Phytoplankton variable stoichiometry modifies key biogeochemical fluxes and the functioning of the ocean biological pump

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The growth of marine phytoplankton is often limited by one of several key nutrients (nitrogen, phosphorus, iron, silicon), and the efficiency of carbon export is constrained by nutrient availability, and the nutrient/carbon ratios in the biota (stoichiometry). Recent field observations suggest widespread variability in phytoplankton stoichiometry (C/N/P/Fe/Si). We show that accounting for phytoplankton dynamic stoichiometry dramatically shifts the magnitude and spatial patterns of carbon export by the biological pump, relative to a model with fixed ratios. Not accounting for dynamic stoichiometry also leads to increases in atmospheric CO 2 , thereby underestimating the ocean carbon inventory. Thus, Earth System Models (ESMs) must account for dynamic plankton stoichiometry to make accurate projections of the carbon cycle and climate. Further research is needed to better constrain environmental controls on the stoichiometry of exported organic matter, particularly ecosystem-level processing of organic matter initially produced by the phytoplankton. Earth and environmental sciences/Ocean sciences Earth and environmental sciences/Biogeochemistry/Carbon cycle Earth and environmental sciences/Biogeochemistry/Element cycles Earth and environmental sciences/Ocean sciences/Marine biology Figures Figure 1 Figure 2 Figure 3 Introduction The biological pump plays a key role in the global carbon cycle, driving ocean uptake of atmospheric carbon dioxide (CO 2 ) 1 . Phytoplankton take up CO 2 and nutrients, converting them to biomass via photosynthesis. Some of this fixed carbon is exported to the ocean interior, lowering surface concentrations, and modifying air-sea exchange. The efficiency of carbon export is dependent on the surface flux, the depth of remineralization, and the time for the carbon to be transported back to the surface via circulation 2 – 4 . These processes are sensitive to climate change, but the direction and magnitude of the sensitivity is poorly understood. For example, increases in sea surface temperatures lead to increased stratification in the surface ocean, which can reduce nutrient supply and therefore reduce phytoplankton productivity, but warming also potentially increases the rates of metabolic processes including phytoplankton growth 5 – 7 . There are many compounding interactions associated with such processes, tied to changes in temperature and nutrient availability, and the global impacts of these changes on the biological pump on longer timescales are poorly constrained. Ocean uptake of CO 2 will strongly impact climate on multi-century timescales 4 , 7 , 8 . Phytoplankton growth is primarily limited by nutrient availability. The ratio of carbon to nutrients in exported organic matter was used in models to simplify biogeochemical cycles, where a fixed, extended Redfield ratio was used to link the cycling of carbon and key growth-limiting nutrients, including nitrogen, phosphorus, iron, and silicon (C:N:P:Fe:Si). However, it is now widely accepted that phytoplankton elemental ratios are not fixed, modulating the efficacy of the biological export of carbon with respect to limiting nutrients 9 – 13 . Field measurements of particulate organic matter (POM) find elevated C:N and C:P ratios in the oligotrophic gyres where nutrients are low 14 . C:P ratios can vary by more than a factor of two, with the lowest values observed in the Southern Ocean (significantly below Redfield C:P < 80 mol/mol), while the highest values are found in the western North Atlantic, exceeding 200 mol/mol 14 . C:N variability is much smaller, with low values of ~ 5 mol/mol in the Southern Ocean, and highest values in the Indian Ocean (> 8 mol/mol) 14 . One study of variable C:N in a steady state ocean found using a fixed C:N underestimated total dissolved inorganic carbon inventory and ocean pCO 2 uptake, while using a variable C:N parameterization in a prognostic climate scenario yielded greater anthropogenic CO 2 uptake 15 . Another study with variable C:N:P found fixed stoichiometry underestimated total 21st century ocean carbon uptake by 0.5–3.5%, and that picophytoplankton stocks did not decline as much as other phytoplankton, due to their greater stoichiometric flexibility, highlighting the importance of variable stoichiometry for predicting changes in marine biodiversity 16 . Other studies utilizing variable phytoplankton C:N:P ratios found that including dynamic ocean biology reduces the sensitivity of biogeochemical cycling to changes in ocean physics 17 – 18 . Kwon et al. showed that CMIP6 models that included flexible C:N:P:Fe ratios projected small increases in net primary productivity (NPP) compared to fixed ratio models which showed decreases 19 . Another study focused on iron cycling in the Pacific found strong links between iron limitation and net primary productivity, and highlighted the uncertainty that Fe:C stoichiometry plays in this response 20 . While previous studies focused on aspects of C:N:P:Fe:Si stoichiometry, none have explicitly investigated the impacts of fully variable versus fully fixed stoichiometry on global marine biogeochemical fluxes. We hypothesize that variable stoichiometry will increase productivity and export in many regions, as phytoplankton acclimate to low nutrient availability by reducing their cellular quotas. Using fixed nutrient:carbon ratios also means that more nutrients will be exported per unit carbon in areas with high nutrient supply, potentially decreasing lateral nutrient transport to adjacent regions. Thus, there are downstream effects and complex nutrient interactions, leading to uncertainty about the effects of varying stoichiometry. Earth system models are valuable tools for investigating the impacts of the these complex, interacting processes. The Community Earth System Model (CESM) includes an ocean ecosystem with three explicit phytoplankton groups: diatoms, pico-nanophytoplankton (a fraction of which acts as an implicit calcifier group, referred to collectively as “small phytoplankton” hereafter), and diazotrophs, with variable nutrient quotas for phosphorus, iron, and silicon 21 – 23 . The model was recently updated to include greater variability in phytoplankton iron quotas, with an improved match to observations 21 . Here, we integrated a variable nitrogen quota, allowing for dynamic computation of phytoplankton C:N:P:Fe:Si ratios, as phytoplankton acclimate to changing ambient nutrient concentrations. For each nutrient, the cellular quota (nutrient:carbon ratio) for new growth is a function of ambient nutrient concentration, with progressive reductions in cellular quotas at lower ambient nutrient concentrations (Methods, SFig.1). Results The modified nutrient quota model can replicate the observed spatial patterns in particulate organic matter stoichiometry. We compare the model particulate flux at 100m to the surface POM C:N:P stoichiometry from the GO-POPCORNv2 database for each observational location where the model grid cell is determined using a least squares calculation 24 . We note the observational bulk POM contains heterotrophic bacteria and non-sinking detritus that are not in our model. The model broadly captures the latitudinal variations in organic matter stoichiometry along multiple cruise transects (Fig. 1 , SFigs. 