The evolutionary relationships of Diprotodontia and improving the accuracy of phylogenetic inference from morphological data

Large-scale molecular datasets have generally outperformed morphological data for inferring phylogeny, and sources of error in the latter are poorly understood. The morphologically and ecologically diverse marsupial order Diprotodontia (kangaroos and their relatives, the koala, wombats and possums) is well suited to considering these issues. Recent molecular results provide a phylogenetic benchmark for comparing previous molecular and morphological studies, encompassing all of the major phylogenetic data sources and methods that have been employed over the past 50 years. We show here that most molecular methodologies and ‘informal-comparative’ morphological studies have inferred diprotodontian relationships that closely resemble the recent molecular consensus. However, and perhaps surprisingly, algorithmic morphology, such as maximum parsimony analysis of morphological matrices, has inferred markedly inaccurate phylogenies, and is not improved by re-analysis with more recently developed, model-based (e.g., likelihood and Bayesian) methods. This is particularly concerning because algorithmic morphology is the primary approach for integrating fossils into the tree of life, and hence, for both calibrating molecular timescales and extending phylogenetic inferences of evolutionary processes beyond the snapshot provided by modern species. A novel simulation study presented here suggests that the inaccuracies in the marsupial algorithmic morphology studies partly stem from functional and body-size correlations among taxa that over-ride phylogenetic signals. We use the results to trial a reverse engineered phylogeny approach to correcting for such functional and developmental correlations among morphological data. In addition, we interrogated a newly published, densely taxon-sampled morphological matrix. Deeper level phylogeny reconstruction was improved by including fossils alongside extant taxa and counterintuitively, by increased effort to resolve relationships among shallow taxa. Matthew J. Phillips [[e-mail]]; Mélina A. Celik [[e-mail]] School of Biology and Environmental Science, Queensland University of Technology, 2 George Street, Brisbane, Qld, 4000, Australia; Robin M.D Beck [[e-mail]] Ecosystems and Environment Research Centre, School of Science, Engineering and Environment, University of Salford, Manchester, UK.

在系统发育（phylogeny）推断方面，大规模分子数据集通常表现优于形态学数据（morphological data），而形态学数据的误差来源仍未得到充分阐明。形态与生态多样性丰富的有袋类双门齿目（Diprotodontia，包含袋鼠及其近缘类群、考拉、袋熊和负鼠）非常适合用于探究上述问题。近期的分子研究结果构建了系统发育基准框架，可用于对比既往的分子与形态学研究，涵盖了过去50年间所采用的各类主要系统发育数据来源与分析方法。本研究表明，绝大多数分子分析方法以及‘非形式化比较’类形态学研究所得出的双门齿目类群亲缘关系，与近期分子研究得到的共识高度吻合。然而，令人意外的是，算法形态学（algorithmic morphology，如基于形态学矩阵（morphological matrices）的最大简约法（maximum parsimony）分析）所推断的系统发育结果却存在显著偏差，即便采用近年来发展出的基于模型的分析方法（如似然法（likelihood）、贝叶斯法（Bayesian））重新分析，也未能改善其准确性。这一现象尤其令人担忧，因为算法形态学是将化石纳入生命之树（tree of life）的核心方法，同时也是校准分子时间尺度（molecular timescales）、以及将演化过程的系统发育推断拓展至现代物种所呈现的演化快照之外的关键手段。本研究提出的全新模拟研究表明，有袋类算法形态学研究中的偏差，部分源于类群间功能性状与体型大小的相关性掩盖了系统发育信号。基于上述结果，我们尝试了一种逆向工程系统发育分析方法，以校正形态学数据中存在的这类功能与发育相关性偏差。此外，我们还对一项新发表的、类群采样密集的形态学矩阵进行了深入分析。将化石与现生类群相结合，且与直觉相悖的是，加大对浅层次类群亲缘关系的解析力度，均可改善深层次系统发育的重建结果。马修·J·菲利普斯 [[e-mail]]；梅利娜·A·塞利克 [[e-mail]] 澳大利亚昆士兰科技大学生物与环境科学学院，布里斯班乔治街2号，昆士兰州4000；罗宾·M·D·贝克 [[e-mail]] 英国索尔福德大学科学、工程与环境学院生态系统与环境研究中心，曼彻斯特。