The choices we make and the impacts they have: Machine learning and species delimitation in North American box turtles (Terrapene spp.).

Model-based approaches that attempt to delimit species are hampered by computational limitations as well as the unfortunate tendency by users to disregard algorithmic assumptions. Alternatives are clearly needed, and machine-learning (M-L) is attractive in this regard as it functions without the need to explicitly define a species concept. Unfortunately, its performance will vary according to which (of several) bioinformatic parameters are invoked.

Contrasting signatures of introgression in North American box turtle (Terrapene spp.) contact zones.

Hybridization occurs differentially across the genome in a balancing act between selection and migration. With the unprecedented resolution of contemporary sequencing technologies, selection and migration can now be effectively quantified such that researchers can identify genetic elements involved in introgression. Furthermore, genomic patterns can now be associated with ecologically relevant phenotypes, given availability of annotated reference genomes.

Sequence-based molecular phylogenetics and phylogeography of the American box turtles (Terrapene spp.) with support from DNA barcoding.

The classification of the American box turtles (Terrapene spp.) has remained enigmatic to systematists. Previous comprehensive phylogenetic studies focused primarily on morphology.