Animal Movement Models with Mechanistic Selection Functions
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Most statistical inference for animal trajectories has primarily arisen from so-called resource selection analyses. While these procedures provide inference relative to approximations of point process models, they have been limited to a few types of specifications that provide inference about relative use and, less commonly, probability of use. For more general spatio-temporal point process models, the most common type of analysis proceeds with a data augmentation approach that is used to create a binary data set that can be analyzed with conditional logistic regression. We show that the conditional logistic regression likelihood can be generalized to accommodate a variety of alternative specifications related to resource selection. We then provide an example of such a case where the resulting inference coincides with that implied by a mechanistic model for movement expressed as a partial differential equation derived from first principles of movement. By analyzing a set of telemetry data from a mountain lion in Colorado, USA, we demonstrate that inference can be made on residence time in units that are meaningful for management and conservation actions in addition to understanding the effects of spatially explicit environmental conditions on movement behavior.
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