Nutrient supply regulates the activity of phytoplankton, but the global biogeography of nutrient limitation and co-limitation is poorly understood. Prochlorococcus adapt to local environments by gene gains and losses, and we used genomic changes as an indicator of adaptation to nutrient stress. We collected metagenomes from all major ocean regions as part of the Global Ocean Ship-based Hydrographic Investigations Program (Bio-GO-SHIP) and quantified shifts in genes involved in nitrogen, phosphorus, and iron assimilation. We found regional transitions in stress type and severity as well as widespread co-stress. Prochlorococcus stress genes, bottle experiments, and Earth system model predictions were correlated. We propose that the biogeography of multinutrient stress is stoichiometrically linked by controls on nitrogen fixation. Our omics-based description of phytoplankton resource use provides a nuanced and highly resolved description of nutrient stress in the global ocean.

Variation in ocean C:N:P of particulate organic matter (POM) has led to competing hypotheses for the underlying drivers. Each hypothesis predicts C:N:P equally well due to regional co-variance in environmental conditions and biodiversity. The Indian Ocean offers a unique positive temperature and nutrient supply relationship to test these hypotheses. Here we show how elemental concentrations and ratios vary over daily and regional scales. POM concentrations were lowest in the southern gyre, elevated across the equator, and peaked in the Bay of Bengal. Elemental ratios were highest in the gyre, but approached Redfield proportions northwards. As Prochlorococcus dominated the phytoplankton community, biodiversity changes could not explain the elemental variation. Instead, our data supports the nutrient supply hypothesis. Finally, gyre dissolved iron concentrations suggest extensive iron stress, leading to depressed ratios compared to other gyres. We propose a model whereby differences in iron supply and N2-fixation influence C:N:P levels across ocean gyres.

Linking ‘omics measurements with biogeochemical cycles is a widespread challenge in microbial community ecology. Here, we propose applying genomic adaptation as ‘biosensors’ for microbial investments to overcome nutrient stress. We then integrate this genomic information with a trait-based model to predict regional shifts in the elemental composition of marine plankton communities. We evaluated this approach using metagenomic and particulate organic matter samples from the Atlantic, Indian and Pacific Oceans. We find that our genome-based trait model significantly improves our prediction of particulate C : P (carbon : phosphorus) across ocean regions. Furthermore, we detect previously unrecognized ocean areas of iron, nitrogen and phosphorus stress. In many ecosystems, it can be very challenging to quantify microbial stress. Thus, a carefully calibrated genomic approach could become a widespread tool for understanding microbial responses to environmental changes and the biogeochemical outcomes. This article is part of the theme issue ‘Conceptual challenges in microbial community ecology’.

While the majority of studies use the environment to describe microbial populations, the high diversity of microbes can conversely be used as a resource to understand subtle environmental variability. Here, we used a high‐resolution spatial and temporal analysis of Prochlorococcus sp. in the Eastern Indian Ocean to determine whether ecotypes and microdiverse taxa can be used to identify fine‐scale biogeochemical regimes in this under‐studied region. A total of 246 DNA samples were collected every 4–6 h in April 2016 on GO‐SHIP cruise I09N, which transected gyre, equatorial, and monsoonal ecosystems between Western Australia and the Bay of Bengal. Using amplicon sequencing of the highly variable rpoC1 marker, we found that the region was largely dominated by the Prochlorococcus HL‐II clade. Conserved single nucleotide polymorphisms (SNPs) were used to identify four microdiverse haplotypes, or SNP‐delineated taxa, within the HL‐II clade of Prochlorococcus. The haplotypes showed regional patterns of relative gene count abundance that were significantly correlated with environmental conditions. Additionally, we used nonlinear least squares models to fit the sine wave function to our data and demonstrate that the haplotypes show distinct patterns in relative diel frequency, providing evidence that these microdiverse populations are ecologically and evolutionarily distinct. Overall, we show how the integration of a genomics data set into a biogeochemical framework can reveal a more nuanced understanding of a complex ocean basin.

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