Analyzing Habitat Selection of Animals from Telemetry Data

Analyzing Habitat Selection of Animals from Telemetry Data

Dates

20-23 April 2026

To foster international participation, this course will be held online

Course overview

This advanced course builds upon the Introduction to Analyzing Animal Movement with R course. It focuses on modeling habitat selection using telemetry data in R. Participants will learn to fit and interpret Habitat Selection Functions (HSFs)—also known as Resource Selection Functions (RSFs)—and gain an in-depth understanding of Step Selection Functions (SSFs). The course also explores model extensions, validation techniques, and integrating behavioral states using Hidden Markov Models (HMMs).

Target Audience and Assumed Background

This course is designed for graduate students and researchers with a working knowledge of R and RStudio, and prior experience analyzing movement data (as covered in the introductory course). A basic understanding of regression models will be helpful.
Example datasets and code will be provided, but participants are encouraged to bring their own telemetry data for hands-on exploration.

Learning outcomes

By the end of the course, participants will be able to:

Fit HSFs (RSFs) and SSFs to animal movement data.
Interpret the results of HSFs and SSFs.
Extend RSF and SSF models to include non-linear effects (e.g., splines) and account for multiple animals using mixed-effects models.
Validate RSFs and SSFs.
Simulate movement paths from fitted SSFs.
Incorporate behavioral states into SSFs by coupling them with Hidden Markov Models (HMMs).

Foundational concepts and introductory methods for analysing animal movement data are covered in the course Introduction to Animal Movement with R, given by the same instructors.