Analyzing spatial autocorrelation in species distributions using Gaussian and logit models

Analyses of spatial distributions in ecology are often influenced by spatial autocorrelation. While methods to deal with spatial autocorrelation in Normally distributed data are already frequently used, the analysis of non-Normal data in the presence of spatial autocorrelation are rarely known to ecologists. Several methods based on the generalized estimating equations (GEE) are compared in their performance to a better known autoregressive method, namely spatially simultaneous autoregressive error model (SSAEM). GEE are further used to analyze the influence of autocorrelation of observations on logistic regression models. Originally, these methods were developed for longitudinal data and repeated measures models. This paper proposes some techniques for application to two-dimensional macroecological and biogeographical data sets displaying spatial autocorrelation. Results are presented for both computationally simulated data and ecological data (distribution of plant species richness throughout Germany and distribution of the plant species Hydrocotyle vulgaris). While for Normally distributed data SSAEM perform better than GEE, GEE provide far better results than frequently used autologistic regressions and remove residual spatial autocorrelation substantially when having binary data.