School of Marine and Environmental Affairs, University of Washington, Seattle, WA, United States of America.
Fisheries Ecology Division, Southwest Fisheries Science Center, National Marine Fisheries Service, National Oceanic and Atmospheric Administration, Santa Cruz, CA, United States of America.
PeerJ. 2023 Nov 28;11:e16487. doi: 10.7717/peerj.16487. eCollection 2023.
Considerable resources are spent to track fish movement in marine environments, often with the intent of estimating behavior, distribution, and abundance. Resulting data from these monitoring efforts, including tagging studies and genetic sampling, often can be siloed. For Pacific salmon in the Northeast Pacific Ocean, predominant data sources for fish monitoring are coded wire tags (CWTs) and genetic stock identification (GSI). Despite their complementary strengths and weaknesses in coverage and information content, the two data streams rarely have been integrated to inform Pacific salmon biology and management. Joint, or integrated, models can combine and contextualize multiple data sources in a single statistical framework to produce more robust estimates of fish populations.
We introduce and fit a comprehensive joint model that integrates data from CWT recoveries and GSI sampling to inform the marine life history of Chinook salmon stocks at spatial and temporal scales relevant to ongoing fisheries management efforts. In a departure from similar models based primarily on CWT recoveries, modeled stocks in the new framework encompass both hatchery- and natural-origin fish. We specifically model the spatial distribution and marine abundance of four distinct stocks with spawning locations in California and southern Oregon, one of which is listed under the U.S. Endangered Species Act.
Using the joint model, we generated the most comprehensive estimates of marine distribution to date for all modeled Chinook salmon stocks, including historically data poor and low abundance stocks. Estimated marine distributions from the joint model were broadly similar to estimates from a simpler, CWT-only model but did suggest some differences in distribution in select seasons. Model output also included novel stock-, year-, and season-specific estimates of marine abundance. We observed and partially addressed several challenges in model convergence with the use of supplemental data sources and model constraints; similar difficulties are not unexpected with integrated modeling. We identify several options for improved data collection that could address issues in convergence and increase confidence in model estimates of abundance. We expect these model advances and results provide management-relevant biological insights, with the potential to inform future mixed-stock fisheries management efforts, as well as a foundation for more expansive and comprehensive analyses to follow.
在海洋环境中追踪鱼类运动需要投入大量资源，其目的通常是估计鱼类的行为、分布和丰度。这些监测工作产生的数据，包括标记研究和遗传采样，通常可以孤立存在。在东北太平洋的太平洋鲑鱼中，鱼类监测的主要数据来源是编码金属标签 (CWT) 和遗传种群鉴定 (GSI)。尽管它们在覆盖范围和信息内容方面具有互补的优势和劣势，但这两个数据流很少被整合起来为太平洋鲑鱼生物学和管理提供信息。联合或综合模型可以将多个数据源结合并置于单个统计框架中，从而更准确地估计鱼类种群。
我们引入并拟合了一个全面的联合模型，该模型整合了 CWT 回收和 GSI 采样的数据，以告知在与正在进行的渔业管理努力相关的时空尺度上的奇努克鲑鱼种群的海洋生活史。与主要基于 CWT 回收的类似模型不同，新框架中的模型种群包括养殖和自然起源的鱼类。我们专门对加利福尼亚州和俄勒冈州南部产卵地的四个不同种群的空间分布和海洋丰度进行建模，其中一个种群被列入美国濒危物种法案。
使用联合模型，我们生成了迄今为止所有模型化奇努克鲑鱼种群的最全面的海洋分布估计，包括历史数据较少和数量较少的种群。联合模型生成的海洋分布估计与简单的、仅基于 CWT 的模型大致相似，但在某些季节的分布上确实存在一些差异。模型输出还包括新颖的、种群、年份和季节特定的海洋丰度估计。我们观察到并部分解决了使用补充数据源和模型约束时模型收敛的几个挑战；类似的困难在综合建模中并不出人意料。我们确定了几种改进数据收集的选择，这可以解决收敛问题并提高对模型丰度估计的信心。我们预计这些模型的改进和结果将提供具有管理意义的生物学见解，有可能为未来的混合种群渔业管理工作提供信息，并为更广泛和全面的分析奠定基础。
