TAP ± SD, mm) 1260 ± 347 1359 ± 289 1391 ± 218 1263 ± 228 2367 ± 517 1199 ± 333 Total precipitation in driest period [3 months rainfall] 1|69|155 75|178|298 86|162|261 106|191|290 48|102|231 45|109|296 (TPdriests. maximum | mean | minimum, mm) Number of dry periods [3 months=1 period] (drySeason, unit) 1 1-2 1-2 2 1 1-2 Number of dry months [<100 month-1] (dryMonths ± SD, unit) 5.0 ± 1.7 3.5 ± 0.8 3.3 ± 0.6 3.8 ± 1.6 3.9 ± 0.8 5.2 ± 2.2 pH [in H2O at depth 0.30 m] (pH) 6.3 ± 0.3 5.8 ± 0.2 6 ± 0.3 5.8 ± 0.3 5.3 ± 0.2 6.1 ± 0.2 Soil organic carbon content [fine earth fraction at depth 0.30 m] 13.3 ± 6.8 25.7 ± 10.4 16.2 ± 6.3 18.4 ± 7 20.7 ± 18.8 24.5 ± 6.2 (OCarbon ± SD, g·kg-1) Sand content [>50-2000 µm at depth 0.30 m] 38.0 ± 3.9 36.5 ± 5.7 38.2 ± 3.8 37.5 ± 3.1 40.4 ± 7.4 35.2 ± 3.4 (Sand ± SD, %) Silt content [2-50 µm at depth 0.30 m] 27.5 ± 2.7 25.5 ± 2.3 25.3 ± 2.9 25.8 ± 2.4 25.7 ± 4.8 26.9 ± 2.7 (Silt ± SD, %) Clay content [<2 µm at depth 0.30 m] 34.5 ± 2.7 37.9 ± 5.2 36.5 ± 2.7 36.4 ± 3 31.8 ± 6.7 38.1 ± 3.3 (Clay ± SD, %) Bulk density [fine earth at depth 0.30 m] 1.44 ± 0.05 1.34 ± 0.07 1.42 ± 0.05 1.38 ± 0.04 1.37 ± 0.2 1.34 ± 0.06 (BulkDens ± SD, kg·m-3) Cation exchange capacity of soil [at depth 0.30 m] 20.6 ± 4.1 21.5 ± 3.9 18.2 ± 2.6 19.6 ± 2.1 11.3 ± 4.1 21.3 ± 4 (CEC ± SD, cmol ·kg-1c ) Absolute depth to bedrock (Bedrock, cm) 59.6 ± 32.5 19.7 ± 10.2 34.7 ± 12.6 19.6 ± 7.1 57.2 ± 23.1 18.6 ± 11.2 28 Ecology of woody plants in Colombian dry forests Table S2. Frequency of plant species in 464 dry forest field sample sites in six regions of TDF in Colombia: most frequent species inside a region (a), most frequent species between regions (b) and most frequent species for all field sample sites (c). The species most commonly found across TDF sites were G. ulmifolia (number of sites: N = 165), Astronium graveolens (N = 158), Attalea butyracea (N = 114), Bursera simaruba (N = 106), Spondias mombin (N = 103), Platymiscium pinnatum (N = 88), Handroanthus chrysanthus (N = 84) and Vachellia farnesiana N = 77). These species represent only 0.9% of the total 881 plant species found, and are shared across 16.6-18% of the total sites sampled in 4-6 regions. The most frequently detected species in each region varied as follows: in the Caribbean, A. graveolens (detected in 46.5% of sites) and B. simaruba (37.6%), in the Cauca valley, G. ulmifolia (50.5%) and Pithecellobium dulce (27.9%), in the Magdalena valley, G. ulmifolia (45%), A. graveolens (34%) and A. butyracea (34%), in the North Andean, Prosopis juliflora (40.0%), Muntingia calabura (36.0%) and Cecropia peltata (36.0%), in the Orinoquia, Vitex orinocensis (44.5%) and A. butyracea (43.8%) and in the Patia, G. ulmifolia (73%) and Zanthoxylum fagara (65.4%). Reference specimens: Caribbean (Herbarium DUGAND H. Cuadros 6439–6751), Cauca valley (Herbarium INCIVA A. Castaño-Naranjo 154–327, Herbarium HUA A. Idárraga-Piedrahíta 5748–5796), Magdalena valley (Herbarium UDBC R. López 15404–15669, J. Aguilar 2150-3214), North Andean (Herbarium CDMB A. Rojas 2146–2234), Orinoquia (Herbarium FMB F. Mijares 1192–1358, F. Castro-Lima 19344-19469), Patia (Herbarium UDENAR R. Jurado 1-19, 354-446, Herbarium CAUP B. Ramírez 23244–23426), TDF All regions (Herbarium FMB R. González-M 1058–2485). *Exotic species. Family Species Growth form Caribbean Cauca valley Magdalena valley North andean Orinoquia Patia All All regions sites Acanthaceae Aphelandra pulcherrima herbaceous 2 1 2 Acanthaceae Bravaisia integerrima tree 6 1 6 Acanthaceae Trichanthera gigantea tree 1 1 1 3 3 Achariaceae Lindackeria paludosa tree 1 1 1 Achariaceae Mayna odorata tree 3 1 3 Achatocarpaceae Achatocarpus nigricans tree 3 16 1 3 20 Anacardiaceae Anacardium excelsum tree 27 15 20 7 4 69 Anacardiaceae Astronium fraxinifolium tree 4 1 4 Anacardiaceae Astronium graveolens tree 94a 7 27a 2 28 5b 158c Anacardiaceae Astronium sp tree 11 1 11 Anacardiaceae Cyrtocarpa velutinifolia tree 3 1 3 Anacardiaceae Mangifera indica tree 1 2 2 3 Anacardiaceae Mauria sp tree 1 1 1 Anacardiaceae Spondias mombin tree 29 2 11 61a 4 103c Anacardiaceae Spondias radlkoferi tree 1 1 2 2 Anacardiaceae Tapirira guianensis tree 41 1 41 Annonaceae Annona edulis tree 1 3 2 4 Annonaceae Annona exsucca tree 1 1 1 Annonaceae Annona glabra tree 1 1 1 Annonaceae Annona jahnii tree 4 1 4 Annonaceae Annona mucosa tree 1 1 2 2 Annonaceae Annona muricata tree 5 1 5 Annonaceae Annona neovelutina tree 1 1 1 Annonaceae Annona rensoniana tree 3 1 3 Annonaceae Annona reticulata tree 2 1 2 Annonaceae Annona sp tree 2 1 2 29 Doctoral Thesis – Roy González-M. Family Species Growth form Caribbean Cauca valley Magdalena valley North andean Orinoquia Patia All All regions sites Annonaceae Annona sp1 tree 2 1 2 Annonaceae Annona sp2 tree 1 2 2 3 Annonaceae Annona sp3 tree 1 7 2 8 Annonaceae Bocageopsis multiflora tree 2 1 2 Annonaceae Duguetia odorata tree 2 1 2 Annonaceae Duguetia riberensis tree 1 1 1 Annonaceae Guatteria maypurensis tree 1 2 2 3 Annonaceae Guatteria metensis tree 1 1 1 Annonaceae Guatteria recurvisepala tree 1 1 1 Annonaceae Guatteria rubrinervis tree 1 1 1 Annonaceae Oxandra espintana tree 2 1 2 Annonaceae Oxandra sp tree 1 1 1 Annonaceae Pseudomalmea sp tree 1 1 1 Annonaceae Sapranthus isae tree 2 1 2 Annonaceae Xylopia aromatica tree 4 9 3 13 4 29 Annonaceae Xylopia emarginata tree 1 1 1 Annonaceae Xylopia sp tree 2 1 2 Annonaceae Xylopia sp1 tree 1 1 1 Apocynaceae Asclepias sp herbaceous 1 1 1 Apocynaceae Aspidosperma cuspa tree 5 1 5 Apocynaceae Aspidosperma polyneuron tree 56 4 2 60 Apocynaceae Aspidosperma spruceanum tree 2 1 2 Apocynaceae Cascabela thevetia* shrub 3 2 2 5 Apocynaceae Forsteronia affinis liana 1 1 1 Apocynaceae Himatanthus articulatus tree 18 1 18 Apocynaceae Mandevilla lancifolia liana 1 1 1 Apocynaceae Mandevilla sp liana 1 1 1 Apocynaceae Odontadenia macrantha tree 1 1 1 Apocynaceae Plumeria alba tree 4 1 4 Apocynaceae Plumeria pudica tree 2 1 2 Apocynaceae Prestonia sp liana 1 1 1 Apocynaceae Tabernaemontana amygdalifolia tree 1 1 1 Apocynaceae Tabernaemontana cymosa tree 12 1 2 13 Apocynaceae Tabernaemontana grandiflora tree 2 1 2 3 Apocynaceae Tabernaemontana sp1 tree 1 1 1 Apocynaceae Thevetia ahouai tree 4 1 4 Apocynaceae Thevetia sp tree 1 1 1 Araceae Monstera adansonii liana 1 1 1 Araliaceae Aralia excelsa shrub 2 1 2 Araliaceae Dendropanax arboreus tree 1 2 2 3 30 Ecology of woody plants in Colombian dry forests Family Species Growth form Caribbean Cauca valley Magdalena valley North andean Orinoquia Patia All All regions sites Araliaceae Dendropanax sp tree 1 1 2 2 Araliaceae Oreopanax acerifolius tree 5 1 5 Araliaceae Oreopanax sp tree 1 1 1 Araliaceae Schefflera bejucosa liana 1 1 1 Araliaceae Schefflera morototoni tree 1 17 2 18 Araliaceae Schefflera sp tree 1 2 2 3 Arecaceae Acrocomia aculeata palm 7 6 2 13 Arecaceae Acrocomia sp palm 1 1 1 Arecaceae Aiphanes aculeata palm 3 1 3 Arecaceae Aiphanes horrida palm 1 1 1 Arecaceae Astrocaryum jauari palm 1 1 1 Arecaceae Attalea butyracea palm 22 1 27a 64a 4 114c Arecaceae Attalea maripa palm 8 1 8 Arecaceae Attalea microcarpa palm 1 1 1 Arecaceae Attalea sp palm 1 1 1 Arecaceae Bactris bidentula palm 1 1 1 Arecaceae Bactris gasipaes palm 3 2 2 5 Arecaceae Bactris gasipaes var. chichagui palm 1 1 2 2 Arecaceae Bactris major palm 4 1 4 Arecaceae Bactris pilosa palm 1 1 1 Arecaceae Chamaedorea linearis palm 1 1 1 Arecaceae Cocos nucifera palm 1 1 2 2 Arecaceae Copernicia tectorum palm 5 1 5 Arecaceae Desmoncus orthacanthos palm 2 1 2 Arecaceae Elaeis guineensis palm 3 1 3 Arecaceae Elaeis oleifera palm 3 1 3 Arecaceae Oenocarpus minor palm 4 1 4 Arecaceae Roystonea oleracea palm 2 1 2 Arecaceae Sabal mauritiiformis palm 13 1 13 Arecaceae Syagrus orinocensis palm 7 1 7 Arecaceae Syagrus sancona palm 1 10 2 11 Aristolochiaceae Aristolochia maxima liana 1 1 1 Aristolochiaceae Aristolochia sp liana 1 1 1 Asparagaceae Agave sp succulent 1 1 1 Asparagaceae Furcraea cabuya succulent 17 1 17 Asteraceae Calea sessiliflora herbaceous 1 1 1 Asteraceae Chromolaena laevigata shrub 1 1 1 Asteraceae Mikania sp shrub 1 1 1 Asteraceae Piptocoma discolor tree 4 14 2 18 Asteraceae Tessaria integrifolia tree 2 1 2 3 31 Doctoral Thesis – Roy González-M. Family Species Growth form Caribbean Cauca valley Magdalena valley North andean Orinoquia Patia All All regions sites Asteraceae Vernonanthura brasiliana tree 1 1 1 Asteraceae Vernonanthura phosphorica tree 9 1 9 Basellaceae Anredera floribunda liana 1 1 1 Bignoniaceae Amphilophium granulosum liana 1 1 1 Bignoniaceae Amphilophium paniculatum liana 1 1 1 Bignoniaceae Arrabidaea sp liana 1 1 1 Bignoniaceae Bignonia corymbosa liana 4 1 4 Bignoniaceae Bignonia pterocalyx liana 1 1 1 Bignoniaceae Bignonia sp liana 1 1 1 Bignoniaceae Fridericia mollissima liana 2 1 2 Bignoniaceae Fridericia pubescens liana 1 1 1 Bignoniaceae Gelseminum insigne tree 13 1 13 Bignoniaceae Godmania aesculifolia tree 4 1 4 Bignoniaceae Gonzalagunia cornifolia herbaceous 1 1 1 Bignoniaceae Handroanthus albus tree 1 1 1 Bignoniaceae Handroanthus billbergii tree 45 1 2 46 Bignoniaceae Handroanthus chrysanthus tree 30 5 41 8 4 84c Bignoniaceae Handroanthus coralibe tree 5 1 5 Bignoniaceae Handroanthus guayacan tree 5 1 5 Bignoniaceae Handroanthus impetiginosus tree 1 1 1 Bignoniaceae Handroanthus ochraceus tree 4 1 4 Bignoniaceae Handroanthus ochraceus tree 9 1 9 Bignoniaceae Handroanthus serratifolius tree 6 1 6 Bignoniaceae Jacaranda copaia tree 2 6 2 8 Bignoniaceae Jacaranda obtusifolia tree 20 1 20 Bignoniaceae Jacaratia digitata tree 1 1 1 Bignoniaceae Roseodendron chryseum tree 1 1 1 Bignoniaceae Tabebuia orinocensis tree 1 1 1 Bignoniaceae Tabebuia rosea tree 12 8 1 3 21 Bignoniaceae Tanaecium tetragonolobum liana 1 1 1 Bignoniaceae Tecoma stans tree 2 5 1 3 8 Bixaceae Bixa urucurana shrub 3 1 3 Bixaceae Cochlospermum orinocense tree 15 1 15 Bixaceae Cochlospermum vitifolium tree 12 18 2 30 Boraginaceae Bourreria cumanensis tree 11 1 11 Boraginaceae Bourreria sp tree 1 1 1 Boraginaceae Cordia alba tree 42 1 1 3 44 Boraginaceae Cordia alliodora tree 25 2 1 1 16 5b 45 Boraginaceae Cordia bicolor tree 3 7 2 10 Boraginaceae Cordia curassavica tree 2 4 2 6 32 Ecology of woody plants in Colombian dry forests Family Species Growth form Caribbean Cauca valley Magdalena valley North andean Orinoquia Patia All All regions sites Boraginaceae Cordia gerascanthus tree 2 1 2 1 4 6 Boraginaceae Cordia lanceolata tree 1 1 1 Boraginaceae Cordia macuirensis tree 1 1 1 Boraginaceae Cordia nodosa tree 4 1 2 5 Boraginaceae Cordia panamensis tree 1 1 2 2 Boraginaceae Cordia sp tree 1 5 2 6 Boraginaceae Cordia sp1 tree 1 1 1 Boraginaceae Cordia sp2 tree 4 1 4 Boraginaceae Cordia tetrandra tree 22 1 22 Boraginaceae Cordia thaisiana tree 5 1 5 Bromeliaceae Bromelia karatas succulent 2 1 2 Bromeliaceae Bromelia pinguin succulent 5 1 5 Bromeliaceae Bromelia sp succulent 2 1 2 Bromeliaceae Pitcairnia pruinosa succulent 1 1 1 Bromeliaceae Pitcairnia sp succulent 2 1 2 Bromeliaceae Tillandsia flexuosa succulent 5 1 5 Burseraceae Bursera glabra tree 8 1 8 Burseraceae Bursera graveolens tree 25 2 1 3 28 Burseraceae Bursera simaruba tree 76a 7 11 3 9 5b 106c Burseraceae Bursera sp tree 2 1 2 Burseraceae Bursera tomentosa tree 1 1 2 2 Burseraceae Commiphora leptophloeos tree 1 1 1 Burseraceae Protium guianense tree 5 1 5 Burseraceae Protium heptaphyllum tree 18 1 18 Burseraceae Protium llanorum tree 3 1 3 Burseraceae Protium sp tree 1 1 1 Burseraceae Protium sp1 tree 1 1 1 Burseraceae Protium subserratum tree 1 1 1 Burseraceae Protium tenuifolium tree 1 1 1 Burseraceae Tetragastris panamensis tree 7 1 7 Burseraceae Trattinnickia aspera tree 1 1 1 Burseraceae Trattinnickia rhoifolia tree 4 1 4 Burseraceae Trattinnickia sp tree 1 1 1 Buxaceae Buxus citrifolia shrub 1 1 1 Cactaceae Acanthocereus sp cactus 3 1 3 Cactaceae Acanthocereus sp1 cactus 2 1 2 Cactaceae Acanthocereus tetragonus cactus 8 1 2 9 Cactaceae Cereus hexagonus cactus 2 3 1 3 6 Cactaceae Hylocereus megalanthus cactus 2 1 2 Cactaceae Melocactus curvispinus cactus 1 1 2 2 33 Doctoral Thesis – Roy González-M. Family Species Growth form Caribbean Cauca valley Magdalena valley North andean Orinoquia Patia All All regions sites Cactaceae Melocactus mazelianus cactus 1 1 1 Cactaceae Opuntia caracassana cactus 10 1 2 11 Cactaceae Opuntia dillenii cactus 3 5 2 8 Cactaceae Opuntia pittieri cactus 4 1 4 Cactaceae Opuntia sp cactus 1 1 1 Cactaceae Opuntia sp1 cactus 3 1 3 Cactaceae Opuntia sp2 cactus 1 1 1 Cactaceae Opuntia sp3 cactus 1 1 1 Cactaceae Pereskia guamacho cactus 26 1 26 Cactaceae Pilosocereus colombianus cactus 1 1 1 Cactaceae Pilosocereus lanuginosus cactus 1 1 1 Cactaceae Pilosocereus sp cactus 5 1 5 Cactaceae Stenocereus griseus cactus 23 2 2 25 Cactaceae Stenocereus humilis cactus 1 1 2 2 Cactaceae Stenocereus sp cactus 5 1 5 Cactaceae Stenocereus sp1 cactus 2 1 2 Calophyllaceae Caraipa llanorum tree 1 1 1 Cannabaceae Celtis iguanaea liana 2 1 3 3 6 Cannabaceae Trema micrantha tree 11 2 2 13 Capparaceae Belencita nemorosa shrub 9 1 9 Capparaceae Capparidastrum frondosum tree 4 5 2 8 Capparaceae Capparidastrum pachaca tree 9 1 9 Capparaceae Capparidastrum tenuisiliquum tree 1 1 1 Capparaceae Cleoserrata sp herbaceous 1 1 1 Capparaceae Cleoserrata speciosa herbaceous 1 1 1 Capparaceae Crateva tapia tree 28 1 2 29 Capparaceae Crescentia cujete tree 1 2 2 3 Capparaceae Cynophalla amplissima tree 4 1 9 3 14 Capparaceae Cynophalla flexuosa tree 3 1 2 4 Capparaceae Cynophalla hastata tree 1 1 2 2 Capparaceae Cynophalla linearis tree 5 1 5 Capparaceae Cynophalla verrucosa tree 5 1 5 Capparaceae Morisonia americana shrub 12 1 12 Capparaceae Quadrella indica tree 7 2 6 3 15 Capparaceae Quadrella odoratissima tree 25 13 3 3 41 Caricaceae Carica papaya tree 2 1 2 Caricaceae Vasconcellea cauliflora tree 2 1 2 Celastraceae Hippocratea volubilis liana 4 1 2 5 Celastraceae Prionostemma aspera liana 1 1 1 Celastraceae Schaefferia frutescens shrub 1 1 1 34 Ecology of woody plants in Colombian dry forests Family Species Growth form Caribbean Cauca valley Magdalena valley North andean Orinoquia Patia All All regions sites Chrysobalanaceae Hirtella americana tree 1 1 1 Chrysobalanaceae Hirtella elongata tree 1 1 1 Chrysobalanaceae Hirtella racemosa tree 1 1 1 Chrysobalanaceae Licania alba tree 2 1 2 Chrysobalanaceae Licania apetala tree 1 24 2 25 Chrysobalanaceae Licania hypoleuca tree 1 1 1 Chrysobalanaceae Licania kunthiana tree 1 1 1 Chrysobalanaceae Licania micrantha tree 1 1 1 Chrysobalanaceae Parinari excelsa tree 3 1 3 Chrysobalanaceae Parinari pachyphylla tree 4 1 4 Clusiaceae Calophyllum brasiliense tree 1 1 1 Clusiaceae Clusia alata tree 2 2 2 4 Clusiaceae Clusia grandiflora tree 2 1 2 Clusiaceae Clusia latipes tree 4 1 4 Clusiaceae Clusia minor tree 1 3 2 4 Clusiaceae Clusia multiflora tree 1 1 1 Clusiaceae Clusia sp tree 1 1 1 Clusiaceae Clusia sp1 tree 3 1 3 Clusiaceae Garcinia madruno tree 2 5 2 7 Combretaceae Buchenavia tetraphylla tree 6 1 6 Combretaceae Combretum aculeatum liana 1 1 1 Combretaceae Combretum fruticosum liana 6 2 2 8 Combretaceae Combretum sp liana 1 1 1 Combretaceae Terminalia amazonia tree 16 1 16 Combretaceae Terminalia oblonga tree 2 1 2 Connaraceae Connarus venezuelanus tree 13 1 13 Costaceae Dimerocostus strobilaceus herbaceous 1 1 1 Cyclanthaceae Carludovica palmata herbaceous 2 1 2 3 Dilleniaceae Curatella americana tree 10 10 1 3 21 Dilleniaceae Davilla kunthii shrub 1 1 1 Dioscoreaceae Dioscorea alata liana 1 1 1 Ebenaceae Diospyros sericea tree 1 1 1 Ebenaceae Diospyros sp2 tree 1 1 1 Elaeocarpaceae Sloanea terniflora tree 6 1 6 Erythroxylaceae Erythroxylum citrifolium tree 1 1 1 Erythroxylaceae Erythroxylum haughtii tree 3 1 3 Erythroxylaceae Erythroxylum havanense tree 2 1 2 Erythroxylaceae Erythroxylum hondense tree 2 1 2 Erythroxylaceae Erythroxylum macrophyllum tree 2 1 2 Erythroxylaceae Erythroxylum suberosum tree 3 1 3 35 Doctoral Thesis – Roy González-M. Family Species Growth form Caribbean Cauca valley Magdalena valley North andean Orinoquia Patia All All regions sites Erythroxylaceae Erythroxylum williamsii tree 1 1 1 Euphorbiaceae Acalypha diversifolia tree 1 1 2 2 Euphorbiaceae Acalypha macrostachya tree 1 1 1 Euphorbiaceae Acalypha sp shrub 2 1 2 Euphorbiaceae Acalypha sp1 shrub 1 1 1 Euphorbiaceae Alchornea discolor tree 14 1 14 Euphorbiaceae Alchornea sp tree 1 1 1 Euphorbiaceae Alchornea triplinervia tree 2 1 2 Euphorbiaceae Allophylus amazonicus tree 2 1 2 Euphorbiaceae Cnidoscolus aconitifolius herbaceous 1 1 1 Euphorbiaceae Cnidoscolus jaenensis herbaceous 1 1 1 Euphorbiaceae Cnidoscolus tubulosus herbaceous 3 1 2 3 6 Euphorbiaceae Cnidoscolus urens herbaceous 11 1 11 Euphorbiaceae Croton argenteus tree 1 1 1 Euphorbiaceae Croton ferrugineus tree 15 1 15 Euphorbiaceae Croton gossypiifolius tree 16 1 1 3 18 Euphorbiaceae Croton hibiscifolius tree 5 1 5 Euphorbiaceae Croton lechleri tree 3 1 3 Euphorbiaceae Croton leptostachyus tree 1 1 2 2 Euphorbiaceae Croton malambo tree 4 1 4 Euphorbiaceae Croton megalodendron tree 18 1 18 Euphorbiaceae Croton niveus tree 11 1 11 Euphorbiaceae Croton punctatus tree 1 1 1 Euphorbiaceae Croton rhamnifolius tree 1 1 1 Euphorbiaceae Croton schiedeanus tree 1 1 1 Euphorbiaceae Croton sp tree 3 1 3 Euphorbiaceae Croton sp1 tree 8 18 2 26 Euphorbiaceae Croton sp2 tree 1 7 2 8 Euphorbiaceae Croton sp3 tree 4 1 4 Euphorbiaceae Euphorbia cotinifolia tree 5 17 1 1 4 24 Euphorbiaceae Euterpe precatoria tree 1 5 2 6 Euphorbiaceae Garcia nutans tree 2 1 2 Euphorbiaceae Hura crepitans tree 62 1 2 63 Euphorbiaceae Jatropha gossypiifolia shrub 1 3 2 4 Euphorbiaceae Mabea montana tree 1 1 1 Euphorbiaceae Mabea sp tree 3 1 3 Euphorbiaceae Mabea trianae tree 16 1 16 Euphorbiaceae Manihot carthaginensis tree 7 1 7 Euphorbiaceae Manihot esculenta shrub 2 1 2 Euphorbiaceae Manihot tristis tree 1 1 1 36 Ecology of woody plants in Colombian dry forests Family Species Growth form Caribbean Cauca valley Magdalena valley North andean Orinoquia Patia All All regions sites Euphorbiaceae Maprounea guianensis tree 1 1 1 Euphorbiaceae Pera glabrata tree 1 1 1 Euphorbiaceae Ricinus communis shrub 1 1 2 2 Euphorbiaceae Sapium glandulosum tree 29 1 29 Euphorbiaceae Sapium jenmannii tree 2 1 2 Fabaceae Abarema jupunba tree 3 1 3 Fabaceae Abarema sp tree 1 1 1 Fabaceae Acacia mangium* tree 1 1 1 Fabaceae Acacia pennatula tree 2 14 2 16 Fabaceae Acacia polyphylla shrub 1 1 1 Fabaceae Acacia sp tree 2 4 2 6 Fabaceae Albizia carbonaria tree 2 1 2 Fabaceae Albizia guachapele tree 12 23 2 7 4 44 Fabaceae Albizia niopoides tree 46 4 10 3 60 Fabaceae Albizia saman tree 28 10 6 1 3 5b 48 Fabaceae Albizia sp tree 1 1 1 Fabaceae Albizia subdimidiata tree 2 1 2 Fabaceae Anadenanthera peregrina tree 17 1 17 Fabaceae Andira inermis tree 1 1 1 Fabaceae Andira sp tree 1 1 1 Fabaceae Andira surinamensis tree 9 1 9 Fabaceae Apuleia leiocarpa tree 19 1 19 Fabaceae Bauhinia aculeata liana 2 1 2 Fabaceae Bauhinia guianensis liana 1 1 1 Fabaceae Bauhinia hymenaeifolia liana 1 1 1 Fabaceae Bauhinia petiolata tree 2 1 2 Fabaceae Bauhinia picta shrub 4 1 4 Fabaceae Bauhinia ungulata liana 1 7 2 8 Fabaceae Bentamantha sp shrub 1 1 1 Fabaceae Brownea ariza tree 2 1 2 Fabaceae Brownea rosa-de-monte tree 2 1 2 Fabaceae Brownea sp tree 1 1 1 Fabaceae Caesalpinia cassioides shrub 11 1 11 Fabaceae Caesalpinia coriaria tree 35 1 35 Fabaceae Caesalpinia ebano tree 2 1 2 Fabaceae Caesalpinia granadillo tree 5 1 5 Fabaceae Caesalpinia punctata tree 1 1 1 Fabaceae Caesalpinia sp tree 1 1 1 Fabaceae Calliandra magdalenae tree 7 1 2 8 Fabaceae Calliandra pittieri tree 7 16 2 23 37 Doctoral Thesis – Roy González-M. Family Species Growth form Caribbean Cauca valley Magdalena valley North andean Orinoquia Patia All All regions sites Fabaceae Calliandra purdiaei tree 3 1 3 Fabaceae Calliandra sp tree 3 1 3 Fabaceae Calliandra sp1 tree 6 1 6 Fabaceae Calliandra trinervia var. carbonaria tree 1 1 1 Fabaceae Cassia fistula tree 2 1 2 Fabaceae Cassia grandis tree 1 1 2 3 4 Fabaceae Cassia moschata tree 1 24 2 25 Fabaceae Cassia sp tree 1 3 2 4 Fabaceae Centrolobium paraense tree 2 1 2 Fabaceae Chloroleucon mangense tree 11 2 2 13 Fabaceae Clathrotropis brachypetala tree 1 1 1 Fabaceae Clathrotropis macrocarpa tree 1 1 1 Fabaceae Clitoria dendrina shrub 4 1 4 Fabaceae Clitoria hermannii shrub 1 1 1 Fabaceae Copaifera pubiflora tree 44 1 44 Fabaceae Coursetia ferruginea tree 3 1 3 Fabaceae Coussarea sp tree 1 1 1 Fabaceae Coutarea hexandra tree 4 1 2 5 Fabaceae Crudia glaberrima tree 2 1 2 Fabaceae Cynometra bauhiniifolia tree 1 1 1 Fabaceae Dalbergia sp tree 1 1 1 Fabaceae Desmodium purpusii liana 2 1 2 Fabaceae Dioclea guianensis liana 1 1 1 Fabaceae Dioclea sericea liana 1 1 1 Fabaceae Dioclea sp liana 1 1 1 Fabaceae Diphysa carthagenensis tree 3 1 3 Fabaceae Dipteryx odorata tree 2 1 2 Fabaceae Entada polystachya liana 3 1 3 Fabaceae Enterolobium barinense tree 12 1 12 Fabaceae Enterolobium cyclocarpum tree 12 2 15 3 29 Fabaceae Enterolobium schomburgkii tree 3 1 3 Fabaceae Enterolobium sp1 tree 3 4 2 7 Fabaceae Enterolobium timbouva tree 2 1 2 Fabaceae Erythrina fusca tree 2 15 2 17 Fabaceae Erythrina poeppigiana tree 19 2 4 4 4 29 Fabaceae Erythrina rubrinervia tree 1 1 1 Fabaceae Erythrina sp tree 1 1 1 Fabaceae Erythrina sp1 tree 1 1 1 Fabaceae Erythrina sp2 tree 1 1 1 Fabaceae Erythrina velutina tree 5 1 5 38 Ecology of woody plants in Colombian dry forests Family Species Growth form Caribbean Cauca valley Magdalena valley North andean Orinoquia Patia All All regions sites Fabaceae Gliricidia sepium tree 2 1 10 3 13 Fabaceae Haematoxylum brasiletto tree 25 3 2 28 Fabaceae Hydrochorea corymbosa tree 8 1 8 Fabaceae Hymenaea courbaril tree 3 32 2 35 Fabaceae Hymenolobium petraeum tree 6 1 6 Fabaceae Inga alba tree 12 1 12 Fabaceae Inga cylindrica tree 1 1 1 Fabaceae Inga densiflora tree 3 1 3 Fabaceae Inga edulis tree 5 7 2 12 Fabaceae Inga fastuosa tree 2 1 2 Fabaceae Inga gracilifolia tree 1 1 1 Fabaceae Inga interrupta tree 2 1 2 Fabaceae Inga leiocalycina tree 1 1 1 Fabaceae Inga semialata tree 4 1 2 5 Fabaceae Inga sp tree 1 10 2 11 Fabaceae Inga sp1 tree 6 1 6 Fabaceae Inga sp2 tree 5 1 5 Fabaceae Inga sp3 tree 4 1 4 Fabaceae Inga sp6 tree 1 1 1 Fabaceae Inga spectabilis tree 1 1 1 Fabaceae Inga vera tree 1 2 2 3 Fabaceae Ipomoea pes-caprae liana 2 1 2 Fabaceae Leucaena glauca tree 1 1 1 Fabaceae Leucaena leucocephala tree 7 5 7 1 4 20 Fabaceae Libidibia ebano tree 6 1 6 Fabaceae Lonchocarpus pictus tree 1 1 1 Fabaceae Lonchocarpus punctatus tree 6 1 6 Fabaceae Lonchocarpus sanctae-marthae tree 16 1 16 Fabaceae Lonchocarpus violaceus tree 1 1 1 Fabaceae Luetzelburgia aff. andina tree 1 1 1 Fabaceae Machaerium arboreum tree 17 14 2 31 Fabaceae Machaerium biovulatum tree 12 1 6 3 19 Fabaceae Machaerium capote tree 7 9 7 1 4 24 Fabaceae Machaerium kegelii liana 1 1 1 Fabaceae Machaerium macrophyllum tree 2 1 2 Fabaceae Machaerium sp tree 2 1 2 Fabaceae Machaerium sp1 tree 2 10 2 12 Fabaceae Machaerium sp2 tree 6 1 6 Fabaceae Machaerium sp3 liana 1 1 1 Fabaceae Machaerium sp4 liana 1 1 1 39 Doctoral Thesis – Roy González-M. Family Species Growth form Caribbean Cauca valley Magdalena valley North andean Orinoquia Patia All All regions sites Fabaceae Machaerium sp6 tree 1 1 1 Fabaceae Macrolobium multijugum tree 2 1 2 Fabaceae Mimosa albida shrub 1 1 1 Fabaceae Mimosa colombiana shrub 1 1 1 Fabaceae Mimosa sp shrub 1 1 1 Fabaceae Mimosa sp1 shrub 1 1 1 Fabaceae Mimosa trianae tree 1 1 1 Fabaceae Muellera broadwayi tree 1 1 1 Fabaceae Myrospermum frutescens tree 7 2 2 9 Fabaceae Myroxylon balsamum tree 1 1 1 Fabaceae Ormosia macrocalyx tree 5 1 5 Fabaceae Parkia pendula tree 1 1 1 Fabaceae Parkinsonia aculeata tree 1 1 1 Fabaceae Parkinsonia sp tree 1 1 1 Fabaceae Peltogyne floribunda tree 10 1 10 Fabaceae Peltogyne purpurea tree 5 1 5 Fabaceae Peltogyne sp tree 1 1 1 Fabaceae Phanera guianensis liana 1 1 1 Fabaceae Piptadenia flava tree 45 1 45 Fabaceae Piptadenia gonoacantha tree 27 1 27 Fabaceae Piptadenia sp tree 3 1 3 Fabaceae Pithecellobium dulce tree 11 26a 17 5 2 5b 61 Fabaceae Pithecellobium lanceolatum tree 3 6 3 7 4 19 Fabaceae Pithecellobium roseum tree 13 1 13 Fabaceae Pithecellobium sp tree 7 1 7 Fabaceae Platycarpum orinocense tree 2 1 2 Fabaceae Platymiscium pinnatum tree 66 1 3 2 16 5b 88c Fabaceae Platypodium elegans tree 1 1 2 2 Fabaceae Prosopis juliflora tree 39 8 10a 3 57 Fabaceae Prosopis sp tree 1 1 1 Fabaceae Pseudosamanea sp tree 1 1 1 Fabaceae Pterocarpus acapulcensis tree 18 36 2 54 Fabaceae Pterocarpus officinalis tree 1 1 2 2 Fabaceae Pterocarpus rohrii tree 13 3 2 3 18 Fabaceae Pterocarpus sp tree 1 1 1 Fabaceae Schizolobium parahyba tree 2 1 2 Fabaceae Schizolobium sp tree 5 3 2 8 Fabaceae Senegalia glabra liana 4 1 4 Fabaceae Senegalia hayesii shrub 1 1 1 Fabaceae Senegalia macbridei liana 1 1 1 40 Ecology of woody plants in Colombian dry forests Family Species Growth form Caribbean Cauca valley Magdalena valley North andean Orinoquia Patia All All regions sites Fabaceae Senegalia multipinnata tree 5 1 5 Fabaceae Senegalia riparia shrub 1 1 1 Fabaceae Senegalia sp tree 1 1 1 Fabaceae Senegalia sp1 tree 3 1 3 Fabaceae Senegalia sp2 tree 1 1 1 Fabaceae Senegalia tamarindifolia tree 1 1 1 Fabaceae Senna atomaria tree 9 1 9 Fabaceae Senna bacillaris tree 2 1 2 Fabaceae Senna bicapsularis tree 2 1 2 Fabaceae Senna fruticosa tree 1 1 1 Fabaceae Senna multijuga tree 1 1 1 Fabaceae Senna obtusifolia tree 7 1 7 Fabaceae Senna silvestris tree 2 1 2 Fabaceae Senna sp tree 3 1 3 Fabaceae Senna spectabilis tree 25 1 25 Fabaceae Stryphnodendron sp tree 1 1 1 Fabaceae Styphnolobium sporadicum tree 1 1 1 Fabaceae Swartzia arborescens tree 1 1 1 Fabaceae Swartzia dipetala tree 1 1 1 Fabaceae Swartzia pittieri tree 1 1 1 Fabaceae Swartzia robiniifolia tree 1 1 1 Fabaceae Swartzia simplex tree 1 1 1 Fabaceae Swartzia sp tree 8 1 8 Fabaceae Swartzia trianae tree 3 1 3 Fabaceae Tachigali guianensis tree 1 1 1 Fabaceae Tachigali sp tree 2 1 2 Fabaceae Uribea tamarindoides tree 2 1 2 Fabaceae Vachellia farnesiana shrub 46 8 4 6 13 5b 77c Fabaceae Vachellia macracantha tree 1 1 1 Fabaceae Vachellia sp tree 1 1 1 Fabaceae Vachellia tortuosa tree 1 1 1 Fabaceae Vigna sp herbaceous 1 1 1 Fabaceae Zapoteca formosa tree 2 1 2 Fabaceae Zygia sp tree 7 1 7 Gentianaceae Adenolisianthus arboreus herbaceous 1 1 1 Goupiaceae Goupia glabra tree 2 1 2 Heliconiaceae Heliconia latispatha herbaceous 5 1 2 6 Hernandiaceae Gyrocarpus americanus tree 20 2 2 22 Hypericaceae Vismia baccifera tree 1 1 2 2 Hypericaceae Vismia lauriformis tree 2 1 2 41 Doctoral Thesis – Roy González-M. Family Species Growth form Caribbean Cauca valley Magdalena valley North andean Orinoquia Patia All All regions sites Hypericaceae Vismia macrophylla tree 1 1 2 2 Hypericaceae Vismia sp tree 2 1 2 Lacistemataceae Lacistema aggregatum tree 1 1 1 3 3 Lamiaceae Gmelina arborea* tree 1 1 1 Lamiaceae Tectona grandis* tree 2 1 2 3 Lamiaceae Vitex capitata tree 3 1 3 Lamiaceae Vitex compressa tree 1 1 1 Lamiaceae Vitex cymosa tree 7 1 7 Lamiaceae Vitex orinocensis tree 65a 1 65 Lamiaceae Vitex sp tree 4 1 4 Lauraceae Aniba sp tree 2 1 2 Lauraceae Cinnamomum triplinerve tree 4 1 2 5 Lauraceae Licaria applanata tree 1 1 1 Lauraceae Nectandra cuspidata tree 5 1 5 Lauraceae Nectandra membranacea tree 1 1 1 Lauraceae Nectandra pichurim tree 16 1 16 Lauraceae Nectandra purpurea tree 1 1 1 Lauraceae Nectandra sp tree 1 1 1 Lauraceae Nectandra sp1 tree 4 1 4 Lauraceae Nectandra turbacensis tree 8 1 8 Lauraceae Ocotea bofo tree 8 1 8 Lauraceae Ocotea cernua tree 1 13 2 14 Lauraceae Ocotea guianensis tree 1 1 1 Lauraceae Ocotea longifolia tree 5 1 5 Lauraceae Ocotea schomburgkiana tree 1 1 1 Lauraceae Ocotea sp tree 2 1 2 3 Lauraceae Ocotea veraguensis tree 15 1 2 16 Lauraceae Persea caerulea tree 2 1 2 Lecythidaceae Eschweilera sp tree 2 1 2 Lecythidaceae Eschweilera tenuifolia tree 2 1 2 Lecythidaceae Gustavia augusta tree 1 1 1 Lecythidaceae Gustavia hexapetala tree 1 1 2 2 Lecythidaceae Gustavia sp tree 1 1 1 Lecythidaceae Gustavia superba tree 4 1 4 Lecythidaceae Lecythis minor tree 9 1 9 Loasaceae Mentzelia scabra herbaceous 1 1 1 Loranthaceae Gaiadendron punctatum tree 1 1 1 Lythraceae Lafoensia punicifolia tree 2 6 2 8 Malpighiaceae Bunchosia armeniaca tree 1 1 1 Malpighiaceae Bunchosia odorata tree 1 1 1 42 Ecology of woody plants in Colombian dry forests Family Species Growth form Caribbean Cauca valley Magdalena valley North andean Orinoquia Patia All All regions sites Malpighiaceae Bunchosia pseudonitida tree 2 2 2 4 Malpighiaceae Bunchosia sp2 liana 1 1 1 Malpighiaceae Byrsonima crassifolia shrub 3 3 2 6 Malpighiaceae Byrsonima crispa shrub 1 1 1 Malpighiaceae Byrsonima japurensis shrub 1 1 1 Malpighiaceae Byrsonima nitidissima shrub 3 1 3 Malpighiaceae Byrsonima sp shrub 1 1 1 Malpighiaceae Byrsonima spicata tree 1 1 1 Malpighiaceae Hiraea reclinata shrub 1 1 1 Malpighiaceae Hiraea sp liana 1 1 1 Malpighiaceae Malpighia glabra tree 9 1 3 1 4 14 Malvaceae Abutilon ibarrense shrub 5 1 5 Malvaceae Apeiba tibourbou tree 2 1 2 Malvaceae Cavanillesia chicamochae tree 1 1 1 Malvaceae Cavanillesia platanifolia tree 6 1 6 Malvaceae Ceiba pentandra tree 14 6 2 3 32 1 5b 58 Malvaceae Ceiba sp tree 1 1 1 Malvaceae Guazuma ulmifolia tree 43 47a 36a 7 13 19a 5b 165c Malvaceae Hampea thespesioides tree 1 1 1 Malvaceae Helicteres baruensis tree 1 1 1 Malvaceae Helicteres sp shrub 2 1 2 Malvaceae Heliocarpus popayanensis tree 2 1 2 Malvaceae Luehea candida tree 1 1 1 Malvaceae Luehea seemannii tree 1 1 5 3 7 Malvaceae Luehea speciosa tree 2 1 2 3 Malvaceae Lueheopsis sp tree 1 1 1 Malvaceae Ochroma pyramidale tree 5 6 14 4 3 12 5b 44 Malvaceae Pachira nukakica tree 1 1 1 Malvaceae Pachira orinocensis tree 2 1 2 Malvaceae Pachira quinata tree 10 1 10 Malvaceae Pachira sp tree 6 1 6 Malvaceae Pavonia sepium herbaceous 1 1 1 Malvaceae Pseudobombax croizatii tree 1 1 1 Malvaceae Pseudobombax maximum tree 2 1 2 Malvaceae Pseudobombax septenatum tree 48 1 6 9 4 64 Malvaceae Sida jamaicensis herbaceous 2 1 2 Malvaceae Sida rhombifolia herbaceous 2 1 2 Malvaceae Sterculia apetala tree 50 1 23 3 74 Malvaceae Sterculia colombiana tree 1 1 1 Malvaceae Theobroma cacao tree 1 1 1 43 Doctoral Thesis – Roy González-M. Family Species Growth form Caribbean Cauca valley Magdalena valley North andean Orinoquia Patia All All regions sites Melastomataceae Acanthella sprucei herbaceous 1 1 1 Melastomataceae Bellucia grossularioides shrub 1 1 1 Melastomataceae Clidemia capitellata shrub 1 1 1 Melastomataceae Miconia albicans shrub 1 1 1 Melastomataceae Miconia holosericea shrub 1 1 1 Melastomataceae Miconia multispicata shrub 1 1 1 Melastomataceae Miconia sp shrub 6 2 2 8 Melastomataceae Miconia sp1 tree 2 1 2 Melastomataceae Miconia sp2 shrub 1 1 1 Melastomataceae Mouriri sp tree 1 1 1 Meliaceae Cedrela odorata tree 4 2 4 7 4 17 Meliaceae Guarea glabra tree 1 1 1 Meliaceae Guarea guidonia tree 4 7 20 3 31 Meliaceae Guarea sp tree 7 1 7 Meliaceae Ruagea glabra tree 3 1 3 Meliaceae Swietenia macrophylla tree 2 1 2 Meliaceae Trichilia acuminata tree 3 1 3 Meliaceae Trichilia carinata tree 3 1 3 Meliaceae Trichilia elegans tree 1 1 1 Meliaceae Trichilia hirta tree 1 1 1 Meliaceae Trichilia oligofoliolata tree 2 1 2 Meliaceae Trichilia pallida tree 4 4 2 3 10 Meliaceae Trichilia sp tree 6 1 6 Meliaceae Trichilia sp1 tree 1 1 1 Moraceae Brosimum alicastrum tree 6 15 2 3 23 Moraceae Brosimum guianense tree 1 1 1 Moraceae Brosimum sp tree 1 1 1 Moraceae Brosimum sp1 tree 7 1 7 Moraceae Brosimum sp2 tree 1 1 1 Moraceae Clarisia biflora tree 1 1 1 Moraceae Ficus americana tree 2 1 2 Moraceae Ficus dendrocida tree 7 19 2 26 Moraceae Ficus insipida tree 4 11 2 19 4 36 Moraceae Ficus lyrata* tree 1 1 1 Moraceae Ficus maxima tree 3 1 3 Moraceae Ficus nymphaeifolia tree 2 1 2 Moraceae Ficus obtusifolia tree 6 2 3 3 11 Moraceae Ficus pandurata tree 1 1 1 Moraceae Ficus pertusa tree 2 1 2 Moraceae Ficus sp tree 10 1 10 44 Ecology of woody plants in Colombian dry forests Family Species Growth form Caribbean Cauca valley Magdalena valley North andean Orinoquia Patia All All regions sites Moraceae Ficus sp1 tree 12 1 12 Moraceae Ficus sp2 tree 1 1 1 Moraceae Ficus sp3 tree 14 1 14 Moraceae Ficus sp4 tree 5 1 5 Moraceae Ficus sp5 tree 1 1 1 Moraceae Ficus trigona tree 1 1 1 Moraceae Ficus zarzalensis tree 1 1 1 Moraceae Helianthostylis sprucei tree 1 1 1 Moraceae Maclura tinctoria tree 20 10 1 6 4 37 Moraceae Maquira coriacea tree 5 1 5 Moraceae Poulsenia armata tree 1 1 1 Moraceae Pseudolmedia sp tree 1 1 1 Moraceae Pseudolmedia sp1 tree 1 1 1 Moraceae Sorocea sp tree 1 1 2 2 Moraceae Sorocea sprucei tree 1 1 2 2 Moraceae Sorocea trophoides tree 1 1 1 Moraceae Trophis racemosa tree 3 1 2 4 Muntingiaceae Muntingia calabura tree 5 3 5 9a 3 5b 25 Musaceae Musa x paradisiaca* shrub 3 1 3 Myristicaceae Virola sebifera tree 4 1 4 Myrtaceae Calyptranthes meridensis tree 1 1 1 Myrtaceae Campomanesia aromatica tree 1 1 1 Myrtaceae Capirona decorticans tree 2 1 2 Myrtaceae Eugenia biflora tree 9 1 9 Myrtaceae Eugenia lambertiana tree 1 1 1 Myrtaceae Eugenia monticola tree 5 1 5 Myrtaceae Eugenia procera tree 3 12 3 3 18 Myrtaceae Eugenia sp tree 6 1 6 Myrtaceae Eugenia sp2 tree 3 1 3 Myrtaceae Eugenia sp3 tree 3 1 3 Myrtaceae Eugenia sp4 tree 1 1 1 Myrtaceae Myrcia fallax tree 1 3 2 4 Myrtaceae Myrcia multiflora tree 1 1 1 Myrtaceae Myrcia paivae tree 1 1 1 Myrtaceae Myrcia popayanensis tree 1 1 1 Myrtaceae Myrcia sp tree 1 1 1 Myrtaceae Myrcia splendens tree 1 1 1 Myrtaceae Myrcia sylvatica tree 1 1 1 Myrtaceae Myrcianthes leucoxyla tree 2 1 2 Myrtaceae Myrcianthes sp tree 5 1 5 45 Doctoral Thesis – Roy González-M. Family Species Growth form Caribbean Cauca valley Magdalena valley North andean Orinoquia Patia All All regions sites Myrtaceae Pseudanamomis umbellulifera shrub 1 1 1 Myrtaceae Psidium guajava tree 3 1 2 4 Myrtaceae Psidium guineense tree 8 2 2 10 Myrtaceae Psidium salutare tree 1 1 1 Myrtaceae Psidium sartorianum tree 3 1 3 Myrtaceae Psidium sp tree 2 4 2 6 Myrtaceae Syzygium jambos* tree 1 1 1 Nyctaginaceae Guapira costaricana tree 2 1 2 Nyctaginaceae Guapira pacurero tree 5 1 5 Nyctaginaceae Guapira sp1 tree 1 1 1 Nyctaginaceae Guapira uberrima tree 3 1 3 Nyctaginaceae Neea amplifolia tree 1 1 1 Nyctaginaceae Neea macrophylla tree 1 1 1 Nyctaginaceae Neea nigricans tree 1 1 1 Nyctaginaceae Neea sp tree 1 1 1 Nyctaginaceae Neea sp1 tree 1 5 2 6 Nyctaginaceae Neea sp2 tree 1 1 1 Nyctaginaceae Pisonia aculeata liana 2 2 1 1 4 6 Nyctaginaceae Pisonia nigricans tree 4 1 4 Nyctaginaceae Pisonia sp liana 3 1 3 Ochnaceae Cespedesia spathulata tree 1 1 1 Ochnaceae Ouratea sp tree 1 1 1 Olacaceae Heisteria acuminata tree 1 1 1 Opiliaceae Agonandra brasiliensis tree 9 1 9 Orchidaceae Vanilla planifolia succulent 1 1 1 Passifloraceae Passiflora edulis liana 2 1 2 Passifloraceae Passiflora sphaerocarpa liana 1 1 1 Peraceae Pera arborea tree 2 1 2 Petiveriaceae Seguieria americana liana 4 1 4 Phyllanthaceae Amanoa guianensis tree 1 1 1 Phyllanthaceae Hieronyma alchorneoides tree 1 1 2 2 Phyllanthaceae Hieronyma sp tree 1 1 1 Phyllanthaceae Margaritaria nobilis tree 1 1 1 Phyllanthaceae Phyllanthus acuminatus shrub 1 1 2 2 Phyllanthaceae Phyllanthus botryanthus shrub 1 1 1 Phyllanthaceae Phyllanthus elsiae shrub 4 1 4 Phyllanthaceae Phyllanthus salviifolius tree 1 1 1 Phyllanthaceae Phyllanthus sp tree 1 1 1 Picrodendraceae Piranhea trifoliata tree 1 1 1 Piperaceae Peperomia sp herbaceous 1 1 1 46 Ecology of woody plants in Colombian dry forests Family Species Growth form Caribbean Cauca valley Magdalena valley North andean Orinoquia Patia All All regions sites Piperaceae Piper aduncum shrub 1 2 2 1 4 6 Piperaceae Piper marginatum shrub 2 1 2 Piperaceae Piper sp shrub 3 1 1 3 5 Piperaceae Piper sp6 shrub 1 1 1 Poaceae Bambusa vulgaris shrub 2 1 2 Poaceae Guadua angustifolia shrub 23 9 4 3 36 Polygalaceae Securidaca sp tree 1 1 1 Polygonaceae Coccoloba acuminata tree 1 1 1 Polygonaceae Coccoloba caracasana tree 1 4 2 5 Polygonaceae Coccoloba coronata tree 1 1 1 Polygonaceae Coccoloba mollis tree 1 1 1 Polygonaceae Coccoloba obovata tree 1 1 1 Polygonaceae Coccoloba obtusifolia tree 3 1 3 Polygonaceae Coccoloba padiformis tree 2 1 2 Polygonaceae Coccoloba sp tree 1 6 1 3 8 Polygonaceae Coccoloba sp1 tree 1 2 2 3 Polygonaceae Coccoloba sp2 tree 1 1 1 Polygonaceae Coccoloba sp4 shrub 1 1 1 Polygonaceae Ruprechtia cruegeri liana 2 1 2 Polygonaceae Ruprechtia ramiflora tree 16 1 16 Polygonaceae Ruprechtia sp1 tree 1 1 1 Polygonaceae Triplaris americana tree 31 3 17 3 6 5b 60 Polygonaceae Triplaris melaenodendron tree 4 1 4 Primulaceae Ardisia guianensis tree 1 1 1 Primulaceae Bonellia frutescens shrub 3 1 3 Primulaceae Geissanthus sp tree 5 1 5 Primulaceae Jacquinia armillaris shrub 1 1 1 Primulaceae Myrsine guianensis tree 12 2 4 1 4 19 Proteaceae Roupala montana tree 1 1 1 Proteaceae Roupala sp tree 2 1 2 Rhamnaceae Frangula goudotiana tree 2 1 2 Rhamnaceae Ziziphus jujuba tree 6 1 6 Rhamnaceae Ziziphus saeri tree 1 1 1 Rhamnaceae Ziziphus sp tree 1 1 1 Rhamnaceae Ziziphus strychnifolia tree 1 1 1 Rubiaceae Alseis blackiana tree 1 1 1 Rubiaceae Amaioua corymbosa tree 1 1 1 Rubiaceae Calycophyllum candidissimum tree 4 1 4 Rubiaceae Chiococca alba shrub 2 1 2 3 Rubiaceae Chomelia spinosa tree 1 1 1 47 Doctoral Thesis – Roy González-M. Family Species Growth form Caribbean Cauca valley Magdalena valley North andean Orinoquia Patia All All regions sites Rubiaceae Genipa americana tree 5 6 5 3 16 Rubiaceae Guettarda roupalifolia tree 1 1 1 Rubiaceae Guettarda sp tree 1 1 1 Rubiaceae Hamelia patens shrub 5 1 5 Rubiaceae Ladenbergia sp tree 1 1 1 Rubiaceae Palicourea rigida shrub 1 1 1 Rubiaceae Pittoniotis trichantha tree 1 1 1 Rubiaceae Pogonopus speciosus tree 5 1 5 Rubiaceae Psychotria carthagenensis shrub 2 1 2 Rubiaceae Psychotria micrantha shrub 2 1 2 Rubiaceae Randia aculeata tree 3 1 2 4 Rubiaceae Randia armata tree 2 1 2 Rubiaceae Randia dioica tree 1 1 1 Rubiaceae Randia obcordata shrub 18 1 18 Rubiaceae Randia pubistyla shrub 1 1 1 Rubiaceae Randia sp shrub 4 1 4 Rubiaceae Rosenbergiodendron formosum shrub 3 1 3 Rubiaceae Rudgea crassiloba tree 6 1 6 Rubiaceae Simira cesariana tree 4 1 4 Rubiaceae Simira cordifolia tree 1 2 2 3 Rubiaceae Simira rubescens tree 1 1 1 Rutaceae Amyris pinnata tree 2 7 1 3 10 Rutaceae Esenbeckia panamensis tree 1 1 1 Rutaceae Esenbeckia pentaphylla tree 1 1 1 Rutaceae Spathelia giraldoana tree 2 1 2 Rutaceae Swinglea glutinosa tree 1 1 1 Rutaceae Zanthoxylum caribaeum tree 1 12 1 2 4 16 Rutaceae Zanthoxylum fagara shrub 19 4 17a 3 40 Rutaceae Zanthoxylum gentryi tree 2 1 2 Rutaceae Zanthoxylum lenticulare tree 2 1 2 Rutaceae Zanthoxylum rhoifolium tree 17 2 1 13 5 5b 38 Rutaceae Zanthoxylum rigidum tree 1 1 1 Rutaceae Zanthoxylum schreberi tree 6 7 2 13 Rutaceae Zanthoxylum sp tree 1 1 1 Rutaceae Zanthoxylum sp1 tree 2 1 2 Rutaceae Zanthoxylum sp4 tree 1 1 1 Rutaceae Zanthoxylum verrucosum tree 5 1 5 Salicaceae Casearia aculeata tree 4 1 4 Salicaceae Casearia arborea tree 3 1 3 Salicaceae Casearia corymbosa tree 13 1 1 3 15 48 Ecology of woody plants in Colombian dry forests Family Species Growth form Caribbean Cauca valley Magdalena valley North andean Orinoquia Patia All All regions sites Salicaceae Casearia guianensis tree 1 1 1 Salicaceae Casearia javitensis tree 2 1 2 Salicaceae Casearia praecox tree 1 3 2 4 Salicaceae Casearia sp tree 20 1 20 Salicaceae Casearia sp1 tree 1 1 1 Salicaceae Casearia sp3 tree 1 1 1 Salicaceae Casearia sylvestris tree 1 3 4 3 4 11 Salicaceae Casearia tremula tree 1 1 1 Salicaceae Casearia ulmifolia tree 2 1 2 Salicaceae Homalium guianense tree 2 1 2 Salicaceae Salix humboldtiana tree 1 1 1 Salicaceae Xylosma spiculifera tree 1 1 1 Sapindaceae Cupania americana tree 4 23 4 3 4 34 Sapindaceae Cupania cinerea tree 5 3 2 8 Sapindaceae Cupania latifolia tree 1 1 1 Sapindaceae Cupania sp tree 2 1 2 Sapindaceae Dilodendron costaricense tree 1 1 1 Sapindaceae Dilodendron elegans tree 2 1 2 Sapindaceae Dodonaea viscosa tree 1 1 1 Sapindaceae Matayba arborescens tree 9 1 9 Sapindaceae Matayba guianensis tree 3 1 3 Sapindaceae Matayba purgans tree 1 1 1 Sapindaceae Matayba sp tree 1 1 1 Sapindaceae Matayba sp1 tree 1 1 1 Sapindaceae Melicoccus bijugatus tree 14 1 2 1 4 18 Sapindaceae Melicoccus oliviformis tree 10 1 10 Sapindaceae Paullinia alata liana 1 1 1 Sapindaceae Paullinia cururu liana 1 1 1 Sapindaceae Paullinia sp2 liana 1 1 1 Sapindaceae Sapindus saponaria tree 4 19 1 1 3 5b 28 Sapotaceae Chrysophyllum cainito tree 1 1 1 Sapotaceae Chrysophyllum sp tree 1 1 2 2 Sapotaceae Elaeoluma sp tree 1 1 1 Sapotaceae Pouteria caimito tree 1 1 1 Sapotaceae Pouteria plicata tree 1 1 1 Sapotaceae Pouteria sp tree 13 1 13 Sapotaceae Pouteria sp1 tree 1 1 1 Sapotaceae Pouteria sp2 tree 1 1 1 Sapotaceae Pouteria sp4 tree 1 1 1 Sapotaceae Pouteria sp7 tree 3 1 3 49 Doctoral Thesis – Roy González-M. Family Species Growth form Caribbean Cauca valley Magdalena valley North andean Orinoquia Patia All All regions sites Sapotaceae Pouteria venosa tree 1 1 1 Sapotaceae Pradosia colombiana tree 19 1 19 Simaroubaceae Castela erecta shrub 1 1 1 Simaroubaceae Quassia amara tree 1 1 1 Simaroubaceae Simarouba amara tree 1 20 2 21 Siparunaceae Siparuna guianensis tree 2 1 2 Siparunaceae Siparuna sp tree 1 1 1 Smilacaceae Smilax sp liana 1 1 1 Solanaceae Cestrum alternifolium shrub 1 1 1 Solanaceae Cestrum mariquitense shrub 1 1 1 Solanaceae Cestrum sp1 tree 1 1 1 Solanaceae Lycium tweedianum tree 1 1 1 Solanaceae Nicotiana tabacum shrub 1 1 1 Solanaceae Solanum laevigatum shrub 1 1 1 Solanaceae Solanum sp shrub 3 1 3 Solanaceae Solanum sp1 shrub 1 1 1 Thymelaeaceae Daphnopsis sp tree 1 1 1 Ulmaceae Ampelocera macphersonii tree 5 1 5 Ulmaceae Ampelocera sp1 tree 2 1 2 Ulmaceae Phyllostylon brasiliense tree 1 1 1 Ulmaceae Phyllostylon rhamnoides tree 4 1 4 Urticaceae Boehmeria sp herbaceous 2 1 2 Urticaceae Cecropia angustifolia tree 5 1 1 3 7 Urticaceae Cecropia engleriana tree 3 1 3 Urticaceae Cecropia longipes tree 2 1 2 Urticaceae Cecropia mutisiana tree 1 1 1 Urticaceae Cecropia peltata tree 23 5 2 9a 21 5b 60 Urticaceae Cecropia sp tree 5 1 2 6 Urticaceae Cecropia sp1 tree 19 1 19 Urticaceae Myriocarpa longipes tree 1 1 1 Urticaceae Myriocarpa sp tree 1 1 1 Urticaceae Myriocarpa stipitata tree 1 1 1 Urticaceae Urera baccifera shrub 8 3 1 3 12 Urticaceae Urera caracasana tree 1 2 2 3 Urticaceae Urera sp tree 1 1 1 Velloziaceae Vellozia tubiflora shrub 1 1 1 Verbenaceae Citharexylum kunthianum shrub 10 11 2 21 Verbenaceae Citharexylum poeppigii tree 2 1 2 Verbenaceae Lantana camara shrub 2 6 2 8 Verbenaceae Lippia origanoides shrub 15 1 15 50 Ecology of woody plants in Colombian dry forests Family Species Growth form Caribbean Cauca valley Magdalena valley North andean Orinoquia Patia All All regions sites Verbenaceae Petrea pubescens tree 5 1 5 Verbenaceae Petrea sp tree 1 1 1 Verbenaceae Verbena litoralis herbaceous 1 1 1 Violaceae Hybanthus prunifolius shrub 1 1 1 Violaceae Leonia sp1 tree 1 1 1 Violaceae Rinorea pubiflora tree 2 1 2 Vochysiaceae Erisma sp tree 1 1 1 Vochysiaceae Erisma uncinatum tree 2 1 2 Vochysiaceae Qualea dinizii tree 4 1 4 Vochysiaceae Vochysia crassifolia tree 3 1 3 Vochysiaceae Vochysia lehmannii tree 29 1 29 Vochysiaceae Vochysia sp tree 1 1 1 Vochysiaceae Vochysia venezuelana tree 8 1 8 Zygophyllaceae Bulnesia arborea tree 49 1 2 50 Zygophyllaceae Bulnesia carrapo tree 4 1 4 Table S3. Number of tropical dry forest field sampling sites across six regions of Colombia used for different analyses Information Number of field sites used for the analysis (N) Type of analysis Environmental conditions and soils 558 Principal component analysis Floristic composition 464 Non-metric multidimensional scaling Environmental conditions, soils and floristic 456 composition Redundancy Analysis Forest fragment size and shape 494 Total fragment size and shape index (perimeter/area) Forest successional status 571 Forest successional status relative frequency Anthropogenic pressures 571 Relative frequency of anthropogenic pressures inside forest and in the surrounding matrix Total 571 51 Doctoral Thesis – Roy González-M. Chapter 3 Climate severity and land-cover transformation determine plant community attributes in Colombian dry forests Roy González-M., Natalia Norden, Juan M. Posada, Camila Pizano, Hernando García, Álvaro Idárraga-Piedrahita, René López-Camacho, Jhon Nieto, Gina Rodríguez, Alba M. Torres, Alejandro Castaño-Naranjo, Rubén Jurado, Rebeca Franke-Ante, Robinson Galindo, Elkin Hernández, Adriana Barbosa and Beatriz Salgado-Negret Published in Biotropica (2019) 51(6): 826-837, doi:10.1111/btp.12715 52 Ecology of woody plants in Colombian dry forests Abstract Tropical dry forests (TDF) are known to be resource-limited due to a marked seasonality in precipitation. However, TDF are also shaped by factors such as solar radiation, wind speed, soil fertility and land-cover transformation. Together, these factors may determine gradients of environmental harshness, that are likely to drive changes in plant community attributes. Here we evaluated the effects of environmental harshness on plant community diversity and structure of Colombian TDF based on floristic and environmental data from 15 1-ha permanent plots. We also analyzed effects on groups of legumes species only (including deciduous and non-deciduous species), deciduous species only (including legumes and non-legumes species), and the whole community excluding either legumes or deciduous separately. Drier conditions and higher land-cover transformation had the strongest negative effects on species diversity, basal area and canopy height. Soil fertility, on the contrary, did not have a significant effect on any of the evaluated variables. Interestingly, legumes maintained their diversity and basal area along the climatic gradient, while deciduous species were negatively affected by drier conditions and an increase in secondary vegetation at the landscape level. Our results suggest that although TDF are limited by water availability, land-cover transformation strongly increases environmental harshness. Yet, both legumes group and deciduous group were differentially impacted by climatic and land transformation variables. Thus, to better understand TDF plant community attributes, it is necessary to consider these gradients and to disentangle their effects on different plant functional groups. Keywords: basal area, branching, canopy height, diversity, deciduous, forest structure, legumes, species richness Introduction Environmental harshness is defined as the combined effects abiotic factors filtering species and shaping forests structure (Whittaker 1965; Marks et al. 2016). The concept of environmental harshness, however, has been difficult to define because different factors affect plant community attributes in distinct ways (Gerstner et al. 2014; Stein et al. 2014). Although this has led many authors to reject the concept of environmental harshness, it may be useful for understanding community structuring in ecosystems experiencing multiple environmental limitations (Marks et al. 2016). This is the case for tropical dry forests (TDF), which experience drought conditions due to recurrent dry seasons related with low rainfall, high temperatures and high potential evapotranspiration (Murphy & Lugo 1986; Galicia et al. 1999; Trejo & Dirzo 2002). Water deficit, as the result of drought conditions, may induce cavitation and hydraulic failure (Markesteijn et al. 2011a; Méndez-Alonzo et al. 2012; Pineda-García et al. 2015), which in turn have negative consequences on plant growth, recruitment and survival (Allen et al. 2010). Thus, the intensity of dry seasons is likely to be one of the main drivers of plant community assembly and forest structure across TDF (Murphy & Lugo 1986, 1995; Allen et al. 2017a). Climatic severity, however, may not be the only factor impacting TDF. In particular, soil fertility has been shown to influence seed germination, morphological adaptations and physiological responses of plants, with consequences for growth and survival (Murphy & Lugo 1986; Singh & Chaturvedi 2017). However, the effects of soil fertility on TDF plant communities have shown contrasting results. In Mexico, for instance, species richness increased with soil fertility (Perroni-Ventura et al. 2006), while species 53 Doctoral Thesis – Roy González-M. diversity declined with soil nutrient availability in Bolivia (Peña-Claros et al. 2012). The extent to which soil conditions determine TDF structure and species diversity therefore deserves further attention. Changes in land-cover resulting from direct and indirect anthropogenic pressures are also important drivers plant community attributes (Gerstner et al. 2014; Stein et al. 2014). TDF are highly threatened in the Neotropics (Sánchez-Azofeifa et al. 2005a), and ongoing deforestation is rapidly converting the remaining natural areas into small forest fragments (Rodríguez et al. 2008; Portillo-Quintero & Sánchez- Azofeifa 2010). Land-cover transformation may directly impact plant diversity and forest structure through selective tree harvesting (Blackie et al. 2014), and indirectly through habitat loss and isolation effects (Stein et al. 2014). For instance, recruitment rates of vertebrate-dispersed plant species tend to be lower as fragmentation increases, as the result of a loss of dispersers in small fragments (Cordeiro & Howe 2001). Besides, deforestation may also increase climate severity, thereby reducing the likelihood of plant establishment and their persistence in forest patches. For example, higher wind exposure and lower forest area are important drivers of mortality rates in Amazonian forest patches (Laurance & Curran 2008), and of lower species richness in the Chaco forests of Argentina (Cagnolo et al. 2006). Land-cover change may also affect biotic interactions such as herbivory (Herrerías-Diego et al. 2008; Benítez-Malvido et al. 2018), or seed dispersal (Jacquemyn et al. 2001), especially of large-seeded plants, which are more dispersal limited (Cramer et al. 2007; Rodríguez-Cabal et al. 2007). For climatic, soils and land-cover variables to constitute measures of environmental harshness in TDF, one must first evaluate whether each of these factors affect TDF diversity and structure. Only factors that effectively impact dry forests, should be considered constituents of this concept. Because TDF occur along a wide range of abiotic conditions (González-M. et al. 2018), evaluating the relative importance of these three key environmental factors on plant community assembly would provide critical insights into how harshness shapes TDF. For instance, at the community level, harder conditions would result in lower plant diversity and changes in forest structure moving towards characteristics that allow plants to survive under stressful environmental conditions. How TDF respond to harsh conditions also depends on the species adaptations. As many species inhabiting TDF exhibit a wide array of strategies to overcome stressful conditions, differences among them may blur the overall community response (Singh & Chaturvedi 2017). In particular, legumes, both evergreen and deciduous, and deciduous species (including deciduous legumes), are two dominant functional groups with successful adaptations to cope with resource limitation in TDF (Houlton et al. 2008; Sprent 2009; Vargas et al. 2015; Santiago et al. 2016). Most legumes are associated with nitrogen-fixing bacteria, which increase their nutrient acquisition capacity and enhance their competitive ability in nutrient- poor soils (Sprent 2009). Their high levels of leaf nitrogen contents allow them to maintain high water use efficiency (Adams et al. 2016), which is also enhanced through the reduction of branching and leaf expansion (Li et al. 2009). These mechanisms alleviate carbon demands, allowing legumes to be physiologically efficient under drought conditions, and the most species-rich group across Neotropical dry forests (Gentry 1995; Lugo et al. 2006). Likewise, deciduous species shed their leaves during the dry season, which reduces their transpiration and respiration rates (Hulshof et al. 2014; Santiago et al. 2016). During the wet season, they produce low-cost leaves with high photosynthetic capacity (Méndez-Alonzo et al. 2012). Together, these mechanisms allow them to be competitive under drought conditions (Givnish 2002). Additionally, deciduous species often have small seeds and long-distance dispersal abilities, which allow them to cross dispersal barriers (Seiwa & Kikuzawa 1991, 1996; Cascante et al. 2002). As these two functional groups are particularly successful in TDF, one may ask whether their response to environmental 54 Ecology of woody plants in Colombian dry forests harshness is similar to that of the community as a whole, or if they tend to increase in dominance as harshness increases due to their ecological adaptations. Here, we explore the effects of climatic conditions, soil fertility and land-cover transformation on TDF plant community attributes in Colombia. Specifically, we addressed the following questions: 1) How do dry forest diversity and structure change across climatic, soil and land-cover gradients in Colombia? We expected that as climate severity increases, low soil fertility and land-cover transformation would increase environmental harshness. Thus, species richness and diversity should decrease, resulting changes in forest structure including shorter forest canopy, lower basal area and an increase of multi-stem individuals. Additionally, 2) Do legume and deciduous species show the same response to environmental harshness than the whole plant community? We predicted legume and deciduous species to respond differently to environmental harshness due to their strategies to overcome stressful conditions. For instance, they should show higher diversity and basal area under harsh conditions compared to the whole community. Methods Vegetation data set Between 2013 and 2015, we established 15 1-ha permanent plots in Colombian dry forests across a wide range of environmental conditions (Supporting Information Figure S1; González-M et al. 2018) and in natural protected areas without evidence of heavy human activities or logging (> 60 years without of human disturbance). The plots were representative of the range of shape, size and land-cover uses of TDF in Colombia (Figure S1c). Within each plot, we tagged, measured and identified all trees, shrubs, lianas and palms ≥ 2.5 cm in diameter at breast height (DBH, measured at 1.3 m height) We measured DBH, total height, and quantified all secondary stems of each individual (van Laar & Akça 2007). We included cacti ≥ 1.5 m in height and ≥ 2.5 cm in basal diameter because of their importance in TDF (Gentry 1995). We did not register DBH for cacti nor height for lianas. Within each plot, botanical samples of all species were collected for identification (see Appendix S1). All plots were visited during dry and rainy seasons to determine species leaf phenology as follows. Deciduous if species dropped their leaves during the drought and had new leaves during rainy conditions, and evergreen if maintained their leaves during both periods (Appendix S1). Climate, soils and land-cover data We compiled a climatic database that included nine variables derived from two sources (Table S1). Annual total number of rainy days (ARD), aridity index (Aridity), isothermality (Isoth), total annual precipitation (TAP), potential evapotranspiration (PET), and total precipitation during the three driest months (TPdriest) were determined using the National Climatic Source, which is based on 2046 weather stations in Colombia, and generates monthly data of temperature, precipitation and rainy days (~90 m spatial resolution, http://institucional.ideam.gov.co/jsp/1769). Aridity was calculated as the TAP/PET ratio (Zomer et al. 2008). PET was determined as the sum of monthly potential evapotranspiration over a year using the Thornthwaite equation (Thornthwaite 1948). Isoth was estimated as the amplitude of day-to-night temperatures relative to the annual amplitude, where annual amplitude is the difference between maximum TºC of the warmest month and minimum TºC of the coldest month (O’Donnell & Ignizio 2012). Solar radiation (SRad), water vapor pressure (WVP) and wind speed (Wind) were derived from WorldClim 2.0 (Fick & Hijmans 2017). 