Ferriss Bridget E, Hunsicker Mary E, Ward Eric J, Litzow Michael A, Rogers Lauren, Callahan Matt, Cheng Wei, Danielson Seth L, Drummond Brie, Fergusson Emily, Gabriele Christine, Hebert Kyle, Hopcroft Russell R, Nielsen Jens, Spalinger Kally, Stockhausen William T, Strasburger Wesley W, Whelan Shannon
Resource Ecology and Fisheries Management Division, Alaska Fisheries Science Center, National Marine Fisheries Service, National Oceanic and Atmospheric Administration, Seattle, Washington, United States of America.
Fish Ecology Division, Northwest Fisheries Science Center, National Marine Fisheries Service, National Oceanic and Atmospheric Administration, Newport, Oregon, United States of America.
PLoS One. 2025 Jun 6;20(6):e0324154. doi: 10.1371/journal.pone.0324154. eCollection 2025.
Ecosystem-based fisheries management requires the successful integration of ecosystem information into the fisheries management process. In the Northeast Pacific Ocean, ecosystem data collection and accessibility have achieved successful milestones, yet application to the harvest specification process remains challenging. The synthesis, interpretation, and application of ecosystem information to groundfish fisheries management in the Gulf of Alaska (GOA) can be supported by the identification of common ecosystem trends and ecosystem states across a diverse set of indicators. In this study, we used Dynamic Factor Analysis (DFA) and hidden Markov models (HMM) to analyze 92 indicators in climate, lower-trophic, mid-trophic, and seabird models for the western and eastern GOA marine ecosystems. Time series ranged from 25 to 52 years in length, analyzed through 2022. The DFA identified common trends across indicators and groups of covarying indicators (e.g., biomass of zooplankton species), highlighting opportunities to streamline communication of these data to management. Non-stationarity analyses revealed past changes in relationships, and can provide early warnings in future annual updates if previously identified correlations change. The HMM identified two to three ecosystem states in each sub-model that largely aligned with previously observed long- and short-term shifts in ecosystem dynamics in the region (i.e., shifts starting in 1975, 1988, and 2014). Annually updating these analyses, within an existing framework of reporting ecosystem information to management bodies, can streamline communication and improve early warning of changes in ecosystem dynamics. These tools can provide ecosystem support to management decisions relative to groundfish productivity and resulting harvest specifications.
基于生态系统的渔业管理要求将生态系统信息成功整合到渔业管理过程中。在东北太平洋，生态系统数据的收集和可获取性已取得了成功的里程碑，但将其应用于捕捞规格制定过程仍具有挑战性。通过识别一系列不同指标中的共同生态系统趋势和生态系统状态，可以支持将生态系统信息综合、解读并应用于阿拉斯加湾（GOA）底层鱼类渔业管理。在本研究中，我们使用动态因子分析（DFA）和隐马尔可夫模型（HMM）分析了GOA西部和东部海洋生态系统的气候、低营养级、中营养级和海鸟模型中的92个指标。时间序列长度从25年到52年不等，分析截至2022年。DFA识别了各指标以及协变指标组（如浮游动物物种生物量）之间的共同趋势，突出了将这些数据简化传达给管理部门的机会。非平稳性分析揭示了过去关系的变化，并可在未来年度更新中提供预警，前提是之前确定的相关性发生变化。HMM在每个子模型中识别出两到三种生态系统状态，这些状态在很大程度上与该地区此前观察到的生态系统动态的长期和短期变化（即始于1975年、1988年和2014年的变化）相一致。在向管理机构报告生态系统信息的现有框架内，每年更新这些分析可以简化沟通并改善对生态系统动态变化的早期预警。这些工具可以为与底层鱼类生产力及由此产生的捕捞规格相关的管理决策提供生态系统支持。
