Data from: One tree to link them all: a phylogenetic dataset for the European Tetrapoda

Since the ever-increasing availability of phylogenetic informative data, the last decade has seen an upsurge of ecological studies incorporating information on evolutionary relationships among species. However, detailed species-level phylogenies are still lacking for many large groups and regions, which are necessary for comprehensive large-scale eco-phylogenetic analyses. Here, we provide a dataset of 100 dated phylogenetic trees for all European tetrapods based on a mixture of supermatrix and supertree approaches. Phylogenetic inference was performed separately for each of the main Tetrapoda groups of Europe except mammals (i.e. amphibians, birds, squamates and turtles) by means of maximum likelihood (ML) analyses of supermatrix applying a tree constraint at the family (amphibians and squamates) or order (birds and turtles) levels based on consensus knowledge. For each group, we inferred 100 ML trees to be able to provide a phylogenetic dataset that accounts for phylogenetic uncertainty, and assessed node support with bootstrap analyses. Each tree was dated using penalized-likelihood and fossil calibration. The trees obtained were well-supported by existing knowledge and previous phylogenetic studies. For mammals, we modified the most complete supertree dataset available on the literature to include a recent update of the Carnivora clade. As a final step, we merged the phylogenetic trees of all groups to obtain a set of 100 phylogenetic trees for all European Tetrapoda species for which data was available (91%). We provide this phylogenetic dataset (100 chronograms) for the purpose of comparative analyses, macro-ecological or community ecology studies aiming to incorporate phylogenetic information while accounting for phylogenetic uncertainty.

随着系统发育信息数据的不断丰富，近十年来整合物种间进化关系信息的生态学研究呈爆发式增长态势。然而，众多大类群与区域仍缺乏详尽的物种水平系统发育树，而这类数据正是开展全面大规模生态系统发育分析的必要前提。为此，我们构建了一套涵盖欧洲所有四足动物的100棵定年系统发育树数据集，该数据集结合了超级矩阵与超级树两种分析方法。针对欧洲四足动物中除哺乳类外的主要类群（即两栖类、鸟类、鳞龙类与龟类），我们基于共识性分类学知识，以科（两栖类与鳞龙类）或目（鸟类与龟类）为层级设置树约束，通过超级矩阵的最大似然（maximum likelihood, ML）分析开展系统发育推断。针对每个类群，我们推导得到100棵最大似然树，以构建可量化系统发育不确定性的系统发育数据集，并通过自举分析评估节点支持度。每棵树均采用惩罚似然法与化石校准进行定年处理，所得树结构符合现有分类知识与既往系统发育研究结论，支持度良好。对于哺乳类，我们对现有文献中最完整的超级树数据集进行了修订，纳入了食肉目（Carnivora）支系的最新更新内容。最后，我们将所有类群的系统发育树进行合并，最终得到一套覆盖91%已记录欧洲四足动物物种的100棵系统发育树集合。本数据集（100棵计时树（chronograms））可供比较分析、宏观生态学或群落生态学研究使用，以助力研究者在纳入系统发育信息的同时量化系统发育不确定性。