Instructor:

Wendy Palen (wpalen@sfu.ca) B8277, 2-4063

Meeting location and time:

Monday, Wednesday, and Thursday. Mix of lectures, computer-based exercises, and discussion. An equal or greater amount of time will be spent on out of class assignments including readings, homework, and development of materials for the group topic.

**Check the Announcements page above for readings, downloads, etc. prior to class each week**

Purpose: To provide a foundation of knowledge and practical experience modeling population dynamics for graduate students interested in ecology, evolution, natural resource management, and conservation practice. Students are not expected to have extensive backgrounds in modeling or theory at the start of this course, but do expect that students will be active participants in the course though hands-on assignments, discussions, and computer exercises outside of class. A basic working knowledge of R will be extremely useful, but not essential for the course.

The first half of the semester (5-6 weeks) will focus on the theory and practice of building structured matrix population models (structured by age, stage, sex, space, etc.) primarily using R. Students will gain an understanding of matrix theory and assumptions, mathematical execution, how to incorporate stochasticity, density dependence, and more complex feedbacks, as well as understand practical considerations regarding data collection and model parameterization. During the second half of the semester, small groups of (2-3) students with similar interests will work with the instructor to develop materials to learn one population modeling approach of their choosing (TBD during the first 2 weeks of the course). Groups will identify appropriate background reading and lead an exploration and discussion of the topic, introducing relevant R-code if possible and useful.

Tentative Class Schedule

Course Schedule

Jan. 28 Introduction to population models

Jan. 30 Structured demographic models

Jan. 31 Matrix models 1 & computer session

Feb. 4Matrix models 2 –adding complexity

Feb. 6 Matrix models 3 –adding stochasticity

Feb. 7        Stochasticity computer session

Feb. 11 Matrix models 4 –correlation, param. estimation*

Feb. 13Final projects 1

Feb. 14Final projects 2