bioRxivEcologyPreprint
Fish microbiomes 101: disentangling the rules governing marine fish mucosal microbiomes across 101 species
J. J. Minich, A. Harer, J. Vechinski, B. W. Frable, Z. Skelton, E. Kunselman, M. Shane, D. Perry, A. Gonzalez, D. McDonald, R. Knight, T. P. Michael, E. E. Allen
Abstract
Fish are the most diverse and widely distributed vertebrates, yet little is known about the microbial ecology of fishes nor the biological and environmental factors that influence the fish microbiome. The microbiota from 101 species of Southern California marine fishes, spanning 22 orders, 55 families, and 83 genera representing ~25% of local marine fish diversity, was analyzed to identify patterns that explain microbial diversity patterns in a geographical subset of marine fish biodiversity. We compared fish microbiomes (gill, skin, midgut, and hindgut) using alpha, beta, and gamma diversity along with establishing a novel method to estimate microbial biomass (Qiime2 plugin katharoseq). For oceanic fishes from the neritic zone, host size and distance from shore were negatively associated with microbial biomass densities and diversity in the gills. Body site was the strongest driver for beta diversity with strong evidence of phylosymbiosis observed across the gill, skin, and hindgut, but not midgut. The majority of microbes from all fish body sites were of unknown origin but overall sea water generally contributes more microbes to fish mucus compared to marine sediment. In a meta-analysis of vertebrate hindguts (569 species), mammals had the highest gamma diversity when controlling for host species number while fishes had the highest percent of unique microbial taxa (92%). In fishes, the midgut, gill, and skin contains the majority of microbial diversity which collectively can be 5.5 times higher than the hindgut. The composite dataset will be useful to vertebrate microbiome researchers and fish biologists interested in microbial ecology with applications in aquaculture and fisheries management.
- organism: multiple species
- assay: metagenomics
- research focus: ecology & biodiversity
- approach: statistical model
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