Key Expoitable Results (KERs)

Global maps of the Ocean microbiome include contemporary and future projections of the ocean microbiome presence probability, abundance, carbon biomass, richness, diversity, and climato-genetic provinces.

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 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).

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 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.

We provide high-quality metagenome-assembled genomes (MAGs) derived from seawater and sea ice samples collected in the Southern Ocean. Several MAGs encode genes associated with dimethylsulfoniopropionate (DMSP) lyase activity and methane oxidation. This resource provides insights regarding the role of microbial communities in the production of key volatile compounds.

We conducted a high-resolution analysis of diatom populations in the microphytoplankton size range using data collected at 30-min intervals over a 20-month period by an automated imaging system deployed near the mouth of Baía de Todos os Santos (BTS), Brazil. Seven diatom taxa were identified and quantified through automated classification using a Convolutional Neural Network (CNN). Frequency-domain analysis revealed distinct environmental drivers acting across different temporal scales. At high-frequency scales (53 h), solar radiation was the predominant factor influencing diatom abundances. At intermediate to monthly scales (53 h–13 days, neap-spring cycles of 13–15 days, and monthly scales), canonical correspondence analysis (CCA) indicated that dissolved oxygen, temperature, and salinity were the primary environmental drivers. Multiple linear regression (MLR) models highlighted colored dissolved organic matter (CDOM) and the north-south wind component as key predictors for Coscinodiscus wailesii abundances. K-strategist marine taxa, including Rhizosolenia robusta and the Rhizosolenia–Proboscia complex, exhibited peak densities during neap tides, coinciding with stronger intrusion events of oligotrophic oceanic waters into the bay. Conversely, r-strategist coastal and estuarine taxa, including C. wailesii, Bacteriastrum-Chaetoceros complex, and Guinardia striata, reached maximum abundances during spring tides, associated with enhanced river discharge and pronounced ebb flow conditions. These taxon-specific distribution patterns demonstrate the influence of environmental forcing across multiple temporal scales on diatom populations. Our findings show the effectiveness of frequency-domain analytical approaches in resolving the complex interactions between environmental variability and phytoplankton dynamics, enhancing understanding of bottom-up regulatory processes and inter-taxa ecological interactions in coastal tropical ecosystems.

Marine heat waves affect ocean ecosystems and are expected to become more frequent and intense. Earth system models’ ability to reproduce extreme ocean temperature statistics has not been tested quantitatively, making the reliability of their future projections of marine heat waves uncertain. We demonstrate that annual maxima of detrended anomalies in daily mean sea surface temperatures (SSTs) over 39 years of global satellite observations are described excellently by the generalized extreme value distribution. If models can reproduce the observed distribution of SST extremes, this increases confidence in their marine heat wave projections. 14 CMIP6 models' historical realizations reproduce the satellite-based distribution and its parameters’ spatial patterns. We find that maximum ocean temperatures will become warmer (by 1.07° ± 0.17°C under 2°C warming and 2.04° ± 0.18°C under 3.2°C warming). These changes are mainly due to mean SST increases, slightly reinforced by SST seasonality increases. Our study quantifies ocean temperature extremes and gives confidence to model projections of marine heat waves.