Bridging Performance and Adaptive Landscapes to Understand Long-Term Functional Evolution.

IF 16.4 1区 化学 Q1 CHEMISTRY, MULTIDISCIPLINARY
Monique Nouailhetas Simon, Daniel S Moen
{"title":"Bridging Performance and Adaptive Landscapes to Understand Long-Term Functional Evolution.","authors":"Monique Nouailhetas Simon,&nbsp;Daniel S Moen","doi":"10.1086/725416","DOIUrl":null,"url":null,"abstract":"<p><p>AbstractUnderstanding functional adaptation demands an integrative framework that captures the complex interactions between form, function, ecology, and evolutionary processes. In this review, we discuss how to integrate the following two distinct approaches to better understand functional evolution: (1) the adaptive landscape approach (ALA), aimed at finding adaptive peaks for different ecologies, and (2) the performance landscape approach (PLA), aimed at finding performance peaks for different ecologies. We focus on the Ornstein-Uhlenbeck process as the evolutionary model for the ALA and on biomechanical modeling to estimate performance for the PLA. Whereas both the ALA and the PLA have each given insight into functional adaptation, separately they cannot address how much performance contributes to fitness or whether evolutionary constraints have played a role in form-function evolution. We show that merging these approaches leads to a deeper understanding of these issues. By comparing the locations of performance and adaptive peaks, we can infer how much performance contributes to fitness in species' current environments. By testing for the relevance of history on phenotypic variation, we can infer the influence of past selection and constraints on functional adaptation. We apply this merged framework in a case study of turtle shell evolution and explain how to interpret different possible outcomes. Even though such outcomes can be quite complex, they represent the multifaceted relations among function, fitness, and constraints.</p>","PeriodicalId":1,"journal":{"name":"Accounts of Chemical Research","volume":null,"pages":null},"PeriodicalIF":16.4000,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Accounts of Chemical Research","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1086/725416","RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CHEMISTRY, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 1

Abstract

AbstractUnderstanding functional adaptation demands an integrative framework that captures the complex interactions between form, function, ecology, and evolutionary processes. In this review, we discuss how to integrate the following two distinct approaches to better understand functional evolution: (1) the adaptive landscape approach (ALA), aimed at finding adaptive peaks for different ecologies, and (2) the performance landscape approach (PLA), aimed at finding performance peaks for different ecologies. We focus on the Ornstein-Uhlenbeck process as the evolutionary model for the ALA and on biomechanical modeling to estimate performance for the PLA. Whereas both the ALA and the PLA have each given insight into functional adaptation, separately they cannot address how much performance contributes to fitness or whether evolutionary constraints have played a role in form-function evolution. We show that merging these approaches leads to a deeper understanding of these issues. By comparing the locations of performance and adaptive peaks, we can infer how much performance contributes to fitness in species' current environments. By testing for the relevance of history on phenotypic variation, we can infer the influence of past selection and constraints on functional adaptation. We apply this merged framework in a case study of turtle shell evolution and explain how to interpret different possible outcomes. Even though such outcomes can be quite complex, they represent the multifaceted relations among function, fitness, and constraints.

桥接性能和适应性景观以理解长期功能演变。
摘要理解功能适应需要一个综合的框架,以捕捉形式、功能、生态和进化过程之间复杂的相互作用。在这篇综述中,我们讨论了如何整合以下两种不同的方法来更好地理解功能进化:(1)适应性景观方法(ALA),旨在找到不同生态环境的适应性峰值;(2)性能景观方法(PLA),旨在找到不同生态环境的性能峰值。我们专注于将Ornstein-Uhlenbeck过程作为ALA的进化模型,并通过生物力学建模来估计PLA的性能。尽管ALA和PLA都对功能适应有深入的了解,但它们无法单独解决性能对适应度的贡献有多大,或者进化约束是否在形式-功能进化中发挥了作用。我们表明,合并这些方法可以更深入地理解这些问题。通过比较表现和适应峰值的位置,我们可以推断出在物种当前环境中表现对适应度的贡献有多大。通过检验历史对表型变异的相关性,我们可以推断过去的选择和限制对功能适应的影响。我们将这一合并框架应用于龟壳进化的案例研究,并解释了如何解释不同的可能结果。尽管这样的结果可能相当复杂,但它们代表了功能、适应度和约束之间的多方面关系。
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
Accounts of Chemical Research
Accounts of Chemical Research 化学-化学综合
CiteScore
31.40
自引率
1.10%
发文量
312
审稿时长
2 months
期刊介绍: Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance. Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.
×
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