{"title":"死亡-出生适应动态:性状进化建模","authors":"Ian Braga, Emmanuel Pereira, Lucas Wardil","doi":"10.1103/physreve.110.l032401","DOIUrl":null,"url":null,"abstract":"Here, we derive stochastic adaptive dynamics from the microscopic death-birth process by explicitly modeling the trait variation from offspring to parent in each reproductive event, thereby accounting for a highly polymorphic population. This generalization enables the construction of a quantitative model that can be subjected to empirical validation. Our mathematical analysis furnishes a formula for estimating the trait variation in the reproductive step by exclusively observing the current trait variation in the population. In addition, we provide a straightforward approach to obtain the fitness function associated with a particular trait by examining its actual evolutionary trajectory, which can be employed to forecast the continued evolution of the trait.","PeriodicalId":20085,"journal":{"name":"Physical review. E","volume":"191 1","pages":""},"PeriodicalIF":2.4000,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Death-birth adaptive dynamics: modeling trait evolution\",\"authors\":\"Ian Braga, Emmanuel Pereira, Lucas Wardil\",\"doi\":\"10.1103/physreve.110.l032401\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Here, we derive stochastic adaptive dynamics from the microscopic death-birth process by explicitly modeling the trait variation from offspring to parent in each reproductive event, thereby accounting for a highly polymorphic population. This generalization enables the construction of a quantitative model that can be subjected to empirical validation. Our mathematical analysis furnishes a formula for estimating the trait variation in the reproductive step by exclusively observing the current trait variation in the population. In addition, we provide a straightforward approach to obtain the fitness function associated with a particular trait by examining its actual evolutionary trajectory, which can be employed to forecast the continued evolution of the trait.\",\"PeriodicalId\":20085,\"journal\":{\"name\":\"Physical review. E\",\"volume\":\"191 1\",\"pages\":\"\"},\"PeriodicalIF\":2.4000,\"publicationDate\":\"2024-09-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Physical review. E\",\"FirstCategoryId\":\"101\",\"ListUrlMain\":\"https://doi.org/10.1103/physreve.110.l032401\",\"RegionNum\":3,\"RegionCategory\":\"物理与天体物理\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"Mathematics\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Physical review. E","FirstCategoryId":"101","ListUrlMain":"https://doi.org/10.1103/physreve.110.l032401","RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"Mathematics","Score":null,"Total":0}
Here, we derive stochastic adaptive dynamics from the microscopic death-birth process by explicitly modeling the trait variation from offspring to parent in each reproductive event, thereby accounting for a highly polymorphic population. This generalization enables the construction of a quantitative model that can be subjected to empirical validation. Our mathematical analysis furnishes a formula for estimating the trait variation in the reproductive step by exclusively observing the current trait variation in the population. In addition, we provide a straightforward approach to obtain the fitness function associated with a particular trait by examining its actual evolutionary trajectory, which can be employed to forecast the continued evolution of the trait.
期刊介绍:
Physical Review E (PRE), broad and interdisciplinary in scope, focuses on collective phenomena of many-body systems, with statistical physics and nonlinear dynamics as the central themes of the journal. Physical Review E publishes recent developments in biological and soft matter physics including granular materials, colloids, complex fluids, liquid crystals, and polymers. The journal covers fluid dynamics and plasma physics and includes sections on computational and interdisciplinary physics, for example, complex networks.