Nitrogen and phosphorus differentially control marine biomass production and stoichiometry

Domain: earth_science
Published: 2025-07-01
Editorial outcome: accept

Abstract
Globally averaged, surface particulate nitrogen and phosphorus approximate the 16:1, N:P “Redfield ratio.” In observations, N:P ratios vary latitudinally at ranges attributable to both phytoplankton community composition and physiological acclimation, but their relative contributions to the N:P ratio remain unclear. Here, results from a 29-day mesocosm experiment highlight how inorganic nitrogen and/or phosphorus nutrient supply can affect the bulk particulate stoichiometry of a North Pacific Subtropical Gyre plankton community. Nitrogen additions, with and without phosphorus, increase total productivity and diatom abundance, whereas treatments with just phosphorus additions remain similar to the no-nutrient addition control. Continual nitrogen supply without phosphorus results in higher particulate N:P ratios than expected based on the phytoplankton community present. Several P-stress markers identified in those treatments highlight the importance of acclimation in extending particulate N:P ratios beyond the Redfield ratio. Phytoplankton’s ability to maintain growth under P-stress conditions has implications for global carbon cycling.

Introduction
Nearly 100 years ago, Alfred Redfield reported on the remarkably similar relationship between the molar ratios of carbon, nitrogen and phosphorus in marine particulate organic matter (POM) (106 C: 16 N: 1 P; Redfield ratio) and deep water dissolved nutrient ratios (16 NO3-: 1 PO43-) across ocean basins1,2. The relative constancy of the Redfield ratio has led to its use as a benchmark for identifying potential nutrient stress in marine surface waters and for modeling marine biogeochemical processes. Observed N:P ratios of surface particles, however, vary latitudinally3 at a range notably captured by both the N:P requirements of different phytoplankton species4,5 (an attribute of species adaptation to specific growth conditions) and the measured nutrient flexibility within individual taxa6–8 (an attribute of physiological acclimation to changing environmental conditions). Nutrient supply rates have been identified as a key regulator of particulate C:N:P ratios in field observations9, and inverse models similarly support a relationship between nutrient supply and particulate biomass N:P stoichiometry across ocean regimes from nutrient-rich upwelling zones to nutrient-poor oligotrophic gyres10. Biological regulation of particulate C:N:P has significant implications for the efficacy of the marine C pump11,12, and thus carbon storage in the oceans. However, since nutrients influence both phytoplankton community composition, and a major driver of cellular acclimation, it remains challenging to determine how nutrient supply dictates biological regulation of particulate C:N:P.

The relative contributions of phytoplankton community composition and acclimation on particulate C:N:P is often evaluated through field observations informed by plankton culture studies. Cultures provide the environmental control that field observations cannot, but they often lack inter-species competition and rare or slow-growing organisms critical to shaping community composition. Mixed communities as well as the presence of detritus can mask cellular-level stoichiometry changes within the POM pool. The relative timescales of community restructuring and cellular acclimation also differ, with physiological responses occurring within the lifetime of a cell and changes in community composition occurring on the longer timescale of cell growth and replication balanced by mortality and grazing13. If bulk particle stoichiometry is driven by phytoplankton community composition, one may expect particulate C:N:P stoichiometry to shift in tandem with community restructuring. The timescale and magnitude for which acclimation imprints on bulk particle stoichiometry in natural communities is less clear.

We used mesocosms, which provide the control of cultures and the complexity of natural systems, to explore how phytoplankton community composition and cellular acclimation influenced bulk particulate stoichiometry under the experimental conditions. An oligotrophic plankton community was collected from 15 m depth in the North Pacific Subtropical Gyre near Oahu, Hawaiʻi, USA and incubated in 115 L mesocosm chambers using semi-continuous culturing techniques14. The community was supplied with inorganic nutrients once daily (except days 5 and 6) at concentrations intended to mimic high and low nutrient supply regions of the ocean. The incubation lasted 29 days to capture community shifts arising from fast and slower growing organisms. This semi-continuous approach allowed us to sample for a variety of biological and chemical features of the community at maximum five times per week and at minimum once at the end of the experiment depending on the sample water requirement (Table S1). Sampling frequency was not short enough to capture instantaneous responses to the nutrient amendment, but did capture integrated responses retained in the particulate pool over time as one might encounter in field observations. This fully triplicated experiment provided a unique opportunity to evaluate how resource supply rates led to distinct patterns of particulate C:N:P within the experimental chambers.

Results
Carbon fixation and C:N:P stoichiometry were nutrient dependent
Experimental treatments consisted of four unique N:P nutrient supply ratios added in small, daily doses: -N-P (negative control, no nutrients added), -N+P (+P=9.4 nmol phosphate L seawater−1 per day), +N-P (+N=150 nmol N L−1 seawater per day added as 1:1 nitrate:ammonium), and +N+P (N:P=16). The four macronutrient treatments consisted of two sub-treatments run in triplicate, one with iron addition (+Fe) and the other without (-Fe; see Methods). Iron concentrations were 1.1 nM in the starting seawater14 and Fe had no significant influence on the concentration of particulate C, N, P, chlorophyll a (chl a), or gross oxygen production at any point in time within the N:P groupings (distribution of pairwise p-values indistinguishable from chance, Table S2). The Fe sub-treatments are differentiated in figures for transparency. The seawater used to fill these mesocosms had phosphate and nitrate plus nitrite concentrations of 67 nM and 41 nM, respectively (Fig. S1), which is within the range of concentrations measured in the region15 and in surface waters (<50 m) of the North Pacific Subtropical Gyre in August for the Hawaii Ocean Time-series using the same nutrient analytical techniques (mean±standard deviation: 64±29 nM and 23±19 nM, respectively; https://hahana.soest.hawaii.edu/hot/hot-dogs accessed December 2024).

Adding N led to increases in chl a (a proxy for phytoplankton biomass) and gross oxygen production (a measure of photosynthetic rates) (Fig. S2). After an initial peak in chl a on day 5 (Fig. S2a), the N supply rate was such that surplus nitrate was not detected when sampled 16 hours after nutrient spikes were added (Fig. S1a). A rapid decline in chl a occurred after day 5, possibly because of the two-day pause in nutrient addition, before it started to accumulate again on day 7. Whether the nutrient pause influenced particulate stoichiometry or the sequential timing of biological responses remains speculative with the data in hand. Low residual nitrate concentrations and increased productivity with N supply indicated that N was the nutrient proximally limiting net phytoplankton production throughout the experiment. Phosphate was drawn down by day 7 of the experiment to the method detection limit (2 nM) in the +N-P treatments (Fig. S1b), and as low as 7 nM in the +N+P treatments. A gradual depletion of phosphate was observed in the -N-P treatments to a minimum of 18 nM, while the -N+P treatments increased to > 100 nM phosphate (Fig. S1b). Chl a and photosynthetic rates remained unchanged in the -N-P treatments. Productivity measures (chl a and photosynthesis) were consistently different between the +N and -N treatments throughout the experiment according to Tukey’s HSD Test (p<0.05), but not within the treatment groupings (+N-P versus +N+P, and -N-P vs -N+P, p>0.05; Table S3, S4) even though the +P treatments were generally more productive than their -P counterparts after day 21 of the experiment (Fig. 1, S2).

In all +N treatments, increases in particulate N (Fig. 1b, S3b) were matched by proportional increases in particulate C (Fig. 1a, d, S3a, d), attributable to new phytoplankton production stimulated by the added N. Particulate C:N molar ratios ranged from 5.5 to 14.1 across all samples analyzed (mean 9.0; median 8.9), including the starting fill water replicates (range 6.5 to 12.0; Fig. S3d). There were few significant differences in C:N ratios between treatment groups or within a treatment over time (Tukey HSD significance here defined as p<0.05; details in Table S3, S5). Yet, two distinct patterns emerged. First, the +N treatments had on average higher C:N ratios than the -N treatments for all days sampled, but only significantly so on day 11 (Tukey HSD p<0.001), the first analysis day when diatom abundances became dominant in the eukaryotic composition of the +N treatments. Higher C:N ratios in the +N treatments could be related to differences in their unique community composition. Second, all treatments, even those without N addition, had increasing C:N ratios until experiment day 18, after which C:N stabilized. This pattern may reflect acclimation to the relatively high light levels used for the incubations16 (Fig. 1d, S3d).

While the C:N ratios remained overall statistically similar between treatments, the C:P and N:P ratios deviated based on the N supply rate and the N:P supply ratio, with significantly higher C:P and N:P in the +N-P treatments after day 18 of the experiment (Tukey HSD p<0.05; Table S3), and similarly low ratios in the other three treatment groups (Fig. 1c, e, f and S3e, f; p>0.05, Table S3). Stable N:P ratios and accumulation of phosphate in the -N+P treatments suggest that adding P alone did not lead to cellular P accumulation when cells were N-limited. Our data are consistent with a non-linear, power law relationship between local phosphate concentration and particulate C:P ratios, as observed in the oceans17 (Fig. S4).

Nutrient supply influenced the final community composition
Community composition was measured weekly by 18S rRNA gene sequencing (Fig. S5) to examine relationships between N and P supply, community structure, and particulate C:N:P stoichiometry (See Table S1 for sample frequency). Most eukaryotic reads (>62% in each sub-treatment) belonged to four major marine phytoplankton groups: diatoms, dinoflagellates, chlorophytes, and haptophytes (Fig. 2). Of these dominant groups, dinoflagellates were most abundant in the experiment fill water (Fig. S5). Metazoa, which made up roughly 21% of the starting community, were nearly absent from all treatments by day 11. Ciliates and Rhizaria had low relative abundances throughout the experiment in all treatments, whereas Syndiniales—diverse parasites—meaningfully contributed to the -N treatments until the end of the experiment (Fig. S5). Significant changes to the eukaryotic community occurred in response to N supply over time (ANOSIM; p<0.05, Fig. 2a). Adding N led to an increase in the relative abundance of diatoms such that they constituted the eukaryotic majority from day 11 onward (Fig. S5). Our ability to resolve how quickly diatoms restructured the community was set by the sampling frequency (days 0, 2, 11, 18, 25, and 29). However, the first chl a peak on day 5 (Fig. S2a) and dominance of diatoms by day 11 (Fig. S5) is consistent with incubation studies from temperate oligotrophic regions, which also suggest that diatoms restructure phytoplankton communities within ~6 days of resource addition18,19. The maintenance of diatoms for weeks under continued N supply in our experiment aligns with diatom community dominance in polar and coastal upwelling regions20. The diatom composition was >90% Pseudo-nitzschia in three of the +N treatments (Fig. S6), and a mix of Pseudo-nitzschia and centric diatoms (primarily Bacteriastrum and Chaetoceros) in the +N-P-Fe treatment. Differences between the +N-P and +N+P treatments were statistically significant (ANOSIM p<0.05). Community composition was not significantly different between the no nutrient control treatment (-N-P) and the P only treatment (-N+P; ANOSIM p>0.05) suggesting P supply alone did not impact the community structure. The -N treatments were consistently dominated by dinoflagellates, as is typically observed at oligotrophic sites such as Station ALOHA21 (Fig. 2b, S5). Consistent changes in community composition were observed in response to the controlled nutrient supply treatments, though factors such as grazing and bottle effects may have influenced the final community structure.

Prokaryotic community structure as determined by 16S rRNA gene sequencing was similar among the different nutrient additions, though as with eukaryotes, the -N treatment communities were significantly different from the +N treatment communities (Fig. S7, S8; ANOSIM p<0.05). Masked in the NMDS ordination analysis of community structure, and in our lumping of taxonomic species, are important species-specific dynamics. Notably, all treatments saw a decline in the relative abundance of Prochlorococcus (Fig. S9a), which is a common observation in long-term incubation studies22,23. The persistence of Synechococcus (Fig. S9b), SAR11, and Rhodobacterales throughout the incubation (Fig. S9) show that other key oligotrophic bacteria were retained. The relative abundance of diazotroph species increased in all -N treatments, but rarely in the +N treatments except for a notable increase in Richelia in the +N+P-Fe treatment (Fig. S10). Even though diazotroph abundances increased in all -N treatments, only the -N+P treatments had elevated photosynthetic rates, particulate N, and chl a concentrations at the end of the experiment, suggestive of a N subsidy from diazotrophic N fixation. Without quantitative N fixation rates, we cannot confirm its influence on total productivity. We do note that the relative abundance of diazotrophs differed based on P and/or Fe supply (Fig. S10) which is important to consider for long-term nutrient regulation in the ocean24.

Particle N:P stoichiometry is related to community composition
A recent study concluded that phytoplankton community composition is a good predictor of particulate stoichiometry across vastly different ocean regions, from nutrient-rich upwelling zones to oligotrophic gyres5. We used Sharoni and Halevy’s5 meta-analysis of plankton grown in culture to calculate expected bulk particulate N:P ratios for the final major phytoplankton assemblages in each treatment as if they were grown under nutrient replete, P-stress, or N-stress conditions. Only the four major eukaryotic plankton groupings listed above were included due to the absence of Prochlorococcus in our incubations (dinoflagellates and chlorophytes were grouped together5). In total, 62% to 92% of the final experimental communities were captured in the calculation (Fig. S11b). In all but the +N-P treatments, the observed ratios (which include all living organisms and detritus) resembled the calculated N:P ratios for cells grown without nutrient limitation (Fig. 3a). The +N-P treatments, where N was continuously supplied without P, exhibited a particulate N:P stoichiometry reflective of P-stressed eukaryotic plankton in culture5. Our incubations therefore support the hypothesis that phytoplankton community composition is indicative of particulate N:P ratios based on the stoichiometry of locally well-adapted species not expressing nutrient limitation.

To better understand the relationship between N:P and community composition, we plotted the Bray-Curtis dissimilarity of 18S ASV relative abundance between pairs of treatments as a function of the Euclidean distances of their N:P ratios (Fig. S12). This calculation considers all eukaryotes sequenced down to their lowest classification level, even zooplankton, which could contribute meaningfully to bulk particulate C:N:P ratios25. A significant, positive relationship was found between the community dissimilarity and N:P Euclidean distance (Spearman ρ=0.26, p<0.001) as would be expected when differences in community composition are associated with differences in N:P ratios. However, there are exceptions, for example, the +N-P and +N+P treatment-comparisons which appear in the upper left quadrant of the figure. These show similar community composition with very different N:P attributable to physiological acclimation. Community composition in our experiments was thus often a good predictor of N:P, but community alone could not explain the range of particulate N:P observed unless acclimation was invoked (Fig. 3b).

Physiological acclimation to P supply drives large shifts in particulate stoichiometry
Cellular phenotype is largely determined by genetic factors, hence the generalized relationship between community composition and bulk particulate stoichiometry, but it can change in response to environmental factors, including both N and P availability13,16. Acclimation, the process of sensing and responding to a shift in resources, occurs on the order of minutes to hours for the microeukaryotes we focus on here26, though the timescale over which the resulting acclimation response manifests as measurable changes in bulk C:N:P may be longer. To examine how physiological acclimation impacted C:N:P stoichiometry, we compared the +N-P and +N+P treatments at the final timepoint of the experiment. Both of these treatments had final timepoint communities dominated by diatom species (Fig. 2) with the same particulate C:N ratios (Fig. 4a), but the particulate N:P and C:P ratios without P added diverged from those with P added (Figs. 1, 4b, c, S3).

Phytoplankton acclimation to low P in culture studies often involves a shift from growth on phosphate to other, less easily accessible, sources of P27. The enzyme alkaline phosphatase (AP) helps cells to meet their P quota by hydrolyzing phosphate from organic P sources27,28, and elevated AP expression has been used to identify P-stressed cells in open ocean surface waters29. Although both +N treatments were depleted in measured phosphate (below 40 nM) and had measurable AP activity, AP activity and C-normalized AP activity doubled in the +N-P treatments relative to the +N+P treatments (Welch’s t-test, p=0.047, Fig. 4e), consistent with a community acclimation response to low P supply. However, this acclimation signal is not necessarily expected to correspond with a change in cellular N:P ratios due to a changed cellular P quota, if sufficient P can be acquired from dissolved organic P for growth.

When insufficient P is available for growth, phytoplankton can conserve it by modulating their cellular P pools30,31. Cellular P requirements are predominantly for nucleic acids and lipids, and P can be stored intracellularly as polyphosphates16. Elevated particulate N:P ratios have been associated with cellular acclimation to P deficits in culture experiments (e.g. 6, 7) and field assessments32, though similarly elevated particulate N:P ratios could be explained by accumulation of detrital material with a high N:P signature due to the preferential regeneration of P33. Both acclimation and preferential regeneration of P likely occur and are important to maintaining biological productivity when P availability is scarce34.

Cellular DNA is generally tied to genome size35 and may not be expected to decrease in response to P-stress, whereas cellular RNA can fluctuate with changing growth conditions36. Here, DNA and RNA as mol P per mol C were both lower in the +N-P treatments relative to the +N+P treatments, although the difference was only statistically significant for DNA (Welch’s t-test, p = 0.033 and p = 0.074, respectively; Fig. S13b, c). A closer look at the results revealed high variability between replicates (Fig. S13b), and a particularly low RNA replicate in the +N+P treatment that likely swayed the significance of the finding. The unexpected difference in DNA P content between treatments could be tied to detritus. Concentrations of the nucleotide adenosine triphosphate (ATP) normalized to C were reduced (p=0.056) in the +N-P treatments (30.1 µmol ATP/mol C) relative to +N+P (57.8 µmol ATP/mol C; Fig. 4d), but no difference was found when normalized to particulate P (Fig. S14). The same ATP normalization trends were observed in P-starved phytoplankton cultures and interpreted as a cellular reduction in ATP in response to P stress37,38, though others have argued that ATP:living C ratios are inflexible and low ATP:particulate C ratios simply a decrease in percent living biomass39. Additionally, the nucleotide adenosine monophosphate was significantly reduced relative to adenosine in the +N-P treatments (Welch’s t-test, p=0.0013, Fig. 4f), as previously demonstrated for P-stressed eukaryotic cultures and P-stressed natural communities30, congruent with an acclimation response as cells try to conserve cellular P.

Cells are thought to reduce their lipid P requirements by replacing P-containing lipid headgroups with non-P headgroups, such as in betaine-lipids31. Here, the C-normalized total lipid P concentrations were similar between the +N+P and +N-P treatments (Fig. S13d). However, the P-containing metabolites phosphocholine and glycerophosphocholine—the precursors to phospholipids—were significantly depleted relative to the non-P containing metabolite choline in the +N-P treatments compared to the +N+P treatments (Welch’s t-test, p=0.016 and 0.0023, respectively; Fig. 4g, h), supporting a P acclimation response. The ratio of N-based betaine lipids to phosphatidylcholine (BL:PC), an indicator of P stress in diatoms40, was not significantly different between the +N-P and +N+P treatments (Welch’s t-test, p=0.065, Fig. 4i), suggesting lipid substitution was not a dominant P-saving strategy in the diatom communities. However, BL:PC was negatively correlated with phosphate across all treatment timepoints (Fig. S15; BL: PC Spearman ρ=−0.64, p<0.001) supporting a moderate lipidome response to the decreased P availability observed in the +N treatments.