2–5). The largest mismatches occur where there are biases in the simulated nutrients. The Spearman's rank correlation coefficient (r s ) for C:N, N:P, and C:P are 0.34, 0.28, and 0.31, respectively (SFig. 6a-c). Model phytoplankton Fe:C ratios are compared to field observations of individual cell iron to carbon 21 . The r s for the phytoplankton community Fe:C is 0.28 (SFig. 6d). Inverse models diagnose elevated C:P ratios (> 140) and N:P ratios (> 20) in the subtropical gyres, and much lower ratios of C:P (< 100) and N:P ( 0.3 µM) 22 , 25 . Combining the regional mean C:P and N:P in export estimated by these two inverse studies gives a mean C:N of 8.5 for the North Pacific gyre, 6.7 for the South Pacific gyre, and 6.0 for the Southern Ocean 25 , 26 . Our model captures these large-scale patterns in C:N:P ratios of the sinking export flux (SFig. 6). The inferred global patterns are also broadly in agreement with the stoichiometry of surface POM in the POPCORN database. (Fig. 1 , SFigs. 2–5,7) 24 . Limited observations make evaluating the Si:C ratios more difficult. The model captures the observed patterns of elevated Si:C in iron-limited regions with elevated surface dSi concentrations, and the low Si:C seen under low Si conditions both in situ and in laboratory studies 27 – 29 . Thus, our simple approach, dynamically linking phytoplankton stoichiometry to ambient nutrient concentrations, captures observed global-scale patterns in the stoichiometry of exported organic matter. We compare a variable C:N:P:Fe:Si model simulation (VarAll) with a fixed-ratio model version (FixAll) to investigate how dynamic plankton stoichiometry influences marine biogeochemistry, in terms of the magnitude and spatial patterns of net primary production, sinking carbon export at 100m depth, air-sea CO 2 flux, nitrogen fixation, and water column denitrification. Both models are able to replicate observations of surface nutrients, with a better fit for the VarAll simulation. Compared to World Ocean Atlas 2018, the r s for VarAll and surface NO 3 , PO 4 , and SiO 3 are 0.91, 0.90, and 0.56, respectively. For FixAll, these values are 0.76, 0.91, and 0.32 respectively 30 . For dissolved iron, we compare primarily to data collected from the GEOTRACES project, supplemented with historical data compilations 31 – 33 . The r s for dissolved iron in the top 200m is 0.39 for the VarAll model and 0.31 for the FixAll model. The models have similar net primary production (NPP), but the FixAll simulation significantly underestimates particulate organic carbon (POC) export and key nitrogen cycle fluxes (N fixation and water-column (WC) denitrification) compared to the fully variable simulation (Figs. 2 A, 2 B, 3 ). The VarAll model has a total integrated NPP of 58 PgC/yr, POC Export at 100m of 8.1 PgC/yr, N fixation of 214 TgN/yr, and WC Denitrification of 57 TgN/yr, which are close to or within the range of previous satellite and model-based estimates (NPP = 52–67 PgC/yr, POC Export = 5–10 PgC/yr, N Fixation = 126–223 TgN/yr, WC Denitrification = 56–73 TgN/yr) 26,34–38 . With fixed stoichiometry, NPP, POC Export, N Fixation, and WC denitrification decrease by 14%, 11%, 29%, and 39%, respectively (FixAll global fluxes: 50 PgC/yr, 7.2 PgC/yr, 153 TgN/yr, 35 TgN/yr). The global export ratio (e-ratio, sinking POC/NPP) distribution in the VarAll model (SFig. 8) shows higher e-ratios in the high latitudes, > 0.175 in the Southern Ocean, and > 0.25 in the North Atlantic and North Pacific. When compared to the FixAll model, we see up to 0.05 increase in e-ratio in the high latitude North Atlantic, compared to up to a 0.05 decrease in the Southern Ocean, both of which are primarily driven by changes in POC export (Fig. 2 C). In the Indian Ocean, which has increases in NPP and POC export throughout the basin, the e-ratio change shows a bimodal pattern, with the Arabian Sea and gyre regions showing increases in e-ratio, while the Bay of Bengal region e-ratio decreases. The lower POC export in the FixAll model is driven by lower surface nutrient concentrations, but is further enhanced by a community dominated by small phytoplankton in the more oligotrophic regions (SFig. 9) 39 . When phytoplankton nutrient uptake ratios are fixed, growth-limiting nutrients are exported more efficiently, furthering nutrient stress in surface waters, favoring smaller phytoplankton and leading to decreases in NPP and POC export. Further, with variable stoichiometry, small phytoplankton may out-compete diatoms within the HNLC regions, resulting in increased small phytoplankton biomass and reduced diatom biomass. This leads to a decrease in Southern Ocean POC export without significant changes in NPP. Additionally, diatoms require silicon and are highly sensitive to silicon availability, which causes them to have significant reductions in biomass in low Si areas in the FixAll case. The only regions where diatoms have higher biomass in the FixAll case are upwelling regions where Si is returned to the surface. When diatoms can vary their Si uptake reducing their quotas, their distribution expands to occupy low-Si regions. This suggests that diatom Si:C acclimation is critical for explaining the global distribution of diatoms, preventing Si-limitation of growth over much of the lower latitudes. Nitrogen fixation and water column denitrification increase under variable stoichiometry (Fig. 3 ). Nitrogen fixation increases globally, up nearly 300%, but particularly in the oligotrophic gyres where diazotrophs can reduce their quotas and maintain their growth (Fig. 3 ). With fixed ratios, surface phosphate declines in the North Atlantic and Indian basins and surface dissolved iron declines in the Pacific, increasing P and Fe stress for diazotrophs. Nitrogen fixation rates in the North Atlantic are particularly dependent on phosphate 40 . WC denitrification decreases 49% with fixed stoichiometry, with little change in the spatial pattern, due