Towards a comprehensive framework for providing management advice from statistical inference using population dynamics models

IF 2.6 3区 环境科学与生态学 Q2 ECOLOGY
{"title":"Towards a comprehensive framework for providing management advice from statistical inference using population dynamics models","authors":"","doi":"10.1016/j.ecolmodel.2024.110836","DOIUrl":null,"url":null,"abstract":"<div><p>There has been substantial progress in fitting population dynamics models to data and this has greatly improved management advice in a variety of situations from exploitation to conservation. One of the major developments has been integrated analysis where multiple diverse data sets are fit simultaneously within the same model. However, issues such as model misspecification, unmodelled process variation, and data weighting make integrated analysis problematic. Here I provide a personal perspective on a framework for Model Development (FMD) based on the Center for the Advancement of Population Assessment Methodology (CAPAM) workshops and special issues, my own research, and other information. The FMD is motivated by fisheries stock assessment but is relevant to any form of population dynamics modelling or modelling in general. I provide an outline of the modeling framework and discuss the important topic of data weighting. The FMD starts with one or more conceptual models which are implemented as population dynamics models fit to data using a comprehensively researched Good Practices Guide (GPG). The models are evaluated, improved, and selected, based on a diagnostic “expert” system that has been rigorously developed using a comprehensive simulation analysis. The final models that are accepted in the ensemble are equally weighted (until the data weighting issue is fully resolved) to provide management advice. I also outline necessary future research.</p></div>","PeriodicalId":51043,"journal":{"name":"Ecological Modelling","volume":null,"pages":null},"PeriodicalIF":2.6000,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0304380024002242/pdfft?md5=f55901d32bcffd20335eb825205d8370&pid=1-s2.0-S0304380024002242-main.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Ecological Modelling","FirstCategoryId":"93","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0304380024002242","RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ECOLOGY","Score":null,"Total":0}
引用次数: 0

Abstract

There has been substantial progress in fitting population dynamics models to data and this has greatly improved management advice in a variety of situations from exploitation to conservation. One of the major developments has been integrated analysis where multiple diverse data sets are fit simultaneously within the same model. However, issues such as model misspecification, unmodelled process variation, and data weighting make integrated analysis problematic. Here I provide a personal perspective on a framework for Model Development (FMD) based on the Center for the Advancement of Population Assessment Methodology (CAPAM) workshops and special issues, my own research, and other information. The FMD is motivated by fisheries stock assessment but is relevant to any form of population dynamics modelling or modelling in general. I provide an outline of the modeling framework and discuss the important topic of data weighting. The FMD starts with one or more conceptual models which are implemented as population dynamics models fit to data using a comprehensively researched Good Practices Guide (GPG). The models are evaluated, improved, and selected, based on a diagnostic “expert” system that has been rigorously developed using a comprehensive simulation analysis. The final models that are accepted in the ensemble are equally weighted (until the data weighting issue is fully resolved) to provide management advice. I also outline necessary future research.

Abstract Image

利用种群动态模型的统计推断提供管理建议的综合框架
在根据数据拟合种群动力学模型方面取得了重大进展,这极大地改进了从开发到保护等各种情况下的管理建议。其中一项主要进展是综合分析,即在同一模型中同时拟合多个不同的数据集。然而,模型的错误定义、未建模的过程变化和数据加权等问题使得综合分析困难重重。在此,我根据人口评估方法推进中心(CAPAM)的研讨会和特刊、我自己的研究以及其他信息,就模型开发框架(FMD)提出个人观点。FMD 以渔业种群评估为动机,但也适用于任何形式的种群动态建模或一般建模。我将简要介绍建模框架,并讨论数据加权这一重要课题。FMD 以一个或多个概念模型为起点,利用经过全面研究的《良好实践指南》(GPG),将其作为与数据相匹配的种群动力学模型来实施。模型的评估、改进和选择以诊断 "专家 "系统为基础,该诊断 "专家 "系统是通过综合模拟分析严格开发的。在数据权重问题完全解决之前,最终被采纳的模型在集合中的权重相同,以提供管理建议。我还概述了今后必须开展的研究。
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
Ecological Modelling
Ecological Modelling 环境科学-生态学
CiteScore
5.60
自引率
6.50%
发文量
259
审稿时长
69 days
期刊介绍: The journal is concerned with the use of mathematical models and systems analysis for the description of ecological processes and for the sustainable management of resources. Human activity and well-being are dependent on and integrated with the functioning of ecosystems and the services they provide. We aim to understand these basic ecosystem functions using mathematical and conceptual modelling, systems analysis, thermodynamics, computer simulations, and ecological theory. This leads to a preference for process-based models embedded in theory with explicit causative agents as opposed to strictly statistical or correlative descriptions. These modelling methods can be applied to a wide spectrum of issues ranging from basic ecology to human ecology to socio-ecological systems. The journal welcomes research articles, short communications, review articles, letters to the editor, book reviews, and other communications. The journal also supports the activities of the [International Society of Ecological Modelling (ISEM)](http://www.isemna.org/).
×
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