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Do species distribution models predict species richness in urban and natural green spaces? A case study using amphibians
Citation:
MILANOVICH, J. R., W. E. Peterman, K. Barrett, AND M. E. HOPTON. Do species distribution models predict species richness in urban and natural green spaces? A case study using amphibians. LANDSCAPE AND URBAN PLANNING. Elsevier Science Ltd, New York, NY, 107(4):409-418, (2012).
Impact/Purpose:
To inform the public.
Description:
Urban green spaces are potentially important to biodiversity conservation because they represent habitat islands in a mosaic of development, and could harbor high biodiversity or provide connectivity to nearby habitat. Presence only species distribution models (SDMs) represent a potential tool for assessing the biodiversity value of urban green space; however, there is limited research to validate SDM results with field surveys to see if the predictions accurately represent observed species distributions and subsequent species richness. We generated a range of SDMs using multiple suitability thresholds for 23 species of amphibians that occur in southwest, Ohio, USA. The distributions were overlaid to enumerate species richness. We surveyed 20 sites for amphibian species within the area to evaluate model predictions. Our models over-predicted species richness relative to survey data. For example, we observed a mean pairwise difference of 14 species between models of species richness relative to observed values. Our results suggest either SDMs built with landscape variables we selected did not represent accurately amphibian richness, or the amphibian surveys did not detect all species present. Analyzing sites that had more than three sampling events suggests the 2 explanation of inadequate sampling effort is only partially correct. Differences between predicted species richness and observed values represent a challenge to land managers and conservation biologist in need of a tool for modeling biodiversity. Species distribution models did project relative species richness well in urban and non-urban green space, which suggests this technique offers a spatially explicit way to identify more species rich areas.
