McClintock Brett T, Lander Michelle E
Marine Mammal Laboratory, Alaska Fisheries Science Center, NOAA, National Marine Fisheries Service, Seattle, Washington, USA.
Ecology. 2024 Jan;105(1):e4186. doi: 10.1002/ecy.4186. Epub 2023 Nov 16.
The identification of important habitat and the behavior(s) associated with it is critical to conservation and place-based management decisions. Behavior also links life-history requirements and habitat use, which are key to understanding why animals use certain habitats. Animal population studies often use tracking data to quantify space use and habitat selection, but they typically either ignore movement behavior (e.g., foraging, migrating, nesting) or adopt a two-stage approach that can induce bias and fail to propagate uncertainty. We develop a habitat-driven Langevin diffusion for animals that exhibit distinct movement behavior states, thereby providing a novel single-stage statistical method for inferring behavior-specific habitat selection and utilization distributions in continuous time. Practitioners can customize, fit, assess, and simulate our integrated model using the provided R package. Simulation experiments demonstrated that the model worked well under a range of sampling scenarios as long as observations were of sufficient temporal resolution. Our simulations also demonstrated the importance of accounting for different behaviors and the misleading inferences that can result when these are ignored. We provide case studies using plains zebra (Equus quagga) and Steller sea lion (Eumetopias jubatus) telemetry data. In the zebra example, our model identified distinct "encamped" and "exploratory" states, where the encamped state was characterized by strong selection for grassland and avoidance of other vegetation types, which may represent selection for foraging resources. In the sea lion example, our model identified distinct movement behavior modes typically associated with this marine central-place forager and, unlike previous analyses, found foraging-type movements to be associated with steeper offshore slopes characteristic of the continental shelf, submarine canyons, and seamounts that are believed to enhance prey concentrations. This is the first single-stage approach for inferring behavior-specific habitat selection and utilization distributions from tracking data that can be readily implemented with user-friendly software. As certain behaviors are often more relevant to specific conservation or management objectives, practitioners can use our model to help inform the identification and prioritization of important habitats. Moreover, by linking individual-level movement behaviors to population-level spatial processes, the multistate Langevin diffusion can advance inferences at the intersection of population, movement, and landscape ecology.
识别重要栖息地及其相关行为对于保护和基于地点的管理决策至关重要。行为还将生活史需求与栖息地利用联系起来，这是理解动物为何使用特定栖息地的关键。动物种群研究通常使用追踪数据来量化空间利用和栖息地选择，但它们通常要么忽略移动行为（例如觅食、迁徙、筑巢），要么采用两阶段方法，这可能会导致偏差且无法传播不确定性。我们为表现出不同移动行为状态的动物开发了一种由栖息地驱动的朗之万扩散模型，从而提供了一种新颖的单阶段统计方法，用于在连续时间内推断特定行为的栖息地选择和利用分布。从业者可以使用提供的R包对我们的集成模型进行定制、拟合、评估和模拟。模拟实验表明，只要观测具有足够的时间分辨率，该模型在一系列采样场景下都能很好地工作。我们的模拟还证明了考虑不同行为的重要性以及忽略这些行为可能导致的误导性推断。我们提供了使用平原斑马（Equus quagga）和北海狮（Eumetopias jubatus）遥测数据的案例研究。在斑马的例子中，我们的模型识别出了不同的“驻营”和“探索”状态，驻营状态的特征是对草地有强烈的选择，而避开其他植被类型，这可能代表了对觅食资源的选择。在海狮的例子中，我们的模型识别出了通常与这种海洋中心地觅食者相关的不同移动行为模式，并且与之前的分析不同，发现觅食型移动与大陆架、海底峡谷和海山等具有更陡峭近海斜坡的区域相关，据信这些区域会提高猎物的聚集度。这是第一种从追踪数据推断特定行为的栖息地选择和利用分布的单阶段方法，并且可以通过用户友好的软件轻松实现。由于某些行为通常与特定的保护或管理目标更相关，从业者可以使用我们的模型来帮助确定重要栖息地并对其进行优先排序。此外，通过将个体层面的移动行为与种群层面的空间过程联系起来，多状态朗之万扩散可以推进在种群、移动和景观生态学交叉领域的推断。
