Marttinen, PekkaHanage, William2017-11-212017Marttinen, Pekka, and William P. Hanage. 2017. “Speciation trajectories in recombining bacterial species.” PLoS Computational Biology 13 (7): e1005640. doi:10.1371/journal.pcbi.1005640. http://dx.doi.org/10.1371/journal.pcbi.1005640.http://nrs.harvard.edu/urn-3:HUL.InstRepos:34375335It is generally agreed that bacterial diversity can be classified into genetically and ecologically cohesive units, but what produces such variation is a topic of intensive research. Recombination may maintain coherent species of frequently recombining bacteria, but the emergence of distinct clusters within a recombining species, and the impact of habitat structure in this process are not well described, limiting our understanding of how new species are created. Here we present a model of bacterial evolution in overlapping habitat space. We show that the amount of habitat overlap determines the outcome for a pair of clusters, which may range from fast clonal divergence with little interaction between the clusters to a stationary population structure, where different clusters maintain an equilibrium distance between each other for an indefinite time. We fit our model to two data sets. In Streptococcus pneumoniae, we find a genomically and ecologically distinct subset, held at a relatively constant genetic distance from the majority of the population through frequent recombination with it, while in Campylobacter jejuni, we find a minority population we predict will continue to diverge at a higher rate. This approach may predict and define speciation trajectories in multiple bacterial species.en-USBiology and Life SciencesBehaviorAnimal BehaviorAnimal MigrationZoologyOrganismsBacteriaStreptococcusPneumococcusMicrobiologyMedical MicrobiologyMicrobial PathogensBacterial PathogensMedicine and Health SciencesPathology and Laboratory MedicinePathogensEcologyEcological NichesEcology and Environmental SciencesBiology and life sciencesGeneticsDNADNA recombinationBiochemistryNucleic acidsHabitatsSimulation and ModelingTheoretical EcologyPhysical SciencesMathematicsApproximation MethodsJournal Article2017-11-2110.1371/journal.pcbi.1005640