We found that P was distributed similarly between the measured P pools (ATP, DNA, RNA, and phospholipids) for both +N+P and +N-P treatments (Fig. S14e, Welch’s t-test, p=0.58). However, when normalized to particulate C, the relative abundance of all P-containing molecules was higher in the +N+P treatments compared to the +N-P treatments, and the C:P ratios were significantly lower in the +N+P treatments compared to the +N-P treatments (C:P=249.8 and 503.2, respectively; Welch’s t-test, p=0.0091). Thus, while it is clear that P-stress likely contributed to the elevated bulk C:P and N:P particle ratios in the +N-P treatments, the increase could not be attributed to any one specific macromolecular pool.

Bulk and community level responses as discussed above are markers of nutrient limitation that have been clearly observed in phytoplankton cultures, but become challenging to interpret in mixed communities due to the presence of detritus and the combined influence of community composition and cellular acclimation on the bulk features. To overcome this, we examined differences in the relative transcript abundance of Pseudo-nitzschia in the +N-P and +N+P treatments where this diatom genus was the most abundant eukaryote (Fig. S6). Consistent with the community-scale measurements, there was a significant increase in the relative abundance of Pseudo-nitzschia AP transcripts in the +N-P treatments relative to the +N+P treatments (Fig. 4j; DESeq, p=0.002). There was also a significant increase in the relative abundance of glycerophosphoryldiester phosphodiesterase (GDPD) transcripts in the +N-P treatment relative to the +N+P treatments (Fig. 4k; DESeq, p=0.026), complementing results from P-stress eukaryotic cultures41,42. The enzyme GDPD participates in lipid degradation by scavenging phosphate from internal phosphodiesters, which is consistent with the lower concentrations of phosphodiesters like glycerophosphocholine observed in our community-scale analysis (Fig. 4h). Both AP and GDPD are commonly up-regulated in response to low P in diatom cultures42–44, and their expression patterns recapitulate the community responses in a single genus. These transcriptional patterns, as well as past evidence of elevated C:P ratios in P-stressed Pseudo-nitzschia cultures8, support the role of physiological acclimation as a control on particle stoichiometry. Taken together, the high bulk particulate C:P and N:P ratios measured when phosphate concentrations were low, combined with multiple indicators of cellular acclimation to P-stress from the community level and within a single species, point toward the role of physiological acclimation as a contributor to changes in particle stoichiometry under conditions of P-stress.

Mesocosm insights into phytoplankton regulation of particulate C:N:P stoichiometry
Mesocosm incubations such as those presented here, where complex communities can be monitored under controlled inorganic nutrient perturbations, provide a way to bridge the gap between culture experiments, short-term incubation studies, and the natural environment in two key ways. First, longer timeframes compared to typical incubations allowed us to explore how responses varied over days and weeks. Second, larger sample volumes allowed us to simultaneously track many types of responses. For example, the immediate response to inorganic N addition in our mesocosm experiment was consistent with shorter incubation studies from both the North Pacific22,45 and North Atlantic18,46 Subtropical Gyres, which find that added N is key to stimulating productivity. As we observed, N supply alone generally stimulated growth similar to N and P co-additions in prior experiments, including deep seawater addition45, except in cases when the starting water was low in phosphate46. We thus support that N is the ‘proximal’ limiting nutrient in much of the surface ocean as described by Tyrrell24. With time, we captured the development of P-limited growth conditions, first noted as a decline in phosphate below 10 nM by day 7, followed by increased AP activity first detected day 11, then elevated N:P ratios detected after day 11. The resulting particulate N:P ratios are consistent with summertime observations made in the Bermuda Atlantic Time-series Study34 (Fig. S4) and are within range of N:P observed when phytoplankton are grown in P-limited media5,6. If the timescales of response are similar in our treatments to the natural environment, we may expect P-stress acclimation responses to manifest in the bulk particulate pool within a week of the onset of P-limitation.

We found that inorganic N supply in the absence of inorganic P supply drove bulk particulate stoichiometry far from the canonical Redfield ratio toward higher N:P (40:1 at the end of the experiment for +N-P treatments) without significantly slowing photosynthetic growth. Higher N:P and C:P ratios persisted for at least two weeks before the 29-day experiment was terminated, whereas the C:N ratio was relatively constant between treatments. Global climate models predict decreasing surface P concentrations due to changing ocean circulation patterns47,48. Maintaining similar phytoplankton productivity under this scenario would require flexibility in cellular N:P akin to what was observed in our experiments. Further, stoichiometric flexibility is important because even small changes in global ocean C:N:P export ratios will lead to dramatic changes in the amount of carbon stored in the oceans11, with implications for global climate. Our work thus illustrates the ways in which phytoplankton communities might respond to future changes in nutrient supply rates and N:P ratios and motivates further work exploring how nitrogen fixation and denitrification couple to return the oceans to a 16:1 ratio over timescales longer than the duration of our experiment.

Methods
Experiment implementation
Data were collected during PERI-FIX (Pelagic Ecosystem Research Incubator- Fe Incubation eXperiment), an experiment conducted at the University of Hawaiʻi Marine Center in Honolulu, HI, USA from August 9, 2021 to September 7, 2021, according to methods which are detailed in Seelen et al.14. Seawater for the incubations was collected between 2 AM and 5 AM HST south-west of Oahu (21.089°N, 158.074°W) in oligotrophic waters generally representative of the North Pacific Subtropical Gyre, such as Station ALOHA49, the Hawaii Ocean Time-series study site50. Unfiltered seawater was pumped from 15 m depth using an ARO non-metallic, polytetrafluoroethylene 1.5” air-operated double diaphragm (AODD) pump aboard the vessel Huki Pau into 1 m3 high density polyethylene water totes. Prior to use, the totes were rinsed sequentially with 1% citranox, 1% HCl, and 4x with distilled water, and then rinsed 2x with seawater on station before filling. After water collection, the totes were covered in opaque, insulated shipping jackets to maintain dark and cool conditions during transport back to shore. Approximately nine metric tons of water were collected to fill the incubation tanks at the beginning of the experiment and to replace water removed as samples were collected during the experiment.

Incubations were performed in twenty-four, 115 L PERIcosm incubation tanks that we designed with all plastic components, enclosed to minimize trace-metal contamination, and fitted with ports for sampling suspended, dissolved, and settled material14. The PERIcosms were operated inside a warehouse bay, within a tented enclosure cooled with portable air conditioning units to temperatures near 25°C (25.4±1.4°C standard deviation). At the start of the experiment, unfiltered seawater was transferred from the totes to the PERIcosms using a ½” all-PFA AODD pump at a rate of ~9 L/minute. All PERIcosms were filled the same day seawater was collected. Light was supplied to each PERIcosm with a Kessil A360X refugium light set to 75% blue/25% red light at 100% brightness for 12-hour day/night cycles. The daily photon flux of ~8 mol quanta m−2 d−1 was chosen to match the light flux at approximately 45 m depth at Station ALOHA51.

Eight different nutrient amendment treatments were tested during the experiment. The nutrients included N, P, and Fe, and were added every afternoon of the experiment after sampling was complete, except days 5 and 6 which were skipped entirely. The +N treatments received 75 nmol of NH4+ and 75 nmol NO3- per L of seawater (1:1 mol N), the +P treatments received 9.4 nmol PO43- per L of seawater, and the +Fe treatments received 1 nmol FeCl per L seawater each day. The starting seawater Fe concentrations exceeded 1 nM14 and were likely not limiting at the start or throughout the experiment. The treatments were labeled according to nutrient addition (e.g., +N-P-Fe signifies only N was added). The treatments were reduced to four groups based on their N and P additions and include -N-P, -N+P, +N-P, and +N+P. Each treatment group consisted of 6 PERIcosm incubations except -N+P which included 5 PERIcosm incubations. Within that treatment, one -N+P-Fe tank started with outlying high C, N, P, and chl a concentrations on Day 4, and decreased through the remaining experiment therefore displaying an opposite pattern to the remaining -N treatments. That tank was removed from analyses, and the data are not presented. Small volume sampling for nutrients and chl a took place almost daily, negligibly decreasing the total incubation volume. Larger sampling events took place twice per week, removing either ~6% or ~20% of the tank volume to meet the water demand of our sampling regime. Sampling frequency is presented in Table S1. The volume removed was replenished using filtered seawater via the AODD pumping system with a Pall AcropakTM 0.2/0.8 µm cartridge filter connected in-line.

Samples were collected from a sampling port installed near the top of the PERIcosms after they were mixed by hand with a customized magnet-controlled paddle (see Seelen et al.14. for more details). Although not measured, we estimate the internal flow rate due to mixing is less than 1 m/sec. It is assumed that these samples represent the suspended particulate composition at the time of sampling. Each PERIcosm was connected from the sampling port to a peristaltic pump head allowing for rapid and simultaneous sample collection from all 24 tanks. For samples processed by vacuum filtration, seawater from the PERIcosms was collected in clean bottles and filtered off-line. Filtered samples could be directly acquired from the PERIcosm using filter cartridges. Sampling and analytical protocols for each sample type are detailed below as well as in a separate PERIcosm method paper14.

Detailed sampling protocol and analytical procedures
Particulate C, N, P, and ATP. The particulate C and N sampling took place mid-afternoon Hawaii Standard Time (HST). Depending on biomass levels, 1 to 2 L of sample water was removed from the PERIcosm and filtered onto a combusted 25 mm diameter glass fiber filter (Whatman GF/F) using a vacuum filtration manifold. The filters were then wrapped in combusted foil and stored at −20°C until they could be dried at 60°C, pelletized, and analyzed at the University of Southern California on a 4010 Costech Elemental Analyzer. Acetanilide (C:N=6.84) was used as a reference material to calibrate the system for carbon and nitrogen at the start of the analytical run and after every 12 sample measurements52. Duplicate samples collected on day 25 of the experiment were analyzed with and without filter acidification to determine the contribution of particulate inorganic carbon in the particulate C measurements. On average, the particulate C concentrations increased with acidification by 20% ± 14% (n=19), and particulate N by 7.5% ± 14% (n=19). The cause of the variable results post acidification is unknown, potentially linked to when the filter duplicates were collected, but ultimately unable to be tested due to insufficient samples. The final particulate C and N values reported here were not corrected for this difference.

Sampling for particulate P took place between 7 and 9 am HST. One liter of water was removed from each PERIcosm and equally divided to provide paired particulate P (measured as particulate phosphate) and ATP samples. For particulate P, 0.5 L was filtered onto a combusted, acid washed (10% HCl) GF/F. The filters were stored in microcentrifuge tubes at room temperature until analysis. The filters were transferred to acid-clean, borosilicate tubes and ashed at 450°C for 4.5 hours. The particulate P filters were subsequently leached in 10 mL 0.15 N HCl for one hour, mixed by vortexing, and centrifuged to remove filter debris. A 5 mL subsample of the supernatant was transferred into new polypropylene tubes and 0.5 ml of reagent mixture was added (53; see Hawaii Ocean Time-series analytical protocol at https://hahana.soest.hawaii.edu/hot/protocols/protocols.html). After one hour, the samples were analyzed by spectrophotometry at 880 nm using a 10 cm pathlength cell. For ATP, 0.5 L was filtered onto a 47 mm diameter GF/F and immediately extracted in 5 ml of boiling Tris buffer. The filters were extracted for 5 minutes, allowed to cool and then stored frozen (−20°C) until analyzed using the firefly bioluminescence method (54; see Hawaii Ocean Time-series analytical protocol at https://hahana.soest.hawaii.edu/hot/protocols/protocols.html). Statistics were run between sub-treatments and the treatment groups for all time-points of the study. The number of data points used was typically n=3 for sub-treatments and n=6 for the treatment groups. Exceptions are for -N+P-Fe for which n=2. Further, missing data-points result in n=2 for +N+P+Fe on day 11 (missing C, N, P, and ATP) and for -N-P+Fe on day 29 (missing P), which impacts their treatment groups on those days.

Dissolved nutrients
Dissolved nutrient samples were either obtained from the amplicon sequencing filtrate or by Pall AcropakTM filtration (0.8/0.2 µm Supor) directly from the PERIcosm. The filtered water was stored frozen at −20°C in VWR® metal-free polypropylene centrifuge tubes or HDPE bottles until analyzed at the University of Hawaiʻi, Mānoa on a Seal Analytical AA3 HR Nutrient Autoanalyzer. Dissolved inorganic nitrogen was determined as the sum of nitrate + nitrite using a diazo reaction modified from Armstrong et al.55. and Hansen and Koroleff56. Phosphate was measured colorimetrically as orthophosphate using the automated procedure adapted from Murphy and Riley53. The nutrient calibration standards were made in low nutrient seawater collected from the HOT 316 cruise in October 2019 using concentrated stocks of ACS grade sodium nitrite (NaNO2) and monopotassium phosphate (KH2PO4) as detailed in the Hawaii Ocean Time-series analytical protocols (https://hahana.soest.hawaii.edu/hot/protocols/protocols.html). A method detection limit for phosphate and nitrate + nitrite was calculated as three standard deviations above the average concentration of the low nutrient seawater blank sample. The detection limit for phosphate was determined to be 0.004 µM, and samples below that concentration (10% of samples) were set to equal half the detection limit (0.002 µM). The nitrate + nitrite detection limit was 0.018 µM, and samples with concentrations below the detection limit (35% of samples) were set to a concentration of 0.009 µM. We note, however, that a high sensitivity chemiluminescence method for nitrate is more typically used for seawater samples with less than 0.05 µM nitrate as detailed in the Hawaii Ocean Time-series analytical protocols.

16S and 18S rRNA gene sequencing
Samples for amplicon sequencing were collected between 11 am and 2 pm HST by filtering 2 L of sample water onto 0.2 µm pore size Supor disc filters using a peristaltic pump directly from the PERIcosms. The loaded filters were flash-frozen in liquid nitrogen and stored at −80°C. DNA was extracted from the filters at the University of California Santa Cruz using a modified Qiagen DNeasy protocol57 after 10 ng of Takara Thermus thermophilus HB8 genomic DNA was added to each sample. Amplicon libraries were prepared at the University of Southern California using 515Y-926R primers (dx.doi.org/10.17504/protocols.io.vb7e2rn), which simultaneously amplify regions of both 16S and 18S SSU rRNA genes to capture prokaryotic and eukaryotic taxa from the same sample58–60. Agencourt AMPure XP beads (dx.doi.org/10.17504/protocols.io.rfvd3n6) were used to purify the PCR products before the samples were normalized, pooled, and sequenced on 2 lanes of a MiSeq v3 2×300 paired-end sequencing platform at the University of Southern California Molecular Genomics Core. The resulting sequences were processed through the bioinformatic pipeline described by Yeh and Fuhrman (59; dx.doi.org/10.17504/protocols.io.vi9e4h6). The raw sequences were demultiplexed and rid of primers using cutadapt61 before separating the 16S and 18S reads using the bbtools package (http://sourceforge.net/projects/bbmap/) and a custom 16S and 18S database59. Low quality reads were removed from the forward and reverse 16S reads by truncating at 210 and 180 base pairs (bp), respectively, and the resulting sequences were passed through DADA2 in the QIIME2 software to create ASVs62,63. Taxonomic identification for the 16S bacterial and archaeal ASVs was assigned using the SILVA database (v. 13864), and for the 16S chloroplast ASVs using the PhytoREF database (from PR2 v. 4.1265). The 18S results were truncated at 210 and 170 bp for the forward and reverse reads, respectively, concatenated using BBduk within the bbtools package, and then used to generate ASVs using DADA2 in QIIME262,63. The 18S eukaryotic ASVs were assigned taxonomy using the PR2 database (v. 4.1266) with default QIIME2 parameters. Contaminant ASVs were isolated and removed from the final ASV tables using the decontam package in R61.

Flow cytometry
Unfiltered samples for flow cytometry were collected mid-afternoon HST. The samples were kept in the dark and quickly transferred to cryovials. The samples were then fixed with 0.25% glutaraldehyde before flash freezing in liquid nitrogen and storage at −80°C until analyzed at the University of Hawaiʻi, Mānoa on a Mariner Influx flow cytometer. We used the same flow cytometry analytical methods as reported for the Hawaii Ocean Time-series, which are adapted from Monger and Landry67. Briefly, the forward light scatter, red fluorescence (692 nm), and orange fluorescence (580 nm) of each sample was detected to resolve the abundance of Prochlorococcus, Synechococcus, and picoeukaryotic algae. The results were processed using FlowJo software (TreeStar).

Gross oxygen production
Sampling to determine gross oxygen production by the 18O-H2O method, as described by Ferrón et al.68, occurred between 8 and 11 am HST. Water from each PERIcosm was sub-sampled into 1 L polycarbonate bottles and then siphoned into two 130 mL Pyrex® bottles with ground-glass stoppers. The incubation bottle was spiked with 650 µL of H218O (Medical Isotopes, 97% isotopic purity) to a final 18O-H2O enrichment of ~2400‰, and then incubated for 5 to 7 hours in a water bath with similar temperature and light conditions as the PERIcosms. The incubation was terminated by pipetting 100 µL of saturated mercuric chloride solution to stop all biological activity. The stopper was then replaced with one that allowed the bottles to be closed without introducing bubbles. The samples were inverted three times to mix and stored in the dark, immersed in water until analysis by membrane inlet mass spectrometry within 48 hours. The other bottle sampled from each treatment was fixed in the same way at the start of the incubation to preserve the initial conditions. Gross oxygen production was determined by the isotopic enrichment of the dissolved O2 pool, following Ferrón et al.68. More details can be found in Salazar Estrada69.

Macromolecules
Bulk particulate RNA and DNA samples were collected late afternoon and filtered onto Nuclepore Track-Etched 47 mm diameter, 0.2 µm pore size polycarbonate filters by vacuum filtration. After filtration, the samples were flash frozen in liquid nitrogen and stored at −80°C until analysis at Dalhousie University. The samples were processed and quantified from the same filter using a modified version of the fluorochrome SYBR Green II method70,71. Bulk particulate lipid samples were collected late afternoon and filtered onto 47 mm diameter pre-combusted GF/F by vacuum filtration. The samples were preserved by flash freezing in liquid nitrogen and stored at −80°C until they were analyzed at Dalhousie University. The RNA and DNA concentrations were converted to P concentrations using the following equations6: DNA-P (µM)=DNA (µg/L) * 0.091/30.97 and RNA-P (µM)=RNA (µg/L) * 0.095/30.97.

