Model-Based Statistical Inference in Ecology

This course is an introduction to the modern theory and practice of scientific data analysis using both standard and innovative approaches. The unifying concepts are those of model, information, and inference. Students will learn and use the basic principles of model formulation, estimation, interpretation, criticism, and refinement.

Specific topics covered will include: exploratory data analysis, data visualization, general and generalized linear models, elements of model structure, stochastic simulation, likelihood, maximum-likelihood and Bayesian inference, and hierarchical/mixed-effects models.

Additional topics that may — according to opportunity and interest — be covered include resampling, survival analysis, time series analysis, dynamical systems models, spatial analysis, and phylogenetic comparative analysis. The course will make use of lectures, readings, and computer exercises in the R statistical computing environment.

Students will obtain hands-on experience in data analysis using data provided by the instructor and/or by students. In particular, students with scientific questions of their own and data sets to analyze will have a chance to work on these in the course.

Although examples will be for the most part drawn from Ecology, students from other disciplines, including Evolutionary Biology and Natural Resources, will learn valuable technique.