Biological species delimitation based on genetic and spatial dissimilarity: a comparative study

The delimitation of biological species, i.e., deciding which individuals belong to the same species and whether and how many different species are represented in a data set, is key to the conservation of biodiversity. Much existing work uses only genetic data for species delimitation, often employing some kind of cluster analysis. This can be misleading, because geographically distant groups of individuals can be genetically quite different even if they belong to the same species. This paper investigates the problem of testing whether two potentially separated groups of individuals can belong to a single species or not based on genetic and spatial data. Various approaches are compared (some of which already exist in the literature) based on simulated metapopulations generated with SLiM and GSpace - two software packages that can simulate spatially-explicit genetic data at an individual level. Approaches involve partial Mantel testing, maximum likelihood mixed-effects models with a population effect, and jackknife-based homogeneity tests. A key challenge is that most tests perform on genetic and geographical distance data, violating standard independence assumptions. Simulations showed that partial Mantel tests and mixed-effects models have larger power than jackknife-based methods, but tend to display type-I-error rates slightly above the significance level. Moreover, a multiple regression model neglecting the dependence in the dissimilarities did not show inflated type-I-error rate. An application on brassy ringlets concludes the paper.