{"title":"研究散布特征和人口特征在预测性系统地理学中的相对作用","authors":"Rilquer Mascarenhas, Ana Carolina Carnaval","doi":"10.1111/ecog.07149","DOIUrl":null,"url":null,"abstract":"Many studies suggest that aside from environmental variables, such as topography and climate, species-specific ecological traits are relevant to explain the geographic distribution of intraspecific genetic lineages. Here, we investigated whether and to what extent incorporating such traits systematically improves the accuracy of random forest models in predicting genetic differentiation among pairs of localities. We leveraged available ecological datasets for birds and tested the inclusion of two categories of ecological traits: dispersal-related traits (i.e. morphology and foraging ecology) and demographic traits (such as species survival rate and generation length). We estimated genetic differentiation from published mitochondrial DNA sequences for 28 species of birds (1578 total genetic samples, 391 localities) in the Atlantic Forest of South America. Aside from the aforementioned ecological traits, we included geographic, topographic and climatic distances between localities as environmental predictors. We then created models using all available data to evaluate model uncertainty both across space and across the different categories of predictors. Finally, we investigated model uncertainty in predicting genetic differentiation individually for each species (a common challenge in conservation biology). Our results show that while environmental conditions are the most important predictors of genetic differentiation, model accuracy largely increases with the addition of ecological traits. Additionally, the inclusion of dispersal traits improves model accuracy to a larger extent than the inclusion of demographic traits. Similar results are observed in models for individual species, although model accuracy is highly variable. We conclude that ecological traits improve predictive models of genetic differentiation, refining our ability to predict phylogeographic patterns from existing data. Additionally, demographic traits may not be as informative as previously hypothesized. Finally, prediction of genetic differentiation for species with conservation concerns may require further careful assessment of the environmental and ecological variation within the species range.","PeriodicalId":51026,"journal":{"name":"Ecography","volume":"11 1","pages":""},"PeriodicalIF":5.4000,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Investigating the relative role of dispersal and demographic traits in predictive phylogeography\",\"authors\":\"Rilquer Mascarenhas, Ana Carolina Carnaval\",\"doi\":\"10.1111/ecog.07149\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Many studies suggest that aside from environmental variables, such as topography and climate, species-specific ecological traits are relevant to explain the geographic distribution of intraspecific genetic lineages. Here, we investigated whether and to what extent incorporating such traits systematically improves the accuracy of random forest models in predicting genetic differentiation among pairs of localities. We leveraged available ecological datasets for birds and tested the inclusion of two categories of ecological traits: dispersal-related traits (i.e. morphology and foraging ecology) and demographic traits (such as species survival rate and generation length). We estimated genetic differentiation from published mitochondrial DNA sequences for 28 species of birds (1578 total genetic samples, 391 localities) in the Atlantic Forest of South America. Aside from the aforementioned ecological traits, we included geographic, topographic and climatic distances between localities as environmental predictors. We then created models using all available data to evaluate model uncertainty both across space and across the different categories of predictors. Finally, we investigated model uncertainty in predicting genetic differentiation individually for each species (a common challenge in conservation biology). Our results show that while environmental conditions are the most important predictors of genetic differentiation, model accuracy largely increases with the addition of ecological traits. Additionally, the inclusion of dispersal traits improves model accuracy to a larger extent than the inclusion of demographic traits. Similar results are observed in models for individual species, although model accuracy is highly variable. We conclude that ecological traits improve predictive models of genetic differentiation, refining our ability to predict phylogeographic patterns from existing data. Additionally, demographic traits may not be as informative as previously hypothesized. 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引用次数: 0