Metabolomics
Samples for metabolomics processing were collected between 6 and 7 pm HST by filtering 2 L of sample water onto 47 mm diameter, 0.2 µm pore size Durapore disc filters using a peristaltic pump. The loaded filters were flash-frozen in liquid nitrogen and then stored at −80°C until analysis at the University of Washington. Samples were extracted using a modified Boysen et al. method72 and analyzed using HILIC chromatography on a Waters Acquity I-Class UPLC with injection volumes of 2 µl coupled to a Q Exactive Orbitrap mass spectrometer (MS). For HILIC, a SeQuant ZIC-pHILIC column (5 µm particle size, 2.1 mm × 150 mm, from Millipore) was used with 10 mM ammonium carbonate in 85:15 water to acetonitrile (Solvent A) and 10 mM ammonium carbonate in 85:15 acetonitrile to water (Solvent B) at a flow rate of 0.15 mL/minute. The column—maintained at 30°C—was held at 100% B for 2 minutes, ramped to 64% A over 18 minutes, ramped up to 100% A over 1 minute, held at 100% A for 7 minutes, and equilibrated at 100% B for 22 minutes (total time is 50 minutes). The mass spectrometer was calibrated weekly in positive and negative modes using solutions provided by the manufacturer. For HILIC, a full scan method employing positive and negative switching was used with a scan range of 60 to 900 m/z and a resolution of 60,000. The capillary temperature was 320°C, the H-ESI spray voltage was 3.5 kV, and the auxiliary gas heater temperature was 90°C. The S-lens RF level was 65. Sheath gas, auxiliary gas, and sweep gas flow rates were maintained at 16, 3, and 1, respectively. To minimize retention time drift, the LC was kept running with the same solvents for the entire run, and Optima-grade LCMS water blanks were run after every 3-4 samples. For the quality control pooled samples, both high-resolution MS scans and data dependent MS/MS scans were collected and separate injections were done for positive and negative ion modes (30,000 resolution and a dynamic exclusion time of 20 seconds for MS/MS data, top five most abundant ions in each cycle were fragmented, collision energies of 20, 35, and 50 eV). The raw files were converted to .mzML formats and centroided via ProteoWizard’s msconvert73. Chromatograms were then extracted using the RaMS package74 in the R programming language with a 5 ppm m/z error tolerance and integrated using a trapezoidal Riemann sum without baseline subtraction.

Lipidomics
One liter of water for lipidomics was collected in polycarbonate bottles between 9 and 11 am HST. The samples were filtered using glass vacuum filtration units onto 47 mm diameter, 0.22 µm pore-size Durapore PVDF membrane filters. Filters were wrapped in combusted aluminum foil, flash frozen in liquid nitrogen, then stored at −80°C. The filters were shipped on dry ice to Woods Hole Oceanographic Institution for analysis, stored in liquid nitrogen until extraction, then extracted via a modified Bligh and Dyer procedure75 according to Popendorf et al.76. Samples were then analyzed on a Q-Exactive MS (ThermoFisher Scientific, Waltham, MA, USA) using an Agilent 1200 (Agilent, Santa Clara, CA, USA) high performance liquid chromatograph system, following a modified method77 originally described by Hummel et al.78. Lipidomic compound annotation and analysis were all performed using ThermoFisher Scientific Xcalibur software and the R programming language, aided by the tidyverse data science package79. Raw data files were converted to the .mzXML format with the ProteoWizard Toolkit73. Mass spectral features were then grouped and annotated using the R-based xcms80–82, CAMERA83, and LOBSTAHS77 packages. Lipids were corrected for recovery using an internal dinitrophenol phosphatidylethanolamine (DNPPE) standard, and quantified using external standard curves of deuterated standards84. Phospholipids were quantified using individual standards for each phospholipid class, and a diacylglycerol-trimethylhomoserine (DGTS) standard was used for all N-based lipids; glycolipids were quantified using a glucosylceramide standard. Phosphatidylglycerol, DNPPE, and DGTS standards were purchased from Avanti Polar Lipids (Alabaster, AL, USA); phosphatidylethanolamine, phosphatidylcholine, and glucosylceramide standards were purchased from Cayman Chemical (Ann Arbor, MI, USA). All samples were corrected for possible contamination by subtracting the average value of four procedural blanks from each identified lipid compound. Total lipid P was determined as the sum of all measured phospholipids in each sample.

Alkaline phosphatase activity
Sampling to determine alkaline phosphatase activity (APA) occurred between 9 and 11 am HST. Approximately 125 ml of sample was removed from each PERIcosm and vacuum filtered onto a 0.2 µm pore size, 47 mm diameter polycarbonate filter. The final volume filtered was recorded, and the filter was transferred to a 47 mm Petri dish and stored at −20°C until analysis. Activity was assayed as previously described28 using the fluorogenic phosphatase substrate 6,8-difluoro-4-methylumbelliferyl phosphate.

Metatranscriptomics
Metatranscriptome sampling took place prior to the genomic sampling, from 8 am to 11 am HST. Samples were collected directly from the PERIcosms using a peristaltic pump to filter 20 L of seawater per sample through 47 mm diameter, 5 μm pore size polycarbonate filters, which were placed in cryovials and flash-frozen and stored in liquid nitrogen until they were extracted. Total RNA was extracted from individual filters using a Qiagen RNeasy Mini Kit with a modified lysis step. Briefly, lysis buffer and Biospec zirconia/silica beads (0.5 mm) beads were added to each filter and vortexed for 1 minute, placed on ice for 30 seconds, and vortexed again for 1 minute. Lysate from each filter was removed with a pipette and pooled into a single 5 mL microcentrifuge tube. Synthetic standards (Ambion ERCC RNA Spike-in mix) were then added based upon estimated total RNA (using extraction values from prior samples collected in this region85). The remainder of the Qiagen RNeasy Mini Kit protocol was then followed according to the manufacturer’s instructions and incorporating the on-column DNase digestion step using a Qiagen RNase-free DNase kit. The resulting total RNA was eluted with RNase-free water and then purified and concentrated with a Qiagen RNeasy MinElute kit according to the manufacturer’s instructions. The quantity and quality of extracted total RNA were assessed on an Agilent 2100 Bioanalyzer (Agilent, Santa Clara, CA).

Treatment samples from the final sampling timepoint (day 29) and tote samples (day 0) were extracted and sequenced. Extracted samples were split into two equal volumes of total RNA, where one volume was sequenced after a polyA pull-down step (Illumina® Truseq library preparation kit) to enrich for eukaryotic mRNA (analyzed herein). Illumina® TruSeq libraries were sequenced with an Illumina NovaSeq 6000 at the JP Sulzberger Columbia Genome Center following Center protocols. RNA targeting just eukaryotic mRNA signals (selected reads) and RNA targeting both eukaryotes and prokaryotes (unselected reads) were sequenced to produce 80 million 100-bp, paired-end reads. Sequence reads are available at the Sequence Read Archive through the National Center for Biotechnology Information under BioProject ID PRJNA1062801.

Metatranscriptomic mapping pipeline is described in Harke et al.86. Pre-processing and trimming of selected reads were done with FastQC (v 0.11.9)87, Trimmomatic (v 0.3.9)88, and bbmap (v 39.01) (https://sourceforge.net/projects/bbmap/) to remove ERCC standards using the bbduk function. Selected reads were aligned with BWA (v 1.1.4, parameters -k 10 -aM)89 and counted with HTSeq (v 2.0.3, parameters -a 1 -m intersection-strict -s no)90. Metatranscriptomic reads were mapped against a curated reference database including Pseudo-nitzschia fraudulenta (MMETSP0851, NCBI SRA SRX551153), Braarudosphaera bigelowii (assembled from Suzuki et al.91 using Trinity v 2.11.092), Chaetoceros sp. (MMETSP0200, SRA SRS619007), Epithemia pelagica (NCBI GCA_946965045.1), Haptolina brevifila (MMETSP1094, SRA SRS621505), Hemiaulus sinensis (NCBI GenBank GAUA00000000.1), and Rhizosolenia setigera (MMETSP0789, SRA SRS618823). Functional annotations were derived by combining available MMETSP Pfam annotations with Eggnog-mapper annotations (v 2.1.9)93,94. All subsequent analysis was done in R (v 4.2.1). DESeq2 was used to determine the significant differential expression of genes between +N-P treatments (+Fe replicates only) and +N+P treatments (all +Fe and -Fe replicates). The +N-P-Fe treatments were not dominated by Pseudo-nitzschia at day 29 and were excluded from this comparison. One +N-P+Fe replicate was also excluded from analyses as it was an outlier in read recruitment. For visualization, reads mapped to genes characteristic of the diatom P-stress response42,43 were normalized to total mapped Pseudo-nitzschia reads, and expressed as tags per million (tpm).

Community based N:P calculations
We estimated particulate N:P ratios for the 8 sub-treatments and 4 treatment groups as if the eukaryotic communities present (average of days 25 and 29 of the experiment) were grown under replete, N-stress, and P-stress nutrient conditions. The major phytoplankton groups and their relative abundances were determined by adding the relative abundances of all ASVs that were annotated as Bacillariophyta (diatoms), Haptophyta (haptophytes), Chlorophyta (chlorophytes), and Dinophyceae (dinoflagellates). Chlorophytes and dinoflagellates were summed into one group for the N:P calculation as reported in Sharoni and Halevy5. The N:P ratios for each plankton group were extracted from the SI of Sharoni and Halevy5 using their classifier of growth condition (Replete, N-stress, and P-stress) and used to calculate a weighted mean with the community composition as the weights. Differences between the estimated N:P ratios for the three growth conditions and the observed N:P value for each treatment were tested using Welch’s t-test and a weighted standard error.

Statistical Approaches
We used parametric pairwise comparisons to determine differences between treatments and time points. Differences between treatments in chl a, gross oxygen production, and particulate C, N, and P, as well as C:N, C:P, and N:P ratios were tested separately at each time point via one-way ANOVA followed by two-tailed Tukey’s HSD post-hoc test (Table S3). All 8 treatments were also compared independently (Table S4). We also compared this same set of measured variables across time points throughout the experiment again using a one-way ANOVA and Tukey’s post-hoc test to determine the day of the experiment on which these variables began to significantly differ (Table S5). Statistics were performed using R’s stats library, version 4.3.1. Differences between the +N-P and +N+P treatment groups in Fig. 3 were tested using unpaired, two-tailed Welch’s two-sample t-tests using the ggpubr R package, version 0.6.0. The same approach was used to test significance in Figures S13 and S14.

The relative abundances of ASVs that were categorized as diatoms, dinoflagellates, haptophytes, and chlorophytes were summed for each day, replicate, and treatment. These summed relative abundances were visualized and used to evaluate broad shifts in plankton community composition over time. The ASV relative abundance data were also used to calculate the Bray-Curtis dissimilarity between every pair of samples in the dataset, and non-metric multidimensional scaling (NMDS) was used to visualize differences in Bray-Curtis dissimilarity over time and between treatments. Finally, analysis of similarities (ANOSIM) tests were carried out using the Bray-Curtis dissimilarities to determine whether specific treatment groups had significantly different 16S and 18S communities. All Bray-Curtis dissimilarity calculations, NMDS analyses, and ANOSIM analyses were conducted using the vegan package in R95.

To determine whether there was a relationship between changes in plankton community composition and changes in the particulate N:P ratio, the Euclidean distances between the N:P ratio measurements were plotted as a function of the Bray-Curtis dissimilarity between every pair of samples collected on day 25 and day 29 of the experiment. Then, Spearman correlation tests were used to determine whether there was a significant correlation between community dissimilarity and the Euclidean distances of the N:P ratios. The distance metrics (Euclidean distances and Bray-Curtis dissimilarity) were calculated using the vegan package in R (Oksanen et al.95, R Core Team 2024), and all Spearman correlation tests were carried out using the stats package in R (R Core Team 2024).

