利用生态指标改进鱼类繁殖的短期预测

IF 5.6 1区 农林科学 Q1 FISHERIES
Eric J. Ward, Mary E. Hunsicker, Kristin N. Marshall, Kiva L. Oken, Brice X. Semmens, John C. Field, Melissa A. Haltuch, Kelli F. Johnson, Ian G. Taylor, Andrew R. Thompson, Nick Tolimieri
{"title":"利用生态指标改进鱼类繁殖的短期预测","authors":"Eric J. Ward,&nbsp;Mary E. Hunsicker,&nbsp;Kristin N. Marshall,&nbsp;Kiva L. Oken,&nbsp;Brice X. Semmens,&nbsp;John C. Field,&nbsp;Melissa A. Haltuch,&nbsp;Kelli F. Johnson,&nbsp;Ian G. Taylor,&nbsp;Andrew R. Thompson,&nbsp;Nick Tolimieri","doi":"10.1111/faf.12850","DOIUrl":null,"url":null,"abstract":"<p>Forecasting the recruitment of fish populations with skill has been a challenge in fisheries for over a century. Previous large-scale meta-analyses have suggested linkages between environmental or ecosystem drivers and recruitment; however, applying this information in a management setting remains underutilized. Here, we use a well-studied database of groundfish assessments from the West Coast of the USA to ask whether environmental variables or ecosystem indicators derived from long-term monitoring datasets offer an improvement in our ability to skilfully forecast fish recruitment. A secondary question is which types of modelling approaches (ranging from linear models to non-parametric methods) yield the best forecast skill. Third, we examine whether simultaneous forecasting of multiple species offers an advantage over generating species-specific forecasts. We find that for approximately one third of the 29 assessed stocks, ecosystem indicators from juvenile surveys yields the highest out of sample predictive skill compared to other covariates (including environmental variables from Regional Ocean Modeling System output) or null models. Across modelling approaches, our results suggest that simpler linear modelling approaches do as well or better than more complicated approaches (reducing out of sample Root Mean Square Error by ~40% compared to null models), and that there appears to be little benefit to performing multispecies forecasts instead of single-species forecasts. Our results provide a general framework for generating recruitment forecasts in other species and ecosystems, as well as a benchmark for future analyses to evaluate skill. The most promising applications are likely for species that are short lived, have relatively high recruitment variability, and moderate amounts of age or length data. Forecasts using our approach may be useful in identifying covariates or mechanisms to include in operational assessments but also provide qualitative advice to managers implementing ecosystem based fisheries management.</p>","PeriodicalId":169,"journal":{"name":"Fish and Fisheries","volume":"25 6","pages":"895-909"},"PeriodicalIF":5.6000,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/faf.12850","citationCount":"0","resultStr":"{\"title\":\"Leveraging ecological indicators to improve short term forecasts of fish recruitment\",\"authors\":\"Eric J. Ward,&nbsp;Mary E. Hunsicker,&nbsp;Kristin N. Marshall,&nbsp;Kiva L. Oken,&nbsp;Brice X. Semmens,&nbsp;John C. Field,&nbsp;Melissa A. Haltuch,&nbsp;Kelli F. Johnson,&nbsp;Ian G. Taylor,&nbsp;Andrew R. Thompson,&nbsp;Nick Tolimieri\",\"doi\":\"10.1111/faf.12850\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Forecasting the recruitment of fish populations with skill has been a challenge in fisheries for over a century. Previous large-scale meta-analyses have suggested linkages between environmental or ecosystem drivers and recruitment; however, applying this information in a management setting remains underutilized. Here, we use a well-studied database of groundfish assessments from the West Coast of the USA to ask whether environmental variables or ecosystem indicators derived from long-term monitoring datasets offer an improvement in our ability to skilfully forecast fish recruitment. A secondary question is which types of modelling approaches (ranging from linear models to non-parametric methods) yield the best forecast skill. Third, we examine whether simultaneous forecasting of multiple species offers an advantage over generating species-specific forecasts. We find that for approximately one third of the 29 assessed stocks, ecosystem indicators from juvenile surveys yields the highest out of sample predictive skill compared to other covariates (including environmental variables from Regional Ocean Modeling System output) or null models. Across modelling approaches, our results suggest that simpler linear modelling approaches do as well or better than more complicated approaches (reducing out of sample Root Mean Square Error by ~40% compared to null models), and that there appears to be little benefit to performing multispecies forecasts instead of single-species forecasts. Our results provide a general framework for generating recruitment forecasts in other species and ecosystems, as well as a benchmark for future analyses to evaluate skill. The most promising applications are likely for species that are short lived, have relatively high recruitment variability, and moderate amounts of age or length data. Forecasts using our approach may be useful in identifying covariates or mechanisms to include in operational assessments but also provide qualitative advice to managers implementing ecosystem based fisheries management.</p>\",\"PeriodicalId\":169,\"journal\":{\"name\":\"Fish and Fisheries\",\"volume\":\"25 6\",\"pages\":\"895-909\"},\"PeriodicalIF\":5.6000,\"publicationDate\":\"2024-08-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://onlinelibrary.wiley.com/doi/epdf/10.1111/faf.12850\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Fish and Fisheries\",\"FirstCategoryId\":\"97\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1111/faf.12850\",\"RegionNum\":1,\"RegionCategory\":\"农林科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"FISHERIES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Fish and Fisheries","FirstCategoryId":"97","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1111/faf.12850","RegionNum":1,"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

