Teaching

I normally teach ESPM 173 in fall semesters and ESPM 215 in spring semesters.

This is changing at least temporarily for Spring 2026, when I will teach ESPM 173 and not ESPM 215.

ESPM 173: Introduction to Analysis of Ecological Data

Fall semesters

This is a first course in Statistics oriented towards environmental science and biology students. We use the third edition of The Analysis of Biological Data by Whitlock & Schluter.

There are lower level intro stats courses at Berkeley (e.g. Stat 2 and Stat 20) that work well for many students. ESPM 173 goes farther than these courses. It also includes introductory use of R. Students interested in the subject matter of biology and environmental science, particularly ecology and evolution, often prefer to learn Statistics in the context of their interests. Students are advised to think carefully about whether they want a gentler (other options) versus faster (ESPM 173) intro stats course.

Course Topics

  • Descriptive statistics and data visualization
  • Probability and sampling distributions
  • Hypothesis testing and confidence intervals
  • Regression and correlation
  • Analysis of variance (ANOVA)
  • Chi-square tests
  • Introduction to R programming

ESPM 215: Hierarchical Statistical Modeling in Ecology

Spring semesters

This is a two-unit seminar for graduate students. The format and content of this seminar evolves each time.

Recent Topics Include

  • Mixed-effects models
  • Generalized linear models (GLMs)
  • Bayesian methods
  • State-space models
  • Spatial capture-recapture
  • NIMBLE software for hierarchical modeling

ESPM 174

ESPM 174 is not being taught on a regular basis right now.

Teaching Philosophy

I believe in learning statistics through hands-on experience with real data and biological problems. My courses emphasize:

  • Active learning: Students work with data throughout the course
  • Computational skills: Learning R as a tool for statistical analysis
  • Biological context: Statistics problems drawn from ecology and evolution
  • Critical thinking: Understanding when and why to use different statistical methods