The energy-diversity relationship of complex bacterial communities in Arctic deep-sea sediments

Abstract

The availability of nutrients and energy is a main driver of biodiversity for plant and animal communities in terrestrial and marine ecosystems, but we are only beginning to understand whether and how energy-diversity relationships may be extended to complex natural bacterial communities. Here, we analyzed the link between phytodetritus input, diversity and activity of bacterial communities of the Siberian continental margin (37-3427 m water depth). Community structure and functions, such as enzymatic activity, oxygen consumption and carbon remineralization rates, were highly related to each other, and with energy availability. Bacterial richness substantially increased with increasing sediment pigment content, suggesting a positive energy-diversity relationship in oligotrophic regions. Richness leveled off, forming a plateau, when mesotrophic sites were included, suggesting that bacterial communities and other benthic fauna may be structured by similar mechanisms. Dominant bacterial taxa showed strong positive or negative relationships with phytodetritus input and allowed us to identify candidate bioindicator taxa. Contrasting responses of individual taxa to changes in phytodetritus input also suggest varying ecological strategies among bacterial groups along the energy gradient. Our results imply that environmental changes affecting primary productivity and particle export from the surface ocean will not only affect bacterial community structure but also bacterial functions in Arctic deep-sea sediment, and that sediment bacterial communities can record shifts in the whole ocean ecosystem functioning.

Figure 1

Changes in bacterial OTU richness, community structure and enzyme activity with pigment concentrations and correlation of changes in community structure with changes in enzyme activities for ARISA and 454 MPTS data. The plots in the left column of the figure (a, d, b) are based on the full ARISA dataset, the ones in the middle column (b, e, h) are based on a reduced ARISA dataset containing only samples used for 454 MPTS and plots in the right column (c, f, i) are based on 454 MPTS data. Linear regression R² values and Spearman's correlations as tested by Mantel tests with 999 permutations are indicated in the plots.

Figure 2

Partitioning of the variation in bacterial community structure (ARISA) and enzyme activity (esterase, lipase, peptidase, beta-glucosidase). The specific effects of contextual parameters (protein concentration, pigment concentrations, water depth, spatial distance) and total co-variation between these parameters are represented. Statistical significance as determined by 999 Monte Carlo permutations under the full multivariate model is indicated by ^***P<0.001 and ^**P<0.01.

Figure 3

Path analysis of the causal relationships between bacterial community structure, bacterial activity and contextual parameters. The Chi-square test is used to test whether the modeled relationships are significantly different from the original correlation matrix, with here a good agreement between the model and the data (P=0.76). The goodness-of-fit index (0.98) and Bentler Comparative Fit Index (1) indicate an optimal fit of the model. The Bayesian Information Criterion (−16.1) is another measure of the goodness of fit, and was the criterion that was iteratively minimized. The coefficient of non-determination (ND=1-R²) indicates the fraction of the variance in bacterial community structure and enzyme activity that is not explained by the model. ^aMarginally significant with P=0.078.

Figure 4

Examples of typical behaviors of taxa (a, b) and individual OTUs (c–e) with pigment concentrations. Significant positive and negative Spearman's rank correlation values are displayed in red and blue, respectively, whereas no significant relationships are in gray. The proportions of significantly correlated taxa and OTUs are for phyla (21% positive, 16% negative), classes (40%, 20%), Proteobacteria (4.6%, 0.6%), Gammaproteobacteria (3.8%, 0.1%), Acidobacteria (4.8%, 1.9%).