Bacterial degradation of ctenophore Mnemiopsis leidyi organic matter.

Blooms of gelatinous zooplankton, an important source of protein-rich biomass in coastal waters, often collapse rapidly, releasing large amounts of labile detrital organic matter (OM) into the surrounding water. Although these blooms have the potential to cause major perturbations in the marine ecosystem, their effects on the microbial community and hence on the biogeochemical cycles have yet to be elucidated. We conducted microcosm experiments simulating the scenario experienced by coastal bacterial communities after the decay of a ctenophore (Mnemiopsis leidyi) bloom in the northern Adriatic Sea. Within 24 h, a rapid response of bacterial communities to the M. leidyi OM was observed, characterized by elevated bacterial biomass production and respiration rates. However, compared to our previous microcosm study of jellyfish (Aurelia aurita s.l.), M. leidyi OM degradation was characterized by significantly lower bacterial growth efficiency, meaning that the carbon stored in the OM was mostly respired. Combined metagenomic and metaproteomic analysis indicated that the degradation activity was mainly performed by Pseudoalteromonas, producing a large amount of proteolytic extracellular enzymes and exhibiting high metabolic activity. Interestingly, the reconstructed metagenome-assembled genome (MAG) of Pseudoalteromonas phenolica was almost identical (average nucleotide identity >99%) to the MAG previously reconstructed in our A. aurita microcosm study, despite the fundamental genetic and biochemical differences of the two gelatinous zooplankton species. Taken together, our data suggest that blooms of different gelatinous zooplankton are likely triggering a consistent response from natural bacterial communities, with specific bacterial lineages driving the remineralization of the gelatinous OM.IMPORTANCEJellyfish blooms are increasingly becoming a recurring seasonal event in marine ecosystems, characterized by a rapid build-up of gelatinous biomass that collapses rapidly. Although these blooms have the potential to cause major perturbations, their impact on marine microbial communities is largely unknown. We conducted an incubation experiment simulating a bloom of the ctenophore Mnemiopsis leidyi in the Northern Adriatic, where we investigated the bacterial response to the gelatinous biomass. We found that the bacterial communities actively degraded the gelatinous organic matter, and overall showed a striking similarity to the dynamics previously observed after a simulated bloom of the jellyfish Aurelia aurita s.l. In both cases, we found that a single bacterial species, Pseudoalteromonas phenolica, was responsible for most of the degradation activity. This suggests that blooms of different jellyfish are likely to trigger a consistent response from natural bacterial communities, with specific bacterial species driving the remineralization of gelatinous biomass.

Global marine phytoplankton dynamics analysis with machine learning and reanalyzed remote sensing.

Phytoplankton are the world's largest oxygen producers found in oceans, seas and large water bodies, which play crucial roles in the marine food chain. Unbalanced biogeochemical features like salinity, pH, minerals, etc., can retard their growth. With advancements in better hardware, the usage of Artificial Intelligence techniques is rapidly increasing for creating an intelligent decision-making system. Therefore, we attempt to overcome this gap by using supervised regressions on reanalysis data targeting global phytoplankton levels in global waters. The presented experiment proposes the applications of different supervised machine learning regression techniques such as random forest, extra trees, bagging and histogram-based gradient boosting regressor on reanalysis data obtained from the Copernicus Global Ocean Biogeochemistry Hindcast dataset. Results obtained from the experiment have predicted the phytoplankton levels with a coefficient of determination score (R2) of up to 0.96. After further validation with larger datasets, the model can be deployed in a production environment in an attempt to complement in-situ measurement efforts.