Time‐varying habitat selection analysis: A model and applications for studying diel, seasonal, and post‐release changes
2024
Dejeante, Romain | Valeix, Marion | Chamaillé-Jammes, Simon | 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 [Occitanie])-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) | Laboratoire de Biométrie et Biologie Evolutive - UMR 5558 (LBBE) ; Université Claude Bernard Lyon 1 (UCBL) ; Université de Lyon-Université de Lyon-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS) | University of Pretoria [South Africa] | REPOS Project | ANR-16-IDEX-0006,MUSE,MUSE(2016)
International audience
Show more [+] Less [-]English. Resource selection functions are commonly used to evaluate animals' habitat selection, for example, the disproportionate use of habitats relative to their availability. While environmental conditions or animal motivations may vary over time, sometimes in an unknown manner, studying changes in habitat selection usually requires an a priori segmentation of time in distinct periods. This limits our ability to precisely answer the question “When is an animal's habitat selection changing?” Here, we present a straightforward and flexible alternative approach based on fitting dynamic logistic models to used/available data. First, using simulated datasets, we demonstrate that dynamic logistic models perform well in recovering temporal variations in habitat selection. We then show real‐world applications for studying diel, seasonal, and post‐release changes in the habitat selection of the blue wildebeest ( Connochaetes taurinus ). Dynamic logistic models allow the study of temporal changes in habitat selection in a framework consistent with resource selection functions but without the need to segment time in distinct periods, which can be a difficult task when little is known about the process studied or may obscure interindividual variability in timing of change. These models should undoubtedly find their place in the movement ecology toolbox. We provide R scripts to facilitate their adoption. We also encourage future research to focus on how to account for temporal autocorrelation in location data, as this would allow statistical inference from location data collected at a high frequency, an increasingly common situation.
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