Greetings, I'm attempting to model the spatial distrubution of avian species richness (SR) across a gradient of distrubance. SR was divided into several groups (all, residents, short-, and long-distance migrants) and analyzed separately. I used point counts (0.8 km) apart to get the species richness data and I used CAPTURE to get estimated SR. I'm linking this to remote sensed landscape data using point count stops as centers. Landscape data come from 100, 200, 1000 m around points so there is considerable overlap in the explanatory variables around points BUT the response variable (SR) should be independent from each other. I used generalized additive models and used the graphics output available in SAS to subjectively select variables to use in subsequent Poisson regression models. All the variables that looked meaningful (95% CI departures from 0 at some point) were curvilinear so I used quadratic forms. I also ran a few models with variables that were not correlated (Spearman r < 0.25).
I then ran Poisson or negative binomial models with a portion (80% randomly selected) and I used AIC to sort the models. The top models had "significant" values for the explanatory variables and were markedly different from null models (difference in AIC > 10). Model fit was also satisfactory (var/df ~ 1). I used the betas from the heuristic data set to get predicted values for a hold out sample. Plotting predicted vs. observed I get complete scatter! Is poor prediction a result of autocorrelation? Anything else to consider (other than the obvious problems of poor data)? Thanks, Jeff **************************************** Jeffrey A. Stratford, Ph.D. Postdoctoral Associate 331 Funchess Hall Department of Biological Sciences Auburn University Auburn, AL 36849 334-329-9198 FAX 334-844-9234 http://www.auburn.edu/~stratja ****************************************