55 Doctoral Thesis – Roy González-M. To evaluate soil fertility, 10 soil samples were randomly taken at each plot and oven-dried at 60ºC until constant weight was reached. Soil acidity (pH) was determined as the concentration of hydrogen ions in a 1:1 soil-water mixture (Burt & Staff 2014), available phosphorus (P) was extracted with a Bray I solution and measured colourimetrically (Bray & Kurtz 1945; Menage & Pridmore 1973), extractable bases (Ca, Mg, K, Na) and cation exchange capacity (CEC) were measured using the absorption-atomic emission spectrophotometry procedure (Reeuwijk 2002), organic carbon (OC) was separated with water-soluble humic and fulvic acids at a pH of 2.0 and quantified by combustion using the Walkley–Black procedure (Walkley 1946), and textural fractions (Sand, Clay, Silt) were determined using the hydrometer method, based on the relation between sedimentary speed and particle sizes according to the Stokes law (Bouyoucos 1962; Powers et al. 2009) (Table S1). Soil analyses were performed at the Agustin Codazzi National Soil Laboratory (Colombia). To evaluate land-cover metrics, we defined a circular area of 500 ha around each plot, based on interpretation of remote-sensing imagery from the Landsat 8 Mosaic 2014 (15x15 m resolution, cloudinness cover < 1%) and Google EarthPro© images of 2014-2015 (0.64x0.64 m resolution, 1500 m flight height) (Xu & Becker 2012; Yu & Gong 2012). Within this area, we estimated total forest cover area (Forest), forest shape index (Shape), the area covered by secondary vegetation (SecVeg), the area covered by human land-uses (ULC), the number of human land use cover types (UCL.type) and topographic roughness (Roughness) (Table S1). Forest, SecVeg, and ULC were quantified as percentages excluding rivers, lakes or rock outcrops (McGarigal & Marks 1995). Shape was estimated as the area/perimeter ratio, where values closer to zero indicate that fragments tend to be narrow (Moser et al. 2002; Berry 2007), while larger values indicate circular fragments. UCL.type included agriculture, cattle ranching and human infrastructure (see Patch Richness, McGarigal & Marks 1995). Roughness was computed as the change in elevation between adjacent cells of a DEM model (15x15 m in 500 ha) following the procedures of (Riley et al. 1999). All spatial analyses were performed using ArcGIS® 10.2 and remote-sensing interpretations were validated in the field. Plant diversity and forest structure Plant diversity was measured based on Hill numbers, also called the effective number of species. This family of diversity metrics is parameterized by the variable q, which controls the sensitivity to species relative abundance. When q = 0, all species are equally weighted, and 0D thus corresponds to species richness. When q = 1, all individuals are equally weighted, and 1D corresponds to the Shannon-derived diversity (Jost 2006; Chiu et al. 2014). Forest structure was assessed by quantifying total basal area (BA), branching index (BI), and forest canopy height (H) in each plot (Table S2; van Laar & Akça 2007). BA was estimated as the sum of all individual basal area for trees, shrubs and lianas within each plot. BI, which is the extent to which individual trees were multi-stemmed, was calculated as: + + BI = 100 × %∑,-. '()*' / ∑,-. 012∑+ 3 ,-. '()*' where ind is the total number of individuals and stems is the total number of branches with DBH ≥ 2.5 cm below 1.3 m height but aboveground, including trees, shrubs, palms, lianas or cactus. A BI of 0% indicates that all the individuals of the plot were single-stemmed, and BI increases as the proportion of multi-stemmed individuals increases. H, which represents the average height of the tallest individuals 56 Ecology of woody plants in Colombian dry forests within a plot, was determined as the mean value of the upper quantile in height (Q3–Q4) of trees, shrubs, palms and cacti. Statistical analysis We performed a principal component analyses (PCA) for each set of environmental variables (i.e. 9 climate- , 11 soils- and 6 land-cover variables), and selected the first two PCA axes for describing the environmental space and as combined predictors (Wigley et al. 2016). To evaluate whether PCA variables were better predictors than raw environmental variables, we predicted 0D, 1D, BA, BI and H using, on one hand, simple and multiple linear models (SLR and MLR, respectively) with all possible combinations of the single variables, so that variance inflation factor (VIF) < 1.5 and, on the other hand, the first two PCA axes of each environmental category (for details see SI Modeling Procedures–MP). Since stem density varies considerably across plots, we ran individual-based rarefaction curves to evaluate whether the rarefied number of species differed from the observed one (Figure S2). In all cases, we found a strong positive correlation between plot species richness and the rarefied number of species, indicating that both metrics were valid (r = 0.99 – 0.73, P < 0.001). Thus, we kept 0D and 1D as response variables. Then, we selected the best-fitted model through stepwise elimination based on the corrected Akaike’s Information Criterion (AICc) (Hurvich & Tsai 1993; Venables & Ripley 2002). We made sure that VIF < 5, so that the models were not affected by a correlation between predictors (Akinwande et al. 2015), and fulfilled all assumptions for the normal distribution of residuals (Royston 1982) and homoscedasticity (Breusch & Pagan 1979). All models showed spatial independence in their residuals (Moran’s I, P > 0.05; Tables S3-S5). To compare the relative effects of the different predictors on each of our response variables, we compared the z- standardized b-coefficients, and performed an analysis of variance on the best-fitted model to calculate the percentage of variance explained by each predictor (Chambers & Hastie 1992). To evaluate whether the response of legumes and deciduous species to environmental harshness was consistent with that observed for at the community level, we repeated the same set of analyses using legumes, deciduous species, and the whole community excluding either legumes or deciduous species separately. We then compared the z-standardized b-coefficients of the best-fitted models with those obtained for the whole plant community. All statistical analyses were performed using R version 3.4.2. Results Climatic, soils and land-cover gradients As revealed by PCA analyses, the 15 1-ha plots occurred across a wide range of climatic, soil and land- cover conditions (Figure 1). The first axis of climatic PCA explained 56.6% of the total variance and was mostly related to climate severity (Figure 1a). Positive values were associated with aridity and high wind speed, solar radiation and water vapour pressure; whereas negative values were associated with high precipitation during the driest season, and more rainy days. Regarding soil variables, the first PCA axis explained 48.8% of the total variance, and was related to soil fertility. Sites with negative values of the PCA axis 1 were characterized by high CEC, high contents of extractable bases (Ca, Mg, K), high contents of clay and silt, and high pH (Figure 1b). Finally, the first axis of land-cover PCA explained 56.5% of the total variance, and was related to a gradient of transformation (Figure 1c). Sites with negative values were associated with higher forest cover, whereas sites with positive values were associated with a higher proportion of secondary vegetation, narrower forest fragments and a higher number of human land-cover types. 57 Doctoral Thesis – Roy González-M. (a) (b) (c) 4 4 4 TAP PET WVP P 2 2 pH 2 UCL K TPdriests Sand Ca ARD SRad OC 0 CECWind 0 0Mg Forest Shape Clay Silt ULC.type Aridity Isoth -2 -2 Na -2 SecVeg Roughness -4 -4 -4 low Climate severity high high Soil fertility low low Land-cover transformation high -4 -2 0 2 4 -4 -2 0 2 4 -4 -2 0 2 4 Climate PC1 (56.6%) Soils PC1 (48.8%) Land-cover PC1 (56.5%) Figure 1. Principal component analysis showing climatic, soils and land-cover transformation environmental space where the 15 1-ha permanent plots are located across tropical dry forest patches (black dots) in Colombia. (a) Climatic variables include: total of annual rainy days (ARD), aridity index (Aridity), isothermality (Isoth), solar radiation (SRad), total annual precipitation (TAP), potential evapotranspiration (PET), total precipitation during the three driest months (TPdriest), water vapor pressure (WVP) and wind speed (Wind). (b) Soil variables are acidity (pH), available phosphorus (P), cation exchange capacity (CEC), extractable bases (Ca, Mg, K, Na), organic carbon (OC) and textural fractions (Sand, Clay, Silt). (c) Land-cover metrics include: forests cover (Forest), forest shape index (Shape), secondary vegetation (SecVeg), land cover used by humans (ULC), types of human land cover uses (ULC.type) and topographic roughness (Roughness). Plant community diversity and forest structure across gradients Overall, we tagged 31,776 individuals (40,341 stems) belonging to 536 species distributed in 79 families of trees, lianas, cacti and palms in the 15 1–ha permanent plots (Table S2). Species richness (0D) varied between 12 and 101, and Shannon-derived diversity (1D) was 3 and 49 species·ha-1 per plot (Table S2). Basal area (BA) varied from 4.82 to 30.34 m2·ha-1, branching index (BI) from 4.11 to 50.46%, and forest canopy height (H) from 5.62 to 18.12 m (Table S2). The best-fitted models predicting forest diversity and structure included the first PCA axes for both climate and land-cover (R2 = 0.42–0.71, P < 0.05; Figure 2, see details in Table S3). Soil PCA axes did not have a significant effect on any of our response variables (Table S3). Overall, all models explained between 27.3 and 70.0% of the total variance (Figure 3a). Land- cover transformation was a strong, significant predictor of all the response variables and explained a considerable proportion of the variance (4.5-46.4%, Figure 3a), except for H (Figure 2 and 3e). Sites surrounded by a higher proportion of secondary vegetation and narrower fragments had lower 0D, 1D and BA, but higher BI. Climate severity was significantly associated with all response variables (Figure 2), and was the only significant predictor of H, explaining 27.3% of the variance (Figure 2e). This relationship was negative in all cases except for BI, which was positive (Figure 2d). In spite of a weak, although significant, correlation between climate and land-cover PCA axis 1 (see Table S3), the interaction between these predictors was not significant for models considering interaction effects, and in all cases VIF < 5. 58 Climate PC2 (21.6%) Soils PC2 (19.7%) Land-cover PC2 (21.6%) Ecology of woody plants in Colombian dry forests (a) β-coefficients 0D (b) β-coefficients 1D (c) β-coefficients BA -15 -10 -5 0 5 10 15 -10 -5 0 5 10 -6 -4 -2 0 2 4 6 Whole plant community (WPC) Whole plant community (WPC) Whole plant community (WPC) R2= 0.60** R2= 0.49* R2= 0.46* WPC (excl. Legumes) WPC (excl. Legumes) WPC (excl. Legumes) R2= 0.65** R2= 0.48* R2= 0.57** Legumes Legumes Legumes R2= 0.12NS R2= 0.33NS R2= 0.12NS WPC (excl. Deciduous) WPC (excl. Deciduous) WPC (excl. Deciduous) R2= 0.75*** R2= 0.58** R2= 0.58** Deciduous Deciduous Deciduous R2= 0.35NS R2= 0.46* R2= 0.39NS (d) β-coefficients BI (e) β-coefficients H -10 -5 0 5 10 -3 -2 -1 0 1 2 3 Whole plant community (WPC) Whole plant community (WPC) R2= 0.71*** R2= 0.42* β-coefficients WPC (excl. Legumes) WPC (excl. Legumes) R2= 0.68*** R2= 0.42* Climate PC1 (56.6%) Legumes Legumes Land-cover PC1 (56.5%) R2= 0.51* R2= 0.43* WPC (excl. Deciduous) WPC (excl. Deciduous) R2= 0.67** R2= 0.46* Deciduous Deciduous R2= 0.73*** R2= 0.45* Figure 2. Best-fitted model b-coefficients showing the effects of climate severity and land-cover transformation (axis 1 of the PCAs) on (a) species richness (0D), (b) species diversity (1D), (c) basal area (BA), (d) branching index (BI) and (e) forest canopy height (H) for the whole plant community (WPC), for each subset of species (legumes-both evergreen and deciduous, and deciduous species-including deciduous legumes) and for the whole community without each of these groups of species. Statistical significance of the models per group are as follows: *** P < 0.001 ** P < 0.01 * P < 0.05 NS (not significant). b coefficients overlapping 0 represents non-significant standardized slopes of climate and land-cover transformation (axis 1 of the PCAs) for the respective model. For details on the models see Table S3. Plant diversity and forest structure of legumes and deciduous species across gradients Legumes, both deciduous and evergreen, had the highest number of species (102), individuals (4696) and stems (7081) in the 15 1-ha permanent plots (Appendix S1), representing 13.7-45.2% of the number of individuals per plot (Table S2). Deciduous species (including deciduous legumes) were also highly abundant, representing 54.4%-88.9% of 0D across plots, and more that 50% of the number of individuals per plot (Table S2). We found contrasting results using the same best-fitted models predicting diversity and forest structure for the whole plant community, when compared to either legumes or deciduous species. Land-cover and climate severity did not predict 0D, 1D nor BA in either of these two functional groups of plants (Figure 2a-c, Table S4-S5), except for 1D in deciduous species. Interestingly, as for the whole plant community, climate severity was the best predictor of BI and H for both legumes and deciduous species (Figure 3), with positive effects on BI (P < 0.001, Figure 3d) and negative effects on H (P < 0.05, Figure 3e). When we ran the models with raw climatic, soils and land-cover variables for predicting forest diversity and structure for legumes and deciduous species, different predictors were found to be significant (Figure 4). For instance, the best-fitted model predicting 0D and 1D in legumes (Table S4) included positive 59 Doctoral Thesis – Roy González-M. effects of solar radiation and total precipitation during the three driest months. Likewise, the best-fitted model predicting BA in legumes only included the positive effect of soil available phosphorus, which had no effects at the community level (Table S4, Figure 4a). In contrast, the best-fitted models explaining 0D, 1D and BA for deciduous species included strong negative effects of land-cover transformation and of the proportion of secondary vegetation, similar to effects observed in the whole plant community (Figure 4b, Table S5). (a) Whole plant community (WPC) 0D 1D BA BI H (b) WPC (excl. Legumes) 0D 1D BA BI H (c) Legumes 0D 1D BA BI H (d) WPC (excl. Deciduous) 0D 1D BA BI H (e) Deciduous 0D 1D BA BI Deciduous H 0 20 40 60 80 100 Explained variance (%) Climate PC1 Land-cover PC1 Unexplained Figure 3. Explained variance of the best models obtained from a series of multiple regression analyses for communities of 15 1-ha Colombian dry forests: (a) whole plant community (WPC), (b) WPC excluding legume species (both evergreen and deciduous), (c) legume species, (d) WPC excluding deciduous species (including deciduous legumes), (e) deciduous species, and their respective plant community attributes: species richness (0D), species diversity (1D), basal area (BA), branching index (BI), and forest canopy height (H). Best-fitted models included as parameters the first PCA axes of climatic and land-cover transformation (56.6% and 56.5%, respectively). Achurated bars indicate non-significant effects of b-coeficientes. For details on the models see Table S4-S5. 60 Ecology of woody plants in Colombian dry forests (a) Legumes 0D SRad (+) 1 TPdriestsD (+) SRad (+) BA P (+) BI TPdriests (-) H Climate PC1 (-) (b) Deciduous 0D Land-cover PC1 (-) 1D Land-cover PC1 (-) BA TPdriests(-) SecVeg (-) BI TPdriests (-) H Land-coverClimate PC1 (-) PC1 (-) 0 20 40 60 80 100 Explained variance (%) Climate Soils Land-cover Unexplained Figure 4. Explained variance for the best models obtained from a series of multiple regression analyses for species richness (0D), species diversity (1D), basal area (BA), branching index (BI) and forest canopy height (H) of legumes (both evergreen and deciduous) (a) and deciduous (including deciduous legumes) (b) species, estimated per hectare (P < 0.05). Climate: climate PC1 (56.6%, C.PC1), solar radiation (SRad) and total precipitation during the three driest months (TPdriest). Soils: available phosphorus (P). Land-cover: land-cover PC1 (56.5%, L.PC1) and secondary vegetation area (SecVeg). (+) indicates positive b-coefficients and (-) negative for the models. Achurated bars indicate non-significant effects of b-coeficientes. For details on the models see Table S4-S5. Discussion We assessed the extent to which climatic, soils and land-cover variables affected TDF diversity and structure, and compared these patterns to those obtained for different functional groups of species known to thrive in dry conditions. Our results showed that: (1) TDF are distributed along gradients of climate severity, soil fertility and land-cover transformation, although climate severity and land-cover transformation were the main determinants of TDF diversity and structure at the community level. Interestingly, (2) the responses of legumes and deciduous species to these gradients were not consistent with that of the whole plant community. Environmental harshness of TDF cannot be defined only by rainfall seasonality Traditionally, TDF have been associated with rainfall seasonality and relatively fertile soils (e.g., Murphy & Lugo 1986; Pennington et al. 2009). Our results show, however, that in Colombia, TDF are distributed across independent gradients of climate severity, soil fertility and land-cover transformation (Figure 1). In particular, water deficit, the main filter shaping TDF species diversity and structure (Pennington et al. 2009; Neves et al. 2015), may result from the combination of different climatic variables, and not just from rainfall 61 | TPdriests Climate PC1 (+) (-) Doctoral Thesis – Roy González-M. regimes (González-M. et al. 2018). For example, at the Colorados site, water deficit was driven by high solar radiation, despite a high total annual rainfall compared to other studied sites. In contrast, at Tayrona and Macuira, we found a low rainfall and high aridity index, solar radiation and wind speed (Table S1). Conversely, other sites were not located under extreme climatic conditions, but were on infertile soils or embedded in heavily transformed landscapes. This was the case of Tuparro, which exhibited the highest annual precipitation, but occurred on sandy soils with low water retention, particularly during the dry season (Medina & Silva 1990; Dezzeo et al. 2008). Soil water deficit is critical in Tuparro, to the point that ~30% of its species are deciduous (Table S2). In addition, land-cover transformations may promote water deficit as they potentially increase exposure to solar radiation or wind (Table S1). This is the case in Taminango, Plato and La Paz, which are all surrounded by heavily transformed landscapes, and show low diversity, basal area and short forest canopy. Dry forest plant community attributes are explained by climate severity and land-cover transformation We hypothesized that TDF diversity and structure would be strongly affected by environmental harshness, as the result of climatic severity, low soils fertility and land-cover transformation. Our results partially support this hypothesis, as 0D, 1D, BA and H were lower, and BI was higher, with increasing land-cover transformation and climate severity (Figure 2), but the soil fertility gradient did not affect any plant community attribute. Two mechanisms may explain these patterns. First, there is strong evidence across the Neotropics that decades of land-uses have resulted in small, isolated TDF fragments exposed to drier conditions (Lambin et al. 2003; Rodríguez et al. 2008; Portillo-Quintero & Sánchez-Azofeifa 2010). Isolation increases dispersal limitation and is likely to be accompanied by local extinctions, thereby reducing diversity (Pimm 1998; Hooke et al. 2012; Gerstner et al. 2014). In TDF plots, 0D, 1D and BA were strongly negatively related to land-cover transformation, suggesting that agriculture, cattle ranching and the historic exploitation of hardwood species, or large trees, for fuelwood may have affected these forest attributes (Blackie et al. 2014; González-M. et al. 2018). Second, land-cover transformation may strengthen climate severity due to edge effects, which change microclimatic conditions within forest patches (Pimm 1998). Edge effects result in a harsher environmental condition, which may affect the overall forest structure (Givnish 1995; Schindler et al. 2012). Indeed, the structural metrics of our plots such as canopy height and branching index, were strongly affected by climate severity, suggesting that land-cover transformation could exacerbate climatic effects. This effect was probably related to the fact that both diversity and structure are sensitive to intensified droughts, or to other environmental forces such as increased wind speed inside forests, which may produce mechanical damages (Givnish 1995; Metzger 2000; Tao et al. 2016). The fact that forest canopies got shorter as climate severity increased may have three different explanations. First, short stature is advantageous because less tension is needed to move water to the canopy leaves, and the probability of cavitation is reduced in scenarios of water deficit (hydraulic limitation hypothesis; Ryan & Yoder 1997; Tao et al. 2016). Second, short trees show lower autotrophic wood respiration, thereby decreasing their allocation of photosynthates to wood, and their resistance to water flow (the respiration hypothesis (Sperry 1995; Ryan & Yoder 1997). Third, taller trees develop larger bending moments at their base, which may trigger damage under strong wind conditions and resulting in a counter- selected strategy (aerodynamic drag hypothesis, Schindler et al. 2012). Data on functional wood and hydraulic traits would improve our mechanistic understanding of why plant stature decreases with climate severity. 62 Ecology of woody plants in Colombian dry forests The increase in stem branching with climatic severity could also be explained as a adaptation to drought. For instance, a reduction in soil water supply coupled with high transpiration demand can cause xylem conduits to become air-filled (cavitate), stopping the flow of water and desiccating plant tissues (McDowell 2011). Inter-vessel pits connect xylem conduits, and air bubbles can travel between neighboring conduits spreading embolism if tension is high. Under this scenario, higher branching may reduce death rates as bubbles would spread across a single stem, given that each branched stem has a group of independent conduits (compartmentalized transport system hypothesis, Zimmermann 1983). In addition, in case of mechanical damages due to external forces such as wind, multi-stemmed individuals would reduce gravitational displacement or sway of trunks (Wilson 1995; van Bloem et al. 2006), and would also reduce near-surface wind speed via different architectural designs (Schindler et al. 2012). Soil fertility did not have significant effects on forest diversity and structure at the community level (Table S3). Previous studies, however, have shown contrasting results. In particular, soil nutrient availability has been positively (Perroni-Ventura et al. 2006), negatively (Huston 1980; Peña-Claros et al. 2012) or not clearly associated (Wright 1992; Gei & Powes 2014) with species richness and biomass in Neotropical dry forests. One explanation for such inconsistencies could be related to the fact that each species or ecological functional group may have different nutrient requirements, thereby obscuring predictions at the community-level across resource gradients (Knoepp et al. 2000). What drives legume and deciduous community attributes in TDF? We hypothesized that both legumes and deciduous species, would be at an advantage under harsh conditions because they have ecological strategies to overcome stressful environments. We found support for our predictions in terms of species richness, species diversity and basal area (Figure 3a-c), but not for branching index nor canopy height, for which the effects of environmental gradients were similar to those observed at the community level (Figure 3d-e, Figure 4). Regarding legumes, their 0D and 1D increased with solar radiation and total precipitation during the three driest months (Figure 4a), although plant community as a whole showed the opposite response with respect to solar radiation in the PCA (Figure 2a). An explanation for this pattern could be that legumes store nitrogen in leaves during rainy periods in TDF, when maximum symbiotic nitrogen fixation happens (Serraj et al. 1999). This stored nitrogen may then be allocated to the photosynthetic machinery of leaves, which, under high solar radiation, increases intercellular CO2 consumption while maintaining low stomatal conductance and low rates of water loss under drought (Adams et al. 2016). Thus, assuming that most legumes are nitrogen-fixers in TDF (Hedin et al. 2009; Sprent 2009), this mechanism could alleviate their water consumption while maintaining their photosynthetic activity under drought conditions. We also found that phosphorus availability had positive effects on legume BA (Figure 4a), which could be related to the fact that net primary productivity in TDF is limited by phosphorus (Gei & Powes 2014). Thus, we may expect an increase in plant biomass as a result of an increase in phosphorus availability. But, why is this relationship important only for legumes? One likely explanation is that N- fixers invest a fraction of their nitrogen in phosphatase enzymes production, which increases plant phosphate capture (Houlton et al. 2008). Thus, an increase in soil P could favour a higher basal area in legumes than in other species due to their high phosphatase activity. Although these mechanisms are plausible explanations for the patterns observed, we call for caution in their generalization as not all legumes are effective nitrogen-fixers (Sprent 2009). On the other hand, land-cover transformation and low precipitation during the dry seasons had negative effects on 0D, 1D and BA of deciduous species (Figure 4b), suggesting that they cannot avoid the 63 Doctoral Thesis – Roy González-M. environmental harshness caused by the effects of land-cover transformation and climate severity in TDF. These findings contrast with our predictions and previous studies showing that deciduous species can break dispersal limitation barriers and maintain their diversity as land-cover transformation increases, due to their small-seed size, which facilitates long-distance dispersal and germination under high light availability (Seiwa & Kikuzawa 1991, 1996). However, deciduous species are characterized by having acquisitive resource-use strategies for high performance during the short window of rainfall pulses (Méndez-Alonzo et al. 2012), increasing the risks of hydraulic failure or mechanical damage under harsh environmental conditions (Markesteijn et al. 2011a). Conclusions We suggest that tropical dry forests cannot be solely defined based on rainfall seasonality. Climate and land-cover transformation are critical factors for a better understanding in how diversity and structure of TDF respond to environmental harshness. While, land-cover transformation negatively impacts species richness, diversity and basal area of TDF plant communities, probably due to forest isolation, canopy height and branching index are stronly driven climate severity. Legumes appear to be the less affected by environmental harshness. Being N-fixers and investors of phosphatase enzymes, they alleviate photosynthetic demands under harsh conditions. But, unexpectedly, deciduous species cannot avoid increases in drougth conditions and secondary vegetation, being nevativelly affected. Thus, given the highly threatened state of TDF, it is urgent to acquire a mechanistic understanding in how species respond to harsh environments, and in such provide tools for comprehensive management of this ecosystem. Acknowledgements We gratefully acknowledge the owners and administrators of the natural areas for their hospitality and logistical support. Botanists Humberto Mendoza, José Aguilar and Hermes Cuadros helped us process and identify plant vouchers. Thanks to Jérôme Chave and two anonymous reviewers by their valuable comments that improved this manuscript. 64 Ecology of woody plants in Colombian dry forests Supporting information (a) (b) 4 MAT Sand N DryMonths 2 150 Km pH Silt 0 TAP OC CEC -2 DrySeason Climate -4 Soils Clay TPdriests -4 -2 0 2 4 5000 PC1 (27.7%) 4000 (c) 3000 2000 Tropical Dry Forest 1000 1-ha Permanent Plot 0 -77 -75 -73 -71 -69 -7 -6 -5 -4 -3 Longitude (W) ln [Perimeter/Area ratio (m −1)] Figure S1. Distribution of 494 dry forest sampling sites in Colombia (a) orange dots (González-M. et al. 2018) and distribution of 15 1-ha permanent plots (black dots). (b) climatic and soils environmental conditions, and (c) forest shape. The first two axes of the PCA explained 52.8% of the variance in climate and soils conditions of TDF in Colombia. Climate included total annual of precipitation (TAP, mm), mean annual temperature (MAT, ºC), total precipitation during the three driest months (TPdriest [<100 mm·month-1], mm), number of dry periods during the year with three continuous driest months (DrySeason [<100 mm·month-1], no.) and total number of dry months in a year (DryMonths, no.). Climatic data from: ~90 m climatic spatial resolution model, http://institucional.ideam.gov.co/jsp/1769). Soils included textural fractions (Sand, Clay, Silt; %), organic carbon (OC, %), acidity (pH), and cation exchange capacity (CEC; cmol+·kg-1). Soils data from: (Hengl et al. 2014). Forests shape (m-1) quantified the relation between perimeter (m) and area (m2) of the forest fragment that surrounded each sampling site and 1-ha permanent plots (Moser et al. 2002). 65 Latitude (N) 0 2 4 6 8 Elevation (masl) PC2 (25.1%) Doctoral Thesis – Roy González-M. (a) (b) ρ Whole plant community (WPC) = 0.97 C.I. 0.88 - 0.99 P < 0.001 rarefyWPC = 713 0 1000 2000 3000 4000 0 20 40 60 80 100 Sample size (number of individuals) Observed number of species (c) (d) ρ ρ = 0.73 WPC (excl. Legumes) = 0.94 Legumes C.I. 0.83 - 0.98 C.I. 0.19 - 0.93 P < 0.001 P = 0.002 0 20 40 60 80 0 5 10 15 20 Observed number of species Observed number of species (e) (f) ρ WPC (excl. Deciduous) = 0.92 ρ Deciduous = 0.92 C.I. 0.77 - 0.97 C.I. 0.8 - 0.98 P < 0.001 P < 0.001 0 10 20 30 40 50 60 0 10 20 30 40 50 60 Observed number of species Observed number of species Figure S2. Pairwise Pearson (r) correlations between rarefy number of species and observe number of species across the plots and for each groups of plants (a-b) rarefy curve and pairwise correlation for the whole plant community, (c) pairwise correlation for the whole plant community excluding legumes, (d) pairwise correlation for legumes, (e) pairwise correlation for the whole plant community excluding deciduous species, and (f) pairwise correlation for deciduous species. 66 Rarefied number of species Rarefied number of species Number of species 0 5 10 15 0 10 20 30 40 50 60 70 0 20 40 60 80 100 120 Rarefied number of species Rarefied number of species Rarefied number of species 0 10 20 30 40 2 3 4 5 6 7 0 20 40 60 80 Ecology of woody plants in Colombian dry forests Table S1. Study sites and environmental conditions (climate, soils and land-cover) of 15 1-ha permanent plots in TDF of Colombia. Climatic variables: total annual rainy days (ARD, no.), aridity index (Aridity, [PET/TAP]), isothermality (Isoth, %), solar radiation (SRad, MJ·m-1 x 100), total annual precipitation (TAP, mm), potential evapotranspiration (PET, mm), total precipitation during the three driest months (TPdriest [<100 mm·month-1], mm), water vapor pressure (WVP, kPa) and wind speed (Wind, m·s-1). Soil variables: acidity (pH), available phosphorus (P, mg·kg-1), cation exchange capacity (CEC, cmol+·kg-1), extractable bases (Ca [Calcium], Mg [Magnesium], K [Potassium], Na [Sodium], cmol+·kg-1), organic carbon (OC, %) and textural fractions (Sand, Clay, Silt, %). Land-cover metrics: forest cover area (Forest, Ha [effective area, %]), forest shape index (Shape [Perimeter/Area], m-1), secondary vegetation area (SecVeg, Ha [effective area, %]), area used by humans (ULC, Ha [effective area, %]), types of human land cover uses (ULC.type, no.) and topographic roughness (Roughness, %). Permanent Plots Caribbean Magdalena river valley Cauca river valley Orinoquía Patia (1-ha) Macuira Matitas Colorados La Paz Plato Tayrona Cardo.P Cardo.L Jabirú Tambor Cotové Támesis Vinculo Tuparro Tamin. Latitude (ºN) 12,20 11.34 9.94 10.37 9.78 11.31 5.08 5.09 5.06 5.17 6.53 5.79 3.84 5.25 1.67 Longitude (ºW) -71.35 -72.95 -75.11 -73.17 -74.71 -74.13 -74.80 -74.77 -74.83 -74.81 -75.83 -75.67 -76.29 -67.86 -77.31 Altitude (m.a.s.l.) 113 32 301 161 21 15 260 322 302 385 509 731 1025 95 591 Climate variables ARD 33 56 96 109 66 95 116 113 116 126 146 180 144 152 138 Aridity 3.42 1.80 1.01 1.74 1.73 2.03 1.29 1.19 1.31 0.88 1.43 0.64 0.97 0.77 1.94 Isoth 75.43 77.62 90.25 86.46 89.59 81.35 85.97 85.91 86.03 85.83 87.29 90.27 93.30 78.07 91.78 SRad 185.69 188.21 192.08 187.33 187.83 196.79 173.36 172.78 173.28 172.87 178.49 176.36 169.95 164.10 159.45 TAP 517.0 1087.4 1528.4 1172.0 1198.3 899.4 1505.9 1541.2 1528.2 1912.5 1193.8 2183.0 1192.4 2697.2 721.4 PET 1768.6 1959.7 1546.0 2043.0 2075.5 1827.7 1946.8 1835.6 2009.1 1689.1 1712.8 1397.9 1161.3 2067.0 1400.8 TPdriest 32.1 31.9 139.3 67.9 98.7 33.4 227.8 222.5 236.8 272.7 112.7 285.8 168.5 177.1 52.4 WVP 2.67 2.77 2.75 2.69 2.92 2.87 2.63 2.53 2.65 2.50 2.55 2.38 2.14 2.80 2.36 Wind 4.84 4.17 2.35 2.74 2.20 4.45 0.93 0.92 0.93 0.90 0.83 0.79 0.87 1.37 0.96 Soils variables pH 6.12 6.16 7.37 5.26 6.89 7.38 6.79 6.87 6.46 6.98 6.54 6.32 6.22 4.39 7.23 P 18.14 6.07 12.17 6.07 147.09 222.59 143.24 19.36 17.08 11.33 20.08 4.09 4.70 3.32 27.89 CEC 10.01 13.65 30.89 12.14 19.43 16.43 15.61 20.08 17.01 14.73 25.71 29.54 26.68 6.69 33.10 Extractable Bases Ca 5.29 6.90 34.83 6.67 16.62 16.05 13.81 22.89 13.13 10.29 22.13 17.57 21.33 0.06 29.14 Mg 2.19 4.88 4.64 1.88 5.45 2.69 3.24 4.11 3.90 2.30 9.95 6.70 17.74 0.06 7.91 K 0.55 0.47 0.65 0.52 0.91 0.82 0.73 0.36 0.70 0.61 0.38 0.73 0.87 0.21 1.08 Na 0.22 0.47 0.09 0.15 0.18 0.07 0.03 0.10 0.08 0.04 0.15 0.09 0.14 0.16 0.16 OC 1.00 1.89 3.22 3.34 1.95 3.58 2.54 2.41 2.80 2.36 2.26 4.48 3.64 1.79 2.61 Textural fractions Sand 57.41 63.89 34.21 42.91 67.72 62.58 61.86 56.45 48.86 72.25 37.42 50.72 60.69 64.27 34.47 Clay 21.54 20.41 31.58 31.02 16.51 19.25 24.96 24.54 21.16 16.33 35.34 22.16 24.36 16.39 29.66 Silt 21.07 15.71 34.21 26.07 15.76 18.18 13.18 19.03 29.98 11.42 27.24 27.12 14.95 19.34 35.9 Land-cover metrics Forest 407.6 456.8 395 31.2 6.4 411.8 153.9 303 145.4 329.6 55.1 13.3 23.4 176.3 56.8 [82.4] (91.3) (79) (6.2) (1.3) (100) (30.8) (60.6) (29.1) (65.9) (11.5) (2.7) (4.7) (51.9) (11.5) Shape 0.004 0.005 0.004 0.034 0.019 0.003 0.028 0.011 0.023 0.017 0.038 0.014 0.025 0.013 0.029 SecVeg 87.1 39.3 57.2 334.5 262.3 0 38.5 52.3 129.9 104 103.4 34.1 180.2 29.5 401.1 (17.6) (7.8) (11.4) (66.9) (53.8) (0) (7.7) (10.5) (26) (20.8) (21.6) (6.8) (36) (8.7) (81.5) ULC 0 1.3 48.1 134.6 218.9 0 254.9 125.5 209 56.7 309.7 452.9 296.7 19.2 30.8 (0) (0.3) (9.6) (26.9) (44.9) (0) (50.9) (25.1) (41.8) (11.3) (64.7) (90.5) (59.3) (5.6) (6.3) 67 Doctoral Thesis – Roy González-M. Permanent Plots Caribbean Magdalena river valley Cauca river valley Orinoquía Patia (1-ha) Macuira Matitas Colorados La Paz Plato Tayrona Cardo.P Cardo.L Jabirú Tambor Cotové Támesis Vinculo Tuparro Tamin. ULC.type 0 2 5 10 6 0 14 3 17 17 34 11 22 2 23 Rougdness 10.4 2.1 14.7 3.7 5.1 19.1 7.3 12.7 9.0 22.5 9.7 8.4 8.0 7.4 20.8 Table S2. Community attributes of 15 1-ha permanent plots in tropical dry forests of Colombia. Plant diversity: Species richness (0D, number of species) and exponential of Shannon entropy (1D, number of species). Forest structure: Basal area (BA, m2·ha-1), branching index (BI, %) and forest canopy height (H, m2). Permanent Plots (1-ha) Caribbean Magdalena river valley Cauca river valley Orinoquía Patia Macuira Matitas Colorados La Paz Plato Tayrona Cardo.P Cardo.L Jabirú Tambor Cotové Támesis Vinculo Tuparro Tamin. Whole plant community 0D 42 42 101 27 58 66 62 58 47 92 32 84 60 89 12 1D 15 11 29 14 15 29 25 21 6 49 10 22 15 41 3 BA 18.00 13.11 20.07 8.73 6.45 24.70 19.67 25.61 20.70 30.34 19.84 22.56 19.17 17.34 4.82 BI 30.23 30.42 7.00 50.46 49.00 35.68 14.16 10.95 6.88 12.12 20.09 4.11 19.84 11.46 21.22 H 11.63 5.62 13.17 6.80 7.42 10.18 10.98 14.11 12.72 15.52 14.23 18.13 9.85 12.08 6.20 Legume species only 0D 7 12 14 11 18 14 9 10 5 14 5 19 9 12 2 1D 4 3 9 6 5 7 6 7 2 7 3 9 3 7 1 BA 3.83 3.00 2.55 5.91 4.90 7.33 5.29 6.38 1.97 2.19 4.11 3.21 4.14 1.72 0.05 BI 26.34 46.56 12.00 53.12 42.58 34.34 17.75 9.92 6.90 8.39 7.81 2.46 19.43 9.21 47.83 H 11.23 5.60 15.49 6.69 7.49 9.90 12.41 13.95 14.74 15.25 16.04 19.66 10.90 11.94 7.40 Deciduous species only 0D 29 28 53 24 37 44 31 28 21 38 18 41 26 30 5 1D 10 6 25 11 8 21 15 14 12 24 11 14 10 19 1 BA 16.29 9.71 8.63 8.14 5.10 17.13 9.00 11.95 3.38 5.74 8.69 9.37 8.76 5.27 4.64 BI 27.96 30.62 8.03 49.60 43.57 27.11 22.03 12.70 17.29 8.56 19.55 2.28 29.07 9.79 19.20 H 11.68 5.66 15.03 6.85 7.49 10.35 11.45 14.77 14.32 16.35 14.30 17.61 9.95 12.72 6.21 Table S3. Best models obtained from a series of multiple regression analysis for each plant community attributes (predicted variables) of the whole plant community (WPC), and climatic, soils and land-cover transformation variables (predictors) in 15 1-ha of Colombian dry forests. Squared R (R2, significant in bold letters), Corrected Akaike’s Information Criterion (AICc), variance inflation factor (VIF, is selected as lowest values, is rejected if values are higher than 5), normal distribution of residuals (Shapiro-Wilk test, is rejected if P < 0.05), Homoscedasticity (Breusch-Pagan test, is rejected P < 0.05), Moran's I Autocorrelation Index (Moran's I, is rejected if P < 0.05). Significant effects of b-coefficients in models are in bold letters. 0 Dimension Predictor VIF Species richness ( D) Species diversity (1D) Basal area (BA) Branching index (BI) Canopy height (H) β-coef. P β-coef. P β-coef. P β-coef. P β-coef. P Climate TPdriests 1.29 PET 1.09 SRad 1.37 PC1 (56.6%) 1.50 -8.31 0.005 -3.45 0.022 -2.04 0.021 6.27 < 0.001 -1.19 0.012 Soil Clay 1.12 pH 1.40 Na 1.12 P 1.36 PC1 (48.8%) 1.25 PC2 (19.7%) 1.22 68 Ecology of woody plants in Colombian dry forests Dimension Predictor VIF Species richness ( 0D) Species diversity (1D) Basal area (BA) Branching index (BI) Canopy height (H) β-coef. P β-coef. P β-coef. P β-coef. P β-coef. P Land-cover SecVeg 1.00 UCL 1.15 Roughness 1.15 PC1 (56.5%) 1.47 -11.43 0.002 -5.3 0.007 -2.72 0.021 4.52 < 0.001 -0.86 0.108 PC2 (21.6%) 1.21 Model R2 0.60 0.50 0.46 0.71 0.42 AICC 137.41 119.47 103.33 115.69 84.04 VIF 2.49 1.98 1.84 3.44 1.72 Shapiro Wilk (P) 0.95 (0.531) 0.95 (0.553) 0.98 (0.969) 0.95 (0.583) 0.97 (0.882) Breusch Pagan (P) 0.13 (0.937) 1.49 (0.476) 0.83 (0.660) 5.64 (0.060) 1.96 (0.376) Moran's I (P) 0.02 (0.384) -0.21 (0.191) 0.04 (0.289) -0.08 (0.922) -0.01 (0.593) Table S4. Best models obtained from a series of multiple regression analysis for each plant community attributes (predicted variables) of legumes species, and climatic, soils and land-cover transformation variables (predictors) in 15 1-ha of Colombian dry forests. Squared R (R2, significant in bold letters), Corrected Akaike’s Information Criterion (AICc), variance inflation factor (VIF, is selected as lowest values, is rejected if values are higher than 5), normal distribution of residuals (Shapiro-Wilk test, is rejected if P < 0.05), Homoscedasticity (Breusch-Pagan test, is rejected P < 0.05), Moran's I Autocorrelation Index (Moran's I, is rejected if P < 0.05). Significant effects of b-coefficients in models are in bold letters. 0 1 Dimension Predictor VIF Species richness ( D) Species diversity ( D) Basal area (BA) Branching index (BI) Canopy height (H) β-coef. P β-coef. P β-coef. P β-coef. P β-coef. P Climate TPdriests 1.29 2.67 0.035 1.72 0.011 -13.80 <0.001 PET 1.09 SRad 1.37 3.52 0.008 1.54 0.019 P C1 (56.6%) 1.50 -1.01 0.021 Soil Clay 1.12 pH 1.40 Na 1.12 P 1.36 1.12 0.027 PC1 (48.8%) 1.25 PC2 (19.7%) 1.22 Land-cover SecVeg 1.00 UCL 1.15 Roughness 1.15 PC1 (56.5%) 1.47 PC2 (21.6%) 1.21 Model R2 0.48 0.48 0.32 0.63 0.35 AICC 90.43 70.18 64.12 120.64 85.01 VIF 1.91 1.93 1.48 2.68 1.53 Shapiro Wilk test (P) 0.95 (0.599) 0.97 (0.920) 0.96 (0.61) 0.99 (0.998) 0.93 (0.311) Breusch Pagan test (P) 1.82 (0.402) 1.79 (0.408) 0.80 (0.37) 3.43 (0.064) 0.09 (0.765) Moran's I (P) -0.03 (0.669) -0.14 (0.546) -0.13 (0.563) -0.15 (0.487) -0.06 (0.913) Table S5. Best models obtained from a series of multiple regression analysis for each plant community attributes (predicted variables) of deciduos species, and climatic, soils and land-cover transformation variables (predictors) in 15 1-ha of Colombian dry forests. Squared R (R2, significant in bold letters), Corrected 69 Doctoral Thesis – Roy González-M. Akaike’s Information Criterion (AICc), variance inflation factor (VIF, is selected as lowest values, is rejected if values are higher than 5), normal distribution of residuals (Shapiro-Wilk test, is rejected if P < 0.05), Homoscedasticity (Breusch-Pagan test, is rejected P < 0.05), Moran's I Autocorrelation Index (Moran's I, is rejected if P < 0.05). Significant effects of b-coefficients in models are in bold letters. 0 Dimension Predictor VIF Species richness ( D) Species diversity (1D) Basal area (BA) Branching index (BI) Canopy height (H) β-coef. P β-coef. P β-coef. P β-coef. P β-coef. P Climate TPdriests 1.29 -2.41 0.011 -8.51 0.010 PET 1.09 SRad 1.37 PC1 (56.6%) 1.50 -1.64 0.045 -1.28 0.009 Soil Clay 1.12 pH 1.40 Na 1.12 P 1.36 PC1 (48.8%) 1.25 PC2 (19.7%) 1.22 Land-cover SecVeg 1.00 -2.71 0.006 UCL 1.15 Roughness 1.15 PC1 (56.5%) 1.47 -3.58 0.029 -2.78 0.009 -1.01 0.071 PC2 (21.6%) 1.21 Model R2 0.32 0.46 0.56 0.42 0.45 AICC 118.92 101.88 82.47 119.14 84.85 VIF 1.46 1.84 2.29 1.71 1.80 Shapiro Wilk (P) 0.99 (0.993) 0.96 (0.655) 0.96 (0.692) 0.93 (0.267) 0.98 (0.965) Breusch Pagan (P) 0.04 (0.827) 0.45 (0.801) 4.19 (0.122) 1.34 (0.247) 2.04 (0.360) Moran's I (P) 0.01 (0.489) -0.11 (0.700) -0.15 (0.442) -0.19 (0.264) -0.02 (0.635) 70 Ecology of woody plants in Colombian dry forests Appendix S1. Taxonomic composition and species abundance (number of stems) of 15 1-ha permanent plots in TDF of Colombia including life forms (LF), total frequency of species across the plots (PPfreq.), total number of individuals per species across plots (Nind), total number of stems per species across plots (Nstems) and leaf phenology. In total, 31776 individuals, 40341 stems, 536 species belonging to 79 families are reported. 