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（statistical species delimitation）通常依托单一类型数据（尤以遗传数据为代表）开展推定物种（putative species）的检测，以及个体向推定物种的归属划分。鉴于物种形成机制（speciation mechanisms）的多样性，单一类型数据往往无法充分表征与物种界定相关的遗传、形态及生态多样性。本研究提出一种融合多元分析与聚类技术的方法论框架，该框架可整合遗传、形态及生态三类数据，实现推定物种的检测与个体的归属划分。我们的方法所识别的物种数量，与传统定性分类学方法所确认的物种数量相当，但这类物种仅依靠纯遗传方法则无法被检出。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。本实证测试表明，尽管遗传、表型与生态维度的分化水平存在差异，但我们用于推定物种检测与个体归属划分的方法，在物种界定工作中具备应用价值。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划分。无论采用何种物种界定方法，所有后续分析流程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问题，均值得审慎考量。