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
{"title":"A roadmap for generating annual bycatch estimates from sparse at-sea observer data","authors":"Yihao Yin, Heather D Bowlby, Hugues P Benoît","doi":"10.1093/icesjms/fsae110","DOIUrl":null,"url":null,"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.","PeriodicalId":51072,"journal":{"name":"ICES Journal of Marine Science","volume":null,"pages":null},"PeriodicalIF":3.1000,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ICES Journal of Marine Science","FirstCategoryId":"97","ListUrlMain":"https://doi.org/10.1093/icesjms/fsae110","RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"FISHERIES","Score":null,"Total":0}
引用次数: 0

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.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
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。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
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.
期刊最新文献
Adaptive small-scale fisheries in the eastern Cantabrian coast through reliance on essential species A roadmap for generating annual bycatch estimates from sparse at-sea observer data Assessing the value of harmful algal bloom forecasts in the Pacific Northwest Uncrewed surface vehicles (USVs) as platforms for fisheries and plankton acoustics Trophic flows to mesozooplankton support the conventional paradigm of pelagic food web structure in ocean ecosystems
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1