Forecasting wildlife movement with spatial capture-recapture

Wildlife movement is an important process affecting species population
biology and community interactions in myriad ways. Studies of wildlife
movement have focused on retrospectively estimating movements of small
numbers of individuals by outfitting them with GPS and telemetry tags.
Recent developments in spatial capture-recapture modeling permit the
integration of movement models that can estimate the movement of untagged
and undetected individuals. Additionally, hidden Markov movement models
provide a framework for forecasting individuals’ movements, which may be
valuable in the conservation of threatened species facing risks that vary
across space and time. We describe maximum likelihood estimators for
spatial capture–recapture models integrated with simple, biased, and
correlated random walk movement models formulated as hidden Markov models.
Additionally, we demonstrate how to forecast wildlife movement based on
these models and hidden Markov model algorithms. We conducted a simulation
study to test the performance of the models’ abundance estimators and
movement forecasts when fit to data simulated under different movement
models. We also fit the models to spatial capture–recapture data collected
on North Atlantic right whales off the Atlantic Coast of the southeastern
United States. Random walk movement models improved abundance estimation
and movement forecasts in our simulation study and received greater
support from the data in the right whale case study than did activity
center movement models. Forecasts of wildlife movement made under
integrated spatial capture–recapture movement models will be most valuable
when individuals have been observed recently, when sampling for
individuals is extensive and efficient, and when the scale of individuals’
movements is small relative to the scale of the study area and sampling
process.

野生动物运动是一类以多种途径影响物种种群生物学与群落互作的关键过程。过往针对野生动物运动的研究，多通过为少量个体佩戴GPS与遥测标签，回溯性估算其运动轨迹与活动范围。近年来，空间捕获-再捕获（spatial capture-recapture）建模技术的进展，使得可集成能够估算未佩戴标签、未被检测个体运动状态的运动模型。此外，隐马尔可夫运动模型（hidden Markov movement models）可为个体运动预测提供分析框架，这对于应对时空异质性风险的濒危物种保护工作具有重要应用价值。本研究针对构建为隐马尔可夫模型的简单偏倚相关随机游走运动模型与空间捕获-再捕获模型的集成形式，推导了其极大似然估计量（maximum likelihood estimators）。此外，本研究还演示了如何基于上述模型与隐马尔可夫模型算法开展野生动物运动预测。我们开展了模拟实验，检验了当模型拟合不同运动模型模拟生成的数据时，其种群丰度估计量与运动预测结果的性能表现。同时，我们将模型应用于美国东南部大西洋沿岸海域采集的北大西洋露脊鲸（North Atlantic right whale）空间捕获-再捕获数据集。在本次模拟实验中，随机游走运动模型优化了丰度估计与运动预测效果；在露脊鲸案例研究中，相较于活动中心运动模型，该模型获得了数据更强的支持度。基于集成型空间捕获-再捕获运动模型开展的野生动物运动预测，在以下场景下将具备最高应用价值：个体近期已被观测到、个体采样工作全面且高效，以及相较于研究区域与采样过程的尺度，个体运动尺度相对较小时。