Mourier Johann, Soria Marc, Silk Matthew, Demichelis Angélique, Dagorn Laurent, Hattab Tarek
MARBEC, Univ Montpellier, CNRS, IFREMER, IRD Sète France.
Centre for Ecology and Conservation University of Exeter Penryn Campus Cornwall UK.
Ecol Evol. 2024 Nov 28;14(12):e70659. doi: 10.1002/ece3.70659. eCollection 2024 Dec.
Animal movements are typically influenced by multiple environmental factors simultaneously, and individuals vary in their response to this environmental heterogeneity. Therefore, understanding how environmental aspects, including biotic, abiotic, and anthropogenic factors, influence the movements of wild animals is an important focus of wildlife research and conservation. We apply Exponential Random Graph Models (ERGMs) to analyze movement networks of a bull shark population in a network of acoustic receivers and identify the effects of environmental, social, or other types of covariates on their movements. We found that intra- and interspecific factors often had stronger effects on movements than environmental variables. ERGMs proved to be a potentially useful tool for studying animal movement network data, especially in the context of spatial attribute heterogeneity.
动物的活动通常会同时受到多种环境因素的影响,而且个体对这种环境异质性的反应也各不相同。因此,了解包括生物、非生物和人为因素在内的环境因素如何影响野生动物的活动,是野生动物研究和保护的一个重要重点。我们应用指数随机图模型(ERGMs)来分析一个牛鲨种群在声学接收器网络中的活动网络,并确定环境、社会或其他类型的协变量对其活动的影响。我们发现种内和种间因素对活动的影响往往比环境变量更强。事实证明,ERGMs是研究动物活动网络数据的一个潜在有用工具,特别是在空间属性异质性的背景下。