The present study was carried out in six fjords within New Zealand’s Fiordland system, specifically Breaksea Sound, Chalky Inlet, Doubtful Sound, Dusky Sound, Long Sound, and Wet Jacket Arm, as described in Tobias-Hünefeldt et al.15. Analyses were divided into three categories: (1) a multi-fjord analysis comprising five of the tested fjords (excluding Long Sound), (2) a high-resolution study along Long Sound’s horizontal axis, and (3) a depth profile of Long Sound’s deepest location (at 421 m). These categories were established to identify trends across multiple fjords, and then test the trends using a fjord analysed at a higher resolution. Total community composition (via 16S and 18S rRNA gene sequencing) and metabolic potential did not significantly covary across the five studied fjords (Mantel, r < 0.01, P = 0.47), Long Sound’s horizontal transect (Mantel, r < 0.01, P > 0.05) (Fig. 1), or Long Sound’s depth profile (Mantel, r < 0.22, P > 0.05) (Fig. 2). However, depth covaried with community structure for five studied fjords (Fig. S1), across the horizontal transect at Long Sound (Figs. S2, S3), and along Long Sound’s depth profile (Fig. 2). Microbial communities differed significantly between the surface and 10 m (Mantel, Multi-fjord—r = 0.21, P < 0.01, Transect—prokaryotes r = 0.47, P ≤ 0.01, eukaryotes r = 0.56, P < 0.01), as opposed to changes along the fjords horizontal axis (Mantel, Multi-fjord—r = 0.08, P = 0.04, Transect—prokaryotes r = 0.21, P = 0.01, eukaryotes r = 0.13, P = 0.07) (Figs. S2, S3, S4). Significant depth dependent metabolic potential differences could also be identified at the regional (multi-fjord; Anosim: R = 0.10, P = 0.03) and individual fjord scale (Anosim: R = 0.27, P ≤ 0.01) (Fig. 1).
Comparison of Biolog Ecoplates results for surface vs. 10m samples by principal component analysis (PCA). Surface and 10m samples compared across 5 sites. (a) Comparison of samples from a transect in a single site (Long Sound). (b) Text labels represent horizontal sample location (head/mouth of the fjord [a], or Km from the outermost sample [b]). Ellipses represent the 95% confidence interval.
Benthic and surface influence on metabolic potential. Two potential metabolic scenarios are depicted (a), the metabolic rate and diversity when driven solely by photosynthetic production, and another model that accounts for additional benthic influences. Biolog Ecoplate plate derived Average Metabolic Rate (AMR, b), Community Metabolic Diversity (c), and the relative metabolic potential (e) are also shown in addition to the bacterial abundance and productivity (d), and taxonomic and Biolog plate derived dissimilarity (Bray–Curtis) from the surface (f). Different colours represent carbon source groups (e; carbohydrates are blue, carboxylic acids are orange, amino acids are light blue, polymers are green, phosphorylated chemicals are yellow, and amines are dark blue), and Bray–Curtis dissimilarity data sources (f; the 16S community is black, 18S community is orange, and Biolog derived metabolic potential is light blue).
Across the fjords (excluding Long Sound), surface samples were more metabolically active (i.e., average metabolic rate [AMR]) compared to the 10 m samples (Wilcox test, W = 425, P < 0.01), and samples closer to the fjord head displayed increased metabolic rates (Wilcox test, W = 0, P < 0.01). Thus, metabolic variability varied with horizontal sampling location (Fig. 1b). While activity was not consistent along the length of Long Sound, surface samples in the low salinity layer were more metabolically active than those collected at 10 m (Fig. S5), although activity at 10 m between 1 and 4 km could not be measured due to sampling limitations. Heterotrophic production (via leucine incorporation) was not significantly correlated with microbial abundance within the five studied fjords and Long Sounds horizontal axis (Mantel—Multi-fjord r = 0.04, P = 0.22, Horizontal r = 0.04, P = 0.32). Along the depth profile, prokaryotic abundance and production were significantly correlated (Mantel, r = 0.60, P = 0.01) exhibiting a large drop in productivity from the surface to 10 m followed by a more gradual decrease.
We hypothesized that metabolic rate and diversity would be driven by marine snow linked to photosynthetic primary producers at the surface (e.g. phytoplankton and macroalgae; Fig. 2a) leading to a steady decrease in metabolic potential as resources were depleted with increases in depth. The high-resolution depth profile was used to explore this topic in more detail (Fig. 2, Fig. S4). Any deviation altering the slow loss of metabolic potential would be linked to extraneous sources of nutrients uncoupled from surface activity (i.e. benthic influences, subsidies from land-based inputs). We observed a steady loss of metabolic diversity and rate from surface to 40 m (Fig. 2b,c), with sustained increases at depths from 100 m onwards. However, abundance did not follow the same pattern, and instead continuously decreased until 360 m (Fig. 2d). Abundance and metabolic changes over depth were associated with shifts in specific carbon utilization potential, where carbohydrate metabolism decreased from 12.7 to 6.8%, as carboxylic acid utilization increased from 12.0 to 29.5% from the surface to 360 m (Fig. 2e). This likely reflected the diminishing abundance of readily mineralizable substrates with depth, and the increase in recalcitrant sources of carbon and energy. Consistently, we also observed increases in phosphorylated chemical metabolism peaking at 40 and 360 m (Fig. 2e) as expected from utilization of phosphorous at the surface during blooms30. However, observed changes in metabolic potential did not reflect changes in prokaryotic or eukaryotic community composition (Fig. 2, Fig. S4), suggesting that while the community members were relatively consistent past a certain depth (i.e., 10 m for eukaryotes and 40 m for prokaryotes) the metabolic potential changed dynamically past 100 m, regaining peak metabolic potential with proximity to the bottom (Fig. 2f). Therefore, we conclude that as bacterial abundance decreases with depth, the functional diversity for carbon utilisation increases. However, this is not due to large prokaryotic or eukaryotic community changes, but may be due to the utilisation of alternative metabolic pathways by the present organisms. However, it may still be possible that fungi play a role in the observed metabolic changes as this study did not assess their community composition. This study is nonetheless one of the first to consider both prokaryotic and eukaryotic community compositions when assessing metabolic changes within a fjord.
Our results demonstrate that metabolic potential and activity in fjords is linked to similar parameters as microbial community composition across surface or near surface sites. However, distinct selective pressures exist at aphotic sites which ultimately affect the link between phylogenetic and metabolic diversity. The observed patterns are contrary to the open ocean carbon pump paradigm and demonstrate that additional refractory sources of organic matter, including resuspension of terrestrial organic matter associated with benthic communities, are important contributors to microbial activity in fjords, which form a major marine biome worldwide (e.g. Patagonian, Scandinavian, Northeastern Pacific systems). We propose that this reflects the influence of the benthic microbial loop and incorporation and breakdown of terrestrial organic matter in fjordic sediments. Sediment resuspension can occur through a variety of abiotic31,32 and biotic sources (known as bioturbation33). The resuspension of organically rich sediments has previously been shown to increase microbial activity34. Observed patterns suggest that resuspension could also be driven by bottom feeding organisms, increasing suspended organic matter and its utilization in near bottom habitats35. Therefore, organic matter sources influence the relationship between microbial communities and their metabolic potential.
Source: Ecology - nature.com
