Data from: Species detection and individual assignment in species delimitation: can integrative data increase efficacy?

Statistical species delimitation usually relies on singular data, primarily genetic, for detecting putative species and individual assignment to putative species. Given the variety of speciation mechanisms, singular data may not adequately represent the genetic, morphological and ecological diversity relevant to species delimitation. We describe a methodological framework combining multivariate and clustering techniques that uses genetic, morphological and ecological data to detect and assign individuals to putative species. Our approach recovers a similar number of species recognized using traditional, qualitative taxonomic approaches that are not detected when using purely genetic methods. Furthermore, our approach detects groupings that traditional, qualitative taxonomic approaches do not. This empirical test suggests that our approach to detecting and assigning individuals to putative species could be useful in species delimitation despite varying levels of differentiation across genetic, phenotypic and ecological axes. This work highlights a critical, and often overlooked, aspect of the process of statistical species delimitation—species detection and individual assignment. Irrespective of the species delimitation approach used, all downstream processing relies on how individuals are initially assigned, and the practices and statistical issues surrounding individual assignment warrant careful consideration.

统计物种界定（species delimitation）通常依赖单一数据（主要为遗传数据），用于检测推定物种（putative species）并将个体归属于推定物种。鉴于物种形成机制的多样性，单一数据往往无法充分体现与物种界定相关的遗传、形态及生态多样性。本研究提出一种结合多变量分析技术（multivariate techniques）与聚类技术（clustering techniques）的方法框架，可利用遗传、形态及生态数据检测推定物种，并将个体归属于此类物种。本方法所识别的物种数量，与采用传统定性分类学方法（taxonomic approaches）所认定的物种数量相当，而这些物种仅依靠纯遗传方法无法被检测到。此外，本方法还能检测到传统定性分类学方法无法识别的类群。本实证检验（empirical test）表明，尽管遗传、表型及生态维度的分化水平存在差异，本用于检测推定物种并将个体归属于此类物种的方法，在物种界定工作中仍具有应用价值。本研究凸显了统计物种界定流程中一个关键且常被忽视的环节：物种检测与个体归属。无论采用何种物种界定方法，所有下游分析流程（downstream processing）均依赖于个体的初始归属方式，而围绕个体归属的操作规范与统计学问题均需谨慎考量。