将动物运动纳入连续时间空间捕获-重捕模型
Incorporating Animal Movement into Continuous-Time Spatial Capture-Recapture Models
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中文总结 AI 辅助
本文针对空间捕获-重捕模型未明确建模动物运动可能导致种群规模估计偏差的问题，开发了纳入运动的连续时间框架，经模拟和美洲貂数据验证可实现无偏推断并提供空间利用信息。
中文摘要 AI 辅助
野生动物种群规模与空间动态的估计是生态学和保护生物学的核心问题。空间捕获-重捕（Spatial Capture-Recapture, SCR）模型利用相机陷阱等传感器的检测数据，通过将检测概率与检测器和潜在个体活动中心之间的距离关联来估计种群丰度。然而，标准SCR公式假设在给定活动中心的情况下，检测结果在时间上条件独立，未明确建模检测事件之间的运动。当个体表现出由运动驱动的检测依赖性时，这一假设可能会引发问题，甚至导致种群规模推断出现偏差。针对这一重要问题，本文开发了一个将运动纳入空间捕获-重捕的连续时间框架。个体运动被建模为离散化景观上的连续时间马尔可夫链，检测事件则作为状态依赖的泊松过程产生，从而得到马尔可夫调制标记泊松过程表示，其中检测结果提供了个体在观测时潜在位置的信息，并支持连续时间下基于似然的推断。我们通过模拟研究表明，忽略运动驱动的依赖性会导致种群规模估计出现正偏差，而所提模型能够恢复无偏估计，并提供关于空间利用的额外推断。将该框架应用于美洲貂的相机陷阱数据，展示了其如何为运动和密度研究提供新见解。这些结果表明，在空间捕获-重捕研究中，明确建模运动对于可靠推断至关重要。
英文摘要
Estimation of wildlife population size and spatial dynamics is central to ecology and conservation. Spatial capture-recapture (SCR) models estimate abundance, using detections at sensors such as camera traps, by linking detection probability to the distance between detectors and latent individual activity centres. However, standard SCR formulations assume detections are conditionally independent over time given activity centres without explicitly modelling movement between detection events. This assumption can be problematic when individuals exhibit movement-driven dependence in detections, potentially leading to biased inference on population size. We address this important issue by developing a continuous-time framework that integrates movement into spatial capture-recapture. Individual movement is modelled as a continuous-time Markov chain over a discretised landscape, and detections arise as state-dependent Poisson events. This yields a Markov-modulated marked Poisson process representation, in which detections provide information about an individual's latent location at the time of observation and allow likelihood-based inference in continuous time. We show through simulation studies that ignoring movement-driven dependence can lead to positively biased estimates of population size, whereas the proposed model recovers unbiased estimates and provides additional inference on space use. An application to camera-trap data of American martens illustrates how the framework yields new insights into movement and density. These results demonstrate that explicitly modelling movement is critical for reliable inference in spatial capture-recapture studies.
