Exploring movement decisions: can Bayesian movement-state models explain crop consumption behaviour in elephants (Loxodonta africana)?

1. Animal movements towards goals or targets are based upon either maximization of resources or risk avoidance, and the way animals move can reveal information about their motivation for movement. 2. We use Bayesian movement models and hourly GPS-fixes to distinguish animal movements into movement states and analyse the influence of environmental variables on being in and switching to a state. Specifically, we apply our models to understand elephant movement decisions surrounding agricultural fields and crop consumption. As it is unclear what the role of habitat features are on this complex issue, we analyse whether elephants target agricultural crops for consumption, or simply pass through them in search of water. 3. Our Hidden-Markov models divide elephant movements into two states: exploratory movements that are fast and directional, and encamped movements that are slow and meandering. For each elephant, we ran 16 models with each possible combination of habitat features (river, elep...1. 动物朝向目标的移动行为基于资源最大化或风险规避原则，而动物的移动模式能够揭示其移动动因的相关信息。2. 本研究采用贝叶斯运动模型（Bayesian movement models）与每小时GPS定位点（GPS-fixes），将动物移动行为划分为不同运动状态，并分析环境变量对动物处于某一状态以及切换至该状态的影响。具体而言，我们将模型应用于解析大象围绕农田的移动决策与作物取食行为。鉴于目前尚不明确栖息地特征在这一复杂议题中所发挥的作用，我们将分析大象是主动瞄准农田作物进行取食，还是仅在寻找水源的途中途经农田。3. 本研究的隐马尔可夫模型（Hidden-Markov Models）将大象的移动行为划分为两种状态：一是快速且具有方向性的探索性移动，二是缓慢且迂回的驻留移动。针对每一头大象，我们构建了16种模型，涵盖栖息地特征（河流、elep...）的所有可能组合。