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Likelihood Inference of Non-Constant Diversification Rates with Incomplete Taxon Sampling

机译:分类单元采样不完全的非恒定分散率的似然推断

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摘要

Large-scale phylogenies provide a valuable source to study background diversification rates and investigate if the rates have changed over time. Unfortunately most large-scale, dated phylogenies are sparsely sampled (fewer than 5% of the described species) and taxon sampling is not uniform. Instead, taxa are frequently sampled to obtain at least one representative per subgroup (e.g. family) and thus to maximize diversity (diversified sampling). So far, such complications have been ignored, potentially biasing the conclusions that have been reached. In this study I derive the likelihood of a birth-death process with non-constant (time-dependent) diversification rates and diversified taxon sampling. Using simulations I test if the true parameters and the sampling method can be recovered when the trees are small or medium sized (fewer than 200 taxa). The results show that the diversification rates can be inferred and the estimates are unbiased for large trees but are biased for small trees (fewer than 50 taxa). Furthermore, model selection by means of Akaike's Information Criterion favors the true model if the true rates differ sufficiently from alternative models (e.g. the birth-death model is recovered if the extinction rate is large and compared to a pure-birth model). Finally, I applied six different diversification rate models – ranging from a constant-rate pure birth process to a decreasing speciation rate birth-death process but excluding any rate shift models – on three large-scale empirical phylogenies (ants, mammals and snakes with respectively 149, 164 and 41 sampled species). All three phylogenies were constructed by diversified taxon sampling, as stated by the authors. However only the snake phylogeny supported diversified taxon sampling. Moreover, a parametric bootstrap test revealed that none of the tested models provided a good fit to the observed data. The model assumptions, such as homogeneous rates across species or no rate shifts, appear to be violated.
机译:大规模的系统发育提供了宝贵的资源,可用于研究背景多样化率以及调查该率是否随时间变化。不幸的是,大多数大型的,过时的系统发育树都是稀疏采样的(少于所描述物种的5%),并且分类群采样也不统一。取而代之的是,经常对分类单元进行采样,以便每个子组(例如家庭)至少获得一名代表,从而使多样性最大化(多样化采样)。到目前为止,这种复杂性已被忽略,可能会使已经得出的结论产生偏差。在这项研究中,我得出了具有非恒定(时间依赖性)分散率和多样化分类群抽样的出生死亡过程的可能性。通过模拟,我测试了树木中小号(少于200个分类单元)时是否可以恢复真实参数和采样方法。结果表明,可以推断出多样化率,估计值对大树无偏,但对小树(少于50个分类单元)有偏见。此外,如果真实比率与替代模型有足够的差异(例如,如果灭绝率较大并与纯出生模型进行比较,则可以恢复出生死亡模型),那么通过Akaike的《信息准则》进行的模型选择会偏向于真实模型。最后,我在三种大规模的经验系统发育上(蚂蚁,哺乳动物和蛇分别具有六种不同的多样化速率模型),从恒定速率的纯出生过程到降低的物种形成速率的出生死亡过程,但不包括任何速率变化模型。 149、164和41个采样物种)。如作者所述,所有这三个系统发育都是通过多样化的分类单元采样构建的。但是,只有蛇的系统发育支持多样化的分类单元采样。此外,参数自举测试表明,没有一个测试模型能够很好地拟合观察到的数据。似乎违反了模型假设,例如跨物种的均一速率或无速率变化。

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    Sebastian Höhna;

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  • 年(卷),期 -1(9),1
  • 年度 -1
  • 页码 e84184
  • 总页数 10
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