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From Two to Many: Multivariate Statistics

Course Details

  • Course number: 78085-01
  • Credit points: 1 ECTS
  • Dates: 24 March 2026 + 25 March 2026
  • Time: 09:15–16:00 each day
  • Format: In-person, hands-on computer lab
  • Locations:
    Tuesday, 24.03.26: Geographie, Seminarraum 5-05, Klingelbergstrasse 27, 4056 Basel
    Wednesday, 25.03.26: Botanik, Seminarraum 00.005, Schönbeinstrasse 6, 4056 Basel
  • Programme: See Course Schedule
  • Prerequisites: Basic R knowledge (data frames, subsetting, plotting)
  • Preparation: See R Setup & Basics
Handouts

Welcome

This site will guide you through both days of the course and remain a helpful resource long after it ends. Over two intensive days, you’ll gain hands-on experience with the multivariate statistical methods most commonly used in ecology and the life sciences.

Here you will find everything you need:

  • The course schedule and session outlines
  • Lecture slides and supplementary reading materials
  • Hands-on exercises and datasets for practical learning
  • Software installation guides and R code examples
  • FAQs and troubleshooting tips

Whether you are encountering multivariate statistics for the first time or looking to sharpen skills you already have, this course will help you understand the methods deeply enough to apply them confidently to your own data.

Why Multivariate Statistics?

Ecological systems are complex, and a single response variable is rarely enough to describe them. Rather than tracking the abundance of one species, ecologists typically work with entire communities: dozens or hundreds of species measured simultaneously across many sites or treatments.

Add to that the multiple environmental drivers you might want to account for (temperature, soil chemistry, land use, disturbance history) and it quickly becomes clear why running separate univariate tests for each variable is both impractical and statistically problematic. Every additional test chips away at your statistical power and inflates the risk of false positives.

Multivariate methods address this directly. By analysing all variables together, they preserve statistical power, account for correlations between variables, and can reveal patterns and gradients that would be invisible if you examined each variable in isolation. The result is a richer, more honest picture of your system.

Textbooks

These are the key references for the course, ranging from accessible introductions to authoritative ecological treatments:

  • Borcard, D., Gillet, F., & Legendre, P. (2018). Numerical Ecology with R (2nd ed.). Springer. ← Most directly relevant to this course
  • Legendre, P. & Legendre, L. (2012). Numerical Ecology (3rd ed.). Elsevier. ← The comprehensive ecological reference
  • James, G., Witten, D., Hastie, T., & Tibshirani, R. (2021). An Introduction to Statistical Learning (2nd ed.). Free PDF ← Accessible introduction
  • Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning (2nd ed.). Free PDF ← The advanced reference

Online Resources

Instructor

Jean-Claude Walser · GDC, ETH Zürich ✉ jean-claude.walser[🙈]usys.ethz.ch