Data from: The influence of locus number and information content on species delimitation: an empirical test case in an endangered Mexican salamander

Perhaps the most important recent advance in species delimitation has been the development of model-based approaches to objectively diagnose species diversity from genetic data. Additionally, the growing accessibility of next-generation sequence datasets provides powerful insights into genome-wide patterns of divergence during speciation. However, applying complex models to large datasets is time consuming and computationally costly, requiring careful consideration of the influence of both individual and population sampling, as well as the number and informativeness of loci on species delimitation conclusions. Here, we investigated how locus number and information content affect species delimitation results for an endangered Mexican salamander species, Ambystoma ordinarium. We compared results for an eight-locus, 137-individual dataset and an 89-locus, seven-individual dataset. For both datasets, we used species discovery methods to define delimitation models and species validation methods to rigorously test these hypotheses. We also used integrated demographic model selection tools to choose among delimitation models, while accounting for gene flow. Our results indicate that while cryptic lineages may be delimited with relatively few loci, sampling larger numbers of loci may be required to ensure that enough informative loci are available to accurately identify and validate shallow-scale divergences. These analyses highlight the importance of striking a balance between dense sampling of loci and individuals, particularly in shallowly-diverged lineages. They also confirm the presence of a currently unrecognized, endangered species in the western part of A. ordinarium's range.近年来，物种界定（species delimitation）领域最为重要的进展之一，便是基于模型的研究方法的问世，该方法可从遗传数据中客观判别物种多样性。此外，下一代测序（next-generation sequence）数据集的可及性日益提升，为深入解析物种形成过程中全基因组水平的分化模式提供了强有力的研究视角。然而，将复杂模型应用于大型数据集往往耗时极久且计算成本高昂，因此需要审慎考量个体与种群采样策略、以及基因座（locus）的数量与信息性对物种界定结果的影响。本研究以濒危的墨西哥钝口螈物种*Ambystoma ordinarium*为研究对象，探讨了基因座数量与信息含量对其物种界定结果的影响。我们分别对两组数据集展开分析并比对结果：一组包含8个基因座、137个个体，另一组则包含89个基因座、7个个体。针对两组数据集，我们均采用物种发现方法构建界定模型，并借助物种验证方法对这些假设开展严格检验。此外，我们还采用整合式种群人口学模型选择工具，在考虑基因流（gene flow）的前提下，从多个界定模型中筛选最优模型。研究结果显示：尽管利用相对少量的基因座即可界定隐秘支系，但要确保拥有足够多的信息性基因座以准确识别并验证浅分化水平的类群分化，往往需要采集更多的基因座数据。本研究结果凸显了在基因座与个体的密集采样之间寻求平衡的重要性，这一点在浅分化支系中尤为关键。同时，本研究证实：在*Ambystoma ordinarium*分布范围的西部区域，存在一种目前尚未被认知的濒危物种。