Students must satisfy the requirements for both their home department and the EEB program to obtain a dual degree PhD or a specialization within their MS. Curriculum requirements for departments can be obtained from the department's graduate office.
To obtain an EEB dual-degree or specialization, the following criteria must be met:
- The major advisor (committee chair) of the student must be an EEB core or affiliated faculty member.
- The student's guidance committee must have at least two EEB core or affiliated faculty members for PhD students and one for MS students. Typically, the guidance committee consists of 4-5 members for PhD students and 2-3 members for MS students. The guidance committee aids the student in designing an appropriate and individualized program of course work (beyond the required courses) and in developing a research project that will lead to an original thesis (MS) or dissertation (PhD).
- The student must complete four graduate-level courses in ecology, evolution, and quantitative methods:
- One 800-level course in ecology (3 credits)
- One 800-level course in evolution (3 credits)
- Two courses in quantitative methods (IBIO/PLB/ENT 830-831; 6 credits)
While EEB allows students to choose their specific ecology and evolution courses based on their backgrounds and interests, all students are required to take the 830-831 quantitative methods course sequence. EEB's quantitative training program is both rigorous and comprehensive. Students gain proficiency in R programming and a deep understanding of classical and modern approaches to data analysis, providing a strong foundation for the many advanced statistical and computational courses that MSU offers.
Formal graduate training is supplemented by a number of academic and professional events sponsored by EEB. Students are strongly encouraged to regularly attend the weekly seminar series, bi-weekly student colloquia, and the annual symposium. Every EEB student should plan to present at least once at colloquia (work in progress) as well as at the annual symposium (completed projects) during their tenure at MSU. Students should consult their guidance committees for more information.
For detailed information on the program please refer to the EEB Graduate Student Handbook. We encourage all EEB graduate students and faculty advisors to regularly review the MSU Guidelines for Graduate Student Advising and Mentoring.
MSU Courses That Satisfy EEB Requirements
Ecology Course (800 Level - One Course)
- IBIO/PLB 896 Population and Community Ecology (core course; every fall)
- PLB 826 Tropical Biology: An Ecological Approach (OTS)
- FW 840 Landscape Ecology
- IBIO/FOR 870 Spatial Ecology
- IBIO 897 Ecosystem Ecology & Global Change
Most EEB students take Population and Community Ecology (IBIO/PLB 896), which is considered a core EEB course as it provides the most general overview of ecology. However, some students choose a more specialized course to fulfill the requirement. Of course, more than one ecology course may be taken upon suggestion by the student's guidance committee.
Evolution Course (800 Level - One Course)
- IBIO/PLB849 Evolutionary Biology (core course; every spring)
- FW828 Conservation Genetics
Most EEB students take Evolutionary Biology (PLB/IBIO 849), which is considered a core EEB course as it provides the most general overview of evolution. However, some students choose a more specialized course to fulfill the requirement. Of course, more than one evolution course may be taken upon suggestion by the student's guidance committee.
Quantitative Methods Courses (800 Level - Two Courses)
All EEB students are required to complete the following two-semester course sequence:
- IBIO/PLB/ENT 830 Statistical Methods in Ecology and Evolution I (every fall)
- IBIO/PLB/ENT 831 Statistical Methods in Ecology and Evolution II (every spring)
This two semester quantitative course sequence focuses on the fundamental elements of data analysis in the fields of ecology and evolution. Students learn how to interpret and model biological data with modern methods for estimation and inference using the R computing language.
Topics include: reproducibility and responsible coding practices, navigating coding errors, study design, probability theory, likelihood-based and Bayesian inference, generalized linear models, mixed and random effects, model comparison and evaluation, power analyses, and numerical simulations.