Estimating abundance and growth of animal populations are central tasks in ecology and natural resource management. Removal models for estimating abundance have a long history in applied ecology, and recent developments provided hierarchical extensions that account for spatially replicated sampling and heterogeneous capture probabilities. Measurement error is common to removal data collected from many broad‐scale monitoring programs, however, and a general framework for population assessment using removal data in the presence of measurement error is lacking. We developed a hierarchical framework for estimating abundance and population trends from removal experiments that are replicated in space and time that accommodates measurement error, as well as heterogeneity in capture probability and animal density. We describe the model for variable‐effort removal sampling and use it to estimate region‐specific abundance and population trends for wild turkeys (Meleagris gallopavo) in Michigan, USA. We used a Bayesian approach for estimation and inference and fit models using daily hunter harvest and effort estimates collected over 5 management regions for 14 annual hunting seasons. Our analyses provide evidence for spatially heterogeneous capture probabilities among regions and turkey densities that were heterogeneous in both space and time, and show that populations increased slightly over the study. Our framework provides a general approach for population assessment using removal data that are collected over broad scales in resource management contexts (e.g., animal harvesting), facilitating formal abundance estimation instead of reliance on unverified indices for tracking populations of managed species. Thus, we provide a useful tool for monitoring programs to assess populations over broad scales, and therefore inform decision makers about population status at spatial scales similar to those for which regulatory decisions are made.

中文翻译：

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从不完全观察到的迁移中估算丰度和人口增长的分层框架

估计动物种群的丰度和增长是生态和自然资源管理的中心任务。用于估计丰度的去除模型在应用生态学中有着悠久的历史，最近的发展提供了分层扩展，这些扩展说明了空间复制的采样和异构捕获概率。从许多大型监测程序收集的去除数据中普遍存在测量误差，但是缺乏在存在测量误差的情况下使用去除数据进行人口评估的通用框架。我们开发了一个层次结构框架，用于根据去除实验估算丰度和种群趋势，这些去除实验在空间和时间上进行了复制，以适应测量误差以及捕获概率和动物密度的异质性。lea鱼）在美国密歇根州。我们使用贝叶斯方法进行估计，推论和拟合模型，其中使用了每天的猎人收获和工作量估计，这些收获是在14个年度狩猎季节从5个管理区域收集的。我们的分析为空间和时间上异质的区域和火鸡密度之间的空间异质捕获概率提供了证据，并表明种群在研究中略有增加。我们的框架使用在资源管理环境（例如动物收获）中广泛收集的清除数据，为种群评估提供了一种通用方法，从而促进了正式的丰度估算，而不是依靠未经验证的指数来追踪被管理物种的种群。因此，我们提供了一个有用的工具，可用于监控各种计划以评估广泛的人口，