Local genomic adaptation of coral reef-associated microbiomes to gradients of natural variability and anthropogenic stressors

Abstract

Holobionts are species-specific associations between macro- and microorganisms. On coral reefs, the benthic coverage of coral and algal holobionts varies due to natural and anthropogenic forcings. Different benthic macroorganisms are predicted to have specific microbiomes. In contrast, local environmental factors are predicted to select for specific metabolic pathways in microbes. To reconcile these two predictions, we hypothesized that adaptation of microbiomes to local conditions is facilitated by the horizontal transfer of genes responsible for specific metabolic capabilities. To test this hypothesis, microbial metagenomes were sequenced from 22 coral reefs at 11 Line Islands in the central Pacific that together span a wide range of biogeochemical and anthropogenic influences. Consistent with our hypothesis, the percent cover of major benthic functional groups significantly correlated with particular microbial taxa. Reefs with higher coral cover had a coral microbiome with higher abundances of Alphaproteobacteria (such as Rhodobacterales and Sphingomonadales), whereas microbiomes of algae-dominated reefs had higher abundances of Gammaproteobacteria (such as Alteromonadales, Pseudomonadales, and Vibrionales), Betaproteobacteria, and Bacteriodetes. In contrast to taxa, geography was the strongest predictor of microbial community metabolism. Microbial communities on reefs with higher nutrient availability (e.g., equatorial upwelling zones) were enriched in genes involved in nutrient-related metabolisms (e.g., nitrate and nitrite ammonification, Ton/Tol transport, etc.). On reefs further from the equator, microbes had more genes encoding chlorophyll biosynthesis and photosystems I/II. These results support the hypothesis that core microbiomes are determined by holobiont macroorganisms, and that those core taxa adapt to local conditions by selecting for advantageous metabolic genes.

Keywords: marine bacteria; metabolic potential; microbial biogeography.

Fig. 1.

The LIs and their nutrient concentrations. (A) The 11 main atolls sampled in this study. The scale on the left indicates latitude and distance between atolls. Atoll sizes are proportionate, but not to scale. (B) Average nutrient concentrations at the 11 atolls. Nutrient concentrations were measured in triplicate for each of the 22 study sites (n = 66) and averaged; sites were then averaged for each atoll. Solid and dashed error bars show the SE for atoll and site replicates, respectively. Average values for each site are provided in Table S2.

Fig. 2.

nMDS plots for the relative abundances of taxonomic similarities (A) and metabolic subsystem similarities (B). Sites were averaged for each atoll. The 2D stress values are 0.05 and 0.03 for the taxonomic and metabolic similarities, respectively. Dark gray circles indicate significant groupings from the SIMPROF analysis (Figs. S2 and S3; Bray–Curtis similarity, P < 0.01). Light gray circles cluster atolls with greatest similarity within each statistically significant group.

Fig. 3.

CCA depicting the correlations between predictor variables (blue) and the relative abundance of taxonomic similarities (A) and metabolic similarities (B) at each LI. Loading vectors for the taxa and subsystems are shown in red. Altero, Alteromonadales; Betaproteo, Betaproteobacteria; cca, crustose coralline algae; calc macro, calcified macroalgae; dist, distance from the equator in degrees latitude; Enterob, Enterobacteriales; macro, fleshy macroalgae; Oceano, Oceanospirillales; OtherAlphas, other Alphaproteobacteria; Pseudomon, Pseudomonadales; Rhodobact, Rhodobacterales; soft, soft coral; Sphing, Sphingomonadales.

Fig. 4.

Metabolic pathways that correlate positively (A) and negatively (B) with increasing distance from the equator (decreasing nutrient concentrations) across the LIs. Pathways are level 3 subsystem annotations from the SEED database. SSU, small subunit.