摘要

一个多世纪以来,如何巧妙地预测鱼类种群的繁殖一直是渔业面临的挑战。以前的大规模荟萃分析表明,环境或生态系统驱动因素与鱼类繁殖之间存在联系;然而,在管理环境中应用这些信息的机会仍然不足。在此,我们利用美国西海岸底层鱼类评估数据库,探讨从长期监测数据集中得出的环境变量或生态系统指标是否能提高我们娴熟预测鱼类繁殖的能力。第二个问题是,哪种建模方法(从线性模型到非参数方法)能产生最佳预测技能。第三,我们研究了同时预测多个物种是否比生成特定物种预测更具优势。我们发现,在 29 个被评估的种群中,约有三分之一的种群与其他协变量(包括区域海洋模拟系统输出的环境变量)或空模型相比,来自幼鱼调查的生态系统指标能产生最高的样本外预测技能。在各种建模方法中,我们的结果表明,较简单的线性建模方法与较复杂的建模方法相比,效果相同或更好(与空模型相比,样本外均方根误差降低了约 40%),而且进行多物种预测而不是单物种预测似乎没有什么好处。我们的研究结果为其他物种和生态系统的招募预测提供了一个总体框架,也为未来评估技能的分析提供了一个基准。最有前景的应用可能是那些寿命较短、招募变异性相对较高、年龄或长度数据量适中的物种。使用我们的方法进行的预测可能有助于确定协变量或机制,以便将其纳入业务评估,同时还能为实施基于生态系统的渔业管理的管理人员提供定性建议。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Leveraging ecological indicators to improve short term forecasts of fish recruitment

Leveraging ecological indicators to improve short term forecasts of fish recruitment

Forecasting the recruitment of fish populations with skill has been a challenge in fisheries for over a century. Previous large-scale meta-analyses have suggested linkages between environmental or ecosystem drivers and recruitment; however, applying this information in a management setting remains underutilized. Here, we use a well-studied database of groundfish assessments from the West Coast of the USA to ask whether environmental variables or ecosystem indicators derived from long-term monitoring datasets offer an improvement in our ability to skilfully forecast fish recruitment. A secondary question is which types of modelling approaches (ranging from linear models to non-parametric methods) yield the best forecast skill. Third, we examine whether simultaneous forecasting of multiple species offers an advantage over generating species-specific forecasts. We find that for approximately one third of the 29 assessed stocks, ecosystem indicators from juvenile surveys yields the highest out of sample predictive skill compared to other covariates (including environmental variables from Regional Ocean Modeling System output) or null models. Across modelling approaches, our results suggest that simpler linear modelling approaches do as well or better than more complicated approaches (reducing out of sample Root Mean Square Error by ~40% compared to null models), and that there appears to be little benefit to performing multispecies forecasts instead of single-species forecasts. Our results provide a general framework for generating recruitment forecasts in other species and ecosystems, as well as a benchmark for future analyses to evaluate skill. The most promising applications are likely for species that are short lived, have relatively high recruitment variability, and moderate amounts of age or length data. Forecasts using our approach may be useful in identifying covariates or mechanisms to include in operational assessments but also provide qualitative advice to managers implementing ecosystem based fisheries management.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Fish and Fisheries
Fish and Fisheries 农林科学-渔业
CiteScore
12.80
自引率
6.00%
发文量
83
期刊介绍: Fish and Fisheries adopts a broad, interdisciplinary approach to the subject of fish biology and fisheries. It draws contributions in the form of major synoptic papers and syntheses or meta-analyses that lay out new approaches, re-examine existing findings, methods or theory, and discuss papers and commentaries from diverse areas. Focal areas include fish palaeontology, molecular biology and ecology, genetics, biochemistry, physiology, ecology, behaviour, evolutionary studies, conservation, assessment, population dynamics, mathematical modelling, ecosystem analysis and the social, economic and policy aspects of fisheries where they are grounded in a scientific approach. A paper in Fish and Fisheries must draw upon all key elements of the existing literature on a topic, normally have a broad geographic and/or taxonomic scope, and provide general points which make it compelling to a wide range of readers whatever their geographical location. So, in short, we aim to publish articles that make syntheses of old or synoptic, long-term or spatially widespread data, introduce or consolidate fresh concepts or theory, or, in the Ghoti section, briefly justify preliminary, new synoptic ideas. Please note that authors of submissions not meeting this mandate will be directed to the appropriate primary literature.
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信