Background: One of the central objectives of microbial ecology is to study the distribution of microbial communities and their association with their environments. Biogeographical studies have partitioned the oceans into provinces and regions, but the identification of their boundaries remains challenging, hindering our ability to study transition zones (i.e. ecotones) and microbial ecosystem heterogeneity. Fuzzy clustering is a promising method to do so, as it creates overlapping sets of clusters. The outputs of these analyses thus appear both structured (into clusters) and gradual (due to the overlaps), which aligns with the inherent continuity of the pelagic environment, and solves the issue of defining ecosystem boundaries. Results: We show the suitability of applying fuzzy clustering to address the patchiness of microbial ecosystems, integrating environmental (Sea Surface Temperature, Salinity) and bacterioplankton data (Operational Taxonomic Units (OTUs) based on 16S rRNA gene) collected during six cruises over 1.5 years from the subtropical frontal zone off New Zealand. The technique was able to precisely identify ecological heterogeneity, distinguishing both the patches and the transitions between them. In particular we show that the subtropical front is a distinct, albeit transient, microbial ecosystem. Each water mass harboured a specific microbial community, and the characteristics of their ecotones matched the characteristics of the environmental transitions, highlighting that environmental mixing lead to community mixing. Further explorations into the OTU community compositions revealed that, although only a small proportion of the OTUs explained community variance, their associations with given water mass were consistent through time. Conclusion: We demonstrate recurrent associations between microbial communities and dynamic oceanic features. Fuzzy clusters can be applied to any ecosystem (terrestrial, human, marine, etc) to solve uncertainties regarding the position of microbial ecological boundaries and to refine the relation between the distribution of microorganisms and their environment.

In response to climate change, biodiversity patterns in the oceans are predicted to shift rapidly, thus increasing the need for efficient monitoring methods. Environmental DNA (eDNA) metabarcoding recently emerged as a potent and cost-effective candidate to answer this challenge. We targeted three molecular markers to determine multicellular metazoan communities from two timepoints across a long-standing transect in the Southern Hemisphere, the Munida Observational Time Series. We detected four community types across the successive water masses—neritic, sub-tropical, frontal, and sub-Antarctic—crossed by the transect, together with important community differences between the two sampling points. From indicator species analysis, we found diversity patterns were mostly driven by planktonic organisms. Mesopelagic communities differed from surface-water communities in the sub-Antarctic water mass, with at-depth communities dominated by single-cellular organisms. We evaluate the ability of eDNA to detect species-compositional changes across surface and depth gradients and lay the foundations for using this technique in multi-trophic environmental monitoring efforts across long time series. We observed community differences across time and space. More intensive sampling will be critical to fully capture diversity across marine gradients, but this multi-trophic method represents an invaluable opportunity to understand shifts in marine biota.

Following publication of the original article [1], the authors reported an error with the positioning of the figures; the positions of Figs. 4 and 5 (not including the captions) had been erroneously swapped. The original article [1] has been updated to correct this error.