An efficient tool to find multispecies MSY for interacting fish stocks

IF 5.6 1区 农林科学 Q1 FISHERIES
T. J. Del Santo O’Neill, A. G. Rossberg, R. B. Thorpe
{"title":"An efficient tool to find multispecies MSY for interacting fish stocks","authors":"T. J. Del Santo O’Neill,&nbsp;A. G. Rossberg,&nbsp;R. B. Thorpe","doi":"10.1111/faf.12817","DOIUrl":null,"url":null,"abstract":"<p>Natural ecological communities exhibit complex mixtures of interspecific biological interactions, which makes finding optimal yet sustainable exploitation rates challenging. Most fisheries management advice is at present based on applying the Maximum Sustainable Yield (MSY) target to each species in a community by modelling it as if it was a monoculture. Such application of single-species MSY policies to strongly interacting populations can result in tragic overexploitation. However, the idea of ‘maximising the yield from each species separately’ can be extended to take into account species interactions. This leads to a form of Nash Equilibrium, where the yields of each species are simultaneously maximised. Here we present ‘<span>nash</span>’, an <span>R</span> package that streamlines the computation of Nash equilibrium reference points for fisheries and other systems represented by a user-defined multispecies or ecosystem model. We present two real-world fisheries management applications alongside performance benchmarks. Satisfactory search results are shown across models with an approximate factor 7 increase in performance when compared to the expensive round-robbing sequential optimisation algorithms used by other authors in the literature. We believe that the <span>nash</span> package can play an instrumental role in fully implementing ecosystem-based management policies worldwide.</p>","PeriodicalId":169,"journal":{"name":"Fish and Fisheries","volume":"25 3","pages":"441-454"},"PeriodicalIF":5.6000,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/faf.12817","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Fish and Fisheries","FirstCategoryId":"97","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1111/faf.12817","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

Abstract

Natural ecological communities exhibit complex mixtures of interspecific biological interactions, which makes finding optimal yet sustainable exploitation rates challenging. Most fisheries management advice is at present based on applying the Maximum Sustainable Yield (MSY) target to each species in a community by modelling it as if it was a monoculture. Such application of single-species MSY policies to strongly interacting populations can result in tragic overexploitation. However, the idea of ‘maximising the yield from each species separately’ can be extended to take into account species interactions. This leads to a form of Nash Equilibrium, where the yields of each species are simultaneously maximised. Here we present ‘nash’, an R package that streamlines the computation of Nash equilibrium reference points for fisheries and other systems represented by a user-defined multispecies or ecosystem model. We present two real-world fisheries management applications alongside performance benchmarks. Satisfactory search results are shown across models with an approximate factor 7 increase in performance when compared to the expensive round-robbing sequential optimisation algorithms used by other authors in the literature. We believe that the nash package can play an instrumental role in fully implementing ecosystem-based management policies worldwide.

Abstract Image

为相互作用的鱼类种群寻找多物种 MSY 的有效工具
自然生态群落呈现出种间生物相互作用的复杂混合物,这使得寻找最佳但可持续的开发率具有挑战性。目前,大多数渔业管理建议都是将群落中的每一个物种都当作单一物种来模拟,从而将最大可持续产量(MSY)目标应用于群落中的每一个物种。这种将单一物种的最大可持续产量(MSY)政策应用于相互作用强烈的种群的做法可能会导致悲惨的过度开发。不过,"最大限度地分别提高每个物种的产量 "这一想法可以扩展到考虑物种间的相互作用。这就产生了一种纳什均衡(Nash Equilibrium),即每个物种的产量同时达到最大化。在这里,我们将介绍一个 R 软件包 "nash",它可以简化用户定义的多物种或生态系统模型所代表的渔业和其他系统的纳什均衡参考点的计算。我们介绍了两个现实世界中的渔业管理应用以及性能基准。与其他作者在文献中使用的昂贵的迂回顺序优化算法相比,各种模型的搜索结果令人满意,性能大约提高了 7 倍。我们相信,nash 软件包可以在全球全面实施基于生态系统的管理政策方面发挥重要作用。
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 求助全文
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