Abstract
Animal movement along repeatedly used, “habitual” routes could emerge from a variety of cognitive mechanisms, as well as in response to a diverse set of environmental features. Because of the high conservation value of identifying wildlife movement corridors, there has been extensive work focusing on environmental factors that contribute to the emergence of habitual routes between protected habitats. In parallel, significant work has focused on disentangling the cognitive mechanisms underlying animal route use, as such movement patterns are of fundamental interest to the study of decision making and navigation. We reviewed the types of processes that can generate routine patterns of animal movement, suggested a new methodological workflow for classifying one of these patterns—high fidelity path reuse—in animal tracking data, and compared the prevalence of this pattern across four sympatric species of frugivorous mammals in Panama. We found the highest prevalence of route-use in kinkajous, the only nocturnal species in our study, and propose that further development of this method could help to distinguish the processes underlying the presence of specific routes in animal movement data.
Highlights
Technological and analytical innovations in animal tracking and remote sensing have led to increased opportunities in animal movement research (Nathan et al, 2008; Kays et al, 2015)
We reviewed the types of processes that can generate routine patterns of animal movement, suggested a new methodological workflow for classifying one of these patterns—high fidelity path reuse—in animal tracking data, and compared the prevalence of this pattern across four sympatric species of frugivorous mammals in Panama
We found the highest prevalence of route-use in kinkajous, the only nocturnal species in our study, and propose that further development of this method could help to distinguish the processes underlying the presence of specific routes in animal movement data
Summary
Technological and analytical innovations in animal tracking and remote sensing have led to increased opportunities in animal movement research (Nathan et al, 2008; Kays et al, 2015). Tracking data are available at high sampling rates and researchers are using them to understand animal movement decisions (Nathan et al, 2008; Fagan et al, 2013; Kays et al, 2015; Gurarie et al, 2016). While the frequency and fidelity of such movement patterns varies, route-use, appears to be taxonomically widespread. Framework for Identifying Animal Route-Use movement is of substantial interest due to its relevance to conservation action and because of the insight it provides into animals’ cognition (Riotte-Lambert et al, 2016). Few quantitative methods exist for identifying such patterns of movement
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