272 species were categorized as deciduous and 254 as evergreen. Botanical samples for all species they were deposited in the Federico Medem Herbarium (FMB, Instituto de Investigación de Recursos Biológicos Alexander von Humboldt, Colombia). Family Species LF PPf Nind Nstems Leaf Phenology Achariaceae no. spp=3 6 721 805 Lindackeria paludosa tree 1 7 7 evergreen Mayna grandifolia tree 1 2 2 evergreen Mayna odorata tree 5 712 796 evergreen Achatocarpaceae no. spp=1 4 101 245 Achatocarpus nigricans treelet 4 101 245 deciduous Amaranthaceae no. spp=2 2 20 21 Iresine sp liana 1 11 12 evergreen Morf sp5 forb 1 9 9 evergreen Anacardiaceae no. spp=6 14 842 955 Anacardium excelsum tree 2 48 49 evergreen Astronium graveolens tree 13 739 850 deciduous Mangifera indica tree 1 1 1 evergreen Spondias mombin tree 4 27 28 deciduous Spondias radlkoferi tree 2 26 26 deciduous Tapirira guianensis tree 1 1 1 evergreen Annonaceae no. spp=9 7 1376 1423 Annona muricata tree 1 3 3 evergreen Annona rensoniana tree 1 2 2 evergreen Duguetia odorata tree 1 5 5 evergreen Guatteria metensis tree 1 2 2 evergreen Malmea sp tree 1 3 3 evergreen Oxandra espintana tree 3 1186 1225 evergreen Oxandra sp tree 1 107 114 evergreen Pseudomalmea sp tree 1 26 26 evergreen Rollinia mucosa tree 2 42 43 evergreen Apocynaceae no. spp=18 13 1408 1707 Asclepias sp forb 1 14 17 evergreen Aspidosperma cuspa tree 2 727 995 evergreen Aspidosperma polyneuron tree 4 360 366 evergreen Aspidosperma sp tree 1 1 2 evergreen Aspidosperma sp1 tree 1 1 1 evergreen Forsteronia affinis liana 1 19 19 deciduous Forsteronia sp1 liana 2 12 13 deciduous Forsteronia spicata liana 1 4 4 deciduous Himatanthus articulatus tree 1 15 15 deciduous Mandevilla sp liana 1 87 88 deciduous Marsdenia sp liana 1 8 8 deciduous Morf sp41 liana 1 1 1 evergreen Plumeria pudica tree 1 41 45 evergreen Prestonia sp liana 2 49 55 deciduous Prestonia trifida liana 1 9 9 evergreen Tabernaemontana grandiflora tree 2 33 40 evergreen Tabernaemontana markgrafiana treelet 1 1 1 evergreen Tabernaemontana sp1 tree 1 26 28 deciduous Araliaceae no. spp=2 2 18 18 Aralia excelsa treelet 1 1 1 evergreen Dendropanax arboreus tree 1 17 17 evergreen Arecaceae no. spp=7 5 267 322 71 Doctoral Thesis – Roy González-M. Family Species LF PPf Nind Nstems Leaf Phenology Achariaceae no. spp=3 6 721 805 Aiphanes horrida palm 1 9 9 evergreen Attalea butyracea palm 1 1 1 evergreen Attalea microcarpa palm 1 55 55 evergreen Bactris bidentula palm 1 145 196 evergreen Bactris pilosa palm 2 32 34 evergreen Bactris sp palm 1 1 3 evergreen Syagrus sancona palm 1 24 24 evergreen Aristolochiaceae no. spp=2 3 38 38 Aristolochia maxima liana 1 18 18 deciduous Aristolochia sp liana 2 20 20 deciduous Asteraceae no. spp=4 3 29 33 Chromolaena perglabra shrub 2 8 10 evergreen Lycoseris mexicana liana 1 18 20 deciduous Verbesina sp tree 1 2 2 evergreen Vernonanthura patens forb 1 1 1 evergreen Basellaceae no. spp=1 1 34 34 Anredera floribunda liana 1 34 34 deciduous Bignoniaceae no. spp=23 14 2289 2800 Adenocalymma aspericarpum liana 2 11 11 deciduous Amphilophium paniculatum liana 1 102 108 deciduous Anemopaegma sp liana 1 3 3 deciduous Arrabidaea sp liana 1 17 18 deciduous Bignonia aequinoctialis liana 2 18 18 deciduous Bignonia pterocalyx liana 1 54 61 deciduous Bignonia sp liana 1 18 18 deciduous Callichlamys latifolia liana 1 2 2 deciduous Fridericia mollissima liana 1 30 44 deciduous Fridericia pubescens liana 1 27 29 deciduous Fridericia sp liana 1 12 13 deciduous Handroanthus barbatus tree 1 5 5 deciduous Handroanthus billbergii tree 3 376 582 deciduous Handroanthus chrysanthus tree 5 1511 1777 deciduous Handroanthus ochraceus tree 2 2 2 deciduous Jacaranda caucana tree 1 3 3 deciduous Morf sp13 tree 1 2 2 deciduous Morf sp36 liana 1 1 2 deciduous Phryganocydia sp1 liana 1 1 1 deciduous Roseodendron chryseum tree 2 3 5 deciduous Tabebuia rosea tree 4 33 36 deciduous Tanaecium tetragonolobum liana 1 56 58 deciduous Xylophragma seemannianum liana 1 2 2 deciduous Bixaceae no. spp=1 1 1 1 Cochlospermum orinocense tree 1 1 1 deciduous Boraginaceae no. spp=8 9 421 696 Bourreria cumanensis tree 2 306 556 evergreen Cordia alba tree 2 14 29 evergreen Cordia alliodora tree 2 4 7 deciduous Cordia bicolor tree 1 1 1 deciduous Cordia gerascanthus tree 3 43 50 deciduous Cordia macuirensis tree 1 1 1 deciduous Cordia panamensis tree 1 49 49 deciduous Cordia sp tree 2 3 3 deciduous Burseraceae no. spp=5 11 269 283 Bursera graveolens tree 1 13 14 deciduous Bursera simaruba tree 10 149 152 deciduous Bursera tomentosa tree 1 9 14 deciduous 72 Ecology of woody plants in Colombian dry forests Family Species LF PPf Nind Nstems Leaf Phenology Achariaceae no. spp=3 6 721 805 Protium guianense tree 1 88 90 evergreen Protium tenuifolium tree 1 10 13 evergreen Cactaceae no. spp=7 9 926 1126 Acanthocereus tetragonus cactus 4 15 23 N/A Cereus hexagonus cactus 3 166 258 N/A Opuntia sp cactus 1 24 42 N/A Pereskia guamacho treelet 5 28 53 deciduous Pilosocereus lanuginosus cactus 2 430 468 N/A Stenocereus griseus cactus 1 6 24 N/A Stenocereus humilis cactus 1 257 258 N/A Cannabaceae no. spp=1 8 242 335 Celtis iguanaea liana 8 242 335 deciduous Capparaceae no. spp=18 12 1255 2030 Belencita nemorosa shrub 1 105 190 deciduous Capparidastrum frondosum tree 1 3 3 evergreen Capparidastrum pachaca tree 2 87 156 evergreen Capparidastrum sola tree 1 2 3 evergreen Capparidastrum tenuisiliquum tree 1 210 276 evergreen Capparis sp2 tree 1 3 3 evergreen Crateva tapia treelet 3 5 13 deciduous Cynophalla amplissima tree 2 31 38 evergreen Cynophalla flexuosa tree 5 102 190 evergreen Cynophalla hastata tree 1 7 8 evergreen Cynophalla linearis tree 2 142 188 evergreen Cynophalla polyantha tree 2 15 19 evergreen Cynophalla verrucosa tree 3 363 626 evergreen Morf sp38 liana 1 15 23 evergreen Morisonia americana tree 3 32 76 evergreen Morisonia sp2 treelet 1 2 3 evergreen Quadrella indica tree 4 43 71 evergreen Quadrella odoratissima tree 5 88 144 evergreen Caricaceae no. spp=1 1 2 2 Vasconcellea sp treelet 1 2 2 evergreen Celastraceae no. spp=5 8 595 655 Cheiloclinium sp liana 1 2 2 deciduous Hippocratea sp liana 2 13 14 deciduous Hippocratea volubilis liana 4 270 300 deciduous Prionostemma aspera liana 1 288 289 deciduous Schaefferia frutescens shrub 1 22 50 evergreen Chrysobalanaceae no. spp=7 2 66 69 Hirtella racemosa tree 1 9 12 evergreen Licania apetala tree 1 3 3 evergreen Licania micrantha tree 1 44 44 evergreen Licania parvifructa tree 1 2 2 evergreen Licania sp tree 1 1 1 evergreen Licania sp1 tree 1 1 1 evergreen Licania sp2 tree 1 6 6 evergreen Clusiaceae no. spp=2 2 2 2 Clusia umbellata tree 1 1 1 evergreen Garcinia intermedia tree 1 1 1 evergreen Combretaceae no. spp=5 6 180 202 Combretum aculeatum liana 1 93 104 deciduous Combretum fruticosum liana 3 74 80 deciduous Combretum sp liana 1 8 13 deciduous Terminalia amazonia tree 1 4 4 evergreen Terminalia oblonga tree 1 1 1 evergreen 73 Doctoral Thesis – Roy González-M. Family Species LF PPf Nind Nstems Leaf Phenology Achariaceae no. spp=3 6 721 805 Connaraceae no. spp=1 1 8 8 Connarus ruber treelet 1 8 8 evergreen Convolvulaceae no. spp=2 2 4 5 Ipomoea carnea liana 1 3 3 deciduous Ipomoea sp liana 1 1 2 deciduous Ebenaceae no. spp=2 4 6 6 Diospyros sp1 tree 1 1 1 evergreen Diospyros sp2 tree 3 5 5 evergreen Erythroxylaceae no. spp=4 5 30 32 Erythroxylum hondense shrub 2 5 7 evergreen Erythroxylum jaimei treelet 1 1 1 evergreen Erythroxylum macrophyllum tree 1 15 15 evergreen Erythroxylum ulei treelet 1 9 9 evergreen Euphorbiaceae no. spp=16 10 1813 2123 Acalypha diversifolia shrub 2 137 139 evergreen Acalypha macrostachya tree 1 3 3 evergreen Acalypha sp shrub 1 1 1 evergreen Croton gossypiifolius tree 1 11 12 deciduous Croton niveus tree 1 140 188 evergreen Croton punctatus shrub 1 1 1 deciduous Croton rhamnifolius treelet 1 1426 1676 deciduous Croton schiedeanus treelet 1 26 32 evergreen Croton sp tree 1 1 1 deciduous Euphorbia cotinifolia treelet 1 7 7 evergreen Hura crepitans tree 2 17 17 deciduous Jatropha gossypiifolia shrub 1 3 4 evergreen Mabea trianae tree 1 3 4 evergreen Manihot carthaginensis tree 2 3 3 deciduous Omphalea diandra liana 1 6 6 evergreen Sapium glandulosum tree 2 28 29 deciduous Fabaceae no. spp=102 15 4696 7081 Abarema sp tree 1 10 14 deciduous Albizia carbonaria tree 2 13 13 deciduous Albizia niopoides tree 1 14 22 deciduous Albizia sp treelet 1 2 2 deciduous Albizia sp2 tree 1 4 4 deciduous Bauhinia glabra liana 3 13 16 deciduous Bauhinia hymenaeifolia liana 2 50 57 deciduous Bauhinia petiolata treelet 2 320 349 evergreen Bentamantha sp tree 1 2 2 deciduous Brownea ariza tree 1 1 1 evergreen Caesalpinia cassioides shrub 1 11 21 deciduous Caesalpinia coriaria treelet 3 166 279 deciduous Caesalpinia ebano tree 2 5 8 deciduous Caesalpinia punctata tree 1 3 3 deciduous Caesalpinia sp tree 1 4 12 deciduous Calliandra magdalenae tree 4 189 286 deciduous Cassia sp tree 1 1 1 deciduous Chloroleucon mangense tree 1 1 1 deciduous Clathrotropis macrocarpa tree 1 25 25 deciduous Coursetia ferruginea tree 4 98 136 deciduous Dioclea sp1 liana 1 4 5 deciduous Enterolobium cyclocarpum tree 2 6 6 deciduous Enterolobium schomburgkii tree 1 4 4 deciduous Enterolobium sp1 tree 2 5 12 deciduous Erythrina poeppigiana tree 1 1 1 deciduous 74 Ecology of woody plants in Colombian dry forests Family Species LF PPf Nind Nstems Leaf Phenology Achariaceae no. spp=3 6 721 805 Erythrina velutina tree 1 19 28 deciduous Gliricidia sepium tree 1 6 23 deciduous Haematoxylum brasiletto treelet 2 185 557 deciduous Humboldtiella arborea tree 1 36 46 deciduous Inga gracilifolia tree 1 48 52 evergreen Inga laurina tree 1 1 1 evergreen Inga marginata tree 1 2 3 evergreen Inga oerstediana tree 1 4 4 evergreen Inga sp tree 1 13 15 evergreen Inga sp1 tree 1 15 18 evergreen Inga sp4 tree 1 5 5 evergreen Inga sp6 tree 1 50 55 evergreen Inga vera tree 1 3 3 evergreen Leucaena leucocephala tree 1 38 39 deciduous Lonchocarpus macrophyllus tree 1 1 1 deciduous Lonchocarpus pictus tree 1 53 73 deciduous Lonchocarpus sanctae-marthae tree 1 3 4 deciduous Lonchocarpus violaceus tree 1 1 1 deciduous Luetzelburgia andina tree 1 16 16 deciduous Machaerium arboreum tree 3 747 1295 deciduous Machaerium biovulatum tree 3 29 34 deciduous Machaerium capote tree 6 312 345 evergreen Machaerium kegelii liana 1 32 33 deciduous Machaerium microphyllum liana 2 10 12 deciduous Machaerium mutisii liana 1 1 1 deciduous Machaerium sp liana 1 23 23 deciduous Machaerium sp1 tree 4 99 129 deciduous Machaerium sp2 liana 1 7 7 deciduous Machaerium sp3 liana 1 22 35 deciduous Machaerium sp6 tree 1 69 72 deciduous Morf sp7 liana 1 7 7 deciduous Muellera broadwayi tree 1 7 10 deciduous Myrospermum frutescens tree 4 52 73 deciduous Peltogyne purpurea tree 1 3 3 deciduous Peltogyne sp tree 1 41 41 deciduous Phanera guianensis liana 1 22 25 deciduous Piptadenia flava treelet 1 47 139 deciduous Piptadenia sp tree 1 3 3 deciduous Pithecellobium dulce tree 2 8 11 deciduous Pithecellobium lanceolatum tree 2 163 272 deciduous Pithecellobium roseum tree 1 7 16 deciduous Pithecellobium sp2 treelet 1 7 14 deciduous Platymiscium pinnatum tree 6 85 90 deciduous Prosopis juliflora treelet 4 176 322 deciduous Pseudosamanea guachapele tree 2 6 7 deciduous Pterocarpus officinalis tree 1 1 1 deciduous Pterocarpus rohrii tree 5 308 401 deciduous Pterocarpus sp tree 1 53 53 deciduous Pterocarpus sp4 tree 1 4 4 deciduous Pueraria phaseoloides liana 1 4 4 deciduous Senegalia gaumeri tree 1 1 1 deciduous Senegalia hayesii shrub 1 20 27 deciduous Senegalia macbridei liana 1 11 16 deciduous Senegalia sp tree 1 15 18 deciduous Senegalia sp1 tree 3 42 54 deciduous Senegalia sp2 treelet 1 6 12 deciduous 75 Doctoral Thesis – Roy González-M. Family Species LF PPf Nind Nstems Leaf Phenology Achariaceae no. spp=3 6 721 805 Senegalia tamarindifolia tree 2 89 161 deciduous Senna atomaria tree 2 10 13 deciduous Senna bacillaris shrub 1 2 2 deciduous Senna pallida treelet 1 1 1 deciduous Senna sp1 tree 1 7 7 deciduous Senna sp2 treelet 1 9 17 deciduous Senna spectabilis tree 1 1 1 deciduous Styphnolobium sporadicum tree 2 35 35 deciduous Swartzia robiniifolia treelet 1 78 78 deciduous Swartzia simplex tree 2 3 3 deciduous Swartzia sp1 treelet 1 7 7 deciduous Swartzia trianae tree 3 150 185 deciduous Tachigali guianensis tree 1 5 5 deciduous Vachellia collinsii tree 1 1 4 deciduous Vachellia farnesiana treelet 3 14 19 deciduous Vachellia macracantha treelet 1 344 561 deciduous Vachellia sp tree 2 11 45 deciduous Vachellia tortuosa treelet 1 23 98 deciduous Vigna caracalla liana 1 1 1 deciduous Zygia inaequalis tree 1 2 2 evergreen Zygia sp tree 1 2 2 evergreen Hernandiaceae no. spp=1 2 14 14 Gyrocarpus americanus tree 2 14 14 deciduous Lacistemataceae no. spp=1 1 17 22 Lacistema aggregatum tree 1 17 22 evergreen Lamiaceae no. spp=4 3 8 10 Aegiphila sp shrub 1 1 1 evergreen Callicarpa acuminata treelet 1 4 6 evergreen Vitex orinocensis tree 1 1 1 deciduous Vitex sp treelet 1 2 2 deciduous Lauraceae no. spp=7 7 370 407 Aiouea sp tree 1 2 2 evergreen Endlicheria sp tree 1 1 1 evergreen Licaria applanata tree 1 108 114 evergreen Licaria guianensis tree 1 1 1 evergreen Nectandra sp tree 1 31 37 evergreen Ocotea schomburgkiana tree 1 24 24 evergreen Ocotea veraguensis tree 3 203 228 evergreen Lecythidaceae no. spp=8 6 422 466 Eschweilera sp tree 1 8 8 evergreen Eschweilera tenuifolia tree 1 94 104 evergreen Gustavia augusta tree 1 67 77 evergreen Gustavia hexapetala tree 1 16 17 evergreen Gustavia sp tree 1 199 218 evergreen Gustavia superba tree 2 20 22 evergreen Lecythis chartacea tree 1 7 7 evergreen Lecythis minor tree 2 11 13 evergreen Loganiaceae no. spp=1 1 5 5 Strychnos panamensis liana 1 5 5 evergreen Malpighiaceae no. spp=11 12 452 746 Bronwenia cornifolia liana 1 9 9 deciduous Bunchosia armeniaca tree 1 6 16 evergreen Bunchosia diphylla shrub 1 1 1 evergreen Bunchosia odorata shrub 1 10 11 evergreen Bunchosia pseudonitida tree 1 61 73 evergreen Bunchosia sp shrub 1 1 2 evergreen 76 Ecology of woody plants in Colombian dry forests Family Species LF PPf Nind Nstems Leaf Phenology Achariaceae no. spp=3 6 721 805 Bunchosia sp2 liana 1 64 96 evergreen Hiraea reclinata shrub 1 21 28 deciduous Hiraea sp liana 3 41 77 deciduous Malpighia glabra tree 8 234 429 evergreen Morf sp9 liana 1 4 4 evergreen Malvaceae no. spp=11 12 212 261 Apeiba tibourbou tree 2 13 13 deciduous Cavanillesia platanifolia tree 1 2 2 deciduous Ceiba pentandra tree 3 10 10 deciduous Guazuma ulmifolia tree 6 66 111 deciduous Hampea thespesioides tree 1 9 10 evergreen Herrania laciniifolia shrub 1 8 8 evergreen Luehea seemannii tree 2 13 13 deciduous Ochroma pyramidale tree 1 12 12 evergreen Pachira nukakica tree 1 16 18 deciduous Pachira quinata tree 3 25 26 deciduous Pseudobombax septenatum tree 6 38 38 deciduous Melastomataceae no. spp=2 1 5 10 Graffenrieda rotundifolia shrub 1 3 8 deciduous Miconia splendens shrub 1 2 2 evergreen Meliaceae no. spp=11 8 3289 3440 Cedrela odorata tree 1 4 4 evergreen Guarea glabra tree 1 37 49 evergreen Guarea guidonia tree 1 2 2 evergreen Guarea sp1 tree 1 2 3 evergreen Trichilia acuminata tree 1 446 474 evergreen Trichilia carinata tree 3 480 493 evergreen Trichilia elegans tree 4 191 205 evergreen Trichilia hirta tree 1 3 3 evergreen Trichilia martiana tree 1 2 2 evergreen Trichilia oligofoliolata tree 3 1889 1953 evergreen Trichilia pallida tree 6 233 252 evergreen Menispermaceae no. spp=1 1 2 2 Morf sp8 liana 1 2 2 evergreen Moraceae no. spp=19 8 1216 1241 Brosimum alicastrum tree 7 224 226 evergreen Brosimum guianense tree 1 9 9 evergreen Brosimum sp tree 1 88 92 evergreen Castilla elastica tree 1 1 1 evergreen Clarisia biflora tree 1 520 522 evergreen Clarisia racemosa tree 1 1 1 evergreen Ficus americana tree 1 13 18 evergreen Ficus obtusifolia tree 1 2 2 evergreen Ficus sp tree 2 3 3 evergreen Ficus trigona tree 1 1 1 evergreen Ficus zarzalensis tree 1 1 1 evergreen Helianthostylis sprucei tree 1 30 31 evergreen Maclura tinctoria tree 3 9 9 deciduous Pseudolmedia sp1 tree 1 19 20 evergreen Sorocea muriculata tree 1 4 4 evergreen Sorocea sp tree 1 10 10 evergreen Sorocea sprucei tree 1 88 91 evergreen Sorocea trophoides tree 1 159 166 evergreen Trophis racemosa tree 1 34 34 evergreen Myrtaceae no. spp=20 11 1673 2076 Calyptranthes multiflora tree 1 2 2 deciduous 77 Doctoral Thesis – Roy González-M. Family Species LF PPf Nind Nstems Leaf Phenology Achariaceae no. spp=3 6 721 805 Calyptranthes speciosa tree 1 1 1 deciduous Eugenia biflora treelet 1 3 3 evergreen Eugenia florida tree 2 8 8 evergreen Eugenia monticola tree 1 141 151 evergreen Eugenia procera tree 5 1348 1710 evergreen Eugenia sp tree 1 1 1 evergreen Eugenia sp1 tree 1 1 1 evergreen Eugenia sp3 tree 2 22 27 evergreen Eugenia sp4 tree 1 13 14 evergreen Eugenia sp5 treelet 1 7 9 evergreen Eugenia venezuelensis shrub 1 1 1 evergreen Morf sp1 shrub 1 1 1 evergreen Morf sp2 tree 1 14 16 evergreen Morf sp3 tree 1 1 1 evergreen Myrcia fallax tree 1 22 22 deciduous Myrcia sp1 tree 1 8 8 deciduous Myrcia sp2 tree 1 3 3 deciduous Pseudanamomis umbellulifera shrub 1 75 96 deciduous Psidium guineense treelet 1 1 1 evergreen Nyctaginaceae no. spp=10 14 625 880 Guapira costaricana tree 1 67 69 deciduous Guapira sp tree 3 8 17 deciduous Guapira sp1 treelet 1 275 327 deciduous Guapira uberrima tree 1 57 104 deciduous Neea ignicola tree 1 2 2 deciduous Neea macrophylla tree 1 14 17 deciduous Neea sp tree 3 6 18 deciduous Neea sp1 tree 2 15 22 deciduous Neea sp2 tree 1 58 106 deciduous Pisonia aculeata liana 9 123 198 deciduous Ochnaceae no. spp=1 1 4 4 Ouratea sp tree 1 4 4 evergreen Olacaceae no. spp=1 1 23 23 Heisteria acuminata tree 1 23 23 evergreen Opiliaceae no. spp=1 1 2 2 Agonandra brasiliensis tree 1 2 2 deciduous Petiveriaceae no. spp=1 3 56 60 Seguieria americana liana 3 56 60 evergreen Phyllanthaceae no. spp=3 4 113 136 Amanoa guianensis tree 1 1 1 evergreen Margaritaria nobilis treelet 1 1 1 deciduous Phyllanthus botryanthus shrub 2 111 134 deciduous Phytolaccaceae no. spp=1 1 20 20 Trichostigma octandrum liana 1 20 20 indet. Piperaceae no. spp=2 2 88 130 Piper amalago shrub 1 1 1 evergreen Piper sp6 treelet 1 87 129 evergreen Polygalaceae no. spp=2 1 22 29 Securidaca sp tree 1 18 25 deciduous Securidaca sp2 shrub 1 4 4 deciduous Polygonaceae no. spp=15 11 506 940 Coccoloba acuminata treelet 1 1 6 evergreen Coccoloba caracasana tree 1 15 30 evergreen Coccoloba densifrons tree 1 1 4 evergreen Coccoloba obovata tree 1 1 1 evergreen Coccoloba obtusifolia tree 2 26 153 evergreen 78 Ecology of woody plants in Colombian dry forests Family Species LF PPf Nind Nstems Leaf Phenology Achariaceae no. spp=3 6 721 805 Coccoloba padiformis tree 1 2 4 evergreen Coccoloba sp tree 1 31 44 evergreen Coccoloba sp1 tree 5 128 215 evergreen Coccoloba sp2 tree 1 21 60 evergreen Coccoloba sp4 shrub 1 15 145 evergreen Ruprechtia ramiflora tree 1 2 4 deciduous Ruprechtia sp1 tree 4 24 28 deciduous Triplaris americana tree 1 2 3 deciduous Triplaris melaenodendron tree 4 236 242 deciduous Triplaris sp tree 1 1 1 deciduous Primulaceae no. spp=7 6 104 186 Ardisia foetida shrub 1 4 5 deciduous Bonellia frutescens shrub 1 14 20 deciduous Jacquinia armillaris shrub 1 70 112 deciduous Jacquinia frutescens shrub 1 10 43 deciduous Morf sp10 shrub 1 2 2 evergreen Myrsine sp1 treelet 1 1 1 evergreen Stylogyne turbacensis tree 1 3 3 evergreen Rhamnaceae no. spp=3 4 62 101 Sageretia elegans liana 1 6 6 deciduous Ziziphus saeri treelet 1 3 10 deciduous Ziziphus strychnifolia tree 2 53 85 deciduous Rubiaceae no. spp=37 14 816 1006 Alibertia sp tree 1 1 1 evergreen Alseis blackiana tree 1 27 33 evergreen Amaioua corymbosa tree 1 5 5 evergreen Bertiera angustifolia tree 1 1 1 deciduous Calycophyllum candidissimum tree 1 9 13 deciduous Chiococca alba shrub 1 34 36 deciduous Chiococca sp shrub 2 8 14 deciduous Chomelia spinosa treelet 1 8 9 deciduous Coffea arabica shrub 1 2 2 evergreen Cordiera myrciifolia treelet 1 2 2 evergreen Coussarea paniculata treelet 1 3 3 evergreen Coutarea hexandra treelet 2 7 10 deciduous Coutarea sp treelet 1 5 11 deciduous Genipa americana tree 2 87 88 evergreen Guettarda comata tree 1 6 20 deciduous Guettarda roupalifolia tree 1 42 47 deciduous Ixora sp tree 1 3 4 evergreen Ladenbergia sp tree 1 4 4 evergreen Morf sp11 liana 1 4 4 evergreen Morf sp17 tree 1 1 1 evergreen Morf sp47 tree 1 1 1 evergreen Morf sp49 liana 1 2 2 deciduous Palicourea rigida shrub 1 9 10 evergreen Palicourea sp shrub 1 1 1 evergreen Pittoniotis trichantha tree 1 20 26 evergreen Psychotria micrantha shrub 1 100 104 evergreen Psychotria sp shrub 1 1 1 evergreen Randia aculeata tree 4 11 31 deciduous Randia armata tree 5 115 143 deciduous Randia dioica tree 2 17 22 deciduous Randia obcordata treelet 3 88 135 deciduous Randia pubistyla treelet 1 3 12 deciduous Rudgea crassiloba tree 1 17 18 evergreen 79 Doctoral Thesis – Roy González-M. Family Species LF PPf Nind Nstems Leaf Phenology Achariaceae no. spp=3 6 721 805 Rudgea sp shrub 1 13 14 evergreen Simira cordifolia tree 4 157 173 evergreen Simira klugei tree 1 1 4 deciduous Simira rubescens tree 1 1 1 deciduous Rutaceae no. spp=13 10 830 1199 Amyris pinnata tree 5 241 268 evergreen Esenbeckia alata tree 2 13 14 deciduous Esenbeckia pentaphylla tree 1 66 67 deciduous Galipea sp tree 1 3 3 evergreen Spathelia sp tree 1 3 3 deciduous Zanthoxylum fagara treelet 3 111 276 deciduous Zanthoxylum lenticulare tree 2 64 67 deciduous Zanthoxylum rhoifolium tree 4 25 47 deciduous Zanthoxylum rigidum tree 2 2 2 evergreen Zanthoxylum schreberi tree 5 193 330 deciduous Zanthoxylum sp2 tree 2 22 25 deciduous Zanthoxylum sp4 tree 1 48 50 deciduous Zanthoxylum verrucosum tree 1 39 47 deciduous Salicaceae no. spp=12 13 635 775 Banara ibaguensis shrub 1 1 1 deciduous Casearia aculeata tree 4 324 372 evergreen Casearia corymbosa tree 4 17 21 evergreen Casearia praecox tree 7 55 62 evergreen Casearia sp1 tree 2 5 6 evergreen Casearia sp2 tree 1 1 1 evergreen Casearia sp6 tree 1 3 3 evergreen Casearia sylvestris tree 4 138 186 evergreen Casearia tremula treelet 1 76 103 evergreen Casearia zizyphoides treelet 1 2 3 evergreen Morf sp31 treelet 1 2 2 evergreen Xylosma intermedia treelet 1 11 15 evergreen Sapindaceae no. spp=18 9 977 1173 Allophylus nitidulus tree 1 1 2 deciduous Allophylus sp tree 1 1 1 deciduous Cupania cinerea tree 1 1 1 evergreen Cupania latifolia tree 1 55 55 evergreen Cupania sp1 tree 1 44 52 evergreen Dilodendron costaricense tree 1 11 13 deciduous Matayba sp tree 1 51 60 evergreen Matayba sp1 tree 1 19 19 evergreen Melicoccus bijugatus tree 4 507 621 evergreen Melicoccus oliviformis tree 4 126 153 deciduous Paullinia alata liana 1 32 32 evergreen Paullinia capreolata liana 1 5 5 evergreen Paullinia cururu liana 2 40 41 evergreen Paullinia globosa liana 1 21 22 evergreen Paullinia sp liana 1 1 1 evergreen Paullinia sp2 liana 1 27 60 evergreen Sapindus saponaria tree 3 19 19 deciduous Serjania sp1 liana 1 16 16 deciduous Sapotaceae no. spp=11 10 443 514 Chrysophyllum cainito tree 1 16 18 evergreen Elaeoluma sp tree 1 1 1 evergreen Manilkara sp tree 1 14 16 evergreen Pouteria plicata tree 1 12 13 evergreen Pouteria sp1 tree 1 159 161 evergreen 80 Ecology of woody plants in Colombian dry forests Family Species LF PPf Nind Nstems Leaf Phenology Achariaceae no. spp=3 6 721 805 Pouteria sp3 tree 1 1 1 evergreen Pouteria sp4 tree 1 26 29 evergreen Pouteria sp7 tree 5 144 169 evergreen Pouteria sp8 tree 1 1 2 evergreen Pradosia colombiana tree 3 51 63 deciduous Sideroxylon obtusifolium treelet 1 18 41 deciduous Siparunaceae no. spp=1 1 21 22 Siparuna guianensis tree 1 21 22 evergreen Smilacaceae no. spp=1 1 1 1 Smilax sp liana 1 1 1 deciduous Solanaceae no. spp=5 2 7 9 Cestrum schlechtendalii shrub 1 1 1 deciduous Cestrum sp1 tree 1 2 3 deciduous Lycianthes sp shrub 1 1 1 evergreen Solanum lepidotum shrub 1 2 3 evergreen Solanum sp shrub 1 1 1 evergreen Stemonuraceae no. spp=1 1 13 13 Discophora sp tree 1 13 13 evergreen Thymelaeaceae no. spp=1 1 3 3 Daphnopsis sp tree 1 3 3 evergreen Ulmaceae no. spp=3 6 739 973 Ampelocera macphersonii tree 1 448 474 evergreen Ampelocera sp1 tree 4 80 89 evergreen Phyllostylon rhamnoides tree 1 211 410 evergreen Urticaceae no. spp=5 5 123 151 Cecropia peltata tree 4 41 44 deciduous Myriocarpa stipitata tree 1 11 15 evergreen Urera baccifera shrub 1 2 2 evergreen Urera caracasana tree 2 63 84 evergreen Urera simplex tree 1 6 6 evergreen Verbenaceae no. spp=4 4 83 106 Citharexylum kunthianum treelet 1 40 48 deciduous Lantana camara forb 1 1 4 evergreen Lippia origanoides tree 1 4 5 evergreen Petrea sp tree 1 38 49 deciduous Violaceae no. spp=3 5 9 11 Leonia sp1 tree 2 2 2 evergreen Rinorea pubiflora shrub 1 1 1 evergreen Rinorea sp1 tree 2 6 8 evergreen Vitaceae no. spp=2 4 19 20 Cissus sp liana 1 6 6 deciduous Cissus verticillata liana 3 13 14 deciduous Vochysiaceae no. spp=1 1 8 8 Vochysia vismiifolia tree 1 8 8 evergreen Zygophyllaceae no. spp=1 3 18 18 Bulnesia arborea tree 3 18 18 evergreen Morf no. fam=8, no. spp=8 6 62 71 Morf sp12 liana 1 4 4 deciduous Morf sp14 tree 1 2 2 deciduous Morf sp15 liana 1 1 1 indet. Morf sp16 liana 1 7 7 indet. Morf sp18 liana 1 9 9 deciduous Morf sp4 tree 1 33 40 evergreen Morf sp40 liana 1 4 6 indet. Morf sp6 treelet 1 2 2 deciduous 81 Doctoral Thesis – Roy González-M. Chapter 4 Importance of species abundances representativeness and trait variability for a functional trait community characterization in tropical dry forests Roy González-M., Juan M. Posada, Carlos P. Carmona, and Beatriz Salgado-Negret Prepared for submission in Functional Ecology 82 Ecology of woody plants in Colombian dry forests Summary 1. The community functional trait characterization in species-rich ecosystems faces sampling trade-offs on species abundances representativeness and trait variability. Overall, sampling designs for dominant species have received broad attention by using community-weighted mean (CWM) to indicate locally optimal phenotypes. 2. CWM, as a descriptor of optimal phenotypes, does not consider trait variability, failing in to explain the mechanisms behind of community trait composition. In particular, when viable co-existing strategies promote divergence, or at fine scales where ecosystems have similar filtering pulses, as is expected in Tropical Dry Forests. 3. We compared different trait sampling designs, vary the species abundance representativeness and trait variability, to evaluate differences in the level of trait community characterization and the trait-environment and trait-biomass relationships in TDF. 4. Following and abundance-weighted trait sampling design, we intensively sampled 15 functional traits in 321 species of ten 1-ha permanent plots in TDF. We sampled a least one individual per species per plot (in the case of ‘rarer’ species) up to 12 individuals when abundant (N=1391 tree individuals). We also monitored stem biomass growth for 19,740 trees for the sampled species, between 2013 and 2017. 5. To evaluate the abundance representativeness, we ran linear correlations between the total species abundances per community and those for the abundance-weighted trait sampling and dominant ones. To assess the main sources of trait variability, we performed a nested variance partitioning with individuals, populations, species, and communities as the ecological scales. We performed linear and linear mixed- effects models for the sampling designs to test the trait-environment and trait-biomass relationships. 6. We found that (i) sampling designs considering only dominant species did not adequately reflect the species abundance representativeness in TDF. (ii) Differences within communities explained a higher proportion of total trait variability than among them. (iii) Trait-environment and trait-biomass had consistently stronger relationships when samplings improve the species abundances representativeness and trait variability. 7. Our results indicate that abundance-weighted trait sampling designs may be useful to reconciling the trade-offs between species abundances representativeness and trait variability in samplings characterizing community trait composition, and to detect trait-environment and trait-biomass relationships in TDF. Key-words: abundance-weighted sampling design, community weighted mean, trait variability, tropical dry forests Introduction Understanding the relationships between plant functional traits (traits), the environment, and forest biomass production, as well as the mechanisms behind these associations, is a recurrent goal of functional ecologists 83 Doctoral Thesis – Roy González-M. (Bruelheide et al. 2018). Traits are expected to reflect the species responses to local environmental conditions, but they should also affect the plant species performance via effects on vegetative biomass growth (Poorter et al. 2008; Paine et al. 2015). Consequently, traits should determine functional species composition within communities and differences between them along environmental gradients (McGill et al. 2006). However, determining these relations at the community scale present critical challenges for trait sampling designs, which are associated with the species’ abundance representativeness and the accuracy of the trait estimations that provide an ‘adequate’ description of the community trait composition (Baraloto et al. 2010b; Messier et al. 2010; Carmona et al. 2015; van der Plas et al. 2017). Overall, studies testing the trait-environment and trait-biomass relationships usually choose sampling designs aimed to characterizing the community weighted mean trait values (CWM; Bruelheide et al., 2018; Wieczynski et al., 2019). The idea behind CWM is that being the abundances a structural parameter for species occupation within communities, it should reflect the locally optimal phenotype in response to the environment (Violle et al., 2007). Therefore, changes in CWM between communities may reflect optimal phenotypic responses to the environment (Muscarella & Uriarte 2016). Likewise, if the vegetative growth depends on species traits, the contribution of the optimal phenotype (CWM) should reflect the effects of traits on biomass within and between communities (Garnier et al. 2004; Finegan et al. 2015a). Although CWM is widely used as a descriptor of the community trait composition (Lavorel & Garnier 2002; Funk et al. 2017), it has some implications that need to be considered. First, it only provides information around the mean value and fails to include the variability of trait values within and among communities (Carmona et al. 2015). Second, the local CWM-optimal phenotype in response to the environment may be much different from the expected because there are different viable strategies co- existing as the result of alternative equally competitive functional phenotypes (Marks & Lechowicz 2006; Poorter et al. 2008), or because biotic interactions may change along abiotic gradients promoting community-trait divergence (Bernard-Verdier et al., 2012; Chauvet, Kunstler, Roy, & Morin, 2017; Kumordzi et al., 2019). Third, at fine spatial scale (e.g., short environmental gradients, same ecosystem abiotic conditions, small geographic extension) trait variability may be more important within communities than among them to reflect changes in community trait composition to environments with the same filtering pulses (Auger & Shipley 2013; Chalmandrier et al. 2017; Petruzzellis et al. 2017). These implications are particularly important for tropical dry forests (TDF), a strongly drought controlled ecosystem in the Neotropics with high diversity and turnover of species (Pennington et al. 2009; Linares-Palomino et al. 2010; DRYFLOR et al. 2016). Under the umbrella of drought conditions, variation in species composition and functional diversity TDF are also determined by variations in temperature, winds, soil fertility, and land-cover transformation (González-M. et al. 2018, 2019) where tree species have different trait strategies to cope with the combinations of those environmental factors (Markesteijn et al. 2011a, b; Méndez-Alonzo et al. 2012; Pineda-García et al. 2015; González-M. et al. 2019). For instance, in response to water-constraints, resulting from arid environments (e.g., harsh climates and land-cover transformation, or high harsh climates with low soil water retention), some strategies are associated with tolerance via morphological hydraulic designs (e.g., small and low density of xylem conduits, high stem wood density; Méndez-Alonzo et al., 2012), while others are associated with drought avoidance or water storage (e.g., reserve water in trunks and roots, and drop the leaves, low stem wood density; Markesteijn, Poorter, Paz, et al., 2011; Sobrado, 1997). These opposing but equivalent trait strategies in response to water-constraints may produce communities highly divergent in community trait composition but similar CWM values. Thus, to adequately describe TDF community trait composition and variation of communities 84 Ecology of woody plants in Colombian dry forests along environmental gradients, sampling designs are required considering community trait abundance structure but without losing the trait variability description. There are sampling trade-offs when considering trait abundance structure and trait variability to characterize the community trait composition in highly diverse ecosystems (Baraloto et al. 2010b). At one extreme, an ideal functional trait characterization of a community requires measuring traits in all individuals of all species among all communities (Carmona et al. 2015). Although this design may reflect the entire trait community abundance structure and its variability with high accuracy, is unfeasible in most ecosystems, particularly for species-rich tropical ones where field trait samplings are time expensive (Baraloto et al. 2010b). To the other extreme, a more practical sampling design is to focus only on dominant species (those accounted for at least 80% of local abundance; Garnier et al., 2004) and measurement a least of one individual per species among the communities (Carmona et al. 2015). With the lesser effort, this design limits our understanding of the entire community trait composition and does not consider trait variability in the approach. These are why trait studies vary through the level of trait measurement and sampling effort (Baraloto et al. 2010b; Carmona et al. 2015). Thus, there is a need to find sampling designs that adequately represent the community trait composition considering both trait abundance structure and trait variability, getting close to the ideal, but generally unfeasible, sampling design when all individuals are measured. Here, we compared the strength of different trait sampling designs to detect the trait-environment and trait-biomass production relationships in Colombian TDF, attempting to account for a high trait abundance structure representativeness and trait variability. To do that we performed an abundance- weighted trait sampling design for fieldwork, consisting in collecting traits for all tree species in all the communities in proportion to their local abundances, and simulated six alternative trait sampling designs differing in the species abundances representativeness and trait variability (Figure 1). We intensively sampled 15 functional traits (N=1391 trees, 321 species) in ten 1-ha permanent plots of TDF, distributed along climatic, soils, and land-cover transformation gradients (N= 29 environmental variables), and monitored tree biomass growth between 2013 and 2017 (N=19,740 trees). Our specific questions were: (i) Are the abundance-weighted trait sampling design reflect the species abundance structure of TDF tree communities? (ii) Do have sampling designs based on dominant species the abundance representativeness of TDF trees communities? (iii) What proportion of trait variation is captured among different ecological scales (i.e., individuals, populations, species and communities) in TDF? (iv) To what extent do different sampling designs detect the trait-environment and trait-biomass production relationships in TDF? Considering that Colombian TDF have a high diversity turnover but are restricted to drought regimes, where communities have species with multiple trait strategies in response to the environmental variation within this ecosystem (González-M. et al. 2018, 2019) we hypothesize that sampling designs incorporating the entire species abundance representativeness and a high characterization of trait variability be optimum to describe the trait composition of its communities and to detect differences of them among environmental gradients. Specifically, we expected that (i) an abundance-weighted trait sampling design be more representative of the species abundance that those designs focused in dominant species, (ii) trait variability within communities (e.g., individuals, populations and species) explained a more faction of variance than trait variability among communities, and (iii) To the extent that sampling designs choose a detailed level of trait estimation should increase the strength of trait-environment and trait-biomass production relationships. 85 Doctoral Thesis – Roy González-M. Materials and methods Field sampling and data sets Field sampling was conducted in ten 1-ha permanent plots distributed across climatic, soils, and land-cover transformation gradients in TDF (González-M. et al., 2019; see details of 29 variables in Table S1). Between 2013 and 2015 we tagged, measured and identified all trees with a diameter at breast height ≥ 2.5 cm (DBH, measured at 1.3 m height). We measured DBH (cm) and tree height (m) and sampled wood sections of mature branches to quantify wood density (g cm-3) by the volume displacement method (Pérez- Harguindeguy et al. 2013; Salgado-Negret et al. 2015). Afterward, between 2016 and 2017, we resampled all surviving trees and measured DBH increments. Overall, we measured 19,740 individuals belonging to 321 species. Based on these inventories, we estimated biomass growth rates (BGR, kg ha-1 yr-1) of each tree as the difference in biomass between the last and the first inventory divided by the time interval of inventories (Prado-Junior et al. 2016; Poorter et al. 2017). For both inventories, biomass was calculated following the allometric formulas type I and type II suggested for TDF in Alvarez et al. (2012). During the last inventory, we collected sun-exposed leaves for leaf traits and mature branches for wood and hydraulic traits. All species in all communities (plots) were sampled (Figure 1a) following an abundance-weighted trait sampling design, which consisted in measuring traits in at least one individual in species with less than two individuals per plot, all the way to 12 individuals for the most abundant species in each plot (Figure 1a). In total, we collected traits in 1391 individuals from 321 species. We measured nine wood traits related to water use efficiency and safety (Méndez-Alonzo, Paz, Cruz, Rosell, & Olson, 2012; Pineda-García, Paz, Meinzer, & Angeles, 2015): fiber wall thickness (FWT, µm), hydraulically weighted diameter (dh, µm), leaf thickness (Lth, mm), maximum vessel area (VA 2max, µm ), pit area (PA, µm2), pit diameter aperture (DApit, µm), vessel area (VA, µm2), vessel density (VD, vessels mm-2), xylem potential hydraulic conductivity (K , kg m-1 s-1 MPa-1), anhydrous wood density (WD , g cm3p 0 ). We also measured six traits that are expected to be good indicators of resource-use strategies and biomass production (Poorter et al. 2010; Pérez-Harguindeguy et al. 2013; Finegan et al. 2015a): leaf area (LA, mm2), leaf dry matter content (LDMC, mg g-1), specific leaf area (SLA, mm2 mg-1), wood density (WD, g cm3), and wood water content at maximal capacity (WCmax, kg kg-1). These traits are known to be sensitive to climate, soils and land-cover transformation gradients, and they have been associated with biomass production of TDF (Prado-Junior et al. 2016; Poorter et al. 2017). For details of the sampled traits and trait descriptions see Table S2. Functional trait sampling designs We performed a sampling design in which we measured individuals of all species in proportion to the local abundance of species in each plot (Figure 1a; abundance-weighted trait sampling). This strategy is as close as one can get to an optimal characterisation to the “real” functional structure with a reasonable but still unfeasible for most cases, amount of trait sampling (Figure 1b; Baraloto et al., 2010; Carmona et al., 2015). We estimated trait values for each species within each community, simulating six different alternative designs. These simulated designs varied on the source of traits used in each community and depend on the degree to which trait variability is taken or not into account. Basically, in each site, we used either the trait values of individuals of each species from the community (Figure 1c; pops design), or the average trait values of all the individuals of each species across all communities (Figure 1d; sps design), or the average trait values of all the individuals of each species across all communities, but only considering the locally most abundant species that accounted for at least 80% of local abundance (Figure 1e; sps80% design). Each 86 Ecology of woody plants in Colombian dry forests of these strategies is placed along a trade-off between sampling effort and trait data quality, with the pops design requiring the measurement of many more individuals than the sps design, and with the sps design requiring the measurement of many more species than the sps design (see details in Figure 1). For each of these approaches, we also estimated the average trait value of the community using the local abundance of each species to weight their contribution towards the local mean (Figure 1c-e; CWM designs). CWM design did not consider trait variability within and among communities because mean traits values are weighted by the relative species abundance, providing only one trait value per community. (a) Com. 1 Com. 2 ... Com. n Abundances Abundance-weighted trait sampling design (b) Abundances trait comm. trait (c) Abundances trait comm. trait Com. 1 Com. 1 inds pops ,- Com. 2 Com. 2 CWM$%$& = (A),0× t$%$& inds 3,-pops )*+ Com. n Com. n inds pops (d) Abundances trait comm. trait (e) Abundances trait comm. trait Com. 1 Com. 1 ,- ,- Com. 2 CWM&$& = (A),0× t&$&3,- Com. 2 CWM45% = (A),0× t&$&78%3,- sps )*+ sps80% )*+ Com. n Com. n Species covering 80% of abundances per community Figure 1. Illustration of the trait sampling for this study. Total species abundances on tree communities (a, upper panel) and the abundance weighted trait sampling design (b) in which all individuals of all species in proportion to the local species abundance were sampled (individuals; inds). The six simulated sampling designs varied in species abundances representativeness and trait variability. Populations (pops) is a design where we calculated average values per trait per species within a community; species (sps) corresponds to the average values per species across all communities; sps80% was calculated using traits for species accounted for at least 80% of species abundance per community (1-ha permanent plot). CWM based on pops (CWMpops), sps (CWMsps) and sps80% (CWMsps80%) sampling designs, where j is each community (i.e., plot), k is a species collected in each community based on its abundances, A is the abundances of the species k in the community j, and t the trait value of the species k in the community j according with the trait sampling design (pops, sps and sps80%). Here, the inds design includes the intraspecific trait variability and the species abundance representativeness within each community. The pops design considers interspecific trait variability within and among communities, but fail to detect species abundance representation of each community. The sps design did not considers trait variability and species abundance representativeness within and among communities. The sps80% design is similar to sps but only includes dominant species. CWM approaches do not consider trait variability within and among communities, but detect the abundance representativeness trait value of each community. 87 … … … … Doctoral Thesis – Roy González-M. Statistical analyses We ran Pearson’ pairwise correlations (r) between the total species abundances and the sampled species abundances to evaluate if our abundance-weighted sampling design was representative of the species abundance structure of each TDF community. We log-transformed the abundances due to the differences in the magnitude of change between the plot species abundances (1-540 individuals sp-1 on average) and the sampled species abundances (1-8 individuals sp-1 on average). Significant positive r (P <0.001) indicates an appropriate sampling effort in each plot. Then, to evaluate if dominant species (sps80%) represent the species abundance structure of each community, we ran r again only with those species that summed at least 80% of the abundances per plot. We considered a significant sps80% abundance representativeness when r showed significant positive associations along the complete set of species per plot (P <0.001). Afterward, we performed a variance component analysis using individuals (inds), populations (pops), species (sps), and plots (coms) as the partitioning factors of the variance in order to account for the proportion of explained variance of the 15 functional traits across the considered scales (individuals within populations, populations within plots, species within plots and variability across plots). To do that, we used the functions ‘lme’ and ‘varcomp’ from R (version 3.5.3, R Core Team, 2020) following the procedures suggested in Messier et al. (2010). This analysis allowed us to how variation in a given trait changes across ecological scales (Messier et al. 2010; Carmona et al. 2015). Finally, to evaluate the trait-environment and trait-biomass relationships, we performed linear and linear mixed-effects models based on our abundance-weighted trait sampling design and the six trait simulated sampling designs. For the inds design, we performed linear mixed-effects models with the measurement traits per individual of each species in each plot as the response variable, and the environmental variables as the predictors. Here, we used species across plots (uSP) and populations within the plots (uP) as the random effects in a nested calculation (Table S3). For the pops design, we performed linear mixed-effects models with the mean trait value of each species in each plot as the response variable, and the environmental variables as the predictors. For these mixed models we used species across the plots (uSP) as the random effects. For the sps and sps80% (mean species trait value), as well as the three CWM designs (abundance-weighted trait values), we ran simple linear models with the environmental variables as the predictors and the respective trait value as the response. The same modelling procedures were done for the trait-biomass production relationships, with BGR as the predicted variable at each organization level. For details in the modelling procedures see Table S3. In both trait-environmental and trait-biomass relationships, we considered that a trait sampling design adequately detected the functional trait composition variation if b1 was significative different from 0 (P<0.05). Models were run with all the 29 environmental variables (Tables S4 and S5) using the functions “lm” and “lmer” from R. In total, we ran 3248 models for the trait-environmental relationships (464 models per design) and 105 models for the trait-biomass relationships (15 models per design). However, to facilitate the interpretation of the results we presented those based on the first axes of a principal component analysis (PCA) performed for three environmental dimensions: (i) Climate (PCA axis 1, 56.6% explained variance) in which negative values indicated low climatic severity (i.e., plots with higher humidity and annual rainfall) while positive values indicated high climate severity (i.e., plots with more aridity, wind speed and solar radiation). (ii) Soil nutrient limitations (PC1 48.8%) in which negative values indicated plots with low soil acidity, high cation exchange capacity and low fractions of sands, and positive values indicated plot with more infertile soils and low water retention capacity. (iii) Land-cover transformation (PC1 56.5%) where negative values were associated with plots with higher mature forest cover and positive values 88 Ecology of woody plants in Colombian dry forests indicated plots with high secondary vegetation covering and high human land cover transformation. For details of the complete set of variables see Table S1 and González-M. et al. (2019) and for the complete set of models see Tables S4 and S5. Results All TDF tree communities accounted for significant positive correlations between the total species abundances and the sampled species abundances (r=0.71–0.87, P<0.001; Figure 2). Plots had between 25 and 74 total species, but the dominant species (those that summed at least 80% of the abundances per hectare) varied between 3 and 29 species per plot (sps80%; Figure 2). On each plot, dominant species represented less than 30% of the total number of species, except for one community that reached 39.2% of the species were required to reach that level of abundance (Tambor, Figure 2). Two communities showed significant and positive correlations by including only dominant species (Macuira and Vinculo, Figure 2) but with low species representativeness (26% and 20%, respectively). 8 CardonalLoma CardonalPlana Colorados Cotove Jabiru 7 sps80%=12 sps80%=14 sps80%=12 sps80%=6 sps80%=3 6 5 4 3 2 1 sps=45 sps=47 sps=66 sps=25 sps=31 ρsps=0.86*** ρsps=0.84*** ρsps=0.78*** ρsps=0.83*** ρsps=0.71*** 0 8 Macuira Tambor Tayrona Tuparro Vinculo 7 sps80%=9 sps80%=29 sps80%=13 sps80%=24 sps80%=9 6 ρsps80%=0.92*** ρsps80%=0.84*** 5 4 3 2 1 sps=34 sps=74 sps=53 sps=73 sps=44 ρsps=0.87*** ρsps=0.73*** ρsps=0.85*** ρsps=0.78*** ρsps=0.79*** 0 0.0 0.5 1.0 1.5 2.0 2.5 0.0 0.5 1.0 1.5 2.0 2.5 0.0 0.5 1.0 1.5 2.0 2.5 0.0 0.5 1.0 1.5 2.0 2.5 0.0 0.5 1.0 1.5 2.0 2.5 ln(Sample, ind ha−1) ln(Sample, ind ha−1) ln(Sample, ind ha−1) ln(Sample, ind ha−1) ln(Sample, ind ha−1) Figure 2. Pearson’s pairwise correlations (r) between the total species abundances (Abundances, ind ha-1) and the sampled species (sps) abundances (Sample, ind ha-1) based on the abundance weighted trait sampling design for ten tree communities of TDF (1-ha permanent plots). Dominant species (sps80%; those that summed at least 80% of the abundances per hectare) are indicated in black dots. Slope lines indicate significant positive correlations (***, P < 0.001) for both the entire community (rsps8) and the dominant species (rsps80%). Differences among plots (coms) accounted for a small proportion of the variance for all the functional traits (9.6±9.2% of the explained variance; Figure 3), except for SLA in which they explained reached 35.7% of the total variation. Differences among populations (pops) and species (sps) accounted for the highest part of the variance in most of the traits (between 24.0 and 41.2% for each scale). Finally, differences between individuals (inds) within plots, plus the error (e), accounted for a higher portion of the variability of the traits than differences between the plots (inds = 20.3±6.0%, Figure 3), with the exception of WD0 for which the proportion of explained variance was similar (inds = coms =16.7%). 89 ln(Abundances, ind ha−1) ln(Abundances, ind ha−1) Doctoral Thesis – Roy González-M. coms sps pops inds + ε LA LDMC SLA Lth PA DApit FWT VD dh Kp VA VAmax WD WD0 WCmax 0 20 40 60 80 100 Explained variance (%) Figure 3. Partitioning of variance of the 15 studied traits across the considered ecological scales (communities or plots = coms, species within coms = sps, populations within sps within coms = pops, and individuals within pops within sps within coms). Functional traits: (a) leaf area (LA, mm2), (b) leaf dry matter content (LDMC, mg g-1), (c) specific leaf area (SLA, mm2 mg-1), (d) leaf thickness (Lth, mm), (e) pit area (PA, µm2), (f) pit diameter aperture (DApit, µm), (g) fiber wall thickness (FWT, µm), (h) vessel density (VD, vessels mm-2), (i) hydraulically weighted diameter (dh, µm), (j) xylem potential hydraulic conductivity (Ks, kg m-1 s-1 MPa-1), (k) vessel area (VA, µm2), (l) maximum vessel area (VAmax, µm2), (m) wood density (WD, g cm3), (n) wood anhydrous density (WD0, g cm3) and (o) water content at maximum capacity (WCmax, kg kg-1). The proportion of significant relationships between trait-environment and trait-biomass increased with ecological scales (Figure 4-6). The abundance-weighted trait sampling design (inds) generated the highest number of models with significant trait-environment and trait-biomass production relationships (P<0.05; Figures 4 and 6). In total, 67.2% of the trait-environment models and 53.5% trait-biomass production models showed b1 significantly different from 0 for inds (Tables S4 and S5). Similar results were found for the three simulated trait sampling designs that considered a high trait variability (pops, sps and sps80%, Figures 4 and 6). However, the number of significant relationships was lower as designs reduced the trait values estimation (i.e., from traits estimated for inds to sps80%). Accordingly, pops had significant b1 for 44.9% of the trait-environment models, while sps had 43.8% and sps80% had 31.5% of models with significant b1 (Table S4). Likewise, the trait-biomass production b1 were significant 53.8% of the time for pops, 50% for sps and 46.2% for sps80% (Table S5). The three CWM designs had a much lower number of significant relationships than the other designs for both studied relationships trait-environment (Figure 5) and the trait-biomass production (Figure 6). For the trait-environment relationships, significant b1 were found in 1.8% models of CWMpops, 3.1% for CWMsps and 2.1% in CWM80% (Table S4), while for the trait- biomass relationships significant b1 were found in 3.8% of the models for CWMpops, 23.1% for CWMsps and 3.8% in CWM80% (Table S5). CWM designs having significant b1 did not show differential detectability 90 Ecology of woody plants in Colombian dry forests with the accuracy of trait values estimation (pops, sps and sps80%; Tables S4 and S5). Interestingly, we did not find a particular trait (e.g., hydraulic, leaf) that consistently fitted with a particular sampling design for the trait-environment relationships (Figure 4-6). However, the hydraulic traits showed a high number of significant models with the trait-biomass production relationships for inds, pops, sps and sps80%, while CWM of leaf traits were more commonly significant (Figure 6). (a) β−coeff. LA (b) β−coeff. LDMC (c) β−coeff. SLA (d) β−coeff. Lth −0.22 0 0.22 −0.05 0 0.05 −0.07 0 0.07 −0.06 0 0.06 sps80 sps pops inds (e) β−coeff. PA (f) β−coeff. DApit (g) β−coeff. FWT (h) β−coeff. VD −0.12 0 0.12 −0.07 0 0.07 −0.05 0 0.05 −0.12 0 0.12 sps80 sps pops inds (i) β−coeff. dh (j) β−coeff. Kp (k) β−coeff. VA (l) β−coeff. VAmax −0.05 0 0.05 −0.2 0 0.2 −0.11 0 0.11 −0.12 0 0.12 sps80 sps pops inds (m) β−coeff. WD (n) β−coeff. WD0 (o) β−coeff. WCmax (p) β−coeff. BGR −0.07 0 0.07 −0.06 0 0.06 −0.09 0 0.09 −0.19 0 0.19 sps80 sps pops inds Figure 4. Fitted models and b-coefficients showing the effects of climate severity (circles), soil nutrient limitations (squares) and land-cover transformation (rhomb) on functional traits at the individuals (inds), populations (pops), species (sps) and dominant species (sps80%, those that summed at least 80% of the abundances per hectare) scale. (a) Leaf area (LA, mm2), (b) leaf dry matter content (LDMC, mg g-1), (c) specific leaf area (SLA, mm2 mg-1), (d) leaf thickness (Lth, mm), (e) pit area (PA, µm2), (f) pit diameter aperture (DApit, µm), (g) fiber wall thickness (FWT, µm), (h) vessel density (VD, vessels mm-2), (i) hydraulically weighted diameter (dh, µm), (j) xylem potential hydraulic conductivity (Ks, kg m-1 s-1 MPa-1), (k) vessel area (VA, µm2), (l) maximum vessel area (VAmax, µm2), (m) wood density (WD, g cm3), (n) wood anhydrous density (WD0, g cm3) and (o) water content at maximum capacity (WCmax, 91 Doctoral Thesis – Roy González-M. kg kg-1), and on the biomass growth rates (BGR, kg ha-1 yr-1). Filled dots represents b1 significantly different from 0 (P<0.05) for the trait-environment relationship with the models based on the combined (PCA axis 1) variables of climate, soil and land-cover transformation; white dots correspond to non-significant models. For details in the modelling procedures see Table S3 and for the complete set of models see Table S4. (a) β−coeff. LA (b) β−coeff. LDMC (c) β−coeff. SLA (d) β−coeff. Lth −0.39 0 0.39 −0.07 0 0.07 −0.13 0 0.13 −0.09 0 0.09 CWM80% CWMsps CWMpops (e) β−coeff. PA (f) β−coeff. DApit (g) β−coeff. FWT (h) β−coeff. VD −0.29 0 0.29 −0.15 0 0.15 −0.2 0 0.2 −0.28 0 0.28 CWM80% CWMsps CWMpops (i) β−coeff. dh (j) β−coeff. Kp (k) β−coeff. VA (l) β−coeff. VAmax −0.13 0 0.13 −0.3 0 0.3 −0.25 0 0.25 −0.26 0 0.26 CWM80% CWMsps CWMpops (m) β−coeff. WD (n) β−coeff. WD0 (o) β−coeff. WCmax (p) β−coeff. BGR −0.08 0 0.08 −0.07 0 0.07 −0.14 0 0.14 −0.61 0 0.61 CWM80% CWMsps CWMpops Figure 5. Fitted models and b-coefficients showing the effects of climate severity (circles), soil nutrient limitations (squares) and land-cover transformation (rhombs) on the community weighted-means (CWM) of functional traits: (a) leaf area (LA, mm2), (b) leaf dry matter content (LDMC, mg g-1), (c) specific leaf area (SLA, mm2 mg-1), (d) leaf thickness (Lth, mm), (e) pit area (PA, µm2), (f) pit diameter aperture (DApit, µm), (g) fiber wall thickness (FWT, µm), (h) vessel density (VD, vessels mm-2), (i) hydraulically weighted diameter (dh, µm), (j) xylem potential hydraulic conductivity (Ks, kg m-1 s-1 MPa-1), (k) vessel area (VA, µm2), (l) maximum vessel area (VAmax, µm2), (m) wood density (WD, g cm3), (n) wood anhydrous density (WD0, g cm3) and (o) water content at maximum capacity (WCmax, kg kg-1), and on the biomass growth rates (BGR, kg ha-1 yr-1). Filled dots represent b1 significantly different from 0 (P<0.05) for the trait-environment relationship with the models based on the combined (PCA axis 1) variables of climate, soil and land-cover transformation. For details in the modelling procedures see Table S3 and for the complete set of models see Table S4. 