摘要
许多研究表明,除了地形和气候等环境变量外,物种特有的生态特征也能解释种内遗传系的地理分布。在此,我们研究了纳入这些特征是否以及在多大程度上系统地提高了随机森林模型预测成对地点间遗传分化的准确性。我们利用现有的鸟类生态数据集,测试了纳入两类生态特征的情况:与扩散相关的特征(即形态学和觅食生态学)和人口特征(如物种存活率和世代长度)。我们通过已发表的线粒体 DNA 序列估计了南美洲大西洋森林中 28 种鸟类(共 1578 个遗传样本,391 个地点)的遗传分化。除上述生态特征外,我们还将各地之间的地理、地形和气候距离作为环境预测因子。然后,我们利用所有可用数据创建了模型,以评估跨空间和不同类别预测因子的模型不确定性。最后,我们研究了模型在预测每个物种遗传分化时的不确定性(这是保护生物学中的一个常见挑战)。我们的结果表明,虽然环境条件是预测遗传分化的最重要因素,但模型的准确性随着生态特征的加入而大大提高。此外,与加入人口特征相比,加入扩散特征能在更大程度上提高模型的准确性。在单个物种的模型中也观察到了类似的结果,尽管模型的准确性差异很大。我们的结论是,生态学特征可以改善遗传分化的预测模型,提高我们从现有数据中预测系统地理格局的能力。此外,人口特征的信息量可能并不像之前假设的那样大。最后,预测需要保护的物种的遗传分化可能需要进一步仔细评估物种分布区内的环境和生态变化。
Investigating the relative role of dispersal and demographic traits in predictive phylogeography
Many studies suggest that aside from environmental variables, such as topography and climate, species-specific ecological traits are relevant to explain the geographic distribution of intraspecific genetic lineages. Here, we investigated whether and to what extent incorporating such traits systematically improves the accuracy of random forest models in predicting genetic differentiation among pairs of localities. We leveraged available ecological datasets for birds and tested the inclusion of two categories of ecological traits: dispersal-related traits (i.e. morphology and foraging ecology) and demographic traits (such as species survival rate and generation length). We estimated genetic differentiation from published mitochondrial DNA sequences for 28 species of birds (1578 total genetic samples, 391 localities) in the Atlantic Forest of South America. Aside from the aforementioned ecological traits, we included geographic, topographic and climatic distances between localities as environmental predictors. We then created models using all available data to evaluate model uncertainty both across space and across the different categories of predictors. Finally, we investigated model uncertainty in predicting genetic differentiation individually for each species (a common challenge in conservation biology). Our results show that while environmental conditions are the most important predictors of genetic differentiation, model accuracy largely increases with the addition of ecological traits. Additionally, the inclusion of dispersal traits improves model accuracy to a larger extent than the inclusion of demographic traits. Similar results are observed in models for individual species, although model accuracy is highly variable. We conclude that ecological traits improve predictive models of genetic differentiation, refining our ability to predict phylogeographic patterns from existing data. Additionally, demographic traits may not be as informative as previously hypothesized. Finally, prediction of genetic differentiation for species with conservation concerns may require further careful assessment of the environmental and ecological variation within the species range.
期刊介绍:
ECOGRAPHY publishes exciting, novel, and important articles that significantly advance understanding of ecological or biodiversity patterns in space or time. Papers focusing on conservation or restoration are welcomed, provided they are anchored in ecological theory and convey a general message that goes beyond a single case study. We encourage papers that seek advancing the field through the development and testing of theory or methodology, or by proposing new tools for analysis or interpretation of ecological phenomena. Manuscripts are expected to address general principles in ecology, though they may do so using a specific model system if they adequately frame the problem relative to a generalized ecological question or problem.
Purely descriptive papers are considered only if breaking new ground and/or describing patterns seldom explored. Studies focused on a single species or single location are generally discouraged unless they make a significant contribution to advancing general theory or understanding of biodiversity patterns and processes. Manuscripts merely confirming or marginally extending results of previous work are unlikely to be considered in Ecography.
Papers are judged by virtue of their originality, appeal to general interest, and their contribution to new developments in studies of spatial and temporal ecological patterns. There are no biases with regard to taxon, biome, or biogeographical area.