Data from: Sampling strategies for delimiting species: genes, individuals, and populations in the Liolaemus elongatus-kriegi complex (Squamata: Liolaemidae) in Andean-Patagonian South America

Recovery of evolutionary history and delimiting species boundaries in widely distributed, poorly-known groups requires extensive geographic sampling, but this is difficult to design a priori because evolutionary diversity is often "hidden" by an inadequate taxonomy. Large data sets are needed, and these provide unique challenges for analysis when they span intra and inter-specific levels of divergence. Protocols have been designed to combine methods of analysis for DNA sequences that exhibit both very shallow and relatively deeper divergences (Crandall and Fitzpatrick, 1996). In this study we combine several tree-based phylogeny reconstruction methods with nested clade analysis, to extract maximum historical signal at various levels, in the poorly-known Liolaemus elongatus-kriegi complex in temperate South America. We implement the basic protocol of Wiens and Penkrot (2002) to test for species boundaries, and propose modifications to accommodate large data sets and gene regions with heterogeneous substitution rates. Combining haplotype trees with nested-clade analyses allowed testing of species boundaries on the basis of a priori defined criteria, and this approach suggests that the number of putative species could be doubled. We discuss these findings in the context of the advantages and limitations of a combined approach for retrieval of maximum historical information in large data sets, in the context of the yet formidable unresolved issues of sampling strategies.

对于广泛分布、认知匮乏的类群而言，重建其演化历史（evolutionary history）并界定物种边界（species boundaries）需要开展大规模地理采样，但由于分类学（taxonomy）研究不足常导致演化多样性被“隐藏”，因此难以预先（a priori）设计采样方案。此类研究亟需大型数据集，而当数据集涵盖种内和种间分化水平时，会给分析带来独特挑战。已有研究设计了可整合分析兼具极浅分化与较深分化的DNA序列（DNA sequences）的分析流程（Crandall和Fitzpatrick，1996）。本研究以南美温带地区认知匮乏的*Liolaemus elongatus-kriegi*物种复合体为研究对象，整合多种基于树状结构的系统发育重建（phylogeny reconstruction）方法与嵌套分支分析（nested clade analysis），以在不同演化水平上提取最大化的历史信号。我们采用Wiens与Penkrot（2002）提出的基础分析流程检验物种边界，并针对大型数据集和替换速率异质的基因区域提出改进方案。将单倍型树（haplotype trees）与嵌套分支分析相结合，可基于预先定义的标准检验物种边界，该方法表明推定物种（putative species）的数量或可翻倍。我们结合该整合方法在大型数据集中提取最大历史信息的优势与局限，并针对目前仍极具挑战性的采样策略未决问题，对本研究结果展开讨论。