AtlantECO-KER-AM-1

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 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 global predictions of surface monthly microplastics concentrations (in #items.km-2, log10 transformed) modelled through five distribution models (GLM, GAM, ANN, RF, GBM). The models were trained on an observational dataset that aggregated measurements from multiple microplastic sampling campaigns compiled by van Sebille et al. (2015) and completed by Schmiz (2021). The original dataset of Schmiz (2021) synthetised no less than 13 159 microplastic concentrations observations spanning from 1979 to 2019. These microplastic concentrations measurements were collected through diverse sampling methods (~83% come from plankton nets) and covered plastics particles ranging from 32µm to 20cm. Giacomo Poli (MSc student, ETH Zürich) analysed this dataset and adapted the modelling pipeline of Knecht et al., (2023) to model global surface microplastics concentrations based on five types of statistical models and an exhaustive suite of environmental and anthropogenic predictors.

This collection of global maps is based on Association Distribution Modeling (ADM), which was conceived as a parallel to well-established Species Distribution Models, but dedicated to the modeling and prediction of pairwise ecological associations, statistical proxies for potential biotic interactions, with the aim of further modelling community biogeography with better reliability. Here, only co-occurrence projections are reported, with 3 possible states: 1 for co-presence of both species, 0 for co-absence, and -1 for exclusion (i.e., one species is present but not the other). On the one hand, 57,082 ADMs were projected in the contemporary era using the World Ocean Atlas 18 dataset (Boyer et al. 2018). On the other hand, 17,517 ADMs were projected throughout the century using IPSL (Institut Pierre-Simon Laplace) climate model under a Representation Concentration Pathway 4.5 climate scenario (Boucher et al. 2020).

This collection of global maps provides the projected probabilities of presence of the 27 “climato-genomic provinces” described in Fremont et al. (2022) and originally defined in Richter 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). Provinces encompass 6 size fractions of plankton that were sampled during the Tara Oceans expeditions (2009-2013): 0-0.2 µm enriched in viruses, 0.22-3 µm enriched in bacteria, 0.8-5 µm enriched in protists, 5-20 µm, 20-180 µm and 180-2000 µm enriched in small metazoans (mainly copepods).

This collection of global maps provides two NetCDF files that record the ensemble predictions of global monthly (12 time levels) biomass concentrations of calcifying Pteropoda (in mg C m-3) and Foraminifera (in µg C m-3). The monthly biomass concentration fields were obtained by training a suite of five distribution models tailored for predicting plankton biomass data obtained from traditional sampling techniques (see Knecht et al., 2023 for details). The observational data and the environmental layers used to train the biomass models were integrated over the epipelagic layer (0-200m) of the global ocean. Therefore, the predicted values are representative of the conditions prevailing in the epipelagic layer. The two NetCDF files thus record the: minimum (Min), maximum (Max), mean, median and the standard deviation (Stdev) of the monthly predictions issued from these five models.

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 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 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).

Ocean currents are a key driver of plankton dispersal across the oceanic basins. However, species specific temperature constraints may limit the plankton dispersal. We propose a methodology to estimate the connectivity pathways and timescales for plankton species with given constraints on temperature tolerances, by combining Lagrangian modeling with network theory. We demonstrate application of two types of temperature constraints: thermal niche and adaptation potential and compare it to the surface water connectivity between sample stations in the Atlantic Ocean. We find that non-constrained passive particles representative of a plankton species can connect all the stations within three years at the surface with pathways mostly along the major ocean currents. However, under thermal constraints, only a subset of stations can establish connectivity. Connectivity time increases marginally under these constraints, suggesting that plankton can keep within their favorable thermal conditions by advecting via slightly longer paths. Effect of advection depth on connectivity is observed to be sensitive to the width of the thermal constraints, along with decreasing flow speeds with depth and possible changes in pathways.