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Here, Seelen and co-workers ran a mesocosm experiments amended with N or P. The underlying scientific aim is to quantify\nhow varying nutrient supply rate and ratios regulate the physiology and composition of plankton communities and in turn the\nregulation of ecosystem C:N:P. The experiment mainly consists of four mesocosm treatments of (+/- N or P). There is also an\nFe treatment which appears in the figures but is not used much in the main text. The mesocosms are analyzed using a\ndiverse set of biogeochemical and molecular approaches. However, I find the wide-ranging extrapolation to overall\nregulation of ocean C:N:P problematic.\nThere are strength and weaknesses with the presented manuscript.\n1. In general, I like the idea of manipulating the nutrient input rates and ratios in mesocosms. This is an interesting setup and\ncomplements both section profiles and culture experiments. The number of measured variables is also impressive. Thus, the\nfindings showing changes to ATP, lipids, and other bio-molecules are really interesting. These are important results.\n2. I also have some issues with the experiments. The complete eradication of Prochlorococcus is an important issue. This\nissue is briefly mentioned in the text (and I appreciate the supplementary figure), and I agree that it is common problem with\nany field manipulations. However, it does make it more challenging when trying to link your mesocosm experiments with the\nbroad-scale regulation of ocean C:N:P. This should be brought up in the discussion (see also below).\n3. Your +N experiments leads to a massive enrichment of diatoms. To me, this suggests that we are seeing opportunists\nresponding to a sudden nutrient enrichment. I am interpreting your text as an evaluation of C:N:P regulation under N vs. P\nlimitation but your results are more aligned with more or less nutrients. We have many N-limited ocean regions where\nProchlorococcus and other picos dominate. Your results are interesting, but it does again make it harder to link your\nobservations with ocean C:N:P.\n4. When you look closely at the data, it does seem that biomass is starting to separate between -N+P and -N-P. I am\nwondering if you are starting to see the impacts of N fixation. In the text, it is stated that these two treatments share the\nresponse – but your data says otherwise. I would expect that feedbacks from Nfixation would be slow. This is a non-trivial\nissue because it leads to a very different biogeochemical model than presented – ie. that it is the supply of P that is important\n(like Tyrrell is arguing).\n5. The manuscript aims at disentangling community composition vs. physiological regulation. You seem to have something\nvery specific in mind with ‘community composition’ – ie. major taxa. But what is community composition? Is it only broad\ngroups or do genomic changes also count? Personally, I find it really hard to separate physiology and composition as they\nare so intertwined. You interpret the changes in AlkP, lipids, etc. to come from change in physiological regulation. However,\ntheir regulation can easily be related to organism. Here, it may be worth checking out Moreno and Martiny, 2018\n(Ecological Stoichiometry of Marine Plankton) as this review gives a careful treatment of the issue of physiological vs. eco-evo\nregulation of C:N:P. In general, I think there is some phylogenetic structuring of biomass composition as well as\nphysiological regulation. Thus, it is not a binary issue. Instead, knowledge lies in understanding the strength of each process\nas well as at what phylogenetic level do biomass composition vary across lineages. So I think the manuscript would benefit\nfrom presenting the observed results without this thick layer of interpretation of the biological regulation. You can then\ndiscuss how you interpret the regulation of these changes while also describe the assumptions you are making.\n6. The stated aim is to test the biological regulation of C:N:P biomass composition (physiology vs. composition). However,\nthe discussion is centered on the biogeochemical regulation of C:N:P. Thus, there is a strong disconnect between on the\none hand the stated aim and observations vs. the discussion. This makes the discussion very speculative. The ideas in the\ndiscussion are definitely interesting (even if I disagree with some it). However, the mesocosm data cannot be used to as\n‘evidence’ for such wide-ranging claims. One reason is the replacement of Prochlorococcus with diatoms in your experiment\n– something that is exceedingly unlikely in the oligotrophic ocean.\nMinor comments:\nL38: I am not sure this statement is right about the mean being 16. The few treatments of this problem suggest a significantly\nhigher value.\nL105: I think it would be worth to be more specific about what you mean with many generations. 29 days is a great\nexperiments but short for ecological re-arrangements – e.g., changes in Nfixer abundances.\nL158: I agree that this decline in Pro is often observed but that is also an issue for using such experimental setups for\nsimulating oceanographic processes.\nL186: I cannot see any data presented that support this statement. There are several theories for why N:P is 16 (or really\nclose to 14) in the deep ocean including ocean mixing + denitrification (so nothing related to phytoplankton biology).\nL302: Although C:N does not vary as much as N:P, it is not constant either. One example is Lee et al., 2021 from the South\nPacific. Here you can see quite elevated C:N in the center of the gyre.\nFigure 1: It does seem that -N+P is starting to separate from -N-P in terms of biomass. Could it be that Nfixation is slow to\ncatch up.\nAdam Martiny", "reviewer_2": "Summary\nThe submission by Seelen et al. presents a mesocosm experiment that examines the effects of N and P supply on the\nstoichiometry (C:N:P) of surface ocean particulate matter and the underlying drivers of these effects. Using a comprehensive\nexperimental design and waters collected from the North Pacific Subtropical Gyre, The authors show that N supply drives\nchanges in phytoplankton biomass and community structure while the subsequent effects of physiological acclimation to low\nP availability following increased N supply phytoplankton production drive changes in particulate C:N:P. This study offers an\nambitious and well-replicated experimental design and a diverse set of measurements to assess its results. Despite these\nstrengths, many of the most relevant results lack statistical support and are given little discussion. This manuscript also\nconveys interesting conclusions regarding the consequences of ocean C:N:P variability but seems to drift from addressing\nits initial focus, which is examining the relative importance of the mechanisms that cause ocean C:N:P to vary. Overall, this\nwork represents a relevant and valuable data set, but requires more thorough analysis and discussion.\nGeneral Comments\n- The issue of C:N variability in phytoplankton and the interpretation of particulate C:N data in this manuscript should be re-\nevaluated. Simply stating that the observed C:N data are “within a tight range” is not well supported and seems off the mark in\nthe context of global C:N:P variability. The overall range of C:N observed globally and in culture experiments is inherently\nsmall compared to variability in N:P and C:P, yet a shift in phytoplankton C:N from 8 to 6 would still represent a large\nreallocation of materials within phytoplankton cells and an impact on export flux of C and N.\n- A key factor that is not addressed in this study are the respective time-scales of the two mechanisms that affect\nphytoplankton C:N:P (changes in community structure vs. physiological acclimation). The experiments in this study span a\n30-day period, but the time-scales over which acclimation or community composition would be expected to change are not\ndiscussed and additional estimates that would help a reader to evaluate this (i.e. estimates of community growth rates or\nlosses) are not attempted. Additionally, the relevant measurements needed to evaluate the relative impact of these two\nmechanisms (i.e. particulate C:N:P, picocyanobacteria abundance, eukaryotic phytoplankton composition, biochemical\ncomposition) were made or are presented with different frequencies. I completely understand the practical limitations of\nlimited sampling time and biomass, but these limitations should be addressed and acknowledged when making\nconclusions.\n- The Methods section should include what days each type of parameter was sampled. The main text does not make the\nfrequency of measurements clear and the reader is left to sort this out from the figures alone and assume that all sampling\npoints are being shown. For example, the main text says that “community composition was measured throughout the\nincubation”, yet the methods do not say which days or how many community composition was sampled and the figures show\nfour sampling points for picocyanobacteria (Fig. S6) and the average of two sampling points near the end of the experiment\nfor eukaryotes (Fig. 2).\n- Many of the key findings (such as trends in C:N:P and changes in community composition) are given little quantitative\ndiscussion and no statistical analysis. This would be understandable for work based on large-scale field observations, but it\nseems to be a major omission for a controlled and well-replicated experiment.\n- Although it seems that the communities examined were not Fe-limited, there are some potentially important confounding\neffects of Fe that are not addressed (for instance, Figures S7, S8, and S10). Overall, I don’t object to pushing the issue of Fe\neffects aside and focusing on N and P that are clearly more important, but the data shown would generate a lot of concern for\na more skeptical reader. Additionally, the outlining used to denote +Fe and -Fe sub-treatments in any of the figures that are\nnot column graphs is very difficult to see.\n- Overall, the focus of the paper seems to drift away from its initial objective: evaluating the relative impact of community\nstructure and physiological acclimation on particulate C:N:P in response to nutrient supply. Large portions of the paper are\ndedicated to results that demonstrate P-stress (ATP, AP, lipidomics, transcriptomics) but do not explain bulk changes in cell\ncomposition that could cause the observed changes in C:P and N:P. Likewise, much of concluding paragraphs focus on the\nimplications of varying N:P for the marine C cycle as a result of potential nutrient supply scenarios with unclear likelihoods. It\nseems that much of this space could have been dedicated to using the data that was collected to explore the underlying\nfactors that allow community structure or acclimation to affect C:N:P and the relative importance of these two mechanisms,\nrather than the long-term consequences of C:N:P variability.\nSpecific Comments\nLines 42-43: The sentence starting with “How these mechanisms…” needs to be reworded. It either contains a typo or is not\ngrammatically correct. Also, it is not clear if you were trying to say that how the mechanisms listed influence phytoplankton\ngrowth and stoichiometry is uncertain or if you are trying to say that how phytoplankton growth and stoichiometry impact the\nmarine carbon cycle is uncertain.\nLines 47-48: For the sentence starting with “Despite N:P…”, you should clarify if you mean part. C:N “remained in a tight range” for both low N and low P conditions.\nLines 62-63: the use of “reflecting” here implies causation and that N:P variability is due to community structure, not\nacclimation. This seems to go against one of the references cited(Galbraith and Martiny, 2015) and a key point of paper.\nLine 68: I think “remained” should be replaced with “remains” since you are talking about the mechanisms continuing to be\nunresolved in general. If your intent was to simply say that Tanioka et al. 2022 did not resolve these mechanisms, you\nshould move the citation to the end of the sentence.\nLines 69-70: This sentence should be revised and expanded. Whether humans “understanding the mechanisms” of any\nocean processes has no effect on marine C cycling. If you’re saying that phytoplankton C:N:P and its response to nutrient\nsupply affects marine C cycling, you should say why this is the case (i.e., it affects efficiency of biological pump, etc.)\nLine 135: “…remained similar across…” could be taken to mean C:N was similar over time in each of these treatments\n(which does not appear to be the case). If the point is that C:N was similar in the +N-P and +N+P treatments, you should\nexchange “across” for “between” or clarify.\nLines 135-136: It’s hard to tell how different C:N is between the +N treatments and -N treatments at any of the sampling\npoints other than Day 10 and Day 18 (Fig. S3d). It seems the patterns in C:N among treatments are much more complex than\nthe statement here would imply.\nLines 138-144: The statements here seem to lack support. No curve fit values are provided for the data from this study.\nMoreover, a large number of data points from all treatments except -N+P appear to have high P:C values at low phosphate\nconcentrations and thus don’t fit the models shown (Fig S4). Also, the statement saying “patterns” that “reflect observations”\nfrom BATS seems to be based on one data point. Much more explanation and/or statistical analysis is needed to support\nthese statements based on the data that is shown.\nLines 135-144: In general, it seems statements like these regarding differences or similarities among well-replicated\nexperimental treatments should be supported by statistical analysis and more quantitative statements.\nLines 164-176: Overall, this section needs more clarity and explanation. First, evaluating this requires a deep dive in the\nSharoni and Halevy work. Additionally, the logic of the argument being made is not entirely clear. I don’t see shift a in both\ncommunity structure and particulate N:P after 25-30 days (a very long time for phytoplankton dynamics) due to +N-P\ntreatment is indicative of “community composition driven by N supply” being “an important mechanism driving particulate\nN:P”. Isn’t physiological acclimation to the +N-P conditions just as reasonable of an explanation for the observed particulate\nN:P? If the implication here is that particulate N:P in the -N+P treatment only diverged from the other treatments later in the\nexperiment and coincided with a shift in community composition, this is difficult to evaluate by comparing trends in N:P over\n30 days (based on 6 sampling days) with essentially one sampling point for eukaryotic phytoplankton composition. Lastly,\nthe fact that community composition appears very similar between +N+P and +N-P treatments, yet the particulate N:P of\nthese treatments is very different seems to undermine any importance of community composition as mechanism for\nshaping N:P patterns and indicate that physiological acclimation is the dominant mechanism.\nThe most salient things I see in the results with respect to community composition are:\n- The community was proximally limited by N rather than P or Fe\n- The growth fueled by N addition favored diatoms, at the expense of dinoflagellates and Prochlorococcus, as one would\nexpect\nOther arguments based solely on the community composition and C:N:P data are not clear.\nLine 210: “… regeneration of particulate P from detrital material”. Your meaning here isn’t clear. Do you mean that a greater\ncontribution detritus to particulate matter relative to live phytoplankton could also cause elevated particulate N:P? Or do you\nmean that phytoplankton growth fueled by regeneration rather than new production will have elevated N:P? If it’s the latter,\nthe reasoning for this is not clear.\nLines 220-222: There is very little discussion of these macromolecular results. And regarding their statistical analysis, this is\nnot explained in the methods, not stated consistently in this text, and not shown in Fig. S7, and is not explained in the\nmethods. Among the macromolecules measured, RNA is the most abundant and dynamic with respect to growth conditions,\nwhile DNA should be function of genome size rather than physiological acclimation. So it is very odd and worth explaining\nwhy RNA is not significantly different between +N-P and +N+P treatments, yet DNA is (sort of). Also these results seem\nsomewhat confounded by the Fe addition sub-treatments and this is not addressed.\nLines 222-253: I agree that the ATP, lipid, and AP data addressed here all indicate that the +N-P treatment experienced\nmore P-stress than the +N+P treatment. But none of these results provide direct explanation for the difference N:P or C:P\nbetween these treatments since ATP and phospholipids represent small fractions of total cellular P (see references #6, #35,\nas well as Garcia et al. 2016 doi: 10.1038/ismej.2016.50, Rhee 1973, Rhee 1978, and others). The P-rich cell contents that\nwould likely drive a change in C:N:P are RNA, DNA, and P storage (e.g. polyphosphates) and these either have\ninconclusive results or were not measured.\nLine 271: Based on the points made in the preceding comments, “confirm the role of physiological acclimation” is overly\nstrong language to use in regards to these transcriptomic data. Again, the ATP, AP, lipidomics, and transcriptomics all\nsupport the expected result that +N-P treatment experienced more P-stress than the +N+P treatment. I do not doubt that\nphysiological acclimation is the key mechanism at play here, but macromolecular data that would directly support this is not\ndiscussed. The strongest argument for physiological acclimation seems to be what I noted in the comments on Lines 164-\n176: The +N+P and +N-P treatments have similar communities, but distinct C:P and N:P at the end of the experiment.\nLines 220-276: When looking at all the biochemical results and their implications for showing physiological acclimation,\nthe elephant in the room seems to be P storage. If RNA and DNA do not explain differences in N:P and C:P, P storage seems\nlike the next suspect. Could P storage or polyphosphates be estimated from the biochemical measurements you have (i.e.\nsubtracting phospholipids and nucleic acids from total P) or from the transcriptomic data (i.e. differential expression of\npolyphosphate kinase or polyphosphatases)\nLines 302-303 and elsewhere: Arguing the N supply and physiological acclimation are key drivers of global C:N:P patterns\nseems to be in conflict with not addressing the variability in C:N observed in your experiments or assuming constant C:N in\nyour projections. A wealth of culture experiments show that N supply affects physiological acclimation of C:N. Your\nargument seems to be that:\n1) N supply increases in phytoplankton biomass and production in many parts of the ocean\n2) Without accompanying P supply, the phytoplankton growth promoted by N supply becomes P-limited\n3) The flexible P content of this new phytoplankton growth drives C:N:P variability as this secondary P-limitation sets in.\nIf this is the case, this needs to be laid out more explicitly, supported more directly, and other key aspects of this argument\n(C:N variability, the underlying causes of flexible P content) should be discussed.\nLines 372-374: Your meaning here is unclear. I think you’re trying to say that all treatments contained 6 replicate incubations\nexcept the -N+P treatment, but your wording doesn’t make it clear whether you removed some replicates from the -N+P\ntreatment or removed that treatment entirely. More importantly, you are saying that some replicates were removed from the -\nN+P treatment, but don’t say how many replicates were kept. The legend for Figure 1 simply says “n = 6” for the treatment\nreplicates. The explanation for why these replicates were removed (the had “anomalous values across datasets”) is also\nquite vague and needs explanation. What “values” and “datasets” do you mean here? Were the values anomalous\ncompared to the other replicates in the treatment or anomalous compared to what you’d expected for realistic values? With\nso much care and effort put into this experimental design, it seems that more explanation is warranted if some treatment\nreplicates are going to be excluded.\nLines 381-382: Do you have any estimates of flow rates during stirring or sampling? Since this impressive experimental\ndesign is meant to mimic natural conditions, it would be good to show that flow rates were not so high as to cause cell lysis.\nLine 403: The comma after “experiment” is unnecessary.\nLine 515: One of references given here (#79) appears to be incomplete in the list of references.", "reviewer_3": "Summary\nA mesocosm experiment was performed with samples collected in the North Pacific Subtropical Gyre. N and P\nmanipulations were performed to assess microbial community responses to variation in N:P supply ratios. In each of the N\nand P manipulations, various analyses were conducted, including 16S and 18S based assessment of microbial community\ncomposition, stoichiometric analyses, and biochemical indicators of nutrient stress. Results indicate that addition of N\npromotes carbon fixation and appeared to shift community composition toward diatoms regardless of whether P was added.\nElevated N:P (by N addition without P) caused a shift toward P stress as indicated by biochemical markers of P stress and a\nreconstruction of plankton N:P combining community composition with laboratory culture data.\nComments\nThe article is very clearly presented and the results are simple and easy to interpret. Based on my knowledge of the\nliterature the findings are novel with important implications for carbon cycling. The authors seem to suggest that their\nfindings could support an argument for N addition to natural systems to promote carbon storage. While this is a potentially\ncontroversial suggestion, with lots of potential flaws that are well-described, I do find the suggestion interesting and novel\nand I suspect will be of considerable interest to the Nat Comm readership. Overall I felt the experimental design and\npresentation of results was very clear and simple and I have no further comments."}, "author_response": "A tale of two nutrients: How nitrogen and phosphorus differentially control marine biomass production\nand stoichiometry\nPlease find our responses below each comment in blue text. Line numbers reflect the clean text, without\ntrack changes.\nReviewer #1 (Remarks to the Author):\nHere, Seelen and co-workers ran a mesocosm experiments amended with N or P. The underlying\nscientific aim is to quantify how varying nutrient supply rate and ratios regulate the physiology and\ncomposition of plankton communities and in turn the regulation of ecosystem C:N:P. The experiment\nmainly consists of four mesocosm treatments of (+/- N or P). There is also an Fe treatment which\nappears in the figures but is not used much in the main text. The mesocosms are analyzed using a\ndiverse set of biogeochemical and molecular approaches. However, I find the wide-ranging\nextrapolation to overall regulation of ocean C:N:P problematic.\nThank you for reviewing our manuscript. We have taken your comments into consideration as detailed\nbelow. Ultimately, we agreed that the extent of extrapolation regarding global-scale biogeochemical\ncontrols on C:N:P was in some cases unwarranted by our data and have scaled back to focus on\nbiological regulation within the surface ocean. The manuscript has thus been thoroughly edited to\nreflect this refined focus, and to provide more context for the study, including its limitations.