Monitoring annual change and long‐term trends in population structure and abundance of white‐tailed deer (Odocoileus virginianus) is an important but challenging component of their management. Many monitoring programs consist of count‐based indices of relative abundance along with a variety of population structure information. Analyzed separately these data can be difficult to interpret because of observation error in the data collection process, missing data, and the lack of an explicit biological model to connect the data streams while accounting for their relative imprecision. We used a Bayesian age‐structured integrated population model to integrate data from a fall spotlight survey that produced a count‐based index of relative abundance and a volunteer staff and citizen classification survey that generated a fall recruitment index. Both surveys took place from 2003–2018 in the parkland ecoregion of southeast Saskatchewan, Canada. Our approach modeled demographic processes for age‐specific (0.5‐, 1.5‐, ≥2.5‐year‐old classes) populations and was fit to count and recruitment data via models that allowed for error in the respective observation processes. The Bayesian framework accommodated missing data and allowed aggregation of transects to act as samples from the larger management unit population. The approach provides managers with continuous time series of estimated relative abundance, recruitment rates, and apparent survival rates with full propagation of uncertainty and sharing of information among transects. We used this model to demonstrate winter severity effects on recruitment rates via an interaction between winter snow depth and minimum temperatures. In years with colder than average temperatures and above average snow depth, recruitment was depressed, whereas the negative effect of snow depth reversed in years with above average temperatures. This and other covariate information can be incorporated into the model to test relationships and provide predictions of future population change prior to setting of hunting seasons. Likewise, post hoc analysis of model output allows other hypothesis tests, such as determining the statistical support for whether population status has crossed a management trigger threshold. © 2020 The Wildlife Society.

中文翻译：

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加拿大萨斯喀彻温省白尾鹿的综合种群建模

监测白尾鹿（Odocoileus virginianus）种群结构的年度变化和长期趋势）是其管理的重要但具有挑战性的组成部分。许多监测程序包括基于计数的相对丰度指数以及各种人口结构信息。单独分析这些数据可能会难以解释，原因是数据收集过程中存在观察错误，数据丢失以及缺乏明确的生物学模型来连接数据流（同时考虑到它们的相对不精确性）。我们使用贝叶斯年龄结构的综合人口模型来整合来自秋季聚光灯调查的数据，该调查产生了基于计数的相对丰度指数，以及志愿者工作人员和公民分类调查产生了秋季招聘指数。两项调查均于2003年至2018年在加拿大萨斯喀彻温省东南部的绿地生态区进行。我们的方法为特定年龄段（0.5岁，1.5岁，≥2.5岁的人群）的人口统计过程建模，并且适合通过允许在各个观察过程中出错的模型来计数和募集数据。贝叶斯框架容纳了丢失的数据，并允许将样条聚合为更大管理单位群体的样本。该方法为管理人员提供了估计的相对丰度，招聘率和表观生存率的连续时间序列，不确定性的完全传播和样线之间的信息共享。我们使用此模型通过冬季降雪深度和最低温度之间的相互作用来证明冬季严酷性对招聘率的影响。在气温低于平均水平且积雪深度高于平均水平的年份，招募工作受到抑制，而在高于平均温度的年份，积雪深度的负面影响会逆转。可以将此信息和其他协变量信息合并到模型中，以测试关系并在设置狩猎季节之前提供对未来种群变化的预测。同样模型输出的事后分析还可以进行其他假设检验，例如确定人口状况是否已超过管理触发阈值的统计支持。©2020野生动物协会。