Presence-only and presence-absence data for comparing species distribution modeling methods

Elith, Jane, Graham, Catherine H., Valavi, Roozbeh, Abegg, Meinrad, Bruce, Caroline, Ford, Andrew, Guisan, Antoine, Hijmans, Robert J., Huettmann, Falk, Lohmann, Lucia, Loiselle, Bette, Moritz, Craig, Overton, Jake, Peterson, A. Townsend, Phillips, Steven, Richardson, Karen, Williams, Stephen, Wiser, Susan K., Wohlgemuth, Thomas, and Zimmermann, Niklaus E. (2020) Presence-only and presence-absence data for comparing species distribution modeling methods. Biodiversity Informatics, 15 (2). pp. 69-80.

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Species distribution models (SDMs) are widely used to predict and study distributions of species. Many different modeling methods and associated algorithms are used and continue to emerge. It is important to understand how different approaches perform, particularly when applied to species occurrence records that were not gathered in structured surveys (e.g. opportunistic records). This need motivated a large-scale, collaborative effort, published in 2006, that aimed to create objective comparisons of algorithm performance. As a benchmark, and to facilitate future comparisons of approaches, here we publish that dataset: point location records for 226 anonymised species from six regions of the world, with accompanying predictor variables in raster (grid) and point formats. A particularly interesting characteristic of this dataset is that independent presence-absence survey data are available for evaluation alongside the presence-only species occurrence data intended for modeling. The dataset is available on Open Science Framework and as an R package and can be used as a benchmark for modeling approaches and for testing new ways to evaluate the accuracy of SDMs.

Item ID: 67914
Item Type: Article (Research - C1)
ISSN: 1546-9735
Copyright Information: Copyright for articles published in this journal is retained by the authors, with first publication rights granted to the journal. All articles are licensed under a Creative Commons Attribution Non-Commercial license.
Date Deposited: 23 Apr 2021 05:16
FoR Codes: 41 ENVIRONMENTAL SCIENCES > 4102 Ecological applications > 410299 Ecological applications not elsewhere classified @ 100%
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