Residual spatial autocorrelation in macroecological and biogeographical modeling: a review

摘要: Macroecologists and biogeographers continue to predict the distribution of species across space based on relationship between biotic processes environmental variables. This approach uses data related to, for example, abundance or presence/absence, climate, geomorphology, soils. Researchers have acknowledged in their statistical analyses importance accounting effects spatial autocorrelation (SAC), which indicates a degree dependence pairs nearby observations. It has been agreed that residual (rSAC) can substantial impact modeling inferences. However, more attention should be paid sources rSAC becomes problematic. Here, we review previous studies identify diverse factors potentially induce presence macroecological biogeographical models. Furthermore, an emphasis is put quantification by seeking unveil magnitude SAC model residuals detrimental process. turned out five categories drive residuals: ecological processes, scale distance, missing variables, sampling design, assumptions methodological approaches. Additionally, noted explicit elaborated discussion presented modeling. Future investigations involving are recommended order understand when adverse effect