92 Ecology of woody plants in Colombian dry forests (a) inds (b) pops (c) sps (d) sps80% WCmax WD0 WD VAmax VA Kp dh VD FWT DApit PA Lth SLA LDMC LA −0.7 0 0.7 −1.17 0 1.17 −1.31 0 1.31 −1.18 0 1.18 β−coeff. BGR β−coeff. BGR β−coeff. BGR β−coeff. BGR (e) CWMpops (f) CWMsps (g) CWM80% WCmax WD0 WD VAmax VA Kp dh VD FWT DApit PA Lth SLA LDMC LA −13.68 0 13.68−17.68 0 17.68 −14 0 14 β−coeff. BGR β−coeff. BGR β−coeff. BGR Figure 6. Fitted models and b-coefficients showing the effects of the functional traits: leaf area (LA, mm2), leaf dry matter content (LDMC, mg g-1), specific leaf area (SLA, mm2 mg-1), leaf thickness (Lth, mm), pit area (PA, µm2), pit diameter aperture (DApit, µm), fiber wall thickness (FWT, µm), vessel density (VD, vessels mm-2), hydraulically weighted diameter (dh, µm), xylem potential hydraulic conductivity (Ks, kg m-1 s-1 MPa-1), vessel area (VA, µm2), maximum vessel area (VAmax, µm2), wood density (WD, g cm3), wood anhydrous density (WD0, g cm3) and water content at maximum capacity (WCmax, kg kg-1) on biomass growth rates (BGR, kg ha-1 yr-1) at the individuals (a, inds), populations (b, pops), species (c, sps) and dominant species (d, sps80%; those that summed at least 80% of the abundances per hectare) and community weighted means for populations (e, CWMpops), species (f, CWMsps), and 80% of the abundances for each community (CWM80%). Filled dots represent b1 significantly different from 0 (P<0.05) for the trait-biomass production relationship with the models based on the PCA axis 1 variables of climate, soil and land- cover transformation. For details in the modelling procedures see Table S3. Discussion In this study, we compared the strength of different trait sampling designs to detect the trait-environment and trait-biomass production relationships in Colombian TDF with a high community trait characterization. An entire community trait characterization was expected for a sampling design considering both species 93 Doctoral Thesis – Roy González-M. abundances representativeness and trait variability simultaneously. Overall, we found that: (i) Our abundance-weighted trait sampling design adequately represents the species abundances structure of TDF tree communities while the sampling design based on dominant species did not. (ii) Differences within communities explained the higher proportion of total trait variation (inds, pops, and, sps) than among them (coms), highlighting the importance of considering trait variability for community trait characterization in TDF. (iii) Our abundance-weighted trait sampling design, which improves accounting both the species abundances representativeness and trait variability, was found to have the highest strength to detect the trait-environment and trait-biomass production relationships. Bias in sampling designs based on dominant species for TDF Community trait analyses based on dominant species assumes that they should clearly reveal directional shifts under environmental gradients or effects on ecosystem processes (Grime 1998; Díaz et al. 2007; Garnier et al. 2007). This assumption has led a broad use of sampling designs based on dominant species to test the trait-environment and trait-biomass relationships in tropical forests (Poorter et al. 2008; Finegan et al. 2015a; Prado-Junior et al. 2016; Poorter et al. 2017), but very little attention has been paid in the contributions of dominant species on the community abundance structure and the consequences for functional analyses. Our results indicate that sampling designs that focus on dominant species can be insufficient to describe the community species abundance structure in TDF adequately. We found that less than 40% of species were “dominants” in all communities (those accounted for at least 80% of local abundance; Figure 2) and that traits sampled for this set of species did not satisfactorily characterized the trait composition in both within and between communities (discussed below). Considering that hyper- diverse tropical communities have a wide range of functional mechanisms for species assembly, measuring traits only in dominant ones can erroneously disregard the contribution of ‘rarer’ species in driving community trait composition (Wright 2002); therefore, it may obscure the entire diversity-environment relationships (Keil & Chase 2019). However, measuring traits in every species for every community is not realistically possible (Pakeman & Quested 2007) in particular for hyper-diverse tropical ecosystems (Baraloto et al. 2010b) (discussed below). Thus, further studies evaluating trait-environment and trait- biomass in TDF need first assess the extent to which a sampling effort may affect the correct characterization of community trait composition and blur the expected relationships. Importance of variability for community trait characterization in TDF Studies that test trait-environment and trait-biomass production relationships based on CWM calculations are relatively common (Lavorel & Garnier 2002; Finegan et al. 2015a; Funk et al. 2017; Miller et al. 2019). However, CWM does not include trait variability for the analysis (Lavorel et al. 2008), which is important for a comprehensive understanding of functional mechanisms supporting community and ecosystem processes (Violle et al. 2012; Chave 2013). An important assumption behind CWM is that traits being weighted by the species abundances structure of the community reflect the ‘optimal’ trait phenotype in response to local environmental conditions (Muscarella & Uriarte 2016) or determining biomass within and between communities (Garnier et al. 2004; Finegan et al. 2015a). However, community trait composition may be very divergent to the extent that species have viable co-existing trait strategies, resulting in alternative equally competitive functional phenotypes (Marks & Lechowicz 2006; Poorter et al. 2008; Bernard-Verdier et al. 2012; Chauvet et al. 2017; Kumordzi et al. 2019). Thus, CWM may blur the mechanisms for a comprehensive understanding of community trait composition in TDF. In agreement with that, we found that for all traits, variance within communities reached three quarters of the portion explained 94 Ecology of woody plants in Colombian dry forests with respect among communities (Figure 3). Where differences within and among populations/species seem to be an important variation source to understand patterns of functional diversity of TDF communities, as well as among them. For instance, within species of a community (e.g., individuals of a population) trait variability may shows the phenotypic plasticity, the presence of different genotypes and ontogenetic variations (Valladares et al. 2006, 2014; Benito Garzón et al. 2011). Additionally, for species within communities (populations) ‘interspecific selective pressures’ may prevent the establishment of each species in a particular location of the community determining local niche differentiation (Kraft et al. 2015). Finally, species between communities can experience different environmental conditions and biogeographic barriers that limit their dispersal resulting selective environmental effects (Bernard-Verdier et al. 2012; Anderegg et al. 2018). For these reasons, further studies exploring trait community composition in TDF based on CWM should consider the limitations to not include trait variability for understanding mechanisms behind of functional trait variation along gradients. Towards a sampling design for an adequate community trait characterization in TDF Of the different motivations to decide for trait sampling design in species-rich plant communities, such as TDF, the cost-efficiency of the sampling efforts is a common one (Baraloto et al. 2010b). In a perfect scenario, all of the individuals of each species in a community should be sampled for an entire community trait composition description (Carmona et al. 2015). However, intensive sampling designs are costly and can be unfeasible in many scenarios for highly diverse ecosystems (Pakeman & Quested 2007; Baraloto et al. 2010b). A common alternative practice in such scenario is to collect traits for a number of individuals in a fraction of species per community (e.g., sps80% in this study) at the expenses of some trait representativeness, or use CWM trait values that are estimated with the mean species trait values and their relative abundances within communities but ignore trait variability (Baraloto et al. 2010b; Messier et al. 2010; Carmona et al. 2015). Here we have shown that intensive trait sampling efforts increase our capacity to detect differences between communities along environmental gradients (Figures 4 and 5), as well as the effect of functional traits on biomass production (Figure 6). We found that sampling designs for accounting trait variability (i.e., from inds to sps80%) detected effects in 67-32% of the trait-environment models and 46-54% trait-biomass production, while those designs for CWM detected effects in less than 3.1% for trait- environment models and 23.1% for trait-biomass production models. It was because sampling designs for accounting trait variability have a higher size effect power to the extent that increasing trait observations within communities. Additionally, we also found that include all variability sources for the analysis improved consistently both studied relationships (Figures 4 and 6). The inds design showed the most substantial detection of trait-environment and trait-biomass production relationships (67.2 and 53.5% of models, respectively). Thus, in order to reconcile the trade-offs between costly sampling efforts for including trait variability and the underpowered of CWM where effects are missed, we recommend the use of abundance-weighted sampling designs for studies (Figure S1), but developing linear mixed models to control the random effects generated by the nested variability among ecological scales. If not considered, random effects may induce erroneously judge the statistical significance of the models wasting the intensive sampling efforts (Messier et al. 2010; Green & Macleod 2016). Finally, we would like to highlight that our results do not claim that all trait sample designs in hyper-diverse tropical ecosystems, in particular TDF, should emphasize in intensive sampling efforts or adopting our abundance-weighted sampling design, because it not only depends on the research question, but also in the financial sources and logistic requirements for fieldwork (Baraloto et al. 2010b; Carmona et al. 2015). 95 Doctoral Thesis – Roy González-M. Acknowledgements We would like to thank the owners of the natural areas where we worked for their logistical support and for allowing us to use their fieldwork facilities. Financial support was provided by the Interamerican Development Bank (Technical Cooperation # ATN/BD-15408-CO), Ministerio de Ambiente y Desarrollo Sostenible of Colombia, the fellowship program of the International Tropical Timber Organization (#020/17A), Fellowship Doctoral Programme (Universidad del Rosario) and Dora Plus Fellowship Programme (University of Tartu). We are thankful to the Colombian TDF Network (Red BST-Col) for their invaluable field collaboration, and many students who helped us with fieldwork and laboratory analyses. Supporting information Meaninds=0.709 N=152 CWMsps=0.711 0.2 0.4 0.6 0.8 1.0 WD g cm3 Figure S1. Example of the trait values distribution based on the abundance-weighted trait sampling design (N=sampled individuals). Mean trait value for the sampled individuals (Meaninds) and the community weighted mean value of the sample trait using the species trait means and the species abundances (CWMsps). Wood density (WD, g cm3). 96 Frequency of sample individuals 0 10 20 30 40 Ecology of woody plants in Colombian dry forests Table S1. Environmental conditions (climate, soils and land-cover transformation) of ten 1-ha permanent plots in tropical dry forests (coms). Climate variables were determined based on 2046 weather stations in Colombia with ~90 m spatial resolution (http://institucional.ideam.gov.co/jsp/1769). Soils variables were obtained from 10 soil samples were randomly taken at each plot and analising at the Agustin Codazzi National Soil Laboratory. Land-cover variables were measured from a circular area of 500 ha around each plot based on interpretation of remote-sensing imagery (Landsat 8 Mosaic and Google EarthPro© images of 2014-2015, 1500 m flight height, 1:2500 in scale and 0.64x0.64 m resolution). For details of the climate, soils and land-cover transformation see PCA axes 1 in González-M. et al. (2019) Permanent Plots (1-ha) CardonalLoma CardonalPlana Colorados Cotove Jabirú Macuira Tambor Tayrona Tuparro Vinculo Climate (Climate severity, PC1 axis 1 – 56.6%*) -0.907 -0.698 0.544 -0.536 -0.688 5.018 -1.717 3.445 -1.218 -2.362 Total annual rainy days (ARD, no.) 113 116 96 146 116 33 126 95 152 144 Aridity index (Aridity, [PET/TAP]) 1.19 1.29 1.01 1.43 1.31 3.42 0.88 2.03 0.77 0.97 Isothermality (Isoth, %) 85.91 85.97 90.25 87.29 86.03 75.43 85.83 81.35 78.07 93.3 Solar radiation (SRad, MJ·m-1 x 100) 172.78 173.36 192.08 178.49 173.28 185.69 172.87 196.79 164.1 169.95 Total annual precipitation (TAP, mm) 1541.2 1505.9 1528.4 1193.8 1528.2 517 1912.5 899.4 2697.2 1192.4 Potential evapotranspiration (PET, mm) 1835.6 1946.8 1546 1712.8 2009.1 1768.6 1689.1 1827.7 2067 1161.3 Total precipitation during the three driest months (TPdriest [<100 mm·month-1], mm) 222.5 227.8 139.3 112.7 236.8 32.1 272.7 33.4 177.1 168.5 Water vapor pressure (WVP, kPa) 2.53 2.63 2.75 2.55 2.65 2.67 2.5 2.87 2.8 2.14 Wind speed (Wind, m·s-1) 0.92 0.93 2.35 0.83 0.93 4.84 0.9 4.45 1.37 0.87 Soils (Soil nutrient limitations, PC1 axis 1 – 48.8%**) -0.237 -1.094 3.481 1.550 -1.310 -2.422 -2.111 -0.066 -3.767 3.661 Acidity (pH) 6.87 6.79 7.37 6.54 6.46 6.12 6.98 7.38 4.39 6.22 Available phosphorus (P, mg·kg-1) 19.36 143.24 12.17 20.08 17.08 18.14 11.33 222.59 3.32 4.7 Cation exchange capacity (CEC, cmol+·kg-1) 20.08 15.61 30.89 25.71 17.01 10.01 14.73 16.43 6.69 26.68 Extractable calcium (Ca, cmol+·kg-1) 22.89 13.81 34.83 22.13 13.13 5.29 10.29 16.05 0.06 21.33 Extractable magnesium (Mg, cmol+·kg-1) 4.11 3.24 4.64 9.95 3.9 2.19 2.3 2.69 0.06 17.74 Extractable potassium (K, cmol+·kg-1) 0.36 0.73 0.65 0.38 0.7 0.55 0.61 0.82 0.21 0.87 Extractable sodium (Na, cmol+·kg-1) 0.1 0.03 0.09 0.15 0.08 0.22 0.04 0.07 0.16 0.14 Organic carbon (OC, %) 2.41 2.54 3.22 2.26 2.8 1 2.36 3.58 1.79 3.64 Sand content (Sand %) 56.45 61.86 34.21 37.42 48.86 57.41 72.25 62.58 64.27 60.69 Clay content (Clay %) 24.54 24.96 31.58 35.34 21.16 21.54 16.33 19.25 16.39 24.36 Silt content (Silt %) 19.03 13.18 34.21 27.24 29.98 21.07 11.42 18.18 19.34 14.95 Land-cover (Land-cover transformation, PC1 axis 1 – 56.5%***) -1.052 1.236 -1.911 3.250 1.207 -2.135 -0.691 -2.953 -1.091 2.410 Forest cover area (Forest, Ha [effective area, %]) 303 [60.6] 153.9 [30.8] 395 [79] 55.1 [11.5] 145.4 [29.1] 407.6 [82.4] 329.6 [65.9] 411.8 [100] 176.3 [51.9] 23.4 [4.7] Forest shape index (Shape [Perimeter/Area], m-1) 0.011 0.028 0.004 0.038 0.023 0.004 0.017 0.003 0.013 0.025 Secondary vegetation area (SecVeg, Ha [effective area, %]) 52.3 [10.5] 38.5 [7.7] 57.2 [11.4] 103.4 [21.6] 129.9 [26] 87.1 [17.6] 104 [20.8] 0 [0] 29.5 [8.7] 180.2 [36] Area used by humans (ULC, Ha [effective area, %]) 125.5 [25.1] 254.9 [50.9] 48.1 [9.6] 309.7 [64.7] 209 [41.8] 0 [0] 56.7 [11.3] 0 [0] 19.2 [5.6] 296.7 [59.3] Types of human land cover uses (ULC.type, no.) 3 14 5 34 17 0 17 0 2 22 Topographic roughness (Roughness, %) 12.7 7.3 14.7 9.7 9 10.4 22.5 19.1 7.4 8 *High values are associated to high aridity and wind speed, high solar radiation and water vapour pressure but low rainfall regimens (low values of annual precipitation, precipitation during the rainy season and number of rainy days) **High values are associated to low cation exchange capacity, low contents of extractable bases (Ca, Mg, K), low contents of clay and silt and low pH but high contents sands. For simplicity we inverted the original PC1 axis of “soils fertility” in González-M. et al. (2019) to the same direction than climate and land-cover axes, “soil nutrient limitations” ***High values are associated to low forest cover, narrow forest shape and high proportion of secondary vegetation and high number of human land-cover types 97 Doctoral Thesis – Roy González-M. Table S2. Description, sampling effort (N) and the general predictions of trait-environment and trait-biomass production relationships for tropical dry forests (TDF). Number of individuals (inds), populations (pops) and species (sps) sampled in this study in 10 1-ha permanent plots of TDF (coms, Table S1) Expected relationships Trait (abbreviation, units) Description N (inds, pops, sps) As gradients become harder* trait High trait values determine References values: ____ biomass production: Fiber wall thickness (FWT, µm) Width wall fibers confer high hydraulic safety 1294, 463, 306 Increase / Unpredicted** / Increase Low Madsen & Gamstedt (2013); Scholz et resistance al. (2013); Sorieul et al. (2016) Hydraulically weighted diameter (d , µm) Large weighted diameters determine high hydraulic h efficiency 1295, 463, 306 Decrease / Unpredicted** / Unpredicted** High Scholz et al. (2013); Rosell et al. (2017) Leaf area (LA, mm2) Large leaves are correlated with low tissue investments 1335, 483, 317 Unpredicted** / Decrease / Increase High Pérez-Harguindeguy et al. (2013); Díaz et al. (2016) Leaf dry matter content (LDMC, mg g-1) High dry mass contents exhibit low tissue 1332, 485, 320 Unpredicted** / Increase / Decrease Low Pérez-Harguindeguy et al. (2013); Díaz investments et al. (2016) Leaf thickness (L , mm) Width leaves are correlated with high tissue th investments 1252, 458, 311 Unpredicted** / Increase / Decrease Low Pérez-Harguindeguy et al. (2013); Onoda et al. (2011) Maximum vessel area (VA , µm2) Large vessels diameters determine high hydraulic max efficiency 1295, 463, 306 Decrease / Decrease / Decrease High IAWA et al. (2007); Scholz et al. (2013) Pit area (PA, µm2) Large pit areas determine high embolism risk 1294, 463, 306 Decrease / Decrease / Decrease High IAWA et al. (2007); Scholz et al. (2013) Pit diameter aperture (DApit, µm) Small pit diameters confer high hydraulic safety 1294, 463, 306 Decrease / Decrease / Decrease High Scholz et al. (2013); Li et al. (2016); Helmling et al. (2018) Specific leaf area (SLA, mm2 mg-1) High specific leaf mass shows low tissue investments 1324, 483, 317 Increase / Decrease / Increase High Wright et al. (2004); Pérez- Harguindeguy et al. (2013) Vessel area (VA, µm2) Large conduits confer high hydraulic efficiency but low hydraulic safety 1295, 463, 306 Unpredicted** / Decrease / Increase High Olson & Rosell (2013); Scholz et al. (2013) Vessel density (VD, vessels mm-2) High conduit per square millimetres confer high hydraulic efficiency but low hydraulic safety 1295, 463, 306 Decrease / Decrease / Unpredicted** Unpredicted** Chave et al. (2009); Scholz et al. (2013); Jacobsen et al. (2005) Wood density (WD, g cm3) High densities are related to high hydraulic safety and high tissue investments 1356, 485, 315 Increase / Increase / Decrease Low Chave et al. (2009); Pérez-Harguindeguy et al. (2013) Wood anhydrous density (WD0, g cm3) High densities are related to high tissue investments and wood mechanical stability 1348, 483, 314 Increase / Increase / Decrease Low Chave et al. (2009); Pérez-Harguindeguy et al. (2013) Water content at maximal capacity High water contents determine low xylem 1348, 483, 314 Decrease / Unpredicted** / Increase High Guevara (2001); Berry & Roderick (WCmax, kg kg-1) mechanical resistance (2005) Xylem potential hydraulic conductivity High potential conductivity confers high hydraulic (K , kg m-1 s-1 MPa-1) efficiency but low hydraulic safety 1295, 463, 306 Decrease / Unpredicted** / Increase High Chave et al. (2009); Poorter et al. p (2010); Méndez-Alonzo et al. (2012) * Increases in climate severity / soils nutrient limitations / land-cover transformation ** There are ambiguous expectations that limit providing of a specific prediction 98 Ecology of woody plants in Colombian dry forests Table S3. Modelling procedures for the abundance weighted trait sampling design (inds) and six functional trait sampling designs, 15 traits, 29 environmental variables of climate, soils, and land-cover transformation, and biomass growth rates (BGR). Functional trait sampling designs: populations (pops), species (sps), dominant species (sps80%, species saturating 80% of the abundances per hectare), community weighted-means based on populations (CWMpops), species (CWMsps) and dominant species (CWM80%). For the linear mixed-effects models the random effects from populations ("#) and the random effects from species ("%#) of each plot. For a description of the environmental variables and traits see Table S1 and S2, respectively. *Trait predictors in the models are respectively calculated based on: inds, pops, sps, sps80%, CWMpops, CWMsps and CWM80% Response Predictors Trait-environment model Trait-biomass production model (Functional trait design) Environmental variable (Envar) Trait* CWM80% Climate severity CWM80% = β. + β0 × Envar + ε Isoth TAP CWM TPdriests sps PET CWMsps = β. + β0 × Envar+ ε Aridity Ard FWT SRad CWMpops WVP dh CWMpops = β. + β0 × Envar + ε Wind LA Soil nutrient limitations LDMC Sand Lth BGRcoms Silt VA BGRcoms = β. + β0 × Envar + ε BGRcoms = β. + β0 × Trait + ε max Clay DApit pH CEC SLA sps80%, BGRsps Ca VA spsC.% = β. + β0 × Envar + ε BGRsps80% = β. + β0 × Trait + ε Mg VD K WD Na WD0 sps, BGRsps P WC sps = β. + β0 × Envar+ ε BGRsps = β. + β0 × Trait + ε max OC K Land-cover transformation p Forest pops, BGRsps Shape pops = β. + β0 × Envar + "%# + ε BGRpops = β. + β0 × Trait + "%# + ε SecVeg UCL ULC.type inds, BGRsps Roughness inds = β. + β0 × Envar + "# + "%# + ε BGRinds = β. + β0 × Trait + "# + "%# + ε 99 Doctoral Thesis – Roy González-M. Table S4. Fitted models and b-coefficients showing the effects of 26 single variables of climate, soils and land-cover transformation on the functional traits for the abundance weighted trait sampling design (inds) and six functional trait sampling designs: populations (pops), species (sps), dominant species (sps80%, species saturating 80% of the abundances per hectare), community weighted-means based on populations (CWMpops), species (CWMsps) and dominant species (CWM80%). Confidence interval at 95% of the probability (CI). Bold letters show significant b-coefficients (P<0.05). For description of the environmental variables and traits see Table S1 and S2, respectively. Environmental inds pops sps sps80% CWM CWM CWMTrait pops sps 80% variable b0 (95% CI) P b0 (95% CI) P b0 (95% CI) P b0 (95% CI) P b0 (95% CI) P b0 (95% CI) P b0 (95% CI) P LA Climate Isoth -0.01 (-0.022, 0.001) 0.087 0.001 (-0.018, 0.02) 0.912 0.002 (-0.021, 0.025) 0.881 0.012 (-0.013, 0.037) 0.350 -0.036 (-0.12, 0.048) 0.346 -0.043 (-0.114, 0.027) 0.195 -0.045 (-0.135, 0.045) 0.283 TAP 0.000 (0.000, 0.001) 0.000 0.000 (0.000, 0.001) 0.000 0.000 (0.000, 0.001) 0.000 0.000 (0.000, 0.001) 0.004 0.001 (0.000, 0.001) 0.026 0.001 (0.000, 0.001) 0.018 0.001 (0.000, 0.001) 0.026 TPdriests 0.003 (0.003, 0.004) 0.000 0.004 (0.002, 0.005) 0.000 0.005 (0.003, 0.006) 0.000 0.004 (0.003, 0.006) 0.000 0.003 (-0.002, 0.008) 0.152 0.003 (-0.002, 0.007) 0.195 0.003 (-0.002, 0.009) 0.189 PET 0.000 (0.000, 0.001) 0.000 0.000 (0.000, 0.001) 0.207 0.000 (0.000, 0.001) 0.288 0.000 (0.000, 0.001) 0.956 0.001 (0.000, 0.003) 0.058 0.001 (0.000, 0.003) 0.033 0.002 (0.000, 0.003) 0.039 Aridity -0.309 (-0.398, -0.22) 0.000 -0.308 (-0.446, -0.169) 0.000 -0.345 (-0.517, -0.173) 0.000 -0.326 (-0.52, -0.132) 0.001 -0.221 (-0.802, 0.36) 0.405 -0.182 (-0.7, 0.336) 0.441 -0.223 (-0.861, 0.414) 0.443 Ard 0.005 (0.003, 0.007) 0.000 0.005 (0.002, 0.008) 0.002 0.005 (0.002, 0.009) 0.006 0.005 (0.000, 0.009) 0.038 0.003 (-0.01, 0.017) 0.579 0.003 (-0.009, 0.015) 0.598 0.003 (-0.011, 0.018) 0.612 SRad 0.000 (0.000, 0.000) 0.000 0.000 (0.000, 0.000) 0.000 0.000 (0.000, 0.000) 0.001 0.000 (0.000, 0.000) 0.008 0.000 (-0.001, 0.000) 0.287 0.000 (-0.001, 0.000) 0.299 0.000 (-0.001, 0.000) 0.322 WVP 0.137 (-0.152, 0.425) 0.353 -0.193 (-0.687, 0.302) 0.444 -0.277 (-0.893, 0.34) 0.378 -0.51 (-1.153, 0.133) 0.120 1.1 (-1.07, 3.27) 0.276 1.115 (-0.756, 2.987) 0.207 1.378 (-0.921, 3.678) 0.204 Wind -0.127 (-0.17, -0.084) 0.000 -0.146 (-0.215, -0.077) 0.000 -0.185 (-0.275, -0.095) 0.000 -0.176 (-0.272, -0.079) 0.000 -0.101 (-0.397, 0.196) 0.456 -0.076 (-0.342, 0.189) 0.526 -0.095 (-0.421, 0.232) 0.521 Soils Sand 0.015 (0.011, 0.02) 0.000 0.014 (0.007, 0.021) 0.000 0.017 (0.008, 0.026) 0.000 0.013 (0.004, 0.023) 0.008 0.027 (0.000, 0.054) 0.049 0.026 (0.003, 0.048) 0.031 0.03 (0.001, 0.059) 0.043 Silt -0.049 (-0.059, -0.039) 0.000 -0.043 (-0.059, -0.027) 0.000 -0.047 (-0.067, -0.027) 0.000 -0.038 (-0.059, -0.016) 0.001 -0.072 (-0.125, -0.018) 0.015 -0.063 (-0.111, -0.014) 0.017 -0.079 (-0.137, -0.021) 0.014 Clay -0.017 (-0.023, -0.01) 0.000 -0.015 (-0.026, -0.003) 0.012 -0.02 (-0.035, -0.005) 0.009 -0.016 (-0.031, 0.000) 0.052 -0.034 (-0.082, 0.014) 0.145 -0.035 (-0.075, 0.006) 0.082 -0.038 (-0.09, 0.014) 0.127 pH -0.138 (-0.201, -0.075) 0.000 -0.093 (-0.193, 0.008) 0.070 -0.05 (-0.161, 0.062) 0.381 -0.003 (-0.128, 0.122) 0.963 -0.249 (-0.765, 0.268) 0.299 -0.207 (-0.669, 0.255) 0.332 -0.282 (-0.842, 0.277) 0.278 CEC -0.021 (-0.028, -0.013) 0.000 -0.015 (-0.028, -0.003) 0.017 -0.015 (-0.03, 0.000) 0.046 -0.009 (-0.025, 0.008) 0.306 -0.039 (-0.094, 0.015) 0.135 -0.041 (-0.086, 0.004) 0.070 -0.045 (-0.103, 0.013) 0.110 Ca -0.017 (-0.023, -0.011) 0.000 -0.014 (-0.023, -0.005) 0.003 -0.013 (-0.024, -0.002) 0.026 -0.008 (-0.021, 0.004) 0.178 -0.029 (-0.071, 0.013) 0.148 -0.027 (-0.064, 0.009) 0.125 -0.033 (-0.078, 0.012) 0.130 Mg -0.032 (-0.044, -0.021) 0.000 -0.025 (-0.045, -0.005) 0.016 -0.029 (-0.054, -0.004) 0.025 -0.015 (-0.041, 0.011) 0.264 -0.073 (-0.144, -0.001) 0.047 -0.076 (-0.13, -0.021) 0.013 -0.084 (-0.159, -0.009) 0.032 K -0.56 (-0.834, -0.287) 0.000 -0.342 (-0.797, 0.113) 0.140 -0.454 (-1.013, 0.105) 0.111 -0.151 (-0.763, 0.461) 0.627 -1.285 (-3.256, 0.686) 0.171 -1.544 (-3.07, -0.018) 0.048 -1.438 (-3.571, 0.694) 0.159 Na -1.652 (-2.745, -0.56) 0.003 -2.233 (-3.969, -0.498) 0.012 -3.425 (-5.666, -1.183) 0.003 -3.871 (-6.364, -1.379) 0.002 -1.755 (-9.592, 6.082) 0.619 -1.731 (-8.648, 5.187) 0.580 -1.829 (-10.386, 6.728) 0.635 P -0.003 (-0.004, -0.002) 0.000 -0.003 (-0.004, -0.002) 0.000 -0.003 (-0.005, -0.001) 0.001 -0.003 (-0.005, -0.001) 0.005 -0.003 (-0.009, 0.003) 0.336 -0.001 (-0.007, 0.004) 0.582 -0.003 (-0.009, 0.004) 0.406 OC -0.161 (-0.241, -0.081) 0.000 -0.125 (-0.255, 0.005) 0.060 -0.146 (-0.309, 0.018) 0.080 -0.094 (-0.274, 0.085) 0.301 -0.296 (-0.836, 0.243) 0.241 -0.339 (-0.784, 0.106) 0.117 -0.331 (-0.916, 0.254) 0.228 Land-cover Forest 0.000 (-0.002, 0.002) 0.817 -0.001 (-0.005, 0.002) 0.422 -0.001 (-0.006, 0.004) 0.716 -0.002 (-0.006, 0.003) 0.517 0.002 (-0.013, 0.017) 0.772 0.004 (-0.009, 0.017) 0.513 0.003 (-0.014, 0.019) 0.711 Shape 1.347 (-4.461, 7.156) 0.649 5.764 (-3.853, 15.381) 0.240 8.594 (-5.156, 22.343) 0.220 9.949 (-4.036, 23.935) 0.162 -1.295 (-41.582, 38.993) 0.943 -3.831 (-39.409, 31.748) 0.810 -2.706 (-46.59, 41.177) 0.890 SecVeg 0.009 (0.003, 0.015) 0.003 0.014 (0.004, 0.024) 0.005 0.01 (-0.003, 0.023) 0.145 0.014 (0.001, 0.028) 0.040 -0.004 (-0.049, 0.042) 0.851 -0.017 (-0.055, 0.021) 0.338 -0.008 (-0.057, 0.042) 0.729 UCL -0.004 (-0.007, -0.002) 0.001 -0.003 (-0.007, 0.002) 0.212 -0.003 (-0.009, 0.002) 0.247 -0.001 (-0.007, 0.005) 0.761 -0.007 (-0.025, 0.011) 0.382 -0.008 (-0.024, 0.007) 0.250 -0.009 (-0.028, 0.011) 0.340 ULC.type 0.004 (-0.002, 0.011) 0.167 0.011 (0.001, 0.021) 0.038 0.012 (-0.002, 0.025) 0.090 0.015 (0.001, 0.029) 0.033 -0.002 (-0.045, 0.04) 0.898 -0.008 (-0.045, 0.029) 0.639 -0.005 (-0.051, 0.041) 0.813 Roughness 0.017 (0.006, 0.028) 0.002 0.021 (0.003, 0.038) 0.020 0.03 (0.008, 0.052) 0.009 0.033 (0.008, 0.058) 0.009 0.019 (-0.071, 0.109) 0.645 0.025 (-0.054, 0.103) 0.485 0.019 (-0.08, 0.117) 0.674 LDMC Climate Isoth 0.004 (0.001, 0.007) 0.006 0.003 (-0.002, 0.007) 0.269 0.000 (-0.005, 0.006) 0.915 -0.009 (-0.034, 0.016) 0.471 0.011 (-0.002, 0.024) 0.089 0.009 (-0.003, 0.02) 0.123 0.011 (-0.002, 0.025) 0.084 TAP 0.000 (0.000, 0.000) 0.274 0.000 (0.000, 0.000) 0.050 0.000 (0.000, 0.000) 0.018 0.000 (0.000, 0.001) 0.002 0.000 (0.000, 0.000) 0.873 0.000 (0.000, 0.000) 0.799 0.000 (0.000, 0.000) 0.886 TPdriests 0.000 (0.000, 0.000) 0.002 0.000 (0.000, 0.001) 0.003 0.000 (0.000, 0.001) 0.037 0.002 (0.000, 0.004) 0.012 0.000 (-0.001, 0.001) 0.526 0.000 (-0.001, 0.001) 0.466 0.000 (-0.001, 0.001) 0.790 PET 0.000 (0.000, 0.000) 0.009 0.000 (0.000, 0.000) 0.010 0.000 (0.000, 0.000) 0.011 0.001 (0.000, 0.001) 0.000 0.000 (0.000, 0.000) 0.843 0.000 (0.000, 0.000) 0.952 0.000 (0.000, 0.000) 0.647 Aridity -0.039 (-0.06, -0.018) 0.000 -0.052 (-0.087, -0.018) 0.003 -0.059 (-0.102, -0.015) 0.008 -0.285 (-0.48, -0.091) 0.004 -0.054 (-0.15, 0.042) 0.229 -0.046 (-0.132, 0.04) 0.254 -0.044 (-0.15, 0.061) 0.359 Ard 0.001 (0.000, 0.001) 0.000 0.001 (0.001, 0.002) 0.001 0.001 (0.000, 0.002) 0.007 0.007 (0.003, 0.011) 0.001 0.001 (-0.001, 0.003) 0.239 0.001 (-0.001, 0.003) 0.405 0.001 (-0.001, 0.003) 0.346 SRad 0.000 (0.000, 0.000) 0.003 0.000 (0.000, 0.000) 0.002 0.000 (0.000, 0.000) 0.023 0.000 (0.000, 0.000) 0.005 0.000 (0.000, 0.000) 0.591 0.000 (0.000, 0.000) 0.665 0.000 (0.000, 0.000) 0.777 WVP -0.024 (-0.093, 0.044) 0.486 -0.035 (-0.156, 0.087) 0.575 0.062 (-0.091, 0.216) 0.424 0.385 (-0.256, 1.026) 0.238 -0.115 (-0.512, 0.283) 0.525 -0.064 (-0.421, 0.294) 0.692 -0.128 (-0.545, 0.289) 0.499 Wind -0.024 (-0.034, -0.013) 0.000 -0.034 (-0.051, -0.017) 0.000 -0.031 (-0.054, -0.008) 0.007 -0.143 (-0.239, -0.046) 0.004 -0.03 (-0.078, 0.017) 0.179 -0.023 (-0.067, 0.02) 0.250 -0.026 (-0.078, 0.026) 0.283 Soils Sand 0.000 (-0.001, 0.001) 0.390 0.001 (-0.001, 0.003) 0.355 0.001 (-0.001, 0.003) 0.469 0.01 (0.000, 0.019) 0.054 -0.003 (-0.009, 0.002) 0.216 -0.002 (-0.007, 0.003) 0.286 -0.004 (-0.01, 0.001) 0.095 Silt -0.001 (-0.003, 0.002) 0.499 -0.004 (-0.008, 0.000) 0.047 -0.005 (-0.01, 0.000) 0.072 -0.033 (-0.055, -0.011) 0.003 0.004 (-0.01, 0.017) 0.547 0.002 (-0.01, 0.014) 0.697 0.007 (-0.007, 0.02) 0.292 Clay 0.002 (0.000, 0.003) 0.066 0.000 (-0.003, 0.003) 0.918 0.000 (-0.003, 0.004) 0.902 -0.008 (-0.024, 0.008) 0.308 0.006 (-0.002, 0.014) 0.113 0.005 (-0.002, 0.013) 0.142 0.008 (0.000, 0.016) 0.051 pH 0.000 (-0.015, 0.015) 0.984 -0.01 (-0.034, 0.015) 0.444 -0.02 (-0.047, 0.008) 0.156 -0.121 (-0.244, 0.002) 0.054 0.009 (-0.088, 0.105) 0.840 0.008 (-0.077, 0.093) 0.828 0.01 (-0.091, 0.112) 0.820 100 Ecology of woody plants in Colombian dry forests Trait Environmental inds pops sps sps 80% CWM pops CWMsps CWM80% variable b0 (95% CI) P b0 (95% CI) P b0 (95% CI) P b0 (95% CI) P b0 (95% CI) P b0 (95% CI) P b0 (95% CI) P CEC 0.001 (-0.001, 0.003) 0.199 -0.001 (-0.004, 0.002) 0.703 -0.001 (-0.005, 0.002) 0.436 -0.015 (-0.031, 0.001) 0.069 0.007 (-0.003, 0.016) 0.146 0.005 (-0.003, 0.014) 0.188 0.008 (-0.001, 0.018) 0.081 Ca 0.002 (0.000, 0.003) 0.021 0.000 (-0.002, 0.002) 0.940 -0.001 (-0.003, 0.002) 0.650 -0.009 (-0.021, 0.003) 0.152 0.005 (-0.002, 0.012) 0.137 0.005 (-0.002, 0.011) 0.145 0.006 (-0.001, 0.013) 0.085 Mg 0.002 (-0.001, 0.005) 0.162 0.001 (-0.004, 0.006) 0.821 -0.002 (-0.009, 0.004) 0.454 -0.02 (-0.046, 0.006) 0.126 0.009 (-0.006, 0.023) 0.202 0.006 (-0.008, 0.019) 0.360 0.011 (-0.004, 0.025) 0.127 K -0.003 (-0.068, 0.062) 0.927 -0.068 (-0.179, 0.044) 0.233 -0.109 (-0.248, 0.03) 0.123 -0.696 (-1.296, -0.095) 0.023 0.063 (-0.322, 0.448) 0.716 0.064 (-0.275, 0.404) 0.673 0.072 (-0.332, 0.477) 0.692 Na -0.447 (-0.705, -0.188) 0.001 -0.521 (-0.948, -0.095) 0.017 -0.372 (-0.936, 0.191) 0.195 -1.944 (-4.454, 0.566) 0.128 -0.363 (-1.717, 0.992) 0.554 -0.343 (-1.538, 0.851) 0.526 -0.148 (-1.602, 1.305) 0.820 P 0.000 (0.000, 0.001) 0.001 0.000 (0.000, 0.001) 0.110 0.000 (0.000, 0.001) 0.435 0.001 (-0.001, 0.003) 0.362 0.000 (-0.001, 0.001) 0.841 0.000 (-0.001, 0.001) 0.949 0.000 (-0.001, 0.001) 0.947 OC 0.033 (0.014, 0.052) 0.001 0.018 (-0.014, 0.05) 0.258 0.009 (-0.032, 0.05) 0.663 -0.023 (-0.202, 0.155) 0.797 0.068 (-0.018, 0.155) 0.107 0.058 (-0.02, 0.136) 0.123 0.069 (-0.023, 0.162) 0.121 Land-cover Forest -0.001 (-0.001, -0.001) 0.000 -0.001 (-0.002, 0.000) 0.002 -0.001 (-0.002, 0.000) 0.058 -0.004 (-0.009, 0.001) 0.100 -0.002 (-0.004, 0.001) 0.125 -0.001 (-0.003, 0.001) 0.250 -0.002 (-0.004, 0.001) 0.128 Shape 2.201 (0.83, 3.572) 0.002 3.504 (1.161, 5.847) 0.003 2.759 (-0.659, 6.177) 0.113 11.802 (-2.112, 25.716) 0.096 3.073 (-3.477, 9.623) 0.311 1.527 (-4.554, 7.608) 0.579 3.066 (-3.877, 10.009) 0.338 SecVeg -0.002 (-0.003, -0.001) 0.004 -0.002 (-0.004, 0.001) 0.125 -0.003 (-0.006, 0.000) 0.081 -0.015 (-0.028, -0.001) 0.037 0.001 (-0.007, 0.009) 0.806 0.001 (-0.006, 0.008) 0.869 0.001 (-0.007, 0.01) 0.696 UCL 0.002 (0.001, 0.002) 0.000 0.002 (0.001, 0.003) 0.001 0.001 (0.000, 0.003) 0.114 0.004 (-0.003, 0.01) 0.255 0.002 (0.000, 0.005) 0.062 0.002 (-0.001, 0.004) 0.169 0.003 (0.000, 0.005) 0.059 ULC.type -0.001 (-0.002, 0.001) 0.465 0.001 (-0.002, 0.003) 0.644 -0.001 (-0.004, 0.003) 0.631 -0.004 (-0.018, 0.01) 0.578 0.002 (-0.005, 0.009) 0.563 0.000 (-0.006, 0.007) 0.865 0.002 (-0.005, 0.01) 0.531 Roughness -0.008 (-0.01, -0.005) 0.000 -0.006 (-0.01, -0.002) 0.004 -0.007 (-0.012, -0.001) 0.015 -0.028 (-0.053, -0.004) 0.024 -0.01 (-0.024, 0.003) 0.112 -0.009 (-0.021, 0.003) 0.137 -0.012 (-0.025, 0.002) 0.085 SLA Climate Isoth 0.003 (-0.001, 0.008) 0.138 0.005 (-0.002, 0.012) 0.154 0.005 (-0.002, 0.012) 0.183 0.013 (-0.012, 0.038) 0.301 -0.001 (-0.034, 0.031) 0.936 0.000 (-0.016, 0.016) 0.971 -0.002 (-0.035, 0.03) 0.875 TAP 0.000 (0.000, 0.000) 0.002 0.000 (0.000, 0.000) 0.285 0.000 (0.000, 0.000) 0.463 0.000 (0.000, 0.000) 0.682 0.000 (0.000, 0.000) 0.574 0.000 (0.000, 0.000) 0.356 0.000 (0.000, 0.000) 0.535 TPdriests 0.001 (0.001, 0.001) 0.000 0.001 (0.000, 0.001) 0.010 0.001 (0.000, 0.001) 0.022 0.001 (0.000, 0.003) 0.150 0.001 (-0.001, 0.003) 0.310 0.001 (0.000, 0.002) 0.078 0.001 (-0.001, 0.003) 0.275 PET 0.000 (0.000, 0.000) 0.024 0.000 (0.000, 0.000) 0.328 0.000 (0.000, 0.000) 0.233 0.000 (-0.001, 0.000) 0.230 0.000 (0.000, 0.001) 0.396 0.000 (0.000, 0.000) 0.710 0.000 (0.000, 0.001) 0.341 Aridity -0.054 (-0.088, -0.02) 0.002 -0.042 (-0.094, 0.01) 0.113 -0.045 (-0.101, 0.012) 0.123 -0.089 (-0.288, 0.109) 0.375 -0.027 (-0.249, 0.194) 0.783 -0.032 (-0.138, 0.075) 0.510 -0.029 (-0.251, 0.194) 0.774 Ard 0.002 (0.001, 0.002) 0.000 0.001 (0.000, 0.002) 0.061 0.001 (0.000, 0.002) 0.089 0.002 (-0.002, 0.007) 0.294 0.001 (-0.004, 0.006) 0.746 0.001 (-0.002, 0.003) 0.471 0.001 (-0.004, 0.006) 0.757 SRad 0.000 (0.000, 0.000) 0.000 0.000 (0.000, 0.000) 0.255 0.000 (0.000, 0.000) 0.217 0.000 (0.000, 0.000) 0.302 0.000 (0.000, 0.000) 0.593 0.000 (0.000, 0.000) 0.206 0.000 (0.000, 0.000) 0.602 WVP -0.045 (-0.154, 0.065) 0.424 -0.015 (-0.198, 0.167) 0.869 -0.203 (-0.401, -0.004) 0.046 -0.55 (-1.192, 0.093) 0.093 0.092 (-0.766, 0.949) 0.811 -0.113 (-0.525, 0.3) 0.546 0.118 (-0.738, 0.975) 0.758 Wind -0.041 (-0.057, -0.024) 0.000 -0.029 (-0.055, -0.004) 0.025 -0.033 (-0.062, -0.003) 0.031 -0.074 (-0.173, 0.024) 0.140 -0.032 (-0.141, 0.078) 0.526 -0.029 (-0.079, 0.021) 0.213 -0.032 (-0.142, 0.078) 0.524 Soils Sand 0.004 (0.003, 0.006) 0.000 0.003 (0.000, 0.005) 0.060 0.001 (-0.002, 0.004) 0.373 0.001 (-0.009, 0.011) 0.852 0.007 (-0.004, 0.018) 0.199 0.004 (-0.001, 0.009) 0.128 0.008 (-0.003, 0.019) 0.140 Silt -0.013 (-0.016, -0.009) 0.000 -0.008 (-0.014, -0.002) 0.009 -0.004 (-0.01, 0.003) 0.293 -0.005 (-0.027, 0.017) 0.668 -0.017 (-0.042, 0.009) 0.173 -0.009 (-0.021, 0.004) 0.142 -0.018 (-0.043, 0.007) 0.134 Clay -0.005 (-0.008, -0.002) 0.000 -0.003 (-0.007, 0.002) 0.222 -0.002 (-0.007, 0.003) 0.492 0.000 (-0.016, 0.016) 0.998 -0.01 (-0.028, 0.009) 0.276 -0.006 (-0.015, 0.003) 0.168 -0.011 (-0.029, 0.007) 0.198 pH -0.022 (-0.046, 0.002) 0.068 -0.008 (-0.045, 0.029) 0.682 0.006 (-0.03, 0.042) 0.761 -0.012 (-0.137, 0.114) 0.856 -0.019 (-0.222, 0.183) 0.831 -0.003 (-0.103, 0.097) 0.945 -0.012 (-0.215, 0.192) 0.898 CEC -0.003 (-0.006, 0.000) 0.086 0.000 (-0.005, 0.005) 0.952 0.001 (-0.004, 0.006) 0.804 0.003 (-0.014, 0.019) 0.753 -0.006 (-0.029, 0.017) 0.565 -0.004 (-0.015, 0.007) 0.413 -0.007 (-0.029, 0.016) 0.503 Ca -0.005 (-0.007, -0.003) 0.000 -0.002 (-0.006, 0.001) 0.191 -0.001 (-0.005, 0.003) 0.617 -0.003 (-0.015, 0.01) 0.672 -0.007 (-0.024, 0.01) 0.370 -0.004 (-0.012, 0.004) 0.274 -0.007 (-0.024, 0.009) 0.342 Mg 0.000 (-0.005, 0.004) 0.864 0.001 (-0.006, 0.009) 0.700 0.004 (-0.004, 0.012) 0.358 0.014 (-0.012, 0.04) 0.294 -0.008 (-0.042, 0.025) 0.586 -0.004 (-0.02, 0.013) 0.637 -0.011 (-0.044, 0.022) 0.478 K 0.146 (0.042, 0.25) 0.006 0.201 (0.034, 0.369) 0.018 0.048 (-0.134, 0.229) 0.606 0.144 (-0.468, 0.756) 0.643 0.089 (-0.726, 0.905) 0.807 -0.036 (-0.437, 0.364) 0.840 0.057 (-0.761, 0.875) 0.876 Na -0.593 (-1.006, -0.181) 0.005 -0.578 (-1.22, 0.065) 0.078 -0.462 (-1.196, 0.273) 0.217 -0.135 (-2.674, 2.404) 0.917 -0.803 (-3.65, 2.044) 0.534 -0.664 (-1.992, 0.664) 0.282 -0.956 (-3.776, 1.864) 0.457 P -0.001 (-0.001, 0.000) 0.000 -0.001 (-0.001, 0.000) 0.044 -0.001 (-0.001, 0.000) 0.033 -0.002 (-0.004, 0.000) 0.020 -0.001 (-0.003, 0.002) 0.532 0.000 (-0.001, 0.001) 0.598 -0.001 (-0.003, 0.002) 0.575 OC 0.006 (-0.025, 0.036) 0.718 0.034 (-0.014, 0.082) 0.163 0.003 (-0.05, 0.056) 0.900 -0.015 (-0.195, 0.164) 0.869 -0.028 (-0.244, 0.187) 0.769 -0.039 (-0.141, 0.062) 0.398 -0.037 (-0.251, 0.178) 0.705 Land-cover Forest -0.002 (-0.003, -0.001) 0.000 -0.002 (-0.003, -0.001) 0.003 -0.001 (-0.003, 0.000) 0.125 -0.004 (-0.008, 0.001) 0.126 -0.001 (-0.007, 0.004) 0.575 -0.001 (-0.003, 0.002) 0.542 -0.001 (-0.007, 0.004) 0.668 Shape 7.106 (4.946, 9.267) 0.000 5.89 (2.377, 9.404) 0.001 5.427 (1.006, 9.848) 0.016 12.639 (-1.313, 26.59) 0.076 5.355 (-8.764, 19.474) 0.407 3.745 (-2.834, 10.324) 0.226 5.069 (-9.141, 19.279) 0.435 SecVeg 0.01 (0.008, 0.012) 0.000 0.009 (0.005, 0.013) 0.000 0.007 (0.003, 0.011) 0.001 0.022 (0.008, 0.035) 0.002 0.008 (-0.008, 0.023) 0.285 0.003 (-0.005, 0.01) 0.483 0.007 (-0.009, 0.022) 0.361 UCL 0.002 (0.001, 0.003) 0.002 0.002 (0.000, 0.003) 0.039 0.001 (-0.001, 0.003) 0.296 0.003 (-0.003, 0.009) 0.337 0.001 (-0.006, 0.008) 0.783 0.001 (-0.003, 0.004) 0.699 0.001 (-0.006, 0.007) 0.866 ULC.type 0.011 (0.008, 0.013) 0.000 0.009 (0.005, 0.012) 0.000 0.008 (0.004, 0.012) 0.000 0.02 (0.006, 0.034) 0.006 0.007 (-0.007, 0.022) 0.279 0.004 (-0.003, 0.011) 0.190 0.007 (-0.007, 0.021) 0.300 Roughness 0.001 (-0.003, 0.006) 0.498 0.000 (-0.006, 0.006) 0.999 0.007 (0.000, 0.014) 0.058 0.009 (-0.016, 0.034) 0.467 0.003 (-0.03, 0.037) 0.822 0.006 (-0.01, 0.021) 0.434 0.006 (-0.027, 0.04) 0.672 Lth Climate Isoth -0.003 (-0.006, 0.000) 0.049 -0.004 (-0.009, 0.000) 0.065 -0.001 (-0.006, 0.005) 0.838 -0.001 (-0.026, 0.024) 0.916 -0.002 (-0.019, 0.016) 0.839 0.000 (-0.012, 0.012) 0.946 0.000 (-0.017, 0.017) 0.993 TAP 0.000 (0.000, 0.000) 0.000 0.000 (0.000, 0.000) 0.000 0.000 (0.000, 0.000) 0.000 0.000 (-0.001, 0.000) 0.000 0.000 (0.000, 0.000) 0.086 0.000 (0.000, 0.000) 0.022 0.000 (0.000, 0.000) 0.117 TPdriests -0.001 (-0.001, -0.001) 0.000 -0.001 (-0.001, -0.001) 0.000 -0.001 (-0.001, -0.001) 0.000 -0.003 (-0.005, -0.002) 0.000 -0.001 (-0.002, 0.000) 0.230 0.000 (-0.001, 0.000) 0.120 0.000 (-0.002, 0.001) 0.308 PET 0.000 (0.000, 0.000) 0.413 0.000 (0.000, 0.000) 0.550 0.000 (0.000, 0.000) 0.280 0.000 (-0.001, 0.000) 0.483 0.000 (0.000, 0.000) 0.983 0.000 (0.000, 0.000) 0.729 0.000 (0.000, 0.000) 0.955 Aridity 0.103 (0.083, 0.124) 0.000 0.11 (0.078, 0.141) 0.000 0.111 (0.074, 0.148) 0.000 0.464 (0.275, 0.653) 0.000 0.061 (-0.044, 0.166) 0.210 0.05 (-0.021, 0.121) 0.140 0.049 (-0.058, 0.156) 0.311 101 Doctoral Thesis – Roy González-M. Environmental inds pops sps sps 80% CWM Trait pops CWMsps CWM80% variable b0 (95% CI) P b0 (95% CI) P b0 (95% CI) P b0 (95% CI) P b0 (95% CI) P b0 (95% CI) P b0 (95% CI) P Ard -0.003 (-0.003, -0.002) 0.000 -0.003 (-0.004, -0.002) 0.000 -0.003 (-0.004, -0.002) 0.000 -0.012 (-0.016, -0.008) 0.000 -0.002 (-0.004, 0.001) 0.137 -0.001 (-0.003, 0.000) 0.088 -0.001 (-0.004, 0.001) 0.211 SRad 0.000 (0.000, 0.000) 0.000 0.000 (0.000, 0.000) 0.000 0.000 (0.000, 0.000) 0.000 0.000 (0.000, 0.000) 0.000 0.000 (0.000, 0.000) 0.134 0.000 (0.000, 0.000) 0.037 0.000 (0.000, 0.000) 0.161 WVP 0.115 (0.047, 0.183) 0.001 0.103 (-0.014, 0.219) 0.084 0.107 (-0.03, 0.244) 0.125 0.448 (-0.193, 1.09) 0.170 0.086 (-0.366, 0.538) 0.667 0.061 (-0.252, 0.373) 0.667 0.081 (-0.361, 0.524) 0.677 Wind 0.049 (0.039, 0.059) 0.000 0.05 (0.034, 0.066) 0.000 0.057 (0.038, 0.077) 0.000 0.225 (0.13, 0.32) 0.000 0.031 (-0.024, 0.086) 0.230 0.027 (-0.008, 0.062) 0.109 0.025 (-0.031, 0.081) 0.329 Soils Sand -0.002 (-0.003, -0.001) 0.000 -0.002 (-0.003, 0.000) 0.078 -0.002 (-0.004, 0.000) 0.036 -0.007 (-0.017, 0.003) 0.179 -0.001 (-0.008, 0.006) 0.654 -0.002 (-0.006, 0.003) 0.460 -0.002 (-0.008, 0.005) 0.609 Silt 0.01 (0.007, 0.012) 0.000 0.009 (0.005, 0.013) 0.000 0.01 (0.005, 0.015) 0.000 0.036 (0.014, 0.059) 0.002 0.007 (-0.009, 0.023) 0.333 0.005 (-0.004, 0.015) 0.245 0.007 (-0.009, 0.022) 0.352 Clay 0.001 (-0.001, 0.002) 0.343 0.000 (-0.003, 0.003) 0.976 0.001 (-0.003, 0.004) 0.642 0.000 (-0.016, 0.016) 0.994 0.000 (-0.011, 0.011) 0.929 0.001 (-0.006, 0.009) 0.702 0.001 (-0.01, 0.012) 0.836 pH 0.054 (0.039, 0.068) 0.000 0.045 (0.022, 0.069) 0.000 0.048 (0.024, 0.072) 0.000 0.233 (0.112, 0.353) 0.000 0.056 (-0.039, 0.152) 0.204 0.046 (-0.019, 0.111) 0.140 0.056 (-0.037, 0.149) 0.196 CEC 0.004 (0.002, 0.006) 0.000 0.002 (-0.001, 0.006) 0.110 0.004 (0.001, 0.008) 0.016 0.017 (0.000, 0.033) 0.048 0.004 (-0.009, 0.016) 0.508 0.003 (-0.005, 0.011) 0.357 0.004 (-0.008, 0.017) 0.417 Ca 0.005 (0.003, 0.006) 0.000 0.003 (0.001, 0.006) 0.002 0.004 (0.002, 0.007) 0.001 0.018 (0.006, 0.03) 0.004 0.005 (-0.004, 0.013) 0.254 0.004 (-0.002, 0.009) 0.196 0.005 (-0.003, 0.013) 0.198 Mg -0.002 (-0.005, 0.001) 0.199 -0.002 (-0.007, 0.003) 0.359 0.000 (-0.005, 0.006) 0.886 -0.002 (-0.029, 0.025) 0.882 0.000 (-0.02, 0.019) 0.965 0.002 (-0.011, 0.014) 0.755 0.000 (-0.018, 0.019) 0.970 K 0.003 (-0.064, 0.07) 0.938 0.005 (-0.105, 0.114) 0.936 0.177 (0.053, 0.3) 0.005 0.767 (0.159, 1.376) 0.014 0.016 (-0.447, 0.479) 0.937 0.138 (-0.141, 0.418) 0.286 0.032 (-0.42, 0.484) 0.873 Na 0.411 (0.147, 0.676) 0.002 0.533 (0.116, 0.951) 0.012 0.233 (-0.274, 0.741) 0.367 0.053 (-2.503, 2.608) 0.967 0.296 (-1.279, 1.871) 0.670 0.181 (-0.884, 1.245) 0.706 0.18 (-1.373, 1.733) 0.792 P 0.000 (0.000, 0.001) 0.000 0.000 (0.000, 0.001) 0.015 0.001 (0.000, 0.001) 0.001 0.003 (0.001, 0.005) 0.001 0.000 (-0.001, 0.002) 0.504 0.000 (0.000, 0.001) 0.364 0.000 (-0.001, 0.002) 0.505 OC 0.000 (-0.019, 0.02) 0.966 -0.018 (-0.049, 0.013) 0.255 0.02 (-0.016, 0.056) 0.281 0.114 (-0.064, 0.293) 0.208 0.014 (-0.101, 0.129) 0.783 0.032 (-0.043, 0.107) 0.356 0.024 (-0.087, 0.135) 0.628 Land-cover Forest 0.002 (0.002, 0.003) 0.000 0.002 (0.002, 0.003) 0.000 0.002 (0.001, 0.003) 0.000 0.009 (0.004, 0.014) 0.000 0.002 (-0.001, 0.005) 0.246 0.001 (-0.001, 0.003) 0.339 0.001 (-0.002, 0.004) 0.342 Shape -7.965 (-9.525, -6.404) 0.000 -7.974 (-10.587, -5.362) 0.000 -7.83 (-11.207, -4.453) 0.000 -29.684 (-45.243, -14.126) 0.000 -5.899 (-14.688, 2.891) 0.157 -2.722 (-7.685, 2.242) 0.242 -5.02 (-13.977, 3.937) 0.227 SecVeg -0.005 (-0.006, -0.004) 0.000 -0.004 (-0.007, -0.002) 0.000 -0.003 (-0.006, 0.000) 0.030 -0.015 (-0.029, -0.002) 0.028 -0.003 (-0.012, 0.006) 0.439 -0.001 (-0.007, 0.005) 0.712 -0.003 (-0.011, 0.006) 0.482 UCL -0.002 (-0.002, -0.001) 0.000 -0.002 (-0.003, -0.001) 0.003 -0.001 (-0.002, 0.000) 0.178 -0.004 (-0.011, 0.002) 0.202 -0.001 (-0.005, 0.003) 0.626 0.000 (-0.003, 0.002) 0.739 -0.001 (-0.005, 0.004) 0.777 ULC.type -0.009 (-0.011, -0.007) 0.000 -0.009 (-0.012, -0.006) 0.000 -0.006 (-0.009, -0.002) 0.001 -0.024 (-0.04, -0.008) 0.004 -0.007 (-0.017, 0.003) 0.151 -0.002 (-0.008, 0.003) 0.346 -0.006 (-0.016, 0.004) 0.212 Roughness 0.003 (0.001, 0.006) 0.013 0.003 (-0.002, 0.007) 0.217 0.002 (-0.003, 0.007) 0.405 0.017 (-0.007, 0.042) 0.171 0.003 (-0.015, 0.02) 0.740 0.001 (-0.011, 0.013) 0.861 0.002 (-0.016, 0.019) 0.827 PA Climate Isoth -0.029 (-0.037, -0.021) 0.000 -0.019 (-0.032, -0.007) 0.003 -0.021 (-0.036, -0.006) 0.006 -0.028 (-0.054, -0.003) 0.029 -0.041 (-0.099, 0.017) 0.140 -0.042 (-0.092, 0.007) 0.082 -0.045 (-0.112, 0.023) 0.166 TAP 0.000 (0.000, 0.000) 0.393 0.000 (0.000, 0.000) 0.150 0.000 (0.000, 0.000) 0.170 0.000 (0.000, 0.000) 0.325 0.000 (-0.001, 0.001) 0.722 0.000 (-0.001, 0.000) 0.732 0.000 (-0.001, 0.001) 0.747 TPdriests -0.001 (-0.001, 0.000) 0.001 -0.001 (-0.001, 0.000) 0.213 -0.001 (-0.002, 0.000) 0.256 -0.001 (-0.003, 0.001) 0.296 -0.003 (-0.006, 0.001) 0.129 -0.003 (-0.006, 0.001) 0.117 -0.003 (-0.007, 0.001) 0.139 PET 0.000 (0.000, 0.000) 0.001 0.000 (0.000, 0.000) 0.275 0.000 (0.000, 0.001) 0.168 0.000 (0.000, 0.001) 0.292 0.000 (-0.002, 0.001) 0.763 0.000 (-0.001, 0.001) 0.877 0.000 (-0.002, 0.001) 0.708 Aridity 0.118 (0.056, 0.18) 0.000 0.023 (-0.071, 0.118) 0.628 0.026 (-0.086, 0.139) 0.645 0.043 (-0.157, 0.242) 0.675 0.242 (-0.17, 0.654) 0.212 0.244 (-0.116, 0.604) 0.157 0.263 (-0.213, 0.74) 0.238 Ard -0.001 (-0.003, 0.000) 0.076 0.001 (-0.002, 0.003) 0.602 0.000 (-0.002, 0.003) 0.772 0.000 (-0.004, 0.004) 0.960 -0.003 (-0.013, 0.007) 0.466 -0.003 (-0.012, 0.005) 0.397 -0.004 (-0.015, 0.008) 0.495 SRad 0.000 (0.000, 0.000) 0.811 0.000 (0.000, 0.000) 0.367 0.000 (0.000, 0.000) 0.431 0.000 (0.000, 0.000) 0.933 0.000 (0.000, 0.000) 0.814 0.000 (0.000, 0.000) 0.810 0.000 (0.000, 0.000) 0.836 WVP 0.326 (0.121, 0.531) 0.002 0.192 (-0.149, 0.534) 0.268 0.304 (-0.104, 0.712) 0.144 0.495 (-0.178, 1.168) 0.149 0.136 (-1.623, 1.895) 0.863 0.219 (-1.359, 1.798) 0.757 0.104 (-1.911, 2.119) 0.908 Wind 0.06 (0.03, 0.09) 0.000 0.029 (-0.018, 0.076) 0.223 0.036 (-0.024, 0.095) 0.236 0.064 (-0.036, 0.163) 0.210 0.113 (-0.098, 0.325) 0.252 0.112 (-0.075, 0.299) 0.203 0.123 (-0.122, 0.368) 0.279 Soils Sand 0.006 (0.002, 0.009) 0.000 0.003 (-0.002, 0.008) 0.205 0.004 (-0.002, 0.01) 0.221 0.006 (-0.005, 0.016) 0.272 -0.003 (-0.029, 0.023) 0.777 -0.001 (-0.024, 0.023) 0.930 -0.005 (-0.034, 0.025) 0.711 Silt -0.009 (-0.016, -0.002) 0.012 -0.007 (-0.019, 0.004) 0.208 -0.009 (-0.023, 0.005) 0.194 -0.011 (-0.034, 0.012) 0.335 0.02 (-0.037, 0.077) 0.439 0.014 (-0.038, 0.066) 0.553 0.025 (-0.04, 0.09) 0.406 Clay -0.01 (-0.015, -0.005) 0.000 -0.005 (-0.012, 0.003) 0.249 -0.005 (-0.015, 0.005) 0.297 -0.009 (-0.026, 0.007) 0.277 -0.001 (-0.043, 0.04) 0.938 -0.005 (-0.042, 0.033) 0.786 0.000 (-0.047, 0.048) 0.984 pH -0.085 (-0.13, -0.041) 0.000 -0.089 (-0.156, -0.022) 0.009 -0.085 (-0.156, -0.014) 0.019 -0.089 (-0.215, 0.036) 0.163 -0.177 (-0.568, 0.214) 0.327 -0.185 (-0.529, 0.159) 0.251 -0.206 (-0.652, 0.239) 0.317 CEC -0.016 (-0.021, -0.01) 0.000 -0.012 (-0.02, -0.003) 0.006 -0.012 (-0.022, -0.002) 0.015 -0.016 (-0.033, 0.001) 0.067 -0.015 (-0.061, 0.03) 0.461 -0.018 (-0.058, 0.023) 0.343 -0.016 (-0.068, 0.037) 0.513 Ca -0.011 (-0.015, -0.007) 0.000 -0.009 (-0.015, -0.002) 0.007 -0.009 (-0.016, -0.002) 0.014 -0.011 (-0.024, 0.001) 0.079 -0.013 (-0.047, 0.022) 0.426 -0.015 (-0.045, 0.015) 0.288 -0.013 (-0.053, 0.027) 0.464 Mg -0.017 (-0.026, -0.009) 0.000 -0.01 (-0.025, 0.004) 0.151 -0.014 (-0.031, 0.003) 0.108 -0.021 (-0.049, 0.006) 0.129 -0.008 (-0.077, 0.062) 0.807 -0.011 (-0.073, 0.051) 0.693 -0.006 (-0.086, 0.073) 0.858 K -0.457 (-0.65, -0.264) 0.000 -0.355 (-0.661, -0.049) 0.023 -0.395 (-0.756, -0.034) 0.032 -0.462 (-1.083, 0.158) 0.143 -0.993 (-2.461, 0.474) 0.157 -0.903 (-2.222, 0.415) 0.153 -1.152 (-2.824, 0.519) 0.151 Na 1.809 (1.043, 2.574) 0.000 1.322 (0.144, 2.501) 0.028 1.713 (0.252, 3.174) 0.022 1.731 (-0.844, 4.305) 0.187 4.967 (0.564, 9.37) 0.032 4.781 (1.058, 8.504) 0.018 5.784 (0.824, 10.743) 0.028 P 0.000 (-0.001, 0.001) 0.886 0.000 (-0.001, 0.001) 0.864 0.000 (-0.002, 0.001) 0.494 0.000 (-0.002, 0.002) 0.973 -0.001 (-0.006, 0.004) 0.637 -0.001 (-0.005, 0.003) 0.537 -0.001 (-0.007, 0.004) 0.559 OC -0.155 (-0.21, -0.099) 0.000 -0.091 (-0.178, -0.004) 0.041 -0.112 (-0.217, -0.006) 0.038 -0.127 (-0.31, 0.055) 0.170 -0.345 (-0.688, -0.002) 0.049 -0.341 (-0.628, -0.054) 0.025 -0.391 (-0.786, 0.005) 0.052 Land-cover Forest 0.003 (0.001, 0.004) 0.000 0.002 (-0.001, 0.004) 0.207 0.002 (-0.001, 0.005) 0.237 0.004 (-0.001, 0.009) 0.125 0.003 (-0.008, 0.014) 0.491 0.003 (-0.007, 0.013) 0.465 0.004 (-0.009, 0.016) 0.526 Shape -7.158 (-11.265, -3.051) 0.001 -4.662 (-11.247, 1.922) 0.165 -7.258 (-16.38, 1.865) 0.119 -11.394 (-26.034, 3.246) 0.127 -7.308 (-36.977, 22.362) 0.586 -7.017 (-33.678, 19.644) 0.561 -7.954 (-41.961, 26.053) 0.604 SecVeg -0.006 (-0.01, -0.002) 0.003 -0.004 (-0.011, 0.003) 0.218 -0.004 (-0.013, 0.004) 0.326 -0.009 (-0.024, 0.005) 0.215 -0.007 (-0.04, 0.027) 0.669 -0.004 (-0.035, 0.027) 0.780 -0.006 (-0.045, 0.033) 0.733 102 Ecology of woody plants in Colombian dry forests Trait Environmental inds pops sps sps 80% CWM pops CWMsps CWM80% variable b0 (95% CI) P b0 (95% CI) P b0 (95% CI) P b0 (95% CI) P b0 (95% CI) P b0 (95% CI) P b0 (95% CI) P UCL -0.005 (-0.007, -0.003) 0.000 -0.004 (-0.007, -0.001) 0.018 -0.005 (-0.009, -0.001) 0.013 -0.007 (-0.014, -0.001) 0.022 -0.006 (-0.019, 0.008) 0.361 -0.006 (-0.018, 0.006) 0.294 -0.006 (-0.022, 0.009) 0.381 ULC.type -0.01 (-0.014, -0.005) 0.000 -0.007 (-0.014, 0.000) 0.062 -0.009 (-0.018, 0.000) 0.059 -0.012 (-0.027, 0.003) 0.114 -0.007 (-0.038, 0.024) 0.611 -0.006 (-0.034, 0.022) 0.624 -0.007 (-0.043, 0.028) 0.651 Roughness 0.003 (-0.005, 0.011) 0.400 0.000 (-0.012, 0.013) 0.942 -0.002 (-0.017, 0.012) 0.751 0.007 (-0.018, 0.033) 0.578 0.000 (-0.069, 0.068) 0.990 0.000 (-0.062, 0.062) 0.998 -0.001 (-0.08, 0.077) 0.975 DApit Climate Isoth -0.017 (-0.022, -0.013) 0.000 -0.013 (-0.019, -0.006) 0.000 -0.015 (-0.023, -0.008) 0.000 -0.043 (-0.068, -0.018) 0.001 -0.022 (-0.053, 0.009) 0.134 -0.022 (-0.047, 0.003) 0.075 -0.024 (-0.06, 0.011) 0.153 TAP 0.000 (0.000, 0.000) 0.175 0.000 (0.000, 0.000) 0.067 0.000 (0.000, 0.000) 0.081 0.000 (0.000, 0.000) 0.074 0.000 (0.000, 0.000) 0.849 0.000 (0.000, 0.000) 0.902 0.000 (0.000, 0.000) 0.830 TPdriests 0.000 (-0.001, 0.000) 0.001 0.000 (-0.001, 0.000) 0.184 0.000 (-0.001, 0.000) 0.202 -0.001 (-0.003, 0.001) 0.319 -0.001 (-0.003, 0.001) 0.199 -0.001 (-0.003, 0.001) 0.184 -0.001 (-0.004, 0.001) 0.202 PET 0.000 (0.000, 0.000) 0.000 0.000 (0.000, 0.000) 0.126 0.000 (0.000, 0.000) 0.027 0.001 (0.000, 0.001) 0.052 0.000 (-0.001, 0.001) 0.879 0.000 (-0.001, 0.001) 0.996 0.000 (-0.001, 0.001) 0.817 Aridity 0.064 (0.031, 0.097) 0.000 0.013 (-0.037, 0.062) 0.613 0.024 (-0.035, 0.082) 0.427 0.031 (-0.169, 0.23) 0.761 0.115 (-0.112, 0.342) 0.276 0.106 (-0.084, 0.296) 0.236 0.131 (-0.127, 0.39) 0.276 Ard 0.000 (-0.001, 0.000) 0.220 0.001 (0.000, 0.002) 0.261 0.000 (-0.001, 0.002) 0.609 0.002 (-0.003, 0.006) 0.491 -0.001 (-0.007, 0.004) 0.591 -0.001 (-0.006, 0.003) 0.584 -0.002 (-0.008, 0.005) 0.570 SRad 0.000 (0.000, 0.000) 0.360 0.000 (0.000, 0.000) 0.151 0.000 (0.000, 0.000) 0.156 0.000 (0.000, 0.000) 0.372 0.000 (0.000, 0.000) 0.942 0.000 (0.000, 0.000) 0.980 0.000 (0.000, 0.000) 0.935 WVP 0.185 (0.077, 0.293) 0.001 0.113 (-0.067, 0.292) 0.218 0.189 (-0.022, 0.4) 0.079 0.627 (-0.044, 