  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. We investigate 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. Existing methods such as the partial
Mantel test and jackknife-based distance-distance regression are considered.
New approaches, i.e., an adaptation of a mixed effects model, a bootstrap
approach, and a jackknife version of partial Mantel, are proposed. All these
methods address the issue that distance data violate the independence
assumption for standard inference regarding correlation and regression; a
standard linear regression is also considered. The approaches are compared on
simulated meta-populations generated with SLiM and GSpace - two software
packages that can simulate spatially-explicit genetic data at an individual
level. Simulations show that the new jackknife version of the partial Mantel
test provides a good compromise between power and respecting the nominal type I
error rate. Mixed-effects models have larger power than jackknife-based
methods, but tend to display type I error rates slightly above the significance
level. An application on brassy ringlets concludes the paper.
