A roadmap for generating annual bycatch estimates from sparse at-sea observer data

IF 3.1 2区 农林科学 Q1 FISHERIES ICES Journal of Marine Science Pub Date : 2024-09-17 DOI:10.1093/icesjms/fsae110
Yihao Yin, Heather D Bowlby, Hugues P Benoît
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Abstract

To support ecosystem-based fisheries management, monitoring data from at-sea observer (ASO) programs should be leveraged to understand the impact of fisheries on discarded species (bycatch). Available techniques to estimate fishery-scale quantities from observations range from simple mean estimators to more complex spatiotemporal models, each making assumptions with differing degrees of support. However, the resulting implementation and analytical trade-offs are rarely discussed when applying these techniques in practice. Using blue shark (Prionace glauca) bycatch in the Canadian pelagic longline fishery as a case study, we evaluated the performance of seven contrasting approaches to estimating total annual discard amounts and assessed their trade-offs in application. Results demonstrated that simple approaches such as mean estimator and nearest neighbors are feasible to implement and can be as efficient for prediction as complex models such as random forest and mixed-effects models. The traditionally used catch-ratio estimator consistently underperformed among all tested models, likely due to misspecified correlative relationships between target and bycatch species. Overall, efforts in model-based approaches were rewarded with very small gains in predictive ability, suggesting that such models relying on environmental, biological, spatial, and/or temporal patterns to improve prediction of bycatch may lack sufficient foundation in data-limited contexts.
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从稀少的海上观测数据生成年度混获物估计值的路线图
为支持基于生态系统的渔业管理,应利用海上观测(ASO)计划的监测数据来了解渔业对丢弃物种(副渔获物)的影响。从观测结果估算渔业规模数量的现有技术包括从简单的平均估算器到更复杂的时空模型,每种技术都有不同程度的支持假设。然而,在实际应用这些技术时,很少讨论由此产生的实施和分析权衡问题。以加拿大中上层延绳钓渔业中混获的大青鲨(Prionace glauca)为案例,我们评估了估算年度丢弃总量的七种对比方法的性能,并评估了它们在应用中的权衡。结果表明,均值估计法和近邻法等简单方法是可行的,其预测效率不亚于随机森林和混合效应模型等复杂模型。在所有测试模型中,传统使用的渔获率估算器一直表现不佳,这可能是由于目标鱼种和副渔获物之间的相关关系描述错误造成的。总体而言,基于模型方法的努力在预测能力方面的收益非常小,这表明依靠环境、 生物、空间和/或时间模式来改进混获预测的此类模型在数据有限的情况下可能缺乏 足够的基础。
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来源期刊
ICES Journal of Marine Science
ICES Journal of Marine Science 农林科学-海洋学
CiteScore
6.60
自引率
12.10%
发文量
207
审稿时长
6-16 weeks
期刊介绍: The ICES Journal of Marine Science publishes original articles, opinion essays (“Food for Thought”), visions for the future (“Quo Vadimus”), and critical reviews that contribute to our scientific understanding of marine systems and the impact of human activities on them. The Journal also serves as a foundation for scientific advice across the broad spectrum of management and conservation issues related to the marine environment. Oceanography (e.g. productivity-determining processes), marine habitats, living resources, and related topics constitute the key elements of papers considered for publication. This includes economic, social, and public administration studies to the extent that they are directly related to management of the seas and are of general interest to marine scientists. Integrated studies that bridge gaps between traditional disciplines are particularly welcome.
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