1.298) 0.067 0.08 (-0.866, 1.025) 0.851 0.107 (-0.694, 0.908) 0.766 0.064 (-1.014, 1.142) 0.894 Wind 0.032 (0.016, 0.047) 0.000 0.016 (-0.009, 0.04) 0.210 0.022 (-0.009, 0.053) 0.158 0.063 (-0.037, 0.162) 0.218 0.053 (-0.064, 0.169) 0.328 0.048 (-0.051, 0.146) 0.295 0.059 (-0.074, 0.192) 0.336 Soils Sand 0.004 (0.002, 0.005) 0.000 0.003 (0.000, 0.005) 0.039 0.004 (0.001, 0.007) 0.021 0.011 (0.001, 0.021) 0.033 0.000 (-0.014, 0.014) 0.964 0.001 (-0.011, 0.013) 0.878 -0.001 (-0.017, 0.015) 0.903 Silt -0.006 (-0.01, -0.003) 0.001 -0.006 (-0.012, 0.000) 0.049 -0.008 (-0.015, -0.001) 0.021 -0.024 (-0.047, -0.002) 0.037 0.008 (-0.024, 0.039) 0.595 0.004 (-0.023, 0.031) 0.738 0.01 (-0.026, 0.045) 0.548 Clay -0.006 (-0.009, -0.004) 0.000 -0.004 (-0.008, 0.000) 0.053 -0.005 (-0.011, 0.000) 0.038 -0.016 (-0.033, 0.000) 0.051 -0.003 (-0.025, 0.019) 0.766 -0.004 (-0.023, 0.015) 0.630 -0.002 (-0.028, 0.023) 0.826 pH -0.057 (-0.08, -0.034) 0.000 -0.061 (-0.096, -0.026) 0.001 -0.06 (-0.097, -0.024) 0.001 -0.16 (-0.285, -0.036) 0.012 -0.098 (-0.307, 0.111) 0.310 -0.1 (-0.272, 0.072) 0.217 -0.11 (-0.349, 0.129) 0.320 CEC -0.01 (-0.013, -0.007) 0.000 -0.009 (-0.013, -0.004) 0.000 -0.01 (-0.015, -0.005) 0.000 -0.027 (-0.044, -0.011) 0.001 -0.01 (-0.034, 0.014) 0.346 -0.011 (-0.031, 0.009) 0.247 -0.011 (-0.038, 0.017) 0.390 Ca -0.007 (-0.009, -0.005) 0.000 -0.007 (-0.01, -0.003) 0.000 -0.007 (-0.011, -0.004) 0.000 -0.02 (-0.032, -0.008) 0.001 -0.009 (-0.027, 0.01) 0.306 -0.009 (-0.024, 0.006) 0.194 -0.009 (-0.03, 0.012) 0.343 Mg -0.01 (-0.014, -0.005) 0.000 -0.006 (-0.014, 0.001) 0.104 -0.01 (-0.018, -0.001) 0.029 -0.03 (-0.058, -0.002) 0.033 -0.006 (-0.043, 0.032) 0.735 -0.007 (-0.038, 0.025) 0.640 -0.005 (-0.048, 0.037) 0.774 K -0.238 (-0.34, -0.136) 0.000 -0.176 (-0.337, -0.015) 0.032 -0.259 (-0.445, -0.073) 0.007 -0.703 (-1.32, -0.086) 0.026 -0.499 (-1.303, 0.305) 0.190 -0.479 (-1.138, 0.181) 0.133 -0.565 (-1.482, 0.352) 0.193 Na 0.948 (0.544, 1.351) 0.000 0.676 (0.056, 1.296) 0.033 1.092 (0.339, 1.846) 0.005 2.345 (-0.222, 4.912) 0.073 2.252 (-0.388, 4.892) 0.085 2.142 (0.037, 4.247) 0.047 2.633 (-0.334, 5.601) 0.075 P 0.000 (0.000, 0.000) 0.404 0.000 (0.000, 0.001) 0.621 0.000 (-0.001, 0.000) 0.628 0.000 (-0.002, 0.002) 0.928 0.000 (-0.003, 0.002) 0.819 0.000 (-0.003, 0.002) 0.661 0.000 (-0.003, 0.002) 0.740 OC -0.092 (-0.121, -0.062) 0.000 -0.055 (-0.101, -0.009) 0.018 -0.081 (-0.136, -0.027) 0.003 -0.196 (-0.377, -0.015) 0.034 -0.184 (-0.369, 0.002) 0.052 -0.172 (-0.319, -0.025) 0.027 -0.211 (-0.421, 0.000) 0.050 Land-cover Forest 0.001 (0.000, 0.002) 0.001 0.001 (-0.001, 0.002) 0.389 0.001 (-0.001, 0.003) 0.229 0.004 (-0.001, 0.009) 0.137 0.002 (-0.004, 0.007) 0.566 0.001 (-0.004, 0.006) 0.534 0.002 (-0.005, 0.008) 0.579 Shape -2.793 (-4.963, -0.623) 0.012 -1.258 (-4.724, 2.209) 0.476 -3.636 (-8.366, 1.094) 0.131 -10.957 (-25.603, 3.688) 0.142 -2.405 (-18.555, 13.745) 0.740 -2.295 (-16.003, 11.414) 0.710 -2.78 (-21.171, 15.61) 0.736 SecVeg -0.004 (-0.006, -0.001) 0.001 -0.002 (-0.006, 0.001) 0.178 -0.003 (-0.007, 0.002) 0.227 -0.011 (-0.026, 0.003) 0.126 -0.004 (-0.022, 0.014) 0.592 -0.003 (-0.018, 0.013) 0.705 -0.004 (-0.025, 0.016) 0.650 UCL -0.002 (-0.003, -0.001) 0.000 -0.002 (-0.003, 0.000) 0.025 -0.003 (-0.005, -0.001) 0.003 -0.009 (-0.015, -0.003) 0.005 -0.003 (-0.01, 0.005) 0.425 -0.003 (-0.009, 0.003) 0.341 -0.003 (-0.011, 0.005) 0.434 ULC.type -0.005 (-0.007, -0.002) 0.000 -0.003 (-0.006, 0.001) 0.135 -0.005 (-0.01, -0.001) 0.025 -0.015 (-0.029, 0.000) 0.053 -0.003 (-0.02, 0.014) 0.700 -0.002 (-0.017, 0.012) 0.720 -0.003 (-0.022, 0.016) 0.720 Roughness 0.000 (-0.004, 0.005) 0.846 -0.001 (-0.007, 0.006) 0.872 -0.002 (-0.01, 0.005) 0.574 0.004 (-0.022, 0.029) 0.783 0.000 (-0.037, 0.037) 0.999 0.001 (-0.03, 0.032) 0.943 0.000 (-0.042, 0.042) 0.990 FWT Climate Isoth 0.001 (-0.002, 0.004) 0.672 0.002 (-0.003, 0.006) 0.502 0.003 (-0.002, 0.009) 0.206 0.025 (0.000, 0.051) 0.053 -0.006 (-0.046, 0.033) 0.715 -0.002 (-0.023, 0.02) 0.865 -0.008 (-0.052, 0.036) 0.688 TAP 0.000 (0.000, 0.000) 0.000 0.000 (0.000, 0.000) 0.008 0.000 (0.000, 0.000) 0.015 0.000 (0.000, 0.000) 0.151 0.000 (0.000, 0.000) 0.441 0.000 (0.000, 0.000) 0.210 0.000 (0.000, 0.001) 0.421 TPdriests 0.000 (0.000, 0.001) 0.001 0.000 (0.000, 0.001) 0.134 0.000 (0.000, 0.001) 0.044 0.001 (0.000, 0.003) 0.120 0.001 (-0.002, 0.003) 0.464 0.001 (-0.001, 0.002) 0.229 0.001 (-0.002, 0.004) 0.450 PET 0.000 (0.000, 0.000) 0.053 0.000 (0.000, 0.000) 0.131 0.000 (0.000, 0.000) 0.212 -0.001 (-0.001, 0.000) 0.035 0.000 (-0.001, 0.001) 0.909 0.000 (0.000, 0.000) 0.873 0.000 (-0.001, 0.001) 0.961 Aridity -0.049 (-0.072, -0.025) 0.000 -0.043 (-0.077, -0.01) 0.012 -0.059 (-0.1, -0.019) 0.004 -0.24 (-0.438, -0.043) 0.017 -0.033 (-0.304, 0.237) 0.783 -0.044 (-0.185, 0.096) 0.488 -0.039 (-0.343, 0.265) 0.775 Ard 0.000 (0.000, 0.001) 0.593 0.000 (-0.001, 0.001) 0.868 0.000 (0.000, 0.001) 0.280 0.000 (-0.004, 0.005) 0.834 -0.001 (-0.008, 0.005) 0.589 0.000 (-0.003, 0.003) 0.964 -0.002 (-0.008, 0.005) 0.614 SRad 0.000 (0.000, 0.000) 0.001 0.000 (0.000, 0.000) 0.456 0.000 (0.000, 0.000) 0.199 0.000 (0.000, 0.000) 0.889 0.000 (0.000, 0.000) 0.595 0.000 (0.000, 0.000) 0.239 0.000 (0.000, 0.000) 0.563 WVP -0.056 (-0.134, 0.021) 0.155 -0.013 (-0.135, 0.11) 0.839 -0.045 (-0.193, 0.103) 0.551 -0.283 (-0.958, 0.393) 0.410 -0.054 (-1.101, 0.993) 0.908 -0.115 (-0.667, 0.437) 0.644 -0.051 (-1.228, 1.126) 0.923 Wind -0.007 (-0.018, 0.005) 0.255 -0.002 (-0.019, 0.014) 0.779 -0.016 (-0.038, 0.005) 0.134 -0.043 (-0.143, 0.057) 0.399 0.019 (-0.117, 0.156) 0.754 -0.008 (-0.082, 0.065) 0.797 0.02 (-0.133, 0.174) 0.771 Soils Sand 0.000 (-0.002, 0.001) 0.562 -0.001 (-0.003, 0.001) 0.311 -0.001 (-0.004, 0.001) 0.187 -0.009 (-0.02, 0.001) 0.069 0.005 (-0.01, 0.02) 0.478 0.002 (-0.006, 0.01) 0.509 0.006 (-0.011, 0.023) 0.438 Silt -0.003 (-0.006, 0.000) 0.034 -0.001 (-0.005, 0.003) 0.733 0.000 (-0.005, 0.004) 0.857 0.008 (-0.015, 0.031) 0.491 -0.018 (-0.05, 0.014) 0.239 -0.011 (-0.027, 0.006) 0.188 -0.021 (-0.057, 0.015) 0.211 Clay 0.002 (0.000, 0.004) 0.018 0.003 (0.000, 0.005) 0.062 0.004 (0.001, 0.008) 0.023 0.021 (0.004, 0.037) 0.014 -0.004 (-0.028, 0.021) 0.745 -0.001 (-0.014, 0.012) 0.867 -0.005 (-0.032, 0.023) 0.702 pH -0.028 (-0.045, -0.011) 0.001 -0.01 (-0.034, 0.014) 0.425 -0.012 (-0.038, 0.014) 0.350 0.019 (-0.107, 0.145) 0.768 -0.049 (-0.294, 0.195) 0.655 -0.05 (-0.176, 0.076) 0.391 -0.06 (-0.334, 0.214) 0.629 CEC -0.002 (-0.004, 0.000) 0.115 0.000 (-0.003, 0.003) 0.931 0.001 (-0.002, 0.005) 0.551 0.013 (-0.004, 0.03) 0.127 -0.012 (-0.039, 0.014) 0.317 -0.007 (-0.021, 0.008) 0.317 -0.015 (-0.044, 0.015) 0.285 Ca -0.001 (-0.002, 0.001) 0.254 0.001 (-0.002, 0.003) 0.598 0.001 (-0.002, 0.004) 0.513 0.01 (-0.002, 0.023) 0.101 -0.007 (-0.028, 0.014) 0.471 -0.004 (-0.015, 0.007) 0.391 -0.008 (-0.032, 0.015) 0.434 103 Doctoral Thesis – Roy González-M. Environmental inds pops sps sps 80% CWM Trait pops CWMsps CWM80% variable b0 (95% CI) P b0 (95% CI) P b0 (95% CI) P b0 (95% CI) P b0 (95% CI) P b0 (95% CI) P b0 (95% CI) P Mg -0.001 (-0.005, 0.002) 0.381 -0.002 (-0.007, 0.003) 0.422 0.000 (-0.006, 0.006) 0.977 0.005 (-0.023, 0.033) 0.708 -0.013 (-0.053, 0.028) 0.489 -0.005 (-0.027, 0.017) 0.622 -0.015 (-0.06, 0.03) 0.470 K -0.002 (-0.075, 0.071) 0.963 -0.013 (-0.123, 0.097) 0.819 -0.031 (-0.163, 0.1) 0.641 0.172 (-0.451, 0.796) 0.586 0.244 (-0.733, 1.22) 0.581 0.055 (-0.476, 0.586) 0.818 0.261 (-0.838, 1.361) 0.598 Na -0.025 (-0.316, 0.266) 0.866 -0.107 (-0.532, 0.317) 0.620 -0.092 (-0.624, 0.441) 0.735 -1.114 (-3.694, 1.466) 0.396 -0.664 (-4.18, 2.851) 0.675 -0.315 (-2.199, 1.568) 0.709 -0.789 (-4.734, 3.155) 0.657 P 0.000 (-0.001, 0.000) 0.006 0.000 (-0.001, 0.000) 0.157 -0.001 (-0.001, 0.000) 0.013 -0.002 (-0.004, 0.000) 0.049 0.000 (-0.003, 0.003) 0.989 0.000 (-0.002, 0.001) 0.644 0.000 (-0.003, 0.003) 0.953 OC 0.000 (-0.021, 0.021) 0.981 0.01 (-0.022, 0.041) 0.548 0.011 (-0.028, 0.049) 0.589 0.119 (-0.063, 0.301) 0.200 0.01 (-0.253, 0.274) 0.931 -0.007 (-0.148, 0.134) 0.910 0.009 (-0.287, 0.306) 0.943 Land-cover Forest 0.000 (0.000, 0.001) 0.143 0.001 (0.000, 0.002) 0.049 0.000 (-0.001, 0.001) 0.826 0.002 (-0.003, 0.007) 0.405 0.003 (-0.003, 0.009) 0.326 0.001 (-0.003, 0.004) 0.669 0.003 (-0.004, 0.01) 0.336 Shape -3.825 (-5.362, -2.288) 0.000 -4.323 (-6.655, -1.992) 0.000 -3.105 (-6.398, 0.189) 0.065 -16.154 (-30.721, -1.588) 0.030 -12.55 (-27.35, 2.251) 0.086 -4.607 (-13.462, 4.248) 0.265 -13.821 (-30.61, 2.968) 0.094 SecVeg 0.000 (-0.002, 0.001) 0.664 -0.001 (-0.004, 0.001) 0.290 0.000 (-0.003, 0.004) 0.788 0.003 (-0.011, 0.018) 0.674 -0.004 (-0.024, 0.016) 0.680 0.000 (-0.011, 0.011) 0.965 -0.005 (-0.027, 0.018) 0.654 UCL -0.001 (-0.002, -0.001) 0.000 -0.002 (-0.003, -0.001) 0.003 -0.001 (-0.002, 0.000) 0.191 -0.004 (-0.011, 0.002) 0.190 -0.005 (-0.012, 0.002) 0.161 -0.002 (-0.006, 0.002) 0.331 -0.006 (-0.014, 0.003) 0.166 ULC.type -0.005 (-0.007, -0.003) 0.000 -0.005 (-0.007, -0.002) 0.000 -0.003 (-0.006, 0.001) 0.099 -0.011 (-0.025, 0.004) 0.162 -0.015 (-0.029, -0.001) 0.041 -0.006 (-0.015, 0.003) 0.148 -0.017 (-0.033, -0.001) 0.041 Roughness -0.002 (-0.005, 0.001) 0.268 0.001 (-0.003, 0.006) 0.512 -0.001 (-0.006, 0.005) 0.797 0.01 (-0.016, 0.036) 0.450 0.005 (-0.036, 0.045) 0.798 0.000 (-0.022, 0.021) 0.961 0.005 (-0.041, 0.051) 0.814 VD Climate Isoth 0.023 (0.015, 0.031) 0.000 0.02 (0.007, 0.033) 0.003 0.016 (0.000, 0.032) 0.048 0.022 (-0.004, 0.047) 0.097 0.035 (-0.022, 0.091) 0.193 0.033 (-0.015, 0.081) 0.147 0.039 (-0.023, 0.101) 0.187 TAP 0.000 (0.000, 0.000) 0.355 0.000 (0.000, 0.000) 0.106 0.000 (0.000, 0.000) 0.054 0.000 (0.000, 0.000) 0.062 0.000 (-0.001, 0.001) 0.865 0.000 (0.000, 0.001) 0.924 0.000 (-0.001, 0.001) 0.855 TPdriests 0.002 (0.001, 0.002) 0.000 0.001 (0.000, 0.002) 0.022 0.000 (-0.001, 0.001) 0.610 0.001 (-0.001, 0.003) 0.432 0.003 (-0.001, 0.006) 0.131 0.002 (-0.001, 0.005) 0.141 0.003 (-0.001, 0.007) 0.124 PET 0.000 (0.000, 0.000) 0.210 0.000 (0.000, 0.000) 0.503 0.000 (0.000, 0.000) 0.713 0.000 (-0.001, 0.000) 0.830 0.000 (-0.001, 0.002) 0.664 0.000 (-0.001, 0.001) 0.850 0.000 (-0.001, 0.002) 0.682 Aridity -0.086 (-0.146, -0.025) 0.006 -0.023 (-0.118, 0.073) 0.638 0.042 (-0.078, 0.162) 0.487 0.091 (-0.109, 0.29) 0.371 -0.166 (-0.575, 0.243) 0.376 -0.146 (-0.502, 0.21) 0.371 -0.189 (-0.639, 0.262) 0.363 Ard 0.001 (-0.001, 0.002) 0.418 -0.001 (-0.003, 0.001) 0.489 -0.002 (-0.005, 0.001) 0.152 -0.004 (-0.008, 0.001) 0.100 0.002 (-0.008, 0.011) 0.693 0.002 (-0.007, 0.01) 0.686 0.002 (-0.009, 0.013) 0.683 SRad 0.000 (0.000, 0.000) 0.031 0.000 (0.000, 0.000) 0.941 0.000 (0.000, 0.000) 0.386 0.000 (0.000, 0.000) 0.677 0.000 (0.000, 0.000) 0.505 0.000 (0.000, 0.000) 0.493 0.000 (0.000, 0.000) 0.491 WVP -0.259 (-0.459, -0.06) 0.011 -0.205 (-0.549, 0.139) 0.243 -0.19 (-0.625, 0.245) 0.390 -0.36 (-1.034, 0.315) 0.294 -0.332 (-1.972, 1.308) 0.653 -0.417 (-1.825, 0.991) 0.514 -0.39 (-2.199, 1.42) 0.633 Wind -0.091 (-0.12, -0.062) 0.000 -0.056 (-0.103, -0.009) 0.020 -0.022 (-0.085, 0.041) 0.498 -0.037 (-0.137, 0.063) 0.462 -0.125 (-0.318, 0.068) 0.173 -0.108 (-0.276, 0.061) 0.179 -0.139 (-0.351, 0.073) 0.170 Soils Sand -0.002 (-0.005, 0.001) 0.119 -0.002 (-0.007, 0.003) 0.435 -0.005 (-0.011, 0.002) 0.167 -0.005 (-0.015, 0.005) 0.343 -0.001 (-0.025, 0.024) 0.947 -0.002 (-0.023, 0.019) 0.838 -0.001 (-0.028, 0.026) 0.954 Silt 0.001 (-0.006, 0.008) 0.783 0.003 (-0.009, 0.014) 0.654 0.011 (-0.003, 0.026) 0.123 0.012 (-0.011, 0.035) 0.290 -0.011 (-0.066, 0.045) 0.671 -0.007 (-0.056, 0.041) 0.742 -0.012 (-0.073, 0.049) 0.663 Clay 0.006 (0.001, 0.01) 0.022 0.004 (-0.004, 0.012) 0.347 0.006 (-0.005, 0.017) 0.262 0.007 (-0.01, 0.023) 0.437 0.007 (-0.032, 0.046) 0.686 0.008 (-0.025, 0.042) 0.575 0.008 (-0.035, 0.051) 0.691 pH 0.072 (0.029, 0.115) 0.001 0.094 (0.027, 0.161) 0.006 0.08 (0.004, 0.155) 0.039 0.114 (-0.011, 0.239) 0.073 0.11 (-0.273, 0.492) 0.527 0.092 (-0.241, 0.426) 0.541 0.123 (-0.3, 0.545) 0.522 CEC 0.012 (0.007, 0.017) 0.000 0.012 (0.003, 0.02) 0.007 0.012 (0.002, 0.023) 0.022 0.016 (-0.001, 0.033) 0.066 0.015 (-0.027, 0.058) 0.432 0.015 (-0.022, 0.052) 0.377 0.017 (-0.031, 0.064) 0.445 Ca 0.011 (0.007, 0.014) 0.000 0.011 (0.004, 0.017) 0.001 0.011 (0.003, 0.018) 0.007 0.014 (0.002, 0.027) 0.025 0.015 (-0.017, 0.047) 0.306 0.014 (-0.014, 0.041) 0.287 0.016 (-0.019, 0.052) 0.315 Mg 0.009 (0.001, 0.017) 0.035 0.008 (-0.006, 0.022) 0.275 0.01 (-0.008, 0.028) 0.264 0.013 (-0.015, 0.041) 0.360 0.01 (-0.056, 0.075) 0.743 0.014 (-0.042, 0.07) 0.585 0.011 (-0.061, 0.083) 0.737 K 0.108 (-0.081, 0.297) 0.261 0.178 (-0.132, 0.488) 0.261 0.214 (-0.172, 0.6) 0.276 0.276 (-0.346, 0.899) 0.383 0.375 (-1.176, 1.926) 0.592 0.436 (-0.895, 1.766) 0.472 0.457 (-1.25, 2.163) 0.554 Na -1.691 (-2.435, -0.947) 0.000 -1.484 (-2.672, -0.297) 0.014 -0.822 (-2.387, 0.743) 0.302 -0.985 (-3.566, 1.596) 0.453 -3.037 (-8.105, 2.032) 0.204 -2.486 (-6.965, 1.992) 0.236 -3.516 (-9.052, 2.02) 0.181 P 0.000 (-0.001, 0.000) 0.663 0.000 (-0.001, 0.001) 0.696 0.000 (-0.001, 0.002) 0.712 0.000 (-0.002, 0.002) 0.917 0.000 (-0.004, 0.005) 0.959 0.000 (-0.004, 0.004) 0.978 0.000 (-0.005, 0.005) 0.903 OC 0.071 (0.017, 0.126) 0.010 0.083 (-0.005, 0.171) 0.065 0.058 (-0.055, 0.171) 0.311 0.045 (-0.138, 0.228) 0.625 0.178 (-0.214, 0.571) 0.325 0.178 (-0.157, 0.512) 0.256 0.203 (-0.229, 0.635) 0.310 Land-cover Forest -0.004 (-0.005, -0.002) 0.000 -0.002 (-0.005, 0.000) 0.053 -0.002 (-0.005, 0.002) 0.308 -0.003 (-0.008, 0.002) 0.310 -0.005 (-0.015, 0.005) 0.311 -0.004 (-0.013, 0.004) 0.275 -0.005 (-0.016, 0.006) 0.309 Shape 9.245 (5.269, 13.221) 0.000 5.308 (-1.331, 11.947) 0.117 4.527 (-5.203, 14.258) 0.361 5.497 (-9.198, 20.192) 0.462 8.109 (-19.649, 35.868) 0.520 6.893 (-17.333, 31.118) 0.530 9.017 (-21.651, 39.684) 0.517 SecVeg 0.002 (-0.002, 0.006) 0.251 0.001 (-0.006, 0.008) 0.778 0.001 (-0.008, 0.011) 0.788 0.004 (-0.01, 0.019) 0.580 0.003 (-0.029, 0.036) 0.815 0.006 (-0.022, 0.033) 0.654 0.003 (-0.032, 0.039) 0.845 UCL 0.007 (0.005, 0.008) 0.000 0.005 (0.002, 0.008) 0.000 0.005 (0.001, 0.009) 0.012 0.007 (0.001, 0.013) 0.027 0.007 (-0.004, 0.019) 0.187 0.007 (-0.004, 0.017) 0.171 0.008 (-0.005, 0.021) 0.184 ULC.type 0.006 (0.001, 0.01) 0.011 0.003 (-0.005, 0.01) 0.484 0.003 (-0.007, 0.012) 0.561 0.002 (-0.012, 0.017) 0.742 0.003 (-0.027, 0.033) 0.834 0.003 (-0.023, 0.029) 0.805 0.003 (-0.03, 0.036) 0.849 Roughness -0.019 (-0.027, -0.012) 0.000 -0.011 (-0.023, 0.001) 0.070 -0.009 (-0.024, 0.007) 0.272 -0.017 (-0.043, 0.008) 0.188 -0.029 (-0.09, 0.031) 0.292 -0.026 (-0.078, 0.026) 0.282 -0.033 (-0.099, 0.034) 0.291 dh Climate Isoth -0.012 (-0.016, -0.008) 0.000 -0.009 (-0.015, -0.002) 0.009 -0.009 (-0.017, -0.001) 0.023 -0.021 (-0.046, 0.005) 0.115 -0.018 (-0.046, 0.009) 0.160 -0.02 (-0.044, 0.005) 0.106 -0.021 (-0.051, 0.008) 0.137 TAP 0.000 (0.000, 0.000) 0.000 0.000 (0.000, 0.000) 0.000 0.000 (0.000, 0.000) 0.001 0.000 (0.000, 0.000) 0.007 0.000 (0.000, 0.000) 0.615 0.000 (0.000, 0.000) 0.611 0.000 (0.000, 0.000) 0.622 TPdriests 0.000 (-0.001, 0.000) 0.068 0.000 (0.000, 0.000) 0.854 0.000 (0.000, 0.001) 0.505 0.000 (-0.002, 0.002) 0.975 -0.001 (-0.003, 0.001) 0.416 -0.001 (-0.002, 0.001) 0.411 -0.001 (-0.003, 0.001) 0.402 PET 0.000 (0.000, 0.000) 0.675 0.000 (0.000, 0.000) 0.758 0.000 (0.000, 0.000) 0.268 0.000 (0.000, 0.001) 0.622 0.000 (-0.001, 0.001) 0.952 0.000 (-0.001, 0.001) 0.828 0.000 (-0.001, 0.001) 0.991 Aridity -0.011 (-0.041, 0.019) 0.487 -0.044 (-0.091, 0.004) 0.074 -0.059 (-0.118, 0.001) 0.054 -0.172 (-0.371, 0.026) 0.089 0.041 (-0.169, 0.251) 0.667 0.048 (-0.15, 0.245) 0.593 0.051 (-0.178, 0.28) 0.619 Ard 0.001 (0.000, 0.002) 0.001 0.002 (0.001, 0.003) 0.002 0.002 (0.001, 0.003) 0.003 0.006 (0.001, 0.01) 0.011 0.000 (-0.005, 0.005) 0.946 0.000 (-0.005, 0.005) 0.993 0.000 (-0.005, 0.005) 0.982 SRad 0.000 (0.000, 0.000) 0.007 0.000 (0.000, 0.000) 0.025 0.000 (0.000, 0.000) 0.008 0.000 (0.000, 0.000) 0.157 0.000 (0.000, 0.000) 0.907 0.000 (0.000, 0.000) 0.922 0.000 (0.000, 0.000) 0.931 104 Ecology of woody plants in Colombian dry forests Trait Environmental inds pops sps sps 80% CWMpops CWMsps CWM80% variable b0 (95% CI) P b0 (95% CI) P b0 (95% CI) P b0 (95% CI) P b0 (95% CI) P b0 (95% CI) P b0 (95% CI) P WVP 0.057 (-0.042, 0.156) 0.261 0.056 (-0.117, 0.229) 0.527 0.061 (-0.156, 0.278) 0.579 0.246 (-0.43, 0.921) 0.474 0.122 (-0.693, 0.936) 0.740 0.185 (-0.578, 0.948) 0.592 0.159 (-0.73, 1.048) 0.690 Wind 0.013 (-0.001, 0.028) 0.070 -0.002 (-0.026, 0.021) 0.841 -0.014 (-0.046, 0.017) 0.380 -0.015 (-0.115, 0.085) 0.766 0.033 (-0.071, 0.137) 0.487 0.032 (-0.066, 0.131) 0.474 0.039 (-0.074, 0.152) 0.451 Soils Sand 0.003 (0.001, 0.004) 0.001 0.002 (-0.001, 0.004) 0.173 0.003 (0.000, 0.006) 0.058 0.006 (-0.005, 0.016) 0.277 0.002 (-0.01, 0.014) 0.692 0.003 (-0.008, 0.014) 0.558 0.003 (-0.011, 0.016) 0.669 Silt -0.006 (-0.009, -0.003) 0.001 -0.006 (-0.011, 0.000) 0.056 -0.01 (-0.017, -0.002) 0.009 -0.018 (-0.041, 0.005) 0.125 -0.001 (-0.029, 0.027) 0.932 -0.003 (-0.029, 0.024) 0.827 -0.001 (-0.032, 0.029) 0.913 Clay -0.004 (-0.006, -0.001) 0.003 -0.002 (-0.006, 0.002) 0.393 -0.003 (-0.008, 0.002) 0.236 -0.006 (-0.022, 0.011) 0.512 -0.005 (-0.024, 0.014) 0.564 -0.006 (-0.024, 0.011) 0.431 -0.006 (-0.026, 0.015) 0.542 pH -0.071 (-0.092, -0.05) 0.000 -0.071 (-0.105, -0.038) 0.000 -0.066 (-0.103, -0.029) 0.001 -0.155 (-0.28, -0.031) 0.015 -0.1 (-0.276, 0.077) 0.229 -0.097 (-0.263, 0.069) 0.213 -0.112 (-0.304, 0.08) 0.216 CEC -0.009 (-0.011, -0.006) 0.000 -0.007 (-0.011, -0.003) 0.001 -0.008 (-0.013, -0.003) 0.002 -0.018 (-0.034, -0.001) 0.040 -0.011 (-0.031, 0.009) 0.233 -0.012 (-0.03, 0.007) 0.181 -0.013 (-0.034, 0.009) 0.218 Ca -0.007 (-0.009, -0.006) 0.000 -0.006 (-0.009, -0.003) 0.000 -0.007 (-0.011, -0.003) 0.000 -0.016 (-0.028, -0.004) 0.011 -0.01 (-0.025, 0.004) 0.142 -0.011 (-0.024, 0.003) 0.108 -0.012 (-0.027, 0.004) 0.132 Mg -0.005 (-0.009, -0.001) 0.014 -0.004 (-0.011, 0.003) 0.253 -0.006 (-0.015, 0.003) 0.198 -0.012 (-0.04, 0.016) 0.385 -0.008 (-0.04, 0.024) 0.594 -0.01 (-0.04, 0.02) 0.463 -0.01 (-0.044, 0.025) 0.546 K -0.221 (-0.314, -0.129) 0.000 -0.213 (-0.367, -0.058) 0.007 -0.254 (-0.445, -0.063) 0.009 -0.526 (-1.146, 0.094) 0.096 -0.376 (-1.093, 0.341) 0.261 -0.407 (-1.068, 0.254) 0.193 -0.424 (-1.205, 0.358) 0.246 Na 0.833 (0.465, 1.2) 0.000 0.609 (0.011, 1.207) 0.046 0.573 (-0.207, 1.352) 0.149 1.153 (-1.427, 3.733) 0.380 1.378 (-1.169, 3.925) 0.247 1.343 (-1.058, 3.745) 0.233 1.598 (-1.159, 4.355) 0.218 P 0.000 (-0.001, 0.000) 0.052 0.000 (-0.001, 0.000) 0.109 -0.001 (-0.001, 0.000) 0.099 -0.001 (-0.003, 0.001) 0.474 0.000 (-0.003, 0.002) 0.713 0.000 (-0.002, 0.002) 0.696 0.000 (-0.003, 0.002) 0.655 OC -0.064 (-0.091, -0.038) 0.000 -0.049 (-0.094, -0.005) 0.029 -0.056 (-0.112, 0.000) 0.050 -0.085 (-0.268, 0.097) 0.358 -0.126 (-0.305, 0.053) 0.144 -0.135 (-0.297, 0.028) 0.092 -0.146 (-0.338, 0.047) 0.119 Land-cover Forest 0.001 (0.000, 0.002) 0.015 0.000 (-0.001, 0.001) 0.833 0.000 (-0.002, 0.001) 0.803 0.001 (-0.004, 0.006) 0.821 0.001 (-0.004, 0.007) 0.533 0.001 (-0.003, 0.006) 0.518 0.002 (-0.004, 0.007) 0.500 Shape -1.849 (-3.825, 0.127) 0.067 -0.002 (-3.346, 3.341) 0.999 0.638 (-4.222, 5.498) 0.796 -0.208 (-14.92, 14.504) 0.978 -1.819 (-15.823, 12.186) 0.772 -1.443 (-14.744, 11.857) 0.809 -2.419 (-17.716, 12.879) 0.725 SecVeg 0.000 (-0.002, 0.002) 0.737 0.000 (-0.003, 0.004) 0.819 0.000 (-0.004, 0.005) 0.893 -0.001 (-0.016, 0.013) 0.840 -0.002 (-0.018, 0.014) 0.806 -0.002 (-0.017, 0.013) 0.769 -0.002 (-0.019, 0.016) 0.823 UCL -0.003 (-0.003, -0.002) 0.000 -0.002 (-0.003, 0.000) 0.014 -0.002 (-0.004, 0.000) 0.048 -0.006 (-0.012, 0.001) 0.082 -0.003 (-0.009, 0.003) 0.272 -0.003 (-0.009, 0.003) 0.262 -0.004 (-0.01, 0.003) 0.240 ULC.type -0.001 (-0.003, 0.001) 0.264 0.000 (-0.003, 0.004) 0.784 0.000 (-0.005, 0.005) 0.994 0.001 (-0.014, 0.016) 0.919 -0.001 (-0.015, 0.014) 0.922 0.000 (-0.014, 0.014) 0.947 -0.001 (-0.017, 0.015) 0.887 Roughness 0.006 (0.002, 0.01) 0.001 0.003 (-0.003, 0.009) 0.397 0.001 (-0.007, 0.009) 0.854 0.01 (-0.016, 0.036) 0.441 0.01 (-0.021, 0.041) 0.499 0.009 (-0.021, 0.038) 0.514 0.01 (-0.024, 0.044) 0.507 Kp Climate Isoth -0.02 (-0.031, -0.008) 0.001 -0.007 (-0.027, 0.013) 0.475 -0.013 (-0.037, 0.012) 0.310 -0.004 (-0.03, 0.022) 0.775 -0.03 (-0.1, 0.04) 0.348 -0.039 (-0.105, 0.027) 0.209 -0.039 (-0.112, 0.035) 0.257 TAP 0.000 (0.000, 0.001) 0.000 0.000 (0.000, 0.001) 0.000 0.000 (0.000, 0.001) 0.000 0.000 (0.000, 0.001) 0.001 0.000 (0.000, 0.001) 0.125 0.000 (0.000, 0.001) 0.174 0.000 (0.000, 0.001) 0.127 TPdriests 0.001 (0.000, 0.002) 0.017 0.001 (0.000, 0.003) 0.041 0.002 (0.000, 0.004) 0.036 0.001 (-0.001, 0.003) 0.209 0.000 (-0.004, 0.005) 0.852 0.000 (-0.005, 0.005) 0.981 0.000 (-0.005, 0.005) 0.877 PET 0.000 (0.000, 0.000) 0.182 0.000 (0.000, 0.001) 0.416 0.000 (0.000, 0.001) 0.224 0.000 (0.000, 0.001) 0.754 0.000 (-0.001, 0.002) 0.800 0.000 (-0.001, 0.002) 0.601 0.000 (-0.001, 0.002) 0.726 Aridity -0.247 (-0.336, -0.157) 0.000 -0.34 (-0.482, -0.198) 0.000 -0.365 (-0.545, -0.186) 0.000 -0.332 (-0.527, -0.137) 0.001 -0.128 (-0.624, 0.369) 0.570 -0.059 (-0.558, 0.441) 0.793 -0.102 (-0.642, 0.438) 0.675 Ard 0.008 (0.006, 0.01) 0.000 0.009 (0.006, 0.013) 0.000 0.01 (0.006, 0.014) 0.000 0.009 (0.004, 0.013) 0.000 0.005 (-0.006, 0.016) 0.300 0.004 (-0.007, 0.015) 0.417 0.004 (-0.007, 0.016) 0.407 SRad 0.000 (0.000, 0.000) 0.000 0.000 (0.000, 0.000) 0.000 0.000 (0.000, 0.000) 0.000 0.000 (0.000, 0.000) 0.039 0.000 (-0.001, 0.000) 0.304 0.000 (-0.001, 0.000) 0.365 0.000 (-0.001, 0.000) 0.309 WVP -0.003 (-0.302, 0.295) 0.983 0.042 (-0.484, 0.567) 0.875 0.043 (-0.624, 0.71) 0.899 0.044 (-0.632, 0.72) 0.897 0.087 (-1.87, 2.044) 0.921 0.284 (-1.638, 2.207) 0.742 0.187 (-1.917, 2.29) 0.843 Wind -0.067 (-0.111, -0.024) 0.002 -0.108 (-0.18, -0.037) 0.003 -0.139 (-0.235, -0.044) 0.004 -0.096 (-0.195, 0.004) 0.059 -0.039 (-0.293, 0.216) 0.736 -0.018 (-0.272, 0.235) 0.871 -0.026 (-0.302, 0.25) 0.834 Soils Sand 0.007 (0.003, 0.012) 0.001 0.004 (-0.004, 0.011) 0.331 0.007 (-0.003, 0.017) 0.157 0.003 (-0.007, 0.013) 0.590 0.008 (-0.02, 0.036) 0.531 0.011 (-0.017, 0.038) 0.399 0.01 (-0.02, 0.04) 0.452 Silt -0.025 (-0.036, -0.015) 0.000 -0.022 (-0.039, -0.004) 0.015 -0.031 (-0.053, -0.009) 0.006 -0.017 (-0.04, 0.006) 0.139 -0.019 (-0.083, 0.045) 0.509 -0.022 (-0.085, 0.041) 0.445 -0.024 (-0.092, 0.044) 0.441 Clay -0.007 (-0.014, 0.000) 0.062 0.001 (-0.011, 0.013) 0.894 -0.002 (-0.019, 0.014) 0.769 0.002 (-0.015, 0.018) 0.849 -0.011 (-0.056, 0.034) 0.591 -0.016 (-0.06, 0.028) 0.419 -0.014 (-0.063, 0.034) 0.511 pH -0.215 (-0.278, -0.151) 0.000 -0.21 (-0.311, -0.108) 0.000 -0.207 (-0.322, -0.093) 0.000 -0.142 (-0.267, -0.018) 0.026 -0.295 (-0.69, 0.101) 0.124 -0.306 (-0.69, 0.077) 0.103 -0.339 (-0.754, 0.076) 0.096 CEC -0.021 (-0.029, -0.013) 0.000 -0.014 (-0.027, -0.001) 0.036 -0.019 (-0.035, -0.003) 0.019 -0.01 (-0.027, 0.007) 0.235 -0.028 (-0.075, 0.02) 0.216 -0.031 (-0.077, 0.014) 0.149 -0.034 (-0.083, 0.016) 0.153 Ca -0.018 (-0.024, -0.013) 0.000 -0.014 (-0.023, -0.004) 0.005 -0.018 (-0.029, -0.006) 0.003 -0.011 (-0.023, 0.002) 0.089 -0.025 (-0.06, 0.009) 0.128 -0.029 (-0.061, 0.004) 0.076 -0.03 (-0.066, 0.006) 0.087 Mg -0.012 (-0.024, 0.001) 0.066 -0.007 (-0.029, 0.015) 0.528 -0.011 (-0.038, 0.017) 0.444 -0.004 (-0.031, 0.024) 0.800 -0.019 (-0.095, 0.057) 0.582 -0.025 (-0.099, 0.049) 0.453 -0.026 (-0.107, 0.055) 0.477 K -0.807 (-1.086, -0.529) 0.000 -0.754 (-1.223, -0.286) 0.002 -0.889 (-1.473, -0.305) 0.003 -0.541 (-1.16, 0.079) 0.087 -1.187 (-2.777, 0.404) 0.124 -1.279 (-2.797, 0.238) 0.088 -1.305 (-3.005, 0.395) 0.115 Na 0.793 (-0.324, 1.91) 0.164 0.187 (-1.636, 2.009) 0.840 0.439 (-1.962, 2.841) 0.719 -0.156 (-2.74, 2.428) 0.905 1.428 (-5.114, 7.97) 0.628 2.03 (-4.329, 8.39) 0.483 1.888 (-5.102, 8.877) 0.551 P -0.001 (-0.002, 0.000) 0.008 -0.001 (-0.003, 0.000) 0.059 -0.002 (-0.004, 0.000) 0.054 -0.001 (-0.003, 0.001) 0.309 -0.001 (-0.007, 0.004) 0.592 -0.001 (-0.007, 0.004) 0.522 -0.002 (-0.007, 0.004) 0.531 OC -0.129 (-0.21, -0.048) 0.002 -0.052 (-0.187, 0.083) 0.454 -0.089 (-0.262, 0.084) 0.314 0.000 (-0.184, 0.183) 0.996 -0.262 (-0.706, 0.182) 0.210 -0.32 (-0.732, 0.092) 0.111 -0.325 (-0.785, 0.136) 0.142 Land-cover Forest -0.001 (-0.003, 0.002) 0.639 -0.003 (-0.006, 0.001) 0.141 -0.004 (-0.009, 0.001) 0.107 -0.002 (-0.007, 0.003) 0.387 0.000 (-0.012, 0.013) 0.961 0.001 (-0.012, 0.013) 0.919 0.001 (-0.013, 0.014) 0.876 Shape 3.14 (-2.831, 9.112) 0.302 8.121 (-2.003, 18.244) 0.116 12.403 (-2.462, 27.268) 0.102 7.723 (-6.956, 22.402) 0.301 3.96 (-29.5, 37.421) 0.792 4.137 (-28.934, 37.208) 0.780 1.954 (-34.216, 38.125) 0.904 SecVeg 0.000 (-0.006, 0.006) 0.907 0.001 (-0.009, 0.012) 0.793 0.002 (-0.013, 0.016) 0.825 0.001 (-0.014, 0.015) 0.924 -0.004 (-0.042, 0.034) 0.805 -0.003 (-0.04, 0.035) 0.865 -0.004 (-0.045, 0.036) 0.807 UCL -0.004 (-0.007, -0.001) 0.005 -0.002 (-0.006, 0.003) 0.501 -0.002 (-0.008, 0.004) 0.539 -0.002 (-0.008, 0.005) 0.592 -0.004 (-0.02, 0.011) 0.546 -0.005 (-0.02, 0.01) 0.498 -0.006 (-0.022, 0.011) 0.443 ULC.type 0.003 (-0.004, 0.009) 0.403 0.008 (-0.003, 0.018) 0.164 0.007 (-0.007, 0.022) 0.324 0.008 (-0.007, 0.022) 0.322 0.004 (-0.031, 0.039) 0.820 0.004 (-0.03, 0.039) 0.777 0.001 (-0.037, 0.039) 0.937 105 Doctoral Thesis – Roy González-M. Environmental inds pops sps sps 80% CWM Trait pops CWMsps CWM80% variable b0 (95% CI) P b0 (95% CI) P b0 (95% CI) P b0 (95% CI) P b0 (95% CI) P b0 (95% CI) P b0 (95% CI) P Roughness 0.011 (-0.001, 0.022) 0.062 0.002 (-0.017, 0.02) 0.869 -0.004 (-0.028, 0.02) 0.764 0.006 (-0.02, 0.031) 0.667 0.015 (-0.06, 0.09) 0.658 0.014 (-0.061, 0.089) 0.676 0.015 (-0.067, 0.096) 0.688 VA Climate Isoth -0.027 (-0.035, -0.019) 0.000 -0.021 (-0.035, -0.008) 0.002 -0.022 (-0.038, -0.005) 0.012 -0.025 (-0.05, 0.001) 0.058 -0.037 (-0.093, 0.019) 0.171 -0.039 (-0.09, 0.012) 0.114 -0.042 (-0.102, 0.019) 0.153 TAP 0.000 (0.000, 0.000) 0.000 0.000 (0.000, 0.000) 0.000 0.000 (0.000, 0.000) 0.000 0.000 (0.000, 0.000) 0.005 0.000 (0.000, 0.001) 0.635 0.000 (0.000, 0.001) 0.620 0.000 (0.000, 0.001) 0.646 TPdriests 0.000 (-0.001, 0.000) 0.105 0.000 (-0.001, 0.001) 0.899 0.001 (-0.001, 0.002) 0.406 0.000 (-0.002, 0.002) 0.875 -0.001 (-0.005, 0.002) 0.427 -0.001 (-0.005, 0.002) 0.428 -0.002 (-0.006, 0.003) 0.404 PET 0.000 (0.000, 0.000) 0.437 0.000 (0.000, 0.000) 0.522 0.000 (0.000, 0.001) 0.169 0.000 (0.000, 0.001) 0.429 0.000 (-0.001, 0.001) 0.983 0.000 (-0.001, 0.001) 0.784 0.000 (-0.001, 0.001) 0.999 Aridity -0.023 (-0.087, 0.04) 0.471 -0.085 (-0.185, 0.015) 0.095 -0.128 (-0.254, -0.002) 0.046 -0.172 (-0.37, 0.026) 0.089 0.075 (-0.354, 0.504) 0.697 0.09 (-0.315, 0.496) 0.621 0.095 (-0.373, 0.563) 0.652 Ard 0.003 (0.002, 0.005) 0.000 0.004 (0.002, 0.007) 0.000 0.005 (0.002, 0.008) 0.000 0.007 (0.003, 0.011) 0.002 0.001 (-0.009, 0.011) 0.792 0.001 (-0.009, 0.01) 0.877 0.001 (-0.01, 0.012) 0.868 SRad 0.000 (0.000, 0.000) 0.000 0.000 (0.000, 0.000) 0.002 0.000 (0.000, 0.000) 0.001 0.000 (0.000, 0.000) 0.036 0.000 (0.000, 0.000) 0.831 0.000 (0.000, 0.000) 0.876 0.000 (0.000, 0.000) 0.882 WVP 0.028 (-0.181, 0.238) 0.792 0.022 (-0.34, 0.385) 0.904 0.04 (-0.419, 0.5) 0.863 0.128 (-0.548, 0.804) 0.710 0.173 (-1.494, 1.84) 0.817 0.347 (-1.217, 1.911) 0.623 0.25 (-1.569, 2.068) 0.760 Wind 0.018 (-0.013, 0.048) 0.260 -0.013 (-0.063, 0.037) 0.615 -0.042 (-0.108, 0.024) 0.215 -0.031 (-0.131, 0.069) 0.540 0.056 (-0.159, 0.271) 0.564 0.057 (-0.146, 0.26) 0.534 0.069 (-0.165, 0.302) 0.516 Soils Sand 0.007 (0.003, 0.01) 0.000 0.005 (0.000, 0.01) 0.046 0.008 (0.002, 0.015) 0.017 0.008 (-0.002, 0.018) 0.119 0.005 (-0.019, 0.03) 0.642 0.007 (-0.016, 0.03) 0.512 0.006 (-0.021, 0.032) 0.638 Silt -0.016 (-0.023, -0.008) 0.000 -0.015 (-0.027, -0.003) 0.012 -0.024 (-0.039, -0.009) 0.002 -0.024 (-0.047, -0.001) 0.041 -0.004 (-0.06, 0.053) 0.884 -0.007 (-0.06, 0.047) 0.783 -0.004 (-0.066, 0.058) 0.885 Clay -0.009 (-0.014, -0.004) 0.000 -0.006 (-0.014, 0.002) 0.147 -0.009 (-0.02, 0.002) 0.108 -0.009 (-0.026, 0.008) 0.291 -0.011 (-0.05, 0.027) 0.520 -0.014 (-0.05, 0.022) 0.389 -0.012 (-0.054, 0.03) 0.514 pH -0.166 (-0.21, -0.121) 0.000 -0.17 (-0.24, -0.1) 0.000 -0.157 (-0.236, -0.079) 0.000 -0.188 (-0.311, -0.064) 0.003 -0.206 (-0.564, 0.151) 0.220 -0.197 (-0.537, 0.142) 0.217 -0.227 (-0.618, 0.164) 0.218 CEC -0.021 (-0.026, -0.015) 0.000 -0.018 (-0.027, -0.009) 0.000 -0.02 (-0.031, -0.009) 0.000 -0.023 (-0.039, -0.006) 0.008 -0.023 (-0.064, 0.018) 0.231 -0.024 (-0.062, 0.014) 0.177 -0.025 (-0.07, 0.019) 0.229 Ca -0.018 (-0.022, -0.014) 0.000 -0.016 (-0.023, -0.01) 0.000 -0.018 (-0.026, -0.01) 0.000 -0.02 (-0.033, -0.008) 0.001 -0.022 (-0.051, 0.008) 0.125 -0.022 (-0.049, 0.005) 0.097 -0.024 (-0.056, 0.008) 0.125 Mg -0.008 (-0.016, 0.001) 0.076 -0.007 (-0.022, 0.009) 0.395 -0.01 (-0.029, 0.009) 0.297 -0.009 (-0.037, 0.018) 0.505 -0.012 (-0.077, 0.054) 0.687 -0.018 (-0.08, 0.043) 0.508 -0.015 (-0.087, 0.056) 0.639 K -0.506 (-0.702, -0.31) 0.000 -0.509 (-0.832, -0.185) 0.002 -0.575 (-0.978, -0.172) 0.005 -0.602 (-1.221, 0.017) 0.057 -0.8 (-2.251, 0.652) 0.240 -0.832 (-2.183, 0.518) 0.193 -0.886 (-2.469, 0.696) 0.233 Na 1.866 (1.088, 2.644) 0.000 1.467 (0.216, 2.717) 0.022 1.302 (-0.345, 2.949) 0.121 1.449 (-1.129, 4.026) 0.269 2.815 (-2.379, 8.009) 0.247 2.613 (-2.343, 7.569) 0.259 3.255 (-2.359, 8.868) 0.218 P 0.000 (-0.001, 0.000) 0.113 -0.001 (-0.002, 0.000) 0.183 -0.001 (-0.002, 0.000) 0.165 -0.001 (-0.003, 0.001) 0.596 -0.001 (-0.005, 0.004) 0.733 -0.001 (-0.005, 0.004) 0.751 -0.001 (-0.006, 0.004) 0.667 OC -0.138 (-0.195, -0.081) 0.000 -0.118 (-0.211, -0.026) 0.012 -0.125 (-0.243, -0.007) 0.039 -0.106 (-0.288, 0.077) 0.256 -0.243 (-0.615, 0.129) 0.170 -0.263 (-0.601, 0.074) 0.110 -0.277 (-0.678, 0.124) 0.150 Land-cover Forest 0.001 (0.000, 0.002) 0.190 -0.001 (-0.003, 0.002) 0.689 -0.002 (-0.005, 0.002) 0.374 -0.001 (-0.006, 0.004) 0.705 0.002 (-0.008, 0.013) 0.645 0.002 (-0.008, 0.012) 0.604 0.003 (-0.009, 0.014) 0.595 Shape -1.09 (-5.282, 3.103) 0.610 2.567 (-4.434, 9.568) 0.472 5.192 (-5.067, 15.452) 0.320 4.625 (-10.075, 19.326) 0.536 -1.095 (-29.808, 27.617) 0.932 -0.809 (-28.092, 26.474) 0.947 -2.349 (-33.702, 29.004) 0.867 SecVeg 0.000 (-0.004, 0.005) 0.843 0.001 (-0.006, 0.009) 0.688 0.002 (-0.008, 0.012) 0.747 0.000 (-0.015, 0.014) 0.978 -0.002 (-0.034, 0.031) 0.901 -0.003 (-0.034, 0.027) 0.813 -0.001 (-0.037, 0.034) 0.925 UCL -0.005 (-0.006, -0.003) 0.000 -0.003 (-0.006, 0.000) 0.052 -0.003 (-0.008, 0.001) 0.146 -0.004 (-0.01, 0.002) 0.203 -0.005 (-0.018, 0.007) 0.362 -0.005 (-0.017, 0.007) 0.336 -0.006 (-0.02, 0.007) 0.315 ULC.type 0.000 (-0.005, 0.004) 0.854 0.002 (-0.005, 0.01) 0.531 0.002 (-0.008, 0.012) 0.726 0.003 (-0.012, 0.018) 0.714 0.001 (-0.029, 0.032) 0.912 0.001 (-0.027, 0.03) 0.920 0.001 (-0.032, 0.034) 0.947 Roughness 0.011 (0.003, 0.019) 0.005 0.004 (-0.009, 0.016) 0.592 -0.001 (-0.018, 0.015) 0.887 0.004 (-0.022, 0.03) 0.748 0.02 (-0.043, 0.083) 0.485 0.018 (-0.042, 0.078) 0.509 0.023 (-0.046, 0.091) 0.472 VAmax Climate Isoth -0.028 (-0.037, -0.02) 0.000 -0.022 (-0.036, -0.007) 0.004 -0.024 (-0.041, -0.006) 0.009 -0.025 (-0.051, 0.001) 0.055 -0.038 (-0.096, 0.02) 0.166 -0.042 (-0.094, 0.01) 0.098 -0.044 (-0.107, 0.018) 0.141 TAP 0.000 (0.000, 0.000) 0.000 0.000 (0.000, 0.000) 0.000 0.000 (0.000, 0.000) 0.000 0.000 (0.000, 0.001) 0.004 0.000 (0.000, 0.001) 0.599 0.000 (0.000, 0.001) 0.588 0.000 (-0.001, 0.001) 0.625 TPdriests 0.000 (-0.001, 0.000) 0.151 0.000 (-0.001, 0.001) 0.946 0.001 (-0.001, 0.002) 0.405 0.000 (-0.002, 0.002) 0.802 -0.001 (-0.006, 0.003) 0.420 -0.001 (-0.005, 0.002) 0.418 -0.002 (-0.006, 0.003) 0.382 PET 0.000 (0.000, 0.000) 0.206 0.000 (0.000, 0.000) 0.347 0.000 (0.000, 0.001) 0.094 0.000 (0.000, 0.001) 0.287 0.000 (-0.001, 0.001) 0.967 0.000 (-0.001, 0.001) 0.723 0.000 (-0.001, 0.002) 0.948 Aridity -0.03 (-0.098, 0.038) 0.389 -0.106 (-0.213, 0.000) 0.051 -0.136 (-0.27, -0.003) 0.045 -0.182 (-0.38, 0.016) 0.072 0.067 (-0.38, 0.515) 0.737 0.09 (-0.328, 0.507) 0.634 0.095 (-0.392, 0.582) 0.666 Ard 0.004 (0.002, 0.005) 0.000 0.005 (0.003, 0.007) 0.000 0.005 (0.003, 0.008) 0.000 0.007 (0.003, 0.011) 0.002 0.001 (-0.009, 0.012) 0.757 0.001 (-0.009, 0.01) 0.862 0.001 (-0.01, 0.012) 0.861 SRad 0.000 (0.000, 0.000) 0.000 0.000 (0.000, 0.000) 0.003 0.000 (0.000, 0.000) 0.001 0.000 (0.000, 0.000) 0.036 0.000 (0.000, 0.000) 0.850 0.000 (0.000, 0.000) 0.885 0.000 (0.000, 0.000) 0.917 WVP 0.068 (-0.156, 0.291) 0.553 0.062 (-0.324, 0.448) 0.752 0.098 (-0.39, 0.585) 0.694 0.191 (-0.485, 0.867) 0.578 0.243 (-1.485, 1.971) 0.754 0.42 (-1.179, 2.02) 0.562 0.341 (-1.541, 2.223) 0.687 Wind 0.018 (-0.015, 0.05) 0.291 -0.019 (-0.072, 0.035) 0.494 -0.044 (-0.114, 0.027) 0.223 -0.035 (-0.135, 0.065) 0.491 0.058 (-0.165, 0.281) 0.566 0.062 (-0.147, 0.27) 0.515 0.074 (-0.167, 0.316) 0.498 Soils Sand 0.007 (0.004, 0.011) 0.000 0.006 (0.000, 0.011) 0.038 0.009 (0.002, 0.017) 0.011 0.009 (-0.001, 0.019) 0.093 0.005 (-0.02, 0.031) 0.645 0.008 (-0.016, 0.031) 0.478 0.006 (-0.022, 0.034) 0.643 Silt -0.018 (-0.025, -0.01) 0.000 -0.017 (-0.03, -0.005) 0.008 -0.027 (-0.043, -0.011) 0.001 -0.025 (-0.048, -0.003) 0.030 -0.004 (-0.062, 0.055) 0.891 -0.008 (-0.063, 0.047) 0.743 -0.004 (-0.068, 0.061) 0.903 Clay -0.01 (-0.016, -0.005) 0.000 -0.007 (-0.016, 0.002) 0.138 -0.01 (-0.022, 0.001) 0.080 -0.01 (-0.026, 0.007) 0.248 -0.012 (-0.051, 0.028) 0.521 -0.015 (-0.052, 0.021) 0.364 -0.013 (-0.057, 0.031) 0.510 pH -0.165 (-0.213, -0.118) 0.000 -0.172 (-0.247, -0.098) 0.000 -0.165 (-0.249, -0.082) 0.000 -0.184 (-0.307, -0.06) 0.004 -0.21 (-0.583, 0.164) 0.232 -0.207 (-0.554, 0.141) 0.208 -0.231 (-0.639, 0.178) 0.229 CEC -0.022 (-0.028, -0.016) 0.000 -0.019 (-0.028, -0.009) 0.000 -0.022 (-0.034, -0.011) 0.000 -0.023 (-0.04, -0.006) 0.007 -0.024 (-0.066, 0.018) 0.227 -0.026 (-0.065, 0.012) 0.154 -0.027 (-0.073, 0.02) 0.221 Ca -0.018 (-0.023, -0.014) 0.000 -0.016 (-0.023, -0.01) 0.000 -0.019 (-0.027, -0.01) 0.000 -0.02 (-0.032, -0.008) 0.001 -0.022 (-0.053, 0.009) 0.136 -0.023 (-0.051, 0.004) 0.087 -0.024 (-0.058, 0.009) 0.133 Mg -0.01 (-0.019, 0.000) 0.040 -0.008 (-0.024, 0.008) 0.328 -0.013 (-0.033, 0.008) 0.220 -0.012 (-0.039, 0.016) 0.407 -0.014 (-0.082, 0.054) 0.646 -0.021 (-0.083, 0.042) 0.462 -0.018 (-0.092, 0.056) 0.590 K -0.52 (-0.73, -0.311) 0.000 -0.528 (-0.873, -0.183) 0.003 -0.615 (-1.043, -0.188) 0.005 -0.615 (-1.234, 0.003) 0.051 -0.855 (-2.354, 0.645) 0.225 -0.866 (-2.252, 0.52) 0.187 -0.951 (-2.585, 0.683) 0.216 106 Ecology of woody plants in Colombian dry forests Trait Environmental inds pops sps sps 80% CWM pops CWMsps CWM80% variable b0 (95% CI) P b0 (95% CI) P b0 (95% CI) P b0 (95% CI) P b0 (95% CI) P b0 (95% CI) P b0 (95% CI) P Na 1.658 (0.826, 2.491) 0.000 1.213 (-0.122, 2.548) 0.075 1.219 (-0.531, 2.969) 0.171 1.114 (-1.466, 3.694) 0.396 2.697 (-2.779, 8.174) 0.289 2.587 (-2.548, 7.722) 0.279 3.207 (-2.696, 9.11) 0.246 P 0.000 (-0.001, 0.000) 0.481 0.000 (-0.001, 0.001) 0.395 -0.001 (-0.002, 0.001) 0.308 0.000 (-0.002, 0.002) 0.816 0.000 (-0.005, 0.004) 0.857 0.000 (-0.005, 0.004) 0.829 -0.001 (-0.006, 0.005) 0.785 OC -0.132 (-0.193, -0.072) 0.000 -0.105 (-0.204, -0.006) 0.037 -0.128 (-0.254, -0.002) 0.046 -0.096 (-0.279, 0.087) 0.302 -0.244 (-0.634, 0.146) 0.188 -0.266 (-0.616, 0.084) 0.117 -0.285 (-0.704, 0.134) 0.155 Land-cover Forest 0.001 (0.000, 0.003) 0.132 0.000 (-0.003, 0.002) 0.714 -0.002 (-0.005, 0.002) 0.418 -0.001 (-0.006, 0.004) 0.734 0.003 (-0.008, 0.013) 0.612 0.003 (-0.008, 0.013) 0.567 0.003 (-0.009, 0.015) 0.551 Shape -1.447 (-5.922, 3.028) 0.526 2.733 (-4.726, 10.191) 0.472 5.271 (-5.624, 16.166) 0.342 4.66 (-10.04, 19.36) 0.533 -1.39 (-31.222, 28.443) 0.917 -1.406 (-29.463, 26.652) 0.911 -2.971 (-35.541, 29.599) 0.839 SecVeg -0.001 (-0.006, 0.003) 0.518 0.000 (-0.008, 0.007) 0.940 0.000 (-0.011, 0.01) 0.955 -0.003 (-0.017, 0.012) 0.715 -0.005 (-0.038, 0.029) 0.747 -0.005 (-0.037, 0.026) 0.722 -0.005 (-0.042, 0.032) 0.764 UCL -0.005 (-0.007, -0.003) 0.000 -0.003 (-0.006, 0.000) 0.070 -0.003 (-0.008, 0.001) 0.147 -0.004 (-0.01, 0.002) 0.220 -0.006 (-0.019, 0.008) 0.355 -0.006 (-0.018, 0.007) 0.308 -0.007 (-0.021, 0.007) 0.299 ULC.type -0.002 (-0.006, 0.003) 0.496 0.002 (-0.006, 0.01) 0.670 0.001 (-0.01, 0.012) 0.896 0.002 (-0.013, 0.017) 0.830 0.000 (-0.031, 0.032) 0.972 0.000 (-0.029, 0.03) 0.983 0.000 (-0.035, 0.034) 0.981 Roughness 0.012 (0.003, 0.02) 0.006 0.004 (-0.01, 0.017) 0.607 -0.002 (-0.019, 0.015) 0.820 0.003 (-0.022, 0.029) 0.796 0.021 (-0.045, 0.086) 0.487 0.019 (-0.043, 0.081) 0.505 0.023 (-0.048, 0.095) 0.477 WD Climate Isoth -0.002 (-0.005, 0.000) 0.099 -0.006 (-0.011, -0.001) 0.012 -0.007 (-0.013, -0.002) 0.013 -0.037 (-0.061, -0.012) 0.004 -0.002 (-0.023, 0.02) 0.845 0.001 (-0.017, 0.019) 0.895 -0.001 (-0.023, 0.021) 0.905 TAP 0.000 (0.000, 0.000) 0.021 0.000 (0.000, 0.000) 0.087 0.000 (0.000, 0.000) 0.230 0.000 (0.000, 0.000) 0.690 0.000 (0.000, 0.000) 0.395 0.000 (0.000, 0.000) 0.353 0.000 (0.000, 0.000) 0.298 TPdriests 0.000 (0.000, 0.000) 0.032 0.000 (-0.001, 0.000) 0.039 -0.001 (-0.001, 0.000) 0.005 -0.001 (-0.003, 0.000) 0.144 0.000 (-0.002, 0.001) 0.649 0.000 (-0.001, 0.001) 0.627 0.000 (-0.002, 0.001) 0.529 PET 0.000 (0.000, 0.000) 0.000 0.000 (0.000, 0.000) 0.000 0.000 (0.000, 0.000) 0.001 0.001 (0.000, 0.001) 0.000 0.000 (0.000, 0.001) 0.648 0.000 (0.000, 0.000) 0.734 0.000 (0.000, 0.001) 0.769 Aridity 0.04 (0.018, 0.062) 0.000 0.062 (0.026, 0.097) 0.001 0.066 (0.023, 0.11) 0.003 0.242 (0.046, 0.438) 0.016 0.058 (-0.081, 0.197) 0.362 0.04 (-0.078, 0.159) 0.457 0.067 (-0.075, 0.209) 0.309 Ard -0.002 (-0.002, -0.001) 0.000 -0.002 (-0.003, -0.001) 0.000 -0.002 (-0.003, -0.001) 0.000 -0.007 (-0.011, -0.002) 0.002 -0.002 (-0.005, 0.001) 0.119 -0.001 (-0.004, 0.001) 0.230 -0.002 (-0.005, 0.001) 0.107 SRad 0.000 (0.000, 0.000) 0.000 0.000 (0.000, 0.000) 0.023 0.000 (0.000, 0.000) 0.013 0.000 (0.000, 0.000) 0.123 0.000 (0.000, 0.000) 0.494 0.000 (0.000, 0.000) 0.487 0.000 (0.000, 0.000) 0.418 WVP 0.211 (0.141, 0.281) 0.000 0.259 (0.134, 0.383) 0.000 0.338 (0.187, 0.488) 0.000 1.268 (0.643, 1.893) 0.000 0.159 (-0.391, 0.71) 0.523 0.108 (-0.358, 0.574) 0.608 0.148 (-0.427, 0.723) 0.570 Wind 0.018 (0.007, 0.028) 0.001 0.026 (0.009, 0.044) 0.004 0.039 (0.016, 0.062) 0.001 0.123 (0.025, 0.22) 0.014 0.026 (-0.045, 0.097) 0.420 0.015 (-0.046, 0.076) 0.577 0.029 (-0.044, 0.103) 0.382 Soils Sand -0.001 (-0.002, 0.000) 0.151 0.000 (-0.002, 0.002) 0.903 0.000 (-0.002, 0.002) 0.961 0.008 (-0.002, 0.018) 0.112 -0.001 (-0.009, 0.007) 0.759 -0.002 (-0.009, 0.005) 0.525 -0.002 (-0.011, 0.007) 0.606 Silt 0.002 (0.000, 0.005) 0.098 0.001 (-0.003, 0.005) 0.625 0.002 (-0.003, 0.007) 0.491 -0.011 (-0.033, 0.012) 0.339 0.002 (-0.017, 0.021) 0.786 0.004 (-0.012, 0.02) 0.570 0.005 (-0.015, 0.024) 0.600 Clay 0.001 (-0.001, 0.003) 0.245 -0.001 (-0.004, 0.002) 0.594 -0.001 (-0.005, 0.003) 0.674 -0.015 (-0.031, 0.001) 0.059 0.002 (-0.011, 0.015) 0.765 0.003 (-0.008, 0.014) 0.538 0.003 (-0.011, 0.016) 0.649 pH 0.016 (0.000, 0.031) 0.052 0.009 (-0.016, 0.035) 0.477 0.003 (-0.025, 0.031) 0.826 0.005 (-0.119, 0.129) 0.939 0.018 (-0.115, 0.151) 0.761 0.018 (-0.093, 0.129) 0.711 0.022 (-0.116, 0.16) 0.721 CEC 0.001 (-0.001, 0.003) 0.260 -0.001 (-0.004, 0.002) 0.600 -0.001 (-0.005, 0.003) 0.595 -0.013 (-0.03, 0.003) 0.115 0.001 (-0.014, 0.016) 0.846 0.003 (-0.009, 0.016) 0.559 0.003 (-0.013, 0.018) 0.718 Ca 0.003 (0.002, 0.004) 0.000 0.001 (-0.001, 0.004) 0.217 0.001 (-0.002, 0.004) 0.470 -0.002 (-0.014, 0.01) 0.740 0.003 (-0.008, 0.014) 0.539 0.004 (-0.005, 0.013) 0.356 0.004 (-0.008, 0.016) 0.456 Mg -0.006 (-0.009, -0.003) 0.000 -0.009 (-0.014, -0.004) 0.001 -0.01 (-0.016, -0.003) 0.002 -0.048 (-0.074, -0.022) 0.000 -0.004 (-0.026, 0.019) 0.721 0.000 (-0.019, 0.019) 0.997 -0.002 (-0.025, 0.021) 0.854 K -0.034 (-0.102, 0.035) 0.335 -0.081 (-0.198, 0.035) 0.171 -0.081 (-0.221, 0.06) 0.259 -0.468 (-1.073, 0.137) 0.129 0.114 (-0.416, 0.644) 0.633 0.101 (-0.342, 0.544) 0.613 0.133 (-0.416, 0.681) 0.592 Na 0.002 (-0.268, 0.272) 0.990 0.219 (-0.232, 0.67) 0.339 0.381 (-0.193, 0.956) 0.193 0.495 (-2.059, 3.05) 0.703 0.253 (-1.657, 2.162) 0.768 0.223 (-1.376, 1.822) 0.756 0.381 (-1.59, 2.351) 0.668 P 0.000 (0.000, 0.001) 0.001 0.000 (0.000, 0.001) 0.042 0.001 (0.000, 0.001) 0.028 0.002 (0.000, 0.004) 0.049 0.000 (-0.001, 0.002) 0.766 0.000 (-0.001, 0.001) 0.824 0.000 (-0.001, 0.002) 0.787 OC -0.005 (-0.025, 0.014) 0.587 -0.028 (-0.061, 0.005) 0.099 -0.026 (-0.067, 0.015) 0.210 -0.164 (-0.342, 0.014) 0.070 0.006 (-0.137, 0.148) 0.929 0.017 (-0.101, 0.136) 0.745 0.008 (-0.14, 0.156) 0.907 Land-cover Forest 0.001 (0.000, 0.001) 0.001 0.001 (0.000, 0.002) 0.004 0.002 (0.001, 0.003) 0.003 0.007 (0.002, 0.012) 0.003 0.001 (-0.003, 0.004) 0.636 0.000 (-0.003, 0.003) 0.938 0.001 (-0.003, 0.004) 0.691 Shape -3.858 (-5.28, -2.437) 0.000 -4.577 (-7.023, -2.131) 0.000 -6.522 (-9.966, -3.078) 0.000 -22.149 (-36.12, -8.177) 0.002 -4.388 (-13.421, 4.646) 0.295 -2.154 (-10.102, 5.793) 0.549 -4.197 (-13.692, 5.298) 0.338 SecVeg -0.005 (-0.007, -0.004) 0.000 -0.006 (-0.009, -0.004) 0.000 -0.008 (-0.011, -0.004) 0.000 -0.029 (-0.043, -0.016) 0.000 -0.003 (-0.013, 0.008) 0.581 -0.001 (-0.01, 0.008) 0.768 -0.002 (-0.014, 0.009) 0.656 UCL 0.000 (-0.001, 0.000) 0.160 -0.001 (-0.002, 0.000) 0.077 -0.002 (-0.003, 0.000) 0.032 -0.007 (-0.014, -0.001) 0.016 0.000 (-0.005, 0.004) 0.810 0.000 (-0.003, 0.004) 0.839 0.000 (-0.005, 0.004) 0.893 ULC.type -0.007 (-0.008, -0.005) 0.000 -0.008 (-0.01, -0.005) 0.000 -0.009 (-0.012, -0.005) 0.000 -0.032 (-0.045, -0.018) 0.000 -0.006 (-0.015, 0.003) 0.142 -0.003 (-0.011, 0.005) 0.354 -0.006 (-0.015, 0.004) 0.190 Roughness -0.008 (-0.01, -0.005) 0.000 -0.006 (-0.01, -0.001) 0.009 -0.006 (-0.011, 0.000) 0.043 -0.013 (-0.038, 0.012) 0.314 -0.011 (-0.031, 0.009) 0.234 -0.011 (-0.027, 0.005) 0.156 -0.012 (-0.033, 0.008) 0.210 WD0 Climate Isoth -0.003 (-0.006, 0.000) 0.026 -0.006 (-0.011, -0.002) 0.010 -0.007 (-0.013, -0.001) 0.015 -0.034 (-0.059, -0.009) 0.008 -0.002 (-0.021, 0.018) 0.845 0.001 (-0.016, 0.018) 0.853 -0.001 (-0.021, 0.019) 0.918 TAP 0.000 (0.000, 0.000) 0.171 0.000 (0.000, 0.000) 0.179 0.000 (0.000, 0.000) 0.368 0.000 (0.000, 0.000) 0.720 0.000 (0.000, 0.000) 0.421 0.000 (0.000, 0.000) 0.412 0.000 (0.000, 0.000) 0.313 TPdriests 0.000 (0.000, 0.000) 0.025 0.000 (-0.001, 0.000) 0.042 -0.001 (-0.001, 0.000) 0.007 -0.001 (-0.003, 0.000) 0.142 0.000 (-0.001, 0.001) 0.626 0.000 (-0.001, 0.001) 0.658 0.000 (-0.002, 0.001) 0.500 PET 0.000 (0.000, 0.000) 0.000 0.000 (0.000, 0.000) 0.000 0.000 (0.000, 0.000) 0.002 0.001 (0.000, 0.001) 0.001 0.000 (0.000, 0.000) 0.584 0.000 (0.000, 0.000) 0.703 0.000 (0.000, 0.000) 0.712 Aridity 0.034 (0.012, 0.056) 0.003 0.056 (0.019, 0.092) 0.003 0.058 (0.014, 0.102) 0.009 0.22 (0.024, 0.416) 0.028 0.05 (-0.076, 0.176) 0.389 0.032 (-0.081, 0.145) 0.536 0.058 (-0.072, 0.187) 0.334 Ard -0.001 (-0.002, -0.001) 0.000 -0.002 (-0.003, -0.001) 0.000 -0.002 (-0.002, -0.001) 0.002 -0.006 (-0.01, -0.002) 0.008 -0.002 (-0.004, 0.001) 0.157 -0.001 (-0.004, 0.001) 0.315 -0.002 (-0.005, 0.001) 0.148 SRad 0.000 (0.000, 0.000) 0.002 0.000 (0.000, 0.000) 0.053 0.000 (0.000, 0.000) 0.043 0.000 (0.000, 0.000) 0.210 0.000 (0.000, 0.000) 0.510 0.000 (0.000, 0.000) 0.555 0.000 (0.000, 0.000) 0.431 WVP 0.229 (0.158, 0.299) 0.000 0.252 (0.126, 0.378) 0.000 0.32 (0.167, 0.473) 0.000 1.151 (0.514, 1.788) 0.000 0.157 (-0.338, 0.652) 0.485 0.099 (-0.341, 0.538) 0.619 0.144 (-0.374, 0.663) 0.539 Wind 0.016 (0.006, 0.027) 0.002 0.025 (0.007, 0.042) 0.007 0.035 (0.012, 0.058) 0.003 0.111 (0.013, 0.209) 0.026 0.022 (-0.043, 0.086) 0.464 0.011 (-0.047, 0.069) 0.676 0.024 (-0.043, 0.091) 0.430 107 Doctoral Thesis – Roy González-M. Trait Environmental inds pops sps sps 80% CWMpops CWMsps CWM80% variable b0 (95% CI) P b0 (95% CI) P b0 (95% CI) P b0 (95% CI) P b0 (95% CI) P b0 (95% CI) P b0 (95% CI) P Soils Sand -0.001 (-0.002, 0.000) 0.232 0.000 (-0.002, 0.002) 0.892 0.000 (-0.003, 0.002) 0.846 0.007 (-0.003, 0.017) 0.198 -0.001 (-0.009, 0.006) 0.721 -0.002 (-0.008, 0.004) 0.487 -0.002 (-0.01, 0.006) 0.552 Silt 0.002 (-0.001, 0.004) 0.211 0.001 (-0.003, 0.005) 0.710 0.002 (-0.004, 0.007) 0.524 -0.009 (-0.032, 0.014) 0.430 0.002 (-0.015, 0.02) 0.754 0.004 (-0.011, 0.019) 0.560 0.005 (-0.013, 0.022) 0.550 Clay 0.001 (-0.001, 0.003) 0.292 -0.001 (-0.004, 0.002) 0.634 0.000 (-0.004, 0.004) 0.882 -0.012 (-0.029, 0.004) 0.129 0.002 (-0.01, 0.014) 0.727 0.003 (-0.007, 0.013) 0.485 0.003 (-0.009, 0.015) 0.597 pH 0.008 (-0.008, 0.024) 0.317 0.004 (-0.022, 0.03) 0.745 -0.003 (-0.031, 0.025) 0.832 -0.01 (-0.134, 0.114) 0.874 0.013 (-0.107, 0.134) 0.804 0.013 (-0.092, 0.118) 0.779 0.017 (-0.108, 0.142) 0.763 CEC 0.001 (-0.001, 0.002) 0.584 -0.001 (-0.004, 0.002) 0.493 -0.001 (-0.005, 0.003) 0.534 -0.012 (-0.029, 0.004) 0.143 0.001 (-0.012, 0.015) 0.809 0.003 (-0.008, 0.015) 0.527 0.003 (-0.011, 0.017) 0.666 Ca 0.003 (0.001, 0.004) 0.001 0.001 (-0.001, 0.004) 0.335 0.001 (-0.002, 0.004) 0.604 -0.002 (-0.015, 0.01) 0.730 0.003 (-0.007, 0.013) 0.509 0.004 (-0.005, 0.012) 0.341 0.004 (-0.007, 0.014) 0.420 Mg -0.007 (-0.01, -0.004) 0.000 -0.009 (-0.014, -0.004) 0.001 -0.009 (-0.015, -0.002) 0.007 -0.042 (-0.069, -0.016) 0.002 -0.003 (-0.023, 0.017) 0.735 0.000 (-0.017, 0.018) 0.956 -0.001 (-0.022, 0.02) 0.890 K -0.07 (-0.138, -0.001) 0.046 -0.101 (-0.219, 0.016) 0.090 -0.095 (-0.236, 0.046) 0.187 -0.453 (-1.061, 0.155) 0.144 0.069 (-0.413, 0.552) 0.749 0.072 (-0.348, 0.493) 0.702 0.085 (-0.416, 0.586) 0.707 Na 0.065 (-0.206, 0.336) 0.638 0.27 (-0.184, 0.725) 0.243 0.459 (-0.115, 1.033) 0.116 0.813 (-1.742, 3.368) 0.532 0.274 (-1.446, 1.993) 0.723 0.224 (-1.282, 1.73) 0.740 0.402 (-1.374, 2.177) 0.616 P 0.000 (0.000, 0.000) 0.003 0.000 (0.000, 0.001) 0.065 0.000 (0.000, 0.001) 0.050 0.002 (0.000, 0.004) 0.070 0.000 (-0.001, 0.002) 0.785 0.000 (-0.001, 0.001) 0.864 0.000 (-0.001, 0.002) 0.809 OC -0.009 (-0.029, 0.011) 0.377 -0.029 (-0.062, 0.005) 0.095 -0.025 (-0.067, 0.016) 0.230 -0.145 (-0.324, 0.033) 0.111 0.005 (-0.124, 0.133) 0.936 0.017 (-0.094, 0.129) 0.731 0.007 (-0.127, 0.14) 0.912 Land-cover Forest 0.001 (0.000, 0.001) 0.001 0.001 (0.000, 0.002) 0.006 0.002 (0.000, 0.003) 0.011 0.006 (0.001, 0.011) 0.013 0.001 (-0.003, 0.004) 0.697 0.000 (-0.003, 0.003) 0.954 0.000 (-0.003, 0.004) 0.775 Shape -3.753 (-5.178, -2.328) 0.000 -4.391 (-6.858, -1.924) 0.001 -5.965 (-9.432, -2.498) 0.001 -19.948 (-34.017, -5.878) 0.006 -3.563 (-11.84, 4.714) 0.350 -1.569 (-9.135, 5.998) 0.645 -3.25 (-11.989, 5.489) 0.416 SecVeg -0.006 (-0.007, -0.004) 0.000 -0.006 (-0.009, -0.004) 0.000 -0.007 (-0.011, -0.004) 0.000 -0.027 (-0.041, -0.014) 0.000 -0.003 (-0.012, 0.007) 0.563 -0.001 (-0.01, 0.008) 0.786 -0.002 (-0.012, 0.008) 0.650 UCL -0.001 (-0.001, 0.000) 0.090 -0.001 (-0.002, 0.000) 0.073 -0.001 (-0.003, 0.000) 0.059 -0.006 (-0.013, 0.000) 0.041 0.000 (-0.004, 0.004) 0.879 0.001 (-0.003, 0.004) 0.745 0.000 (-0.004, 0.004) 0.989 ULC.type -0.007 (-0.008, -0.005) 0.000 -0.007 (-0.01, -0.005) 0.000 -0.008 (-0.012, -0.005) 0.000 -0.03 (-0.044, -0.016) 0.000 -0.005 (-0.013, 0.003) 0.171 -0.003 (-0.011, 0.005) 0.418 -0.005 (-0.014, 0.004) 0.241 Roughness -0.008 (-0.01, -0.005) 0.000 -0.006 (-0.01, -0.001) 0.012 -0.007 (-0.012, -0.001) 0.018 -0.017 (-0.042, 0.009) 0.196 -0.011 (-0.029, 0.007) 0.198 -0.011 (-0.026, 0.004) 0.122 -0.012 (-0.03, 0.006) 0.169 WCmax Climate Isoth 0.004 (-0.001, 0.009) 0.081 0.009 (0.002, 0.017) 0.018 0.011 (0.002, 0.02) 0.020 0.034 (0.009, 0.059) 0.007 0.004 (-0.032, 0.04) 0.794 -0.001 (-0.031, 0.029) 0.937 0.003 (-0.035, 0.041) 0.842 TAP 0.000 (0.000, 0.000) 0.013 0.000 (0.000, 0.000) 0.059 0.000 (0.000, 0.000) 0.135 0.000 (0.000, 0.000) 0.510 0.000 (0.000, 0.000) 0.395 0.000 (0.000, 0.000) 0.349 0.000 (0.000, 0.000) 0.305 TPdriests 0.000 (0.000, 0.001) 0.030 0.001 (0.000, 0.001) 0.039 0.001 (0.000, 0.002) 0.003 0.001 (0.000, 0.003) 0.106 0.000 (-0.002, 0.003) 0.660 0.000 (-0.002, 0.002) 0.640 0.001 (-0.002, 0.003) 0.548 PET 0.000 (0.000, 0.000) 0.000 0.000 (0.000, 0.000) 0.001 0.000 (0.000, 0.000) 0.003 -0.001 (-0.001, 0.000) 0.001 0.000 (-0.001, 0.001) 0.651 0.000 (-0.001, 0.001) 0.749 0.000 (-0.001, 0.001) 0.760 Aridity -0.07 (-0.105, -0.035) 0.000 -0.1 (-0.156, -0.044) 0.000 -0.111 (-0.179, -0.043) 0.001 -0.257 (-0.452, -0.061) 0.010 -0.105 (-0.337, 0.127) 0.327 -0.073 (-0.271, 0.125) 0.419 -0.121 (-0.361, 0.119) 0.278 Ard 0.003 (0.002, 0.004) 0.000 0.003 (0.002, 0.004) 0.000 0.003 (0.002, 0.004) 0.000 0.007 (0.003, 0.011) 0.001 0.004 (-0.001, 0.009) 0.090 0.003 (-0.002, 0.007) 0.184 0.004 (-0.001, 0.009) 0.080 SRad 0.000 (0.000, 0.000) 0.000 0.000 (0.000, 0.000) 0.022 0.000 (0.000, 0.000) 0.010 0.000 (0.000, 0.000) 0.124 0.000 (0.000, 0.000) 0.489 0.000 (0.000, 0.000) 0.490 0.000 (0.000, 0.000) 0.424 WVP -0.348 (-0.46, -0.236) 0.000 -0.381 (-0.577, -0.186) 0.000 -0.501 (-0.739, -0.263) 0.000 -1.183 (-1.819, -0.547) 0.000 -0.272 (-1.199, 0.656) 0.518 -0.177 (-0.961, 0.608) 0.618 -0.257 (-1.237, 0.722) 0.561 Wind -0.03 (-0.046, -0.013) 0.000 -0.042 (-0.07, -0.014) 0.003 -0.063 (-0.098, -0.027) 0.001 -0.125 (-0.222, -0.028) 0.012 -0.047 (-0.166, 0.072) 0.389 -0.028 (-0.13, 0.074) 0.544 -0.053 (-0.177, 0.071) 0.354 Soils Sand 0.001 (0.000, 0.003) 0.125 0.000 (-0.003, 0.003) 0.930 0.001 (-0.003, 0.005) 0.654 -0.006 (-0.016, 0.004) 0.231 0.002 (-0.012, 0.016) 0.804 0.003 (-0.008, 0.015) 0.555 0.003 (-0.012, 0.017) 0.663 Silt -0.004 (-0.008, 0.000) 0.071 -0.002 (-0.009, 0.004) 0.490 -0.004 (-0.013, 0.004) 0.282 0.007 (-0.016, 0.03) 0.545 -0.003 (-0.035, 0.029) 0.819 -0.006 (-0.033, 0.02) 0.598 -0.007 (-0.04, 0.027) 0.649 Clay -0.002 (-0.004, 0.001) 0.225 0.001 (-0.004, 0.005) 0.736 0.000 (-0.006, 0.006) 0.959 0.012 (-0.004, 0.029) 0.132 -0.002 (-0.025, 0.02) 0.813 -0.005 (-0.023, 0.014) 0.568 -0.004 (-0.027, 0.019) 0.707 pH -0.026 (-0.051, -0.001) 0.038 -0.016 (-0.056, 0.024) 0.427 -0.005 (-0.049, 0.038) 0.807 -0.004 (-0.129, 0.12) 0.946 -0.03 (-0.254, 0.194) 0.767 -0.03 (-0.216, 0.157) 0.725 -0.035 (-0.27, 0.2) 0.739 CEC -0.002 (-0.005, 0.001) 0.233 0.001 (-0.004, 0.006) 0.716 0.001 (-0.005, 0.007) 0.772 0.011 (-0.005, 0.028) 0.181 -0.002 (-0.027, 0.024) 0.893 -0.005 (-0.026, 0.016) 0.596 -0.003 (-0.03, 0.023) 0.783 Ca -0.005 (-0.007, -0.003) 0.000 -0.003 (-0.006, 0.001) 0.162 -0.002 (-0.007, 0.002) 0.351 0.001 (-0.011, 0.013) 0.882 -0.005 (-0.024, 0.014) 0.576 -0.006 (-0.022, 0.009) 0.385 -0.006 (-0.026, 0.014) 0.506 Mg 0.011 (0.006, 0.015) 0.000 0.014 (0.005, 0.022) 0.001 0.014 (0.004, 0.024) 0.007 0.044 (0.017, 0.07) 0.001 0.007 (-0.03, 0.045) 0.664 0.001 (-0.031, 0.032) 0.953 0.005 (-0.035, 0.044) 0.782 K 0.056 (-0.052, 0.165) 0.309 0.118 (-0.063, 0.3) 0.201 0.106 (-0.114, 0.325) 0.345 0.405 (-0.204, 1.013) 0.191 -0.183 (-1.078, 0.711) 0.649 -0.168 (-0.914, 0.578) 0.617 -0.214 (-1.15, 0.722) 0.613 Na -0.037 (-0.466, 0.393) 0.866 -0.398 (-1.102, 0.305) 0.266 -0.731 (-1.623, 0.161) 0.108 -0.864 (-3.419, 1.691) 0.506 -0.481 (-3.694, 2.731) 0.739 -0.434 (-3.117, 2.25) 0.719 -0.704 (-4.054, 2.646) 0.641 P 0.000 (-0.001, 0.000) 0.003 0.000 (-0.001, 0.000) 0.071 -0.001 (-0.002, 0.000) 0.040 -0.002 (-0.004, 0.000) 0.067 0.000 (-0.003, 0.002) 0.835 0.000 (-0.002, 0.002) 0.901 0.000 (-0.003, 0.003) 0.857 OC 0.015 (-0.016, 0.046) 0.353 0.045 (-0.007, 0.097) 0.087 0.039 (-0.025, 0.104) 0.228 0.153 (-0.025, 0.332) 0.092 0.002 (-0.238, 0.242) 0.982 -0.02 (-0.22, 0.18) 0.820 0.001 (-0.251, 0.253) 0.994 Land-cover Forest -0.002 (-0.002, -0.001) 0.000 -0.002 (-0.003, -0.001) 0.003 -0.003 (-0.004, -0.001) 0.004 -0.007 (-0.011, -0.002) 0.007 -0.002 (-0.007, 0.004) 0.578 0.000 (-0.005, 0.005) 0.885 -0.001 (-0.008, 0.005) 0.629 Shape 6.584 (4.329, 8.84) 0.000 7.468 (3.66, 11.276) 0.000 10.251 (4.884, 15.618) 0.000 21.861 (7.837, 35.886) 0.002 8.013 (-6.998, 23.024) 0.253 4.157 (-9.109, 17.422) 0.491 7.853 (-8.105, 23.811) 0.289 SecVeg 0.009 (0.006, 0.011) 0.000 0.01 (0.006, 0.013) 0.000 0.012 (0.006, 0.017) 0.000 0.028 (0.014, 0.041) 0.000 0.004 (-0.014, 0.023) 0.598 0.002 (-0.014, 0.017) 0.803 0.004 (-0.016, 0.023) 0.670 UCL 0.001 (0.000, 0.002) 0.096 0.002 (0.000, 0.003) 0.072 0.002 (0.000, 0.005) 0.053 0.007 (0.001, 0.013) 0.032 0.001 (-0.006, 0.009) 0.730 0.000 (-0.007, 0.006) 0.912 0.001 (-0.007, 0.009) 0.804 ULC.type 0.011 (0.009, 0.014) 0.000 0.012 (0.008, 0.016) 0.000 0.013 (0.008, 0.019) 0.000 0.031 (0.018, 0.045) 0.000 0.011 (-0.004, 0.026) 0.123 0.006 (-0.007, 0.02) 0.319 0.011 (-0.005, 0.026) 0.162 Roughness 0.012 (0.008, 0.016) 0.000 0.009 (0.002, 0.017) 0.009 0.01 (0.002, 0.019) 0.020 0.017 (-0.008, 0.042) 0.187 0.018 (-0.016, 0.052) 0.263 0.018 (-0.009, 0.045) 0.169 0.02 (-0.016, 0.055) 0.234 Models with