\nThere are strength and weaknesses with the presented manuscript.\n1. In general, I like the idea of manipulating the nutrient input rates and ratios in mesocosms. This is an\ninteresting setup and complements both section profiles and culture experiments. The number of\nmeasured variables is also impressive. Thus, the findings showing changes to ATP, lipids, and other bio-\nmolecules are really interesting. These are important results.\nThanks for this positive feedback. We agree that the wide range of analyses presented here is a strength\nof the manuscript, and we hope showing such a widespread dataset for a single experiment not only\nstrengthens our interpretations, but also serves as a basis for future work interpreting similar analyses in\nculture and in situ.\n2. I also have some issues with the experiments. The complete eradication of Prochlorococcus is an\nimportant issue. This issue is briefly mentioned in the text (and I appreciate the supplementary figure),\nand I agree that it is common problem with any field manipulations. However, it does make it more\nchallenging when trying to link your mesocosm experiments with the broad-scale regulation of ocean\nC:N:P. This should be brought up in the discussion (see also below).\nThe loss of Prochlorococcus is a ubiquitous problem in incubation studies (e.g. Ref 18), and we agree\nthat the loss of Prochlorococcus presents a challenge for extrapolating the results to natural oligotrophic\nenvironments. Although we do not go into great detail on this, the abundance of Synechococcus\nincreased throughout all experimental treatments (Fig. S10b), so that the niche filled by\npicocyanobacteria was occupied in our experiments by a related genus. We have revised the manuscript\nto better acknowledge the limitation arising from the loss of Prochlorococcus (LINE 240-243). We more\nclearly state that that our N:P calculations are without contributions from cyanobacteria (LINE 262-269)\nand are used mainly to describe the changing ratios observed in our study, although we do compare the\nresults to field observations for context. In doing so, we have refined the text to more specifically focus\non how these experiments can test community composition and acclimation as mechanisms that\nunderpin shifts in particulate C:N:P stoichiometry.\nLINE 240-243: The decline of Prochlorococcus limits our ability to extrapolate results exactly to\noligotrophic environments, but the persistence of Synechococcus (Fig. S10b), SAR11, and\nRhodobacterales throughout the incubation (Fig. S9) show that other key oligotrophic bacteria were\nretained through the incubation.\nLINE 263-267: We narrowed our evaluation to the final experimental timepoints where most sample\ntypes were analyzed and included only the four major eukaryotic plankton groupings listed above due to\nthe absence of Prochlorococcus in our incubations (dinoflagellates and chlorophytes were grouped\ntogether5).\n3. Your +N experiments leads to a massive enrichment of diatoms. To me, this suggests that we are\nseeing opportunists responding to a sudden nutrient enrichment. I am interpreting your text as an\nevaluation of C:N:P regulation under N vs. P limitation but your results are more aligned with more or\nless nutrients. We have many N-limited ocean regions where Prochlorococcus and other picos\ndominate. Your results are interesting, but it does again make it harder to link your observations with\nocean C:N:P.\nYou are of course correct that there are many N limited regions where picocyanobacterial dominate,\nand we have now limited the broad extrapolations to ocean C:N:P in our study. Your feedback also\nhighlighted an important gap in our rationale which we have edited to better outline the study\nmotivation (LINE 265-268), which is focused on how oligotrophic communities respond to variations in\nnutrient supply, and the biological mechanisms that underpin resulting particulate C:N:P ratios.\nSpecifically, we added nutrients in a way we felt would be representative of naturally high and low\nnutrient supply environments (LINE 108) and aimed to simulate conditions resembling upwelling vs gyre\nnutrient supply, such as polar vs mid-latitude regions. We hypothesized that adding N would induce a\ndiatom bloom—consistent with our current understanding of nutrient driven community succession in\nthe NPSG (ref 24), natural N additions (e.g. Wilson, Hawco et al. 2019 Science), and as observed here—\nand thus lower the C:P and N:P ratio relative to the starting condition. If the community composition\nchanged but the C:N:P ratios did not follow, it would suggest that community alone could not account\nfor observed ratios and would support a role for acclimation. Both processes are dictated by nutrient\nsupply but in slightly different ways. So yes, the study is about high versus low nutrients (unrestricted to\nthe oligotrophic ocean), but also the ratio of that nutrient supply. Our findings allowed us to identify\ndifferences in how the community composition and acclimation influenced particulate C:N:P as a\nfunction of known nutrient supply, a critical step forward in understanding the how these factors\ndifferentially influence particulate C:N:P.\nLINE 268- 271: Comparing the calculated N:P ratios to the measured bulk particulate N:P ratios indicated\nwhether the observed values were driven by communities optimized to local nutrient supply (mechanism\n1) or were influenced by acclimation to the nutrient environment (mechanism 2).\nLINE 106-109: An oligotrophic plankton community was collected in the North Pacific Subtropical Gyre\nand supplied nutrients once daily (except days 5 and 6) to mimic high and low nutrient supply regions of\nthe ocean, and for 29 days to capture community shifts arising from fast and slower growing organisms.\n4. When you look closely at the data, it does seem that biomass is starting to separate between -N+P\nand -N-P. I am wondering if you are starting to see the impacts of N fixation. In the text, it is stated that\nthese two treatments share the response – but your data says otherwise. I would expect that feedbacks\nfrom Nfixation would be slow. This is a non-trivial issue because it leads to a very different\nbiogeochemical model than presented – ie. that it is the supply of P that is important (like Tyrrell is\narguing).\nYes, there is a noticeable split between +N-P and +N+P treatments (and within the -N treatments) for\nsome datasets, however, the treatment averages are, for the most part, not statistically different (Tukey\nHSD, p > 0.05, Table S3-S5). Statistical comparisons are now presented in greater detail that help clarify\nthis point, but have acknowledged some divergences in the text (e.g., LINES 145-146, 159-162). Your\npoint on Nfixation is very interesting. While our incubation experiments were continued for much longer\nthan typical incubation experiments, and long enough to potentially see response from diazotrophs (e.g.\nBonnet et al. 2016 Biogeosciences found an increase in N fixation ~10 days after phosphate addition), it\n2\nis likely that our month-long timescale was insufficient to reflect nutrient balancing processes in the\noceans. We have modified our manuscript to better clarify that while our conclusions hold over the\n~month timescale of our experiments, further exploration would be required to know how these\nprocesses develop over longer periods of time (LINES 419-422). Indeed, the slight increase in biomass in\nthe -N+P relative to the -N-P incubations noted by the reviewer is an intriguing hint that nitrogen\nfixation may have started to impact nutrient dynamics at the very end of our experiment, but this\nincrease was not significant (Tukey HSD, p > 0.05). This is now discussed in more detail in our\nmanuscript, including a discussion of the signs of diazotrophy (LINE 146-147, 247-249), actual diazotroph\nabundances (LINE 245-247) and note their significant role in regulating marine productivity in the\nimplications (LINE 250-253).\nLINE 245-253: The relative abundance of diazotroph species increased in all -N treatments, but rarely in\nthe +N treatments except for a notable increase in Richelia in the +N+P-Fe treatment (Fig. S11). Even\nthough diazotroph abundances increased in all -N treatments, only the -N+P treatments had elevated\nphotosynthetic rates, particulate N, and chl a concentrations at the end of the experiments suggestive of\na N subsidy. We note that the relative abundance of diazotrophs differed based on P and/or Fe supply\n(Fig. S11) which is important to consider for long-term nutrient regulation in the ocean27. Our\nexperiment duration and lack of quantitative N2 fixation rates leave us unable to confirm the role of\ndiazotrophs in our study or their role in regulating particle stoichiometry.\nLINE 419-422: These high N:P ratios persisted for at least the 29-day timescale of our experiments,\nmotivating future research into the timescales at which natural ocean communities restore N:P towards\n16:1 and the mechanisms which drive this process.\n5. The manuscript aims at disentangling community composition vs. physiological regulation. You seem\nat something very specific in mind with ‘community composition’ – ie. major taxa. But what is\ncommunity composition? Is it only broad groups or do genomic changes also count? Personally, I find it\nreally hard to separate physiology and composition as they are so intertwined. You interpret the\nchanges in AlkP, lipids, etc. to come from change in physiological regulation. However, their regulation\ncan easily be related to organism. Here, it may be worth checking out Moreno and Martiny, 2018\n(Ecological Stoichiometry of Marine Plankton) as this review gives a careful treatment of the issue of\nphysiological vs. eco-evo regulation of C:N:P. In general, I think there is some phylogenetic structuring of\nbiomass composition as well as physiological regulation. Thus, it is not a binary issue. Instead,\nknowledge lies in understanding the strength of each process as well as at what phylogenetic level do\nbiomass composition vary across lineages. So I think the manuscript would benefit from presenting the\nobserved results without this thick layer of interpretation of the biological regulation. You can then\ndiscuss how you interpret the regulation of these changes while also describe the assumptions you are\nmaking.\nYou correctly state that our community composition is defined by grouping major taxa, smoothing over\ngenomic differences within those groupings. We agree that the changes attributed to ‘physiology’\ncannot be fully separated from those resulting from changes in community structure with that\napproach, and in general is quite challenging. The text has been revised to clarify that such a distinction\nis unresolvable in a rich ecological community such was present in our experiments, and to more clearly\ndefine what we mean by attributing observations to ‘physiological regulation’. We also added in a figure\ndepicting Bray-Curtis dissimilarity between the treatments versus the N:P Euclidean distance to help\nunderstand whether our interpretation was influenced by the taxonomic grouping (Fig. S7).\nLINE 192-194: Most eukaryotic reads (> 62 % in each sub-treatment) belonged to four major marine\nphytoplankton groups: diatoms, dinoflagellates, chlorophytes, and haptophytes (Fig. 2) and were\ntherefore the focus of our analysis.\nLINE 217-231: We make the assumption that the relatively similar elemental make-up of species within\nour broad taxonomic groupings is sufficient for evaluating community-level regulation of particulate\nC:N:P, but acknowledge that taxa within these groups have unique nutrient requirements and\nphysiological capabilities15,21. To evaluate the relationship between ASV relative abundances and N:P\nstoichiometry, we plotted Bray-Curtis dissimilarity between pairs of treatments as a function of the\nEuclidean distances of their N:P ratios (more details in the Statistical Methods). A significant, positive\nrelationship was found between the community dissimilarity and N:P Euclidean distance (Spearman ρ =\n0.26, p < 0.001), indicating that large differences in community composition were associated with large\ndifferences in N:P ratios. This finding suggests that on average, similar communities in terms of\ncomposition will also have similar N:P stoichiometry supporting mechanism 1. Importantly, the +N\ntreatments where Pseudo-nitzschia remained dominant (+N+P+/-Fe, +N-P+Fe) did not follow this trend,\nnoting that these points fell distinctly away from the regression line having similar communities with\ndiffering N:P expression supporting mechanism 2 (Fig. S7; yellow triangles in top left quadrant).\n6. The stated aim is to test the biological regulation of C:N:P biomass composition (physiology vs.\ncomposition). However, the discussion is centered on the biogeochemical regulation of C:N:P. Thus,\nthere is a strong disconnect between on the one hand the stated aim and observations vs. the\ndiscussion. This makes the discussion very speculative. The ideas in the discussion are definitely\ninteresting (even if I disagree with some it). However, the mesocosm data cannot be used to as\n‘evidence’ for such wide-ranging claims. One reason is the replacement of Prochlorococcus with diatoms\nin your experiment – something that is exceedingly unlikely in the oligotrophic ocean.\nThank you for this comment. We initially approached this manuscript with a more biogeochemistry-\nfocused mindset, which led to some of the ideas previously held in the discussion. Upon revision, we\nagreed that these speculations were broad and moved some ideas to the implication section and\nremoved others all-together. The manuscript is therefore updated to focus more on the biological\nregulation of C:N:P under the two stated mechanisms (composition vs physiology) while considering that\nthe lack of Prochlorococcus means that our outcomes inherently differ from natural oligotrophic waters\n(particularly for the -N treatments).\nThe new starting paragraph to the implications helps describe this point:\nLINES 382-397: Here we show that an oligotrophic phytoplankton community from the North Pacific\nsubtropical gyre transformed to a diatom-dominated community in response to N supply with just a\nsmall contribution of cyanobacteria (9 %) to the estimated diatom plus cyanobacterial biomass\n(Methods). Dinoflagellates dominated the eukaryotic community and cyanobacteria constituted a larger\nfraction of the estimated biomass (30 %) in treatments without N addition. Particulate N:P ratios\nreflected the characteristic N:P ratios of the eukaryotic communities as if they were grown under\nnutrient replete conditions (Fig. 4a), until phosphate was depleted to < 10 nM in the +N-P treatments.\nOnce phosphate was depleted in the +N-P treatment, the diatom-based community had N:P ratios\nresembling diatoms grown under P-stress conditions in culture5, and expressed further signs of\nacclimation both in bulk community measures (e.g., elevated AP activity) and at the cellular level (here\nshown with Pseudo-nitzschia metatranscripts). These results support a role for both community\ncomposition (mechanism 1) and acclimation (mechanism 2) as having an influence\nover how biology mediates particle stoichiometry in mixed phytoplankton communities.\nMinor comments:\nL38: I am not sure this statement is right about the mean being 16. The few treatments of this problem\nsuggest a significantly higher value.\nWe updated the sentence to: Globally, nitrate and phosphate are delivered from deep to surface waters\nand measured in marine particles at molar ratios near 16:1, the N:P “Redfield ratio.” Line 38-40\nL105: I think it would be worth to be more specific about what you mean with many generations. 29\ndays is a great experiments but short for ecological re-arrangements – e.g., changes in Nfixer\nabundances.\nTo clarify the study and its objectives in response to your major comments, this paragraph has been\nrewritten and no longer includes this sentence.\nL158: I agree that this decline in Pro is often observed but that is also an issue for using such\nexperimental setups for simulating oceanographic processes.\nWe have edited the text to acknowledge this limitation.\nLINE 241-244: The decline of Prochlorococcus limits our ability to extrapolate results exactly to\noligotrophic environments, but the persistence of Synechococcus (Fig. S10b), SAR11, and\nRhodobacterales throughout the incubation (Fig. S9) show that other key oligotrophic bacteria were\nretained through the incubation.\nLINE 263-267: We narrowed our evaluation to the final experimental timepoints where most sample\ntypes were analyzed and included only the four major eukaryotic plankton groupings listed above due to\nthe absence of Prochlorococcus in our incubations (dinoflagellates and chlorophytes were grouped\ntogether5).\nL186: I cannot see any data presented that support this statement. There are several theories for why\nN:P is 16 (or really close to 14) in the deep ocean including ocean mixing + denitrification (so nothing\nrelated to phytoplankton biology).\nThis entire paragraph was removed from the discussion as we felt it extrapolated too far from our\nexperimental outcomes. However, a similar sentiment is now included in the implications section but\nworded differently.\nLINE 403-415: Under the condition of low surface nutrient N:P, as is true for most of the global ocean\nexcept for the Mediterranean Sea43 and seasonally in the North Atlantic Gyre44, we suggest that\ncommunity composition is most directly influenced by N supply rates. The general availability of P\nrelative to N allows for the N-selected phytoplankton groups to assimilate nutrients near their optimums,\nwhich converge toward the Redfield ratio and means that community composition is most often\npredictive of particle stoichiometry in our modern ocean5. In short, P does not limit growth in most of the\nmodern ocean, and N is the ‘proximal’ limiting nutrient as described by Tyrrell et al.27. Once a system is P\nlimited (here < 10 nM P, although that may fluctuate based on community type and other confounding\nfactors), acclimation becomes more important as cells modify their physiology to remain competitive\nunder such non-ideal growth conditions, supporting the phosphate frugality hypothesis4.\nL302: Although C:N does not vary as much as N:P, it is not constant either. One example is Lee et al.,\n2021 from the South Pacific. Here you can see quite elevated C:N in the center of the gyre.\nThis portion of the discussion has been removed, but the point is still valid. We have added more\ndiscussion on C:N ratios further up (LINE 155-168) in the text to elaborate on the C:N patterns we\nobserved, and more clearly specify why we focus on N:P and C:P instead of C:N.\nLINE 181-184: While phytoplankton species have variable C:N requirements and undergo physiological\nchanges in response to N-stress important to the regulation of the marine C cycle21, the lack of significant\ndifference in our observed C:N ratios led us to focus the remainder of this discussion on the acclimation\nresponses to P-stress.\nFigure 1: It does seem that -N+P is starting to separate from -N-P in terms of biomass. Could it be that\nNfixation is slow to catch up.\nThis observation has been added to the text. Originally, we did not discuss the potential role of\ndiazotrophs because we do not have N2 fixation rate measurements to confirm this hypothesis, but\nafter your comments felt it was worth noting, e.g., LINE 245-253 as pasted above.\nAdam Martiny\nReviewer #2 (Remarks to the Author):\nSummary\nThe submission by Seelen et al. presents a mesocosm experiment that examines the effects of N and P\nsupply on the stoichiometry (C:N:P) of surface ocean particulate matter and the underlying drivers of\nthese effects. Using a comprehensive experimental design and waters collected from the North Pacific\nSubtropical Gyre, The authors show that N supply drives changes in phytoplankton biomass and\ncommunity structure while the subsequent effects of physiological acclimation to low P availability\nfollowing increased N supply phytoplankton production drive changes in particulate C:N:P. This study\noffers an ambitious and well-replicated experimental design and a diverse set of measurements to\nassess its results. Despite these strengths, many of the most relevant results lack statistical support and\nare given little discussion. This manuscript also conveys interesting conclusions regarding the\nconsequences of ocean C:N:P variability but seems to drift from addressing its initial focus, which is\nexamining the relative importance of the mechanisms that cause ocean C:N:P to vary. Overall, this\nwork represents a relevant and valuable data set, but requires more thorough analysis and discussion.\nWe appreciated your thorough review, which has improved the manuscript. We have added statistical\ntests where appropriate and have thoroughly edited the text to focus on these data and their\ninterpretation in the context of the relative importance of the mechanisms that cause ocean C:N:P to\nvary. We removed the broad biogeochemical implications at the end of the text. More details are\nprovided in the below comments.\nGeneral Comments\n- The issue of C:N variability in phytoplankton and the interpretation of particulate C:N data in this\nmanuscript should be re-evaluated. Simply stating that the observed C:N data are “within a tight range”\nis not well supported and seems off the mark in the context of global C:N:P variability. The overall range\nof C:N observed globally and in culture experiments is inherently small compared to variability in N:P\nand C:P, yet a shift in phytoplankton C:N from 8 to 6 would still represent a large reallocation of\nmaterials within phytoplankton cells and an impact on export flux of C and N.\nWe agree with the reviewer that even though C:N ratios vary less than C:P ratios, the amount of\nparticulate C fixed (or stored as detritus) relative to N is an important variable to consider when thinking\nabout biological regulation of the C cycle and nutrient utilization in general. Although there were\npatterns in the C:N ratios in our experiment, the difference between treatments at each timepoint, and\nwithin a single treatment over time, were not significant (Tukey HSD, with significance defined as p <\n0.05). However, the data show that the treatments with added N had on average higher C:N ratios than\nthose without N addition, which one might expect from the relief of N-limited growth after the nutrient\nspike was added and the community shift toward diatoms. We feel that the similar pattern across all\ntreatments (slight dip then slow increase in values throughout the experiment) is likely a sign of cellular\nacclimation. One explanation could be the supplied light levels. We have now expanded the discussion\non the patterns in C:N ratios in the main text including p-values from Welch’s t-test for multiple\ncomparisons. Full statistical output is given in a supporting excel file (Tables S2-S5).