Predictors, spatial distribution, and occurrence of woody invasive plants in subtropical urban ecosystems

Staudhammer, Christina L.; Escobedo, Francisco J.; Holt, Nathan; Young, Linda J.; Brandeis, Thomas J.; Zipperer, Wayne

doi:https://doi.org/10.1016/j.jenvman.2015.03.012

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Solo Metadatos

Predictors, spatial distribution, and occurrence of woody invasive plants in subtropical urban ecosystems

https://repository.urosario.edu.co/handle/10336/26024
https://doi.org/10.1016/j.jenvman.2015.03.012

Autores

Staudhammer, Christina L.

Escobedo, Francisco J.

Holt, Nathan

Young, Linda J.

Brandeis, Thomas J.

Zipperer, Wayne

Fecha

2015-05-15

Editor

Elsevier

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Abstract

We examined the spatial distribution, occurrence, and socioecological predictors of woody invasive plants (WIP) in two subtropical, coastal urban ecosystems: San Juan, Puerto Rico and Miami-Dade, United States. These two cities have similar climates and ecosystems typical of subtropical regions but differ in socioeconomics, topography, and urbanization processes. Using permanent plot data, available forest inventory protocols and statistical analyses of geographic and socioeconomic spatial predictors, we found that landscape level distribution and occurrence of WIPs was not clustered. We also characterized WIP composition and occurrence using logistic models, and found they were strongly related to the proportional area of residential land uses. However, the magnitude and trend of increase depended on median household income and grass cover. In San Juan, WIP occurrence was higher in areas of high residential cover when incomes were low or grass cover was low, whereas the opposite was true in Miami-Dade. Although Miami-Dade had greater invasive shrub cover and numbers of WIP species, San Juan had far greater invasive tree density, basal area and crown cover. This study provides an approach for incorporating field and available census data in geospatial distribution models of WIPs in cities throughout the globe. Findings indicate that identifying spatial predictors of WIPs depends on site-specific factors and the ecological scale of the predictor. Thus, mapping protocols and policies to eradicate urban WIPs should target indicators of a relevant scale specific to the area of interest for their improved and proactive management.