Global maps of the Ocean microbiome: Taxonomic and functional diversity of prokaryotes in the epipelagic layer (0-200 m), projected under contemporary (2005-2017) environmental conditions, based on the modelled habitat suitability of 1,210 marker gene-ba…

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.