Evaluating presence‐only species distribution models with discrimination accuracy is uninformative for many applications

By Dan L. Warren, N. Matzke, T. Iglesias. Journal of Biogeography, 2019

90 citations recorded by the source corpus.

Species distribution models are used across evolution, ecology, conservation and epidemiology to make critical decisions and study biological phenomena, often in cases where experimental approaches are intractable. Choices regarding optimal models, methods and data are typically made based on discrimination accuracy: a model's ability to predict subsets of species occurrence data that were withheld during model construction. However, empirical applications of these models often involve making biological inferences based on continuous estimates of relative habitat suitability as a function of environmental predictor variables. We term the reliability of these biological inferences ‘functional accuracy.’ We explore the link between discrimination accuracy and functional accuracy.

Subfields: Ecological Modeling, Ecology, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/96bc154b1ed69b8aebf621070db0dd5ce6a08afb

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In Ecological Modeling

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In Nature and Landscape Conservation

Citations in the corpus

High uncertainty in the effects of data characteristics on the performance of species distribution models (2021, background): “Here we must point out that, although we acknowledge that model training and evaluation would benefit from selecting reliable absences coming from well-sampled cells (Lobo et al., 2010; Warren et al., 2020), we”