Abstract
Bioclimatic envelope models are commonly used to assess the influence of climate change on species' distributions and biodiversity patterns. Understanding how methodological choices influence these models is critical for a comprehensive evaluation of the estimated impacts. Here we systematically assess the performance of bioclimatic envelope models in relation to the selection of predictors, modeling technique, and pseudo‐absences. We considered (a) five different predictor sets, (b) seven commonly used modeling techniques and an ensemble model, and (c) three sets of pseudo‐absences (1,000 pseudo‐absences, 10,000 pseudo‐absences, and the same as the number of presences). For each combination of predictor set, modeling technique, and pseudo‐absence set, we fitted bioclimatic envelope models for 300 species of mammals, amphibians, and freshwater fish, and evaluated the predictive performance of the models using the true skill statistic (TSS), based on a spatially independent test set as well as cross‐validation. On average across the species, model performance was mostly influenced by the choice of predictor set, followed by the choice of modeling technique. The number of the pseudo‐absences did not have a strong effect on the model performance. Based on spatially independent testing, ensemble models based on species‐specific nonredundant predictor sets revealed the highest predictive performance. In contrast, the Random Forest technique yielded the highest model performance in cross‐validation but had the largest decrease in model performance when transferred to a different spatial context, thus highlighting the need for spatially independent model evaluation. We recommend building bioclimatic envelope models according to an ensemble modeling approach based on a nonredundant set of bioclimatic predictors, preferably selected for each modeled species.
Highlights
In the face of ongoing climate change it is crucial to quantify and understand its impacts on biodiversity
Threats of future climate change to biodiversity are commonly quantified with bioclimatic envelope models, that is, species distribution models (SDMs) that link species presence records with climatic variables to project the future distribution of species in response to climate change (Elith & Leathwick, 2009; Guisan & Thuiller, 2005; Pacifici et al, 2015; Pearson & Dawson, 2003; Thomas et al, 2004)
We evaluated the sensitivity of bioclimatic envelope model performance to choices in predictor set, modeling technique, and number of pseudo-absences
Summary
In the face of ongoing climate change it is crucial to quantify and understand its impacts on biodiversity. Many studies rely on a (semi-)automated selection of complementary (i.e., not too highly correlated) predictors from a broader set of variables that are expected to be relevant (Barbet-Massin & Jetz, 2014; Bradie & Leung, 2017; Dormann et al, 2013; Petitpierre et al, 2017) When it comes to the choice of modeling technique, there is a lack of consensus as to which technique is most suited for which SDM purpose (Araújo et al, 2019; Benito et al, 2013). To what extent are the outcomes contingent on the evaluation method, that is, independent testing as opposed to cross-validation?
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