a–c, Global ecological networks were built for the prokaryotic functions using the WGCNA methodology (see Methods) and correlated to classical oceanographic parameters as well as carbon export. a, Two bacterial functional subnetworks (n = 441 and n = 220, N = 37,832) are associated to carbon export (r = 0.54, P = 1 × 10⁻⁷ and r = 0.42, P = 1 × 10⁻⁴). b, The WGCNA approach directly links subnetworks to environmental parameters, that is, the more the features contribute to the subnetwork structure (topology), the more their abundance are correlated to the parameter. This measure allows to identify subnetworks for which the overall structure, summarized as the eigenvector of the subnetwork, is related to the carbon export. The bacterial function subnetwork structures correlate to carbon export (FNET1 r = 0.68, P = 3 × 10⁻⁶¹, and FNET2 r = 0.47, P = 6 × 10⁻¹³). c, Two functional subnetworks (light and dark green, FNET1 (n = 220) and FNET2 (n = 441), respectively) are significantly associated with carbon export (FNET1: r = 0.42, P = 4 × 10⁻⁹ and FNET2: r = 0.54, P = 7 × 10⁻⁶). The highest VIP score functions from top to bottom correspond to red dots from right to left. d, PLS regression was used to predict carbon export using abundances of functions (OGs) in selected subnetworks. LOOCV was performed and VIP scores computed for each function. Light green subnetwork (FNET1) functions predict carbon export with a R² of 0.41. Dark green subnetwork (FNET2) functions predict carbon export with a R² of 0.48. e, Cumulative abundance of genus-level taxonomic annotations of genes encoding functions from FNET1 and FNET2 subnetworks and bacterial function subnetworks predict carbon export. Genes contributing to the relative abundance of FNET1 and FNET2 subnetwork functions were taxonomically annotated by homology searches against a non-redundant gene reference database using a last common ancestor (LCA) approach (see Methods).