Abstract
AbstractSpatial autocorrelation (SAC) is a common feature of ecological data where observations tend to be more similar at some geographic distance(s) than expected by chance. Despite the implications of SAC for data dependencies, its impact on the performance of species distribution models (SDMs) remains controversial, with reports of both strong and negligible impacts on inference. Yet, no study has comprehensively assessed the prevalence and the strength of SAC in the residuals of SDMs over entire geographic areas. Here, we used a large‐scale spatial inventory in the western Swiss Alps to provide a thorough assessment of the importance of SAC for (1) 850 species belonging to nine taxonomic groups, (2) six predictors commonly used for modeling species distributions, and (3) residuals obtained from SDMs fitted with two algorithms with the six predictors included as covariates. We used various statistical tools to evaluate (1) the global level of SAC, (2) the spatial pattern and spatial extent of SAC, and (3) whether local clusters of SAC can be detected. We further investigated the effect of the sampling design on SAC levels. Overall, while environmental predictors expectedly displayed high SAC levels, SAC in biodiversity data was rather low overall and vanished rapidly at a distance of ~5–10 km. We found low evidence for the existence of local clusters of SAC. Most importantly, model residuals were not spatially autocorrelated, suggesting that inferences derived from SDMs are unlikely to be affected by SAC. Further, our results suggest that the influence of SAC can be reduced by a careful sampling design. Overall, our results suggest that SAC is not a major concern for rugged mountain landscapes.
Highlights
Assessing how species occurrence or abundance varies across space and along environmental gradients is a long-standing goal of ecology, recently revived by an increasing interest about the consequences of global change on biodiversity (Bellard et al 2012, Guisan et al 2017)
We used a large-scale spatial inventory in the western Swiss Alps to provide a thorough assessment of the importance of spatial autocorrelation (SAC) for (1) 850 species belonging to nine taxonomic groups, (2) six predictors commonly used for modeling species distributions, and (3) residuals obtained from species distribution models (SDMs) fitted with two algorithms with the six predictors included as covariates
We found a low prevalence of SAC in model residuals for the majority of the taxa, suggesting that in our study, SDMs are likely robust with respect to SAC (Diniz-Filho et al 2003; see Thibaud et al 2014 for similar evidence in the same area)
Summary
Assessing how species occurrence or abundance varies across space and along environmental gradients is a long-standing goal of ecology, recently revived by an increasing interest about the consequences of global change on biodiversity (Bellard et al 2012, Guisan et al 2017). SAC generated by exogenous processes results from independent responses to autocorrelated environmental gradients and is often referred as induced spatial dependence (Fortin and Dale 2005) or spatial dependency (Legendre et al 2002) In this case, spatially structured environmental factors such as geomorphology or climate are responsible for the spatial structure in species distribution data (Legendre et al 2002, Diniz-Filho et al 2003). Least-cost modeling can be used to identify routes with the lowest cumulative resistance between target locations on a landscape (Adriaensen et al 2003) and may reveal new insights about SAC
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