{"title":"Predicting species invasiveness with genomic data: Is genomic offset related to establishment probability?","authors":"Louise Camus, Mathieu Gautier, Simon Boitard","doi":"10.1111/eva.13709","DOIUrl":null,"url":null,"abstract":"<p>Predicting the risk of establishment and spread of populations outside their native range represents a major challenge in evolutionary biology. Various methods have recently been developed to estimate population (mal)adaptation to a new environment with genomic data via so-called Genomic Offset (GO) statistics. These approaches are particularly promising for studying invasive species but have still rarely been used in this context. Here, we evaluated the relationship between GO and the establishment probability of a population in a new environment using both in silico and empirical data. First, we designed invasion simulations to evaluate the ability to predict establishment probability of two GO computation methods (Geometric GO and Gradient Forest) under several conditions. Additionally, we aimed to evaluate the interpretability of absolute Geometric GO values, which theoretically represent the adaptive genetic distance between populations from distinct environments. Second, utilizing public empirical data from the crop pest species <i>Bactrocera tryoni</i>, a fruit fly native from Northern Australia, we computed GO between “source” populations and a diverse range of locations within invaded areas. This practical application of GO within the context of a biological invasion underscores its potential in providing insights and guiding recommendations for future invasion risk assessment. Overall, our results suggest that GO statistics represent good predictors of the establishment probability and may thus inform invasion risk, although the influence of several factors on prediction performance (e.g., propagule pressure or admixture) will need further investigation.</p>","PeriodicalId":168,"journal":{"name":"Evolutionary Applications","volume":null,"pages":null},"PeriodicalIF":3.5000,"publicationDate":"2024-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/eva.13709","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Evolutionary Applications","FirstCategoryId":"99","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1111/eva.13709","RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"EVOLUTIONARY BIOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Predicting the risk of establishment and spread of populations outside their native range represents a major challenge in evolutionary biology. Various methods have recently been developed to estimate population (mal)adaptation to a new environment with genomic data via so-called Genomic Offset (GO) statistics. These approaches are particularly promising for studying invasive species but have still rarely been used in this context. Here, we evaluated the relationship between GO and the establishment probability of a population in a new environment using both in silico and empirical data. First, we designed invasion simulations to evaluate the ability to predict establishment probability of two GO computation methods (Geometric GO and Gradient Forest) under several conditions. Additionally, we aimed to evaluate the interpretability of absolute Geometric GO values, which theoretically represent the adaptive genetic distance between populations from distinct environments. Second, utilizing public empirical data from the crop pest species Bactrocera tryoni, a fruit fly native from Northern Australia, we computed GO between “source” populations and a diverse range of locations within invaded areas. This practical application of GO within the context of a biological invasion underscores its potential in providing insights and guiding recommendations for future invasion risk assessment. Overall, our results suggest that GO statistics represent good predictors of the establishment probability and may thus inform invasion risk, although the influence of several factors on prediction performance (e.g., propagule pressure or admixture) will need further investigation.
预测种群在其原生地之外建立和扩散的风险是进化生物学的一大挑战。最近开发了多种方法,通过所谓的基因组偏移(GO)统计,利用基因组数据估计种群对新环境的(不)适应性。这些方法对研究入侵物种特别有前景,但在这方面还很少使用。在这里,我们利用硅学数据和经验数据评估了 GO 与种群在新环境中建立概率之间的关系。首先,我们设计了入侵模拟,以评估两种GO计算方法(几何GO和梯度森林)在多种条件下预测种群建立概率的能力。此外,我们还旨在评估几何 GO 绝对值的可解释性,该值在理论上代表了来自不同环境的种群之间的适应性遗传距离。其次,我们利用原产于澳大利亚北部的农作物害虫 Bactrocera tryoni(一种果蝇)的公开经验数据,计算了 "源 "种群与入侵区域内不同地点之间的 GO 值。在生物入侵的背景下,GO 的这一实际应用凸显了其在为未来入侵风险评估提供见解和指导建议方面的潜力。总体而言,我们的研究结果表明,GO统计量可以很好地预测生物入侵的建立概率,从而为入侵风险提供信息,但还需要进一步研究一些因素对预测结果的影响(如传播压力或混合)。
期刊介绍:
Evolutionary Applications is a fully peer reviewed open access journal. It publishes papers that utilize concepts from evolutionary biology to address biological questions of health, social and economic relevance. Papers are expected to employ evolutionary concepts or methods to make contributions to areas such as (but not limited to): medicine, agriculture, forestry, exploitation and management (fisheries and wildlife), aquaculture, conservation biology, environmental sciences (including climate change and invasion biology), microbiology, and toxicology. All taxonomic groups are covered from microbes, fungi, plants and animals. In order to better serve the community, we also now strongly encourage submissions of papers making use of modern molecular and genetic methods (population and functional genomics, transcriptomics, proteomics, epigenetics, quantitative genetics, association and linkage mapping) to address important questions in any of these disciplines and in an applied evolutionary framework. Theoretical, empirical, synthesis or perspective papers are welcome.