Aim Processes and variables measured in ecology are almost always spatially autocorrelated, potentially leading to the choice of overly complex models when performing variable selection. One way to solve this problem is to account for residual spatial autocorrelation ( RSA ) for each subset of variables considered and then use a classical model selection criterion such as the A kaike information criterion ( AIC ). However, thi...