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.野生动物移动是一类关键过程，可通过多种途径影响物种种群生物学特性与群落相互作用。过往野生动物移动研究多通过为少量个体佩戴GPS与遥测标签，回顾性估算其移动行为。近年来空间捕获-再捕获（spatial capture-recapture）模型的发展，使得整合移动模型成为可能，此类模型可估算未佩戴标签且未被检测到的个体的移动情况。此外，隐马尔可夫移动模型（hidden Markov movement models）提供了个体移动预测的分析框架，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。本研究针对整合了简单有偏相关随机游走（correlated random walk）移动模型的空间捕获-再捕获模型，推导了其极大似然估计量（maximum likelihood estimators），并将这类整合模型构建为隐马尔可夫模型形式。此外，本研究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移动预测。我们开展了模拟研究，针对不同移动模型下生成的模拟数据拟合模型，以此检验模型的丰度估计量与移动预测的性能表现。同时，我们将模型应用于美国东南部大西洋沿岸海域收集的北大西洋露脊鲸（North Atlantic right whales）空间捕获-再捕获数据中。在本研究的模拟实验中，随机游走移动模型优化了丰度估计与移动预测效果；在北大西洋露脊鲸的案例研究中，该模型相较于活动中心移动模型，获得了数据更强的支持。基于整合型空间捕获-再捕获移动模型的野生动物移动预测，在以下场景中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，以及个体移动尺度相较于研究区域与采样流程的尺度更小。