to decreases in diatom production and export over the Bay of Bengal and in the eastern equatorial Pacific. The volume of low oxygen (< 30 mmol/m 3 ) waters decreases 42% with fixed stoichiometry. Both models were run for 300 years with dynamic atmospheric CO 2, initiated at 284ppm. Averaged over the final 20 years, the FixAll scenario had 313ppm atmospheric CO 2 concentration, while the VarAll scenario had 296ppm atmospheric CO 2 . This indicates that the FixAll scenario is underestimating the capacity of the ocean carbon inventory when compared to the simulation with fully variable stoichiometry. Discussion The phytoplankton stoichiometry model captures large-scale observed elemental stoichiometries. It is built on the frugal phytoplankton concept, whereby phytoplankton acclimate to increasing nutrient stress by decreasing the cellular quota of the nutrient 41 . Applying this concept to all the growth-limiting nutrients, with prescribed minimum and maximum nutrient:carbon ratios, allows for dynamic phytoplankton stoichiometry tied to available nutrients, that captures observed global patterns. The acclimation to low nutrient conditions (reducing quotas) strongly impacts marine biogeochemical cycles, increasing the strength and modifying the spatial patterns of carbon export by the biological pump. The coupling of biogeochemical cycles is strongly influenced by variable phytoplankton stoichiometry. Although it is possible to reproduce surface nutrient and net primary productivity distributions with both models, the VarAll model utilizes additional observational constraints of POM and individual phytoplankton cell stoichiometric variability, not captured by the FixAll model. This highlights the importance of using multiple observational constraints to better capture complex interactions between the biogeochemical cycles. The phytoplankton nutrient quotas are lower than the elemental ratios in the exported organic matter diagnosed with inverse models. This suggests substantial ecosystem processing of organic matter produced by phytoplankton prior to export. In our model this is achieved by assuming zooplankton mortality is exported with a modified Redfield ratio, thus, both zooplankton and phytoplankton stoichiometry influences the elemental export ratios. Further research is needed on the stoichiometry of the biota and the processes contributing to ecosystem processing of phytoplankton biomass. For Earth System models to project ocean carbon cycling with climate change, they must account for variable phytoplankton stoichiometry. Fixed ratio models will overestimate the declines in NPP and export with increasing stratification, with biases in the spatial patterns of carbon export. The fixed ratio models may also introduce significant biases in the ocean CO 2 inventory, with the ocean taking up less CO 2 . Integrated nitrogen fixation and water column denitrification are also significantly lower in the FixAll model, which could lead to biases in bioavailable nitrogen, the primary limiting nutrient over much of the global ocean, also impacting the carbon cycle. These results stress the need for Earth System Models to capture the complexities of the cycling of nitrogen, phosphorus, iron, and silicon in the oceans, as they all influence and modify the strength and spatial patterns of the biological pump. Methods We used a modified version of CESM v1.2, which includes biogeochemical modifications that were introduced in CESM v2, including an explicit ligand, iron model 21 – 23 . The model includes three explicit phytoplankton groups (pico-nanophytoplankton, diatoms, and diazotrophs), one implicit group (coccolithophores, which are a variable fraction of the small phytoplankton group), and one zooplankton group 21 – 23 . The model was initiated at preindustrial CO 2 levels (284ppm) for 300 years using COREv2 interannual forcing, and we analyzed the output averaged over the last 20 years. Atmospheric CO 2 was allowed to respond to air-sea CO 2 fluxes. This version of the model includes new equations and parameterization for phytoplankton nutrient quotas. For each of the potentially growth-limiting nutrients, the stoichiometry for new phytoplankton growth (nutrient:carbon ratio) is a linear function of the ambient nutrient concentration, with prescribed minimum and maximum values (SFig. 1). This allows stoichiometry to dynamically adjust to changing environmental nutrient availability. Phytoplankton N:C ratios for new growth (gQn) are a function of dissolved inorganic nitrogen (DIN), which includes both nitrate and ammonia (Eq. 1). \(\:gQn=\left\{\begin{array}{cc}{gQn}_{max}&\:whereDIN>NOpt\\\:max\left({gQn}_{max}\times\:\frac{DIN}{NOpt},{gQn}_{min}\right)&\:whereDIN<NOpt\end{array}\right.\) (Eq. 1) There are some modifications to the P:C formulation for new growth (gQp) in order to maintain appropriate N:P values as N:C ratios acclimate (Eqs. 2–3). \(\:gQp=\left\{\begin{array}{cc}{gQp}_{max}&\:whereDIP>POpt\\\:max\left({gQp}_{max}\times\:\frac{DIP}{POpt},{gQp}_{min}\right)&\:whereDIP<POpt\\\:max\left({gQp}_{max}\times\:\frac{DIN}{NOpt},{gQp}_{min}\times\:\frac{{gQn}_{min}}{{gQn}_{max}}\right)&\:whereDINFeOpt\\\:max\left({gQfe}_{max}\times\:\frac{dFe}{FeOpt},{gQfe}_{min}\right)&\:wheredFe<FeOpt\end{array}\right.\) (Eq. 3) There are also modifications to the Si:C formulation in order to allow for diatoms to increase the Si:C uptake ratio when dFe is low (Eq. 4). \(\:gQsi=\left\{\begin{array}{cc}\left\{\begin{array}{cc}{gQsi}_{max}&\:wheredFe=0\\\:min\left(0.133\times\:\frac{FeOpt}{dFe},{gQsi}_{min}\right)&\:where0<dFeFeOpt\end{array}\right.&\:where{Si\left(OH\right)}_{4}>SiOpt\\\:max\left(0.133\times\:\frac{{Si\left(OH\right)}_{4}}{SiOpt},{gQsi}_{min}\right)&\:where{Si\left(OH\right)}_{4}>SiOpt\end{array}\right.\) (Eq. 4) Observations of particulate organic matter (POM) are from the GO-POPCORNv2 database 24 . 