Hello Elaine, It depends on the focus of your study. (1)If you want to work out which of your three explanatory variables is the most important for bird richness, then you will probably compare parameter estimates from non-spatial and spatial (autoregressive) models. See Dormann et al (2007) Ecography 30:609-628 for excellent examples and code. They use simulated data, and you use actual data so you will not know the true value of your parameters. If there is a lot of autocorrelation in your data then your parameter estimates will be poor. The effect of autocorrelation in your data is to inflate the importance of variables - but you may not know which ones or by how much.
(2)If you want to make good predictions, then compare the predicted values from your spatial and non-spatial models to your actual observed values. For this you could calculate AUC (or ROC) and percent of deviance explained by partitioning your data and using some for training, and some for testing. Cross validation would be better. And an independent data set, better yet. See Betts et al (2006) Ecological Modelling 191, 197-224. (3)If you just want to show that there IS autocorrelation in your data, then you could calculate or plot Moran's I, and show any differences in parameter estimates from the non-spatial and spatial models. Cheers Beth. _______________________________________________ R-sig-Geo mailing list R-sig-Geo@stat.math.ethz.ch https://stat.ethz.ch/mailman/listinfo/r-sig-geo