Fitting and evaluating univariate and multivariate models of within-lineage evolution.

IF 4.6 Q2 MATERIALS SCIENCE, BIOMATERIALS ACS Applied Bio Materials Pub Date : 2023-11-01 Epub Date: 2023-04-17 DOI:10.1017/pab.2023.10
Kjetil Lysne Voje
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Abstract

The nature of phenotypic evolution within lineages is central to many unresolved questions in paleontology and evolutionary biology. Analyses of evolutionary time-series of ancestor-descendant populations in the fossil record are likely to make important contributions to many of these debates. However, the limited number of models that have been applied to these types of data may restrict our ability to interpret phenotypic evolution in the fossil record. Using uni- and multivariate models of trait evolution that make different assumptions regarding the dynamics of the adaptive landscape, I evaluate contrasting hypotheses to explain evolution of size in the radiolarian Eucyrtidium calvertense and armor in the stickleback Gaserosteus doryssus. Body size evolution in E. calvertense is best explained by a model where the lineage evolves as a consequence of a shift in the adaptive landscape that coincides with the initiation of neosympatry with its sister lineage. Multivariate evolution of armor traits in a stickleback lineage (Gasterosteus doryssus) show evidence of adaptation towards independent optima on the adaptive landscape at the same time as traits change in a correlated fashion. The fitted models are available in a the R package evoTS, which builds on the commonly used paleoTS framework.

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拟合和评估单变量和多变量谱系内进化模型
谱系内表型进化的本质是古生物学和进化生物学中许多尚未解决的问题的核心。对化石记录中祖先-后代种群进化时间序列的分析可能会对其中的许多争论做出重要贡献。然而,应用于这些类型数据的模型数量有限,可能会限制我们解释化石记录中表型进化的能力。使用对适应性景观的动力学做出不同假设的性状进化的单变量和多变量模型,我评估了对比假设,以解释放射虫calvertense Eucyrdidium和刺鱼Gastrosteus doryssus盔甲的大小进化。卡尔弗滕斯E.calvertense的体型进化最好用一个模型来解释,在这个模型中,谱系进化是适应性景观变化的结果,而适应性景观的变化与其姊妹谱系的新症状学的开始相吻合。刺鱼谱系(G.doryssus)盔甲特征的多变量进化表明,在特征以相关方式变化的同时,在适应性景观上适应独立最优。拟合模型可在R软件包evoTS中获得,该软件包建立在古TS框架上。
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来源期刊
ACS Applied Bio Materials
ACS Applied Bio Materials Chemistry-Chemistry (all)
CiteScore
9.40
自引率
2.10%
发文量
464
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