Multi-mode movement decisions across widely ranging behavioral processes

• Published in: PLOS ONE
• Main Authors: Prima, Marie-Caroline, Duchesne, Thierry, Merkle, Jerod, A, Chamaillé-Jammes, Simon, Fortin, Daniel
• Other Authors: Université Laval Québec (ULaval), University of Wyoming (UW), Centre d’Ecologie Fonctionnelle et Evolutive (CEFE), Université Paul-Valéry - Montpellier 3 (UPVM)-École Pratique des Hautes Études (EPHE), Université Paris Sciences et Lettres (PSL)-Université Paris Sciences et Lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD France-Sud )-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro Montpellier, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Université de Montpellier (UM), University of Pretoria South Africa, CNRS Zone Atelier Hwange, ANR-16-CE02-0001,LANDTHIRST,Les paysages de la soif: changement climatique et ajustements comportementaux face au manque d'eau(2016)
• Format: Article in Journal/Newspaper
• Language: English
• Published: HAL CCSD 2022
• Subjects: [SDE]Environmental Sciences; [SDV]Life Sciences [q-bio]; Bison bison bison; Plains Bison
• Online Access: https://hal.science/hal-03818094; https://hal.science/hal-03818094/document; https://hal.science/hal-03818094/file/Prima_2022_PloSOne.pdf; https://doi.org/10.1371/journal.pone.0272538

International audience Movement of organisms plays a fundamental role in the evolution and diversity of life. Animals typically move at an irregular pace over time and space, alternating among movement states. Understanding movement decisions and developing mechanistic models of animal distribution dynamics can thus be contingent to adequate discrimination of behavioral phases. Existing methods to disentangle movement states typically require a follow-up analysis to identify state-dependent drivers of animal movement, which overlooks statistical uncertainty that comes with the state delineation process. Here, we developed population-level, multi-state step selection functions (HMM-SSF) that can identify simultaneously the different behavioral bouts and the specific underlying behavior-habitat relationship. Using simulated data and relocation data from mule deer ( Odocoileus hemionus ), plains bison ( Bison bison bison ) and plains zebra ( Equus quagga ), we illustrated the HMM-SSF robustness, versatility, and predictive ability for animals involved in distinct behavioral processes: foraging, migrating and avoiding a nearby predator. Individuals displayed different habitat selection pattern during the encamped and the travelling phase. Some landscape attributes switched from being selected to avoided, depending on the movement phase. We further showed that HMM-SSF can detect multi-modes of movement triggered by predators, with prey switching to the travelling phase when predators are in close vicinity. HMM-SSFs thus can be used to gain a mechanistic understanding of how animals use their environment in relation to the complex interplay between their needs to move, their knowledge of the environment and navigation capacity, their motion capacity and the external factors related to landscape heterogeneity.