Reddick, Edward2011-01-102011-01-102011-01-10http://hdl.handle.net/10222/13184Inference in generalized linear mixed models (GLMM) remains a topic of debate. Baayen, Davidson, and Bates (2008) outlines criticism against conventional ways of performing inference for GLMMs. There are various alternatives proposed but lit- tle consistency is found on which is the most reasonable. Our focus is on assessing temporal trends for mainly ecological count data. That is, we hope to provide a prag- matic approach to Poisson GLMMs for ecological researchers within the statistical programming environment R. To achieve this, we start by providing a description of the selected estimation and inferential procedures. We then complete a large scale simulation to evaluate each of the estimation methods. We implement a power analy- sis to assess each of the selected inferential procedures. We then go on to apply these procedures to data sampled by The National Parks of Canada. Finally, we conclude by giving a summary of our ?ndings and outlying work for the future.enGLMMPoissonTemporal TrendP-valueParametric Bootstrapping