Abstract
It is usually assumed that landscape heterogeneity influences animal movements, but understanding of such processes is limited. Understanding the effects of landscape heterogeneity on the movements of large herbivores such as North American elk is considered very important for their management. Most simulation studies on movements of large herbivores use predetermined behavioral rules based on empirical observations, or simply on what seems reasonable for animals to do. Here we did not impose movement rules but instead we considered that animals had higher fitness (hence better performance) when they managed to avoid predators, and when they acquired important fat reserves before winter. Individual decision-making was modeled with neural networks that received as input those variables suspected to be important in determining movement efficiency. Energetic gains and losses were tracked based on known physiological characteristics of ruminants. A genetic algorithm was used to improve the overall performance of the decision processes in different landscapes and ultimately to select certain movement behaviors. We found more variability in movement patterns in heterogeneous landscapes. Emergent properties of movement paths were concentration of activities in well-defined areas and an alternation between small, localized movement with larger, exploratory movements. Even though our simulated individuals moved shorter distances that actual elk, we found similarities in several aspects of their movement patterns such as in the distributions of distance moved and turning angles, and a tendency to return to previously visited areas.
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Morales, J., Fortin, D., Frair, J.L. et al. Adaptive models for large herbivore movements in heterogeneous landscapes. Landscape Ecol 20, 301–316 (2005). https://doi.org/10.1007/s10980-005-0061-9
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DOI: https://doi.org/10.1007/s10980-005-0061-9