Key Expoitable Results (KERs)

The Southern Ocean hosts a high degree of endemic plants and animals, yet the genetic diversity, function and evolutionary relationships of microbial communities remains unexplored, particularly in the aphotic “dark ocean,” where microbes play critical roles in local and global food webs. Here, we performed a metagenomic analysis of 44 aphotic seawater samples collected from multiple depths across the Southern Ocean to characterize the functional gene repertoire of these microbial communities. Of the 11,896,546 species-level unigenes1 identified, ~ 87% appear specific to the Southern Ocean and are distinct from other major ocean datasets. We reconstructed 502 bacterial and 108 archaeal metagenome-assembled genomes (MAGs), revealing widespread capacities for inorganic carbon fixation via the Calvin cycle, the hydroxypropionate-hydroxybutyrate cycle, and the 3-hydroxypropionate bi-cycle. Metapangenomic analyses indicated that several genes involved in the oxidation of reduced nutrients including ammonia, nitrite, and thiosulfate, are shared across the aphotic water column through horizontal gene transfer. MAGs belonging to Acidimicrobia, Gammaproteobacteria, and SAR324 were abundant throughout the dark Southern Ocean and showed potential for both chemolithoautotrophy and carbohydrate degradation, suggesting mixotrophy as a key metabolic strategy. Together, these findings reveal the unique functional and genomic diversity of deep Southern Ocean microbiomes and provide insights into their roles in carbon cycling within one of Earth’s most important marine carbon sinks.

Understanding how climate change affects marine primary productivity requires examining the evolving causal relationships between physical and biogeochemical processes. We applied the PCMCI+ causal discovery algorithm to investigate how the mechanisms controlling Net Primary Productivity (NPP) in the North Atlantic Subpolar Gyre evolve under different climate scenarios across five Earth System Models. Using 100-year sliding windows, we compare causal relationships in future scenarios against pre-industrial conditions, focusing on the roles of mixed layer nutrients, vertical mixing and horizontal transport. Our analysis reveals three main categories of relationship evolution: the emergence of links, the disappearance of links, and changes in link strengths. For example, while the link between stratification and NPP emerges under climate change in CanESM5-CanOE, it strengthens in CMCC-ESM2 and remains stable with moderate to high intensities in other models. At the end of the 21st century, the spread between models regarding the effect of stratification on NPP is reduced compared to pre- industrial conditions, suggesting a reduction in inter-model uncertainty. However, the transport and vertical mixing controls on the supply of nutrients to the mixed layer exhibit a more diverse evolution among the ESMs studied. The CMCC-ESM2 model has a strengthening of the relationships between winter vertical mixing and nutrients, while IPSL-CM6A-LR and CanESM5CanOE show weakening of these relationships. Furthermore, the evolution of the link between nutrient supply to the mixed layer for NPP exhibits a large variability between models. These divergent pathways reveal that the dynamics of nutrients has uncertain evolution between models. Lastly, model-specific dynamics are also observed, such as the strengthening of the link between horizontal transport and the nutrient content of the mixed layer in IPSL-CM6A-LR. Together with the decreasing strength of the vertical mixing/nutrients link, this suggests the presence of compensation mechanisms and a shift from vertical mixing dominance to enhanced horizontal transport control over the course of the scenario. These findings offer mechanistic insights into the dynamics of ESMs, specifically in the evolving relationships between physical and biogeochemical processes that shape the projections of NPP and nutrients. The causality-based approach identifies mechanisms that traditional analyses miss, offering a novel framework for model intercomparison and understanding ecosystem responses to climate change.

