Anthropogenic impacts on riverine systems have, in part, led to management concerns regarding the population status of species using these systems. In an effort to assess the efficacy of restoration actions, and in order to improve monitoring of species of concern, managers have turned to PIT (passive integrated transponder) tag studies with in‐stream detectors to monitor movements of tagged individuals throughout river networks. However, quantifying movements in a river network using PIT tag data with incomplete coverage and imperfect detections presents a challenge. We propose a flexible Bayesian analytic framework that models the imperfectly detected movements of tagged individuals in a nested PIT tag array river network. This model structure provides probabilistic estimates of up‐stream migration routes for each tagged individual based on a set of underlying nested state variables. These movement estimates can be converted into abundance estimates when an estimate of abundance is available for a location within the river network. We apply the model framework to data from steelhead (Oncorhynchus mykiss) in the Upper Columbia River basin and evaluate model performance (precision/variance of simulated population sizes) as a function of population tagging rates and PIT tag array detection probability densities within the river system using a simulation framework. This simulation framework provides both model validation (precision) and the ability to evaluate expected performance improvements (variance) due to changes in tagging rates or PIT receiver array configuration. We also investigate the impact of different network configurations on model estimates. Results from such investigations can help inform decisions regarding future monitoring and management.

中文翻译：

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贝叶斯嵌套式斑块占用模型，用于估计杆头运动和丰度。

人为因素对河流系统的影响在一定程度上导致了管理方面对使用这些系统的物种的种群状况的担忧。为了评估恢复措施的有效性，并改善对关注物种的监控，管理人员已转向使用PIT（无源集成转发器）标签研究和流内探测器，以监控被标记个体在整个河网中的运动。但是，使用覆盖范围不完整和检测不完善的PIT标签数据来量化河流网络中的运动提出了挑战。我们提出了一个灵活的贝叶斯分析框架，该框架对嵌套的PIT标签阵列河网中的标签个体的不完全检测到的运动进行建模。该模型结构基于一组基础嵌套状态变量，为每个加标签的个体提供了上游迁移路径的概率估计。当河流网络中某个位置的丰度估算可用时，这些运动估算可以转换为丰度估算。我们将模型框架应用于来自Steelhead的数据（上哥伦比亚河流域的Oncorhynchus mykiss），并使用模拟框架评估模型性能（模拟种群大小的精度/方差）与人口标记率和PIT标签阵列检测概率密度的函数关系。该仿真框架提供了模型验证（精度）和评估由于标记率或PIT接收器阵列配置变化而导致的预期性能改进（差异）的能力。我们还将调查不同网络配置对模型估计的影响。这些调查的结果可以帮助您做出有关未来监视和管理的决策。