Using species distribution models to inform IUCN Red List assessments

Mindy M Syfert, Lucas N Joppa, Matthew J Smith, David A Coomes, Steven P Bachman, and Neil A Brummitt

Abstract

Characterising a species' geographical extent is central to many conservation assessments, including those of the IUCN Red List of Threatened Species. The IUCN recommends that extent of occurrence (EOO) to be quantified by drawing a minimum convex polygon (MCP) around known or inferred presence localities. EOO calculated from verified specimens is commonly used in Red List assessments when other data are scarce, as is the case for many threatened plant species. Yet rarely do these estimates incorporate inferred localities from species distribution models (SDMs). A key impediment stems from uncertainty about how SDM predictions relate to EOO. Here we address this issue by comparing the EOOs estimated from specimen localities with EOOs derived from SDMs for plant species occurring in Costa Rica and Panama. We first analyse 20 plant species, with well-known and well-sampled distributions, and train SDMs to subsamples of the data and assess how well the SDM-derived MCPs predict both the MCPs of the subsamples and the MCPs of the complete dataset. We find that when sample sizes are small (5 or 10 samples) the SDM-derived MCPs are actually closer to the complete dataset than to the MCPs of the subsamples, both in terms of EOO and geographically. This occurs when using a probability threshold based on maximum geographical similarity between the SDM-derived MCP and the subsample MCP; other threshold methods performed less well. For the species with less well-known distributions, the SDM-derived EOOs correlate strongly with, but tend to be larger than, EOOs estimated by point data. This implies that a SDM-derived EOO may be more representative of the full EOO than that drawn around known localities. Our findings reveal situations in which SDMs provide useful information that complements the IUCN Red Listing process.

Details

Publication typeArticle
Published inBiological Conservation
PublisherElsevier

Previous versions

Cory Merow, John A. Silander, and Matthew J. Smith. A practical guide to MaxEnt for modeling species' distributions: what it does, and why inputs and settings matter, Ecography, Wiley, April 2013.

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Mindy M. Syfert, Matthew J. Smith, and David A. Coomes. The effects of sampling bias and model complexity on the predictive performance of MaxEnt species distribution models , PLOS One, PLoS, February 2013.

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Greg J. McInerny and Rampal S. Etienne. Ditch the niche – is the niche a useful concept in ecology or species distribution modelling?, Journal of Biogeography, 2012.

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