Uniting Statistical and Individual-Based Approaches for Animal Movement Modelling

Authored by Lael Parrott, Guillaume Latombe, Mathieu Basille, Daniel Fortin

Date Published: 2014

DOI: 10.1371/journal.pone.0099938

Sponsors: National Science and Engineering Research Council of Canada (NSERC) Canada Foundation for Innovation (CFI)

Platforms: No platforms listed

Model Documentation: Other Narrative Mathematical description

Model Code URLs: Model code not found

Abstract

The dynamic nature of their internal states and the environment directly shape animals' spatial behaviours and give rise to emergent properties at broader scales in natural systems. However, integrating these dynamic features into habitat selection studies remains challenging, due to practically impossible field work to access internal states and the inability of current statistical models to produce dynamic outputs. To address these issues, we developed a robust method, which combines statistical and individual-based modelling. Using a statistical technique for forward modelling of the IBM has the advantage of being faster for parameterization than a pure inverse modelling technique and allows for robust selection of parameters. Using GPS locations from caribou monitored in Quebec, caribou movements were modelled based on generative mechanisms accounting for dynamic variables at a low level of emergence. These variables were accessed by replicating real individuals' movements in parallel sub-models, and movement parameters were then empirically parameterized using Step Selection Functions. The final IBM model was validated using both k-fold cross-validation and emergent patterns validation and was tested for two different scenarios, with varying hardwood encroachment. Our results highlighted a functional response in habitat selection, which suggests that our method was able to capture the complexity of the natural system, and adequately provided projections on future possible states of the system in response to different management plans. This is especially relevant for testing the long-term impact of scenarios corresponding to environmental configurations that have yet to be observed in real systems.

Tags

Habitat selection Resource selection functions State-space models Conditional logistic-regression Yellowstone-national-park Multiple spatial scales Adult female caribou Woodland caribou Heterogeneous landscapes Functional-responses