Courses

Forest Ecology and Management

EAS 447 - Fall

In this course, we use ecological concepts as they apply to forests in the context of current forest ecology research and forest management. We study the biological and ecological basis behind the current challenges forest ecosystems face under global change (i.e., climate change, landscape fragmentation, pollution, introduced species). We also review the role and impact of humans on these communities, focusing on the services forests ecosystems provide and the emergence of urban ecology.

Part of the course involves developing forest research and management plans, in which we apply the concepts reviewed in both lecture and lab to real world problems. Students have the choice to work on their plans as either a research project or a management project. Each student presents a management/research plan during the presentation sessions scheduled at the end of the semester.

Analysis and Modeling of Environmental Data

EAS 549 - Winter odd years

This course will consist on an overview of standard and innovative techniques in environmental data analysis and modeling. Topics will include: linear regression, mixed effects models (fixed and random effects), maximum likelihood, general linear models and general additive models, survival analysis, time series, spatial analysis and Bayesian and hierarchical Bayesian approaches. The course will be a combination of lectures and computer labs, for which we will be using two open source programs, R, JAGS and OpenBUGS.

The course is designed for students to work on their own data, or simulated data, related to their research projects or scientific interests. While reviewing the major statistical techniques, students will work on their projects and will be presenting their work to the class along the semester, these presentations will consist on: initial exploratory data analysis, selection of statistical analysis or modeling approach, implementation, and results.

By the end of course students will have an understanding of the basic approaches used in ecological data analysis and modeling and they will be ready to jump-off into more advance understanding and use of these methods. They will have become proficient in the specific techniques chosen for their work and will have the necessary knowledge and tools to understand work done using all the other methods addressed in the lectures.

Woody Plants

EAS/EEB/ENVIRON 436 - Fall

Woody Plants is an intensive field- and lecture-based learning experience, in which you will learn to identify 140 woody plant species (trees, shrubs and vines) that are important in Michigan environments. You'll learn about their taxonomy, distribution, habitat associations, and biogeographic history and even how to identify them in their leafless winter condition. The lab component (see web page on field sites) consists of weekly field trips in the Ann Arbor area, which include riparian and floodplain habitats, glacial lakes, moraines, bogs, fens and mesic forests. The lectures cover elementary aspects of plant identification, taxonomy and ecology; however, the broader themes include biogeographic history and the assembly of Michigan plant communities, both before and after major glaciations, ecological specialization, and impacts of global warming and other anthropogenic environmental changes.

Natural Resource Statistics

EAS 538 - Fall

The study of natural resources, sustainability, and the environment is increasingly focused on quantitative methods to characterize systems, test hypotheses, and develop solutions to real-world problems. As such, an understanding of statistical analyses is essential to anyone working in these fields. This course covers applied introductory statistics, focusing on when and why different statistical techniques should be used to analyze different datasets. Through this course, students will be introduced to one of the most common statistical programming languages, R.