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4602062","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Physical Sciences - Article","associatedPublications":[],"authors":[{"id":322349665,"identity":"cfcb6951-8357-48de-8369-260ecbad5656","order_by":0,"name":"Nicola Wiseman","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0001-9296-7566","institution":"University of Bristol","correspondingAuthor":true,"prefix":"","firstName":"Nicola","middleName":"","lastName":"Wiseman","suffix":""},{"id":322349666,"identity":"eeaee00d-1caf-46e0-ab57-8fbb3f21ba76","order_by":1,"name":"Jefferson Keith Moore","email":"","orcid":"https://orcid.org/0000-0001-8188-6779","institution":"University of California, Irvine","correspondingAuthor":false,"prefix":"","firstName":"Jefferson","middleName":"Keith","lastName":"Moore","suffix":""},{"id":322349667,"identity":"c407c070-98b1-418e-b818-7ae5911e39d4","order_by":2,"name":"Adam Martiny","email":"","orcid":"https://orcid.org/0000-0003-2829-4314","institution":"University of California, Irvine","correspondingAuthor":false,"prefix":"","firstName":"Adam","middleName":"","lastName":"Martiny","suffix":""},{"id":322349668,"identity":"a7ca7230-d9d1-407a-a49b-b8b836c50eda","order_by":3,"name":"Robert Letscher","email":"","orcid":"https://orcid.org/0000-0002-3768-9003","institution":"University of New Hampshire","correspondingAuthor":false,"prefix":"","firstName":"Robert","middleName":"","lastName":"Letscher","suffix":""}],"badges":[],"createdAt":"2024-06-18 21:20:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4602062/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4602062/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":60127289,"identity":"b75accb8-3334-497c-80d6-32d75242fa66","added_by":"auto","created_at":"2024-07-12 06:15:49","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":106499,"visible":true,"origin":"","legend":"\u003cp\u003eObserved and simulated phytoplankton and organic matter stoichiometry (A-C) and surface nutrients (D-F) for GO-SHIP cruise transect P18 in the eastern Pacific. Observed particulate organic matter stoichiometry from GO-POPCORNv2 is shown as individual blue dots and smoothed as the blue line\u003csup\u003e24\u003c/sup\u003e. Simulated phytoplankton surface community and export at 100m stoichiometries are shown as the dotted and solid yellow lines respectively (A-C). Observed surface nutrients from GO-POPCORNv2 are also shown for dissolved nitrate and phosphate as blue dots, while model surface nitrate, iron, and phosphate are shown as yellow lines (D-F).\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4602062/v1/0f50f4e3621ab99345a9908b.png"},{"id":60127694,"identity":"ba9668de-5482-4207-873a-1ca523df675e","added_by":"auto","created_at":"2024-07-12 06:23:49","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":636661,"visible":true,"origin":"","legend":"\u003cp\u003eThe differences between annual A) net primary production, B) sinking particulate organic carbon export, and C) e-ratio comparing the VarAll - FixAll simulations. Net primary production (A) and POC export (B) changes are shown as percent change, while e-ratio (C) change is shown as absolute change in value.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4602062/v1/7b5711106881fc10ca5a6e6a.png"},{"id":60127290,"identity":"bfc9afd7-091c-42a2-a113-48da9ca7f962","added_by":"auto","created_at":"2024-07-12 06:15:49","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":149973,"visible":true,"origin":"","legend":"\u003cp\u003eDifferences in annual nitrogen fixation comparing the VarAll and FixAll simulations. Red indicates increased nitrogen fixation when phytoplankton can vary their resource acquisition.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4602062/v1/f2d405cadc11cfbb0f6d0da3.png"},{"id":60128299,"identity":"1a899132-f3bd-4b93-b0a6-158f405d610a","added_by":"auto","created_at":"2024-07-12 06:31:51","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1142712,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4602062/v1/45734724-ec36-4e76-ae44-31af40542284.pdf"},{"id":60127292,"identity":"aa28d4ca-910f-41d2-ae0b-80948c81565b","added_by":"auto","created_at":"2024-07-12 06:15:50","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1048810,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryInformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-4602062/v1/2362f5595b1970b8f4153496.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Phytoplankton variable stoichiometry modifies key biogeochemical fluxes and the functioning of the ocean biological pump","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe biological pump plays a key role in the global carbon cycle, driving ocean uptake of atmospheric carbon dioxide (CO\u003csub\u003e2\u003c/sub\u003e)\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Phytoplankton take up CO\u003csub\u003e2\u003c/sub\u003e and nutrients, converting them to biomass via photosynthesis. Some of this fixed carbon is exported to the ocean interior, lowering surface concentrations, and modifying air-sea exchange. The efficiency of carbon export is dependent on the surface flux, the depth of remineralization, and the time for the carbon to be transported back to the surface via circulation\u003csup\u003e\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. These processes are sensitive to climate change, but the direction and magnitude of the sensitivity is poorly understood. For example, increases in sea surface temperatures lead to increased stratification in the surface ocean, which can reduce nutrient supply and therefore reduce phytoplankton productivity, but warming also potentially increases the rates of metabolic processes including phytoplankton growth\u003csup\u003e\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. There are many compounding interactions associated with such processes, tied to changes in temperature and nutrient availability, and the global impacts of these changes on the biological pump on longer timescales are poorly constrained. Ocean uptake of CO\u003csub\u003e2\u003c/sub\u003e will strongly impact climate on multi-century timescales\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003ePhytoplankton growth is primarily limited by nutrient availability. The ratio of carbon to nutrients in exported organic matter was used in models to simplify biogeochemical cycles, where a fixed, extended Redfield ratio was used to link the cycling of carbon and key growth-limiting nutrients, including nitrogen, phosphorus, iron, and silicon (C:N:P:Fe:Si). However, it is now widely accepted that phytoplankton elemental ratios are not fixed, modulating the efficacy of the biological export of carbon with respect to limiting nutrients\u003csup\u003e\u003cspan additionalcitationids=\"CR10 CR11 CR12\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Field measurements of particulate organic matter (POM) find elevated C:N and C:P ratios in the oligotrophic gyres where nutrients are low\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. C:P ratios can vary by more than a factor of two, with the lowest values observed in the Southern Ocean (significantly below Redfield C:P\u0026thinsp;\u0026lt;\u0026thinsp;80 mol/mol), while the highest values are found in the western North Atlantic, exceeding 200 mol/mol\u003csup\u003e14\u003c/sup\u003e. C:N variability is much smaller, with low values of ~\u0026thinsp;5 mol/mol in the Southern Ocean, and highest values in the Indian Ocean (\u0026gt;\u0026thinsp;8 mol/mol)\u003csup\u003e14\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eOne study of variable C:N in a steady state ocean found using a fixed C:N underestimated total dissolved inorganic carbon inventory and ocean pCO\u003csub\u003e2\u003c/sub\u003e uptake, while using a variable C:N parameterization in a prognostic climate scenario yielded greater anthropogenic CO\u003csub\u003e2\u003c/sub\u003e uptake\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Another study with variable C:N:P found fixed stoichiometry underestimated total 21st century ocean carbon uptake by 0.5\u0026ndash;3.5%, and that picophytoplankton stocks did not decline as much as other phytoplankton, due to their greater stoichiometric flexibility, highlighting the importance of variable stoichiometry for predicting changes in marine biodiversity\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Other studies utilizing variable phytoplankton C:N:P ratios found that including dynamic ocean biology reduces the sensitivity of biogeochemical cycling to changes in ocean physics\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Kwon et al. showed that CMIP6 models that included flexible C:N:P:Fe ratios projected small increases in net primary productivity (NPP) compared to fixed ratio models which showed decreases\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Another study focused on iron cycling in the Pacific found strong links between iron limitation and net primary productivity, and highlighted the uncertainty that Fe:C stoichiometry plays in this response\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWhile previous studies focused on aspects of C:N:P:Fe:Si stoichiometry, none have explicitly investigated the impacts of fully variable versus fully fixed stoichiometry on global marine biogeochemical fluxes. We hypothesize that variable stoichiometry will increase productivity and export in many regions, as phytoplankton acclimate to low nutrient availability by reducing their cellular quotas. Using fixed nutrient:carbon ratios also means that more nutrients will be exported per unit carbon in areas with high nutrient supply, potentially decreasing lateral nutrient transport to adjacent regions. Thus, there are downstream effects and complex nutrient interactions, leading to uncertainty about the effects of varying stoichiometry. Earth system models are valuable tools for investigating the impacts of the these complex, interacting processes.\u003c/p\u003e \u003cp\u003eThe Community Earth System Model (CESM) includes an ocean ecosystem with three explicit phytoplankton groups: diatoms, pico-nanophytoplankton (a fraction of which acts as an implicit calcifier group, referred to collectively as \u0026ldquo;small phytoplankton\u0026rdquo; hereafter), and diazotrophs, with variable nutrient quotas for phosphorus, iron, and silicon\u003csup\u003e\u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. The model was recently updated to include greater variability in phytoplankton iron quotas, with an improved match to observations\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. Here, we integrated a variable nitrogen quota, allowing for dynamic computation of phytoplankton C:N:P:Fe:Si ratios, as phytoplankton acclimate to changing ambient nutrient concentrations. For each nutrient, the cellular quota (nutrient:carbon ratio) for new growth is a function of ambient nutrient concentration, with progressive reductions in cellular quotas at lower ambient nutrient concentrations (Methods, SFig.1).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThe modified nutrient quota model can replicate the observed spatial patterns in particulate organic matter stoichiometry. We compare the model particulate flux at 100m to the surface POM C:N:P stoichiometry from the GO-POPCORNv2 database for each observational location where the model grid cell is determined using a least squares calculation\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. We note the observational bulk POM contains heterotrophic bacteria and non-sinking detritus that are not in our model. The model broadly captures the latitudinal variations in organic matter stoichiometry along multiple cruise transects (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, SFigs. 2\u0026ndash;5). The largest mismatches occur where there are biases in the simulated nutrients. The Spearman's rank correlation coefficient (r\u003csub\u003es\u003c/sub\u003e) for C:N, N:P, and C:P are 0.34, 0.28, and 0.31, respectively (SFig. 6a-c). Model phytoplankton Fe:C ratios are compared to field observations of individual cell iron to carbon\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. The r\u003csub\u003es\u003c/sub\u003e for the phytoplankton community Fe:C is 0.28 (SFig. 6d).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eInverse models diagnose elevated C:P ratios (\u0026gt;\u0026thinsp;140) and N:P ratios (\u0026gt;\u0026thinsp;20) in the subtropical gyres, and much lower ratios of C:P (\u0026lt;\u0026thinsp;100) and N:P (\u0026lt;\u0026thinsp;16) in regions with elevated surface phosphate concentrations (\u0026gt;\u0026thinsp;0.3 \u0026micro;M)\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Combining the regional mean C:P and N:P in export estimated by these two inverse studies gives a mean C:N of 8.5 for the North Pacific gyre, 6.7 for the South Pacific gyre, and 6.0 for the Southern Ocean\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Our model captures these large-scale patterns in C:N:P ratios of the sinking export flux (SFig. 6). The inferred global patterns are also broadly in agreement with the stoichiometry of surface POM in the POPCORN database. (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, SFigs. 2\u0026ndash;5,7)\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Limited observations make evaluating the Si:C ratios more difficult. The model captures the observed patterns of elevated Si:C in iron-limited regions with elevated surface dSi concentrations, and the low Si:C seen under low Si conditions both \u003cem\u003ein situ\u003c/em\u003e and in laboratory studies\u003csup\u003e\u003cspan additionalcitationids=\"CR28\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. Thus, our simple approach, dynamically linking phytoplankton stoichiometry to ambient nutrient concentrations, captures observed global-scale patterns in the stoichiometry of exported organic matter.