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，重建其演化历史并界定物种边界需要开展大范围的地理采样，但这类采样往往难以预先（a priori）设计方案——这是因为不完善的分类学研究常会掩盖演化多样性。此类研究亟需大规模数据集，但当数据集涵盖种内与种间分化水平时，会给分析带来独特挑战。已有研究设计了相关分析流程，可整合处理兼具极浅分化与较深分化的DNA序列数据（Crandall和Fitzpatrick，1996）。本研究以南美洲温带地区认知匮乏的Liolaemus elongatus-kriegi复合群为研究对象，整合多种基于建树的系统发育重建方法与嵌套支系分析（nested clade analysis），以在不同分化层级中提取最大化的历史演化信号。我们采用Wiens和Penkrot（2002）提出的基础流程检验物种边界，并针对大规模数据集以及替换速率存在异质性的基因区域，提出了适配性改进方案。将单倍型树（haplotype trees）与嵌套支系分析相结合，可基于预先定义的标准检验物种边界，该研究方法表明推定物种的数量或可翻倍。我们结合该整合方法在大规模数据集下提取最大化历史信息的优势与局限，并结合当前采样策略领域仍存在诸多棘手的未决问题，对本研究结果展开了讨论。