Quantitative Methods in Ecology and Evolution (Fall)
This graduate level survey course focuses on the fundamental elements of data analysis in the fields of ecology and evolution. Students will learn how to interpret and model biological data with computationally intensive methods for estimation and inference using the R language. Topics include probability theory, frequentist and Bayesian inference, linear models, generalized linear models, mixed and random effects, hierarchical models, zero-inflation models, power analyses, and model selection.
This graduate level course provides an introduction to modern statistical models used in the analysis of population and community dynamics in ecology. The class covers some theory but primarily focuses on practical applications including model development and analysis using the programs R, BUGS, and JAGS. The first third of the class reviews (generalized) linear (mixed) models and their use in ecology. The remainder of the course explores more advanced topics including state-space models, mark-recapture models, binomial mixture models for estimating population abundance and demographic rates from count data, occupancy models for the analysis of species distributions, and integrated population models.