In a recent paper in TREE, Ben Bolker (from the University of Florida) and colleagues describe the use of generalized linear mixed models for ecology and evolution. GLMMs are used more and more in evolution and ecology given how powerful they are, basically because they allow the use of random and fix effects and can analyze non-normal data better than other models. The authors made a really good job at explaining what to use when. Despite the fact that you need more than basic knowledge of stats to fully understand this guide, I think that people should take a look at it before starting to plan their projects, since it outlines really well all the possible alternatives (and challenges) that one can have when analyzing data. This article also describes what is available in each software package; this is really useful since is not obvious with program in SAS or R you need to use when dealing with some specific GLMMs.
Bolker, B., Brooks, M., Clark, C., Geange, S., Poulsen, J., Stevens, M., & White, J. (2009). Generalized linear mixed models: a practical guide for ecology and evolution Trends in Ecology & Evolution, 24 (3), 127-135 DOI: 10.1016/j.tree.2008.10.008