How different are species distribution model predictions?-Application of a new measure of dissimilarity and level of significance to giant panda Ailuropoda melanoleuca

Species distribution models (SDMs) are widely used for predicting species' spatial distributions. Different model setup and data input however can lead to variable model predictions. Existing studies on quantifying SDM dissimilarity primarily rely on partitioning the variability in SDM-produced community level metrics such as species richness and turnover rate which are threshold-dependent and are generated with binary range maps of multiple species. Most existing measurements of spatial dissimilarity constitute geometric comparisons, which is limited compared to a more information-theoretic application of statistical dissimilarity measures using SDM predictions as direct input without renormalization. We introduce a novel method to quantify the degree of dissimilarity and its level of significance between unscaled SDM predictions of a single species. We apply the method to giant panda Ailuropoda melanoleuca data as well as pairs of simulated species distributions. We utilize a pixel-based Bhattacharyya distance to quantify the degree of dissimilarity among predictions of giant panda habitat of different combinations of model types, Global Climate Models (GCMs) and Representative Concentration Pathways (RCPs). Comparisons are also made between pairs of simulated species with different degrees of dissimilarity in spatial distribution. To evaluate the level of significance, the observed dissimilarity measure is compared against a null distribution that captures the level of dissimilarity caused by small and random variations. Specific pairs of climate scenarios (HadGEM2-ES with HadGEM2-AO and HadGEM2-AO with MIROC5) consistently produce statistically similar predictions of giant panda habitat; the highest level of RCP tends to result in more similar predictions, suggesting a convergence of model predictions. The simulated scenarios also show that the proposed method is able to effectively differentiate a range of artificial species with varying degree of dissimilarity in their resource selection preference. Our method can also reflect the dissimilarity that cannot be quantified by traditional metrics that rely on geometric comparisons. The proposed method supplements existing studies by utilizing a novel application of statistical comparisons to measure dissimilarity between user-defined pairs of models. It provides a robust way to construct the null distribution of dissimilarity that contrasts the degree of the observed dissimilarity with the intrinsic model variability. Our study provides useful insight to facilitate building more computationally efficient and robust ensemble SDMs, and it lends a practical tool to help understand the processes that contribute to prediction variability among SDMs.