This course provides a theoretical and quantitative exploration of the processes and principles associated with population dynamics. We will learn about key ideas in population ecology (such as density dependence, competition, evolution, predation, and parasitism) and then learn about how to represent these theories as mathematical models. We will learn to use the programming language R to implement these models and visualize the results. Possible topics include fitting theoretical models to data, general linear statistical models, multivariate analysis, model comparison, and sensitivity analysis. This course is relevant and generalized for all biology and environmental science students and examples will include a range of ecosystems.
- Learn key concepts in population ecology.
- Connect ecological theories with the elements of theoretical models representing them.
- Create your own R programs to simulate theoretical model results.
- Fit theoretical models to population data and compare the fit of multiple potential models.
- Explain what a sensitivity analysis shows about a model and its parameters.
- Complete a project involving research, developing a question, defining a theoretical model, implementing that model, assessing the results, and communicating the results in writing and orally.
Delivery Method: Fully in-person
Prerequisites: To be ready to take this course, students need to have completed at least one ecology or population biology class and either have completed or also be co-enrolled in Statistical Methods for Data Analysis or another R-based statistics course (or permission of the instructor). Fill out this form by November 28, 2022 to request enrollment in this course. Full consideration will be given for all responses recieved by 8am on Monday, Nov. 28.
Course Level: 4000-level
T/F 2:10PM - 4:00PM (Full-term)
Maximum Enrollment: 18
Course Frequency: One time only
Categories: 4000 , All courses , Biology , Environment , Four Credit , Fully In-Person , Mathematics
Tags: climate change , Complex systems , data analysis , dynamics , Environment , Nature , problem solving