\u003c/p\u003e \u003cp\u003eWe compare a variable C:N:P:Fe:Si model simulation (VarAll) with a fixed-ratio model version (FixAll) to investigate how dynamic plankton stoichiometry influences marine biogeochemistry, in terms of the magnitude and spatial patterns of net primary production, sinking carbon export at 100m depth, air-sea CO\u003csub\u003e2\u003c/sub\u003e flux, nitrogen fixation, and water column denitrification. Both models are able to replicate observations of surface nutrients, with a better fit for the VarAll simulation. Compared to World Ocean Atlas 2018, the r\u003csub\u003es\u003c/sub\u003e for VarAll and surface NO\u003csub\u003e3\u003c/sub\u003e, PO\u003csub\u003e4\u003c/sub\u003e, and SiO\u003csub\u003e3\u003c/sub\u003e are 0.91, 0.90, and 0.56, respectively. For FixAll, these values are 0.76, 0.91, and 0.32 respectively\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. For dissolved iron, we compare primarily to data collected from the GEOTRACES project, supplemented with historical data compilations\u003csup\u003e\u003cspan additionalcitationids=\"CR32\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. The r\u003csub\u003es\u003c/sub\u003e for dissolved iron in the top 200m is 0.39 for the VarAll model and 0.31 for the FixAll model.\u003c/p\u003e \u003cp\u003eThe models have similar net primary production (NPP), but the FixAll simulation significantly underestimates particulate organic carbon (POC) export and key nitrogen cycle fluxes (N fixation and water-column (WC) denitrification) compared to the fully variable simulation (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA, \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB, \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The VarAll model has a total integrated NPP of 58 PgC/yr, POC Export at 100m of 8.1 PgC/yr, N fixation of 214 TgN/yr, and WC Denitrification of 57 TgN/yr, which are close to or within the range of previous satellite and model-based estimates (NPP\u0026thinsp;=\u0026thinsp;52\u0026ndash;67 PgC/yr, POC Export\u0026thinsp;=\u0026thinsp;5\u0026ndash;10 PgC/yr, N Fixation\u0026thinsp;=\u0026thinsp;126\u0026ndash;223 TgN/yr, WC Denitrification\u0026thinsp;=\u0026thinsp;56\u0026ndash;73 TgN/yr)\u003csup\u003e26,34\u0026ndash;38\u003c/sup\u003e. With fixed stoichiometry, NPP, POC Export, N Fixation, and WC denitrification decrease by 14%, 11%, 29%, and 39%, respectively (FixAll global fluxes: 50 PgC/yr, 7.2 PgC/yr, 153 TgN/yr, 35 TgN/yr).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe global export ratio (e-ratio, sinking POC/NPP) distribution in the VarAll model (SFig. 8) shows higher e-ratios in the high latitudes, \u0026gt;\u0026thinsp;0.175 in the Southern Ocean, and \u0026gt;\u0026thinsp;0.25 in the North Atlantic and North Pacific. When compared to the FixAll model, we see up to 0.05 increase in e-ratio in the high latitude North Atlantic, compared to up to a 0.05 decrease in the Southern Ocean, both of which are primarily driven by changes in POC export (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). In the Indian Ocean, which has increases in NPP and POC export throughout the basin, the e-ratio change shows a bimodal pattern, with the Arabian Sea and gyre regions showing increases in e-ratio, while the Bay of Bengal region e-ratio decreases.\u003c/p\u003e \u003cp\u003eThe lower POC export in the FixAll model is driven by lower surface nutrient concentrations, but is further enhanced by a community dominated by small phytoplankton in the more oligotrophic regions (SFig. 9)\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. When phytoplankton nutrient uptake ratios are fixed, growth-limiting nutrients are exported more efficiently, furthering nutrient stress in surface waters, favoring smaller phytoplankton and leading to decreases in NPP and POC export. Further, with variable stoichiometry, small phytoplankton may out-compete diatoms within the HNLC regions, resulting in increased small phytoplankton biomass and reduced diatom biomass. This leads to a decrease in Southern Ocean POC export without significant changes in NPP. Additionally, diatoms require silicon and are highly sensitive to silicon availability, which causes them to have significant reductions in biomass in low Si areas in the FixAll case. The only regions where diatoms have higher biomass in the FixAll case are upwelling regions where Si is returned to the surface. When diatoms can vary their Si uptake reducing their quotas, their distribution expands to occupy low-Si regions. This suggests that diatom Si:C acclimation is critical for explaining the global distribution of diatoms, preventing Si-limitation of growth over much of the lower latitudes.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eNitrogen fixation and water column denitrification increase under variable stoichiometry (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Nitrogen fixation increases globally, up nearly 300%, but particularly in the oligotrophic gyres where diazotrophs can reduce their quotas and maintain their growth (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). With fixed ratios, surface phosphate declines in the North Atlantic and Indian basins and surface dissolved iron declines in the Pacific, increasing P and Fe stress for diazotrophs. Nitrogen fixation rates in the North Atlantic are particularly dependent on phosphate\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. WC denitrification decreases 49% with fixed stoichiometry, with little change in the spatial pattern, due to decreases in diatom production and export over the Bay of Bengal and in the eastern equatorial Pacific. The volume of low oxygen (\u0026lt;\u0026thinsp;30 mmol/m\u003csup\u003e3\u003c/sup\u003e) waters decreases 42% with fixed stoichiometry.\u003c/p\u003e \u003cp\u003eBoth models were run for 300 years with dynamic atmospheric CO\u003csub\u003e2,\u003c/sub\u003e initiated at 284ppm. Averaged over the final 20 years, the FixAll scenario had 313ppm atmospheric CO\u003csub\u003e2\u003c/sub\u003e concentration, while the VarAll scenario had 296ppm atmospheric CO\u003csub\u003e2\u003c/sub\u003e. This indicates that the FixAll scenario is underestimating the capacity of the ocean carbon inventory when compared to the simulation with fully variable stoichiometry.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe phytoplankton stoichiometry model captures large-scale observed elemental stoichiometries. It is built on the frugal phytoplankton concept, whereby phytoplankton acclimate to increasing nutrient stress by decreasing the cellular quota of the nutrient\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. Applying this concept to all the growth-limiting nutrients, with prescribed minimum and maximum nutrient:carbon ratios, allows for dynamic phytoplankton stoichiometry tied to available nutrients, that captures observed global patterns. The acclimation to low nutrient conditions (reducing quotas) strongly impacts marine biogeochemical cycles, increasing the strength and modifying the spatial patterns of carbon export by the biological pump.