significant b0 (P<0.05, %) 67.2 44.9 43.8 31.5 1.8 3.1 2.1 108 Ecology of woody plants in Colombian dry forests Table S5. Fitted models and b-coefficients showing the effects of 26 single variables of climate, soils and land-cover transformation on biomass grow rates (BGR) for the abundance weighted trait sampling design (inds) and six functional trait sampling designs: populations (pops), species (sps), dominant species (sps80%, species saturating 80% of the abundances per hectare), community weighted-means based on populations (CWMpops), species (CWMsps) and dominant species (CWM80%). Confidence interval at 95% of the probability (CI). Bold letters show significant b-coefficients (P<0.05). For description of the environmental variables see Table S1. Environmental inds pops sps sps CWM 80% pops CWMsps CWM80% Trait variable b0 (95% CI) P b0 (95% CI) P b0 (95% CI) P b0 (95% CI) P b0 (95% CI) P b (95% CI) P 0 b0 (95% CI) P BGR Climate Isoth -0.008 (-0.018, 0.003) 0.139 -0.009 (-0.026, 0.009) 0.326 -0.007 (-0.029, 0.014) 0.501 -0.005 (-0.03, 0.02) 0.682 -0.018 (-0.17, 0.133) 0.790 -0.083 (-0.22, 0.053) 0.195 -0.006 (-0.158, 0.146) 0.932 TAP 0.000 (0.000, 0.000) 0.000 0.000 (0.000, 0.000) 0.000 0.000 (0.000, 0.001) 0.000 0.000 (0.000, 0.001) 0.001 0.001 (0.000, 0.002) 0.054 0.001 (0.000, 0.002) 0.018 0.001 (0.000, 0.002) 0.040 TPdriests 0.001 (0.000, 0.002) 0.005 0.002 (0.001, 0.004) 0.000 0.003 (0.002, 0.005) 0.000 0.003 (0.002, 0.005) 0.000 0.005 (-0.003, 0.014) 0.185 0.005 (-0.003, 0.014) 0.195 0.005 (-0.004, 0.014) 0.254 PET 0.000 (0.000, 0.000) 0.156 0.000 (0.000, 0.001) 0.068 0.000 (0.000, 0.001) 0.072 0.000 (0.000, 0.001) 0.185 0.001 (-0.002, 0.004) 0.623 0.003 (0.000, 0.005) 0.033 0.000 (-0.003, 0.003) 0.813 Aridity -0.084 (-0.164, -0.004) 0.040 -0.193 (-0.323, -0.063) 0.004 -0.293 (-0.454, -0.132) 0.000 -0.291 (-0.485, -0.096) 0.004 -0.513 (-1.465, 0.438) 0.249 -0.351 (-1.35, 0.648) 0.441 -0.622 (-1.529, 0.286) 0.153 Ard 0.003 (0.001, 0.004) 0.006 0.005 (0.002, 0.008) 0.001 0.006 (0.003, 0.01) 0.001 0.006 (0.002, 0.01) 0.005 0.012 (-0.01, 0.033) 0.242 0.006 (-0.018, 0.029) 0.598 0.015 (-0.006, 0.035) 0.139 SRad 0.000 (0.000, 0.000) 0.000 0.000 (0.000, 0.000) 0.000 0.000 (0.000, 0.000) 0.000 0.000 (0.000, 0.000) 0.001 -0.001 (-0.001, 0.000) 0.122 0.000 (-0.001, 0.000) 0.299 0.000 (-0.001, 0.000) 0.175 WVP -0.069 (-0.329, 0.192) 0.605 -0.181 (-0.638, 0.275) 0.436 -0.151 (-0.725, 0.424) 0.606 -0.221 (-0.864, 0.421) 0.498 -0.218 (-4.225, 3.789) 0.903 2.15 (-1.457, 5.758) 0.207 -0.241 (-4.247, 3.765) 0.893 Wind -0.042 (-0.08, -0.003) 0.033 -0.103 (-0.167, -0.04) 0.002 -0.157 (-0.241, -0.073) 0.000 -0.151 (-0.247, -0.054) 0.002 -0.201 (-0.7, 0.299) 0.382 -0.147 (-0.659, 0.365) 0.526 -0.2 (-0.7, 0.3) 0.383 Soils Sand 0.006 (0.002, 0.01) 0.002 0.013 (0.006, 0.019) 0.000 0.015 (0.006, 0.023) 0.001 0.015 (0.005, 0.025) 0.002 0.031 (-0.023, 0.085) 0.221 0.049 (0.006, 0.093) 0.031 0.022 (-0.034, 0.079) 0.386 Silt -0.02 (-0.029, -0.011) 0.000 -0.038 (-0.053, -0.024) 0.000 -0.042 (-0.061, -0.024) 0.000 -0.042 (-0.064, -0.02) 0.000 -0.106 (-0.21, -0.002) 0.047 -0.121 (-0.214, -0.028) 0.017 -0.089 (-0.203, 0.025) 0.111 Clay -0.007 (-0.013, -0.001) 0.033 -0.015 (-0.025, -0.004) 0.006 -0.016 (-0.03, -0.002) 0.023 -0.018 (-0.034, -0.002) 0.026 -0.027 (-0.119, 0.065) 0.519 -0.067 (-0.144, 0.011) 0.082 -0.014 (-0.108, 0.08) 0.745 pH -0.108 (-0.165, -0.051) 0.000 -0.135 (-0.227, -0.043) 0.004 -0.117 (-0.22, -0.014) 0.026 -0.1 (-0.223, 0.024) 0.114 -0.587 (-1.405, 0.232) 0.137 -0.398 (-1.289, 0.492) 0.332 -0.551 (-1.386, 0.284) 0.166 CEC -0.013 (-0.02, -0.005) 0.001 -0.02 (-0.032, -0.009) 0.001 -0.02 (-0.034, -0.006) 0.004 -0.02 (-0.036, -0.004) 0.016 -0.054 (-0.152, 0.045) 0.245 -0.078 (-0.165, 0.008) 0.070 -0.041 (-0.143, 0.062) 0.388 Ca -0.012 (-0.017, -0.006) 0.000 -0.018 (-0.026, -0.009) 0.000 -0.017 (-0.027, -0.006) 0.002 -0.017 (-0.029, -0.005) 0.006 -0.054 (-0.123, 0.016) 0.114 -0.052 (-0.123, 0.018) 0.125 -0.044 (-0.118, 0.03) 0.210 Mg -0.006 (-0.017, 0.005) 0.288 -0.015 (-0.034, 0.003) 0.109 -0.021 (-0.045, 0.002) 0.076 -0.018 (-0.044, 0.008) 0.170 -0.03 (-0.187, 0.126) 0.666 -0.146 (-0.251, -0.041) 0.013 -0.017 (-0.176, 0.141) 0.808 K -0.129 (-0.378, 0.12) 0.309 -0.254 (-0.674, 0.167) 0.236 -0.474 (-0.993, 0.044) 0.073 -0.31 (-0.916, 0.295) 0.314 -0.482 (-4.276, 3.311) 0.777 -2.976 (-5.918, -0.034) 0.048 -0.567 (-4.353, 3.218) 0.738 Na 0.058 (-0.926, 1.043) 0.908 -1.13 (-2.748, 0.487) 0.170 -2.057 (-4.158, 0.044) 0.055 -2.737 (-5.236, -0.238) 0.032 -1.439 (-14.999, 12.121) 0.813 -3.337 (-16.673, 9.999) 0.580 -1.471 (-15.028, 12.087) 0.809 P -0.001 (-0.002, 0.000) 0.018 -0.001 (-0.003, 0.000) 0.032 -0.002 (-0.003, 0.000) 0.083 -0.001 (-0.003, 0.001) 0.258 -0.005 (-0.015, 0.005) 0.281 -0.003 (-0.013, 0.008) 0.582 -0.005 (-0.015, 0.006) 0.324 OC -0.069 (-0.141, 0.003) 0.060 -0.11 (-0.23, 0.01) 0.072 -0.132 (-0.284, 0.02) 0.088 -0.125 (-0.303, 0.052) 0.166 -0.068 (-1.076, 0.94) 0.880 -0.653 (-1.511, 0.205) 0.117 0.072 (-0.935, 1.08) 0.872 Land-cover Forest -0.002 (-0.003, 0.000) 0.091 -0.003 (-0.006, 0.000) 0.084 -0.003 (-0.008, 0.001) 0.134 -0.003 (-0.008, 0.002) 0.211 -0.004 (-0.03, 0.021) 0.694 0.007 (-0.018, 0.032) 0.513 -0.003 (-0.029, 0.022) 0.762 Shape 4.061 (-1.185, 9.308) 0.129 8.858 (-0.024, 17.741) 0.051 13.251 (0.471, 26.032) 0.042 13.231 (-0.704, 27.167) 0.063 5.303 (-63.414, 74.021) 0.863 -7.385 (-75.974, 61.205) 0.810 3.014 (-65.795, 71.824) 0.922 SecVeg 0.006 (0.001, 0.012) 0.017 0.009 (0.000, 0.019) 0.042 0.004 (-0.009, 0.016) 0.564 0.005 (-0.009, 0.018) 0.517 0.033 (-0.04, 0.106) 0.328 -0.032 (-0.106, 0.041) 0.338 0.029 (-0.045, 0.103) 0.397 UCL 0.000 (-0.003, 0.002) 0.701 0.000 (-0.004, 0.004) 0.874 -0.001 (-0.006, 0.005) 0.838 0.000 (-0.006, 0.006) 0.969 -0.006 (-0.038, 0.026) 0.698 -0.016 (-0.046, 0.014) 0.250 -0.006 (-0.038, 0.025) 0.653 ULC.type 0.004 (-0.002, 0.01) 0.165 0.008 (-0.001, 0.017) 0.092 0.008 (-0.005, 0.02) 0.230 0.009 (-0.005, 0.023) 0.201 0.009 (-0.063, 0.08) 0.786 -0.015 (-0.086, 0.056) 0.639 0.009 (-0.062, 0.081) 0.773 Roughness -0.002 (-0.012, 0.008) 0.701 0.003 (-0.013, 0.019) 0.727 0.004 (-0.017, 0.025) 0.705 0.007 (-0.017, 0.032) 0.556 0.016 (-0.14, 0.171) 0.822 0.048 (-0.103, 0.199) 0.485 0.032 (-0.122, 0.186) 0.641 Models with significant b0 (P<0.05, %) 53.8 53.8 50.0 46.2 3.8 23.1 3.8 109 Doctoral Thesis – Roy González-M. Chapter 5 Diverging functional strategies but high sensitivity to an extreme drought in tropical dry forests Roy González-M., Juan M. Posada, Carlos P. Carmona, Fabián Garzón, Viviana Salinas, Álvaro Idárraga-Piedrahita, Camila Pizano, Andrés Avella, René López-Camacho, Natalia Norden, Jhon Nieto, Sandra P. Medina, Gina M. Rodríguez-M., Rebeca Franke- Ante, Alba M. Torres, Rubén Jurado, Hermes Cuadros, Alejandro Castaño-Naranjo, Hernando García and Beatriz Salgado-Negret Submitted for publication in Ecology Letters 110 Ecology of woody plants in Colombian dry forests Abstract Extreme drought events have negative effects on forest diversity and functioning. At the species level, however, these effects are still unclear, as species vary in their response to drought through specific functional traits combinations. We used long-term demographic records of 21,821 trees and extensive databases of traits to understand the responses of 338 tropical dry forests tree species to ENSO2015, the driest event in decades in Northern South America. Functional differences between species were related to hydraulic safety-efficiency trade-offs, but unexpectedly, dominant species were characterized by high investment in leaf and wood tissues regardless of their leaf phenology habit. Despite strong species functional differences, tree mortality covered a higher space of trait combinations than tree growth. Our results indicate that irrespective of the drought adaptations, most trees will be negatively affected under drier scenarios predicted for tropical dry forests. Introduction Tropical forest diversity and functioning are continuously threatened by a global increase in drought events (Allen et al. 2010, 2015; Nunes Garcia et al. 2018). Extreme droughts can increase tree mortality and significantly reduce ecosystem biomass gain due to low tree growth and recruitment (Condit et al. 1996; Slik 2004; Allen et al. 2010; Maza-Villalobos et al. 2013), even in forests considered to be historically water-limited such as tropical dry forests (TDF; Allen et al. 2015; Powers et al. 2020). However, it is still not entirely clear how extreme drought events affect ecosystem processes, nor are the mechanisms mediating species’ drought responses (Allen et al. 2010). Trait-based ecology provides a framework to understand how species respond to environmental filters, how they vary their demography, and, ultimately, what their fitness is (Pistón et al. 2019). Thus, studying the functional responses of species to extreme drought events, and how these translate into changes in biomass, can improve our ability to predict forests changes to future climatic drier scenarios (Aubry-Kientz et al. 2013; McDowell et al. 2018). There is a general expectation that TDF tree species are adapted to cope with water limitation and should be more resistant to drought than species from mesic ecosystems (Dodd & Ryan 2016). Studies have shown changes in traits in response to both more extended and more frequent dry periods, such as an increase in shorter stature, and denser wood species (Esquivel-Muelbert et al. 2019), or more deciduous species (Fauset et al. 2012), highlighting that some traits should provide an advantage under water constraints (Dodd & Ryan 2016). Nevertheless, critical knowledge gaps remain regarding the ecological viability of multiple trait combinations (Méndez-Alonzo et al. 2012), trait relationships with demographic rates under droughts (Mendivelso et al. 2013; Allen et al. 2017b), or the role of traits in explaining species’ dominance (Prado-Junior et al. 2016; Aguirre-Gutiérrez et al. 2019). This lack of knowledge is especially acute given the difficulty of having simultaneous trait and demographic data for a large number of tree species during extreme droughts, information which will ultimately be essential to help us forecast how TDF may respond to future drier scenarios (Allen et al. 2010; Aguirre-Gutiérrez et al. 2019). Previous studies examining traits in TDF suggest that tree species are distributed along a continuum of functional traits related to a hydraulic safety-efficiency trade-off, which in turn should be associated with the degree of investment in tissues (from ‘costly’ to ‘cheap’ tissues) (Figure 1; Markesteijn et al. 2011a, b; Méndez-Alonzo et al. 2012; Pineda-García et al. 2015). On one side of this continuum, species have a high density of narrow xylem conduits, high carbon investment in thicker fibre cell walls, denser wood and leaves, and high leaf retention time (Méndez-Alonzo et al. 2012). These species with costly, but 111 Doctoral Thesis – Roy González-M. hydraulically-safe tissues, are expected to be drought-tolerant, have the low photosynthetic capacity and low growth rates because of their slow water transport, and a low mortality risk since their narrow and reinforced conduits support negative water potentials and enhance mechanical stability (Poorter et al. 2008; Markesteijn et al. 2011a; Beeckman 2016). On the other side of this continuum, species have soft woods with larger xylem conduits and thin leaves with short retention time (Méndez-Alonzo et al. 2012). These species are expected to have fast growth rates during the rainy seasons due to their high water transport capacity and photosynthetic rates (Santiago et al. 2004a; Markesteijn et al. 2011a). However, they also have low hydraulic safety margins and may suffer higher mortality when water becomes limited (Méndez- Alonzo et al. 2012; Pineda-García et al. 2015). Based on the above, we can expect drought-tolerant species to be more dominant and less vulnerable to extreme droughts in TDF than species with ‘cheap’ but hydraulically-efficient tissues (Figure 1b) (Méndez-Alonzo et al. 2012; Pineda-García et al. 2015). Yet, the predominance of deciduous species in TDF (Pennington et al. 2009; Linares-Palomino et al. 2011) suggests that lower leaf retention time in species that are hydraulically-efficient, but with low investment in tissues, may also be a successful strategy to survive under high water constraints (Figure 1c; Markesteijn et al. 2011a, b). By dropping their leaves, these drought-avoidance species reduce hydraulic cavitation risks via lower transpiration rates and prevent drought-induced mortality (McDowell et al. 2018). Accordingly, both drought-tolerant and drought- avoidance functional strategies could be successful in TDF (Sterck et al. 2011). The pervasive existence of these functional strategies in TDF (Méndez-Alonzo et al. 2012) carries the idea that other trait combinations are unlikely to occur or, when present, they will not favour the performance of species under strong water constraints (Figure 1a-c; Ziemińska et al. 2015; Gleason et al. 2016). For instance, it has been demonstrated that high hydraulic conductivity and high wood density do not simultaneously occur in stems (Gleason et al. 2016), or that species cannot simultaneously grow stems that have high wood density, high fibre wall fractions, and large fibre lumens (Ziemińska et al. 2015). Likewise, large-leaves species with dense wood would have higher risks of cavitation in low rainfall ecosystems, as the result of major transpiration demands (Baraloto et al. 2010a). However, the prevalence of trade-offs in hydraulic safety-efficiency and tissues investment in determining species dominance in TDF or demographic responses to extreme droughts is still untested. Similarly, little is known about the existence of other trait combinations and their consequences on species performance in TDF. Here, we use a network of 11 1-ha permanent plots in TDF with a comprehensive dataset of standing biomass and demography for 21,821 individual trees belonging to 338 species, and measurements of 15 leaf and hydraulic functional traits, to construct the functional traits space and relate functional trait combinations to species dominance and responses to the extreme “El Niño” drought of 2015 in Northern South America (ENSO2015). We assessed three main questions: (1) Does functional trait space reflect trade- offs in hydraulic safety-efficiency and investment in tissues for TDF tree species? (2) What are the dominant functional trait combinations of tree species in TDF? (3) How is functional space to demographic changes in biomass and net biomass balances after an extreme drought in TDF? Overall, we expected that the dominant (high biomass) species in the functional space would be associated with traits shaping the trade- offs in hydraulic safety-efficiency and investment in tissues (Figure 1), but we also expected to find species with trait combinations outside these trade-offs, with low dominance and poor performance after ENSO2015. Specifically, species with either high safety and expensive tissues, or high efficiency and cheap tissues should both experience low mortality and have high growth rates under ENSO2015, because they are adapted to cope with water constraints via drought-tolerance or drought-avoidance strategies (Figure 1b, c). These species would, in turn, have high positive net biomass balances after ENSO2015 (Figure 1b-c). In contrast, 112 Ecology of woody plants in Colombian dry forests species with other functional trait combinations should be less adapted to cope with water limitations, showing negative net biomass balances due to high mortality and low growth rates after ENSO2015 (Figure 1d). Figure 1. Schematic diagram representing hypotheses about the distribution of functional trait combinations across the continuum of hydraulic safety-efficiency and investment in tissues trade-offs (a, adapted from Méndez-Alonzo et al. 2012). Within this trait space, dominant species (higher biomass) are expected to be bounded by trade-offs in trait combinations that favour drought tolerance (b) or drought avoidance (c). Therefore, positive biomass net changes (red areas in the continuum) can be expected for both strategies under an extreme drought (d). Here, both drought tolerance and drought avoidance are alternative optimal strategies for species in response to water-constraints in TDF. Because species with other trait combinations are not expected to cope with drought conditions, they should not be present or be associated to lower performance under extreme drought conditions. Material and methods Study area and censuses data Between 2013 and 2014, we established 11 1-ha permanent plots in mature TDF of Colombia, Northern South America (Figure 2a). Plots were located in areas of floristic representativeness of the three main dry formations among the region (i.e., Caribbean lowlands, Inter Andean region, and dry Savannas, Table S1; Portillo-Quintero & Sánchez-Azofeifa 2010; DRYFLOR et al. 2016; González-M et al. 2018) and without evidence of logging. Mean annual temperature varied between 23.4 and 28.3 ºC, mean annual precipitation between 517.0-2697.2 mm, and mean annual potential evapotranspiration between 1161-2067 mm. All sites experienced between one and two dry seasons (4-9 dry months) and soils had high proportions of sand (34.2-72.3%) and high aridity (0.8-3.4; see Table S1 for more details). Within each plot, all individual trees with a diameter at breast height ≥ 2.5 cm (DBH, censused at 1.3 m height) were tagged, and their DBH and height (m) were recorded (van Laar & Akça 2007). Subsequently, between 2016 and 2017, all plots were resampled, and DBH of all surviving trees (which allowed to estimate growth as an increment in DBH) and recruits (new individuals with DBH ≥2.5 cm) were measured. In total, 21,821 individual trees (26,132 stems) were measured. Between censuses, all sites experienced one of the strongest drought events of the 113 Doctoral Thesis – Roy González-M. last 36 years, “El Niño” Southern Oscillation 2015-2016 (Figure 2b, ENSO2015, Kogan & Guo 2017). During this event, mean annual temperatures were almost 3ºC above, normal and cumulative rainfall anomalies reached -250 mm (Anyamba et al. 2019), inducing intense water deficits in several areas of Neotropical dry forests (Figure 2b, Table S1). ENSO2015 started in October 2014 with dry peaks between May 2015 and January 2016 and lasted until June 2016 (L’Heureux et al. 2017). It was considered catastrophic for trees with low hydraulic safety margins and resulted in high tree mortality rates in TDF of Central America (Powers et al. 2020). Figure 2. Geographic distribution and average inter-annual drought regimes of the study sites. (a) Distribution of dry ecosystems in Northern South America (orange area, adapted from Portillo-Quintero & Sánchez-Azofeifa 2010; DRYFLOR et al. 2016). Blue circles indicate the location of the 11 1-ha permanent plots installed for monitoring mature forests across the region. (b) The Standardised Precipitation-Evapotranspiration Index (SPEI; Vicente-Serrano et al. 2012) was calculated based on long-term data from weather stations near the plots (1980 to 2019). SPEI 114 Ecology of woody plants in Colombian dry forests determines the magnitude and strength of drought conditions during the period of analysis, where negative values indicate the SPEI mean for drought periods (red colour) and positive values correspond to wet periods (blue colour). All plots experienced the extreme ENSO2015 (red area between 2015 and 2016). For extended details see Table S1 in supporting information. Functional traits We measured 15 functional traits in 1553 individual trees, in 524 populations belonging to 338 species. Here, a population refers to all sampled individuals of the same species within a plot; we considered individual populations separately to account for local trait and biomass variations of species among plots. We measured four leaf traits and eleven wood traits, which characterizes the hydraulic safety-efficiency trade-off as well as investment in tissues across a broad range of values (Table 1, Scholz et al. 2013; Salgado-Negret et al. 2015). We collected traits for all tree species in each plot following an abundance- weighted trait sampling scheme (Carmona et al. 2015). Accordingly, within each plot, we measured traits in 5-8 individuals for the most abundant species, 1-3 individuals for species with less than five individuals per plot, and one individual for species with only one individual per plot. For individual trees for which some traits had missing values (only 4% of sampled individuals, for a total of 0.98%), we imputed trait values using the R package “missForest” (missing value imputation for mixed-type, Stekhoven & Bühlmann, 2012). We accounted for differences in the imputation process by including plots and species as predictors. Species with imputed individual-trait values were strongly linearly correlated with their not- imputed trait values (P<0.001, Figure S1). Standing biomass and biomass changes To estimate biomass (tons, t) of each individual stem, we used the allometric formulas Type I and Type II for TDF from Alvarez et al. (2012), which consider DBH (cm), tree height (m) and stem wood density (g cm-3). Stem wood density was measured using the water displacement method and calculated as dry mass divided by fresh volume for 1-8 samples individual trees per species in each plot (Pérez-Harguindeguy et al. 2013). Standing biomass of each species (t ha-1) was estimated as the sum of biomass of all its trees in each plot for the first census (t0). Biomass growth of survivors for each species (BGS, t ha-1 yr-1) was estimated as the annual biomass increment produced by the growth of all trees surviving from t0 to the final census (tfin) in a plot. Biomass growth of recruits for each species (BG , -1 -1R t ha yr ) was estimated as the annual biomass increment obtained from all trees that attaining at least 2.5 cm DBH in tfin and that were not sampled in t0 in a plot. Here, we considered that each new tree was recruited immediately after t0 and assumed that had an initial DBH of 0 to avoid biomass overestimation (Talbot et al. 2014). Biomass mortality for each species (BM, t ha-1 yr-1) was estimated as the biomass loss obtained from dead trees between t0 and tfin. To correctly compare BM with BGS and BGR, we calculated the biomass mortality of each tree as the biomass in t0 minus the biomass of the same tree calculated with a DBH of 2.5 cm (Talbot et al. 2014). Finally, we estimated the net biomass change for each species (NBC, -1 -1 t ha yr ) as the net annual change in biomass during the time interval between t0 and tfin (Prado-Junior et al. 2016; Poorter et al. 2017), so that: NBC = BGS + BGR – BM. 115 Doctoral Thesis – Roy González-M. Table 1. Description of the selected functional traits, trait function dimensions, mean–ranges and global reference ranges. Trait Units Description Trait function Trait mean ±SD Reference (abbreviation) (dimension) (Q"#$.&– Q"'$.() range References a Double wall between adjacent fibres Fibre wall thickness Madsen & Gamstedt (2013); µm b (FWT) Resistance of internal and external stresses Water exploitative 5.54 ±1.52 safety (wood) (3.24–8.55) 4–12 Scholz et al. (2013); Sorieul c Greater walls, higher hydraulic safety et al. (2016) a Sum of circle conduits diameters d divided by the number of conduits N in a surface area Hydraulically +, $./0 weighted diameter )∑ . Water exploitative 58.64 ±22.39 µm 1–300 Scholz et al. (2013); Rosell - (d b h) Conductance of conduits efficiency (wood) (31.23–105.74) et al. (2017) c Larger weighted diameters, higher hydraulic efficiency a Projected area of a leaf b Light interception, energy and water balance Investment in tissues 1.25x104 4Leaf area (LA) mm2 ±2.15x10 c Larger LA, cheaper tissues and high water Water exploitative 6 Pérez-Harguindeguy et al. efficiency (leaves) (1.05x10 3–5.77x104) 1–>20x10 (2013); Díaz et al. (2016) demands a Dry mass per unit of lamina surface area Leaf dry matter -1 b content (LDMC) mg g Tissue investments and carbon-gain strategies Investment in tissues 379.38 ±91.31 Pérez-Harguindeguy et al. c (leaves) Higher LDMC, robust tissues (209.46–533.64) 50–700 (2013); Díaz et al. (2016) a Leaf mesophilic density (or thickness) Leaf thickness (Lth) mm b Physical strength and leaf longevity Investment in tissues 0.21 ±0.06 Pérez-Harguindeguy et al. c (leaves) 0.11–0.74 (2013); Onoda et al. (2011) Thicker leaves, higher tissue investments (0.13–0.33) a Average conduit surface area of the last VA percentile (>75, Q –Q ) Maximum vessel 3 4 µm2 b Hydraulically efficiency Water exploitative 2942.59 ±2623.32 IAWA et al. (2007); Scholz area (VAmax) c efficiency (wood) Greater conduits, higher water flows but higher (589.12–8904.27) 7853–31415 et al. (2013) conduits embolism risk a Pit aperture surface area b Pit area (PA) µm2 Air-water interfaces for conduits Water exploitative 19.68 ±16.30 12–78 IAWA et al. (2007); Scholz c Larger pits, higher water flows but higher efficiency (wood) (4.37–55.15) et al. (2013) conduits embolism risk a Horizontal pit membrane diameter Pit diameter aperture b Embolism resistance inter-conduits Water exploitative 2.90 ±1.19 Scholz et al. (2013); Li et al. (DA µm pit) c Smaller and denser pits, higher hydraulic safety safety (wood) (1.38–5.38) 0.5–7 (2016); Helmling et al. (2018) Specific leaf area a mm2 mg-1 Area of a fresh leaf divided by its oven-dry mass Water exploitative 15.39 ±7.33 Wright et al. (2004); Pérez-(SLA) b Carbon capture and leaf longevity efficiency (leaves) (7.24–32.22) <1–300 Harguindeguy et al. (2013) 116 Ecology of woody plants in Colombian dry forests Trait Units Description Trait function Trait mean ±SD Reference (abbreviation) (dimension) (Q" – Q" range References #$.& '$.() c Higher SLA, lower tissue investments a Average conduit surface area b Vessel area (VA) µm2 Hydraulic conductivity Water exploitative c efficiency and safety 1676.93 ±1484.11 196–37600 Olson & Rosell (2013); Greater conduits, higher hydraulic efficiency but (wood) (391.73–5094.72) Scholz et al. (2013) lower hydraulic safety a Number of conduits per cross-sectional area Chave et al. (2009); Scholz Vessel density (VD) vessels mm-2 b Resistance to strength and vessel implosion Water exploitative 71.71 ±50.54 safety (wood) (15.22–181.83) 1–1000 et al. (2013); Jacobsen et al. c Higher density, higher hydraulic safety (2005) a Oven-dry mass divided by saturated volume of the wood section b Wood density (WD) g cm3 Wood stability, aboveground biomass Investment in tissues 0.63 ±0.15 construction and carbon-gain strategies Water exploitative Chave et al. (2009); Pérez- safety (wood) (0.32–0.84) 0.1–1.2 Harguindeguy et al. (2013) c Harder woods, lower water demands and higher tissue investments a Oven-dry mass divided by anhydrous volume of the wood section Wood anhydrous 3 b density (WD ) g cm Wood stability Investment in tissues 0.72 ±0.17 Chave et al. (2009); Pérez- 0 c (wood) Greater wood anhydrous densities, higher tissue (0.38–0.96) 0.1–1.5 Harguindeguy et al. (2013) investments a Free and fixed water capacity in cells. [(1.5 − WD$)× 1.5WD$]+WC>?@ (Water content at Water content at fibre saturation point); WC = & − & maximal capacity kg kg-1 >?@ BC BC Water exploitative 1.05 ±0.61 0.2–5.0 Guevara (2001); Berry & $ (WC ) b Shrinkage and swelling of xylem cells efficiency (wood) (0.53–2.54) Roderick (2005) max c Higher water content, lower xylem mechanical resistance a Theoretical specific xylem hydraulic conductivity per cross-sectional area. DEF × &/G H Xylem potential Kkg m-1 s-1 ∑dJ × VD; MN=998.2 kg m-3; O=1.002x10–9 Water exploitative 25.09 ± 43.78 Chave et al. (2009); Poorter hydraulic –1MPa-1 MPa s ; dh and VD by m units efficiency (wood) (2.25–113.72) 0.3–200 et al. (2010); Méndez-conductivity (Kp) b Water exploitation abilities Alonzo et al. (2012) c Higher potential conductivity, higher hydraulic efficiency a Trait-based ecology definition and method of calculation b Trait association to functions and mechanisms of a tree c Trait association to hydraulic safety-efficiency trade off of a tree 117 Doctoral Thesis – Roy González-M. Statistical analyses To characterize the functional trait space in TDF, we performed a Principal Component Analysis (PCA) with trait values at the individual level (i.e., each score in the PCA refers to an individual tree). We selected the two first PCA axes, which explained 61.32% of the variance, and performed a varimax rotation to improve the interpretability of the resulting 2-dimensional functional trait space. The rotation did not change the coordinate system of the initial PCA (r=0.9, P<0.001). Both axes of the functional space were defined as multidimensional traits for evaluating the trait probability density (TPD) of species, and their respective biomass dimensions (i.e., standing biomass, biomass demographic changes, and biomass net changes), following the procedures in Carmona et al. (2016, 2019). The TPD approach is based on the estimation of Gaussian kernel density functions (bivariate in this case) around each observation. Here, the TPD function of a given species represented the probabilities of observing different trait values (or combinations of them in the case of our 2-dimensional space) in those species, considering all sampled individuals (Carmona et al. 2016). For species with at least three individuals, the standard deviation (bandwidth) around each observation was selected using the unconstrained bandwidth estimation implemented in the R package ks (Chacón & Duong 2018), as implemented in the TPDs function of the R package “TPD” (Carmona et al. 2019). For species with less than or equal to two individuals within a plot, the standard deviation for each PCA axis and plot were predicted by regressing standard deviations against the mean value of species (considering all species within the plot). For extended details see Figure S2. To calculate the trait probability densities for each biomass dimension (TPDC), we combined all TPD of the individual species based on the sum of the probability functions, rescaled by the relative biomass dimension of each species (see Figure S2). Then, to evaluate the amount of functional space occupied by each TPDC, we estimated the Functional Richness (FRic) index suggested by Carmona et al. (2016, 2019). FRic refers to the sum of each biomass dimensions’ hypervolumes, considering their probability distributions for values above 0. We estimated differences in FRic between biomass dimensions (e.g., BGS vs. BM) by calculating FRic 999 times, with half the species randomly selected each time. To evaluated dissimilarities on the occupancy of the functional space between biomass dimensions, we ran an Overlap-based functional dissimilarity (bO) index, where values vary from 0 to 1 and indicate maximum dissimilarity between hypervolumes when bO amounts to 1 (Carmona et al. 2019). We estimated if bO was higher than expected by chance with a null model, in which TPDs scores were randomized and bO was calculated 999 times (Traba et al. 2019). All statistical analyses were performed using R (v3.5.3; www.r-project.org). Results Functional trait space and biomass dominance in TDF The functional trait space of 524 populations belonging 338 TDF tree species is summarized in the first two-dimensions of the PCA (Figure 3a). The first PCA axis (36.75% of explained variance) reflected the hydraulic safety-efficiency trade-off. The high safety side was characterized by a high density of narrow vessels with high fibre wall thickness, whereas the hydraulic-efficiency side had wide vessels and pits with high xylem potential hydraulic conductivity. The second PCA axis (24.57% of explained variance) reflected differences of investment in tissues, where negative values were related to large leaves with high SLA, and high content of water at maximal capacity (‘cheap’ tissues), while positive values corresponded to high LDMC and high wood density (‘costly’ tissues). The 50% probability threshold in the TPD (TPD<50%; Figure 3a) showed that more than half of the populations (288) occurred along the hydraulic safety- 118 Ecology of woody plants in Colombian dry forests efficiency trade-off but were restricted to the side of high investment in tissues. Surprisingly, many populations had combinations of traits outside of these trade-offs (TPD50-99% = 236; Figure 3a). When TPD was rescaled by standing biomass, the TPD<50% was narrower and included 220 populations, which accounted for 61% of total biomass (592.8 t, Figure 3b). Populations with costly and hydraulically-safe tissues and costly and hydraulically-efficient tissues accounted for 37.4% of total biomass (TPD<20%= 76 populations with 362.9 t, Figure 3b), while populations with other trait combinations were widespread but with low biomass across the functional space (Figure 3b). For instance, species with the dominant trait combinations such as Trichilia oligofoliolata (a costly and hydraulically-safe evergreen species) or Astronium graveolens (a costly and hydraulically-efficient deciduous species) reached to 53.6 and 21.5 t ha-1, respectively. In contrast, species with different trait combinations and low investment in tissues such as Urera simplex (an evergreen species with intermediate hydraulically-efficiency) or Pseudobombax septenatum (the typical deciduous and hydraulically-efficient species) only reached 0.02 and 4.7 t ha-1, respectively (Figure 3b). For extended details of functional trait combinations and biomass scores of species see Table S2. Figure 3. Trait probability densities (TPD) showing the functional trait combinations of species populations along an axis hydraulic safety-efficiency trade-off (hs-he, PC1 36.75% explained variance) and an axis of investment in tissues (it, PC2 24.57% explained variance). (a) TPD where each species at each plot has an equivalent weight (grey points). (b) TPD where each species population is rescaled by its equivalent biomass at each plot (green points represent evergreen species and orange points deciduous species). Functional traits: Fibre wall thickness (FWT, µm), hydraulically weighted diameter (dh, µm), leaf area (LA, mm2), leaf dry matter content (LDMC, mg g-1), leaf thickness (Lth, mm), maximum vessel area (VAmax, µm2), pit area (PA, µm2), pit diameter aperture (DApit, µm), specific leaf area (SLA, mm2 mg-1), vessel area (VA, µm2), vessel density (VD, vessels mm-2), wood density (WD, g cm3), wood anhydrous density (WD0, g cm3), water content at maximum capacity (WCmax, kg kg-1), and xylem potential hydraulic conductivity (Ks, kg m-1 s-1 MPa-1). The whole pool (TWP) of populations in the TPD. Functional Richness (FRic). Examples of species with different functional trait combinations in TDF: Anacardium excelsum (Aex), Aspidosperma polyneuron (Apo), Astronium graveolens (Agr), Cavanillesia platanifolia (Cpl), Cecropia peltate (Cpe), Eugenia procera (Epr), Gustavia superba (Gsu), Machaerium capote (Mca), Pradosia colombiana (Pco), Prosopis juliflora 119 Doctoral Thesis – Roy González-M. (Pju), Pseudobombax septenatum (Pse), Randia armata (Rar), Spondias mombin (Smo), Trichilia oligofoliolata (Tol), Trichilia elegans (Tel), Urera simplex (Usi), Zanthoxylum rhoifolium (Zrh). Functional Richness (FRic). TDF functional and biomass changes after ENSO2015 Total biomass growth of surviving trees was 24.5 t yr-1 (2.23 t ha-1 yr-1 ± 0.67), biomass growth of recruiting trees was 1.10 t yr-1 (0.10 t ha-1 yr-1 ± 0.11) and biomass mortality was 7.2 t yr-1 (0.65 t ha-1 yr-1 ± 0.35; Table S1). We found high dissimilarity between functional trait spaces of all demographic dimensions, at all probability thresholds (P<0.001, Figure 1b, c, f, Figure S3), but resulting in contrasting responses to ENSO2015. The two dominant functional trait combinations, hydraulically safe with high investment and hydraulically efficient with high investment, showed the largest biomass growth of surviving trees (TPD20%= 50 populations, 10.4 t yr-1 [42% total biomass] and TPD50%= 167 populations, 14.3 t yr-1 [58%]; Figure 4a). In contrast, the largest biomass for recruited trees was restricted to species with combinations of costly and hydraulically-safe traits or ‘cheap’ and hydraulically-efficient traits (TPD50%= 27 species, 0.64 t yr-1 [57%]; Figure 4e). Species with low investment in tissues and high hydraulic efficiency were mainly deciduous (71%, e.g. Zanthoxylum lenticulare = 0.17 t ha-1 yr-1). The highest loss of biomass by mortality was mainly ascribed to species with costly but hydraulically-safe tissues (TPD20%= 142 populations, 4.61 t yr-1 [64%]; Figure 4i), and a second group of functional trait combinations was related to species with costly and hydraulically-efficient tissues (TPD20-50%= 97 populations, 2.88 t yr-1 [40%]; Figure 4i). Here, it is important to highlight that biomass loss by mortality accounted for a higher functional space of trait combinations with respect to the other demographic dimensions (FRic=27.67, P<0.001, Figure 1d, g, h). These results were consistent across all tested probability thresholds (Figure S4). Net biomass change was 18.4 t yr-1 (1.68 t ha-1 yr-1 ± 0.56), varying between 0.82 and 2.50 t ha-1 yr- 1 per plot (Table S1). Positive changes in net biomass reached 20.3 t yr-1 (1.84 t ha-1 yr-1 ± 0.61) but were restricted to the dominant functional trait combinations (Figure 5a, TPD<50%). Negative changes in net biomass attained 1.9 t yr-1 (0.17 t ha-1 yr-1 ± 0.11) and included species with costly hydraulically-safe tissues, but also ‘intermediate’ species with traits between both ends of the hydraulic safety and efficiency trade- offs and with intermediate investment in tissues (Figure 5b, TPD<50%). Functional dissimilarities between positive and negative biomass dimensions (bO) were significant at all probability thresholds (P<0.001; Figure 5c). Negative biomass changes occupied a higher fraction of the functional space when compared to positive biomass changes for all probability thresholds (Diff. FRic=4.93, P<0.001; Figure 5c). Notably, total biomass in all plots after ENSO2015 was 989.1 t, but the 50% threshold probability showed a clear divergence of both dominant functional trait combinations that were not detected for the initial biomass (see Figures 3b and 5c). 120 Ecology of woody plants in Colombian dry forests Figure 4. Trait probability densities (TPD) showing the functional trait combinations of species populations rescaled by biomass growth of survivors (a, BGS in green colour), biomass growth of recruits (e, BGR in orange colours) and biomass mortality (i, BM in blue colour). Null models for Functional Dissimilarity (FDiss, b, c, f) between biomass growth and mortality TPD’s. Significant bO (P<0.001) indicates that dissimilarity between paired TPD demographic dimensions is greater than the expected by chance (999 randomizations). Functional Richness (FRic, d, g, h) between biomass growth and mortality TPD’s (b, c, f). Significative differences between the paired frequency distributions indicate different FRic of the contrasted TPD’s demographic dimensions (P<0.001, 999 randomizations). Hydraulic safety (hs), hydraulic efficiency (he), investments in tissues (ti). 121 Doctoral Thesis – Roy González-M. Figure 5. Trait probability densities (TPD) showing the functional space of trait combinations for species populations rescaled by positive net biomass changes (a, NBC(+) in blue colours), by negative net biomass changes (b, NBC(–) in green colours) and by standing biomass after ENSO2015 (c). FRic refers to functional richness and FDiss to functional dissimilarity. Significant O (P<0.001) indicate that functional dissimilarity between positive and negative net biomass change TPD’s is greater than expected by chance (999 randomizations). Differences in functional richness (Diff. FRic, P<0.001) indicate that negative net biomass changes TPD’s had a higher FRic than positive net biomass changes TPD’s (999 randomizations). Hydraulic safety (hs), hydraulic efficiency (he), investments in tissues (ti). 122 Ecology of woody plants in Colombian dry forests Discussion Tropical dry forests (TDF) experience frequent water limitations as the result of annual rainfall seasonality (Linares-Palomino et al. 2011) and inter-annual extreme droughts (Allen et al. 2010). In response to these conditions, species have developed a particular suite of functional traits (Pennington et al. 2009; Aguirre- Gutiérrez et al. 2019), with important consequences for ecosystem functioning. To understand the functional responses of TDF tree species to extreme drought events, we assessed the functional trait space of a large number of tree species and evaluated if particular suites of trait combinations determined differences in species standing biomass and demographic biomass changes to one of the driest events in decades (ENSO2015). Our results showed that: (1) TDF tree species are distributed across a broad functional trait space associated with trade-offs in hydraulic safety-efficiency and investment in tissues. (2) Biomass- dominant species were located along the hydraulic safety-efficient trade-off but only at the high investment in tissues side. Yet, almost half of the species had other trait combinations but with lower biomass dominance. (3) Biomass loss by mortality covered a broader functional trait space than biomass growth, but net biomass losses were mostly experienced by species with intermediated hydraulically safety- efficiency and lower investment in tissues. The hydraulic safety-efficiency trade-off and costly tissues govern species dominance in TDF As expected, most TDF tree species were distributed along the hydraulic safety-efficiency trade-off (Figure 3). Yet, another important fraction of species was found to occupy the functional space with alternative trait combinations. We found a first numerous group of species with high standing biomass in the functional space combining hydraulically-safe traits (Figure 3b) such as narrow vessels and pit areas and high vessel density (Markesteijn et al. 2011b; Onoda et al. 2011; Méndez-Alonzo et al. 2012). A second group of dominant species was associated with traits of high water transport efficiency (Figure 2a) such as wide vessels, large pit areas and high xylem potential hydraulic conductivity (Sobrado 1997; Pineda-García et al. 2015). However, and contrary to our expectations, this trade-off was not related to a parallel trade-off in tissue investments since both groups of dominant species were found to have dense leaves and stems (Figure 2). This unexpected result disagrees with previous studies that have suggested that species with high hydraulic-efficiency, traditionally associated with deciduousness for avoiding cavitation risks under water- constraints, have low investment in tissues but high nutrient concentration which would enable them to maximize growth rates in their reduced growing season (Brodribb et al. 2010; Markesteijn et al. 2011a, b; Méndez-Alonzo et al. 2012). In contrast, our results suggest that not investing in expensive tissues has negative consequences on biomass dominance, irrespective of leaf habit (Figure 5c). Despite this result being novel, it is consistent with the physiological mechanisms of species to deal with drought. For instance, high wood density, which may result from a different combination of tissue and cell distributions such as high investments in fibre wall thickness (Ziemińska et al. 2015) or an increasing abundance of fibre (Jacobsen et al. 2007), can protect vessels from implosion when water shortage creates strong negative xylem potentials (Hacke et al. 2001b; Pratt et al. 2007). Likewise, thick and dense leaves may be more resistant to drought because living cells have rigid walls preventing cell collapse caused by negative turgor pressures developing in them under substantial water loss (Salleo & Nakdini 2000). Additionally, dense and rigid leaves have smaller transpiring surfaces, hence reducing wilting and water requirements (Niinemets 2001; Poorter et al. 2009). However, it is important to acknowledge that investment in costly tissues may also respond to other factors that favour species dominance. Tough tissues may provide 123 Doctoral Thesis – Roy González-M. protection against herbivores and against other physical hazards (Turner 1994; Cunningham et al. 1999). For instance, Aspidosperma polyneuron, a dominant species with dense leaves, showed lower leaf area removed by herbivory than Sapium glandulosum, which have low dominance and thin tissues (Silva et al. 2015; Table S2). Moreover, dense tissues in deciduous species may retard leaf loss during the dry seasons increasing the carbon gain window, or reduce cavitation risk on instantaneous dry conditions during the rainy seasons (Powers & Tiffin 2010; Lopezaraiza-Mikel et al. 2013). The fact that investment in expensive tissues relates to high standing biomass in TDF opens new questions about the mechanisms that may mediate the dominance patterns of species under future climatic scenarios. For example, if extreme droughts become more frequent and intense in the tropics (Allen et al. 2010, 2015), and building dense tissues is energetically expensive and time-consuming (Chave et al. 2009; Osnas et al. 2013), how will the functional space be modified in tropical forests in the future? Answering this question requires models that integrate not only species losses but also changes in the functional patterns at community scales (Lawlor & Tezara 2009; McDowell et al. 2018). Biomass demographic changes in TDF following the ENSO2015 After ENSO2015 the functional space of biomass loss by mortality was significantly broader than the space of growth and recruitment (Figure 4), and negative net biomass change showed a higher covering of this functional space respect to positive net biomass change (Figure 5). These findings support the idea that under extreme droughts, tree mortality may be more widespread than forest recovery via the growth of trees (Allen et al. 2010), and that is an important driver of functional trait composition and forests functioning (Fauset et al. 2012; Aguirre-Gutiérrez et al. 2019; Esquivel-Muelbert et al. 2019). Growth, recruitment, and mortality of trees were mostly shaped by differences in the hydraulic safety-efficiency and investments in tissue (Figure 4). However, we did not find support for a coordinated demographic response of both dominant strategies along the hydraulic safety-efficiency trade-off and “costly-to-cheaper” tissue investments (Méndez-Alonzo et al. 2012). The higher biomass gained by survivors’ growth was restricted to species with costly, but hydraulically-safe tissues or costly and hydraulically-efficient tissues, which is consistent with the prediction that species having dominant functional trait combinations would perform better under water constraints. Our results also demonstrate that irrespective of the hydraulic designs or leaf habits, as long as species invest in expensive leaf and wood tissues, they would effectively respond to extreme drought conditions. This result may be related to the fact that building expensive tissues implies more biomass per volume fraction, where despite the expected low growth rates for the costly and hydraulically-safety tissue species, they can pack high carbon stocks at constant growth rates during long periods (Poorter et al. 2017). Likewise, costly and hydraulically-efficient tissue species have important carbon gains during the reduced growing season when they not only invest in performance but also protect their structures from coping with water-constraints (Somavilla et al. 2014). Interestingly, biomass of recruited trees was the only demographic dimension following the expected coordination between the hydraulic safety-efficiency trade-off with the “costly” to “cheap” investment in tissues axis. Markesteijn et al. (2011a) found that pioneer and deciduous tree species generally combine high hydraulic conductivity with low stem densities, which favours short-term gain in biomass at the expense of long-term survival. This strategy favours high volumetric and height gain, to rapidly ascend in the canopy and gain a photosynthetic edge. This same explanation may apply in our study if we take into account that the high recruitment of hydraulically-efficient species, also correspond to species mainly deciduous with high values of specific leaf area and water content at maximum capacity, which are traits associated with fast growth (Wright et al. 2004; Poorter et al. 2008). 124 Ecology of woody plants in Colombian dry forests The broader functional space associated with tree mortality, in comparison to biomass gain by growth, suggests that most TDF species are sensitive to extreme droughts. Additionally, we observed that high mortality was concentrated in species with costly and hydraulically-safe tissues, followed, to a lesser extent, by species with costly and hydraulically-efficient tissues (Figure 4c). This unexpected result contrasts with previous studies suggesting that ‘conservative’ traits associated with high hydraulic-safety should be positively related to survival rates in TDF (Prado-Junior et al. 2016; Powers et al. 2020), where biomechanical resistance or narrow hydraulic conduits are enough to counteract water-constraints (Pineda- García et al. 2015; Beeckman 2016). Likewise, it supports the idea that drought-induced mortality can be countered by avoidance strategies such as leaf dropping (Sobrado 1997). However, it is important to note that although biomass losses by mortality and functional space reduction were evident in this study as the result of one extreme drought episode (ENSO2015), long time monitoring is necessary to determine if under normal climatic conditions, TDF recover the loss of biomass and functionality that follow extreme droughts; or if on the contrary, these species have widespread mortality risks across the whole hydraulic safety- efficiency functional space under these particular strong droughts (Powers et al. 2020). The sensitivity of TDF to future drier scenarios Future drier scenarios are expected to change the functional space and functioning of TDF (Allen et al. 2010, 2015). However, the strength and direction of these changes are still unclear because of the absence of studies on the functional sensitivity of species to extreme droughts, and the absence of long monitoring trait-demographic data to evaluate the consequences of extreme droughts on forest functioning. Recently, it was demonstrated that ENSO2015 caused high mortality of TDF species with low hydraulic safety margins (Powers et al. 2020). Our results suggest that, irrespective of their hydraulic designs, species with low investment in tissues were strongly sensitive to ENSO2015, resulting in important negative net biomass balances. In a broader context, biomass net balance after ENSO2015 was over two times lower than balances for a rainy period. For instance, between 2009 and 2011 (wet period for TDF in Northern South America, SPEI > 1; Figure 2b), El Vinculo, one of our study sites, had a net biomass gain of 3.4 t ha-1 yr-1 (tress with DBH > 5cm; Torres et al. 2012), while after ENSO2015 it only reached 1.73 t ha-1 yr-1 (trees with DBH > 2.5cm). This result, together with the narrower functional space of positive biomass gain than of negative biomass gains (Figure 5c), suggests that both functional diversity and biomass productivity should decrease in future drier scenarios. Further studies should model how future scenarios of changes in rainfall regimes may impact forest biomass dynamics (Allen et al. 2017a) but also if the functional trait space will become narrower due to these events. Acknowledgements We would like to thank the owners of the natural areas where we worked for their logistical support and for allowing us to use their fieldwork facilities. Financial support was provided by the Interamerican Development Bank (Technical Cooperation # ATN/BD-15408-CO), Ministerio de Ambiente y Desarrollo Sostenible of Colombia, the fellowship program of the International Tropical Timber Organization (#020/17A), the Estonian Research Council (PSG293), the European Regional Development Fund (Centre of Excellence EcolChange), and Dora Plus Fellowship Programme (University of Tartu). AI-P was supported by COLCIENCIAS call 727 of 2015. We are thankful to the Colombian TDF Network (Red BST-Col) for their invaluable field collaboration, and many students who helped us with fieldwork and laboratory analyses. 