Animal Tracking in Conservation Biology
MSc Biologia Ambientale — University of Rome Tor Vergata, 2026-2027
Giuliano Colosimo 
Welcome. These pages hold everything you need for Animal Tracking in Conservation Biology: slides, R practicals, datasets and further reading.
Everything here is public and permanent. Slides open in your browser — press F for fullscreen, S for speaker notes, E for print/PDF view, ? for all shortcuts. Practicals are plain web pages you can work through at your own pace.
What this course is about
Biologging has changed what we can ask about animals. A collar that reports a position every hour produces more raw observations in a week than a generation of field naturalists could collect in a career. The bottleneck is no longer data — it is asking a well-posed question and knowing which analysis answers it.
The spine of the course is the movement-ecology paradigm (Nathan et al. 2008): every path is the joint outcome of an animal’s internal state, its motion capacity, its navigation capacity, and the external factors around it. Tracking data record only the path. Every session is an attempt to reason back from the path to those four components — and to be honest about how far the data let us go.
Every concept is paired with code you run yourself, and three sessions use simulated animals whose true behaviour we know, so you can see exactly how each method succeeds and fails.
Course at a glance
| Degree | MSc Biologia Ambientale |
| Credits | 2 CFU — 16 hours |
| Structure | 8 sessions × 2 hours: ~45 minutes of theory, ~75 minutes of R |
| When | Fridays 11:00–13:00, 16 October – 4 December 2026 — see the calendar |
| Language | English |
| Software | R, RStudio, Quarto — all free and open source |
| Assessment | Reproducible report, weekly practicals, participation — see the syllabus |
The eight sessions
Session 1
Why animals move, and how we watch them
The movement-ecology paradigm; the ecological drivers of movement and their trade-offs; tracking technologies and the 3–5% rule; how the fix interval shapes the path you see.
Session 2
Spatial data in R: the survival kit
Vector and raster, coordinate reference systems, choosing a projection for the analysis, the sf and terra stack, and where environmental rasters come from. Ends with a publication-quality map.
Session 3
From fixes to trajectories
Positions are not paths. Location error, cleaning, bursts, gaps and resampling; which analyses tolerate irregular sampling; annotating a track with NDVI matched in time.
Session 4
Quantifying movement
Net squared displacement, straightness, first-passage time; classifying movement strategies; thresholds and behavioural change-point analysis — and whether their answers survive a change of fix rate.
Session 5
Movement models and simulation
Random, correlated, biased and Lévy walks. You write a simulator from scratch, use it as a null model for real buffalo, and build the ground-truth animal for the next two sessions.
Session 6
Home ranges and space use
MCP, KDE, Brownian bridges and autocorrelated KDE on the same animal; variograms and range residency; and a test of every estimator against a simulated animal whose true home range we know.
Session 7
Habitat selection on the move
Resource and step selection functions, integrated step selection, and relative selection strength — fitted to New York fishers, and checked against a simulated animal with a known selection coefficient.
Session 8
Before the first session
- Install R, RStudio and the course packages — see Software setup.
- Download the course project, which includes the datasets.
- Bring a laptop. Every session has hands-on time.
Reusing this material
All material is released under CC BY-NC-SA 4.0. Source code lives on GitHub — corrections and issues are welcome.