Species delimitation using machine learning recovers a phylogenomically consistent classification for North American box turtles (Terrapene spp.)

ABSTRACT Model-based approaches to species delimitation are constrained by computational capacities as well as frequently violated algorithmic assumptions applied to biologically complex systems. An alternate approach employs machine learning to derive species limits without explicitly defining an underlying species model. Herein, we demonstrate the capacity of these approaches to identify phylogenomically relevant groups in North American box turtles (Terrapene spp.). We invoked several machine learning-based species delimitation algorithms and a multispecies coalescent approach to parse a large ddRAD sequencing SNP dataset. We highlight two major findings: 1) Machine learning delimitations were variable among replicates, but heterogeneity only occurred within major species tree clades; 2) in this sense unsupported splits echoed patterns of phylogenetic discordance among several species-tree methods. Discordance, as corroborated by previously observed patterns of differential introgression, may reflect biogeographic history, gene flow, incomplete lineage sorting, or their combinations. Our study underscores machine learning as a species delimitation method, and provides insight into how commonly observed patterns of phylogenetic discordance may similarly affect machine learning classification.