Global maps of the Ocean microbiome: Abundance of 17 groups of autotrophs in the surface mixed layer, available as monthly climatologies projected under contemporary (2005-2012) environmental conditions, using an ensemble of habitat suitability models.

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.