\u003c/p\u003e \u003cp\u003eThe coupling of biogeochemical cycles is strongly influenced by variable phytoplankton stoichiometry. Although it is possible to reproduce surface nutrient and net primary productivity distributions with both models, the VarAll model utilizes additional observational constraints of POM and individual phytoplankton cell stoichiometric variability, not captured by the FixAll model. This highlights the importance of using multiple observational constraints to better capture complex interactions between the biogeochemical cycles.\u003c/p\u003e \u003cp\u003eThe phytoplankton nutrient quotas are lower than the elemental ratios in the exported organic matter diagnosed with inverse models. This suggests substantial ecosystem processing of organic matter produced by phytoplankton prior to export. In our model this is achieved by assuming zooplankton mortality is exported with a modified Redfield ratio, thus, both zooplankton and phytoplankton stoichiometry influences the elemental export ratios. Further research is needed on the stoichiometry of the biota and the processes contributing to ecosystem processing of phytoplankton biomass.\u003c/p\u003e \u003cp\u003eFor Earth System models to project ocean carbon cycling with climate change, they must account for variable phytoplankton stoichiometry. Fixed ratio models will overestimate the declines in NPP and export with increasing stratification, with biases in the spatial patterns of carbon export. The fixed ratio models may also introduce significant biases in the ocean CO\u003csub\u003e2\u003c/sub\u003e inventory, with the ocean taking up less CO\u003csub\u003e2\u003c/sub\u003e. Integrated nitrogen fixation and water column denitrification are also significantly lower in the FixAll model, which could lead to biases in bioavailable nitrogen, the primary limiting nutrient over much of the global ocean, also impacting the carbon cycle. These results stress the need for Earth System Models to capture the complexities of the cycling of nitrogen, phosphorus, iron, and silicon in the oceans, as they all influence and modify the strength and spatial patterns of the biological pump.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eWe used a modified version of CESM v1.2, which includes biogeochemical modifications that were introduced in CESM v2, including an explicit ligand, iron model\u003csup\u003e\u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. The model includes three explicit phytoplankton groups (pico-nanophytoplankton, diatoms, and diazotrophs), one implicit group (coccolithophores, which are a variable fraction of the small phytoplankton group), and one zooplankton group\u003csup\u003e\u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. The model was initiated at preindustrial CO\u003csub\u003e2\u003c/sub\u003e levels (284ppm) for 300 years using COREv2 interannual forcing, and we analyzed the output averaged over the last 20 years. Atmospheric CO\u003csub\u003e2\u003c/sub\u003e was allowed to respond to air-sea CO\u003csub\u003e2\u003c/sub\u003e fluxes. This version of the model includes new equations and parameterization for phytoplankton nutrient quotas.\u003c/p\u003e \u003cp\u003eFor each of the potentially growth-limiting nutrients, the stoichiometry for new phytoplankton growth (nutrient:carbon ratio) is a linear function of the ambient nutrient concentration, with prescribed minimum and maximum values (SFig. 1). This allows stoichiometry to dynamically adjust to changing environmental nutrient availability. Phytoplankton N:C ratios for new growth (gQn) are a function of dissolved inorganic nitrogen (DIN), which includes both nitrate and ammonia (Eq.\u0026nbsp;1).\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:gQn=\\left\\{\\begin{array}{cc}{gQn}_{max}\u0026amp;\\:whereDIN\u0026gt;NOpt\\\\\\:max\\left({gQn}_{max}\\times\\:\\frac{DIN}{NOpt},{gQn}_{min}\\right)\u0026amp;\\:whereDIN\u0026lt;NOpt\\end{array}\\right.\\)\u003c/span\u003e \u003c/span\u003e (Eq.\u0026nbsp;1)\u003c/p\u003e \u003cp\u003eThere are some modifications to the P:C formulation for new growth (gQp) in order to maintain appropriate N:P values as N:C ratios acclimate (Eqs.\u0026nbsp;2\u0026ndash;3).\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:gQp=\\left\\{\\begin{array}{cc}{gQp}_{max}\u0026amp;\\:whereDIP\u0026gt;POpt\\\\\\:max\\left({gQp}_{max}\\times\\:\\frac{DIP}{POpt},{gQp}_{min}\\right)\u0026amp;\\:whereDIP\u0026lt;POpt\\\\\\:max\\left({gQp}_{max}\\times\\:\\frac{DIN}{NOpt},{gQp}_{min}\\times\\:\\frac{{gQn}_{min}}{{gQn}_{max}}\\right)\u0026amp;\\:whereDIN\u0026lt;NOpt\\end{array}\\right.\\)\u003c/span\u003e \u003c/span\u003e (Eq.\u0026nbsp;2)\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:gQfe=\\left\\{\\begin{array}{cc}{gQfe}_{max}\u0026amp;\\:wheredFe\u0026gt;FeOpt\\\\\\:max\\left({gQfe}_{max}\\times\\:\\frac{dFe}{FeOpt},{gQfe}_{min}\\right)\u0026amp;\\:wheredFe\u0026lt;FeOpt\\end{array}\\right.\\)\u003c/span\u003e \u003c/span\u003e (Eq.\u0026nbsp;3)\u003c/p\u003e \u003cp\u003eThere are also modifications to the Si:C formulation in order to allow for diatoms to increase the Si:C uptake ratio when dFe is low (Eq.\u0026nbsp;4).\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:gQsi=\\left\\{\\begin{array}{cc}\\left\\{\\begin{array}{cc}{gQsi}_{max}\u0026amp;\\:wheredFe=0\\\\\\:min\\left(0.133\\times\\:\\frac{FeOpt}{dFe},{gQsi}_{min}\\right)\u0026amp;\\:where0\u0026lt;dFe\u0026lt;FeOpt\\\\\\:0.133\u0026amp;\\:wheredFe\u0026gt;FeOpt\\end{array}\\right.\u0026amp;\\:where{Si\\left(OH\\right)}_{4}\u0026gt;SiOpt\\\\\\:max\\left(0.133\\times\\:\\frac{{Si\\left(OH\\right)}_{4}}{SiOpt},{gQsi}_{min}\\right)\u0026amp;\\:where{Si\\left(OH\\right)}_{4}\u0026gt;SiOpt\\end{array}\\right.\\)\u003c/span\u003e \u003c/span\u003e (Eq.\u0026nbsp;4)\u003c/p\u003e \u003cp\u003eObservations of particulate organic matter (POM) are from the GO-POPCORNv2 database\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Phytoplankton cellular iron to carbon ratios were previously compiled from multiple sources\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. The parameters determined for each cellular nutrient ratio are provided (Table S1).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eVolk T, Hoffert MI (2013) Ocean carbon pumps: Analysis of relative strengths and efficiencies in ocean-driven atmospheric CO2 changes. in \u003cem\u003eThe Carbon Cycle and Atmospheric CO2: Natural Variations Archean to Present\u003c/em\u003e vol. 32 99\u0026ndash;110\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu Y, Moore JK, Primeau F, Wang WL (2022) Reduced CO2 uptake and growing nutrient sequestration from slowing overturning circulation. 