R scripts for "From randomness to traplining: A framework for the study of routine movement behavior"
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Here are provided all R functions necessary to run the methods presented in Riotte-Lambert, L., Benhamou, S. and S. Chamaillé-Jammes. 2017. From randomness to traplining: A framework for the study of routine movement behavior. Behavioral Ecology. 28:280-287
Open "script.R" to run the methods on one example.
All details on the inputs and outputs of the R functions are given as comments in the R scripts.
Abstract
Memory allows many animals to benefit from the spatial predictability of their environment by revisiting known profitable places. Travel route optimization or resource-acquisition constraints usually lead to repeated sequences of visits, which may have major evolutionary and ecological implications. However, the study of this behavior has been impaired by a lack of concepts and methodologies. We here formally define routine movement behavior, provide an index that quantifies the degree of repetitiveness in movement sequences in terms of minimal conditional entropy, and design a flexible procedure that detects the specific sub-sequences that are repeated. We demonstrate our framework using computer simulations and real-world movement data of black-tailed deer (Odocoileus hemionus) introduced into a novel environment. The simulation example showed that our methods can suitably reveal the increase in the level of routine movement behavior during home range establishment. Black-tailed deer did not show such an increase, suggesting that home range establishment occurred very fast. In both examples, our procedure determining the sub-sequences that are repeated provides a precise visualization of routine movements. Our approach solves limitations in the study of routine movement behavior and thus opens promising perspectives for the study of the linkages between cognition, foraging strategies and environments. Although we developed it to study routine movement behavior, it can be applied to any type of behavioral sequence and should thus be of interest to a broad range of behavioral ecologists.
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Publication Details
Subfield
Ecology
Field
Environmental Science
Domain
Physical Sciences
Confidence Score
44%
Source
Scholar Data Model