Hi there! our response variable is the number of grasshoppers (abundance) on ski slopes/non ski study plots in alpine ecosystems. We are interested in the fixed effects of skiing, fertilization, management intensity and other properties of the study plots on grasshopper abundance. We have 82 study plots arranged as pairs (one plot on a ski slope, the other away from the slope), we call this a BLOCK. We studied these plots in three different study areas. This lead us to believe we have a nested design with random effect= 1~AREA/BLOCK data table arranged as follows: AREA BLOCK grasshopper fertilizer management skiing 1 0 8 1 2 0 1 1 4 1 1 1 1 0 16 0 3 0 1 1 3 1 0 1 2 0 4 0 2 0 2 1 5 1 2 1 etc. The number of grasshoppers recorded is a count variable, so we used Poisson-distribution. model1 <- glmmPQL(abundance~fertilizer+management,random=~1| AREA/BLOCK, family="poisson") I was wondering, whether a GLMM like this would be appropriate to decide which predictor variables have distinct effects on grasshopper numbers. We are especially unsure, whether the random effect has been properly defined, because one of the predictor variables (skiing) is also our BLOCK factor. We have 15 predictor variables in total and in order not to overfit our model we did the following: 1. Calculate separate models for single predictor variables (linear, quadratic and logarithmic terms) 2. We included only the most significant terms of each variable in the initial model 3. Model was simplified by stepwise removing variables according to their p-values 4. This was done until all remaining varibles had p-values below 0.05 As I followed the former discussions in your group on p-values, you will probably want to slaughter me for that approach. But I am an absolute beginner in mixed models and it would be very helpful for me to receive a hint, how I could decide which variables play a major role in determining my grasshoppers abundance while taking into account the nested study design structure. Comments would be very welcome!!! Best wishes from cold Berlin, Frank
Prof. Dr. Frank Dziock Animal Ecologist and Head of Department (Juniorprofessor) Department of Biodiversity Dynamics Technische Universitaet Berlin Sekr. AB 1 Rothenburgstr. 12 D - 12165 Berlin Germany Tel: 030 ? 314 71368 Secretary Gisela Falk gisela.falk at tu-berlin.de Tel: 030 - 314 71350 Fax: 030 ? 314 71355 http://www.biodiv.tu-berlin.de/