Interactions between mesoscale eddies and seamounts can strongly modify local circulation, influencing nutrient transport and planktonic communities. This study investigates the coupling between hydrodynamics features and zooplankton dynamics along the Vitória–Trindade Seamount Chain (VTC) in the southwestern Atlantic, using hydrographic (CTD) measurements and Underwater Vision Profiler (UVP5) observations collected along two transects: one crossing the VTC and a second located slightly south of the chain. Flow–topography interactions involving the Vitória Eddy (VE) and regional seamounts were found to modulate chlorophyll-a distributions and zooplankton abundance. Although classical seamount-induced upwelling was not detected, elevated chlorophyll-a concentrations within thermocline depths and downstream of Davis Bank indicate enhanced vertical mixing associated with flow–seamount interactions and eddy deformation. Rhizaria numerically dominated the zooplankton community, particularly within the euphotic zone and thermocline layers. Analysis of the normalized biovolume size spectrum (NBSS) revealed flatter slopes and higher intercepts near the shelf and in VE-influenced regions, indicating a greater contribution of large-bodied organisms and higher trophic transfer efficiency. In contrast, offshore and seamount-only stations exhibited steeper size spectra consistent with oligotrophic, energy-limited conditions. Particulate organic carbon (POC) fluxes were highest at stations influenced by the VE, reflecting the combined effects of lateral shelf inputs and particle retention driven by flow–topography interactions. Enhanced subsurface and seamount-associated POC fluxes further highlight the joint role of mesoscale eddies and seamounts in regulating carbon export along the VTC.

Ocean warming can affect plankton both directly, through altered metabolic activities, and indirectly, modifying the physical–chemical properties of the water column, with possible effects on ecosystem functioning. To evaluate the combined action of warming-related physiological responses and environmental changes on plankton functioning, we carried out a long-term analysis (from 1994 to 2019) of the Bermuda Atlantic Time-series Study (BATS) dataset where ocean warming and stratification have driven a decrease in the net primary production over the last decade. Using the time series of plankton observations, we assembled 1000 replicates of a food web model for each year. We observed that the total flow of matter through the model remained constant over time, despite the increased oligotrophication, due to global warming, after 2014. In fact, the plankton food web remained robust through re-modulated trophic interactions with an increased detritivory to herbivory ratio of the food web over time. However, it was problematic to re-establish the trophic connections of the food web broken by ocean warming, as remarked by the increased relative internal ascendency. Thanks to trophic plasticity, the reduced zooplankton dependence on herbivory was compensated by a significant increase in the reliance on carnivory and detritivores, highlighting the crucial role of trophic interactions in buffering significant environmental short-term changes.

Marine plankton are key drivers of ocean productivity and global carbon cycling, yet their quantitative biogeography remains poorly characterized. Environmental genomic datasets are inherently compositional, restricting analyses to relative abundances and limiting their integration into ecological and biogeochemical models. Here we combine DNA mass measurements, filtered seawater volumes, and metagenomic relative abundances to generate absolute estimates of cell concentrations and carbon biomass of plankton across the global ocean. Leveraging thousands of samples, this approach provides quantitative estimates for hundreds of eukaryotic and thousands of prokaryotic environmental genomes, unveiling ecological associations not captured by compositional data. Using the psbO marker gene, we reconstruct quantitative biogeographies of photosynthetic lineages and, through global-scale modeling, project their distributions to generate quantitative maps of phytoplankton communities across the world’s ocean. By bridging genomic data with biogeochemical metrics, this study provides novel resources for integrating plankton at genomic resolution into next-generation biogeochemical models.

This collection of global maps provides species-level monthly fields of habitat suitability indices (HSI, ranging between 0 and 1; also called ‘presence probability’) for phytoplankton and zooplankton, for the contemporary time period. Each NetCDF file records the monthly: minimum (Min), maximum (Max), mean, median and the standard deviation (Stdev) of the species-specific HSI predictions issued from three species distribution models. The species distribution models used to generate the maps relied on background data were based on a “total target group approach”. These species distribution models were trained on species-level georeferenced occurrence (presence-only) data collectively gathered in the ZooBase dataset. Phytoplankton species (n=341) were separated from zooplankton species (n=504) on the basis that these two categories constitute two broadly-defined trophic levels (i.e., primary and secondary producers, respectively).

This collection of global maps provides phytoplankton species richness estimated from the binarized (presence—absence transformed) habitat suitability patterns of 536 – 538 phytoplankton species including diatoms, dinoflagellates, coccolithophores, and a few minor groups. We used Total and group-specific target group approach.

This collection of global maps provides monthly global biogeography of 14 diazotroph species including cyanobacterial and non-cyanobacterial diazotrophs. Each netCDF file contains the ensemble average, standard deviation, maximum and minimum across all ensemble members. We used total, group-specific and cruise-specific target group approaches.

