Accounting for spatial autocorrelation from model selection to statistical inference: Application to a national survey of a diurnal raptor

a r t i c l e i n f o Planning actions for species conservation involves working at both an ecologically meaningful spatial scale and a scale suitable for implementing management or conservation plans. Animal populations and conservation policies often operate across wide areas. Large-extent spatial datasets are thus often used, but their analyses rarely deal with problems inherent to spatial datasets such as residual spatial autocorrelation, which can bias or even reverse results. Here we propose a procedure for analysing a large-scale count dataset integrating residual spatial autocorrelation in a Generalized Linear Model framework by combining and extending previously published methods. The first step concerns the selection of the environmental variables by a modified cross-validation procedure allowing for residual spatial autocorrelation. Then the second step consists in evaluating the spatial effect of the model using a spatial filtering approach based on the variogram parameters. We apply this method to the Black kite (Milvus migrans) to estimate the distribution and population size of this species in France. We found some divergence in estimated population size between spatial and non spatial models, as well as in the distribution map. We also found that the uncertainty of the model was underestimated by the residual spatial autocorrelation. Our analysis confirms previous results, that residual spatial autocorrelation should be always accounted for, especially in conservation where false results may lead to poor management decisions. Animal populations and conservation policies often operate across wide areas. Large-extent spatial datasets (Scheiner et al., 2000) can therefore be extremely valuable to determine population parameters for conservation purposes, e.g. the geographical distribution of species , its population size or trends. However, the statistical analyses used often ignore issues that may bias conclusions. In particular, they rarely deal with inference problems inherent from spatial data-sets such as residual spatial autocorrelation (hereafter RSA), which may actually reverse observed patterns (Kühn, 2007). Spatial autocorrelation arises when the measure of a variable of interest in multiple sample units are not independent of each other (Griffith, 1987), which often occurs in ecological data. Such spatial patterns are usually explained by environmental features (e.g. climatic variables or habitat structure) that are themselves spatially structured. Therefore, including all environmental variables that are spatially structured may be sufficient to remove RSA of a regression model (Diniz-Filho et al., 2003). However, it is often impossible to measure all spatially structured variables: for instance, variables accounting for social …