125 Doctoral Thesis – Roy González-M. Supporting information Table S1. Extended information of the 11 1-ha permanent Tropical Dry Forests (TDF) plots. Biomass: Standing biomass (t ha-1) estimated as the sum of biomass of all trees for the first census (t0). Biomass growth of survivors (BGS, t ha-1 yr-1) was estimated as annual biomass increments resulting from the growth of all trees that survived from t0 to the final census (tfin). Biomass growth of recruits (BGR, t ha-1 yr-1) was estimated as annual biomass increment obtained from all trees that attained at least 2.5 cm DBH in tfin and were not sampled in t0. To avoid biomass overestimation, we consider that each new tree was recruited immediately after t0 and assumed that they had an initial DBH of 0 (Talbot et al. 2014). Biomass mortality (BM, t ha-1 yr-1) was estimated as biomass loss from all trees that died between t0 and tfin; biomass for a DBH of 2.5 cm was subtracted to each dead tree (Talbot et al. 2014). Net biomass change (NBC, t ha-1 yr-1) corresponded to the net annual change in biomass per plot between t0 and tfin (Prado-Junior et al. 2016; Poorter et al. 2017); it was estimated as: NBC = BGS + BGR – BM. The Standardised Precipitation-Evapotranspiration Index (SPEI; Vicente-Serrano et al. 2012) shows the mean for wet periods (wet), dry periods (dry), and ENSO2015. SPEI was calculated based on long-term data from weather stations near the plots (1980 to 2019), and shows the magnitude and strength of drought conditions during the period of analysis, where negative values indicate the SPEI mean for drought periods (red colour) and positive values correspond to wet periods (blue colour). Climatic conditions: Total annual rainy days (ARD, no.), aridity index (Aridity, [PET/TAP]), isothermality (Isoth, %), solar radiation (SRad, MJ·m-1 x 100), total annual precipitation (TAP, mm), potential evapotranspiration (PET, mm), number of periods with three consecutive dry months (DryPeriods, # [three month with <100 mm·month-1]), mean annual temperature (MAT, ºC), total precipitation during the three driest months (TPDriest [<100 mm·month-1], mm), number of dry months (DMonths, months with <100 mm·month-1), water vapor pressure (WVP, kPa) and wind speed (Wind, m·s-1). Soil conditions: Acidity (pH), available phosphorus (P, mg·kg-1), cation exchange capacity (CEC, cmol+·kg-1), extractable bases (Ca [Calcium], Mg [Magnesium], K [Potassium], Na [Sodium], cmol+·kg- 1), organic carbon (OC, %) and textural fractions (Sand, Clay, Silt, %). Land-cover and terrain characteristics: Surrounding forest area (ha) and topographic roughness (Roughness, %). Study sites TDF in the Caribbean lowlands’ TDF in the Inter TDF in the region Andean region dry Savannas Macuira Sanctuary of Tayrona Cardonal Cardonal Jabirú Tambor El Site name National Flora and National Plana Loma Private Private Cotove Taminango Fauna Natural Natural Research Vinculo Tuparro Park Park Forests Forests Station Regional Research Colorados Reserve Reserve Park Station National Park Latitude (ºN) 12.20 9.94 11.31 5.08 5.09 5.06 5.17 6.53 3.84 1.67 5.25 Longitude (ºW) -71.35 -75.11 -74.13 -74.80 -74.77 -74.83 -74.81 -75.83 -76.29 -77.31 -67.86 Altitude (masl) 113 301 15 260 322 302 385 509 1025 591 95 Number of species 34 66 53 49 47 35 77 28 45 9 76 Deciduous/Evergreen 24/10 30/36 34/19 21/28 22/25 15/20 27/50 15/13 19/26 5/4 22/54 Standing biomass 80.9 103.8 110.7 87.6 132.9 106.1 116.1 76.9 62.0 19.9 73.8 BGS 1.45 2.68 1.85 1.65 2.63 3.59 2.58 2.44 2.22 1.18 2.25 BGR 0.002 0.007 0.048 0.212 0.027 0.360 0.166 0.178 0.087 0.020 0.007 BM 0.63 0.63 0.40 0.87 1.01 1.45 0.66 0.41 0.59 0.25 0.30 BNC 0.82 2.06 1.49 0.99 1.64 2.50 2.09 2.21 1.73 0.95 1.96 Weather stations (Dt=1980-2019) 2 4 1 SPEI wet 0.82 0.81 0.74 SPEI dry -0.77 -0.80 -0.80 126 Ecology of woody plants in Colombian dry forests Study sites TDF in the Caribbean lowlands’ TDF in the Inter region Andean region TDF in the dry Savannas SPEI ENSO2015 (t=May/2015-Jan/2016) -1.42 -1.21 -1.87 Climate Ard 33 96 95 116 113 116 126 146 144 138 152 Aridity 3.42 1.01 2.03 1.29 1.19 1.31 0.88 1.43 0.97 1.94 0.77 DryPeriods 1 1 1 2 2 2 2 1 2 2 1 DMonths 10 5 9 5 5 5 4 6 5 9 4 Isoth 75.43 90.25 81.35 85.97 85.91 86.03 85.83 87.29 93.30 91.78 78.07 MAT 27.11 26.1 27.38 27.86 27.44 27.86 26.81 26.92 23.38 25.25 28.25 SRad 185.69 192.08 196.79 173.36 172.78 173.28 172.87 178.49 169.95 159.45 164.10 TAP 517.0 1528.4 899.4 1505.9 1541.2 1528.2 1912.5 1193.8 1192.4 721.4 2697.2 PET 1768.6 1546.0 1827.7 1946.8 1835.6 2009.1 1689.1 1712.8 1161.3 1400.8 2067.0 TP Driest 32.1 139.3 33.4 227.8 222.5 236.8 272.7 112.7 168.5 52.4 177.1 WVP 2.67 2.75 2.87 2.63 2.53 2.65 2.50 2.55 2.14 2.36 2.80 Wind 4.84 2.35 4.45 0.93 0.92 0.93 0.90 0.83 0.87 0.96 1.37 Soils pH 6.12 7.37 7.38 6.79 6.87 6.46 6.98 6.54 6.22 7.23 4.39 P 18.14 12.17 222.59 143.24 19.36 17.08 11.33 20.08 4.70 27.89 3.32 CEC 10.01 30.89 16.43 15.61 20.08 17.01 14.73 25.71 26.68 33.10 6.69 Extractable Bases Ca 5.29 34.83 16.05 13.81 22.89 13.13 10.29 22.13 21.33 29.14 0.06 Mg 2.19 4.64 2.69 3.24 4.11 3.90 2.30 9.95 17.74 7.91 0.06 K 0.55 0.65 0.82 0.73 0.36 0.70 0.61 0.38 0.87 1.08 0.21 Na 0.22 0.09 0.07 0.03 0.10 0.08 0.04 0.15 0.14 0.16 0.16 OC 1.00 3.22 3.58 2.54 2.41 2.80 2.36 2.26 3.64 2.61 1.79 Textural fractions Sand 57.41 34.21 62.58 61.86 56.45 48.86 72.25 37.42 60.69 34.47 64.27 Clay 21.54 31.58 19.25 24.96 24.54 21.16 16.33 35.34 24.36 29.66 16.39 Silt 21.07 34.21 18.18 13.18 19.03 29.98 11.42 27.24 14.95 35.9 19.34 Forest area 407.6 395.0 411.8 153.9 303.0 145.4 329.6 55.1 23.4 56.8 176.3 Roughness 10.4 14.7 19.1 7.3 12.7 9.0 22.5 9.7 8.0 20.8 7.4 127 Doctoral Thesis – Roy González-M. ●● ● ● ● ● ρ = 0.96 ρ = 0.94 ●●● ●● ρ = 0.96 ρ = 0.97 ρ = 0.97 ●● ● ● ● ●●●● ● ● P < 0.001 ●● P < 0.001 ●●● P < 0.001 ● P < 0.001 ● P < 0.001 ●●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●●● ● ●●● ● ● ●● ● ●●● ● ●●● ● ●● ●● ● ● ● ●●● ● ● ●● ●●●●● ●● ●●●●● ● ●● ●●●●● ● ● ● ● ● ● ● ●●● ●● ●●● ●● ● ● ● ● ● ● ● ● ●●● ● ● ● ●●● ● ●● ●●● ● ●● ● ●●● ● ●●●● ● ● ● ●●● ● ●● ● ●●● ●● ● ● ● ●●●● ●● ● ● ●●● ● ● ● ●● ●● ● ● ● ● ● ●●● ● ● ● ●● ● ●●●● ● ●● ● ●● ● ●●● ● ● ● ● ●● ●● ● ●●● ● ● ● ●● ●●● ●● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● 6 7 8 9 10 12 4.5 5.0 5.5 6.0 6.5 1.5 2.0 2.5 3.0 3.5 4.0 1 2 3 4 5 0.0 0.5 1.0 1.5 2.0 LA (mm2) LDMC (mg g−1) SLA (mm2 mg−1) PA (µm2) DApit (µm) ● ● ●● ●● ●● ρ = 0.96 ● ● ρ = 0.95 ρ = 0.97 ● ρ = 0.98 ρ = 0.97 ● ● ● ● P < 0.001 ● P < 0.001 ●● P < 0.001 ● P < 0.001 P < 0.001 ●● ●●●● ●● ●● ● ● ●● ●● ●● ● ● ● ● ● ● ●●●● ●● ● ●●●● ● ● ●● ●● ● ●● ● ●●●● ● ● ●●● ●● ● ● ● ●● ● ● ● ●●● ● ● ●● ● ● ● ●●● ● ●● ●● ● ●●●● ● ●● ●● ● ●●● ●●● ● ●● ● ●●● ●● ●● ●●● ●●● ●●● ● ●● ● ●● ●● ● ●● ●● ●● ● ● ● ●●● ●● ● ● ●●●●● ● ●● ● ●● ●● ●● ●● ● ● ●● ● ●● ● ●●● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ●●● ● ● ●● ● ●●● ● ●● ● ●● ●● ● ● ●● ● ●● ● ● ●● ● ● ●●● ● ● ●●● ● ● ● ● ●●● ●● ● ● ●● ● ● ●● ● ● ● 0.5 1.0 1.5 2.0 2 3 4 5 3.5 4.0 4.5 5.0 0 2 4 6 6 7 8 9 TFW (µm) VD (no.mm2) dh (µm) Kp (kg m −1 s−1 MPa−1) VA (µm2) ● ● ● ● ● ●●● ● ● ρ = 0.97 ● ρ = 0.99 ●● ● ●●●●● ρ = 0.97● ●●●● ● ρ = 0.99 ● ● ●● P < 0.001 P < 0.001 ●●●● P < 0.001 ●●●● P < 0.001 ● ● ● ● ●● ● ●● ● ●● ● ● ● ●●● ● ● ●● ●● ● ● ● ●● ● ● ●● ●● ● ●●● ● ●● ●● ●● ● ● ●● ● ● ●● ● ● ●● ● ● ● ● ●● ●●● ●● ●● ● ● ● ● ● ● ●● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ●●● ● ● ●● ● ●● ●● ●● ● ● ● ● ● ●●● ●● ● ● ●●● ● ●●●● ● ●● ● 6 7 8 9 10 −1.5 −1.0 −0.5 −1.4 −1.0 −0.6 −0.2 −0.5 0.0 0.5 1.0 1.5 VA (µm2) WD (g cm−3max ) WD0 (g cm−3) WCmax (kg kg−1) Figure S1. Pairwise correlations between imputed individual-trait values (IMP) and not-imputed individual-trait values. Functional traits: Fibre wall thickness (FWT, µm), hydraulically weighted diameter (dh, µm), leaf area (LA, mm2), leaf dry matter content (LDMC, mg g-1), leaf thickness (Lth, mm), maximum vessel area (VAmax, µm2), pit area (PA, µm2), pit diameter aperture (DApit, µm), specific leaf area (SLA, mm2 mg-1), vessel area (VA, µm2), vessel density (VD, vessels mm-2), wood density (WD, g cm3), anhydrous wood density (WD0, g cm3), water content at maximum capacity (WCmax, kg kg-1), and xylem potential hydraulic conductivity (Ks, kg m-1 s-1 MPa-1). 128 2 2 IMP VAmax (µm ) IMP TFW (µm) IMP LA (mm ) 7.0 8.0 9.0 1.4 1.6 1.8 2.0 7 8 9 10 11 IMP WD (g cm−3) VD (no.mm2IMP ) IMP LDMC (mg g−1) −1.2 −0.8 −0.4 3.0 3.5 4.0 4.5 5.0 5.4 5.6 5.8 6.0 6.2 WD (g cm−3IMP 0 ) IMP dh (µm) 2 −1IMP SLA (mm mg ) −1.0 −0.6 −0.2 3.6 4.0 4.4 4.8 2.0 2.5 3.0 3.5 IMP WCmax (kg −1 −1 −1 kg−1) IMP Kp (kg m s MPa ) IMP PA (µm2) −0.5 0.0 0.5 1.0 2 3 4 5 6 1.5 2.5 3.5 4.5 IMP VA (µm2) IMP DApit (µm) 6.5 7.5 8.5 0.5 1.0 1.5 Ecology of woody plants in Colombian dry forests (Step 1) Multidimensional trait space axes (PCA) (Step 2) Trait Probability Density (TPD) TPDi Sum of kernx el fxunx cx tioxns xx x x x xx xx xx x x x TPDPn TPDP1 TPDP2 TPDP3 TPDP −2 −1 0 1 2 TPDP TPDi 2 22 2 3 3 3 3 3 3 NN == 55 BBaannddwwididtthh = = 0 0.0.125317653992952 6 9 5 3 9 3 7 1 1 6 8 7 x 6 1 1 1 x . 0. . . x x 0 0 0 1 11 1 5. .5 .5 .5 5. 5x x 0 1 1 1 1 1 .1 x = = = yx = x x yx x h h h x yy t th t yx y t d d d t2 2 t t2 2 2 2i di i yi x yy0 00 0 ai i i ti i t it y w w w yyy w 0 rT 0 r a 0 ra 0 ra 0 ar 0 a d d d A A T A T A T A T A T r y d n n n n PC Cya a a P P C PC CP CP a B B B & B x 1 11 1 x − −− − 5 5 5 5 1. . 5 .5 5 5 5 TPD = "TPD 5 − 1− −1 .1 .1 1. x C &. − − −= = = y = x /$% x y −3 −1.5 0 1.5 N N NN 32 22 2 −3 −1.5 0 1.5 3 −3 −1.5 0 1.5 3y PCATrait 1 − −− − PCATrait 1 PCATrait 1 y y 3− 3− 3− −3 −3 3− 0.0 1.0 2.0 3.0 Populations (P) Sampled traits 2-dimentional traits −2 −1 0 1 2 3 −2 3 5 . 1−1 5 . 1 0 0 0 5 . 1 −1 5 . 1 −3 − 2 3 − 3 −2 3 5 . 1−1 5 . 1 0 0 0 5 . 1 −1 5 . 1 −3 − 2 3 − 3 3 5 . 1 5 . 1 0 0 5 . 1 − 5 . 1 −3 − 3 − PCATrait 1 1 t iPaCrATTArCaPi1t t1i ar T A C P 1 t iPaCrATArCaPi1t t1i ar T A C P 1 t i ar T A C P 1 t i ar T A C P (Step 3) TPD rescaled by biomass (TPDCs) (Step 4) TPDCs and probability thresholds Density Population TPDCs biomass (t ha-1) 19.7 TPD20% TPD50% 21.5 TPD99% 18.4 & TPDCs = "SB) ×TPD&. 4.2 #$% −2−2 −1−1 00 11 22 −2−2 −1−1 00 11 22 −2−2 −1−1 00 11 22 PCPACTArTariat i1t 1 PCPACTArTariat i1t 1 PCPACTArTariat i1t 1 Figure S2. Conceptual framework showing the steps to calculate Trait Probability Densities (TPD) in this study. In the example, we used four populations (P; in total we had 524 populations) belonging to three species (sp; in total we had 338 species) sampled in four plots (p; in total we had 11 plots). Here, a population refers to all sampled individuals (i) of the same species within a plot, for both traits and biomass. Step 1. To describe each individual's functional characterization in a 2-dimensional trait space, we performed a PCA analysis that reduces the matrix of sampled traits (in total 15 functional traits) to the principal axes of variation. The PCA analysis provides the uncorrelated and compressed first components as the new trait values (PCAtrait1 and PCAtrait2). In this study, the first PCA axis (36.75% of explained variance) reflected the hydraulic safety-efficiency trade-off. Negative values in PC1, high safety, describe species having a high density of narrow vessels with high fibre wall thickness, whereas positive values, hydraulic efficiency, characterized species having large vessels and pits with high xylem potential hydraulic conductivity. The second PCA axis (24.57% of explained variance) reflected differences of investment in tissues, where negative values were related to large leaves with high SLA, and high content of water at maximal capacity (‘cheap’ tissues), while positive values corresponded to high LDMC and high wood density (‘costly’ tissues). Step 2. TPD was calculated following the procedures suggested by Carmona et al. (2016) based on the bivariate gaussian kernel density functions. In the example, we first calculated the TPD of each population (TPDP) as the sum of all individual trait density 129 PCATrait 2 PCATrait 2−2 −1 0 1 2 −2 −1 0 1 2 PCATrait 2 −2 −1 PCA Trait 2 0 1 2 −2 −1 0 1 2 DDDDeeeennnnssssiiiittttyyyy PCATrait 2 −2 −1 PCA Trait 2 0 1 2 PCATrait 2PCATrait 2 0000....0000 1111....0000 2222....0000 3333....0000 −2 −1 0 1 2 −2 −3−1 −1.50 0 1 1.52 3 y y y y y yy yy y y yy yy yy y y y PCATrait 2PCATrait 2 −2 −3−1 −1.50 0 1 1.52 3 PCATrait 2PCATrait 2 −2 −3−1 −1.50 0 1 1.52 3 Doctoral Thesis – Roy González-M. probabilities (TPDi). We then summed all bivariate density functions for populations (TPDP) to obtain the trait probability density for all species in TDF (TPDC). Step 3. To calculated the TPDC rescaled by biomass (TPDCs) each TPDP was multiplying by the relative biomass of each population. As the relative biomass sum to 1 across all TDF populations, the sum of all the rescaled TPDP functions’ integrals was 1. We made this procedure for all biomass dimensions used in this study (e.g., standing biomass–SB, biomass growth of survivors, biomass growth of recruits, and net biomass change). Step 4. At last, we calculated the functional trait space occupied by the TPDC and TPDCs at three probability thresholds (20%, 50%, and 99%) using the Functional Richness (FRic) index suggested by Carmona et al. (2016, 2019). 130 Ecology of woody plants in Colombian dry forests Table S2. Functional trait space scores and biomass values for 524 populations belonging to 338 species in TDF. Functional trait space: The first PCA axis (PC1, 36.75% of explained variance) reflected the hydraulic safety-efficiency trade-off; negative values in PC1 corresponded to high safety for species having a high density of narrow vessels with high fibre wall thickness, whereas positive values corresponded to high hydraulic efficiency, characterized by species having large vessels and pits, with high xylem potential hydraulic conductivity. The second PCA axis (PC2, 24.57% of explained variance) reflected differences in tissue investment, where negative values were related to large leaves with high SLA, and high content of water at maximal capacity (‘cheap’ tissues), while positive values refereed to high LDMC and high wood density (‘costly’ tissues). Biomass: Standing biomass (kg ha-1) refers to the sum of biomass for all trees of each species in each plot for the first census (t0). Biomass growth of survivors (BGS, kg ha-1 yr-1) refers to the annual biomass increment produced by the growth of all trees of each species that survived from t0 to the final census (tfin) in a plot. Biomass growth of recruits (BGR, kg ha-1 yr-1) refers to the annual biomass increment obtained from all trees of each species that attained at least 2.5 cm DBH in tfin and that were not sampled in t0 in a plot. Biomass mortality (BM, kg ha-1 yr-1) refers to the biomass loss obtained from all trees of each species between t0 and tfin. Net biomass change (NBC, kg ha-1 yr-1) refers to the net annual change in biomass during the time interval between t0 and tfin (Prado-Junior et al. 2016; Poorter et al. 2017). Values were rounded to two decimals. Voucher code (v) and individual tagged number (tag) are provided when a species was not fully identified. Study sites Family Species PC1 PC2 Standing Biomass BGS BGR BM NBC Caribbean lowlands’ region Macuira National Park Anacardiaceae Astronium graveolens 0.72 0.86 18409.31 343.19 0.00 124.88 218.31 Bignoniaceae Handroanthus billbergii 0.77 0.89 27936.60 632.26 0.00 24.00 608.26 Bignoniaceae Handroanthus chrysanthus 0.20 -0.17 90.30 3.63 0.00 0.00 3.63 Boraginaceae Cordia alba 0.85 -0.85 83.04 0.57 0.00 0.00 0.57 Boraginaceae Cordia macuirensis -0.74 0.24 1.11 0.00 0.00 0.00 0.00 Burseraceae Bursera graveolens 0.84 -1.26 3455.96 45.83 0.00 19.35 26.48 Burseraceae Bursera simaruba 0.77 -1.27 4258.10 103.30 0.00 14.87 88.43 Capparaceae Capparidastrum pachaca -1.18 1.09 312.30 0.00 0.00 29.44 -29.44 Capparaceae Capparis sp2 [v. HC-6659, tag. 129] -0.91 0.24 44.91 0.31 0.00 4.99 -4.68 Capparaceae Cynophalla linearis -0.80 1.26 2715.36 30.16 0.00 30.49 -0.34 Capparaceae Cynophalla verrucosa -0.93 0.66 17.67 0.31 0.00 3.24 -2.92 Euphorbiaceae Croton punctatus -1.21 -0.33 3.27 0.14 0.00 0.00 0.14 Fabaceae Erythrina velutina 1.46 -1.42 4361.96 18.78 0.00 29.57 -10.79 Fabaceae Lonchocarpus pictus 1.21 1.00 4629.47 45.76 1.40 149.13 -101.97 Fabaceae Lonchocarpus violaceus 2.14 1.42 177.17 1.57 0.00 0.00 1.57 Fabaceae Machaerium arboreum 0.95 0.66 4397.31 24.01 0.00 112.22 -88.21 Fabaceae Myrospermum frutescens 0.89 1.05 214.38 5.19 0.00 0.00 5.19 Fabaceae Myrospermum sp [tag. 578] 0.51 1.29 60.49 0.41 0.00 0.00 0.41 Fabaceae Pithecellobium dulce 0.77 0.44 14.03 0.57 0.00 0.00 0.57 Fabaceae Prosopis juliflora 0.59 0.81 628.57 49.89 0.00 8.54 41.35 Hernandiaceae Gyrocarpus americanus 0.82 -2.07 878.74 11.22 0.00 13.42 -2.21 131 Doctoral Thesis – Roy González-M. Study sites Family Species PC1 PC2 Standing Biomass BGS BGR BM NBC Malpighiaceae Bunchosia odorata -0.90 0.27 31.31 1.82 0.12 5.48 -3.54 Malvaceae Pachira quinata 1.54 -0.60 2490.44 35.96 0.00 0.00 35.96 Nyctaginaceae Neea sp [tag. 707] -0.23 0.04 113.45 1.38 0.00 0.00 1.38 Polygonaceae Coccoloba caracasana -0.84 0.59 811.09 24.73 0.00 0.00 24.73 Polygonaceae Coccoloba sp [tag. 218] -0.24 0.55 1715.88 21.97 0.00 18.11 3.86 Rubiaceae Chiococca sp [v. HC-6652, tag. 461] -1.92 -0.04 120.51 1.75 0.00 7.83 -6.07 Rubiaceae Coutarea sp [v. HC-6641, tag. 35] -1.81 0.37 62.18 1.89 0.00 2.70 -0.81 Rubiaceae Randia aculeata -1.72 0.54 230.77 4.58 0.00 19.10 -14.51 Rutaceae Zanthoxylum schreberi -1.57 0.65 280.67 3.10 0.00 7.19 -4.09 Salicaceae Casearia sp6 [v. HC-6635, tag. 262] -1.64 -0.19 133.73 0.79 0.00 8.48 -7.69 Sapindaceae Melicoccus bijugatus 0.26 0.34 717.19 2.06 0.00 0.00 2.06 Sapindaceae Melicoccus oliviformis 1.05 1.07 958.62 13.98 0.00 0.00 13.98 Sapotaceae Pradosia colombiana -0.13 1.23 560.42 22.72 0.00 0.00 22.72 Sanctuary of Flora and Fauna Colorados Achariaceae Mayna grandifolia -0.60 0.22 3.45 0.53 0.00 0.00 0.53 Achatocarpaceae Achatocarpus nigricans -0.68 -1.27 477.08 15.90 0.00 0.85 15.06 Anacardiaceae Astronium graveolens 0.64 0.18 25.21 0.93 0.00 0.00 0.93 Anacardiaceae Spondias radlkoferi -0.02 -1.27 2035.18 38.86 0.00 9.52 29.34 Annonaceae Oxandra sp [v. HC-6491, tag. 13] -0.86 0.75 2041.64 39.03 1.06 34.36 5.73 Apocynaceae Aspidosperma polyneuron -1.21 0.37 2565.56 92.31 0.00 1.77 90.54 Apocynaceae Aspidosperma sp [tag. 2991] 0.94 0.35 0.00 0.00 0.00 0.00 0.00 Apocynaceae Tabernaemontana cymosa -1.19 -1.18 20.21 0.07 0.00 0.00 0.07 Apocynaceae Tabernaemontana sp1 [v. HC-6532, tag. 202] -0.28 -3.49 365.03 7.03 0.02 4.84 2.21 Asteraceae Chromolaena perglabra -1.34 -0.11 305.73 0.94 0.00 12.05 -11.11 Burseraceae Bursera simaruba -0.20 -3.06 2274.64 15.26 0.00 63.36 -48.09 Capparaceae Cynophalla verrucosa -1.01 0.48 2158.41 40.76 0.03 14.89 25.89 Capparaceae Quadrella indica -0.33 -0.81 136.08 0.33 0.00 0.00 0.33 Capparaceae Quadrella odoratissima -0.30 0.44 1002.69 9.76 0.00 5.15 4.61 Euphorbiaceae Hura crepitans 1.33 -0.29 4.45 0.07 0.00 0.00 0.07 Fabaceae Albizia sp2 [v. HC-6494, tag. 81] 0.78 0.29 336.48 20.87 0.00 0.00 20.87 Fabaceae Coursetia ferruginea 1.16 0.16 13037.52 32.53 0.00 6.43 26.10 Fabaceae Inga vera 0.80 -0.20 19.81 5.07 0.00 0.00 5.07 Fabaceae Peltogyne purpurea 0.29 1.15 13.26 0.00 0.00 0.00 0.00 Fabaceae Peltogyne sp [v. HC-6497, tag. 31] 0.05 0.73 2291.65 22.04 0.00 69.46 -47.42 Fabaceae Platymiscium pinnatum 0.78 0.48 1096.60 77.17 0.00 0.00 77.17 Fabaceae Pterocarpus rohrii 0.19 0.13 196.01 1.59 0.23 0.60 1.22 Fabaceae Senegalia sp [v. HC-6506, tag. 44] 0.80 -0.62 1753.04 32.52 0.00 57.33 -24.81 132 Ecology of woody plants in Colombian dry forests Study sites Family Species PC1 PC2 Standing Biomass BGS BGR BM NBC Fabaceae Swartzia simplex -0.06 0.65 2.24 0.00 0.04 0.00 0.04 Fabaceae Zygia sp [tag. 836] 0.67 1.49 95.74 0.00 0.00 0.00 0.00 Lamiaceae Aegiphila sp [v. HC-6531, tag. 221] 1.34 -0.57 14.18 1.33 0.00 0.00 1.33 Lamiaceae Vitex sp [tag. 307] -0.46 0.01 2.52 0.00 0.00 0.00 0.00 Lecythidaceae Gustavia superba -0.98 -0.65 154.51 5.70 0.07 0.73 5.04 Lecythidaceae Lecythis minor 0.05 0.12 0.00 0.00 0.00 0.00 0.00 Malpighiaceae Malpighia glabra -0.97 -0.25 26.00 2.11 0.00 0.00 2.11 Malvaceae Cavanillesia platanifolia 1.23 -1.58 5827.36 1.58 0.00 0.00 1.58 Malvaceae Ceiba pentandra 1.89 -0.77 5318.54 0.00 0.00 0.00 0.00 Malvaceae Guazuma ulmifolia 0.78 -1.07 246.46 10.04 0.00 0.00 10.04 Malvaceae Pachira quinata -0.34 -3.85 66.58 2.03 0.00 1.25 0.78 Meliaceae Trichilia acuminata -1.01 1.09 6437.26 116.53 2.00 122.73 -4.20 Meliaceae Trichilia elegans -0.86 -0.19 186.96 3.71 0.00 0.00 3.71 Moraceae Brosimum alicastrum 0.71 0.61 2335.12 680.01 1.10 7.69 673.42 Moraceae Brosimum sp [v. HC-6558, tag. 1613] 0.48 0.54 7426.03 121.18 0.00 25.31 95.87 Moraceae Sorocea sprucei 0.54 0.53 1333.92 61.54 0.00 2.17 59.37 Myrtaceae Eugenia procera -1.14 0.98 599.88 10.68 0.12 5.88 4.92 Nyctaginaceae Guapira sp [v. HC-6578, tag. 974] -0.01 -0.73 142.22 1.49 0.00 0.00 1.49 Phyllanthaceae Margaritaria nobilis -0.01 -0.07 17.73 0.00 0.00 0.00 0.00 Polygonaceae Coccoloba padiformis -0.05 0.59 3.02 0.18 0.08 0.00 0.27 Polygonaceae Coccoloba sp1 [v. HC-6507, tag. 52] 0.33 0.86 3111.72 167.95 0.00 1.51 166.44 Primulaceae Ardisia foetida -0.26 -0.49 48.89 0.50 0.09 1.31 -0.71 Rubiaceae Alibertia sp [v. HC-6536, tag. 236] -0.55 0.40 15.30 0.00 0.00 0.00 0.00 Rubiaceae Chiococca sp [v. HC-6595, tag. 270] -1.50 0.29 40.93 0.06 0.00 0.00 0.06 Rubiaceae Coutarea hexandra -0.97 -0.92 15.42 2.17 0.00 0.00 2.17 Rubiaceae Morf sp17 [tag. 1001] -0.63 -2.49 35.07 0.00 0.00 0.00 0.00 Rubiaceae Pittoniotis sp [tag. 1190] 0.17 -0.38 131.74 0.00 0.00 0.00 0.00 Rubiaceae Pittoniotis trichantha -0.64 0.09 2279.77 18.46 0.00 25.37 -6.90 Rubiaceae Rudgea sp [v. HC-6599, tag. 1065] -1.01 0.16 55.10 2.42 0.13 0.00 2.55 Rutaceae Amyris pinnata -1.11 0.07 1291.98 50.45 0.04 0.00 50.49 Rutaceae Esenbeckia pentaphylla -0.89 -0.04 5109.60 267.80 0.00 2.96 264.83 Rutaceae Galipea sp [tag. 1236] -0.63 0.51 144.94 9.17 0.03 0.00 9.20 Rutaceae Zanthoxylum sp4 [v. HC-6494, tag. 17] -1.10 0.75 1078.15 32.41 0.00 13.09 19.32 Salicaceae Casearia sylvestris -1.15 -0.39 8.72 0.65 0.00 0.00 0.65 Sapindaceae Allophylus sp [v. HC-6619, tag. 159] -0.76 -0.14 0.00 0.00 0.00 0.00 0.00 Sapindaceae Melicoccus bijugatus 0.71 1.50 29.25 1.29 0.00 0.00 1.29 Sapindaceae Melicoccus oliviformis 1.05 1.06 74.68 4.13 0.00 0.00 4.13 133 Doctoral Thesis – Roy González-M. Study sites Family Species PC1 PC2 Standing Biomass BGS BGR BM NBC Sapotaceae Manilkara sp [tag. 318] 0.05 1.27 211.63 10.99 0.00 0.00 10.99 Sapotaceae Pouteria sp1 [v. HC-6508, tag. 739] -0.41 0.87 5949.59 95.35 0.28 39.00 56.62 Sapotaceae Pouteria sp2 [tag. 955] 0.13 0.19 0.00 0.00 0.00 0.00 0.00 Sapotaceae Pouteria sp3 [tag. 163] -0.40 0.93 79.45 2.24 0.00 0.00 2.24 Sapotaceae Pouteria sp7 [v. HC-6480, tag. 124] -0.53 0.91 351.82 9.93 0.04 14.39 -4.41 Sapotaceae Pradosia colombiana 0.06 0.64 3054.36 53.25 0.00 0.00 53.25 Stemonuraceae Discophora sp [v. HC-6557, tag. 581] -0.85 0.42 118.59 3.66 0.00 0.00 3.66 Ulmaceae Ampelocera macphersonii 0.12 0.61 16791.18 435.47 2.07 82.40 355.14 Zygophyllaceae Bulnesia arborea 0.03 1.09 1394.12 38.86 0.00 0.00 38.86 Tayrona National Park Anacardiaceae Astronium graveolens 0.71 0.72 15716.00 388.46 0.07 0.00 388.53 Anacardiaceae Spondias mombin -0.28 -1.68 5446.50 58.83 0.00 6.18 52.64 Asteraceae Chromolaena perglabra -1.39 -1.06 26.77 0.15 0.00 1.46 -1.31 Boraginaceae Cordia alba 0.20 -1.18 335.60 6.29 11.15 6.16 11.28 Boraginaceae Cordia sp [v. HC-6723, tag. 355] 0.15 -0.55 4.37 0.32 0.11 0.00 0.43 Burseraceae Bursera simaruba 0.65 -1.93 2812.19 82.75 0.00 14.64 68.11 Capparaceae Capparidastrum pachaca -0.28 0.90 3562.54 59.14 2.98 5.38 56.73 Capparaceae Capparidastrum tenuisiliquum -1.21 -0.18 1072.35 12.74 7.69 10.22 10.20 Capparaceae Crateva tapia -0.01 -0.79 130.66 1.32 0.00 0.00 1.32 Capparaceae Cynophalla flexuosa -0.19 0.82 273.64 3.65 0.00 3.70 -0.05 Capparaceae Cynophalla verrucosa -0.98 0.59 2666.03 39.82 4.67 12.16 32.33 Capparaceae Quadrella indica -0.54 0.12 278.74 5.71 0.05 0.00 5.76 Capparaceae Quadrella odoratissima -0.13 0.78 14742.47 81.11 1.45 15.39 67.17 Erythroxylaceae Erythroxylum hondense -1.61 0.49 54.78 0.66 0.00 0.00 0.66 Euphorbiaceae Croton niveus -0.74 0.11 970.89 6.56 1.76 37.91 -29.59 Euphorbiaceae Hura crepitans 1.58 -1.37 2791.77 13.54 0.00 0.00 13.54 Euphorbiaceae Manihot carthaginensis 2.78 -0.43 45.26 2.72 0.00 0.00 2.72 Fabaceae Albizia niopoides 1.06 0.62 809.64 22.77 5.29 2.80 25.26 Fabaceae Caesalpinia punctata 1.65 1.74 4546.64 39.54 0.00 0.00 39.54 Fabaceae Coursetia ferruginea 1.14 -0.21 203.30 11.40 0.71 2.24 9.87 Fabaceae Machaerium capote 1.40 0.48 938.48 170.91 0.00 0.64 170.27 Fabaceae Machaerium sp1 [v. HC-6729, tag. 144] 1.32 0.41 961.96 11.58 0.00 2.64 8.94 Fabaceae Muellera broadwayi 1.61 0.42 2510.94 26.68 0.00 0.00 26.68 Fabaceae Pithecellobium roseum 0.41 1.40 148.99 0.70 0.00 2.84 -2.14 Fabaceae Platymiscium pinnatum 1.28 1.11 1925.33 16.85 0.00 0.45 16.40 Fabaceae Prosopis juliflora 0.58 0.30 1391.18 29.63 0.00 0.00 29.63 Fabaceae Pterocarpus rohrii 0.77 0.24 19481.61 323.73 0.60 145.43 178.90 134 Ecology of woody plants in Colombian dry forests Study sites Family Species PC1 PC2 Standing Biomass BGS BGR BM NBC Fabaceae Senegalia tamarindifolia 0.44 0.18 1261.20 30.78 2.28 34.57 -1.50 Fabaceae Senna atomaria 1.10 0.56 167.96 5.72 0.00 0.00 5.72 Fabaceae Senna sp [tag. 272] 1.18 -0.10 116.60 3.69 0.00 0.00 3.69 Fabaceae Senna sp1 [v. HC-6714, tag. 1179] 0.43 0.39 247.13 3.63 0.00 3.48 0.15 Fabaceae Vachellia farnesiana 0.76 0.99 588.53 18.60 0.46 11.23 7.84 Hernandiaceae Gyrocarpus americanus -0.25 -1.33 894.85 8.84 0.00 0.00 8.84 Indet Morf sp14 [tag. 746] -0.58 0.43 11.76 0.97 0.00 0.00 0.97 Lecythidaceae Eschweilera sp [v. HC-6681, tag. 109] -1.90 0.34 81.97 0.58 0.09 0.00 0.67 Malvaceae Guazuma ulmifolia -0.28 -0.84 273.76 4.78 0.00 2.70 2.08 Malvaceae Pseudobombax septenatum 1.03 -2.03 738.37 77.31 0.05 0.00 77.37 Moraceae Brosimum alicastrum 0.20 0.16 7.89 0.08 0.00 0.61 -0.53 Myrtaceae Eugenia sp1 [v. HC-6699, tag. 731] -0.88 1.39 3.21 0.06 0.00 0.00 0.06 Nyctaginaceae Guapira sp [tag. 335] -0.51 -0.75 67.03 1.22 2.77 0.54 3.44 Nyctaginaceae Guapira uberrima -0.67 -0.55 1418.54 10.24 0.27 36.49 -25.98 Polygonaceae Coccoloba obtusifolia -0.24 -0.03 528.40 13.33 0.61 8.45 5.49 Polygonaceae Triplaris americana -0.03 -0.60 201.87 1.25 0.00 0.00 1.25 Primulaceae Bonellia frutescens -1.18 -0.02 197.15 1.02 0.00 0.54 0.48 Rubiaceae Calycophyllum candidissimum -0.32 0.00 227.01 3.62 0.46 2.00 2.08 Rubiaceae Psychotria sp [v. HC-6751, tag. 1375] -1.26 -1.23 2.57 0.15 0.00 0.00 0.15 Rubiaceae Randia aculeata -1.20 0.43 18.45 0.12 0.00 0.00 0.12 Rubiaceae Simira cordifolia -1.46 -0.50 8.78 0.10 0.00 0.00 0.10 Salicaceae Casearia praecox -1.65 -0.37 141.08 3.21 0.00 0.80 2.41 Sapindaceae Melicoccus bijugatus 1.23 0.84 7216.29 104.82 4.17 0.00 108.99 Sapindaceae Melicoccus oliviformis 0.66 0.96 4161.97 93.63 0.22 0.00 93.85 Sapindaceae Sapindus saponaria 0.77 0.77 109.50 10.52 0.00 0.00 10.52 Sapotaceae Pradosia colombiana -0.21 0.63 8115.70 31.90 0.00 30.86 1.04 Inter Andean region Cardonal Loma Forests Achariaceae Mayna odorata -1.17 0.65 2284.78 50.21 2.84 70.86 -17.81 Anacardiaceae Astronium graveolens 0.98 0.65 21467.34 409.48 0.00 55.33 354.15 Apocynaceae Aspidosperma polyneuron -0.61 0.69 19754.58 413.49 0.15 48.91 364.73 Bignoniaceae Tabebuia rosea 1.13 1.15 3876.59 113.01 0.00 0.00 113.01 Boraginaceae Cordia gerascanthus 0.44 0.54 1314.39 17.22 4.81 22.97 -0.94 Burseraceae Bursera simaruba 1.37 -0.73 4669.39 64.98 0.00 47.15 17.83 Capparaceae Cynophalla polyantha 0.34 1.26 496.55 8.59 0.00 0.00 8.59 Capparaceae Quadrella odoratissima 0.22 1.17 658.24 11.19 0.00 0.00 11.19 Ebenaceae Diospyros sp2 [v. RG-1910, tag. 2731] 0.27 0.70 3.62 0.15 0.00 0.00 0.15 135 Doctoral Thesis – Roy González-M. Study sites Family Species PC1 PC2 Standing Biomass BGS BGR BM NBC Euphorbiaceae Croton schiedeanus -0.36 0.06 159.04 3.04 0.04 2.18 0.90 Euphorbiaceae Croton sp [v. RG-1874, tag. 245] 0.03 1.11 19.60 0.00 0.00 0.00 0.00 Fabaceae Bauhinia petiolata -0.57 1.25 1626.90 48.15 6.01 46.87 7.29 Fabaceae Calliandra magdalenae -0.01 1.39 880.81 11.15 0.00 46.40 -35.25 Fabaceae Machaerium capote 0.81 0.54 9203.82 137.01 1.61 63.81 74.82 Fabaceae Machaerium sp1 [v. RG-1891, tag. 368] 1.19 1.08 711.25 9.26 0.00 7.06 2.21 Fabaceae Machaerium sp6 [v. RG-1850, tag. 2917] 1.02 0.80 3414.98 28.98 0.39 93.39 -64.02 Fabaceae Platymiscium pinnatum 1.34 0.96 3485.97 59.63 0.00 27.60 32.03 Fabaceae Pterocarpus rohrii 0.92 0.69 10523.74 185.76 0.00 91.40 94.35 Fabaceae Senegalia sp1 [v. RG-1895, tag. 996] 1.75 0.21 902.60 15.07 1.28 36.85 -20.50 Fabaceae Swartzia trianae -0.26 0.80 3054.22 49.11 0.02 21.91 27.22 Malpighiaceae Bunchosia sp [v. RG-1894, tag. 981] -1.57 0.58 4.93 0.11 0.00 0.00 0.11 Malpighiaceae Malpighia glabra -0.99 0.34 142.82 1.71 0.05 12.60 -10.84 Malvaceae Pseudobombax septenatum 1.89 -1.11 4245.40 14.96 0.00 0.00 14.96 Meliaceae Trichilia carinata -0.95 0.47 1076.77 31.80 0.59 0.00 32.39 Meliaceae Trichilia elegans -0.49 0.59 3937.68 79.62 0.77 37.35 43.04 Meliaceae Trichilia oligofoliolata -1.38 0.85 12045.54 442.70 4.53 25.55 421.69 Meliaceae Trichilia pallida -0.97 0.22 4405.16 74.92 0.02 25.26 49.69 Myrtaceae Eugenia procera -1.51 1.03 1545.02 46.44 2.40 8.16 40.68 Myrtaceae Morf sp1 [v. RG-1881, tag. 376] -1.22 1.23 1.75 0.00 0.00 0.00 0.00 Nyctaginaceae Neea sp1 [v. RG-1871, tag. 161] -0.12 0.24 208.16 1.81 0.00 11.87 -10.06 Polygonaceae Coccoloba sp1 [v. RG-1843, tag. 2103] -0.09 1.64 3112.52 62.82 0.84 2.86 60.80 Polygonaceae Ruprechtia sp1 [v. RG-1914, tag. 3445] -0.31 -0.53 1.84 0.20 0.00 0.00 0.20 Polygonaceae Triplaris melaenodendron 0.73 0.20 1378.28 11.83 0.37 34.78 -22.58 Rhamnaceae Ziziphus strychnifolia 0.42 0.88 1189.79 11.16 0.00 13.78 -2.62 Rubiaceae Guettarda comata -0.92 0.05 393.97 3.37 0.00 1.98 1.40 Rubiaceae Randia aculeata -1.48 -0.12 3.79 0.00 0.00 0.82 -0.82 Rubiaceae Randia armata -1.03 0.41 630.48 10.75 0.00 34.21 -23.46 Rutaceae Amyris pinnata -0.91 0.64 1032.39 23.31 0.00 21.35 1.96 Rutaceae Esenbeckia alata -1.55 0.92 3.62 0.05 0.00 0.00 0.05 Rutaceae Zanthoxylum sp2 [v. RG-1848, tag. 2890] -0.40 -0.09 781.82 30.46 0.00 6.73 23.73 Salicaceae Casearia corymbosa -1.39 0.68 784.87 6.10 0.00 6.54 -0.44 Salicaceae Casearia praecox -1.79 0.28 1182.97 11.29 0.03 0.29 11.03 Salicaceae Casearia sp1 [v. RG-1897, tag. 1086] -1.81 0.35 39.69 0.18 0.00 0.00 0.18 Salicaceae Casearia sylvestris -1.60 0.45 4714.76 96.70 0.00 74.45 22.25 Sapotaceae Pouteria sp7 [v. RG-1865, tag. 111] -0.27 0.73 1254.55 21.70 0.11 12.97 8.84 Ulmaceae Ampelocera sp1 [v. RG-1870, tag. 166] 0.16 1.23 141.07 8.85 0.00 0.00 8.85 136 Ecology of woody plants in Colombian dry forests Study sites Family Species PC1 PC2 Standing Biomass BGS BGR BM NBC Violaceae Rinorea sp1 [tag. 788] -0.91 1.34 114.25 2.17 0.00 0.00 2.17 Cardonal Plana Forests Achariaceae Mayna odorata -1.25 0.54 2754.22 46.62 0.00 126.23 -79.61 Achatocarpaceae Achatocarpus nigricans -0.70 -0.35 2796.34 66.47 15.62 36.78 45.31 Anacardiaceae Astronium graveolens 1.10 0.48 7433.11 99.65 0.22 43.32 56.54 Anacardiaceae Spondias mombin 2.40 -0.45 111.97 0.61 0.00 0.00 0.61 Annonaceae Oxandra espintana -0.45 0.37 4184.72 66.75 45.21 47.11 64.84 Apocynaceae Aspidosperma polyneuron -0.99 0.57 117.44 1.37 0.00 0.00 1.37 Bignoniaceae Handroanthus chrysanthus 0.73 -0.06 357.53 0.68 0.00 0.00 0.68 Bignoniaceae Tabebuia rosea 0.39 0.06 397.18 15.92 0.00 0.00 15.92 Boraginaceae Cordia gerascanthus 0.18 -0.06 872.97 13.91 0.00 28.09 -14.18 Boraginaceae Cordia sp [v. RG-1938, tag. 248] -0.64 -0.64 60.73 0.74 0.00 5.42 -4.69 Capparaceae Capparidastrum frondosum 0.04 0.52 0.00 0.00 0.00 0.00 0.00 Capparaceae Morisonia americana 0.37 0.69 43.91 0.00 0.00 0.00 0.00 Ebenaceae Diospyros sp2 [v. RG-1957, tag. 472] 0.32 -0.16 130.60 0.89 0.00 0.00 0.89 Fabaceae Bauhinia petiolata -0.94 1.06 1041.69 18.28 0.00 20.00 -1.71 Fabaceae Calliandra magdalenae 0.19 1.14 2016.74 55.83 2.24 24.74 33.34 Fabaceae Machaerium capote 0.99 0.43 7619.72 179.43 11.26 17.83 172.86 Fabaceae Machaerium sp1 [tag. 1525] 1.11 0.98 1.19 0.07 0.00 0.00 0.07 Fabaceae Pterocarpus rohrii 1.61 0.20 1046.32 9.66 0.02 0.00 9.68 Fabaceae Senegalia sp1 [tag. 458] 0.48 -0.66 8141.87 171.32 0.65 122.91 49.07 Fabaceae Swartzia trianae 0.54 0.15 7312.02 170.46 0.00 5.93 164.52 Lamiaceae Aegiphila sp1 [tag. 832] 0.63 -1.01 458.57 8.03 0.00 0.00 8.03 Lauraceae Ocotea veraguensis 0.64 0.57 5356.22 27.54 0.16 37.93 -10.23 Lecythidaceae Gustavia sp [v. RG-1917, tag. 7] -0.89 0.67 803.96 12.26 37.94 26.13 24.08 Malpighiaceae Malpighia glabra -1.46 0.19 57.34 1.68 0.07 1.97 -0.21 Malvaceae Guazuma ulmifolia 0.68 -1.41 47.85 2.86 0.12 0.00 2.98 Meliaceae Trichilia carinata -1.05 0.52 4816.82 87.28 7.54 54.14 40.69 Meliaceae Trichilia elegans -0.89 0.41 515.18 11.54 0.03 6.68 4.90 Meliaceae Trichilia oligofoliolata -1.39 0.98 79.21 3.49 0.00 0.00 3.49 Meliaceae Trichilia pallida -0.80 -0.47 3059.99 32.61 0.05 35.52 -2.85 Moraceae Brosimum alicastrum 0.94 -0.03 30.71 0.00 0.00 0.00 0.00 Myrtaceae Eugenia procera -1.20 1.08 743.38 16.29 0.12 13.44 2.97 Myrtaceae Eugenia sp5 [v. RG-1928, tag. 1901] -0.84 1.36 259.65 0.78 10.27 21.40 -10.35 Nyctaginaceae Neea sp1 [tag. 1414] 0.25 0.46 10.70 0.08 0.00 1.23 -1.15 Polygonaceae Coccoloba acuminata 0.06 0.51 19.50 0.11 0.00 0.00 0.11 Polygonaceae Coccoloba sp1 [tag. 30] -0.94 1.21 3298.38 45.58 11.87 21.13 36.32 137 Doctoral Thesis – Roy González-M. Study sites Family Species PC1 PC2 Standing Biomass BGS BGR BM NBC Polygonaceae Ruprechtia sp1 [v. RG-1941, tag. 47] -0.01 -0.70 1422.93 33.43 0.09 0.00 33.52 Polygonaceae Triplaris melaenodendron 0.64 0.12 4160.65 66.97 11.88 53.36 25.49 Rhamnaceae Ziziphus strychnifolia 1.33 0.64 2835.82 40.88 0.92 4.42 37.38 Rubiaceae Randia armata -0.94 0.47 798.70 37.34 49.60 25.19 61.75 Rubiaceae Simira cordifolia -0.95 0.37 3744.38 69.36 0.85 57.17 13.04 Rutaceae Esenbeckia alata -0.99 1.01 1455.53 9.88 0.00 0.00 9.88 Rutaceae Zanthoxylum rhoifolium -0.41 -0.54 462.96 22.03 0.00 9.98 12.05 Rutaceae Zanthoxylum rigidum -0.77 -0.13 1.13 0.29 0.00 0.00 0.29 Salicaceae Casearia corymbosa -1.57 -0.16 33.32 1.60 0.00 0.00 1.60 Salicaceae Casearia praecox -1.81 0.17 133.88 2.18 0.00 0.00 2.18 Salicaceae Casearia sylvestris -1.03 0.50 1592.36 22.26 0.00 14.56 7.70 Sapotaceae Pouteria sp7 [tag. 10] -0.24 1.02 2867.13 51.62 0.17 4.45 47.33 Sapotaceae Pouteria sp8 [v. RG-1925, tag. 1629] 0.29 0.95 70.93 72.65 0.00 0.00 72.65 Ulmaceae Ampelocera sp1 [v. RG-1918, tag. 6] 0.70 1.49 2061.80 53.54 5.29 5.53 53.31 Cotove Research Station Achatocarpaceae Achatocarpus nigricans -1.26 -1.52 267.39 9.68 0.00 1.72 7.95 Anacardiaceae Astronium graveolens 1.04 -0.34 3618.92 30.89 0.08 51.48 -20.52 Apocynaceae Tabernaemontana grandiflora -1.68 -2.14 18.04 0.00 0.00 3.78 -3.78 Araliaceae Aralia excelsa -0.36 -2.53 0.00 0.00 0.00 0.00 0.00 Burseraceae Bursera simaruba 0.64 -2.18 3768.94 26.65 0.11 28.94 -2.18 Capparaceae Quadrella indica -0.23 -0.21 218.43 10.02 0.19 5.43 4.78 Erythroxylaceae Erythroxylum hondense -1.12 0.28 1.20 0.10 0.00 0.00 0.10 Fabaceae Enterolobium cyclocarpum 0.93 -1.48 6953.70 4.02 0.00 60.81 -56.79 Fabaceae Leucaena leucocephala 1.04 -0.68 2162.75 7.03 0.00 71.77 -64.75 Fabaceae Platymiscium pinnatum 0.28 -0.25 139.58 1.49 0.00 0.00 1.49 Fabaceae Pseudosamanea guachapele 0.95 -0.67 3250.49 16.61 0.00 60.55 -43.94 Indet. Morf sp6 [tag. 461] -1.05 -0.18 3.33 0.05 0.00 0.00 0.05 Malpighiaceae Bunchosia armeniaca 0.10 0.58 388.21 2.67 0.00 7.73 -5.06 Malpighiaceae Malpighia glabra -0.60 -0.22 1208.35 15.56 2.39 3.00 14.94 Malvaceae Ceiba pentandra 2.27 -1.15 1077.99 52.46 0.00 0.85 51.61 Moraceae Brosimum alicastrum 0.71 0.90 70.42 0.58 0.00 0.00 0.58 Moraceae Castilla elastica 0.49 -2.49 0.95 0.00 0.00 0.46 -0.46 Myrtaceae Eugenia venezuelensis -0.08 1.02 2.92 0.10 0.00 0.00 0.10 Phyllanthaceae Phyllanthus botryanthus -0.75 -0.50 568.43 13.02 2.79 17.77 -1.96 Rubiaceae Chomelia spinosa -1.18 -0.81 93.40 0.40 0.00 2.07 -1.67 Rutaceae Amyris pinnata -0.20 0.49 372.87 4.06 0.00 8.64 -4.58 Rutaceae Zanthoxylum fagara -0.04 -0.20 1169.75 23.07 0.40 37.14 -13.68 138 Ecology of woody plants in Colombian dry forests Study sites Family Species PC1 PC2 Standing Biomass BGS BGR BM NBC Rutaceae Zanthoxylum lenticulare 0.20 -0.65 1119.22 31.23 170.26 6.38 195.11 Rutaceae Zanthoxylum schreberi -0.46 -0.73 1466.26 21.07 1.31 9.11 13.27 Salicaceae Casearia corymbosa -2.15 0.28 39.57 0.30 0.00 2.80 -2.51 Salicaceae Casearia praecox -0.32 -0.06 104.51 0.71 0.05 8.82 -8.06 Sapindaceae Melicoccus bijugatus 0.80 0.45 48647.07 2165.66 0.61 16.57 2149.70 Sapindaceae Sapindus saponaria 1.80 -0.17 120.06 2.96 0.00 0.00 2.96 Jabirú Private Natural Reserve Achariaceae Mayna odorata -1.21 -0.58 78.59 9.41 0.00 0.00 9.41 Anacardiaceae Astronium graveolens 0.24 -0.56 5465.49 72.52 0.00 12.98 59.54 Annonaceae Oxandra espintana -1.42 -0.21 8387.17 427.89 2.32 195.58 234.63 Apocynaceae Aspidosperma polyneuron -1.34 -0.15 183.57 7.77 0.00 0.00 7.77 Bignoniaceae Handroanthus chrysanthus 0.09 -0.21 1263.06 29.48 0.00 43.38 -13.90 Boraginaceae Cordia gerascanthus -0.25 -0.67 206.10 9.33 0.00 0.00 9.33 Burseraceae Bursera simaruba 0.22 -2.61 635.96 11.06 0.00 0.00 11.06 Capparaceae Cynophalla flexuosa -0.25 -0.44 45.38 1.05 0.00 0.00 1.05 Capparaceae Cynophalla polyantha -0.64 -0.51 389.28 6.57 0.00 0.00 6.57 Ebenaceae Diospyros sp2 [v. JAC-2202, tag. 1830] 0.11 0.65 399.30 4.89 0.00 0.00 4.89 Fabaceae Machaerium capote -0.09 -0.70 8017.24 114.57 0.00 37.15 77.42 Fabaceae Piptadenia sp [v. JAC-2187, tag. 1196] 0.25 -0.62 1166.38 0.14 0.00 90.05 -89.91 Fabaceae Platymiscium pinnatum 1.06 0.20 219.90 9.82 0.00 0.00 9.82 Fabaceae Swartzia trianae -0.25 0.28 255.91 11.52 0.00 0.00 11.52 Lauraceae Ocotea veraguensis -0.07 -0.21 863.26 9.31 0.00 0.00 9.31 Malvaceae Pseudobombax septenatum -0.01 -2.51 1285.20 15.41 0.00 0.00 15.41 Meliaceae Trichilia carinata -1.06 0.07 3734.54 153.10 0.00 216.59 -63.49 Meliaceae Trichilia oligofoliolata -1.54 0.34 53642.81 2172.34 357.75 265.73 2264.36 Meliaceae Trichilia pallida -1.38 -0.73 3215.02 82.06 0.00 46.27 35.79 Myrtaceae Eugenia procera -1.40 0.45 2261.94 119.08 0.28 121.55 -2.19 Nyctaginaceae Guapira sp [v. JAC-2204, tag. 2498] -0.15 -1.84 78.79 1.91 0.00 7.04 -5.13 Polygonaceae Coccoloba sp1 [tag. 1275] -0.38 0.68 4.65 0.38 0.00 0.00 0.38 Polygonaceae Coccoloba sp2 [v. JAC-2171, tag. 618] -0.71 0.51 5832.28 115.60 0.00 174.90 -59.30 Polygonaceae Ruprechtia sp1 [v. JAC-2189, tag. 1093] -0.41 -1.37 547.98 6.98 0.00 0.00 6.98 Polygonaceae Triplaris melaenodendron 0.64 -0.82 1775.49 51.12 0.00 38.85 12.26 Rubiaceae Randia armata -1.41 -0.23 832.59 23.45 0.00 24.14 -0.69 Rubiaceae Randia dioica -0.33 0.06 7.39 1.10 0.00 0.00 1.10 Rutaceae Amyris pinnata -1.70 -0.67 0.00 0.00 0.00 0.00 0.00 Rutaceae Zanthoxylum rhoifolium -0.27 -2.33 416.47 11.06 0.00 35.83 -24.77 Rutaceae Zanthoxylum rigidum -0.34 -0.44 25.32 0.87 0.00 0.00 0.87 139 Doctoral Thesis – Roy González-M. Study sites Family Species PC1 PC2 Standing Biomass BGS BGR BM NBC Rutaceae Zanthoxylum schreberi -0.48 -0.44 146.79 0.00 0.00 2.74 -2.74 Salicaceae Casearia praecox -1.59 -0.60 1023.44 12.53 0.00 121.45 -108.92 Salicaceae Casearia sp1 [tag. 157] -1.70 -0.58 538.81 19.35 0.00 0.00 19.35 Sapotaceae Pouteria sp7 [v. JAC-2152, tag. 120] -0.97 -0.14 1750.20 42.08 0.00 6.23 35.86 Ulmaceae Ampelocera sp1 [v. JAC-2154, tag. 79] -0.23 0.20 1057.49 32.49 0.00 6.29 26.21 Violaceae Leonia sp1 [v. JAC-2207, tag. 2494] -0.51 -2.31 374.12 1.72 0.00 0.00 1.72 Tambor Private Natural Reserve Achariaceae Mayna odorata -0.68 -0.47 87.78 5.61 0.00 0.38 5.22 Anacardiaceae Anacardium excelsum 1.54 -1.48 66199.73 790.50 0.04 0.19 790.35 Anacardiaceae Astronium graveolens 1.15 -0.44 3939.02 45.08 0.34 0.00 45.42 Anacardiaceae Spondias mombin 2.06 -1.74 3243.11 49.98 0.00 0.00 49.98 Annonaceae Malmea sp [v. JAC-3209, tag. 167] 0.54 0.57 201.93 0.29 0.00 0.00 0.29 Annonaceae Oxandra espintana 0.20 0.63 87.49 2.60 0.00 0.00 2.60 Annonaceae Pseudomalmea sp [v. RLC-15595, tag. 7] 0.14 -0.56 1012.64 23.11 0.20 17.00 6.32 Annonaceae Rollinia mucosa 1.41 -1.20 134.12 13.26 1.08 2.56 11.78 Apocynaceae Aspidosperma sp1 [v. RLC-15602, tag. 30] 0.05 0.03 0.00 0.00 0.00 0.00 0.00 Apocynaceae Tabernaemontana grandiflora -1.33 -1.63 798.04 18.76 2.09 17.88 2.97 Apocynaceae Tabernaemontana markgrafiana -1.58 -1.10 2.09 0.11 0.00 0.00 0.11 Bignoniaceae Jacaranda caucana 0.58 -1.31 1599.37 0.06 0.97 0.00 1.03 Bignoniaceae Tabebuia rosea 0.86 -0.35 3805.51 28.44 0.38 58.03 -29.21 Boraginaceae Cordia alliodora 0.47 -0.05 16.16 11.77 0.00 0.00 11.77 Boraginaceae Cordia bicolor 1.68 -1.83 8.98 1.72 1.96 0.00 3.68 Burseraceae Protium tenuifolium 1.23 -0.41 157.68 10.92 0.00 3.15 7.78 Chrysobalanaceae Licania sp1 [v. RLC-15703, tag. 678] -1.46 -0.34 6.08 0.42 0.00 0.00 0.42 Ebenaceae Diospyros sp1 [v. RLC-15612, tag. 23] -0.43 -1.21 0.00 0.00 0.00 0.00 0.00 Euphorbiaceae Acalypha diversifolia 0.18 0.13 2.56 0.43 0.00 0.00 0.43 Fabaceae Albizia sp2 [v. RLC-15687, tag. 254] 2.07 -0.07 9.85 0.13 0.00 0.00 0.13 Fabaceae Brownea ariza 0.90 0.62 23.06 0.00 0.00 0.00 0.00 Fabaceae Cassia sp [tag. 623] -0.02 -2.26 0.71 0.55 0.00 0.00 0.55 Fabaceae Enterolobium sp1 [v. RLC-15687, tag. 476] 1.01 -1.11 370.61 4.26 0.00 0.00 4.26 Fabaceae Inga sp1 [v. RLC-15590, tag. 73] 0.83 -0.44 693.54 58.36 3.34 12.71 48.99 Fabaceae Inga sp4 [v. RLC-15634, tag. 181] 1.74 -0.55 53.80 3.90 0.00 0.00 3.90 Fabaceae Inga sp6 [v. RLC-15589, tag. 87] 1.64 -1.06 2930.90 59.78 32.08 78.20 13.67 Fabaceae Machaerium capote 1.13 0.10 2032.30 86.52 6.19 0.00 92.70 Fabaceae Platymiscium pinnatum -0.27 -1.63 0.00 0.00 0.00 0.00 0.00 Fabaceae Senegalia sp1 [v. RLC-15669, tag. 510] 1.01 -0.90 250.80 11.90 10.79 0.00 22.69 Fabaceae Styphnolobium sporadicum 0.86 -0.35 56.14 2.44 0.00 0.00 2.44 140 Ecology of woody plants in Colombian dry forests Study sites Family Species PC1 PC2 Standing Biomass BGS BGR BM NBC Fabaceae Swartzia simplex 0.94 1.44 1128.24 0.00 0.00 0.00 0.00 Fabaceae Swartzia sp1 [v. RLC-15604, tag. 31] 1.64 0.67 2486.80 35.44 0.00 0.00 35.44 Lamiaceae Callicarpa acuminata -0.34 -1.34 26.98 5.44 1.24 2.13 4.55 Lauraceae Nectandra sp [v. RLC-15618, tag. 116] 0.66 0.03 242.35 16.87 7.01 6.65 17.23 Lecythidaceae Gustavia hexapetala -1.02 0.20 490.78 14.84 0.00 16.96 -2.12 Lecythidaceae Gustavia superba 0.23 -0.91 110.43 2.34 0.00 0.00 2.34 Malvaceae Apeiba tibourbou 0.19 -2.82 34.91 14.40 0.00 1.35 13.04 Malvaceae Guazuma ulmifolia -0.40 -1.43 348.90 2.06 0.00 0.00 2.06 Malvaceae Hampea thespesioides 0.36 -2.20 65.79 18.52 4.08 4.32 18.27 Malvaceae Herrania laciniifolia 0.08 -1.49 0.00 0.00 0.00 0.00 0.00 Malvaceae Ochroma pyramidale 0.02 -2.94 1441.24 72.10 0.00 42.79 29.31 Malvaceae Pachira quinata 0.83 -0.13 1414.20 3.67 0.19 54.15 -50.30 Malvaceae Pseudobombax septenatum 0.07 -2.83 4.70 0.94 1.06 0.00 2.00 Meliaceae Guarea sp1 [v. RLC-15645, tag. 230] 1.37 0.80 30.82 1.26 0.32 0.00 1.58 Meliaceae Trichilia hirta 0.06 -1.47 576.64 14.65 0.00 0.00 14.65 Moraceae Brosimum alicastrum 0.50 -0.37 681.37 691.79 0.00 0.00 691.79 Moraceae Ficus sp [tag. 24] -0.12 -0.15 0.00 0.00 0.00 0.00 0.00 Moraceae Helianthostylis sprucei 0.48 0.34 2872.28 52.11 0.16 46.12 6.15 Moraceae Maclura tinctoria -0.31 -1.92 7.93 1.38 0.35 0.00 1.74 Moraceae Sorocea sp [v. RLC-15598, tag. 39] 1.47 0.35 372.33 4.19 0.33 6.11 -1.59 Myrtaceae Eugenia sp3 [v. JAC-3196, tag. 479] -0.47 0.35 467.60 17.91 0.00 0.00 17.91 Myrtaceae Eugenia sp4 [v. RLC-15644, tag. 217] 0.54 0.57 729.97 5.53 0.65 0.00 6.17 Nyctaginaceae Neea macrophylla -0.15 -2.37 192.32 7.48 0.00 10.23 -2.75 Piperaceae Piper sp6 [v. RLC-15584, tag. 1] -0.07 -0.79 443.93 28.87 36.04 23.05 41.86 Polygonaceae Coccoloba obovata 0.41 0.02 2.18 0.59 1.23 0.00 1.82 Polygonaceae Ruprechtia sp1 [tag. 768] -0.18 1.33 9.33 1.47 0.00 0.00 1.47 Polygonaceae Triplaris melaenodendron 0.89 -0.32 391.87 40.92 0.00 7.96 32.96 Rubiaceae Alseis blackiana -0.80 -0.69 3140.66 100.95 4.55 0.00 105.50 Rubiaceae Ixora sp [v. RLC-15721, tag. 899] -0.42 -0.49 186.04 5.17 7.94 0.00 13.11 Rubiaceae Ladenbergia sp [v. RLC-15578, tag. 47] -0.56 -1.27 1090.69 2.56 0.00 0.00 2.56 Rubiaceae Randia dioica -0.93 -0.03 1451.78 26.92 1.74 7.69 20.97 Rubiaceae Randia sp [v. RLC-15620, tag. 99] -0.44 -0.70 2.32 0.38 0.00 0.00 0.38 Rubiaceae Simira cordifolia -0.77 -0.24 1122.33 29.12 0.27 0.00 29.39 Rutaceae Zanthoxylum rhoifolium 0.16 -1.40 0.56 0.09 0.00 0.00 0.09 Salicaceae Banara ibaguensis -1.11 0.14 16.33 0.34 0.00 0.00 0.34 Salicaceae Casearia aculeata -0.83 -0.40 6.74 0.18 0.00 0.00 0.18 Salicaceae Casearia praecox -1.02 -0.18 124.59 13.41 21.42 4.99 29.84 141 Doctoral Thesis – Roy González-M. Study sites Family Species PC1 PC2 Standing Biomass BGS BGR BM NBC Sapindaceae Allophylus nitidulus 0.33 0.03 19.94 0.65 0.00 2.22 -1.57 Sapindaceae Cupania cinerea -1.26 -0.10 0.98 0.12 0.00 0.00 0.12 Sapindaceae Dilodendron costaricense 0.68 -0.36 1585.88 6.30 0.04 65.79 -59.44 Sapindaceae Matayba sp1 [v. RLC-15587, tag. 10] 0.76 0.57 2168.33 17.56 0.12 0.00 17.68 Sapotaceae Pouteria sp5 [v. RLC-15728, tag. 770] 0.28 0.50 94.72 17.07 0.00 1.53 15.54 Solanaceae Solanum lepidotum -0.73 -1.29 1.15 0.00 0.00 0.47 -0.47 Ulmaceae Ampelocera sp1 [v. JAC-3203, tag. 865] 1.14 0.88 41.44 3.37 1.06 0.00 4.43 Urticaceae Cecropia peltata 0.20 -2.62 1640.81 48.66 0.00 77.06 -28.40 Urticaceae Myriocarpa stipitata -0.42 -2.66 288.93 7.62 0.00 8.64 -1.02 Urticaceae Urera caracasana -0.62 -3.33 791.45 17.34 16.62 78.32 -44.36 Violaceae Rinorea sp1 [v. JAC-3197, tag. 843] -1.44 0.13 8.14 0.57 0.00 0.00 0.57 Taminango Research Station Bignoniaceae Handroanthus chrysanthus 0.66 0.87 17265.44 1115.92 16.42 210.68 921.66 Burseraceae Bursera tomentosa 0.55 -1.39 65.58 1.15 0.40 3.98 -2.42 Capparaceae Cynophalla flexuosa -0.45 0.68 640.83 14.77 0.00 9.69 5.08 Erythroxylaceae Erythroxylum jaimei -0.95 0.76 16.35 0.06 0.00 0.00 0.06 Euphorbiaceae Jatropha gossypiifolia -0.22 -3.22 4.19 0.65 0.67 0.34 0.98 Fabaceae Caesalpinia cassioides 0.27 0.45 135.86 1.92 2.13 3.21 0.85 Fabaceae Vachellia pennatula 1.65 0.33 153.92 5.65 0.24 6.25 -0.36 Rutaceae Zanthoxylum fagara -0.72 0.73 1562.63 41.00 0.36 13.27 28.09 Verbenaceae Lippia origanoides -0.33 1.09 21.01 0.26 0.00 1.97 -1.71 El Vinculo Regional Park Achatocarpaceae Achatocarpus nigricans -0.41 -0.16 1552.08 64.76 2.33 18.92 48.16 Anacardiaceae Anacardium excelsum 1.44 -1.08 2794.51 13.44 0.00 0.00 13.44 Annonaceae Annona muricata 1.56 -0.84 16.65 0.31 0.00 0.00 0.31 Asteraceae Verbesina sp [v. VLL-224, tag. D67] -0.43 -3.68 6.38 0.00 0.26 1.49 -1.23 Capparaceae Cynophalla amplissima 0.33 0.14 3123.39 92.40 0.00 28.23 64.17 Erythroxylaceae Erythroxylum ulei -0.73 0.29 25.89 0.98 0.42 0.00 1.39 Euphorbiaceae Croton gossypiifolius 1.17 0.03 56.12 0.25 2.03 13.77 -11.49 Euphorbiaceae Euphorbia cotinifolia -0.78 -3.36 86.64 0.48 0.00 7.47 -6.99 Fabaceae Enterolobium sp1 [v. VLL-228, tag. E24] 0.09 -2.19 3442.38 20.16 0.00 0.00 20.16 Fabaceae Gliricidia sepium 0.91 -1.58 714.11 22.47 0.00 0.00 22.47 Fabaceae Machaerium capote 0.74 0.23 4513.18 200.13 0.11 4.08 196.16 Fabaceae Pithecellobium dulce 0.40 -1.97 68.54 2.34 0.00 0.00 2.34 Fabaceae Pithecellobium lanceolatum 1.11 -0.24 4259.99 108.38 0.43 62.22 46.59 Fabaceae Pseudosamanea guachapele 1.58 -0.10 28.99 3.20 0.00 0.00 3.20 Fabaceae Senna spectabilis 1.81 -1.22 37.33 0.20 0.00 0.00 0.20 142 Ecology of woody plants in Colombian dry forests Study sites Family Species PC1 PC2 Standing Biomass BGS BGR BM NBC Fabaceae Vachellia farnesiana -0.13 1.63 15.41 0.66 0.00 0.00 0.66 Lauraceae Ocotea veraguensis 0.36 0.21 7704.26 359.17 2.01 3.64 357.54 Malpighiaceae Bunchosia pseudonitida -0.93 0.68 260.20 14.04 0.82 2.90 11.96 Malpighiaceae Malpighia glabra -0.60 -0.43 105.17 4.94 0.00 0.00 4.94 Malvaceae Ceiba pentandra 0.21 -1.54 199.03 11.11 0.00 0.00 11.11 Malvaceae Guazuma ulmifolia 0.19 -1.50 2967.65 40.96 0.00 37.52 3.44 Meliaceae Trichilia pallida -0.81 -0.71 27.81 1.38 0.09 0.00 1.47 Moraceae Brosimum alicastrum 0.88 0.19 2154.56 134.82 3.70 1.31 137.21 Moraceae Ficus zarzalensis -0.21 -0.67 7.75 0.72 0.00 0.00 0.72 Moraceae Sorocea trophoides 0.52 0.11 1136.64 35.97 3.46 7.50 31.93 Myrtaceae Eugenia monticola -0.25 0.84 1100.86 53.69 6.10 51.80 7.99 Myrtaceae Eugenia procera -0.79 0.86 10038.93 586.20 47.36 32.98 600.58 Myrtaceae Psidium guineense -0.77 0.34 1.09 0.00 0.00 0.00 0.00 Nyctaginaceae Guapira sp1 [v. VLL-187, tag. O129] -0.26 -0.43 2704.48 100.50 5.00 29.35 76.15 Piperaceae Piper amalago -0.83 -0.73 2.47 0.05 0.00 0.00 0.05 Rubiaceae Chiococca alba -0.16 -0.53 422.31 9.17 1.11 0.58 9.69 Rubiaceae Coffea arabica -0.87 0.31 2.94 0.03 0.10 0.00 0.13 Rubiaceae Genipa americana 0.12 -0.19 346.39 30.92 0.11 7.12 23.91 Rutaceae Amyris pinnata -0.61 0.12 2143.52 19.79 0.77 100.86 -80.31 Rutaceae Zanthoxylum fagara -0.29 0.57 6.27 0.48 0.00 0.00 0.48 Rutaceae Zanthoxylum rhoifolium -0.14 -1.07 182.77 5.06 0.00 0.00 5.06 Rutaceae Zanthoxylum schreberi -0.61 -0.37 2987.50 130.46 7.88 20.89 117.45 Rutaceae Zanthoxylum verrucosum -0.26 -1.18 1199.99 58.49 1.85 0.00 60.34 Salicaceae Casearia aculeata -1.37 0.01 297.68 15.83 0.07 3.08 12.81 Salicaceae Xylosma intermedia -1.43 -0.19 67.91 4.29 0.00 1.68 2.61 Sapindaceae Cupania sp1 [v. VLL-230, tag. E72] 0.27 0.00 3107.66 56.90 0.00 86.01 -29.11 Sapindaceae Sapindus saponaria 0.98 -0.28 1481.37 6.03 0.00 33.45 -27.43 Thymelaeaceae Daphnopsis sp [v. VLL-243, tag. L150] -0.18 -0.71 196.68 7.66 0.00 0.00 7.66 Urticaceae Urera simplex -0.38 -3.44 21.71 0.33 0.23 2.44 -1.88 Verbenaceae Citharexylum kunthianum 0.56 -0.11 373.58 5.70 0.29 26.38 -20.39 Dry Savannas Tuparro National Park Achariaceae Lindackeria paludosa -0.51 -0.14 74.64 10.24 0.00 0.00 10.24 Achariaceae Mayna odorata -1.27 -0.79 1.68 0.08 0.03 0.38 -0.27 Anacardiaceae Astronium graveolens 0.95 -0.38 130.31 8.33 0.00 0.19 8.14 Anacardiaceae Spondias mombin 0.68 -0.83 12.33 0.54 0.00 0.00 0.54 Annonaceae Duguetia odorata -0.26 0.29 115.85 6.99 0.00 0.00 6.99 143 Doctoral Thesis – Roy González-M. Study sites Family Species PC1 PC2 Standing Biomass BGS BGR BM NBC Annonaceae Guatteria metensis 1.56 0.48 20.99 0.37 0.00 1.36 -0.99 Apocynaceae Himatanthus articulatus -0.05 -1.20 1867.66 49.40 1.44 24.62 26.22 Bignoniaceae Handroanthus barbatus 1.56 1.00 693.35 5.75 0.00 26.94 -21.19 Bixaceae Cochlospermum orinocense 1.75 -2.31 262.20 6.44 0.00 0.00 6.44 Burseraceae Bursera simaruba 0.74 -1.60 382.90 7.79 0.00 1.53 6.25 Burseraceae Protium guianense 0.51 0.28 10057.62 271.40 0.26 0.00 271.66 Capparaceae Capparidastrum sola -0.99 0.25 10.10 0.19 0.00 0.00 0.19 Chrysobalanaceae Hirtella racemosa 0.92 1.11 68.18 2.17 0.00 0.00 2.17 Chrysobalanaceae Licania apetala 0.74 0.94 531.98 16.13 0.00 0.00 16.13 Chrysobalanaceae Licania micrantha 1.32 1.20 7600.18 192.13 0.00 0.54 191.59 Chrysobalanaceae Licania parvifructa 1.62 1.22 399.74 4.98 0.00 0.00 4.98 Chrysobalanaceae Licania sp [v. RG-2387, tag. 116] 2.58 0.74 154.17 4.45 0.00 0.00 4.45 Chrysobalanaceae Licania sp2 [v. RG-2367, tag. 35] 0.80 0.82 712.47 12.98 0.00 0.00 12.98 Clusiaceae Clusia umbellata 0.72 1.06 31.49 0.81 0.00 0.00 0.81 Combretaceae Terminalia amazonia 1.34 0.19 594.60 30.11 0.00 0.00 30.11 Connaraceae Connarus ruber 0.48 0.46 31.85 1.14 0.00 0.00 1.14 Erythroxylaceae Erythroxylum macrophyllum -0.06 0.87 415.09 15.66 0.00 0.00 15.66 Euphorbiaceae Mabea trianae 0.20 -0.22 33.16 0.93 0.00 0.00 0.93 Euphorbiaceae Sapium glandulosum 1.51 -1.53 2290.53 55.65 0.00 22.72 32.93 Fabaceae Clathrotropis macrocarpa 1.27 -0.05 301.34 29.14 0.00 2.41 26.72 Fabaceae Enterolobium schomburgkii 1.53 0.06 765.43 8.70 0.00 0.58 8.12 Fabaceae Inga gracilifolia 1.48 1.26 3107.58 171.98 0.05 43.69 128.33 Fabaceae Inga laurina 1.00 1.20 235.64 0.96 0.00 0.00 0.96 Fabaceae Inga sp [v. RG-2433, tag. 692] 1.03 1.21 1800.61 113.10 0.00 51.81 61.29 Fabaceae Machaerium biovulatum -0.01 -2.58 114.22 3.11 0.00 0.00 3.11 Fabaceae Pterocarpus sp4 [v. RG-2420, tag. 389] 1.12 0.02 695.79 10.18 0.00 0.00 10.18 Fabaceae Tachigali guianensis 1.88 0.18 442.68 86.53 0.00 0.00 86.53 Indet Morf sp4 [v. RG-2430a, tag. 107] 0.74 -1.50 6063.79 132.92 0.00 0.00 132.92 Lamiaceae Vitex orinocensis -0.06 0.62 322.24 3.97 0.00 0.00 3.97 Lauraceae Ocotea schomburgkiana 0.70 0.53 216.84 12.51 0.38 2.65 10.25 Lecythidaceae Eschweilera tenuifolia 1.17 1.19 8005.90 284.92 0.00 49.02 235.90 Lecythidaceae Gustavia augusta -0.31 -0.67 2370.81 43.24 0.29 10.54 33.00 Lecythidaceae Lecythis chartacea 0.87 0.06 468.58 10.39 0.61 0.00 11.00 Malvaceae Apeiba tibourbou 0.90 -0.31 2067.01 28.11 0.00 0.00 28.11 Malvaceae Pachira nukakica 2.21 -2.43 4015.82 119.42 0.00 0.00 119.42 Melastomataceae Graffenrieda rotundifolia 0.88 -1.42 47.52 3.40 0.00 1.50 1.91 Melastomataceae Miconia splendens -0.86 -0.22 10.64 0.63 1.48 0.00 2.12 144 Ecology of woody plants in Colombian dry forests Study sites Family Species PC1 PC2 Standing Biomass BGS BGR BM NBC Meliaceae Guarea glabra -0.36 0.05 578.66 23.83 0.00 2.37 21.46 Moraceae Brosimum guianense 0.79 0.28 504.68 9.00 0.00 0.00 9.00 Moraceae Clarisia racemosa 1.88 0.33 5.69 0.81 0.00 0.00 0.81 Moraceae Ficus americana 1.97 -0.94 3569.14 84.94 0.00 4.40 80.54 Moraceae Ficus sp [v. RG-2396, tag. 186] 1.67 -1.15 1.22 0.37 0.00 0.00 0.37 Moraceae Ficus sp1 [tag. 495] 2.16 -0.88 725.85 0.00 0.00 0.00 0.00 Moraceae Ficus trigona 2.30 0.81 734.31 0.00 0.00 0.00 0.00 Moraceae Pseudolmedia sp1 [v. RG-2361, tag. 189] 0.93 0.31 399.61 9.22 0.00 1.81 7.41 Moraceae Sorocea muriculata 0.75 0.20 6.18 0.21 0.00 0.00 0.21 Myrtaceae Calyptranthes multiflora 0.83 -0.57 4.19 0.00 0.00 1.05 -1.05 Myrtaceae Eugenia florida 0.84 0.67 172.88 1.75 0.00 0.00 1.75 Myrtaceae Morf sp2 [v. RG-2372, tag. 47] -1.58 0.05 227.82 7.49 0.00 0.00 7.49 Myrtaceae Morf sp3 [v. RG-2458, tag. ] -0.63 0.75 320.37 9.47 0.00 0.00 9.47 Myrtaceae Myrcia sp1 [v. RG-2378, tag. 69] -0.52 0.66 1306.41 59.27 0.29 0.37 59.19 Myrtaceae Myrcia sp2 [v. RG-2416, tag. 341] -0.03 0.82 2.71 0.40 0.00 0.31 0.09 Nyctaginaceae Neea ignicola -0.96 0.28 5.27 0.19 0.00 0.00 0.19 Ochnaceae Ouratea sp [v. RG-2380, tag. 83] -0.01 0.91 67.03 5.14 0.10 0.00 5.23 Olacaceae Heisteria acuminata 1.30 1.15 1107.08 32.93 0.00 21.44 11.49 Phyllanthaceae Amanoa guianensis 1.63 1.67 408.97 13.12 0.00 0.00 13.12 Rubiaceae Amaioua corymbosa -0.36 -0.15 109.98 2.96 0.00 2.56 0.41 Rubiaceae Cordiera myrciifolia -0.45 0.85 11.70 0.57 0.09 0.00 0.66 Rubiaceae Coussarea paniculata -1.04 -0.74 8.54 0.33 0.00 0.00 0.33 Rubiaceae Palicourea rigida -1.14 -1.02 75.98 5.01 0.00 0.00 5.01 Rubiaceae Rudgea crassiloba -1.50 -0.53 123.27 9.28 1.24 0.00 10.52 Rubiaceae Simira rubescens -0.67 0.39 229.34 3.17 0.09 0.00 3.26 Sapindaceae Matayba sp [v. RG-2382, tag. 117] 0.26 0.99 2352.02 72.45 0.23 6.33 66.35 Sapotaceae Elaeoluma sp [v. RG-2392, tag. 163] 0.29 0.51 82.88 6.64 0.00 0.00 6.64 Sapotaceae Pouteria plicata -0.23 0.38 1095.64 22.98 0.00 2.29 20.68 Sapotaceae Pouteria sp4 [v. RG-2406, tag. 234] -0.31 0.20 1275.92 59.13 0.08 0.00 59.21 Siparunaceae Siparuna guianensis -0.17 -0.11 185.49 14.78 0.04 6.55 8.27 Urticaceae Cecropia peltata 2.63 -1.40 23.70 4.51 0.00 1.24 3.27 Verbenaceae Petrea sp [v. RG-2371, tag. 44] 0.13 -0.76 482.12 16.81 0.29 0.00 17.10 Violaceae Rinorea pubiflora -1.31 0.01 35.50 0.20 0.06 5.88 -5.63 Vochysiaceae Vochysia vismiifolia 0.19 0.18 60.27 3.77 0.00 0.42 3.35 145 Doctoral Thesis – Roy González-M. hs he hs he hs he 99 it 99 it it 99 99 9 99 50 50 5200 20 5200 50 50 20 20 50 50 99 99 BGRsurv BGRrecr BGRsurv BMR BGRrecr BMR −3 −2 −1 0 1 2 3 −3 −2 −1 0 1 2 3 −3 −2 −1 0 1 2 3 PC1 (36.75%) PC1 (36.75%) PC1 (36.75%) null mean= 0.4 null mean= 0.22 null mean= 0.43 βO=0.67 βO=0.63 βO=0.74 P=0.027 P<0.001 P=0.03 ● ● ● 0.08 0.54 1.00 0.07 0.35 0.63 0.12 0.56 1.00 FDiss20% FDiss20% FDiss20% null mean= 0.23 null mean= 0.11 null mean= 0.25 βO=0.76 βO=0.43 βO=0.72 P<0.001 P<0.001 P<0.001 ● ● ● 0.09 0.42 0.76 0.05 0.24 0.43 0.09 0.41 0.72 FDiss50% FDiss50% FDiss50% null mean= 0.15 null mean= 0.08 null mean= 0.17 βO=0.45 βO=0.22 βO=0.4 P<0.001 P<0.001 P<0.001 ● ● ● 0.07 0.26 0.45 0.04 0.13 0.22 0.09 0.25 0.40 FDiss99% FDiss99% FDiss99% Figure S3. Null models for functional dissimilarity (FDiss) between biomass growth and mortality TPD’s at thresholds 20%, 50%, and 99% of probability. Significant bO (P<0.001) indicated that dissimilarity between paired TPD demographic dimensions was more significant than expected by chance (999 randomizations). Hydraulic safety (hs); hydraulic efficiency (he); investments in tissues (it). 