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Prokaryotic communities forming symbiotic relationships with the vent shrimp,  Rimicaris exoculata , are well studied components of hydrothermal ecosystems at the Mid-Atlantic Ridge (MAR). Despite the tight link between host and symbiont, the observed lack of spatial genetic structure seen in  R .  exoculata  contrasts with the geographic differentiation detected in specific bacterial ectosymbionts. The geographic clustering of bacterial lineages within a seemingly panmictic host suggests either the presence of finer scale restriction to gene flow not yet detected in the host, horizontal transmission (environmental selection) of its endosymbionts as a consequence of unique vent geochemistry, or vertically transmitted endosymbionts that exhibit genetic differentiation. To identify which hypothesis best fits, we tested whether bacterial assemblages exhibit differentiation across sites or host populations by performing a 16S rRNA metabarcoding survey on  R .  exoculata  digestive prokaryote samples (n = 31) taken from three geochemically distinct vents across MAR: Rainbow, Trans-Atlantic Geotraverse (TAG) and Logatchev. Analysis of communities across two organs (digestive tract, stomach), three molt colors (white, red, black) and three life stages (eggs, juveniles, adults) also provided insights into symbiont transmission mode. Examining both whole communities and operational taxonomic units (OTUs) confirmed the presence of three main epibionts:  Epsilonproteobacteria ,  Mollicutes  and  Deferribacteres . With these findings, we identified a clear pattern of geographic segregation by vent in OTUs assigned to  Epsilonproteobacteria . Additionally, we detected evidence for differentiation among all communities associated to vents and life stages. Overall, results suggest a combination of environmental selection and vertical inheritance of some of the symbiotic lineages.

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Investigation of bacterial communities within the digestive organs of the hydrothermal vent shrimp Rimicaris exoculata provide insights into holobiont geographic clustering