# Uniting Statistical and Individual-Based Approaches for Animal Movement Modelling

> Can a computer model predict how caribou will move when a forest changes? Using GPS data from 27 woodland caribou in Québec, researchers built a model that accounts for hunger and memory. When simulated logging turned forest to deciduous stands, the virtual caribou changed their habitat choices in ways that could bring them closer to wolves.

Summary by Kersey Lawrence, Original Wisdom. Updated 7 November 2026. Page: https://www.originalwisdom.com/tracking-literature/2014-latombe-parrott-caribou-movement-model/

## Citation and access

- **Authors:** Guillaume Latombe, Lael Parrott, Mathieu Basille, Daniel Fortin
- **Year published:** 2014
- **Journal or source:** PLoS ONE 9(6): e99938
- **Full citation:** Latombe, G., Parrott, L., Basille, M., and Fortin, D. 2014. Uniting Statistical and Individual-Based Approaches for Animal Movement Modelling. PLoS ONE 9(6): e99938.
- **DOI:** 10.1371/journal.pone.0099938
- **Access status:** Open access (CC BY)
- **PDF:** https://www.originalwisdom.com/wp-content/uploads/2026/11/2014_Latombe_and_Parrott_etal_uniting-statistical-and-individual-based-approaches-for-animal-movement-modelling.pdf

## Tags

- **Species:** Mammals > Woodland caribou
- **Region:** North America > Canada
- **Behaviour and ecology:** Behaviour; Feeding; Habitat selection; Home range; Movement
- **Methods and technology:** Global Positioning System (GPS) collars; Individual-based models; Movement simulation; Resource selection function; Step selection function
- **Data type:** Modelling or theoretical; New field data
- **Discipline:** Behaviour and ecology
- **Year published:** 2014

## Detailed keywords

- **Species:** Studied: woodland caribou (Rangifer tarandus caribou), threatened in the Canadian boreal forest; 27 GPS-collared individuals. Food: terrestrial lichens. Discussed but not modelled: moose (Alces alces) and gray wolf (Canis lupus).
- **Location and habitat:** Managed boreal forest of the Côte-Nord region, Québec, Canada (50°N to 52°N, 68°W to 71°W); caribou density 1.9 per 100 km² in March 2007. Land cover in 15 classes, including open conifer stands with lichen, heath, wetlands, burned areas, recent and regenerating cuts and roads.
- **Topics:** individual-based (agent-based) modelling of animal movement; combining statistical and simulation models; internal states (stored energy and spatial memory) in habitat selection; costs and benefits of moving; lichen foraging and energy budgets; avoidance of roads and cuts; functional response in habitat selection; model validation; forest harvesting and hardwood encroachment scenarios; apparent competition with moose and wolves
- **Statistics or models:** Biased correlated random walk individual-based model; step selection function fitted by conditional logistic regression (20 random steps per observed step) with robust sandwich standard errors and clusters of autocorrelated steps; quasi-likelihood information criterion (QIC) for three candidate models; 5-fold cross-validation with Spearman rank correlation; resource selection functions by generalized linear mixed models; Pearson and Spearman correlations; Kruskal–Wallis and Wilcoxon–Mann–Whitney tests; Michaelis–Menten functional response for lichen intake; 95% minimum convex polygon home ranges.
- **Data type:** New field data: 27 caribou with GPS or ARGOS/GPS collars from 2005 to 2009, locations every 4 hours, winter periods (28 December to 15 April); Landsat land cover (2000) updated yearly with cut and burn data; elevation model. Modelling: an individual-based model and simulations of two landscape scenarios. Inconsistencies in the paper: (1) the methods say 27 caribou were monitored, but Figure S2 refers to 22 radio-collared caribou; (2) the simplest model is called the “memory model” in the text and the “Mechanistic model” in Table 3; (3) the text says the “best” and “best90” methods performed significantly better than the others, but for Pearson R on all coefficients the “best” method gave p = 0.0738.

## Synopsis

### 1. What they did

Predicting where animals will go when landscapes change needs models that account for their internal state, such as hunger and memory, which are hard to measure in the field. The authors combined a statistical model of habitat selection with an individual-based simulation model, using GPS data from 27 woodland caribou in winter in the managed boreal forest of Québec.

They replayed each real caribou’s movements in a simulated landscape to calculate hidden variables (energy gained from lichen against energy spent moving, energy stored over three days, and memory of previously visited patches), then fitted a step selection function using those variables plus canopy cover, altitude and distances to roads and cuts. They checked the model by cross-validation and by comparing simulated and real habitat selection and home ranges, then ran it on the current landscape and on one where 70% of each 225-km² block became mixed and deciduous forest.

### 2. What they found

- The model with memory, energy and environmental variables explained caribou movements best.
- Caribou moved back towards previously visited patches, chose steps with a high ratio of energy gained to energy spent, preferred lower canopy cover and higher altitude, and avoided roads and regenerating cuts.
- When their energy over the previous three days was high, caribou selected open, low-cover areas even more.
- The model was robust in cross-validation (rs = 0.94), and simulated caribou chose land cover types much like real ones; simulated home ranges were slightly smaller than real ones.
- In the hardwood encroachment scenario, simulated caribou selected fixed open areas, burned areas and water less, and selected mixed and deciduous forest, which they had avoided in the current landscape. Home range sizes did not differ between scenarios.

### 3. What they concluded

Using a statistical model to set the rules of an individual-based model, and generating hidden internal-state variables by replaying real movements, gives a robust model that can predict how animals will respond to landscapes not yet observed. The simulated change in habitat selection with hardwood encroachment could expose caribou to more wolves, because more mixed and deciduous forest is likely to mean more moose and therefore more wolves.

### 7. Implications for conservation (from the paper)

- Woodland caribou are threatened in the Canadian boreal forest, most likely because of human activities such as forest harvesting. Models like this can test the long-term effects of forest management plans before they are applied, including the risk of increased wolf predation through more moose.

## Cite this summary

Lawrence, K. 2026. Summary of: Uniting Statistical and Individual-Based Approaches for Animal Movement Modelling (Latombe and Parrott et al., 2014). Original Wisdom, Tracking Literature. https://www.originalwisdom.com/tracking-literature/2014-latombe-parrott-caribou-movement-model/