A comprehensive evaluation of predictive performance of 33 species distribution models at species and community levels


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A comprehensive evaluation of predictive performance of 33 species distribution models at species and community levels
Ecological Monographs
Norberg A., Abrego N., Blanchet F.G., Adler F., Anderson B., Anttila J., Araújo M, Dallas T., Dunson D., Elith J., Fox R., Franklin J., Godsoe W., Guisan A., O'Hara R., Hill N., Holt R., Hui F., Husby M., Kålås J., Lehikoinen A., Luoto M., Mod H., Newell G., Renner I., Roslin T., Soininen J., Thuiller W., Vanhatalo J., Warton D., White M., Zimmermann N.E., Gravel D., Ovaskainen O.
Statut éditorial
In Press
A large array of species distribution model (SDM) approaches have been
developed for explaining and predicting the occurrences of individual
species or species assemblages. Given the wealth of existing models, it
is unclear which models perform best for interpolation or extrapolation of
existing data sets, particularly when one is concerned with species
assemblages. We compared the predictive performance of 33 variants of
15 widely applied and recently emerged SDMs in the context of
multispecies data, including both joint SDMs that model multiple species
together, and stacked SDMs that model each species individually
combining the predictions afterwards. We offer a comprehensive
evaluation of these SDM approaches by examining their performance in
predicting withheld empirical validation data of different sizes
representing five different taxonomic groups, and for prediction tasks
related to both interpolation and extrapolation. We measure predictive
performance by twelve measures of accuracy, discrimination power,
calibration, and precision of predictions, for the biological levels of
species occurrence, species richness, and community composition. Our
results show large variation among the models in their predictive
performance, especially for communities comprising many species that
are rare. The results do not reveal any major trade-offs among measures
of model performance; the same models performed generally well in
terms of accuracy, discrimination, and calibration, and for the biological
levels of individual species, species richness, and community
composition. In contrast, the models that gave the most precise
predictions were not well calibrated, suggesting that poorly performing
models can make overconfident predictions. However, none of the
models performed well for all prediction tasks. As a general strategy, we
therefore propose that researchers fit a small set of models showing
complementary performance, and then apply a cross-validation
procedure involving separate data to establish which of these models
performs best for the goal of the study.
Création de la notice
20/02/2019 9:18
Dernière modification de la notice
21/08/2019 5:32
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