{"title":"Chronospaces: An R package for the statistical exploration of divergence times promotes the assessment of methodological sensitivity","authors":"Nicolás Mongiardino Koch, Pablo Milla Carmona","doi":"10.1111/2041-210x.14404","DOIUrl":null,"url":null,"abstract":"<jats:list> <jats:list-item>Much of our understanding of the history of life hinges upon time calibration, the process of assigning absolute times to cladogenetic events. Bayesian approaches to time‐scaling phylogenetic trees have dramatically grown in complexity, and depend today upon numerous methodological choices. Arriving at objective justifications for all of these is difficult and time‐consuming. Thus, divergence times are routinely inferred under only one or a handful of parametric conditions, often times chosen arbitrarily. Progress towards building robust biological timescales necessitates the development of better methods to visualize and quantify the sensitivity of results to these decisions.</jats:list-item> <jats:list-item>Here, we present an R package that assists in this endeavour through the use of chronospaces, that is, graphical representations summarizing variation in the node ages contained in time‐calibrated trees. We further test this approach by estimating divergence times for three empirical datasets—spanning widely differing evolutionary timeframes—using the software PhyloBayes.</jats:list-item> <jats:list-item>Our results reveal large differences in the impact of many common methodological decisions, with the choice of clock (uncorrelated vs autocorrelated) and loci having strong effects on inferred ages. Other decisions have comparatively minor consequences, including the use of the computationally intensive site‐heterogeneous model CAT‐GTR, whose effect might only be discernible for exceedingly old divergences (e.g. the deepest eukaryote nodes).</jats:list-item> <jats:list-item>The package <jats:italic>chronospace</jats:italic> implements a range of graphical and analytical tools that assist in the exploration of sensitivity and the prioritization of computational resources in the inference of divergence times.</jats:list-item> </jats:list>","PeriodicalId":208,"journal":{"name":"Methods in Ecology and Evolution","volume":null,"pages":null},"PeriodicalIF":6.3000,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Methods in Ecology and Evolution","FirstCategoryId":"93","ListUrlMain":"https://doi.org/10.1111/2041-210x.14404","RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ECOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Much of our understanding of the history of life hinges upon time calibration, the process of assigning absolute times to cladogenetic events. Bayesian approaches to time‐scaling phylogenetic trees have dramatically grown in complexity, and depend today upon numerous methodological choices. Arriving at objective justifications for all of these is difficult and time‐consuming. Thus, divergence times are routinely inferred under only one or a handful of parametric conditions, often times chosen arbitrarily. Progress towards building robust biological timescales necessitates the development of better methods to visualize and quantify the sensitivity of results to these decisions.Here, we present an R package that assists in this endeavour through the use of chronospaces, that is, graphical representations summarizing variation in the node ages contained in time‐calibrated trees. We further test this approach by estimating divergence times for three empirical datasets—spanning widely differing evolutionary timeframes—using the software PhyloBayes.Our results reveal large differences in the impact of many common methodological decisions, with the choice of clock (uncorrelated vs autocorrelated) and loci having strong effects on inferred ages. Other decisions have comparatively minor consequences, including the use of the computationally intensive site‐heterogeneous model CAT‐GTR, whose effect might only be discernible for exceedingly old divergences (e.g. the deepest eukaryote nodes).The package chronospace implements a range of graphical and analytical tools that assist in the exploration of sensitivity and the prioritization of computational resources in the inference of divergence times.
期刊介绍:
A British Ecological Society journal, Methods in Ecology and Evolution (MEE) promotes the development of new methods in ecology and evolution, and facilitates their dissemination and uptake by the research community. MEE brings together papers from previously disparate sub-disciplines to provide a single forum for tracking methodological developments in all areas.
MEE publishes methodological papers in any area of ecology and evolution, including:
-Phylogenetic analysis
-Statistical methods
-Conservation & management
-Theoretical methods
-Practical methods, including lab and field
-This list is not exhaustive, and we welcome enquiries about possible submissions. Methods are defined in the widest terms and may be analytical, practical or conceptual.
A primary aim of the journal is to maximise the uptake of techniques by the community. We recognise that a major stumbling block in the uptake and application of new methods is the accessibility of methods. For example, users may need computer code, example applications or demonstrations of methods.