Data from: 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, elephant corridor, agricultural field, trees), and repeated these analyses including interaction effects with both season and time of day. We used cross-validation to select the best performing model, and GLMMs to analyse the influence of habitat features on being in and switching to a state. 4. Our results show that in corridors, exploratory movements are dominant. Elephants mainly showed encamped movements at the river during the dry season, when temporary water sources have dried out and elephants rely on this permanent water source. In fields, males most often exhibited exploratory movements to and from the river, while females showed an increase in the frequency of encamped movements at night –when most crop consumption and movements through fields occur- and during the dry season. 5. The predation-risk hypothesis could explain this behaviour, since foraging in fields might be less risky under the cover of darkness and during the dry season when farmers are absent from fields. This sexual segregation in elephant movement decisions highlights the importance of risk in movement patterns, while the increase in encamped movements in the dry season suggests the importance of agricultural timing. Taking this into account could increase efficiency of elephant crop consumption mitigation. 08-Jan-2020

1. 动物朝向目标的运动行为，基于资源摄取最大化或风险规避两大原则，而动物的运动模式能够揭示其运动动机相关信息。2. 本研究采用贝叶斯运动模型（Bayesian movement models）与每小时GPS定位点，将动物运动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及状态切换的影响。具体而言，我们将模型应用于解析大象围绕农田与作物取食的运动决策。鉴于目前尚不明确栖息地特征在这一复杂问题中的作用，我们将分析大象是主动取食农田作物，还是仅在寻找水源的途中途经农田。3. 我们的隐马尔可夫模型（Hidden-Markov models）将大象运动划分为两类状态：快速且具有方向性的探索性运动，以及缓慢蜿蜒行进的驻留运动。针对每头大象，我们基于栖息地特征（河流、大象廊道、农田、林木）的所有可能组合构建了16个模型，并重复开展了纳入季节与时段交互效应的分析。我们采用交叉验证选择最优模型，并通过广义线性混合模型（Generalized Linear Mixed Models, GLMMs）分析栖息地特征对动物处于某一状态及状态切换的影响。4. 研究结果表明，在大象廊道中，探索性运动占据主导地位。在旱季，当临时水源枯竭、大象依赖永久性河流水源时，它们主要在河流区域开展驻留运动。在农田中，雄性大象大多往返河流时表现为探索性运动；而雌性大象在夜间（此时多数作物取食及农田穿行活动发生）与旱季的驻留运动频率显著提升。5. 捕食风险假说可以解释这一行为模式：由于在夜间遮蔽以及旱季农民不在农田值守时，农田觅食的风险相对更低。大象运动决策中的这种性别分化，凸显了风险在运动模式中的重要性；而旱季驻留运动频率的提升，则体现了农作时节的关键作用。将上述结论纳入考量，能够提升大象作物取食防控工作的效率。08-Jan-2020