Fukasawa Keita, Higashide Daishi
Biodiversity Division, National Institute for Environmental Studies, Tsukuba, Ibaraki, Japan.
Faculty of Bioresources and Environmental Sciences, Ishikawa Prefectural University, Nonoichi, Ishikawa, Japan.
Ecology. 2025 Feb;106(2):e70046. doi: 10.1002/ecy.70046.
Spatial capture-recapture models (SCRs) provide an integrative statistical tool for analyzing animal movement and population patterns. Although incorporating home range formation with a theoretical basis of animal movement into SCRs can improve the prediction of animal space use in a heterogeneous landscape, this approach is challenging owing to the sparseness of recapture events. In this study, we developed an advection-diffusion capture-recapture model (ADCR), which is an extension of SCRs incorporating home range formation with advection-diffusion formalism, providing a new framework to estimate population density and landscape permeability. we tested the unbiasedness of the estimator using simulated capture-recapture data generated by a step selection function. We also compared the accuracy of population density estimates and home range shapes with those from SCR incorporating the least-cost path and basic SCR. In addition, ADCR was applied to a real dataset of Asiatic black bear (Ursus thibetanus) in Japan to demonstrate the capacity of the ADCR to detect geographical barriers that constrain animal movements. Population density and permeability of ADCR were substantially unbiased for simulated datasets. ADCR could detect environmental signals on connectivity more sensitively and could estimate population density, home range shapes, and size variations better than the existing models. For the application to the bear dataset, ADCR could detect the effect of water bodies as a barrier to movement, which is consistent with previous studies, whereas estimates by SCR with the least-cost path were difficult to interpret. ADCR provides unique opportunities to elucidate both individual- and population-level ecological processes from capture-recapture data. By offering a formal link with step selection functions to estimate animal movement, it is suitable for simultaneously modeling capture-recapture data and animal movement data. This study provides a basis for studies of the interplay between animal movement processes and population patterns.
空间捕获-重捕模型（SCRs）为分析动物运动和种群模式提供了一种综合统计工具。尽管将具有动物运动理论基础的家域形成纳入SCRs可以改善对异质景观中动物空间利用的预测，但由于重捕事件的稀疏性，这种方法具有挑战性。在本研究中，我们开发了一种平流-扩散捕获-重捕模型（ADCR），它是SCRs的扩展，将家域形成与平流-扩散形式相结合，提供了一个估计种群密度和景观渗透率的新框架。我们使用由步长选择函数生成的模拟捕获-重捕数据测试了估计器的无偏性。我们还将种群密度估计的准确性和家域形状与纳入最小成本路径的SCR和基本SCR的估计结果进行了比较。此外，ADCR被应用于日本亚洲黑熊（Ursus thibetanus）的真实数据集，以证明ADCR检测限制动物运动的地理障碍的能力。对于模拟数据集，ADCR的种群密度和渗透率基本无偏。与现有模型相比，ADCR能够更敏感地检测连通性的环境信号，并能更好地估计种群密度、家域形状和大小变化。对于熊数据集的应用而言，ADCR能够检测到水体作为运动障碍的影响，这与先前研究一致；而使用最小成本路径的SCR估计则难以解释。ADCR为从捕获-重捕数据中阐明个体和种群水平的生态过程提供了独特机会。通过提供与步长选择函数的正式联系来估计动物运动，它适用于同时对捕获-重捕数据和动物运动数据进行建模。本研究为研究动物运动过程与种群模式之间的相互作用提供了基础。
