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多元化综合生态系统方法克服了与大流行相关的渔业监测挑战。

Diverse integrated ecosystem approach overcomes pandemic-related fisheries monitoring challenges.

机构信息

Fisheries Ecology Division, Southwest Fisheries Science Center, National Marine Fisheries Service, National Oceanic and Atmospheric Administration, Santa Cruz, California, 95060, USA.

Department of Applied Math, University of California Santa Cruz, Santa Cruz, California, 95060, US.

出版信息

Nat Commun. 2021 Nov 11;12(1):6492. doi: 10.1038/s41467-021-26484-5.

Abstract

The COVID-19 pandemic caused unprecedented cancellations of fisheries and ecosystem-assessment surveys, resulting in a recession of observations needed for management and conservation globally. This unavoidable reduction of survey data poses challenges for informing biodiversity and ecosystem functioning, developing future stock assessments of harvested species, and providing strategic advice for ecosystem-based management. We present a diversified framework involving integration of monitoring data with empirical models and simulations to inform ecosystem status within the California Current Large Marine Ecosystem. We augment trawl observations collected from a limited fisheries survey with survey effort reduction simulations, use of seabird diets as indicators of fish abundance, and krill species distribution modeling trained on past observations. This diversified approach allows for evaluation of ecosystem status during data-poor situations, especially during the COVID-19 era. The challenges to ecosystem monitoring imposed by the pandemic may be overcome by preparing for unexpected effort reduction, linking disparate ecosystem indicators, and applying new species modeling techniques.

摘要

COVID-19 大流行导致渔业和生态系统评估调查前所未有地取消,导致全球管理和保护所需的观测出现衰退。这种不可避免的调查数据减少对通知生物多样性和生态系统功能、开发未来捕捞物种的存量评估以及为基于生态系统的管理提供战略建议带来了挑战。我们提出了一个多元化的框架,涉及监测数据与经验模型和模拟的整合,以告知加利福尼亚当前大海洋生态系统的生态系统状况。我们利用有限渔业调查中收集的拖网观测数据,结合调查工作减少模拟、利用海鸟的饮食作为鱼类丰度的指标,以及基于过去观测数据训练的磷虾物种分布模型。这种多元化的方法允许在数据匮乏的情况下评估生态系统状况,特别是在 COVID-19 时代。可以通过为意外的工作量减少做准备、链接不同的生态系统指标和应用新的物种建模技术来克服大流行对生态系统监测造成的挑战。

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