[R-sig-ME] Data structure issue for GLMM models

[Anne Blach Overgaard]

Dear List,
I hope that some of you may be able to help us with a data structure issue.
We have collected plant cover data (count data) for selected species along a climatic gradient in random stratified sampling plots. The hierarchical structure of the data is as follows:
We have sampled at five sites placed along a large-scale climatic gradient. Within each of the five sites we placed three plot groups 500 meters apart on each of the altitudes 20 m 100, 200, 300, 400 and 500 m above sea level, whenever possible, as not all isoclines were present at each site. Each plot group consisted of six plots that were placed 10 meters apart.
In total we have 5 sites x a varying number of altitudes per site x 3 plot groups per altitude x 6 plots = 414 plots in the entire data set.
Overall we would like to assess the relative importance of different predictor groups (altitude, climate, and biotic interactions) on the variation in cover per species. We are including the predictor groups as fixed effects in our models using lme4::glmer (family = poisson). We include site and plot group as nested random effects and plots as an observation-level random factor due to overdispersion in the data.
Our question is whether altitude should be entered as a random factor, as a fixed effect, or possibly as both a fixed and a random effect. Altitude is a part of the nested structure of the data, but we also have an interest in including it as a fixed effect to assess how much of the variation in the data is due to altitude.
We hope that some of you can guide us how to deal with altitude in this data analysis.
Thanking you in advance.
Best regards,
Anne & co-workers





	[[alternative HTML version deleted]]