\nLINE 155-168: Particulate C:N molar ratios ranged from 5.5 to 14.1 across all samples analyzed (mean\n9.0; median 8.9), including the starting fill water replicates (range 6.5 to 12.0; Fig. S3d). There were few\nsignificant differences in C:N ratios across treatment groups or within a treatment over time (Tukey HSD\nsignificance here defined as p < 0.05; details in Table S3, S5). Yet, two distinct patterns emerged. First,\nthe +N treatments had on average higher C:N ratios than the -N treatments for all days sampled, but\nparticularly magnified on day 11, the first analysis day when diatom abundances became dominant in\nthe +N treatment eukaryotic composition. Although this pattern was not significant, it is consistent with\nboth an alleviation of N-limited growth and related community responses (see below). Second, all\ntreatments, even those without N addition, had increasing C:N ratios until experiment day 18, after\nwhich C:N stabilized. The similar time-trend in all treatments is suggestive of an acclimation response,\nwhich we hypothesize could be due to the light levels of the incubation21 (Fig. 1d, S3d).\nLINE 181-184: While phytoplankton species have variable C:N requirements and undergo physiological\nchanges in response to N-stress important to the regulation of the marine C cycle21, the lack of significant\ndifference in our observed C:N ratios led us to focus the remainder of this discussion on the acclimation\nresponses to P-stress.\n- A key factor that is not addressed in this study are the respective time-scales of the two mechanisms\nthat affect phytoplankton C:N:P (changes in community structure vs. physiological acclimation). The\nexperiments in this study span a 30-day period, but the time-scales over which acclimation or\ncommunity composition would be expected to change are not discussed and additional estimates that\nwould help a reader to evaluate this (i.e. estimates of community growth rates or losses) are not\nattempted. Additionally, the relevant measurements needed to evaluate the relative impact of these\ntwo mechanisms (i.e. particulate C:N:P, picocyanobacteria abundance, eukaryotic phytoplankton\ncomposition, biochemical composition) were made or are presented with different frequencies. I\ncompletely understand the practical limitations of limited sampling time and biomass, but these\nlimitations should be addressed and acknowledged when making conclusions.\nWe thank the reviewer for this valuable feedback, and now explicitly discuss the expected response\ntime-scale for each mechanism (e.g. LINE 101-104, 106-109, 282-286). In the revised manuscript we also\nnow note the sampling frequency in more places and created a table that outlines when each sample\nwas collected (Table S1). We hope this more clearly outlines the sampling frequencies, which were\nsometimes offset, or only available at the end of the experiment, because of water and biomass\nrestrictions.\nHow fast community composition changes in response to nutrient conditions—here focused on primary\nproducers—is indeed a function of the growth rate and losses of each community member. While we\ndon’t have a mechanism for estimating losses, we do have community composition over time and see\nthe relative abundance of diatoms shift in all +N treatments by day 11, coincident with shifts in C:N\nrelative to other treatments (LINE 160). We note that the experiment was designed to try and capture\nthe response of both fast (e.g. diatoms) and slower growing organisms (e.g. diazotrophs) (LINE 282-286),\nwhich can take days or weeks to become apparent in the data . To address this, we also now discuss the\npotential influence of diazotrophs and longer time-scale shifts in community composition and their\npotential influence on C:N:P (LINE 245-253). Sensing and responding to a shift in resources can happen\non the order of minutes to hours for the microeukaryotes we focus on here (ref 28). However, the time-\nscale of when the resulting acclimation response may manifest with a measurable change in bulk C:N:P,\nmay be longer and is poorly constrained. For example, it takes >24 hrs for diatoms to significantly reduce\ntheir lipid P pool when cells are transferred into no P medium, but <24 hrs for P-stressed diatoms to\nincrease their P lipid pool when they are exposed to new phosphate (ref 38). These dynamic responses\nwhich happen distinctly for different community members can make interpreting acclimation signals\nparticularly challenging. In our study phosphate in both +N-P and +N+P treatments was drawn down to\nsimilarly low levels by day 11 (Fig S1) and this potentially points to why some of the biochemical\nmeasurements like lipid profiles are correlated with phosphate concentration and not the supply\ntreatment (e.g. there is a rapid <24 hr drawdown of P in the +N+P treatments). Regardless, there were\nconsistent changes in P stress markers, P sensitive biochemical pools, and shifts in the C:N:P between\nsupply treatments that are remarkably consistent with a P acclimation response mechanism given the\nchallenges noted above. In sum, we have edited the manuscript as suggested and make particular note\nof how the two mechanisms may drive changes in C:N:P across different time scales (in particular, see\nLINE 268-277, 328-332, 382-397).\nLINE 268-277: Comparing the calculated N:P ratios to the measured bulk particulate N:P ratios indicated\nwhether the observed values were driven by communities optimized to local nutrient supply (mechanism\n1) or were influenced by acclimation to the nutrient environment (mechanism 2). In all but the +N-P\ntreatments, the observed ratios (which include all living organisms and detritus) resembled the\ncalculated N:P ratios under nutrient-replete conditions consistent with mechanism 1 (Fig. 4a). The +N-P\ntreatments, where N was continuously supplied more than 16:1 N:P, exhibited a particulate N:P\nstoichiometry reflective of P-stressed eukaryotic plankton in culture5 suggesting the importance of\nmechanism 2 in these treatments.\nLINE 330-333: Additionally, the nucleotide adenosine monophosphate was significantly reduced relative\nto adenosine in the +N-P treatments (Welch’s t-test, p = 0.0013, Fig. 3f), as previously demonstrated for\nP-stressed eukaryotic cultures and P-stressed natural communities31 congruent with an acclimation\ninterpretation (mechanism 2).\nLINE 382-397: Here we show that an oligotrophic phytoplankton community from the North Pacific\nsubtropical gyre transformed to a diatom-dominated community in response to N supply with just a\nsmall contribution of cyanobacteria (9 %) to the estimated diatom plus cyanobacterial biomass\n(Methods). Dinoflagellates dominated the eukaryotic community and cyanobacteria constituted a larger\nfraction of the estimated biomass (30 %) in treatments without N addition. Particulate N:P ratios\nreflected the characteristic N:P ratios of the eukaryotic communities as if they were grown under\nnutrient replete conditions (Fig. 4a) supporting mechanism 1, until phosphate was depleted to < 10 nM in\nthe +N-P treatments supporting mechanism 2. Once phosphate was depleted in the +N-P treatment, the\ndiatom-based community had N:P ratios resembling diatoms grown under P-stress conditions in culture5,\nand expressed further signs of acclimation both in bulk community measures (e.g., elevated AP activity)\nand at the cellular level (here shown with Pseudo-nitzschia metatranscripts). These results support a role\nfor both community composition (mechanism 1) and acclimation (mechanism 2) as having an influence\nover how biology mediates particle stoichiometry in mixed phytoplankton communities.\n- The Methods section should include what days each type of parameter was sampled. The main text\ndoes not make the frequency of measurements clear and the reader is left to sort this out from the\nfigures alone and assume that all sampling points are being shown. For example, the main text says that\n“community composition was measured throughout the incubation”, yet the methods do not say which\ndays or how many community composition was sampled and the figures show four sampling points for\npicocyanobacteria (Fig. S6) and the average of two sampling points near the end of the experiment\nfor eukaryotes (Fig. 2).\nThe sampling design is now more clearly presented. In particular, we added a new table listing each\nparameter and what days it was analyzed in the supplemental material (Table S1). We also now provide\nthe full time-series results for the ASV derived eukaryotic and prokaryotic communities (Fig. S5 and S9),\nwhich allows us to more easily demonstrate how the community composition changed in response to\nnutrient supply.\n- Many of the key findings (such as trends in C:N:P and changes in community composition) are given\nlittle quantitative discussion and no statistical analysis. This would be understandable for work based on\nlarge-scale field observations, but it seems to be a major omission for a controlled and well-replicated\nexperiment.\nWe have added statistical output to our data comparisons whenever possible and agree that it\nstrengthens our arguments. Most of the time we used Tukey HSD post-hoc tests to evaluate differences\nbetween the means of our treatments, and now include four tables of statistical output as a supporting\nexcel file (Tables S2-S5). We also now include p-values in our boxplot figures (Welch’s t-test) and in the\ntext as needed. The figures were updated with accurate n values for the statistical output. Changes were\nmade throughout the text, a few examples included:\nLINE 157-159: There were few significant differences in C:N ratios across treatment groups or within a\ntreatment over time (Tukey HSD significance here defined as p < 0.05; details in Table S3, S5).\nLINE 168-171: In contrast, the C:P and N:P ratios were dependent on P supply, with significantly higher\nC:P and N:P in the +N-P treatments after day 18 of the experiment (Tukey HSD p < 0.05; Table 3), and\nsimilarly low ratios in the other three treatment groups (Figs. 1c,e,f and S3e,f; p > 0.05, Table 3).\nLINE 195-198: Significant changes to the eukaryotic community occurred in response to N supply over\ntime (ANOSIM; p < 0.05, Fig. 2a). Adding N led to an increase in the relative abundance of diatoms such\nthat they constituted the majority of eukaryotes from day 11 and onward (Fig. S5).\n- Although it seems that the communities examined were not Fe-limited, there are some potentially\nimportant confounding effects of Fe that are not addressed (for instance, Figures S7, S8, and S10).\nOverall, I don’t object to pushing the issue of Fe effects aside and focusing on N and P that are clearly\nmore important, but the data shown would generate a lot of concern for a more skeptical reader.\nAdditionally, the outlining used to denote +Fe and -Fe sub-treatments in any of the figures that are\nnot column graphs is very difficult to see.\nWe agree that there are some small differences between the +Fe and -Fe treatments that are intriguing,\nthough a thorough investigation is beyond the scope of this manuscript which focuses on\nmacronutrients. Further, we found no significant differences between the +Fe and -Fe sub-treatments\nwithin each treatment group for the particulate C, N, P, chl a, and GOP as shown in the supporting\ninformation (Table S2).\nLINE 123-126: Overall, Fe had no significant influence on the concentration of particulate C, N, P,\nchlorophyll a (chl a), and gross oxygen production at any point in time within the N:P groupings\n(distribution of pairwise p-values indistinguishable from chance, Table S2), but the sub-treatments are\ndifferentiated in figures for data reporting and transparency.\nSeveral changes have been made to the figures (main and supporting) that make differentiating the sub-\ntreatments easier, and we feel better represent the effect of iron on the treatment outcomes. For\nexample, instead of using grey and black borders around points to differentiate -Fe from +Fe, we use\npoint fill colors that are either white or black. When able, the point shapes also differ between the Fe\nsub-treatments. Also, instead of presenting separate boxplots for the sub-treatments in Fig 3, S13, and\nS14 (Fig. S7 and S8 in the original file), we present them as the treatment groupings (+N-P, +N+P) and\nadded the individual data points over the boxes. This way, the variability in the points is more evident\nand the differences between the +Fe and -Fe treatments better depicted. The effect of iron is now\npointed out in a couple places, in particular:\nLINE 249-251: We note that the relative abundance of diazotrophs differed based on P and/or Fe supply\n(Fig. S11) which is important to consider for long-term nutrient regulation in the ocean27.\nLINE 318-320: A closer look at the DNA normalized to particulate C (Fig. S13b) reveals that the difference\nin treatment means is influenced by low DNA concentrations in the +N-P-Fe sub-treatments.\n- Overall, the focus of the paper seems to drift away from its initial objective: evaluating the relative\nimpact of community structure and physiological acclimation on particulate C:N:P in response to\nnutrient supply. Large portions of the paper are dedicated to results that demonstrate P-stress (ATP, AP,\nlipidomics, transcriptomics) but do not explain bulk changes in cell composition that could cause the\nobserved changes in C:P and N:P. Likewise, much of concluding paragraphs focus on the implications of\nvarying N:P for the marine C cycle as a result of potential nutrient supply scenarios with unclear\nlikelihoods. It seems that much of this space could have been dedicated to using the data that was\ncollected to explore the underlying factors that allow community structure or acclimation to affect C:N:P\nand the relative importance of these two mechanisms, rather than the long-term consequences of C:N:P\nvariability.\nMuch of the implications for marine C cycling have now been removed in favor of expanding text on\nhow biology regulates C:N:P signatures of surface ocean particles as requested, specifically as a function\nof optimal cellular nutrient requirements which differ by community composition and as a function of\nacclimation as impacted by nutrient supply. We have also included more discussion for how we think\nthe macromolecular composition of cells impact bulk stoichiometry under low P availability (LINES 349-\n355). Unfortunately, this section remains speculative as we were unable to measure polyP and our\nsamples were collected on dissimilar filters making a mass balance challenging. This is further outlined in\nthe specific comments below. We decided to keep the implications as a discussion on how changing\nnutrient supply to surface waters could impact total C fixation and particulate C:N:P. However, as the\nreviewer suggested we have greatly shortened this section in order to focus more clearly on the relative\nimportance of the two mechanisms.\nLINE 349-355: The amount of P in the molecular pools (ATP, DNA, RNA, phospholipid) as a fraction of\ntotal particulate P was similar between the +N treatments (Fig. S14e, Welch’s t-test, p = 0.58). However,\nwhen normalized to particulate C, the relative abundance of all P-containing molecules was higher in the\n+N+P treatments compared to the +N-P treatments (C:P= 503.2 and 249.8, respectively; Welch’s t-test, p\n= 0.0091; Fig. S13e). This demonstrates that while P stress led to an overall decrease in community C:P,\nthe decrease was not attributable to one of these specific macromolecular pools.\nSpecific Comments\nLines 42-43: The sentence starting with “How these mechanisms…” needs to be reworded. It either\ncontains a typo or is not grammatically correct. Also, it is not clear if you were trying to say that how the\nmechanisms listed influence phytoplankton growth and stoichiometry is uncertain or if you are trying to\nsay that how phytoplankton growth and stoichiometry impact the marine carbon cycle is uncertain.\nThe sentence has been updated to: How these mechanisms influence plankton growth and\nstoichiometry, and further dictate the marine carbon cycle, remains uncertain (LINE 42-43).\nLines 47-48: For the sentence starting with “Despite N:P…”, you should clarify if you mean part.\nC:N “remained in a tight range” for both low N and low P conditions.\nThe sentence has been removed during the course of our revisions.\nLines 62-63: the use of “reflecting” here implies causation and that N:P variability is due to community\nstructure, not acclimation. This seems to go against one of the references cited(Galbraith and Martiny,\n2015) and a key point of paper.\nWe intended to say that field observations of N:P vary at ranges captured both by community structure\nand cellular acclimation. The sentence has been revised to clarify this point:\nLines 61-65: Observed N:P ratios of surface particles, however, vary latitudinally3 at a range notably\ncaptured by both the N:P requirements of different phytoplankton species4,5—an attribute of species\nadaptation to specific growth conditions—and the measured nutrient flexibility within individual taxa—\nan attribute of cellular acclimation to changing environmental conditions6–8.\nLine 68: I think “remained” should be replaced with “remains” since you are talking about the\nmechanisms continuing to be unresolved in general. If your intent was to simply say that Tanioka et al.\n2022 did not resolve these mechanisms, you should move the citation to the end of the sentence.\nThe end of this paragraph has been revised to:\nLines 66-72: Nutrient supply rates, in particular, have been linked to patterns in particulate C:N:P ratios\nin field observations, especially in the tropics and subtropics9, and inverse models support a relationship\nbetween nutrient supply and observed particulate biomass N:P stoichiometry from polar to sub-Antarctic\nlatitudes10. Biological regulation of particulate C:N:P has significant implications for the efficacy of the\nmarine C pump11,12. Yet, the mechanisms linking nutrient supply and biological regulation of particulate\nC:N:P remain unresolved.\nLines 69-70: This sentence should be revised and expanded. Whether humans “understanding the\nmechanisms” of any ocean processes has no effect on marine C cycling. If you’re saying that\nphytoplankton C:N:P and its response to nutrient supply affects marine C cycling, you should say why\nthis is the case (i.e., it affects efficiency of biological pump, etc.)\nThis is a good point, and the sentence has been revised to: Biological regulation of particulate C:N:P has\nsignificant implications for the efficacy of the marine C pump12,13. Yet, the mechanisms linking nutrient\nsupply and biological regulation of particulate C:N:P remain unresolved. (Lines 69-72)\nLine 135: “…remained similar across…” could be taken to mean C:N was similar over time in each of\nthese treatments (which does not appear to be the case). If the point is that C:N was similar in the +N-P\nand +N+P treatments, you should exchange “across” for “between” or clarify.\nWe switched across for between as suggested, and made several changes to this section for clarity:\nLINE 157-168: There were few significant differences in C:N ratios between treatment groups or within a\ntreatment over time (Tukey HSD significance here defined as p < 0.05; details in Table S3, S5). Yet, two\ndistinct patterns emerged. First, the +N treatments had on average higher C:N ratios than the -N\ntreatments for all days sampled, but particularly magnified on day 11, the first analysis day when diatom\nabundances became dominant in the +N treatment eukaryotic composition. Although this pattern was\nnot significant, it is consistent with both an alleviation of N-limited growth and related community\nresponses (see below). Second, all treatments, even those without N addition, had increasing C:N ratios\nuntil experiment day 18, after which C:N stabilized. The similar time-trend in all treatments is suggestive\nof an acclimation response, which we hypothesize could be due to the light levels of the incubation21\n(Fig. 1d, S3d).\nLines 135-136: It’s hard to tell how different C:N is between the +N treatments and -N treatments at any\nof the sampling points other than Day 10 and Day 18 (Fig. S3d). It seems the patterns in C:N among\ntreatments are much more complex than the statement here would imply.\nWe have edited the manuscript to clarify this point. There is quite a bit of variability in the C:N ratios,\nmost of which is not significant between treatments (except between the +N+P treatments and the -N\ntreatments on day 11). We have hypotheses as to why the C:N ratio changed that is now elaborated in\nthe revised text: (LINE 166-168): The similar time-trend in all treatments is suggestive of an acclimation\nresponse, which we hypothesize could be due to the light levels of the incubation21 (Fig. 1d, S3d).\nWe also clarified why our focus was on C:P for the majority of the text: LINE 181-185: While\nphytoplankton species have variable C:N requirements and undergo physiological changes in response to\nN-stress important to the regulation of the marine C cycle21, the lack of significant difference in our\nobserved C:N ratios led us to focus the remainder of this discussion on the acclimation responses to P-\nstress.\nLines 138-144: The statements here seem to lack support. No curve fit values are provided for the data\nfrom this study. Moreover, a large number of data points from all treatments except -N+P appear to\nhave high P:C values at low phosphate concentrations and thus don’t fit the models shown (Fig S4). Also,\nthe statement saying “patterns” that “reflect observations” from BATS seems to be based on one data\npoint. Much more explanation and/or statistical analysis is needed to support these statements based\non the data that is shown.\nThe dataset was indeed too variable (and too few points) to find a significant relationship between the\nP:C and phosphate concentrations. Instead, the point of this section was to acknowledge that there are\nseveral proposed relationships between those variables and we agree that it is likely a power law\nrelationship (Fig S4).\nLines 175-178: Our data support a non-linear, power law relationship between local phosphate\nconcentration and particulate C:P ratios23 (Fig. S4), though the dataset was too small and variable to\nascribe significance to this pattern.\nThe relation to BATS is now clarified through text (which is still more observational than statistically\nbased), and further demonstrated by showing the range of values observed at BATS and HOT in fig S4.\nLINE 172-175: Observations of high particulate C:P ratios when phosphate concentrations were low in\nthe +N-P treatments are consistent with observations made in the Bermuda Atlantic Time-series Study22\n(Fig. S4), and are within range of ratios observed when phytoplankton are grown in P-limited media5,6\nLines 135-144: In general, it seems statements like these regarding differences or similarities among\nwell-replicated experimental treatments should be supported by statistical analysis and more\nquantitative statements.\nWe now have edited the manuscript to reflect a full series of statistical analyses and our more detailed\ndiscussion of the significant patterns.\nLines 164-176: Overall, this section needs more clarity and explanation. First, evaluating this requires a\ndeep dive in the Sharoni and Halevy work. Additionally, the logic of the argument being made is not\nentirely clear. I don’t see shift a in both community structure and particulate N:P after 25-30 days (a very\nlong time for phytoplankton dynamics) due to +N-P treatment is indicative of “community composition\ndriven by N supply” being “an important mechanism driving particulate N:P”. Isn’t physiological\nacclimation to the +N-P conditions just as reasonable of an explanation for the observed particulate\nN:P? If the implication here is that particulate N:P in the -N+P treatment only diverged from the other\ntreatments later in the experiment and coincided with a shift in community composition, this is difficult\nto evaluate by comparing trends in N:P over 30 days (based on 6 sampling days) with essentially one\nsampling point for eukaryotic phytoplankton composition. Lastly, the fact that community composition\nappears very similar between +N+P and +N-P treatments, yet the particulate N:P of these treatments is\nvery different seems to undermine any importance of community composition as mechanism for\nshaping N:P patterns and indicate that physiological acclimation is the dominant mechanism.