146 Number of randomizations Number of randomizations Number of randomizations PC2 (24.57%) 10 30 50 70 10 30 50 70 10 30 50 70 −4 −3 −2 −1 0 1 2 20 99 Number of randomizations Number of randomizations Number of randomizations PC2 (24.57%) 10 30 50 70 10 30 50 70 10 30 50 70 −4 −3 −2 −1 0 1 2 20 Number of randomizations Number of randomizations Number of randomizations PC2 (24.57%) 10 30 50 70 10 30 50 70 10 30 50 70 −4 −3 −2 −1 0 1 2 99 50 50 Ecology of woody plants in Colombian dry forests P<0.001 BGRsurv <0.001 BGRBGR P surv BMR P<0.001 BGRrecr recr BMR 0.07 0.67 1.27 0.25 0.73 1.20 0.07 0.67 1.27 FRic20% FRic20% FRic20% BGR P<0.001 survBGR P<0.001 BGRsurv P<0.001 BGRrecr recr BMR BMR 0.23 2.24 4.24 1.60 2.91 4.21 0.23 2.24 4.24 FRic50% FRic50% FRic50% <0.001 BGRsurv <0.001 BGRP P survBGR BMR P<0.001 BGRrecr recr BMR 11.51 19.61 27.71 18.18 24.18 30.18 11.51 20.84 30.18 FRic99% FRic99% FRic99% Figure S4. Functional richness (FRic) between biomass growth and mortality TPD’s at thresholds 20%, 50%, and 99% of probability. Significant differences between paired frequency distributions indicated different FRic of contrasted TPD’s demographic dimensions (P<0.001, 999 randomizations). Biomass growth of survivors (BGS), biomass growth of recruits (BGR), and biomass mortality (BM). 147 Number of randomizations Number of randomizations Number of randomizations 10 110 210 310 10 110 210 310 10 110 210 310 Number of randomizations Number of randomizations Number of randomizations 10 110 210 310 10 110 210 310 10 110 210 310 Number of randomizations Number of randomizations Number of randomizations 10 110 210 310 10 110 210 310 10 110 210 310 Doctoral Thesis – Roy González-M. Chapter 6 El Bosque Seco Tropical en Colombia Reportes de estado y tendencias de la biodiversidad Roy González-M. 148 Ecology of woody plants in Colombian dry forests El Bosque Seco Tropical en Colombia: Distribución y estado de conservación Camila Pizano, Roy González-M., René López, Rubén Jurado, Hermes Cuadros, Alejandro Castaño-Naranjo, Alicia Rojas, Karen Pérez, Hernando Vergara-Varela, Álvaro Idárraga, Paola Isaacs y Hernando García Publicado en Reporte de Estado y Tendencias de la Biodiversidad Continental de Colombia (2016). Instituto de Investigación de Recursos Biológicos Alexander von Humboldt Síntesis El bosque seco se encuentra en un estado crítico de fragmentación y degradación en Colombia. La mayoría de sus áreas están expuestas a presiones antropogénicas como la ganadería, la infraestructura humana y la agricultura. Reporte El Bosque Seco Tropical (BST) se encuentra en tierras bajas (0-1000 msnm) y se caracteriza por presentar una fuerte estacionalidad de lluvias con al menos tres meses de sequía (<100 mm de precipitación). Este ecosistema sostiene una diversidad única de plantas, animales y microorganismos, cuyas especies se han adaptado a condiciones extremas. El BST contiene aproximadamente 2.600 especies de plantas (Pizano et al. 2014b), al menos 230 de aves (Gómez & Robinson 2014) y 60 de mamíferos (Díaz-Pulido et al. 2014), con 83, 33 y 3 especies exclusivas, respectivamente. Adicionalmente, el BST presta servicios fundamentales, tales como la regulación hídrica, la retención de suelos y la captura de carbono (Maass et al. 2005; Wall et al. 2011). La distribución del BST en suelos relativamente fértiles y en condiciones climáticas específicas ha convertido sus áreas en escenarios históricos de asentamiento humano. En consecuencia, es considerado como uno de los ecosistemas más amenazados del Neotrópico (Janzen 1988b); tanto así, que fue declarado como estratégico para la conservación de la biodiversidad por el Ministerio del Medio Ambiente y Desarrollo Sostenible. Hasta ahora, la imposibilidad de contar con un insumo cartográfico detallado había impedido la gestión integral del BST. Por tal razón, fue necesario cuantificar su distribución y determinar cuáles eran las presiones antropogénicas que más lo afectaban (Figura 1). La situación actual refleja una severa fragmentación, que se traduce en un número exiguo de remanentes boscosos que podrían limitar la provisión de servicios ecosistémicos. Teniendo en cuenta que el BST constituye un porcentaje muy pobre de las áreas del Sistema Nacional de Áreas Protegidas (García et al. 2014) (SINAP) (6.4%), y que las 9.000.000 ha que cubría originalmente, solo queda el 8%, es imperante establecer estrategias integrales para su gestión (García et al. 2014). Estas deben considerar zonas prioritarias para la conservación, la restauración ecológica mediante enriquecimiento de áreas degradadas (rastrojos y bosques secundarios) y la conectividad de fragmentos estratégicos en territorios productivos (Urbina-Cardona et al. 2014; Vargas & Ramírez 2014). De tal forma, es necesario adelantar acciones inmediatas: (a) orientar una agenda de investigación y monitoreo para enfocar mejor los esfuerzos de restauración; (b) revisar aquellos insumos cartográficos oficiales que registren remanentes menores a 25 ha; (c) lograr una mayor representatividad en el SINAP con base en las áreas de distribución original y no en los remanentes actuales; (d) incluir áreas de BST en figuras regionales de protección y en instrumentos de ordenamiento territorial; y (e) articular los esfuerzos de la red de reservas de la sociedad civil por medio de estrategias complementarias (Miles et al. 2006; Chazdon et al. 2009). 149 Doctoral Thesis – Roy González-M. Para poner en práctica las recomendaciones mencionadas, se debe garantizar un esfuerzo colectivo que involucre al Gobierno, a las entidades ambientales, a la Academia y al sector privado. Esto permitirá estudiar este ecosistema en mayor detalle y orientar las decisiones necesarias para conservar lo que queda del mismo. Figura 1. Distribución actual y estado de conservación del bosque seco tropical en Colombia. Las barras de color indican el área total de cobertura para los sitios de muestreo (653 remanentes de bosque seco) valorados en tres estados sucesionales: (i) rastrojo (coberturas en estadio de sucesión temprana, color fucsia), (ii) bosques secundarios (coberturas en estadios de sucesión intermedia, color magenta claro) y (iii) bosques maduros (coberturas en estadios de sucesión tardía, color magenta oscuro). La cobertura evaluada sin presencia de vegetación se representa en color gris. Los iconos color naranja indican las presiones antropogénicas identificadas en campo para los sitios de muestreo. 150 Ecology of woody plants in Colombian dry forests Monitoreo de la vegetación en los bosques secos de Colombia: Una herramienta para el análisis y la gestión integral de este ecosistema a escala de país Roy González-M., Camila Pizano, René López, Gina Rodríguez, Álvaro Idárraga, Álvaro Duque, Alba Marina Torres, Alejandro Castaño, Karen Pérez, Rubén Jurado, Beatriz Salgado-Negreta, Julián Aguirre, Juan Phillips, Adriana Barbosa, José Aguilar, Jhon Nieto, Rebeca Franke, Robinson Galindo, Augusto Repizo, Natalia Norden, Hernando García Publicado en Reporte de Estado y Tendencias de la Biodiversidad Continental de Colombia (2017). Instituto de Investigación de Recursos Biológicos Alexander von Humboldt Síntesis El monitoreo permanente en ecosistemas con prioridad de conservación, como el bosque seco, es fundamental para comprender las dinámicas ecológicas y proponer medidas de manejo para su gestión integral. Reporte En el Neotrópico los bosques secos son considerados ecosistemas con alta prioridad para la conservación1, en ellos se encuentran especies exclusivas de estos ecosistemas, que resisten altas temperaturas y fuertes limitaciones de agua durante gran parte del año (Pizano & García 2014; DRYFLOR et al. 2016). Sin embargo, las áreas donde se distribuye este ecosistema han albergado tradicionalmente grandes asentamientos humanos, lo que le confiere una larga historia de transformación y perdida de su biodiversidad (Miles et al. 2006; DRYFLOR et al. 2016). Alarmados por el grado de amenaza que sufren estos bosques en Colombia (Pizano et al. 2016) y la falta de conocimiento sobre su dinámica y funcionamiento (Sánchez-Azofeifa et al. 2005a; Parrado-Rosselli et al. 2016), en 2013 investigadores regionales iniciaron, una estrategia nacional para el monitoreo de la vegetación del bosque seco (Red BST- Col), cuyo objeto es generar datos científicos relevantes para la gestión integral de este ecosistema de cara a los motores de cambio que enfrenta y los escenarios socioecológicos complejos que presenta (García et al. 2014). Estos esfuerzos de monitoreo aportan información de alta calidad que debe ser la base para la toma de decisiones en términos de su conservación. Se considera, entonces, que el monitoreo permanente de la vegetación se traducirá́ en un proceso sistemático de toma y análisis de datos, que no solo explorará las tendencias de cambio de los atributos propios de las especies y comunidades vegetales en el tiempo, sino que también permitirá́ verificar como los diferentes esquemas de conservación que Colombia tiene para este ecosistema aportan a la gestión integral de su biodiversidad. Hasta el momento, a partir del análisis de la información registrada para la primera toma de datos, 623 especies de plantas (33.559 individuos) entre árboles, arbustos, palmas, lianas y cactus, están siendo monitoreadas en todas las parcelas (62±29 especies ha-1) (Figura 1). Al sobreponer las parcelas con el Sistema Nacional de Áreas Protegidas (SINAP), se encontró́ que tanto las áreas con medidas de protección estricta como las áreas con iniciativas de conservación privada resguardan un mayor número de especies respecto a los bosques sin figuras de manejo. Para los Parques Nacionales Naturales y Parques Regionales se registra ~72 especies ha-1), para las Reservas Privadas de la Sociedad Civil ~74 especies ha-1 y en predios privados ~51 especies ha-1 . Sin embargo, independientemente de la figura de manejo, llama la atención la alta exclusividad y unicidad 151 Doctoral Thesis – Roy González-M. florística de cada sitio de monitoreo, sumado a la presencia de especies endémicas para la mayoría de las regiones. Lo anterior resalta la importancia del SINAP para la gestión integral de la biodiversidad en los bosques secos y la necesidad de proponer alternativas para la conservación de las plantas en las áreas privadas que no cuentan hoy en día con alguna figura de manejo y sobre la base del acoplamiento con los escenarios productivos que subyacen a cada sitio. Aunque esta iniciativa está en su fase preliminar, en el futuro, y gracias al monitoreo permanente, se podrán puntualizar necesidades de conservación derivadas de los análisis sobre la dinámica, el funcionamiento y la capacidad de respuesta de las plantas ante diferentes motores de transformación. Figura 1. Sistema de monitoreo permanente de la vegetación en los bosques secos de Colombia. Los diagramas en anillo para cada parcela permanente de 1 ha indican el porcentaje de individuos correspondientes a especies de árboles, arbolitos, arbustos, lianas, cactus palmas y hierbas. 152 Ecology of woody plants in Colombian dry forests Diversidad funcional en los bosques de Colombia Jhon Nieto, Roy González-M., Ana Aldana, Esteban Álvarez, Andrés Avella, Mary Lee Berdugo, Laura Cano, Nicolás Castaño, Carolina Castellanos, Álvaro Duque, Fernando Fernández, Claudia Garnica, Diego González, René López, Luis López, Johanna Martínez, Sandra Medina, Natalia Norden, Luisa Pinzón, Juan Posada, Esperanza Pulido, Sebastián Saldarriaga, Pablo Stevenson, John Sánchez, Selene Torres, Maribel Vásquez-Valderrama y Beatriz Salgado- Negret Publicado en Reporte de Estado y Tendencias de la Biodiversidad Continental de Colombia (2017). Instituto de Investigación de Recursos Biológicos Alexander von Humboldt Síntesis Los rasgos funcionales de las plantas leñosas son claves para entender la vulnerabilidad de los bosques frente al cambio climático, su capacidad para ofrecer servicios ecosistémicos y para garantizar su adecuado manejo y conservación. Sin embargo, existen grandes vacíos de información para todos los ecosistemas forestales del país. Reporte Los bosques de Colombia abarcan cerca del 53 % del territorio nacional (FAO 2002) y ofrecen servicios ecosistémicos tan importantes como la regulación del clima o del ciclo hidrológico, de los cuales depende el bienestar de la población humana. La oferta de estos servicios está relacionada con los procesos de los ecosistemas, los cuales están influenciados por las características de las especies arbóreas que ahí habitan. Es decir, la oferta de servicios ecosistémicos está determinada por la diversidad funcional, que hace referencia a la variedad de formas y estrategias que tienen las plantas para usar los recursos y transformar con su actividad el ambiente (Salgado Negret 2015). Los rasgos funcionales de las plantas pueden agruparse de acuerdo a su función en: (1). Rasgos de las hojas, relacionados con la captura de carbono y relaciones hídricas de las plantas; (2). Rasgos del tallo, relacionados con la función hidráulica y el sostenimiento mecanico; (3). Rasgos de raíces, importantes para el transporte de agua y nutrientes; 4. Rasgos reproductivos, asociados al establecimiento y dispersión de los individuos (Figura 1). Aunque todavía no existen datos consolidados ni un análisis a escala regional sobre los rasgos funcionales de las plantas leñosas en Colombia, los estudios en diversidad funcional en los ecosistemas forestales del país se han incrementado en los últimos años. Esta ficha evidencia el creciente interés por incorporar esta dimensión de la diversidad en los estudios de ecología en los bosques del país. Este análisis se elaboró a partir de la información colectada por cerca de 60 investigadores en 2265 especies de árboles distribuidas en los diferentes bosques del país. Los rasgos foliares fueron los mejor representados en todos los bosques estudiados y son importantes por su influencia en la productividad primaria, la descomposición de la hojarasca y el ciclaje de nutrientes (Pérez-Harguindeguy et al. 2013). Llama la atención la poca información que se encuentra sobre los rasgos radiculares en los todos los ecosistemas forestales del país. 153 Doctoral Thesis – Roy González-M. Figura 1. Diversidad de rasgos funcionales de las plantas leñosas en los bosques de Colombia y número de especies medidas por cada rasgo funcional. Rasgos foliares: Los rasgos foliares hacen referencia a los caracteres fisiológicos o morfológicos de las hojas de las plantas. Son probablemente los más sensibles a la variación ambiental e influencian procesos de los ecosistemas como la productividad primaria, la descomposición de hojarasca y el ciclaje de nutrientes. Rasgos reproductivos: Los rasgos reproductivos pueden ser tanto sexuales como vegetativos y proveen información sobre las estrategias de regeneración y dispersión, así ́ como la capacidad de los individuos de colonizar diferentes ambientes. Rasgos hidráulicos: Son los rasgos funcionales del tronco se han estudiado principalmente desde su relación con la conductividad hidráulica de la planta. Rasgos vegetativos: Los rasgos vegetativos están relacionados con el potencial de establecimiento de las especies en nuevos ambientes y determinan la posición de la planta en el gradiente vertical, así ́como su vigor competitivo. Hacen referencia a las características propias de la planta, como su altura máxima, forma de crecimiento, entre otros. El bosque seco es el ecosistema que tiene el mayor número de especies con información funcional (Figura 2). Mientras que los rasgos de tallo y vegetativos fueron muestreados principalmente en bosques andinos, los reproductivos en los bosques húmedos del Pacífico. Es importante destacar que la densidad de madera fue el rasgo mejor representado en todos los bosques debido principalmente a la reciente necesidad de estimar el carbono en muchos proyectos nacionales. Los rasgos foliares fueron los mejor representados en todos los bosques estudiados. Sin embargo, los rasgos vegetativos fueron muestreados principalmente en 154 Ecology of woody plants in Colombian dry forests los bosque andino y bosque seco. Los ecosistemas con mayor número de rasgos reproductivos medidos fueron los bosques húmedos del Pacífico y bosque seco. Figura 2. Síntesis de información sobre rasgos funcionales en los principales ecosistemas de Colombia e intensidad de muestreo. La diversidad funcional para la gestión de la biodiversidad Aunque el enfoque de ecología funcional ha sido adoptado por muchas instituciones en Colombia, aún hay grupos de rasgos clave y ecosistemas con poca información en el país. El reto actual no solo consiste en aumentar el número de especies y ecosistemas con información de rasgos funcionales sino en enlazar estos conocimientos a preguntas de investigación y gestión a diferentes escalas biológicas como la identificación de áreas prioritarias para la conservación, la restauración de ecosistemas enfocada en la recuperación de los procesos de los ecosistemas, el manejo de las invasiones biológicas, adaptación al cambio climático, entre otras. Esta información debe estar a disposición de la comunidad científica, traducida e integrada en r ecomendaciones que apunten a disminuir la pérdida de las funciones ecosistémicas del territorio. 155 Doctoral Thesis – Roy González-M. Chapter 7 General discussion Roy González-M. 156 Ecology of woody plants in Colombian dry forests Discussion outline The objective of this thesis was to identify the factors determining environmental harshness in Tropical Dry Forests (TDF) of Colombia and to study its influence on plant community attributes and functioning. Additionally, this thesis evaluated how the extreme “El Niño” Southern Oscillation drought of 2015 (ENSO2015) affected biomass dynamics of this ecosystem. Overall, we found that: (1) Colombian TDF are widely heterogeneous in their climate, soil, and land-cover transformation and that the interaction of these factors is what determines the environmental harshness in this ecosystem (Chapter 2). (2) Environmental harshness drives changes in species composition (Chapter 2), species diversity and forest structure (Chapters 3), and trait community composition and biomass productivity (Chapter 4) of TDF. (3) TDF tree species are adapted to cope with environmental harshness via different functional trait combinations (Chapters 3 and 5); nevertheless, all tree species seem to be sensitive to extreme droughts (Chapter 5). Accordingly, extreme drought events cause negative net biomass balances, particularly for species having traits associated with high hydraulic efficiency and ‘cheap’ tissue investments (Chapters 4 and 5). This dissertation provides new information about the drivers of environmental harshness and their ecological consequences on TDF diversity and functioning (Chapters 2-5), with clear implications for a comprehensive management of the most threatened ecosystem in Colombia (Chapter 6). The complexity of environmental harshness driving plant community attributes in TDF Rainfall seasonality determines plant community attributes of TDF, where variations in frequency, intensity, or length of drought conditions are commonly suggested as the most substantial controllers of environment harshness for this ecosystem (Murphy & Lugo 1986; Allen et al. 2015, 2017a; Dexter et al. 2018). Multiple studies have shown how differences in drought conditions drive species turnover (Muenchow et al. 2013; Neves et al. 2015), species diversity and forest structure (Gillespie et al. 2000; Marcelo-Peña et al. 2007; Pineda-García et al. 2007), and trait community composition (Fauset et al. 2012; Ouédraogo et al. 2016; Álvarez-Dávila et al. 2017; Aguirre-Gutiérrez et al. 2019). However, TDF have important variations in other environmental conditions such as temperature, isothermality, solar radiation, soil fertility, and even in land-cover transformation (Portillo-Quintero & Sánchez-Azofeifa 2010; Peña- Claros et al. 2012; Muenchow et al. 2013; Neves et al. 2015). This motivated us to evaluate how these multiple environmental conditions may also be responsible for ‘environmental harshness’ and its role in shaping plant community attributes in TDF. We found that TDF in Colombian are widely distributed across six biogeographical regions with contrasting climatic, soil, and land-transformation conditions. For instance, forests in the Inter Andean valleys have two dry periods per year (3-4 continuous dry months twice a year), where precipitation in each dry period reaches between 178 and 191 mm, while Caribbean forests only experience one dry period (5-8 dry months) where total seasonal precipitations reach 69 mm on average. In addition, Inter Andean valley forests are not exposed to high temperatures like Caribbean forests or to low soil water retention as Orinoquía forests. Nevertheless, all six regions showed fragments highly irregular in shape and comprising mostly early and intermediate successional stages, with some mature forest concentrated in the Caribbean region (see details in Chapter 2). These results not only show the high environmental heterogeneity of Colombian TDF but also reinforce the idea that the attributes of plant communities may be determined by the interaction of multiple environmental conditions (Peña-Claros et al. 2012; Muenchow et al. 2013; Neves 157 Doctoral Thesis – Roy González-M. et al. 2015; Ouédraogo et al. 2016). The results of Chapters 2-4, show how the combined effects of these factors exert important controls on TDF plant community attributes (e.g., species turnover and diversity, forest structure and trait community composition), demonstrating that environmental harshness in this ecosystem is more complex than just rainfall seasonality claimed for several studies (Dexter et al. 2015, 2018). In particular, we found strong climatic and soil control on floristic turnover among (dissimilarity >80%) and within regions (between 67% and 88%) (Chapter 2). Floristic composition in the Caribbean region was characteristic of forests with more arid conditions and prolonged droughts, with coastal marine influence, where the most common species are predominantly fast deciduous (e.g., Piptadenia spp. and Pithecellobium roseum), mesomorphic water-storing (e.g., Pereskia guamacho) and ‘bombacoids’ (e.g., Pachira quinata), with water-stress avoidance strategies for long and hot drought seasons (Rangel-Ch et al. 1997; Rojas-P. & León-H. 2020), or adapted to tolerate saline conditions (e.g., Caesalpinia coriaria; IRENA 1992). In contrast, Inter Andean valley forests floristic composition changed along more fertile soil gradients and were exposed to moister conditions, with a bimodal drought regime, when compared to Caribbean forests. In this region, we found local-dominances and unique species that were restricted to these particular environmental conditions (e.g., Trichilia carinata, Trichilia oligofoliolata; Villanueva Tamayo et al. 2015), but also common generalist species (e.g., Casearia sylvestris, Croton spp. Cecropia, Leucaena leucocephala, Zanthoxylum fagara) that were favored by the predominance of secondary forests in the landscape (García et al. 2014). Finally, in the Orinoquía region, species turnover was determined by high temperatures, and sandy and infertile soils that result from nutrient leaching induced by high annual precipitations. Across this region, the most common species (e.g., Spondias mombin, Vitex orinocensis) are associated with riparian habitats and have adaptations for water dispersal (Duvall 2006), but also to have sclerophylly due to infertile soil (Amaya 2014; Cabrera-Amaya & Rivera-Díaz 2016). Several environmental harshness factors were driving species diversity and forests structure. We found that increases in climate severity (i.e., high aridity, solar radiation, wind speed, and temperature, but low precipitation) and greater land-cover transformation (i.e., narrow forests with low area, and location in a highly transformed landscape) had synergic effects that reduced species richness and diversity, basal area, and canopy height (Chapter 3). Interestingly, the plant community attributes for legumes and deciduous species did not change along those gradients (discussed below), with the exception of diversity for deciduous species (Chapter 3). Several studies have shown that soils with a low water supply and high temperatures may limit the persistence of tall tree species that demand water for supporting their size, but low nutrient availability can also limit growth and impair osmotic regulation (Sperry 1995; Ryan & Yoder 1997; Tao et al. 2016), which could explain why basal area and canopy height decreased in arid environments. In addition, the influence of land-cover transformation on species diversity, basal area, and canopy height may be the result of an increase in temperature, wind exposure, and solar radiation due to increases in forest edge effects and forest isolation that affected species with low mechanical resistance (van Bloem et al. 2006) or low tolerance to water-stress (Sperry 1995). Two main conclusions were derived from these results. First, we identified the need that future studies in TDF take an ‘environmental harshness’ approach to comprehensively understand processes underlying changes in plant community attributes. Second, we showed the importance of including land- cover transformation, which we found was one of the most important drivers shaping the composition, structure, and functioning of forests given the broad and global human degradation of ecosystems (Lindborg & Eriksson 2004; Stein et al. 2014; Baynes et al. 2016). 158 Ecology of woody plants in Colombian dry forests Functional traits strategies of TDF tree species It has widely suggested that TDF tree species are adapted to cope with environmental harshness, particularly to drought conditions, via multiple functional strategies (Markesteijn et al. 2011a; Méndez-Alonzo et al. 2012; Pineda-García et al. 2015). However, different studies have also shown that TDF species may be at their limits of drought tolerance (Choat et al. 2012; Allen et al. 2015, 2017), and therefore, it is a priority to explore the mechanisms underlying species drought responses. Additionally, as we previously discussed, species not only respond to drought but also to soil conditions and land cover change. Thus, another motivation of this study was also to relate the functional strategies of TDF tree species in response to environmental harshness, and identify the mechanisms responsible for changes in plant community attributes across gradients. Overall, we found that dominant TDF tree species are characterized by having traits with high investment in tissues (i.e., dense leaves and wood; see Chapter 5) but to have a broad range of traits along the hydraulic safety-efficiency trade off. We found species with the expected hydraulically-safe and costly tissues (i.e., Aspidosperma, Cynophalla, Eugenia, Trichilia), but, interestingly and contrary to general expectation, we also found species with hydraulically-efficient and costly tissues (e.g., Astronium graveolens, Platymiscium pinnatum, Lonchocarpus spp. and Machaerium spp.). This last finding contrasts with studies suggesting that species having high hydraulic-efficiency, commonly associated with deciduous leaf habits and high cavitation risk under water constraints, have consequently low investment in tissues due to their low leaf lifespan and high nutrient concentration which enable them to maximize growth rates in their reduced growing season (Brodribb et al. 2010; Markesteijn et al. 2011a, b; Méndez-Alonzo et al. 2012). Thus, one of the main finding of this dissertation was the finding that species with hydraulically efficient vessel and dense tissues (in leaves and woods) are dominant, highlighting the importance of ‘costly’ tissues in response to harshness conditions in TDF. Costly tissue investments, such as high wood density and thick fiber walls prevent cell collapse caused by negative xylem potentials (Salleo & Nakdini 2000; Hacke et al. 2001a), but can also increase mechanical resistance to external forces such as strong winds (Chave et al. 2009; Beeckman 2016; Díaz et al. 2016), while small, dense and rigid leaves have can have lower wilting risks (Niinemets 2001; Poorter et al. 2009). Additionally, this result may help understand why under increasing harsh conditions, species richness and basal area of legumes and deciduous species did not decrease in our forests (Chapter 3). Legumes, having denser leaves with smaller surfaces (see Powers & Tiffin 2010), reduce their wilting risks and transpiration surface under water limitations (Niinemets 2001), which in combination with their resource acquisition abilities (nitrogen-fixing bacteria, Sprent 2009; Adams et al. 2016) can maintain a high photosynthetic capacity under harsher conditions. On the other hand, deciduous (non-legume) species, that make costly investments in their tissues may benefit from higher protection against herbivores during the rainy seasons (Turner 1994; Cunningham et al. 1999), reduce cavitation risk during unexpected droughts that can occur during the growing season (Powers & Tiffin 2010; Lopezaraiza-Mikel et al. 2013) and retard leaf shedding during the onset of the dry seasons, increasing the carbon gain window. However, we also found that about half of the TDF tree species have trait combinations differing from the dominant functional strategies (Chapter 5). This result highlight that plants have evolved different strategies to cope with harsh conditions. For instance, a group of species with intermediate dominance, were characterized by a high hydraulic efficiency, low investments in tissues, a high content of water at maximal capacity and predominance of deciduous leaf habits (e.g., Cavanillesia platanifolia, Bursera simaruba, Pachira quinate, Pseudobombax septenatum). These species are known as resource acquisitive 159 Doctoral Thesis – Roy González-M. but fugaciously deciduous, or water storage species (Méndez-Alonzo et al. 2012). Another group of species, evergreen with low dominance, made low investments in leaf tissues but had intermediate hydraulic safety- efficiency (e.g., Croton punctatus, Guazuma ulmifolia, Jatropha gossypiifolia and Aralia excelsa), which may be associated with pioneer ecological guilds (Markesteijn et al. 2011a). Our results are also in agreement with the idea that some trait combinations would be non-viable or would be related to very poor performance in TDF (Méndez-Alonzo et al. 2012; Gleason et al. 2016). For instance, species with low tissue investments and high hydraulic safety such as Tabernaemontana spp (Chapter 5) showed the lowest dominance. Overall, these results suggest that there are multiple trait combinations that may emerge as alternative responses to environmental harshness in TDF, and that interspecific variations may blur functional community patterns along environmental gradients. In agreement with this idea, the weak relationships found between traits and environment when analyses were performed at community-weighted level (CWM, Chapter 4) can be related explained by species differing in their functional trait combinations in response to environmental harshness. Specifically, we demonstrated that using CWM trait failed to detect relationships in about 96.9% of 464 models evaluating the effects of climate, soils and land-cover transformation on 15 functional traits (Chapter 4). Additionally, we found that communities account for the lowest fraction of explained variance for the studied traits (9.6±9.2%), with the exception of specific leaf area that reached 35.7%. Our results highlight the importance of improving sampling design to gain a comprehensive understanding of the ecological mechanisms that drive plant community attributes and functioning of tropical forests (Baraloto et al. 2010b; Carmona et al. 2015), and suggest that new studies should (i) sample traits of both dominant and ‘rare’ species, and (ii) make important efforts to take into account trait variability within and among communities, for an adequate trait characterization. As an important methodological contribution of this dissertation, we proposed a new trait sampling scheme based on the species abundance-weighted efforts (see details in Chapter 4). Explaining biomass changes in TDF through functional traits and environment The interaction between functional traits and environmental conditions play an important role in determining the functioning of tropical forests (Brown & Lugo 1982; Ruiz-Jaen & Potvin 2010; Poorter et al. 2015). For instance, both traits and environment have effects on biomass productivity (Finegan et al. 2015b; Poorter et al. 2017), and it has been suggested that environments with favorable conditions (e.g., high light intensity, water supply and soil nutrient availability) favor species with ‘acquisitive’ resource use strategies (e.g., high hydraulic efficiency, and high specific leaf area, among others), with fast growth but also high mortality rates, which in turn have high biomass stands (Poorter et al. 2008, 2010, 2017; Finegan et al. 2015b). This generalization is not necessary true for ecosystems with harsh environmental conditions, where plants may display contrasting ecological strategies to overcome environmental limitations (Marks & Lechowicz 2006; Méndez-Alonzo et al. 2012). It is the case of TDF, where high standing biomass and growth have been associated with the dominance of acquisitive species (i.e., large canopy height and low wood density; Conti & Díaz 2013) or species with conservative traits (e.g., low specific leaf area, high wood density) (Prado-Junior et al. 2016). In this sense, we explore the functional mechanisms behind biomass demographic rates in TDF, and the effects of environmental harshness on net biomass productivity for this ecosystem. Biomass demographic changes (i.e., survival, mortality and recruitment) were strongly determined by traits related to the hydraulic safety-efficiency trade-off and by investment in tissues (Chapter 5). We 160 Ecology of woody plants in Colombian dry forests found that the growth of surviving trees after a strong drought, caused by el Niño 2015 event, was stronger in trees that were already dominant before the drought, supporting the hypothesis that dominant trait combinations would perform better under particularly harsh conditions. Tree recruitment occurred in species with a broad range of traits related to the hydraulic safety-efficiency trade-off that had “costly” tissues, but also in species with “cheap” tissues that were nor dominant before the drought. Unexpectedly, however, tree mortality was widespread and affected species that invest in costly tissues (Chapter 5). Net biomass balances following el Niño showed that the most negative balances were associated to species that produced cheap tissues. More importantly, negative net balances of biomass were more widespread in the functional space than positive net balances. These findings confirmed that positive biomass changes in TDF are strongly determined dense tissues (Prado-Junior et al. 2016), where high investments is one of the most critical mechanisms to cope with water-constraints in this ecosystem. However, the results also revealed the high sensitivity of TDF to future drier climatic scenarios (Allen et al. 2015, 2017a). Extreme droughts, such as that TDF experienced during the 2015 el Niño, can cause widespread mortality and significantly reduce the biomass gain of species (Condit et al. 1996; Slik 2004; Allen et al. 2010; Maza-Villalobos et al. 2013). In our forests, the predominance of species with ‘costly tissues’ driving biomass productivity may even be at a disadvantage under drier scenarios. Investment in costly tissues is energetically expensive and require long payback times (Chave et al. 2009; Osnas et al. 2013) and an increase in the frequency and intensity of extreme droughts can play against species with these traits. Changes in these climatic conditions may cause significant reductions in aboveground biomass stock in forests (Venturas et al. 2016; Powers et al. 2020) and accelerate the risk of extinctions and permanent changes of this ecosystem in the next 50 years (Rowland et al. 2019). Ecological knowledge for decision-makers Fifteen years ago, Sánchez-Azofeifa et al. (2005) called the attention of the need for including human transformation in ecological studies of TDF. Their publications highlighted the existence of widespread efforts to generate information for a comprehensive management of TDF, which are ecosystems that are restricted to a few sites worldwide in comparison with other tropical ecosystems (Sánchez-Azofeifa et al. 2005a, b). The most important priorities identified by authors were: i) integrate land-cover transformation and climatic change as the main drivers of TDF future, ii) improve the ecological approaches for understanding responses to the drivers of transformation of these ecosystems, and, iii) to communicate the level of threat and ecological significance of this ecosystem to decision-makers. In Colombia, TDF underpin the wellbeing of many rural populations, but have suffered intensive land-cover transformation which, in combination to the intensification of drier conditions, can trigger cascading effects that could lead to desertification and affect food security (Norden et al. 2020). However, the interest of researcher to support conservation actions in Colombian TDF only became relevant in 2013 (Pizano et al. 2017), while one of the most important challenge has been to translate the research findings to a flexible language for decision makers. In this sense, each chapter of this thesis had the purpose of filling gaps in our knowledge of TDF and to be the base for an infographic offprint for decision-makers, indicating the importance of scientific knowledge for the conservation of TDF and to take informed management decisions. In 2016 we made a report on the distribution and conservation status of this ecosystem in Colombia (Chapter 6 – Infographic offprint 1). This offprint was the first extensive fieldwork sampling effort for this ecosystem, where the recorded information revealed its current extent across six important regions of the country, and its 161 Doctoral Thesis – Roy González-M. degradation derived from human infrastructure, cattle ranches, agriculture, and others anthropogenic pressures. This information is currently included in the list of ecosystems that are a conservation priority in the Environmental Ministry of Colombia, and guides mining moratorium in this ecosystem. In 2017 we published in the national biodiversity report our 1-ha permanent plots network for monitoring TDF (Chapter 6 – Infographic offprint 2), highlighting the importance of long-term monitoring strategies that will evaluate the response on TDF to future climatic change. Currently, this information is part of the socio-ecological monitoring platform for the comprehensive management of TDF in Colombia (Norden et al. 2020), and it has been included in the monitoring programs for National and Regional Parks, and the Biodiversity Information System of Colombia. Additionally, we reported the status of knowledge of functional diversity in Colombian forests and the trait sampling effort (Chapter 6 – Infographic offprint 3, 2017), where we emphasizing in the importance of plant functional traits to evaluate the responses of forests to future climatic change scenarios, and the direction for its adaptive conservation. That infographic offprints not only had the purpose of communicating the current ecological knowledge on TDF in Colombia, but also to inform decision-makers about the significance of conserving this ecosystem for the country. 162 Ecology of woody plants in Colombian dry forests Agradecimientos Los años de mi doctorado han sido, quizás, los más inspiradores, desafiantes y llenos de emociones de mi vida hasta hoy. Durante este tiempo he sentido un crecimiento como investigador, hoy aún en estado de aprendizaje, pero lo más enriquecedor ha sido poder compartir este proceso con una diversidad infinita de pensamientos, opiniones, sentimientos y motivaciones, representados en personas, que no alcanzaría a describir con gran detalle. Por todo esto, le agradezco a ese gran equipo de personas que estuvieron siempre en el momento indicado y sin las cuales no hubiera sido posible desarrollar mi tesis. Querida Ángela Parrado, gracias por tocar la puerta en la Universidad del Rosario y presentarme ante Juan Posada para iniciar este proyecto. Hernando García, muchas gracias por creer en mí, secundar mis ideas, y siempre estar ahí para ofrecer un buen consejo; en particular cuando me recomendaste hablar con Beatriz Salgado para que asesorara mi trabajo. Juan y Beatriz, quiero expresarles mis más sinceros agradecimientos, no han sido solo cinco años de acompañamiento y asesoría a mi investigación, sino también, cinco años cargados de emociones y aprendizajes que como una esponja he tomado de ustedes. Queridos compañeros del Instituto Humboldt, durante estos años algunos se han ido y otros han llegado, pero siempre han estado ahí presentes apoyándome en el desarrollo de mí tesis. Camila Pizano, junto a ti y Hernando iniciamos el camino de una agenda de investigación y monitoreo del Bosque Seco en Colombia, y la iniciativa de la red (Red BST-Col), estrategias que ha sido la base para la generación de información de cada sección en esta tesis, gracias. Camila gracias por el apoyo en varias secciones del documento. Jhon Nieto, Sandra Medina, Viviana Salinas, Fabián Garzón y Beatriz, gracias a ustedes logramos esta ambiciosa tarea de construir una base de referencia nacional sobre la ecología funcional del bosque seco en Colombia, gracias por apoyarme siempre en la toma de datos en campo y el fuerte trabajo en laboratorio. Creo que hoy podemos decir que es una de las mejores fuentes de información para seguir investigando en este ecosistema y aportar elementos desde la investigación para su gestión. Andrés Avella, Natalia Norden, Ana Belén Hurtato y Carolina Alcázar, ustedes han estado en unos momentos muy especiales de los cuales siempre estaré agradecido. Andrés gracias por las discusiones florísticas y filosóficas sobre el bosque seco. Natalia gracias por las discusiones ecológicas y el apoyo en varias secciones de esta tesis. Ana gracias por sus criticas constructivas y filosóficas sobre mi investigación. Carolina, gracias por llamar siempre la atención en resaltar la aplicación práctica de este estudio. Maylin González, Paola Isaacs, Carolina Castellanos y Susana Rodríguez, gracias por sus ideas y por haber abierto espacios de discusión particulares conmigo en torno a la tesis. Carlos Carmona, gracias por recibirme en la Universidad de Tartu, orientar con generosidad gran parte de los análisis para este trabajo, y apoyar varias secciones del documento. Horacio Paz, Jérôme Chave y Marius Bottin gracias por sus acertados comentarios y recomendaciones. Amigos y colaboradores de la Red BST-Col gracias por toda su ayuda en campo y los espacios de discusión, los aportes de todos ustedes fueron fundamentales para cada resultado y conclusión de este trabajo. Álvaro Idárraga, René López, Hermes Cuadros, José Aguilar Cano, Alicia Rojas, Alejandro Castaño, Francisco Mijares, Francisco Castro, Juan Lázaro Toro, Alba Marina Torres y Gerardo Aymard, muchas gracias por sus contribuciones en la identificación botánica de las especies en este estudio. Sinceramente, creo que muy pocos trabajos pueden contar con este gran equipo de botánicos; sus aportes 163 Doctoral Thesis – Roy González-M. son invaluables para mí. Álvaro y René les extiendo un agradecimiento muy especial, fueron jornadas intensas de campo y herbario en las que generosamente me enseñaron sobre las plantas del bosque seco. Quiero también agradecerle a un gran número de personas que me abrieron las puertas en sus territorios, y resaltar la labor de quienes me acompañaron en cada ejercicio de campo. Fueron jornadas extensas instalando las parcelas permanentes, midiendo miles de árboles, colectando material botánico, tomando muestras de suelo, muestras de hojas, muestras de madera, entre otras cosas, por las que estoy muy agradecido. En el Parque Nacional Natural La Macuira, le agradezco al profesor Alberto González y el resguardo indígena de Kajashiwoü por recibirnos, y a los jefes del parque Robinson Galindo y Borish Cuadrado por abrirnos las puertas de esta hermosa área natural. En el Santuario de Fauna y Flora Los Colorados, le agradezco al jefe del parque Julio Ferrer y Rebeca Franke por permitirnos trabajar en el área. En el Parque Nacional Natural Tayrona le agradezco al funcionario Elkin Rodríguez por toda su colaboración para trabajar en el área. En el Parque Nacional Natural El Tuparro, le agradezco al jefe Orlando Patiño y Augusto Repizo por facilitar el espacio de trabajo y la entrada al parque en cada jornada. Le agradezco especialmente a Pablo Pizano y Lucía Gómez (Reserva Natural Jabirú), a José Pablo Mesa (Reserva Natural El Tambor), Cesar Díaz (Hacienda El Cardonal), Alejandro, Alba Marina y Juan Adarve (Parque Regional El Vinculo), Álvaro Duque (Estación de Investigación Cotové) por permitirnos adelantar la investigación y facilitarnos el trabajo en sus bosques. Por su apoyo en campo, le agradezco al equipo de la Fundación Ecosistemas Secos de Colombia – Gina Rodríguez, Lino Olivares, David Hernández, Viviana Andrade, Fredy Vargas, María del Mar Gallego y Alejandra Díaz, el Instituto para la Investigación y la Preservación del Patrimonio Cultural y Natural del Valle del Cauca y la Unidad Central del Valle del Cauca – Alejandro Castaño, Juan Adarve y Wilson Devia, el Sistema de Parques Nacionales Naturales – Alexander González, Bienvenido Bastidas, Cesar Buelvas, Chayanne Pausany, Dilia Naranjo, Elkin Ortiz, Elkin Rodríguez, Gabriel Carmona, Luis Rodríguez, Onesimo Añes, Reycler Iguaran, Victor Cifuentes, la Universidad de Antioquia y Universidad Nacional de Colombia, sede Medellín – Álvaro I., Paula Morales, Diego Molina, Iván López, Katia Vargas, Camilo Sánchez, Esteban Domínguez y Yenny Cardona, la Universidad del Atlántico – Hermes e Isabel Pozzo, la Universidad del Cauca y Asociación GAICA – Hernando Vergara, Rubén Jurado, Sandra Urbano, la Universidad Distrital Francisco José de Caldas – René, Maribel Vásquez (Mari), Fabián, Viviana, Yeferson Gutiérrez, Juliana Mora, Blanca Caleño, la Universidad Javeriana – Mariana Florián, la Universidad Nacional de Colombia, sede Bogotá – Beatriz y Nelly Rodríguez, la Universidad de Sucre – Jorge Mercado, la Universidad del Tolima – Mariana Cortés, la Universidad del Valle – Viviana Londoño, y por supuesto al equipo del Instituto Humboldt – Jhon, Sandra, Hernando, Camila, Daniel, José, Beatriz, Natalia, Adriana Quintana, Henry Arenas, Susana y Maylin. Alma, Mara, Timote y Marto. ¡Amigos! Gracias por dejar en evidencia que la amistad no tiene limites, con ustedes no solo comparto la profesión y el gusto por la naturaleza, sino también grandes historias de vida. ¡Mil gracias por nunca tener que hablar de mi trabajo de doctorado, pero siempre querer hacerlo! Aunque sea imperceptible muchas de sus reflexiones hacen parte de este documento. Mari, gracias por tu apoyo y amor! Sin tu soporte emocional creo que nunca hubiera terminado este trabajo. Tú y Bongo son el impulso de energía más importante para emprender cualquier proyecto. 164 Ecology of woody plants in Colombian dry forests References Adams, M.A., Turnbull, T.L., Sprent, J.I. & Buchmann, N. (2016). Legumes are different: Leaf nitrogen, photosynthesis, and water use efficiency. Proc. Natl. Acad. Sci. U. S. A., 113, 4098–4103. Aguirre-Gutiérrez, J., Oliveras, I., Rifai, S., Fauset, S., Adu-Bredu, S., Affum-Baffoe, K., et al. (2019). 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