A Gulf Stream frontal eddy harbors a distinct microbiome compared to adjacent waters

Abstract

Mesoscale oceanographic features, including eddies, have the potential to alter productivity and other biogeochemical rates in the ocean. Here, we examine the microbiome of a cyclonic, Gulf Stream frontal eddy, with a distinct origin and environmental parameters compared to surrounding waters, in order to better understand the processes dominating microbial community assembly in the dynamic coastal ocean. Our microbiome-based approach identified the eddy as distinct from the surround Gulf Stream waters. The eddy-associated microbial community occupied a larger area than identified by temperature and salinity alone, increasing the predicted extent of eddy-associated biogeochemical processes. While the eddy formed on the continental shelf, after two weeks both environmental parameters and microbiome composition of the eddy were most similar to the Gulf Stream, suggesting the effect of environmental filtering on community assembly or physical mixing with adjacent Gulf Stream waters. In spite of the potential for eddy-driven upwelling to introduce nutrients and stimulate primary production, eddy surface waters exhibit lower chlorophyll a along with a distinct and less even microbial community, compared to the Gulf Stream. At the population level, the eddy microbiome exhibited differences among the cyanobacteria (e.g. lower Trichodesmium and higher Prochlorococcus) and in the heterotrophic alpha Proteobacteria (e.g. lower relative abundances of specific SAR11 phylotypes) versus the Gulf Stream. However, better delineation of the relative roles of processes driving eddy community assembly will likely require following the eddy and surrounding waters since inception. Additionally, sampling throughout the water column could better clarify the contribution of these mesoscale features to primary production and carbon export in the oceans.

Fig 1

Locations and microbiome of field samples (A) Map of sampling sites for five ~10–15 km long ship-board transects off the coast of Cape Hatteras, North Carolina, USA September 5–7, 2021. Background color represents average sea surface temperatures from August 20^th to September 10^th obtained from the Geostationary Operational Environmental Satellite 16 (GOES-16). Each sampling point is shown along the transect and categorized by temperature and salinity (shapes) and microbial-community based k-means clusters (colors), generated using the self-organizing map (SOM) R package ‘kohonen’. Isobaths for 500m (white), 1000m (grey) and 2000m (black) are included for context. (B) Non-metric multidimensional scaling (NMDS) ordination computed based on Bray-Curtis dissimilarity for 16S rRNA gene libraries. Ellipses show the multivariate t-distribution 95% confidence interval for the mean of each SOM cluster. Environmental factors found to be statistically significant (Multiple regression permutation test, p<0.05) are indicated with an ‘*’.

Fig 2. Satellite overview of the observed frontal eddy from formation to in situ sampling.

Black dotted lines are all 5 transects from this study. Black boxes outline the eddy location across five days from August 27th to September 6th. SST is from GOES-16 and chlorophyll a is from Sentinel-3’s OLCI sensor. Boxes were determined manually by identifying an anomaly in SST and ocean color measurements that is consistent with a cyclonic, frontal eddy and associated warm streamer.

Fig 3. Key environmental factors for Self-Organizing Map (SOM) clusters.

Numbered clusters correspond to the most common environments: Continental shelf (Cluster 1), Continental slope (Cluster 2), Gulf Stream (Cluster 3), Eddy (Cluster 4). For the box and whisker plots, the center lines denote the median value while the box encompasses the 25^th to 75^th percentiles of the dataset. Whiskers denote the 5^th and 95^th percentiles and values that fall outside these ranges are shown as individual points. Brackets indicate p-values of Wilcoxon Rank Sum pairwise comparisons. Additional environmental parameter comparisons are shown in S4 Fig in S1 File.

Fig 4. Alpha diversity metrics for 16S rRNA gene community composition for each Self-Organizing Map (SOM) cluster.

(A) Shannon’s H index, (B) species richness, and (C) Pielou’s evenness were calculated based on the absolute abundance of ASVs using ‘vegan’ (v2.6.2) in R (v4.0.0). SOM cluster numbers correspond to the following classifications: 1 –continental shelf, 2-continental slope, 3- Gulf Stream, 4- eddy. Brackets indicate p-values associated with Wilcoxon Rank Sum test pairwise comparisons.

Fig 5. Average family-level composition for each Self-Organizing Map (SOM) cluster.

The average community was calculated by taking the mean relative abundance of each taxon across all samples within that cluster, then normalizing relative abundances to 100%. Amplicon Sequence Variants (ASVs) were generally grouped at the family level, but when family is undefined they are labeled at the phylum level. ASVs with a relative abundance of less than 1% are grouped as “Other.” Additionally, the family Synechococcaceae was resolved at the genus level to illustrate the Synechococcus-Prochlorococcus inversion between clusters.

Fig 6

Amplicon sequence variant (ASV) associations with specific Self Organizing Map (SOM) groupings (A) Heatmap of ln(absolute abundance+1) of the 22 ASVs identified with at least one significant SOM cluster beta coefficient obtained from generalized joint attribute modeling (GJAM) analysis of the 250 most abundant ASVs across all samples. Samples are organized by SOM grouping (labeled at top) (B) Heatmap of statistically significant SOM cluster-associated beta coefficients (95% confidence interval does not overlap 0), non-significant beta coefficients are shown as white. Colored labels at the top of each column identifies the SOM cluster (as in panel A). ASVs are organized maximum likelihood phylogenetic tree constructed using Smart Model Selection in PhyML 3.0 with taxonomic assignments based on the RDP naïve Bayesian classifier using the Greengenes version 13.5 database.

Fig 7. Microbiome differences between the Gulf Stream and eddy.

Left panel: Phylotypes (ASVs) that exhibited significant differential abundance in the eddy relative to the Gulf Stream as identified using DESeq2, left side indicates enrichment in the Gulf Stream, right side eddy. Dots indicates the Log₂ fold changes for each ASV identified as significant (Benjamin-Hochberg multiple hypothesis adjusted p<0.05), error bars indicate the standard error of the Log₂ fold changes as calculated by DESeq2. Only ASVs with a minimum average relative abundance of 0.05% across the samples being compared were included in the analysis. Y-axis represents maximum likelihood phylogenetic tree constructed using Smart Model Selection in PhyML 3.0. Taxonomic assignments were obtained from the RDP database. Right hand column indicates those ASVs that were also found to have a significant association with either the Gulf Stream (dark blue) or the eddy (light blue) as identified using Linear discriminant analysis Effect Size (LEfSE).