Methods to account for spatial autocorrelation in the analysis of species distributional data: a review

作者：Carsten F. Dormann, Jana McPherson, Miguel B. Araújo, Roger Bivand, Janine Bolliger, Gudrun Carl, R. G. Davies, Alexandre H. Hirzel, Walter Jetz, W. Daniel Kissling, Ingolf Kühn, Ralf Ohlemüller, Pedro R. Peres‐Neto, Björn Reineking, Boris Schröder, Frank M. Schurr, Robert J. Wilson · 发表于：Ecography · 年份：2007 · DOI：10.1111/j.2007.0906-7590.05171.x · 被引用次数：3433 · 研究领域：Species Distribution and Climate Change、Ecology and Vegetation Dynamics Studies、Wildlife Ecology and Conservation

Species distributional or trait data based on range map (extent‐of‐occurrence) or atlas survey data often display spatial autocorrelation, i.e. locations close to each other exhibit more similar values than those further apart. If this pattern remains present in the residuals of a statistical model based on such data, one of the key assumptions of standard statistical analyses, that residuals are independent and identically distributed (i.i.d), is violated. The violation of the assumption of i.i.d. residuals may bias parameter estimates and can increase type I error rates (falsely rejecting the null hypothesis of no effect). While this is increasingly recognised by researchers analysing species distribution data, there is, to our knowledge, no comprehensive overview of the many available spatial statistical methods to take spatial autocorrelation into account in tests of statistical significance. Here, we describe six different statistical approaches to infer correlates of species’ distributions, for both presence/absence (binary response) and species abundance data (poisson or normally distributed response), while accounting for spatial autocorrelation in model residuals: autocovariate regression; spatial eigenvector mapping; generalised least squares; (conditional and simultaneous) autoregressive models and generalised estimating equations. A comprehensive comparison of the relative merits of these methods is beyond the scope of this paper. To demonstrate each method's impl...