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 Québec, 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.
Highlights
Predictive models of animal distribution are central to many fields of theoretical and applied ecology, and wildlife conservation and management strongly rely on such models, e.g., [1,2,3,4]
We related the generative mechanisms to energetic requirements, spatial memory, and habitat characteristics, to represent the trade-offs between costs and benefits of moving, and modelled the movement of caribou monitored by GPS telemetry based on a Step Selection Function calibrated by forward modelling
We modelled a memory effect, 3 days defined as the minimum angle acluster between the step direction and previous clusters of locations, because animals tend to come back to familiar areas for forage and safety [44]. acluster is related to the internal state of the modelled individuals because each modelled individual can remember a limited number of clusters PatchArray, characterized by their locations and order of visits
Summary
Predictive models of animal distribution are central to many fields of theoretical and applied ecology, and wildlife conservation and management strongly rely on such models, e.g., [1,2,3,4]. We related the generative mechanisms to energetic requirements, spatial memory, and habitat characteristics, to represent the trade-offs between costs and benefits of moving, and modelled the movement of caribou monitored by GPS telemetry based on a Step Selection Function calibrated by forward modelling. To assess the differences in the selection of the different landcover types with respect to different availability, we computed, for each simulation run, an RSF model over all the individuals, with random points drawn over the whole study area, P15 taking the form: w(x)~exp( bixi), where xi is a boolean i~1 variable of presence/absence of the corresponding landcover type at the location of the observed of random point.
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