Assessing the reliability of species distribution projections in climate change research

Abstract

AimForecasting changes in species distribution under future scenarios is one of the most prolific areas of application for species distribution models (SDMs). However, no consensus yet exists on the reliability of such models for drawing conclusions on species distribution response to changing climate. In this study we provide an overview of common modelling practices in the field and assess model predictions reliability using a virtual species approach. LocationGlobal MethodsWe first provide an overview of common modelling practices in the field by reviewing the papers published in the last 5 years. Then, we use a virtual species approach and three commonly applied SDM algorithms (GLM, MaxEnt and Random Forest) to assess the estimated (cross-validated) and actual predictive performance of models parameterized with different modelling settings and violations of modelling assumptions. ResultsOur literature review shows that most papers that model species distribution under climate change rely on single models (65%) and small samples (< 50 presence points, 62%), use presence-only data (85%), and binarize models output to estimate range shift, contraction or expansion (74%). Our virtual species approach reveals that the estimated predictive performance tends to be over-optimistic compared to the real predictive performance. Further, the binarization of predicted probabilities of presence reduces models predictive ability considerably. Sample size is one of the main predictors of real accuracy, but has little influence on estimated accuracy. Finally, the inclusion of irrelevant predictors and the violation of modelling assumptions increases estimated accuracy but decreases real accuracy of model projections, leading to biased estimates of range contraction and expansion. Main conclusionsOur study calls for extreme caution in the application and interpretation of SDMs in the context of biodiversity conservation and climate change research, especially when modelling a large number of species where species-specific model settings become impracticable.