a–c, Global ecological networks were built using the WGCNA methodology (see Methods) and correlated to classical oceanographic parameters as well as carbon export (estimated at 150 m from particle size distribution and abundance). Each domain-specific global network is decomposed into smaller coherent subnetworks (depicted by distinct colours on the y axis) and their eigenvector is correlated to all environmental parameters. Similar to a correlation at the network scale, this approach directly links subnetworks to environmental parameters (that is, the more the taxa contribute to the subnetwork structure, the more their abundance is correlated to the parameter). a, A single eukaryotic subnetwork (n = 58, N = 1,870) is strongly associated to carbon export (r = 0.81, P = 5 × 10⁻¹⁵). b, A single prokaryotic subnetwork (n = 109, N = 1,527) is moderately associated to carbon export (r = 0.32, P = 9 × 10⁻³). c, A single viral subnetwork (n = 277, N = 5,476) is strongly associated to carbon export (r = 0.93, P = 2 × 10⁻¹⁵). d–f, 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. d, The eukaryotic subnetwork structure correlates to carbon export (r = 0.87, P = 5 × 10⁻¹⁶). e, The prokaryotic subnetwork structure correlates to carbon export (r = 0.47, P = 5 × 10⁻⁶). f, The viral population subnetwork structure correlates to carbon export (r = 0.88, P = 6 × 10⁻⁹³). g–i, Lineage subnetworks predict carbon export. PLS regression was used to predict carbon export using lineage abundances in selected subnetworks. LOOCV was performed and VIP scores computed for each lineage. g, The eukaryotic subnetwork predicts carbon export with a R² of 0.69. h, The prokaryotic subnetwork predicts carbon export with a R² of 0.60. i, The viral population subnetwork predicts carbon export with a R² of 0.89. j–l, Synechococcus (rather than Prochlorococcus) absolute cell counts correlate well to carbon export. j, Prochlorococcus cell counts estimated by flow cytometry do not correlate to carbon export (mean carbon flux at 150 m, r = −0.13, P = 0.27). k, Synechococcus cell counts estimated by flow cytometry correlate significantly to carbon export (r = 0.64, P = 4.0 × 10⁻¹⁰). l, Synechococcus / Prochlorococcus cell counts ratio correlates significantly to carbon export (r = 0.54, P = 4.0 × 10⁻⁷).