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

Abstract

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.