Biol 206: Methods in Biology of Organisms
I coordinate Biol 206, which is held every fall. We cover a range of methods used in organismal biology with an emphasis on introductory level statistics. The course is based around 3-4 modules on topics including: urban ecology, the evolution of form and function, parasitism, and citizen science. Students collect their own data, form their own hypotheses and analyses their own data.
Biol 310: Biodiversity and Ecosystems
Biol 310 is a team-taught class where we progress through concepts in ecology and biogeography that allow us to understand how and why biodiversity is distributed across the landscape and organized into ecosystems. Topics include: community assembly, species-area relationships, alpha/beta/gamma diversity patterns, functional ecology, food webs, and diversity change over time.
Biol 651: Living Data Project
The Living Data Project is a Canadian-wide initiative to rescue important ecological and evolutionary data from languishing in dusty file cabinets and hard drives. Graduate students can be involved in the courses, workshops, and data rescue internships.
The courses are 1-credit modules taught by instructors and Living Data postdocs and cover scientific collaboration, data management, reproducibility and synthesis statistics. For more information, see our OSF page..
BIOL 603: Organismal Biology Research and Professional Skills
This course covers the skills graduate students need to be successful in their degrees. We focus on critical thinking skills, peer review, communicating your research, and writing your research proposal.
Biol 5xx: Topics in Macroecology
This course will be a top-down view of the patterns and processes that shape biodiversity. The course will include short lectures, discussions of (mostly) recent hot-topic papers, and some computer sessions on methods for analyzing ‘big’ ecological data.
Biol 5xx: Ecological Modelling and Statistics
This course is currently being developed with Brian Leung and will cover intermediate level statistics and ecological modelling. We begin with generalized linear models (GLMs) and work our way through hierarchical models. We will explore parameter estimation using maximum likelihood and Bayesian approaches. Although we will cover a range of fundamental topics, we will also dive in a bit more each year into a focal topic of interest to students in the course.