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Nat Geosci 7:895\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang W-L, Moore JK, Martiny AC, Primeau FW (2019) Convergent estimates of marine nitrogen fixation. Nature 566:205\u0026ndash;211\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTakeda S (1998) Influence of iron availability on nutrient consumption ratio of diatoms in oceanic waters. Nature 393:774\u0026ndash;777\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMosseri J, Qu\u0026eacute;guiner B, Armand L, Cornet-Barthaux V (2008) Impact of iron on silicon utilization by diatoms in the Southern Ocean: A case study of Si/N cycle decoupling in a naturally iron-enriched area. Deep Sea Res Part 2 Top Stud Oceanogr 55:801\u0026ndash;819\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChristina L, Hutchins DA, Brzezinski MA, Zhang Y (2000) Effects of iron and zinc deficiency on elemental composition and silica production by diatoms. Mar Ecol Prog Ser 195:71\u0026ndash;79\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGarcia H et al (2018) World Ocean Atlas. Vol. 4: Dissolved Inorganic Nutrients (phosphate, nitrate and nitrate\u0026thinsp;+\u0026thinsp;nitrite, silicate). \u003cem\u003eNOAA Atlas NESDIS 84\u003c/em\u003e 35pp (2019)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMoore JK, Braucher O (2008) Sedimentary and mineral dust sources of dissolved iron to the world ocean. Biogeosciences 5:631\u0026ndash;656\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGEOTRACES Intermediate Data Product Group. The GEOTRACES Intermediate Data Product 2021 (IDP2021). Preprint at (2021)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTagliabue A et al (2012) A global compilation of dissolved iron measurements: focus on distributions and processes in the Southern Ocean. Biogeosciences 9:2333\u0026ndash;2349\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBehrenfeld MJ, Boss E, Siegel DA (2005) Shea. Carbon-based ocean productivity and phytoplankton physiology from space. Global Biogeochem Cycles 19:GB1006\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWestberry T, Behrenfeld MJ et al (2008) Carbon-based primary productivity modeling with vertically resolved photoacclimation. Global Biogeochem Cycles\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSilsbe GM, Behrenfeld MJ, Halsey KH, Milligan AJ (2016) Westberry. The CAFE model: A net production model for global ocean phytoplankton. Global Biogeochem Cycles 30:1756\u0026ndash;1777\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDeVries T, Weber T (2017) The export and fate of organic matter in the ocean: New constraints from combining satellite and oceanographic tracer observations. Global Biogeochem Cycles 31:535\u0026ndash;555\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBopp L, Resplandy L, Orr JC, Doney SC, Dunne JP, Gehlen M et al (2013) Multiple stressors of ocean ecosystems in the 21st century: Projections with CMIP5 models. Biogeosciences 10:6225\u0026ndash;6245\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFu W, Randerson JT, Moore JK (2016) Climate change impacts on net primary production (NPP) and export production (EP) regulated by increasing stratification and phytoplankton community structure in the CMIP5 models. Biogeosciences 13:5151\u0026ndash;5170\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLetscher RT, Moore JK (2015) Preferential remineralization of dissolved organic phosphorus and non-Redfield DOM dynamics in the global ocean: Impacts on marine productivity, nitrogen fixation, and carbon export. Global Biogeochem Cycles 29:325\u0026ndash;340\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGalbraith ED, Martiny AC (2015) A simple nutrient-dependence mechanism for predicting the stoichiometry of marine ecosystems. \u003cem\u003eProc. Natl. Acad. Sci. U. S. A.\u003c/em\u003e 112, 8199\u0026ndash;8204\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":false,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-4602062/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4602062/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eOcean biota take up carbon in surface waters and export some of it to the ocean interior (the biological pump), modifying surface carbon concentrations, air-sea CO\u003csub\u003e2\u003c/sub\u003e exchange, and thus, Earth's climate. The growth of marine phytoplankton is often limited by one of several key nutrients (nitrogen, phosphorus, iron, silicon), and the efficiency of carbon export is constrained by nutrient availability, and the nutrient/carbon ratios in the biota (stoichiometry). Recent field observations suggest widespread variability in phytoplankton stoichiometry (C/N/P/Fe/Si). We show that accounting for phytoplankton dynamic stoichiometry dramatically shifts the magnitude and spatial patterns of carbon export by the biological pump, relative to a model with fixed ratios. Not accounting for dynamic stoichiometry also leads to increases in atmospheric CO\u003csub\u003e2\u003c/sub\u003e, thereby underestimating the ocean carbon inventory. Thus, Earth System Models (ESMs) must account for dynamic plankton stoichiometry to make accurate projections of the carbon cycle and climate. Further research is needed to better constrain environmental controls on the stoichiometry of exported organic matter, particularly ecosystem-level processing of organic matter initially produced by the phytoplankton.\u003c/p\u003e","manuscriptTitle":"Phytoplankton variable stoichiometry modifies key biogeochemical fluxes and the functioning of the ocean biological pump","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-12 06:15:45","doi":"10.21203/rs.3.rs-4602062/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"2680d998-1841-41e0-82d1-5eaf5c1f1654","owner":[],"postedDate":"July 12th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":34086797,"name":"Earth and environmental sciences/Ocean sciences"},{"id":34086798,"name":"Earth and environmental sciences/Biogeochemistry/Carbon cycle"},{"id":34086799,"name":"Earth and environmental sciences/Biogeochemistry/Element cycles"},{"id":34086800,"name":"Earth and environmental sciences/Ocean sciences/Marine biology"}],"tags":[],"updatedAt":"2025-06-09T19:00:52+00:00","versionOfRecord":[],"versionCreatedAt":"2024-07-12 06:15:45","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4602062","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4602062","identity":"rs-4602062","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}