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Jaguar movement behavior: using trajectories and association rule mining algorithms to unveil behavioral states and social interactions

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  • Suelane Garcia Fontes
  • Ronaldo Gonçalves Morato
  • Silvio Luiz Stanzani
  • Pedro Luiz Pizzigatti Corrêa

Abstract

Animal movement data are widely collected with devices such as sensors and collars, increasing the ability of researchers to monitor animal movement and providing information about animal behavioral patterns. Animal behavior is used as a basis for understanding the relationship between animals and the environment and for guiding decision-making by researchers and public agencies about environmental preservation and conservation actions. Animal movement and behavior are widely studied with a focus on identifying behavioral patterns, such as, animal group formation, the distance between animals and their home range. However, we observed a lack of research proposing a unified solution that aggregates resources for analyses of individual animal behavior and of social interactions between animals. The primary scientific contribution of this work is to present a framework that uses trajectory analysis and association rule mining [Jaiswal and Agarwal, 2012] to provide statistical measures of correlation and dependence to determine the relationship level between animals, their social interactions, and their interactions with other environmental factors based on their individual behavior and movement data. We demonstrate the usefulness of the framework by applying it to movement data from jaguars in the Pantanal, Brazil. This allowed us to describe jaguar behavior, social interactions among jaguars and their behavior in different landscapes, thus providing a highly detailed investigation of jaguar movement decisions at the fine scale.

Suggested Citation

  • Suelane Garcia Fontes & Ronaldo Gonçalves Morato & Silvio Luiz Stanzani & Pedro Luiz Pizzigatti Corrêa, 2021. "Jaguar movement behavior: using trajectories and association rule mining algorithms to unveil behavioral states and social interactions," PLOS ONE, Public Library of Science, vol. 16(2), pages 1-18, February.
  • Handle: RePEc:plo:pone00:0246233
    DOI: 10.1371/journal.pone.0246233
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    References listed on IDEAS

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    1. Hahsler, Michael & Grün, Bettina & Hornik, Kurt, 2005. "arules - A Computational Environment for Mining Association Rules and Frequent Item Sets," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 14(i15).
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