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. 动物朝向目标的移动行为，均基于资源最大化获取或风险规避原则，而其移动方式可揭示自身的移动动机。
2. 本研究采用贝叶斯运动模型（Bayesian movement models）与每小时一次的GPS定位点（GPS-fixes）数据，将动物移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。鉴于目前尚不明确生境特征在这一复杂议题中所扮演的角色，我们将分析大象是主动靶向农田作物进行取食，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）将大象移动划分为两种状态：一是快速且具有方向性的探索性移动，二是缓慢且蜿蜒徘徊的驻留移动。针对每头大象，我们基于生境特征（河流、大象廊道、农田、林木）的所有可能组合构建了16组模型，并重复开展分析，同时纳入季节与每日时段的交互效应。我们通过交叉验证选取最优模型，并采用广义线性混合模型（Generalized Linear Mixed Models, GLMMs）分析生境特征对动物处于某一状态及切换至该状态的影响。
4. 研究结果显示，在大象廊道中，探索性移动占据主导地位。旱季时，临时水源枯竭，大象依赖永久性河流作为水源，此时它们在河流区域多呈现驻留移动。在农田区域，雄性大象大多会在往返河流的过程中展现探索性移动；而雌性大象则在夜间（此时农田内的作物取食与移动行为最为频发）与旱季的驻留移动频率显著提升。
5. 捕食风险假说可解释这一行为模式：由于在夜间与旱季，农民通常不在农田中活动，此时在农田觅食的风险相对更低。大象移动决策中的这种性别分化现象，凸显了风险在运动模式中的重要性；而旱季驻留移动频率的提升，则表明了农业活动时序的关键作用。将上述结论纳入考量，可提升大象作物取食防控措施的实施效率。
08-Jan-2020