Predicting the Fate of Biodiversity Using Species’ Distribution Models: Enhancing Model Comparability and Repeatability

Ecology and Evolution, Stony Brook University, Stony Brook, New York, United States of America.
PLoS ONE (Impact Factor: 3.23). 09/2012; 7(9):e44402. DOI: 10.1371/journal.pone.0044402
Source: PubMed


Species distribution modeling (SDM) is an increasingly important tool to predict the geographic distribution of species. Even though many problems associated with this method have been highlighted and solutions have been proposed, little has been done to increase comparability among studies. We reviewed recent publications applying SDMs and found that seventy nine percent failed to report methods that ensure comparability among studies, such as disclosing the maximum probability range produced by the models and reporting on the number of species occurrences used. We modeled six species of Falco from northern Europe and demonstrate that model results are altered by (1) spatial bias in species' occurrence data, (2) differences in the geographic extent of the environmental data, and (3) the effects of transformation of model output to presence/absence data when applying thresholds. Depending on the modeling decisions, forecasts of the future geographic distribution of Falco ranged from range contraction in 80% of the species to no net loss in any species, with the best model predicting no net loss of habitat in Northern Europe. The fact that predictions of range changes in response to climate change in published studies may be influenced by decisions in the modeling process seriously hampers the possibility of making sound management recommendations. Thus, each of the decisions made in generating SDMs should be reported and evaluated to ensure conclusions and policies are based on the biology and ecology of the species being modeled.

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    Acta Oecologica 08/2014; 59:62–71. DOI:10.1016/j.actao.2014.05.005 · 1.62 Impact Factor
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    Ecology and Evolution 04/2014; 4(8):1209-21. DOI:10.1002/ece3.985 · 2.32 Impact Factor
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