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.大规模分子数据集在系统发育推断方面通常优于形态学数据，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，袋鼠及其近缘类群、考拉、袋熊与袋貂）非常适合用于探究此类问题。近期的分子研究结果为对比此前的分子与形态学研究提供了系统发育基准，涵盖了过去50年间所采用的所有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与“非正式比较”形态学研究所推断的双门齿目类群亲缘关系，与近期的分子学共识高度吻合。然而，或许出人意料的是，基于算法的形态学分析——例如对形态学矩阵（morphological matrix）开展最大简约法（maximum parsimony）分析——所推断的系统发育结果却存在显著偏差，且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）与贝叶斯法（Bayesian））重新分析，也未能改善这一问题。这一结果尤其令人担忧，因为基于算法的形态学分析是将化石整合至生命之树的核心手段，同时也是校准分子演化时间尺度、以及将演化过程的系统发育推断拓展至现生类群（extant taxa）所提供的演化快照之外的关键途径。本文提出的一项全新模拟研究（simulation study）显示，有袋类算法形态学研究中的误差，部分源于类群间的功能与体型相关性掩盖了系统发育信号。我们利用该研究结果，尝试了一种逆向工程系统发育方法（reverse engineered phylogeny approach），以校正形态学数据中的此类功能与发育相关性偏差。此外，我们还对一项最新发表的、类群采样密度极高的形态学矩阵开展了分析。结果显示，将化石与现生类群一并纳入分析，以及反直觉地增加对浅层级类群亲缘关系的解析力度，均可改善深层系统发育的重建结果。 马修·J·菲利普斯 [[e-mail]]；梅利娜·A·塞尔克 [[e-mail]] 澳大利亚昆士兰科技大学生物与环境科学学院，乔治街2号，布里斯班，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