Evaluating the Accuracy and Precision of Multiple Abundance Estimators Using State-Space Models: A Case Study for a Threatened Population of Chinook Salmon in Johnson Creek, Idaho

Over the last century, Chinook Salmon Oncorhynchus tshawytscha populations in the Pacific Northwest have experienced dramatic declines, leading to many of them being listed as threatened under the Endangered Species Act. The abundance of these threatened populations relative to the thresholds for delisting remains the primary metric for assessing recovery, yet determining the true population abundances from multiple survey types with unknown levels of accuracy and precision remains difficult. The abundance of the spring–summer Chinook Salmon population in Johnson Creek, Idaho, has been measured using a mark–recapture survey and three different redd count surveys (RCSs) that vary temporally and spatially. Using a state-space model, we determined the accuracy and precision of each survey type by decoupling the observation error of the survey from the process error describing the annual variability in the true population abundance. We then extended the results of the model to determine the risk of managers’ incorrectly delisting the population (a type I error) or incorrectly keeping it listed (a type II error). Finally, we show that salmon managers with data-limited populations (primarily those with only single-pass index RCSs) might use the results of our risk analysis to determine whether expanding survey efforts to minimize management risks is appropriate when they are confronted with dwindling financial resources. For example, we determined that although both the multiple-pass extended RCS (CV = 0.06) and mark–recapture surveys (CV = 0.14) provide unbiased estimates of salmon abundance in Johnson Creek, the mark–recapture study can have annual costs that are 30–100 times greater. Managers may determine that directing research funds toward acquiring information unique to weir-based mark–recapture surveys (i.e., migration timing, good genetics samples, etc.) may not be justified for all populations.Received October 7, 2013; accepted March 5, 2014

近一个世纪以来，太平洋西北地区（Pacific Northwest）的奇努克鲑鱼（Chinook Salmon，Oncorhynchus tshawytscha）种群数量大幅下降，致使其中多个种群被列入《濒危物种法案（Endangered Species Act）》的受威胁物种名录。这些受威胁种群相对于除名阈值的种群丰度，仍是评估其恢复状况的核心衡量指标；然而从准确性与精度未知的多种调查类型中估算真实种群丰度，仍颇具挑战。爱达荷州约翰逊溪的春夏奇努克鲑鱼种群丰度，已通过标记-重捕调查（mark–recapture survey）以及3种在时空尺度上存在差异的产卵场计数调查（redd count surveys，RCSs）完成估算。本研究借助状态空间模型（state-space model），将调查的观测误差与描述真实种群丰度年际波动的过程误差解耦，以此确定每种调查类型的准确性与精度。随后，我们将模型结果拓展应用，以评估管理人员错误将该种群从受威胁名录中除名（第一类错误，type I error）或错误保留其受威胁名录资格（第二类错误，type II error）的风险。最后，本研究表明，针对种群数据受限的鲑鱼管理人员（主要指仅开展单通行指数型产卵场计数调查的人员），在面临财政资源缩减的困境时，可借助本风险分析结果，判断是否应扩大调查力度以降低管理风险。例如，本研究发现，尽管多通行扩展型产卵场计数调查（CV=0.06）与标记-重捕调查（CV=0.14）均可对约翰逊溪的鲑鱼丰度做出无偏估计，但标记-重捕研究的年度成本可达前者的30至100倍。管理人员可判断，将研究经费投入以获取基于鱼道的标记-重捕调查所特有的信息（如洄游时间、高质量遗传学样本等），并非适用于所有种群的合理选择。收稿日期：2013年10月7日；录用日期：2014年3月5日