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 ge...

统计物种界定通常依赖单一数据源，主要为遗传数据，用于检测推定物种（putative species）以及将个体划归至推定物种。鉴于物种形成机制的多样性，单一数据源可能无法充分表征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与聚类技术的方法框架，可利用遗传、形态及生态数据完成推定物种的检测与个体划归。我们的方法能够复原出与传统定性分类学方法所识别的物种数量相近的类群，而这些类群无法通过纯遗传方法检测得到。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。本实证检验表明，我们用于检测推定物种并完成个体划归的方法在物种界定工作中仍具有应用价值，尽管不同类群间的分化水平 across ge...