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

作者：Le Rest Kevin^*; Pinaud David; Bretagnolle Vincent

来源：Ecological Informatics, 2013, 14: 17-24.

DOI：10.1016/j.ecoinf.2012.11.008

• Generalized Linear Models
• Spatial cross-validation
• Population size
• Residual spatial autocorrelation
• Spatial filtering
• Species distribution

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.

• 出版日期2013-3