Pengjuan Zu, Florian P Schiestl, Daniel Gervasi, Xin Li, Daniel Runcie, Frédéric Guillaume
{"title":"在人工和传粉昆虫的选择下,甘蓝型油菜的花信号以可预测的方式进化。","authors":"Pengjuan Zu, Florian P Schiestl, Daniel Gervasi, Xin Li, Daniel Runcie, Frédéric Guillaume","doi":"10.1186/s12862-020-01692-7","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Angiosperms employ an astonishing variety of visual and olfactory floral signals that are generally thought to evolve under natural selection. Those morphological and chemical traits can form highly correlated sets of traits. It is not always clear which of these are used by pollinators as primary targets of selection and which would be indirectly selected by being linked to those primary targets. Quantitative genetics tools for predicting multiple traits response to selection have been developed since long and have advanced our understanding of evolution of genetically correlated traits in various biological systems. We use these tools to predict the evolutionary trajectories of floral traits and understand the selection pressures acting on them.</p><p><strong>Results: </strong>We used data from an artificial selection and a pollinator (bumblebee, hoverfly) evolution experiment with fast cycling Brassica rapa plants to predict evolutionary changes of 12 floral volatiles and 4 morphological floral traits in response to selection. Using the observed selection gradients and the genetic variance-covariance matrix (G-matrix) of the traits, we showed that the observed responses of most floral traits including volatiles were predicted in the right direction in both artificial- and bumblebee-selection experiment. Genetic covariance had a mix of constraining and facilitating effects on evolutionary responses. We further revealed that G-matrices also evolved in the selection processes.</p><p><strong>Conclusions: </strong>Overall, our integrative study shows that floral signals, especially volatiles, evolve under selection in a mostly predictable way, at least during short term evolution. Evolutionary constraints stemming from genetic covariance affected traits evolutionary trajectories and thus it is important to include genetic covariance for predicting the evolutionary changes of a comprehensive suite of traits. Other processes such as resource limitation and selfing also need to be considered for a better understanding of floral trait evolution.</p>","PeriodicalId":9111,"journal":{"name":"BMC Evolutionary Biology","volume":null,"pages":null},"PeriodicalIF":3.4000,"publicationDate":"2020-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7517814/pdf/","citationCount":"0","resultStr":"{\"title\":\"Floral signals evolve in a predictable way under artificial and pollinator selection in Brassica rapa.\",\"authors\":\"Pengjuan Zu, Florian P Schiestl, Daniel Gervasi, Xin Li, Daniel Runcie, Frédéric Guillaume\",\"doi\":\"10.1186/s12862-020-01692-7\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Angiosperms employ an astonishing variety of visual and olfactory floral signals that are generally thought to evolve under natural selection. Those morphological and chemical traits can form highly correlated sets of traits. It is not always clear which of these are used by pollinators as primary targets of selection and which would be indirectly selected by being linked to those primary targets. Quantitative genetics tools for predicting multiple traits response to selection have been developed since long and have advanced our understanding of evolution of genetically correlated traits in various biological systems. We use these tools to predict the evolutionary trajectories of floral traits and understand the selection pressures acting on them.</p><p><strong>Results: </strong>We used data from an artificial selection and a pollinator (bumblebee, hoverfly) evolution experiment with fast cycling Brassica rapa plants to predict evolutionary changes of 12 floral volatiles and 4 morphological floral traits in response to selection. Using the observed selection gradients and the genetic variance-covariance matrix (G-matrix) of the traits, we showed that the observed responses of most floral traits including volatiles were predicted in the right direction in both artificial- and bumblebee-selection experiment. Genetic covariance had a mix of constraining and facilitating effects on evolutionary responses. We further revealed that G-matrices also evolved in the selection processes.</p><p><strong>Conclusions: </strong>Overall, our integrative study shows that floral signals, especially volatiles, evolve under selection in a mostly predictable way, at least during short term evolution. Evolutionary constraints stemming from genetic covariance affected traits evolutionary trajectories and thus it is important to include genetic covariance for predicting the evolutionary changes of a comprehensive suite of traits. Other processes such as resource limitation and selfing also need to be considered for a better understanding of floral trait evolution.</p>\",\"PeriodicalId\":9111,\"journal\":{\"name\":\"BMC Evolutionary Biology\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.4000,\"publicationDate\":\"2020-09-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7517814/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"BMC Evolutionary Biology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1186/s12862-020-01692-7\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"Agricultural and Biological Sciences\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"BMC Evolutionary Biology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1186/s12862-020-01692-7","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"Agricultural and Biological Sciences","Score":null,"Total":0}
Floral signals evolve in a predictable way under artificial and pollinator selection in Brassica rapa.
Background: Angiosperms employ an astonishing variety of visual and olfactory floral signals that are generally thought to evolve under natural selection. Those morphological and chemical traits can form highly correlated sets of traits. It is not always clear which of these are used by pollinators as primary targets of selection and which would be indirectly selected by being linked to those primary targets. Quantitative genetics tools for predicting multiple traits response to selection have been developed since long and have advanced our understanding of evolution of genetically correlated traits in various biological systems. We use these tools to predict the evolutionary trajectories of floral traits and understand the selection pressures acting on them.
Results: We used data from an artificial selection and a pollinator (bumblebee, hoverfly) evolution experiment with fast cycling Brassica rapa plants to predict evolutionary changes of 12 floral volatiles and 4 morphological floral traits in response to selection. Using the observed selection gradients and the genetic variance-covariance matrix (G-matrix) of the traits, we showed that the observed responses of most floral traits including volatiles were predicted in the right direction in both artificial- and bumblebee-selection experiment. Genetic covariance had a mix of constraining and facilitating effects on evolutionary responses. We further revealed that G-matrices also evolved in the selection processes.
Conclusions: Overall, our integrative study shows that floral signals, especially volatiles, evolve under selection in a mostly predictable way, at least during short term evolution. Evolutionary constraints stemming from genetic covariance affected traits evolutionary trajectories and thus it is important to include genetic covariance for predicting the evolutionary changes of a comprehensive suite of traits. Other processes such as resource limitation and selfing also need to be considered for a better understanding of floral trait evolution.
期刊介绍:
BMC Evolutionary Biology is an open access, peer-reviewed journal that considers articles on all aspects of molecular and non-molecular evolution of all organisms, as well as phylogenetics and palaeontology.