This course uses modern Bayesian statistical modeling approaches to analyze ecological and environmental data, with an emphasis on applied hierarchical models. This course will focus on models that are used to examine population and community dynamics, spatial patterns, species abundance and diversity, community organization, metapopulations, and landscape processes. To be successful in the course students should have taken a course in statistics and have working knowledge of the R programming language.

This course is an introduction to data management, analysis, and visualization using the R programming language. Class topics and examples will have an emphasis on problems in environmental science (both natural and social science examples). The course is broken down into three sections: 1. Working with Data, 2. Statistics and Modeling, and 3. Advanced Topics Classes will consist of short introductions to new concepts followed by hands-on computing exercises using R. No backgroundin programming is required. Students are encouraged to bring their own laptop, either Mac or PC, to this course.