AtlantECO-KER-AM-1

This collection of global maps provides global marine prokaryotic diversity using a standardized ensemble pipeline that integrates metagenomic profiles with environmental predictors. Metagenomic samples from the Ocean Microbiomics Database were processed into domain- and class-level taxonomic profiles, and diversity indices (richness, Shannon, Chao1) were estimated from rarified mOTU counts. Multiple algorithms—Generalized Linear and Additive Models, Random Forests, Boosted Regression Trees, Support Vector Machines, and shallow neural networks—were trained and tuned using five-fold cross-validation. Models were evaluated using root mean square error and R², with only well-performing models (R² ≥ 0.25) retained. Ensemble predictions were derived as the mean across all successful models, while associated uncertainty was quantified as the standard deviation among them. The resulting dataset contains annual, global projections of domain- and class-level prokaryotic diversity indices (richness, Shannon, Chao1). Each netCDF file provides both the ensemble average and standard deviation, offering spatially explicit estimates alongside model-based uncertainty.

This collection of global maps is based on metagenomic data collected during multiple oceanographic expeditions, including Tara Oceans, Malaspina, GO-SHIP, bioGEOTRACES, and OSD2014. Sample metadata, including geographic coordinates and sampling depth, were curated and merged with the environmental raster stack using bilinear extraction (terra::extract). Only samples with complete environmental metadata and valid coordinates were retained, and with samples from the 0.22-3 µm size fraction. The metagenomic data is from AtlantECO-BASEv2, a database developed within the AtlantECO project. All the metagenomic data were analyzed using the version 5.0 of MGnify’s pipeline. Taxonomic profiles were generated by running mOTUs on MGnify. For each sample (n = 1,210), tables containing mOTUs taxonomic assignments were downloaded and compiled into a single table. Diversity was then calculated from this dataset using the Shannon and Simpson indices in R (vegan package). We conducted our analysis separately, using either data from all expeditions pooled together or using only GO-SHIP data, as this was the only dataset covering the latitudinal gradient. We focused on the sunlit zone due to its ecological relevance for primary production and microbial functional diversity. To predict Simpson and Shannon diversity across global marine expeditions, we applied a species distribution modeling (SDM) framework using four algorithms within R, using the package “h2o” (v3.42.0.2). All models were trained to predict Simpson or Shannon functional/taxonomic diversity based on a selected set of non-collinear environmental predictors: mixed layer depth (MLD), chlorophyll (Chl), salinity (Sal), silicate (Si), temperature (T), and the excess of nitrates over phosphates (N*) according to Redfield ratio [NO3−] − 16[PO43−]. Models were trained on the final dataset using 5-fold cross-validation, for four algorithms (Artificial Neural Network, Random Forest, Generalized Linear Model, Gradient Boosting Machine). Predictions were projected using each trained model. Extracted predictions at sampling locations were compared against observed Simpson/Shannon diversity values using Pearson correlation, RMSE, and R². An ensemble prediction was calculated as the pixelwise mean across the four models.

This collection of global maps provides global gridded estimates of the functional diversity (FD) of marine copepod assemblages. FD indices were estimated by combining species distribution models (SDMs) with species-level functional traits (i.e., body size, trophic group, feeding mode, myelination and spawning mode) for > 300 copepod species. The dataset includes two complementary products: a contemporary baseline representing average copepod FD patterns for the present-day ocean and a future projection representing expected end-of-century (2081–2100) average changes in FD under a high-emission climate scenario (RCP8.5). Both products are provided as monthly climatologies at 1° × 1° spatial resolution for the surface ocean (0–10 m). Multiple FD indices were computed to cover multiple aspects of functional diversity including: functional richness, evenness, divergence, dispersion, and trait dissimilarity. These data allow researchers to investigate how copepod trait composition is structured across the global ocean today, and how it may be reshuffled in response to anthropogenic climate change. This dataset offers a valuable resource to assess potential impacts of changing zooplankton communities on marine productivity, carbon cycling, and climate feedbacks.

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 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 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 global picozoa richness for the epipelagic layer, obtained as the sum of mean pOTU-level HSI values across all 53 pOTUs, and the coefficient of variation over all pOTUs-level HSI maps.

This collection of global maps provides global projections of habitat suitability index (HSI) estimated for each of 53 different picozoa operational taxonomic units (pOTUs), as described in Huber et al. 2024. For each pOTU-level map, mean, max, min, stdev, and coefficient of variation of HSI values computed across several algorithms are provided. We developed the single pOTU HSI maps from 2,394 eDNA samples relative to the pico-size fraction (i.e. 0.2 to 5 μm), which were retrieved from the EukBank database. Picozoa samples were classified based on their depth into epipelagic (0-200 m), mesopelagic (200-1000 m) and bathypelagic (>1000 m) samples. To estimate the HSI, we used species distribution models based on a presence/absence design; among all retrieved samples of pico-eukaryotes, georeferenced samples where a specific pOTU was detected were treated as presences, while sampling locations where that pOTU was not detected were treated as (pseudo-) absences. For each pOTU, we contrasted average multi-year depth-specific environmental conditions at presence vs absence points using an ensemble of modelling algorithms, including Generalized Linear Models (GLM), Artificial Neural Network (ANN), Boosted Regression Trees (BRT) and Random Forest (RF). Models were calibrated using multi-year average conditions across epipelagic, mesopelagic and bathypelagic layers. HSI maps were projected over epipelagic conditions only, due to lack of sufficient data coverage for deeper layers. Environmental predictors were retrieved from the World Ocean Atlas 2018.

This collection of global maps provides the projected presence probabilities of 374 Single-cell and Metagenome Assembled Genomes (SMAGs) from Delmont et al. (2022). They are projected using 10 year average ensemble climatologies (CMIP5) in the present day (2006-15) and in the future (2090-99, RCP8.5).

This collection of global maps provides contemporary and future projections of marine planktonic species (n = 843) based on the Habitat Suitability Index retrieved from an ensemble across Species Distribution Models accounting for several sources of uncertainty (algorithm complexity and climate models). Each species file can be opened and contains 5 dimensions (rcp, statistic, timesteps, latitude and longitude). The data covers the time period from 2012 until 2100 in monthly timesteps for three different RCPs (RCP2.6, RCP4.5, RCP8.5). The maps cover the time period from 2012 until 2100 in monthly timesteps for three different RCPs (RCP2.6, RCP4.5, RCP8.5). The statistics (mean, standard deviation, minimum and maximum) are calculated across three different Earth System Models (CNRM-CM5, GFDL-ESM2M, IPSL-CM5A-LR) and three different algorithms (General Linear Model, General Additive Model and an Artificial Neural Network). The resolution of the data is 1° by 1° monthly resolution.