Process‐based species delimitation leads to identification of more biologically relevant species. (21st November 2019)

• Record Type: Journal Article
• Authors: Smith, Megan L.; Carstens, Bryan C.
• Is Part Of: Evolution. Volume 74:Number 2(2020)
• Page Start: 216
• Page End: 229
• Publication Date: 2019-11-21
• Subjects: Ecological speciation -- machine learning -- reinforcement -- speciation -- species delimitation; Evolution -- Periodicals; Heredity -- Periodicals; Évolution (Biologie) -- Périodiques; Hérédité -- Périodiques; 338.47004094
• DOI: 10.1111/evo.13878
• Languages: English
• ISSNs: 0014-3820

Abstract

Abstract: Most approaches to species delimitation to date have considered divergence‐only models. Although these models are appropriate for allopatric speciation, their failure to incorporate many of the population‐level processes that drive speciation, such as gene flow (e.g., in sympatric speciation), places an unnecessary limit on our collective understanding of the processes that produce biodiversity. To consider these processes while inferring species boundaries, we introduce the R‐package delimitR and apply it to identify species boundaries in the reticulate taildropper slug ( Prophysaon andersoni ). Results suggest that secondary contact is an important mechanism driving speciation in this system. By considering process, we both avoid erroneous inferences that can be made when population‐level processes such as secondary contact drive speciation but only divergence is considered, and gain insight into the process of speciation in terrestrial slugs. Further, we apply delimitR to three published empirical datasets and find results corroborating previous findings. Finally, we evaluate the performance of delimitR using simulation studies, and find that error rates are near zero when comparing models that include lineage divergence and gene flow for three populations with a modest number of Single Nucleotide Polymorphisms (SNPs; 1500) and moderate divergence times (<100, 000 generations). When we apply delimitR to a complex model set (i.e., including divergence, gene flow, and population size changes), error rates are moderate (∼0.15; 10, 000 SNPs), and, when present, misclassifications occur among highly similar models.