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    Although biogeographic patterns are the product of complex ecological processes, the increasing com-plexity of correlative species distribution models (SDMs) is not always motivated by ecological theory,but by model fit. The validity of model projections, such as shifts in a species’ climatic niche, becomesquestionable particularly during extrapolations, such as for future no-analog climate conditions. To exam-ine the effects of model complexity on SDM predictive performance, we fit statistical models of varyingcomplexity to simulated species occurrence data arising from data-generating processes that assume differing degrees of distributional symmetry in environmental space, interaction effects, and coverage inclimate space. Mismatches between data-generating processes and statistical models (i.e., different functional forms) led to poor predictive performance when extrapolating to new climate-space and greater variation in extrapolated predictions for overly complex models. In contrast, performance issues werenot apparent when using independent evaluation data from the training region. These results draw intoquestion the use of highly flexible models for prediction without ecological justification.

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    Bell, David M.; Schlaepfer, Daniel R. 2016. On the dangers of model complexity without ecological justification in species distribution modeling. Ecological Modelling, Vol. 330: 10 pages.: 50-59.


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    Prediction, Extrapolation, Model fitting, Species distribution modeling, Transferability.

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