\nTo clarify this section, our approach is now more clearly outlined including how we calculated and\ninterpreted the results (LINE 257-277). As you note, the change in community and shift in N:P do not\nhappen simultaneously. Instead, the community is reshaped by the addition of N observed as the\ninsurgence of diatoms within ~the first week of the experiment, while P acclimation occurred in the final\nweeks of the experiment after the local P was drawn down to limiting concentrations (based on the\ntimeseries trend of N:P results and comparing diatom N:P ratios grown in culture to our experimental\nresults). Therefore, we are using physiological acclimation to explain the particulate N:P observations in\nthe +N-P treatments. To better demonstrate this, we now include time-series results for the community\ncomposition in the supporting information. (Fig. S5 and S9).\nLines 257-277: The distinct eukaryotic communities that resulted from the resource additions,\nparticularly the dominance of diatoms in the +N treatments and the dominance of dinoflagellates in the -\nN treatments, allowed us to evaluate if changes in community composition were reflected in bulk particle\nstoichiometry. To do this, we calculated expected bulk particulate N:P ratios for the final major plankton\nassemblages in each treatment as if they were grown under nutrient replete, P-stress, or N-stress\nconditions based on Sharoni and Halevy’s5 meta-analysis of plankton grown in culture. We narrowed our\nevaluation to the final experimental timepoints where most sample types were analyzed and included\nonly the four major eukaryotic plankton groupings listed above due to the absence of Prochlorococcus in\nour incubations (dinoflagellates and chlorophytes were grouped together5). In total, 62% to 92% of the\nfinal experimental communities were included in the calculation (Fig. S12b). Comparing the calculated\nN:P ratios to the measured bulk particulate N:P ratios indicated whether the observed values were driven\nby communities optimized to local nutrient supply (mechanism 1) or were influenced by acclimation to\nthe nutrient environment (mechanism 2). In all but the +N-P treatments, the observed ratios (which\ninclude all living organisms and detritus) resembled the calculated N:P ratios under nutrient-replete\nconditions consistent with mechanism 1 (Fig. 4a). The +N-P treatments, where N was continuously\nsupplied more than 16:1 N:P, exhibited a particulate N:P stoichiometry reflective of P-stressed eukaryotic\nplankton in culture5 suggesting the importance of mechanism 2 in these treatments.\nThe most salient things I see in the results with respect to community composition are:\n- The community was proximally limited by N rather than P or Fe\n- The growth fueled by N addition favored diatoms, at the expense of dinoflagellates and\nProchlorococcus, as one would expect\nOther arguments based solely on the community composition and C:N:P data are not clear.\nWe hope that by addressing the above comments with substantial revisions to the text it is more\nobvious how we attribute community composition to C:N:P stoichiometry.\nLine 210: “… regeneration of particulate P from detrital material”. Your meaning here isn’t clear. Do you\nmean that a greater contribution detritus to particulate matter relative to live phytoplankton could also\ncause elevated particulate N:P? Or do you mean that phytoplankton growth fueled by regeneration\nrather than new production will have elevated N:P? If it’s the latter, the reasoning for this is not clear.\nOur intent was the former. This sentence has been changed to: “…though similarly elevated particulate\nN:P ratios can be explained by accumulation of detrital material with a high N:P signature due to the\npreferential regeneration of P.” (LINE 292-293)\nLines 220-222: There is very little discussion of these macromolecular results. And regarding their\nstatistical analysis, this is not explained in the methods, not stated consistently in this text, and not\nshown in Fig. S7, and is not explained in the methods. Among the macromolecules measured, RNA is the\nmost abundant and dynamic with respect to growth conditions, while DNA should be function of\ngenome size rather than physiological acclimation. So it is very odd and worth explaining why RNA is not\nsignificantly different between +N-P and +N+P treatments, yet DNA is (sort of). Also these results seem\nsomewhat confounded by the Fe addition sub-treatments and this is not addressed.\nWe have substantively revised the manuscript in this section, especially through the addition of\nstatistical comparisons, a Statistical Approaches section in the Methods (LINE 530-570) and a broader\nacknowledgement of some of the methodological constraints that influence their interpretation (LINE\n318-323). The results are included in Figure 3, S13, and S14.\nWe agree that the RNA and DNA results were not as expected. However, there is still an interesting\nresult where the pools remain equally distributed normalized to P, but not to C. Therefore, it seems as\nthough all the macromolecular P pools that we analyzed were reduced in response to P-stress. It is\nunclear if this response is regulated by the cell during growth or a function of preferential P\nremineralization. The role of iron remains unclear with our limited dataset.\nLines 349-355: The amount of P in the molecular pools (ATP, DNA, RNA, phospholipid) as a fraction of\ntotal particulate P was similar between the +N treatments (Fig. S14e, Welch’s t-test, p = 0.58). However,\nwhen normalized to particulate C, the relative abundance of all P-containing molecules was higher in the\n+N+P treatments compared to the +N-P treatments (C:P= 503.2 and 249.8, respectively; Welch’s t-test, p\n= 0.0091; Fig. S13e). This demonstrates that while P stress led to an overall decrease in community C:P,\nthe decrease was not attributable to one of these specific macromolecular pools.\nLines 222-253: I agree that the ATP, lipid, and AP data addressed here all indicate that the +N-P\ntreatment experienced more P-stress than the +N+P treatment. But none of these results provide direct\nexplanation for the difference N:P or C:P between these treatments since ATP and phospholipids\nrepresent small fractions of total cellular P (see references #6, #35, as well as Garcia et al. 2016 doi:\n10.1038/ismej.2016.50, Rhee 1973, Rhee 1978, and others). The P-rich cell contents that would likely\ndrive a change in C:N:P are RNA, DNA, and P storage (e.g. polyphosphates) and these either have\ninconclusive results or were not measured.\nWithout reliable macromolecular polyphosphate data, it is unfortunately not possible for us to\ndefinitively determine which pools of phosphorous are responsible for the observed changes in total\nparticulate P between treatments. We still find it interesting to present the macromolecular results as\nwe observed them alongside the results which are more frequently attributable to P-stress in natural\nphytoplankton communities, like the high AP activity and elevated N:P ratios when phosphate\nconcentrations were low, since this range of datasets for this type of long-term incubation are not\ncommon in the literature but informative to studies looking to bridge phytoplankton culture results with\nfield observations.\nLine 271: Based on the points made in the preceding comments, “confirm the role of physiological\nacclimation” is overly strong language to use in regards to these transcriptomic data. Again, the ATP, AP,\nlipidomics, and transcriptomics all support the expected result that +N-P treatment experienced more P-\nstress than the +N+P treatment. I do not doubt that physiological acclimation is the key mechanism at\nplay here, but macromolecular data that would directly support this is not discussed. The strongest\nargument for physiological acclimation seems to be what I noted in the comments on Lines 164-\n176: The +N+P and +N-P treatments have similar communities, but distinct C:P and N:P at the end of the\nexperiment.\nWe agree with the reviewer that the similar community yet differing N:P ratios across the +N treatments\nis highly suggesting of physiological acclimation to P in the +N-P treatments, which we note in the\n“Nitrogen supply …” section of the manuscript. We then used our available datasets in the “Physiological\nacclimation to P…” section to explore mechanisms that could explain that deviation in particulate N:P\nbetween +N-P and +N+P. Unfortunately, we were unable to generate time-series datasets for all data\ntypes due to volume and time constraints, but the results we do have all give insight toward what might\nbe underlying the observation. We changed the sentence first stated to: LINE 373-374: …support the role\nof physiological acclimation as a control on particle stoichiometry.\nWe also more explicitly linked our findings to the two proposed mechanisms in the Main to further\nclarify our point throughout the text. For example:\nLINE 268-277: Comparing the calculated N:P ratios to the measured bulk particulate N:P ratios indicated\nwhether the observed values were driven by communities optimized to local nutrient supply (mechanism\n1) or were influenced by acclimation to the nutrient environment (mechanism 2). In all but the +N-P\ntreatments, the observed ratios (which include all living organisms and detritus) resembled the\ncalculated N:P ratios under nutrient-replete conditions consistent with mechanism 1 (Fig. 4a). The +N-P\ntreatments, where N was continuously supplied more than 16:1 N:P, exhibited a particulate N:P\nstoichiometry reflective of P-stressed eukaryotic plankton in culture5 suggesting the importance of\nmechanism 2 in these treatments.\nLINE 329-333: Additionally, the nucleotide adenosine monophosphate was significantly reduced relative\nto adenosine in the +N-P treatments (Welch’s t-test, p = 0.0013, Fig. 3f), as previously demonstrated for\nP-stressed eukaryotic cultures and P-stressed natural communities31 congruent with an acclimation\ninterpretation (mechanism 2).\nLINE 387-390: Particulate N:P ratios reflected the characteristic N:P ratios of the eukaryotic communities\nas if they were grown under nutrient replete conditions (Fig. 4a) supporting mechanism 1, until\nphosphate was depleted to < 10 nM in the +N-P treatments supporting mechanism 2.\nLines 220-276: When looking at all the biochemical results and their implications for showing\nphysiological acclimation, the elephant in the room seems to be P storage. If RNA and DNA do not\nexplain differences in N:P and C:P, P storage seems like the next suspect. Could P storage or\npolyphosphates be estimated from the biochemical measurements you have (i.e. subtracting\nphospholipids and nucleic acids from total P) or from the transcriptomic data (i.e. differential expression\nof polyphosphate kinase or polyphosphatases)\nAgain, we sympathize with the reviewers wish for reliable macromolecular data. Unfortunately, we are\nunable to extrapolate patterns in polyphosphate abundance from the transcriptome data, in part,\nbecause the genes that underpin the utilization of polyphosphate stores and their regulation, are not\nwell understood in microalgae (Plouviez et al. 2021 Journal Phycol). Because the macromolecules were\ncollected on dissimilar filter types with differing pore sizes we didn’t feel it was prudent to estimate\npolyP pools with the available data.\nLines 302-303 and elsewhere: Arguing the N supply and physiological acclimation are key drivers of\nglobal C:N:P patterns seems to be in conflict with not addressing the variability in C:N observed in your\nexperiments or assuming constant C:N in your projections. A wealth of culture experiments show that N\nsupply affects physiological acclimation of C:N. Your argument seems to be that:\n1) N supply increases in phytoplankton biomass and production in many parts of the ocean\n2) Without accompanying P supply, the phytoplankton growth promoted by N supply becomes P-limited\n3) The flexible P content of this new phytoplankton growth drives C:N:P variability as this secondary P-\nlimitation sets in.\nIf this is the case, this needs to be laid out more explicitly, supported more directly, and other key\naspects of this argument (C:N variability, the underlying causes of flexible P content) should be\ndiscussed.\nWe now more clearly acknowledge that there is variability in C:N in our experiments, though the\ndifferences between treatments were not statistically significant, nor were they significantly different\nover time (LINE 157-159). Additionally, we have reduced our focus on future projections of changes in\nC:N:P, including parts of the manuscript which relied on assumptions of constant C:N and variable P,\nthough we still do explore some possible implications of C:N being less flexible than C:P on C fixation by\nphytoplankton (424-434). The implications section states these findings more succinctly than previously\nwritten\nLINE 382-397: Here we show that an oligotrophic phytoplankton community from the North Pacific\nsubtropical gyre transformed to a diatom-dominated community in response to N supply with just a\nsmall contribution of cyanobacteria (9 %) to the estimated diatom plus cyanobacterial biomass\n(Methods). Dinoflagellates dominated the eukaryotic community and cyanobacteria constituted a larger\nfraction of the estimated biomass (30 %) in treatments without N addition. Particulate N:P ratios\nreflected the characteristic N:P ratios of the eukaryotic communities as if they were grown under\nnutrient replete conditions (Fig. 4a) supporting mechanism 1, until phosphate was depleted to < 10 nM in\nthe +N-P treatments supporting mechanism 2. Once phosphate was depleted in the +N-P treatment, the\ndiatom-based community had N:P ratios resembling diatoms grown under P-stress conditions in culture5,\nand expressed further signs of acclimation both in bulk community measures (e.g., elevated AP activity)\nand at the cellular level (here shown with Pseudo-nitzschia metatranscripts). These results support a role\nfor both community composition (mechanism 1) and acclimation (mechanism 2) as having an influence\nover how biology mediates particle stoichiometry in mixed phytoplankton communities.\nLINE 424-438: The abundance and stoichiometry of marine phytoplankton has important implications for\nhow much C is fixed in the surface ocean, which ultimately impacts C storage in the deep ocean4,11. For\nexample, assume that marine phytoplankton retain near Redfield C:N ratios but fluctuate C:P\nrequirements in response to local P availability. Decreasing N supply to surface waters, through\nenhanced denitrification, increased stratification, or both, would decrease total phytoplankton biomass.\nConversely, increasing N supply to surface waters through N2 fixation or anthropogenic dust would have\nthe opposite effect, accelerating C fixation. While most of the surface ocean likely has sufficient surface P\nfor pulsed growth in response to episodic N supply events, more frequent events or changing ocean\ncirculation patterns as predicted in global climate models45,46 could reduce surface P to concentrations\nforcing acclimation in more ocean regions. For acclimation to maintain C fixation under increasing P\nlimitation, it would require total productivity to remain relatively unchanged by the extended physiology\nand N loss to be balanced by N2 fixation47. Long-term incubations provide a platform to test this\nhypothesis in future studies.\nLines 372-374: Your meaning here is unclear. I think you’re trying to say that all treatments contained 6\nreplicate incubations except the -N+P treatment, but your wording doesn’t make it clear whether you\nremoved some replicates from the -N+P treatment or removed that treatment entirely. More\nimportantly, you are saying that some replicates were removed from the -N+P treatment, but don’t say\nhow many replicates were kept. The legend for Figure 1 simply says “n = 6” for the treatment\nreplicates. The explanation for why these replicates were removed (the had “anomalous values across datasets”) is also\nquite vague and needs explanation. What “values” and “datasets” do you mean here? Were the values\nanomalous compared to the other replicates in the treatment or anomalous compared to what\nyou’d expected for realistic values? With so much care and effort put into this experimental design, it\nseems that more explanation is warranted if some treatment replicates are going to be excluded.\nWe clarified the wording to better state that only one replicate (-N+P-Fe 3) was removed from the -N+P\ntreatment, and that it was removed due to consistently anomalous results that we felt misrepresented\nthe treatment average. Specifically, this tank displayed 3x higher particulate C, N, P concentrations on\nday 4 of the experiment compared to the other 5 -N+P treatments which then declined as the\nexperiment progressed. Chl a declined to nearly zero for the first 10 days of the experiment in the\nanomalous tank before displaying a boom-bust type behavior during the second half of the experiment,\nwhile the other 5 followed more similar patterns of ~0.1 ug/L chl a through the first ten days of the\nexperiment and generally increasing thereafter as shown in the figure below. Further, all figure captions\nhave been updated with specific n values for each group used in the statistical analyses.\nLINE 481-485: Each treatment group consisted of 6 PERIcosm incubations except -N+P which included 5\nPERIcosm incubations because one tank within that treatment (-N+P-Fe 3) displayed particulate C, N, P,\nand chl a patterns opposite that of the remaining 5 treatments which we felt misrepresented the\ntreatment averages. That tank was removed from analyses and the data are not presented.\nLines 381-382: Do you have any estimates of flow rates during stirring or sampling? Since this impressive\nexperimental design is meant to mimic natural conditions, it would be good to show that flow rates\nwere not so high as to cause cell lysis.\nThe suitability of the sampling pumps and mixing methods in terms of not causing cell lysis was\ndiscussed in the L&O Methods manuscript which described the PERIcosm design (Ref 16). It is\nchallenging to estimate the flow rate of stirring since it was done manually, but the interior flow rate is\nestimated to be less than 1 m/sec. We have added estimates of flow rate during sampling to the\nmethods (LINE 496). Luckily, in both cases, the rates are similar to those used throughout the\noceanographic community and thus we conclude that they are unlikely to cause significant cell lysis.\nLINE 465-466: At the start of the experiment, unfiltered seawater was transferred from the totes to the\nPERIcosms using a ½” all-PFA AODD pump at a rate of ~9 L/min.\nLINE 493-496: Samples were collected from a sampling port installed near the top of the PERIcosms after\nthey were mixed by hand with a customized magnet-controlled paddle. Although not measured, we\nestimate the internal flow rate due to mixing is less than 1 m/sec.\nLine 403: The comma after “experiment” is unnecessary.\nIt has been deleted.\nLine 515: One of references given here (#79) appears to be incomplete in the list of references.\nWe now use the lipidomic results to quantify the total lipid P so this reference has been removed.\nReviewer #3 (Remarks to the Author):\nSummary\nA mesocosm experiment was performed with samples collected in the North Pacific Subtropical Gyre. N\nand P manipulations were performed to assess microbial community responses to variation in N:P\nsupply ratios. In each of the N and P manipulations, various analyses were conducted, including 16S and\n18S based assessment of microbial community composition, stoichiometric analyses, and biochemical\nindicators of nutrient stress. Results indicate that addition of N promotes carbon fixation and appeared\nto shift community composition toward diatoms regardless of whether P was added. Elevated N:P (by N\naddition without P) caused a shift toward P stress as indicated by biochemical markers of P stress and a\nreconstruction of plankton N:P combining community composition with laboratory culture data.\nComments\nThe article is very clearly presented and the results are simple and easy to interpret. Based on my\nknowledge of the literature the findings are novel with important implications for carbon cycling. The\nauthors seem to suggest that their findings could support an argument for N addition to natural systems\nto promote carbon storage. While this is a potentially\ncontroversial suggestion, with lots of potential flaws that are well-described, I do find the suggestion interesting and novel\nand I suspect will be of considerable interest to the Nat Comm readership. Overall I felt the experimental design and\npresentation of results was very clear and simple and I have no further comments.\nWe thank the reviewer for evaluating the manuscript and for these comments.\nReviewer #1 (Remarks to the Author):\nThis is my second review of the Seelen and co-workers study using a mesocosm to understand\nthe biological regulation of C:N:P. I appreciate how some issues have been resolved (e.g., Fe-\ntreatments, absence of Prochlorococucs. However, the attempt to link the results from a\nmesocosm and the large-scale regulation of ocean C:N:P ratios is still very central to paper but\nalso vague and tenuous. I will give a few examples:\nThank you for reviewing our manuscript a second time. We are happy that we sufficiently\naddressed most of your comments during the first revision, and hope that our second revision\nresolves the remaining questions that you outline here.\nInitially, we compared the mesocosm results to observations in the ocean and used our findings\nas support for two hypotheses regarding biological regulation of surface particulate C:N:P in the\noceans. In doing so we drew some conclusions that were beyond the scope of the mesocosms\nas you point out. Now we have flipped the discussion points such that we are only testing\nhypotheses for biological regulation of surface particulate C:N:P within our incubation itself. With\nthis approach, the important findings of the mesocosm are clearer for the reader and less\nspeculative toward global ocean C:N:P ratios.\n1. The summary paragraph is vague and leaves in unclear what we have learned from the\nstudy. There is one line hinting at the results.\nThe summary paragraph has been rewritten to highlight the results of our study, especially lines\n45-54:\nTreatments with added nitrogen, with and without added phosphorus, increased in total\nproductivity and had communities dominated by diatoms, whereas treatments without added\nnitrogen remained more similar to the starting oligotrophic conditions. The absence of\nphosphorus with added nitrogen led to higher particulate N:P stoichiometry than expected\nbased on the phytoplankton community composition, without much impact on total\nphytoplankton growth or carbon fixation. We identified several P-stress markers in those\ntreatments using a suite of analyses ranging from molecular characteristics to\nmetatranscriptomics, supporting an important role for acclimation in extending particulate N:P\nand C:P ratios beyond the Redfield ratio.