Effects of incorporating spatial autocorrelation into the analysis of species distribution data

TLDR: In this article, the authors address the question of whether incorporating spatial autocorrelation (SAC) in data affects estimates of model coefficients and inference from statistical models, and show that these biased estimates and incorrect model specifications have implications for predicting species occurrences under changing environmental conditions.

Abstract: Aim Spatial autocorrelation (SAC) in data, i.e. the higher similarity of closer samples, is a common phenomenon in ecology. SAC is starting to be considered in the analysis of species distribution data, and over the last 10 years several studies have incorporated SAC into statistical models (here termed ‘spatial models’). Here, I address the question of whether incorporating SAC affects estimates of model coefficients and inference from statistical models. Methods  I review ecological studies that compare spatial and non-spatial models. Results  In all cases coefficient estimates for environmental correlates of species distributions were affected by SAC, leading to a mis-estimation of on average c. 25%. Model fit was also improved by incorporating SAC. Main conclusions  These biased estimates and incorrect model specifications have implications for predicting species occurrences under changing environmental conditions. Spatial models are therefore required to estimate correctly the effects of environmental drivers on species present distributions, for a statistically unbiased identification of the drivers of distribution, and hence for more accurate forecasts of future distributions.