IBIO 860 - Modern Statistical Models in Ecology (Spring)
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 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.
IBIO/PLB/ENT 831 - Statistical Methods in Ecology and Evolution II (Spring)
This graduate level survey course is the second semester in a two semester sequence (830 is a required prerequisite), focused 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 modern methods for estimation and inference using the R computing language.
Topics covered in 830: Quantitative toolbox, introduction to R, reproducibility and responsible coding practices, Rmarkdown, GitHub, plotting, navigating errors/getting help, study design, introduction to probability theory, demystifying probability distributions, introduction to deterministic relationships, likelihood-based inference, Bayesianism vs. Frequentism, modes of inference. (Co-taught by Marjorie Weber and Gideon Bradburd) Topics covered in 831: Frequentist and Bayesian inference, linear models, generalized linear models, mixed and random effects, hierarchical models, zero-inflation models, model comparison and evaluation, power analyses, numerical simulations, temporal and spatial correlations, and a holistic view of quantitative tools throughout the scientific process.
Co-taught by Elise Zipkin and Will Wetzel in 2019 and 2020. Currently taught by Will Wetzel.