估计复杂特征的祖先状态:羽毛进化案例研究

Pierre Cockx, Michael J. Benton, Joseph N. Keating
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摘要

羽毛是鸟类在进化过程中取得成功的关键因素,但人们对羽毛的起源仍然知之甚少。关于羽毛进化的争论主要集中在丝状体是一次进化还是多次进化的问题上。这些相互矛盾的结果源于统计祖先状态估计方法上的主观差异。在此,我们对应用于干系鸟类的祖先状态估计方法进行了全面比较,检验了外群包含、树时间比例方法、模型选择和特征编码策略的作用。我们根据阿凯克信息准则(AIC)、互信息以及边际祖先状态估计值的不确定性对模型进行了比较。我们的研究结果表明,树时比例法、外群选择和模型选择对茎鸟整骨的祖先状态估计影响很大,而特征编码策略似乎对祖先状态估计的影响较小。我们利用 AIC 分数和留一交叉验证方法(LOOCV)确定了最佳拟合模型。我们的分析大体上支持恐龙和翼龙的丝状整脊的独立起源,并支持羽毛的进化起源比以前提出的更早。更广泛地说,我们的研究强调了在进行 ASE 分析时必须特别注意选择外群、树和模型。在模型选择方面,我们的研究结果表明,当分析涉及的类群数量有限时,考虑 LOOCV 方法可能会比简单地比较 AIC 分数产生更可靠的结果。
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Estimating ancestral states of complex characters: a case study on the evolution of feathers
Feathers are a key novelty underpinning the evolutionary success of birds, yet the origin of feathers remains poorly understood. Debates about feather evolution hinge upon whether filamentous integument has evolved once or multiple time independently on the lineage leading to modern birds. These contradictory results stem from subjective methodological differences in statistical ancestral state estimates. Here we conduct a comprehensive comparison of ancestral state estimation methodologies applied to stem-group birds, testing the role of outgroup inclusion, tree time scaling method, model choice and character coding strategy. Models are compared based on their Akaike Information Criteria (AIC), mutual information, as well as the uncertainty of marginal ancestral state estimates. Our results demonstrate that ancestral state estimates of stem-bird integument are strongly influenced by tree time scaling method, outgroup selection and model choice, while character coding strategy seems to have less effect on the ancestral estimates produced. We identify the best fitting models using AIC scores and a leave-one-out cross-validation approach (LOOCV). Our analyses broadly support the independent origin of filamentous integument in dinosaurs and pterosaurs and support a younger evolutionary origin of feathers than has been suggested previously. More generally, our study highlights that special care must be taken in selecting the outgroup, tree and model when conducting ASE analyses. With respect to model selection, our results suggest that considering a LOOCV approach, may yield more reliable results than simply comparing AIC scores when the analyses involve a limited number of taxa.
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