This collection of global maps provides a global ensemble modeling framework allying cell abundance dataset derived from empirical quantification of phytoplankton groups in metagenomic samples with global environmental variables. Cell abundances of phytoplankton groups were estimated from the quantification of the psbO-gene in metagenomics samples from the Tara Oceans and Tara Pacific expeditions. Phytoplankton communities were resolved into 16 taxonomic groups. Separate models were trained for each of 17 responses (16 groups + total phytoplankton), using only samples with complete predictors and targets. Hyperparameters (tree number, minimum leaf size) were tuned by Bayesian optimization. Optimal configurations were determined via 5-fold cross-validation, balancing predictive accuracy and data use. Performance was quantified using RMSE and R². Final models were retrained with all valid data and applied to the global prediction grid. The ensemble models relied on a suite of key environmental predictors, sea surface temperature (SST), sea surface salinity (SSS), chlorophyll-a, iron, nitrate, phosphate, and silicate. Model predictions were made over monthly global environmental grids, with phytoplankton maps generated at 1° × 1° resolution. We computed Mahalanobis distances between environmental conditions at each grid point and the training dataset to identify regions outside the empirical environmental envelope and quantify prediction confidence and extrapolation risk. In parallel, we conducted sensitivity analyses by varying subsets of the data, quantifying how training data composition influences global predictions.

This collection of global maps provides monthly global marine diazotroph ensemble richness estimates from binarized (presence-absence transformed) habitat suitability patterns of 14 diazotroph species including cyanobacterial and non-cyanobacterial diazotrophs. We used a total, group-specific and cruise-specific target group approach.

This collection of global maps is based on protein data from 1,379 metagenomes (from AtlantECO-BASEv2), we define Operational Protein Units (OPUs) through a sensitive heuristic clustering strategy that captures both known and unknown amino acid sequences, including those associated with "functional dark matter" often missed by traditional analyses. Based on abundance and occurrence, 20 representative OPUs were selected for the modelling process. These OPUs were assigned to different functional pathways. For each of 20 OPUs we ran species distribution models to predict their global distribution. We modeled using four models- Generalized Linear Models (GLM), Artificial Neural Network (ANN), Boosted Regression Trees (BRT), Random Forest (RF), resulting in 80 maps. With this, with the ensemble approach, we used the mean of the suitability among the four models, resulting in the potential distribution maps. The OPUs were modeled to sunlit ocean regions ( 200 meters). The models showed different OPUs distribution patterns: widely, polar, no polar, tropical, temperate and sub polar distributions. These resulting maps are stored in the folder “SDM OPUs”. We used species distribution models to predict the global distribution of 20 OPUs well represented across all the ocean basins.

This collection of global maps provides a global Bray-Curtis dissimilarity index (accounting for projected changes in the different size fraction) comparing present day and end of century projected provinces is included. This index is also projected on EEZ and main fisheries (from Watson, 2017).

This collection of global maps provides functional group-level global monthly fields of species richness (estimated through the sum of species-level HSI) for the contemporary and future time periods (i.e., folder labelled ‘Groups_species_richness_Benedettietal.2021’). There are two NetCDF files per functional group, one for each time period: contemporary (2012-2031) and future (2081-2100). Each NetCDF file records the monthly: minimum (Min), maximum (Max), mean, median and the standard deviation (Stdev) of the species richness estimated for 14 different plankton functional groups (Amphipoda, Appendicularia, Calanoida, Chaetognatha, Coccolithophores, Diatoms, Dinoflagellates, Euphausiids, Foraminifera, Jellyfish, Oithonida, Poecilostomatoida, Pteropoda and Thaliacea). For each functional group, species richness was estimated as the sum of the HSI predicted for the species composing that functional group. The species distribution models used to generate the maps relied on background data were based on a “group-specific target group approach”. Projections were made using an ensemble of five Earth System Models from the MAREMIP project: the Community Earth System Model version 1 (CESM1, POP-BEC), the Geophysical Fluid Dynamics Laboratory Earth System Model with Modular Ocean Model version 4 (GFDL-ESM2M; MOM-TOPAZ), the Institut Pierre Simon Laplace Climate Model version 5A-LR (IPSL-CM5A-LR; NEMO-PISCES), the Centre National de Recherches Météorologiques Climate Model version 5 (CNRM- CM5; NEMO-PISCES) and the Model for Interdisciplinary Research on Climate version 5 (MIROC5; MRI.COM-MEM).