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An animal movement model incorporating home range and habitat selection

In short: How do you tell where a bear prefers to be from where it simply happens to wander? This 2008 paper built one model that estimates an animal’s home range and habitat preferences together from GPS collar data. They tested it on a female brown bear in Southeast Alaska during the salmon run, who strongly favoured large forest and avoided open, non-forested ground.

Summary by Kersey Lawrence, Original Wisdom · Updated

Synopsis

1. What they did

Wildlife biologists usually study two questions separately: where an animal lives (its home range), and which habitats it prefers within that area. The authors argue the two are linked, because animals move to reach the habitats they need. So they built a single model that estimates both at once. It also deals with a problem of frequent GPS locations: each location depends on the one before, so they are not independent.

The model combines three parts: a random step from the animal’s last location, a pull back toward the centre of its home range, and a preference for some habitats over others. It also copes with missed GPS locations.

They tested it on one female brown bear in Southeast Alaska. Her GPS collar was set to take a location every 20 minutes for 11 days during the October 2001 salmon run. Early-generation antenna and satellite problems meant many locations were missed, leaving 157. Habitat came from a map of 50 m squares with seven habitat types, plus distance to the nearest stream.

2. What they found

  • Large forest was strongly preferred, and medium forest less so.
  • Clearcut, scrub forest and small forest could not be told apart statistically.
  • Nonforest was strongly avoided. The bear was never located there, although it was within reach.
  • Tidal flats were left out, because they were too rarely within reach to estimate a preference.
  • The likelihood of the bear’s next location dropped by about 1% for every 50 m away from a stream.
  • The model also produced a home range map (a 95% contour) with uncertainty bands. The outer band was larger than the data alone suggest, because the locations were strongly linked over a short period.

3. What they concluded

  • Movement, home range and habitat preference can be estimated together in one model. This avoids the researcher deciding, often arbitrarily, which habitat counts as “available” to the animal, and it accounts for linked GPS locations.
  • Limitations: the method becomes slow with many locations or many habitat types. Home range shape and habitat effects can be confused when habitats run in long strips, as here. Some estimation issues remained under investigation.
  • Next steps: a model covering several animals at once, to estimate selection for both individuals and populations. The authors provide their R code.

6. Other technologies, skills or innovations

  • A GPS collar set for 20-minute locations; early antenna and satellite problems caused many missed locations
  • A habitat map in a Geographic Information System (GIS) raster of 50 m squares, including distance to streams
  • Bayesian Markov chain Monte Carlo (MCMC) estimation written in R

8. Implications for tracking (Original Wisdom's observations)

These are Original Wisdom's observations, not the authors'.

  • The authors note that telemetry shows where an animal was, but not what it was doing there: feeding, sleeping or just passing through. Tracks and signs at those locations can supply exactly that, for example fishing sign at salmon streams or bed sites in large forest.

Questions and answers

What did Christ and Ver Hoef et al. (2008) study, and how?

How do you tell where a bear prefers to be from where it simply happens to wander? This 2008 paper built one model that estimates an animal’s home range and habitat preferences together from GPS collar data. They tested it on a female brown bear in Southeast Alaska during the salmon run, who strongly favoured large forest and avoided open, non-forested ground. Read the full section

What did Christ and Ver Hoef et al. (2008) find?

Large forest was strongly preferred, and medium forest less so. Clearcut, scrub forest and small forest could not be told apart statistically. Nonforest was strongly avoided. The bear was never located there, although it was within reach. Read the full section

What did Christ and Ver Hoef et al. (2008) conclude?

Movement, home range and habitat preference can be estimated together in one model. This avoids the researcher deciding, often arbitrarily, which habitat counts as “available” to the animal, and it accounts for linked GPS locations. Read the full section

Cite this summary

Lawrence, K. 2026. Summary of: An animal movement model incorporating home range and habitat selection (Christ and Ver Hoef et al., 2008). Original Wisdom, Tracking Literature. https://www.originalwisdom.com/tracking-literature/2008-christ-ver-hoef-animal-movement-model-incorporating/

For the paper itself, cite the full citation above.

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