\n2. On L178, you draw a big conclusion about the relative importance of supply rate vs. ratio in\nthe middle of the result section. The is non-trivial issue. Tanioka et al., 2022 showed that there\nis a complex interaction between the two. Most chemostat culture and theoretical modeling work\nsupport this view. In brief, elemental flexibility is typically highest a slow growth – something that\ngenerally occurs at low supply rates. So I struggle with taking one observation from a\nmesocosm and then conclude something this wide-ranging.\nIn the statement at hand, we inadvertently created too definitive of a separation between rate\nand ratio, when our results support that the two interact. The point of that statement was to\nhighlight that our results support P-acclimation of diatoms in a high N-supply regime (with low\nlocal phosphate) similar to what has been previously shown for cyanobacteria collected in a low\nN-supply regime (with low local phosphate). We cannot use our incubation results to decipher\nthe growth rate of a single organism and did not intend to insinuate that the same organism\nshould or does have the same stoichiometry if grown under high or low nutrient supply\nconditions. Ultimately, we decided to remove the statement altogether as it no longer fits with\nthe structure of the manuscript.\n3. You observe parallel changes in C:N:P and community composition and conclude that this\nshows a biodiversity control. However, an alternative explanation is that both community\ncomposition and C:N:P change when you increase your nutrient supply. In other words, without\na causal link.\nWe now use a simpler argument to support that, in general, particulate C:N:P reflected the\n“optimal” nutrient requirement of the phytoplankton communities present, with optimal referring\nto the measured C:N:P stoichiometry of phytoplankton grown under replete nutrient conditions\nin cultures (based on the work of Sharoni and Halevy, 2020). You make a good point that this\nrelationship is not sufficient to support a causal rather than coincidental link. We note that the\nsupply of phosphate alone did not leave an obvious imprint on the particle stoichiometry, and\nwhen supplied without nitrate the excess phosphate was not taken up. When nitrate +\nammonium was supplied without phosphate, the particles did not immediately reflect the nutrient\nN:P supply ratio, but rather the higher particulate N:P ratio developed more than a week after\nthe background phosphate was depleted below 10 nM. Therefore, we can less clearly tie\nparticulate C:N:P to nutrient supply alone. We now acknowledge the possibility for C:N:P\nchanges to be unrelated to the shift in community, while still arguing that that the greatest\ndeviations in N:P within our incubations are driven by acclimation:\nLine 262-265: Community composition in our experiments was thus often a good predictor of\nN:P, but community alone could not explain the range of particulate N:P observed unless\nacclimation was invoked.\n4. The paragraph starting at L399 is very speculative. You have four observations from one site.\nI cannot see how this data can firmly constrain the large-scale regulation of C:N:P. It is also\nsomewhat incorrect. N:P is not 16 in N limited oligotrophic systems and also distinct from\nregions with higher nutrient concentrations.\nThis paragraph in the final section of the manuscript (formerly Implications of nutrient regulated\nparticle stoichiometry now Summary and implications) has been completely rewritten. We now\nstrive to highlight the novel results of our incubation, but we no longer discuss what may drive\nnatural surface ocean plankton structure or patterns in observed particle C:N:P stoichiometry.\nSee the new section Summary and Implications (Lines 373-411) for all changes.\nAs I mention in my first review, the study includes a wealth of rare biochemical observations that\nare shown in the results. However, this information does not feed into the main discussion and\nconclusions. This is a shame and leaves the main conclusions without much support.\nA key goal during revision of this manuscript was to focus more squarely on the rich dataset we\nhave assembled. Key takeaways of our incubation were that characteristics of the community\ncomposition were generally predictive of the bulk particulate stoichiometry in our mesocosm\nincubation, except when P concentrations were low enough to induce P-stress, though even\nwhen the cells became P-stressed, they appeared to remain as productive as their more P-\nreplete counterparts. P-stress could be identified at the bulk molecular level and within genus-\nspecific transcriptomics. Excitingly, the data in our mixed community aligns with observations of\ndiatoms in culture (diatoms were the dominant phytoplankton species in the treatments with\nadded N). While we no longer strive to completely disentangle the relative importance of each\nbiological process on the observed particle stoichiometry, the dataset offers insight towards how\nbiology may contribute to bulk C:N:P stoichiometry via community composition and acclimation\nresponses. We hope that our presentation of such a rich dataset will contribute to the broader\nconversation about the controls on C:N:P in the global oceans.\nSincerely,\nAdam Martiny\nReviewer #2 (Remarks to the Author):\nThis is my second review of the manuscript by Seely and coauthors. I appreciate the authors\nthoughtful consideration and thorough responses to my comments as well as the insightful\ncomments provided by Reviewer #1. The authors have greatly improved this manuscript by\nproviding more statistical support for their findings, clarifying their approach and conclusions,\nand providing a discussion that more closely matches the scope their study. My initial critiques\nof this manuscript have been thoroughly addressed and I have no further comments or revisions\nto contribute.\nThank you for reading our manuscript a second time and providing this positive review.\nReviewer #4 (Remarks to the Author):\nThe study investigates how nitrogen and phosphorus influence marine biomass production and\nstoichiometry through a mesocosm nutrients manipulation experiment. The experimental design\nfollows standard mesocosm practices for examining ecological dynamics, but the authors have\ngone further by collecting an exceptionally broad and detailed set of data, covering various\naspects of the ecosystem. This comprehensive approach is unique and demonstrates significant\neffort in capturing community-level changes. Based on their data, the authors propose two\nmechanisms: first, that nitrogen addition affects total productivity and the particulate CNP ratio\nby altering community composition; second, phosphorus availability raises community CNP\nratios through physiological acclimation. However, the data in the manuscript do not fully\nsupport these proposed mechanisms. Specifically, the evidence for how nutrient additions\nimpact the particulate CNP ratio through changes in phytoplankton community composition or\nphysiological responses is not convincing.\nWe appreciate your thorough, and overall positive review of our manuscript. Based on this\ncomment and those that follow, it seems we miscommunicated some goals of our discussion.\nWe were not proposing two new mechanisms based on our data. Rather, these two\nmechanisms (perhaps they should have been stated as ‘hypotheses’) are founded in the\nliterature, and we use our results as a case study showcasing how nutrient supply dynamics\nmay influence phytoplankton regulation of particle stoichiometry under those hypotheses. We\nhave rewritten the manuscript to clarify this; essentially, we focus on the experimental results\nand then use the established hypotheses to explain our dataset (rather than using our data to\nbetter understand global patterns). To further eliminate confusion, we removed the concept of\ntwo mechanisms throughout the manuscript. We hope this restructuring clarified the intent of our\nmanuscript.\nHere are the main concerns with the study’s validity and interpretation:\n1. The setup of the experiment was to add nutrient(s) continuously to specific treatments,\nincluding +N, +P, or both. However, the addition process was skipped on Days 4 and 5, leading\nto visible declines in Chl a as shown in Figure S2. This interruption likely caused a \"crash\" in the\nphytoplankton community across all enrichment treatments, as seen by significant drops in Chl\na level, with some even falling below Day 0 levels. Consequently, the phytoplankton community\nlikely experienced restructuring around Day 7. Since community composition data were not\ncollected until Day 11, it is very hard to tell what exactly happened after these two days of\ninterruption. Therefore, the findings at the end of the experiment may reflect changes beginning\nfrom Day 7 rather than Day 0, complicating the interpretation of nutrient addition impacts from\nthe experiment’s start. This was not discussed in the paper yet.\nThis is an important point—the pause in nutrient addition likely did play a part in the rapid\ndecline of chl a on day 7, and likely did have an impact on which phytoplankton species were\nultimately favored at the end of the experiment. However, our study was not intended to\ndiagnose or propose how nutrients fundamentally restructure plankton communities, but rather\nhow that restructuring impacted the particle stoichiometry. Phytoplankton cells respond to\nnutrient addition faster than the 24 days of the experiment post the pause in nutrients, so how\nthe pause manifested in the final community structure and biochemical composition remains\nspeculative. As you point out, timing of key responses in the community could be impacted by\nthe pause in nutrients, and we briefly discuss timing at the end of the manuscript. The potential\nimpact of the nutrient supply on the experiment are now acknowledged.\nLines 132-136: After an initial peak in chl a on day 5 (Fig. S2a), the N supply rate was such that\nsurplus nitrate was not detected when sampled 16 hours after nutrient spikes were added (Fig.\nS1a). A rapid decline in chl a occurred after day 5, possibly because of the two-day pause in\nnutrient addition, before it started to accumulate again on day 7.\n2. The study used a GF/F filter with a pore size of 0.7 µm to collect particulate CNP,\nassuming this captured only phytoplankton community composition. However, in all your\nmesocosms, you used the unfiltered seawater pumped from 15m depth without pre-filtration,\nunfiltered seawater likely included zooplankton, which were not explicitly removed. Therefore,\nthe samples collected on GF/F filters probably contain larger bacteria, phytoplankton, detrital\nmaterial and their consumers e.g. zooplankton and so on. As the phytoplankton community grows\nin response to nutrient additions, zooplankton populations would also likely increase in the later\nstage of experiment, posing top-down grazing pressure and creating different grazing preferences\non phytoplankton community. In turn, consumer can alter the phytoplankton community structure\nand, thus, their C:N:P ratios. The periodic declines in Chl a (Figure S2) observed in later stages\ncould be attributed to this top-down control by zooplankton, particularly in the +N or +N+P\ntreatments, rather than -N-P or -N+P ones. This pattern suggests that the shifts in\nphytoplankton community composition are not solely attributed to nitrogen addition\n(Mechanism 1) but the result of both nutrient effects and increased zooplankton grazing.\nDo you have the zooplankton data? This dynamic—how zooplankton grazing interacts with\nnutrient additions to shape phytoplankton community composition and stoichiometry—should be\naddressed with zooplankton community data, as it significantly impacts the validity of\nMechanism 1 in your discussion\nThe discussion of our study focuses on phytoplankton due to their prominence in the eukaryotic\ncommunity composition as determined by 18S gene sequencing, and due to prior studies\nshowing their predictability for bulk C:N:P particle stoichiometry. We do not assume that our\nsampling only captured phytoplankton, nor do we assume that phytoplankton are the only\nimportant organism present in the incubation (see Fig. S5). Although we do not have direct\nzooplankton counts or measures of grazing, the ASV taxonomic groups generally consisting of\ngrazers (Metazoa, Ciliates, and Rhizaria) were of low abundance, especially in the second half\nof the experiment. The parasite Syndiniales did retain a significant population in the -N\ntreatments. While grazing is an important top-down control on phytoplankton structure, the\nsubject of this paper is not on the holistic determinant of phytoplankton community structure. For\nthe purpose of this paper we did not go into detail on the potential influence of grazers and\nbacteria on the bulk particulate pool or its C:N:P.\nMore details on the non-phytoplankton community are now stated in the text: Lines 186-190:\nMetazoa, which made up roughly 21% of the starting community, were nearly absent from all\ntreatments by Day 11. Ciliates and Rhizaria had low relative abundances throughout the\nexperiment, whereas Syndiniales—diverse parasites—meaningfully contributed to the -N\ntreatments until the end of the experiment (Fig. S5).\nAnd we note that zooplankton ASVs are included in the Bray-Curtis dissimilarity analysis: Lines\n254-256: This calculation considers all eukaryotes sequenced down to their lowest classification\nlevel, even zooplankton which could contribute meaningfully to bulk particulate C:N:P ratios.\nThis line also includes a reference to Talmy et al., 2016 in which the role of zooplankton in\nsetting ocean particle stoichiometry is discussed.\nMore generally, we have now removed specific discussion relating to mechanism 1 and\nmechanism 2, since they created confusion regarding the main points of our paper.\n3. Your Figure S1 shows that the initial N:P ratio in the collected seawater was around 1:1. I\nalso checked the data from the Hawaii HOT-DOGS system, for example, in station 2, 2023\nAugust, the phosphate concentration is pretty similar to what you measure on Day0, however,\nthe nitrate plus nitrite concentration is quite different. Your figure showed a 0.05uM level, but the\nfield data showed 0.229uM which is much higher than yours. Maybe you want to double-check\nthis? If this is correct, why your initial water N (Day 0) concentration is very low?\nTo ensure that we are similarly obtaining data from HOT-DOGS, I also went back to the\nwebpage (hahana.soest.hawaii.edu/hot/hot-dogs -> Horizontal Profiles -> Time-series -> Bottle).\nI then selected the following: Operator: Nitrate + Nitrite; Function Grouping: Depth; Function:\nMean/StDev; Grouping: Month. If I keep the default value/range as 0-200m, it is true that the\nAugust mean is ~0.45 uM. However, if you change the depth range to better represent the\nmixed layer, 0-100m, the August mean is ~0.025 uM. In the manuscript we present results from\nthe surface 50m, which is about the same as the surface 100m. It seems the values we reported\nin the manuscript are higher than what is currently reported in the system, so they have been\nupdated (previously 63 ± 29 nM phosphate and 28 ± 26 nM nitrate plus nitrite, now 64 ± 29 nM\nphosphate and 23 ± 19 nM nitrate plus nitrite). Thus, our starting N+N seems to be very\nconsistent with values typically observed at station ALOHA.\nWe did not measure low level nitrogen (which is analytically different than the autoanalyzer\nresults presented in the manuscript and used for the nitrate + nitrite value in HOT-DOGS),\nbut we recognize that the N+N analysis results in higher concentrations than the low-level protocol.\nLow level nitrogen provides a more accurate picture of surface nitrate at ALOHA, which is\ngenerally below 10 nM in the summer months. We clarify this method in Line 127.\n4. In Figure 3, only the final data points for the +N+P and +N-P treatments are shown, which\ncould lead to a somewhat misleading interpretation of the experiment as a whole, especially\naccording to Figure S3. Figure S3 indicates notable changes in CNP ratios over time.\nSpecifically, Figures S3e and S3f indicate a clear phosphorus limitation in the +N-P treatment\nas the experiment progressed. More importantly, the CP and NP ratios in the +N+P treatment\nremained quite similar to those in the -N-P and -N+P treatments, suggesting that adding both\nnitrogen and phosphorus (+N+P) did not significantly alter particulate CNP compared to the\noriginal system without any nutrient addition. This implies that adding phosphorus alone does\nnot promote phosphorus accumulation due to N-limitation.\nThis outcome is likely due to the initial particulate NP ratio of around 16 (Fig. S3f), which\nmatches the NP ratio in the nutrient additions. This balance means that, if the system started off\nnitrogen-limited, it would stay nitrogen-limited in the +N+P treatment. As a result, any\nstoichiometric shifts would be minimal compared to the +N-P treatment, where phosphorus\nlimitation became more pronounced. In Mechanism 2, you argued that higher phosphorus\navailability increased community NP ratios through physiological acclimation. While this\ninterpretation may seem accurate when only comparing the +N+P and +N-P treatments, it does\nnot hold when comparing -N-P with -N+P treatments.\nThe development of the experiment over time is important, and a unique feature that remains a\nhighlight of our experimental datasets. As the reviewer notes, the C:P and N:P ratios diverge\nonly after day 11 of the experiment, as shown in Figure 1 and Figure S3, and now stated in the\nrevised Implications section Lines 387-390: With time, we captured the development of P-limited\ngrowth conditions, first noted as a decline in phosphate below 10 nM by day 7, followed by\nincreased AP activity first detected day 11, then elevated N:P ratios detected after day 11.\nIt is intriguing that excess phosphate added to the system was not taken up/ stored by cells.\nConsidering the sampling water dilutions, the measured phosphate in the tanks aligns with how\nmuch we added in the -N treatments. This discussion point was added to the text for clarity:\nLines 172-174: Stable N:P ratios and accumulation of phosphate in the -N+P treatments\nsuggest that adding P alone does not lead to cellular P accumulation when cells are N-\nlimited.\nWe agree with the reviewer that the similarity in C:P and N:P ratios in the +N+P treatment and -\nN treatments is likely due to the relatively similar nutrient requirements of the main\nphytoplankton comprising those communities. In fact, the relative similarity in CNP between\ndiatoms, dinoflagellates, and haptophytes makes interpreting differences between the non-P\nlimited treatments challenging, unlike the natural environment where Prochlorococcus is thought\nto play a role in increasing bulk particulate N:P ratios. Because our new manuscript has a\ndecreased focus on validating Mechanism 2, we rely less on the data from +N+P and +N-P\nshown in Fig 3 to make broad statements about nutrient stoichiometry in the oceans (which\ncould be contradicted by the -N+P and -N-P data), and focus more on the results of our\nincubations, for example Lines 275-279: To examine how physiological acclimation impacted\nC:N:P stoichiometry, we compared the +N-P and +N+P treatments at the final time point of the\nexperiment. Both of these treatments had final timepoint communities dominated by diatom\nspecies (Fig. 2), but the particulate N:P ratios without P added diverged from those with P\nadded (Fig. 1, 3c, S3).\nWe do not argue that higher phosphorus availability increased community N:P ratios, rather,\nlack of phosphorus increased community N:P ratios due to decreasing particulate P\nconcentrations, which we interpret as a result of physiological adaptation to P stress. For\ntreatments with sufficient P addition, we see either an incomplete drawdown of phosphate or an\naccumulation of phosphate in the dissolved phase, and maintenance of particulate N:P ratios\nnear what the dominant phytoplankton stoichiometries are when grown in nutrient replete\nconditions. As mentioned above, the phytoplankton that we considered all have similar\nelemental stoichiometries when grown under nutrient replete conditions (see figure below\nderived from the meta-analysis of Sharoni and Halevy 2020 Sci Adv), so if they do dictate\nparticle stoichiometry, we might not expect large differences between the treatments.\nIn short, we agree with the reviewer that the only treatment with significantly different N:P at the\nend of the experiment was +N-P. This is an important finding of the manuscript, as presented in\nFig 4a where we show that only the +N-P treatment matches the N:P stoichiometry expected for\nP-stress conditions, while the +N+P, -N+P and -N-P treatments all have similar N:P which\nmatches predictions for nutrient replete conditions of N-stress conditions (as the reviewer points\nout in Fig. 3S). We have significantly revised the first paragraph of the Summary and\nImplications section in hopes of clarifying this point to the reviewer, and to other readers of the\nmanuscript.\nIn summary, several major issues need to be addressed: (1) the impact of the interruption in\nnutrient addition on Days 4 and 5 and how this may have influenced community changes need\nto be addressed; (2) the interpretation that particulate C:N reflects only the phytoplankton\ncommunity is not convincing, therefore your proposing mechanism1 is not fully correct; and (3)\nthe relative N-limitation observed in the +N+P treatment could be another important pathway to\nexplain the community stoichiometric changes, but it was not considered. These issues could\nsignificantly impact the discussion in the paper, so I will withhold further detailed comments at\nthis time.\nWe agree with the reviewer that all three of the points mentioned above required further\nclarification, and have revised our manuscript in order to address these issues. More broadly,\nwe hope that by focusing less on mechanism 1 vs. mechanism 2, our revised manuscript more\nclearly describes the results of our work, and the important conclusions which can be made\nbased upon our data. See detailed responses above.\nNitrogen and phosphorus differentially control marine biomass production and\nstoichiometry\nSeelen et al. 2025\nResponse to Reviewers\nReviewer #1 (Remarks to the Author):\nThis is my third review of this manuscript. I will mainly comment on the changes linked to\nmy overall criticism about the unclear link between observations and overall conclusions\nregarding the regulation of ocean C:N:P. I like the new format. There is much more clear\nconnection between the observations and conclusions. Hence, my comments are fairly\nminor.\nWe greatly appreciate the thorough reviews and advice provided by Adam through this\nprocess. Thank you for reviewing again and providing positive feedback.\nL129: Perhaps the url should be a reference\n(now L119) The url was left in the text, which is typical for the HOT-DOGS dataset, although\nit was shortened by removing “/bseries.html”.\nL176: You may feel that I contradict myself. However, it seems unnecessary to include\n‘though the dataset was too small and variable to ascribe significance to this pattern’. I\nthink it suffice to say ‘consistent with’ as you are not stating that this is proof.\n(now L166) Thank you for this comment. We have decided to remove the second half of the\nsentence as you suggest.\nL289: That is correct from the viewpoint of the P quota. However, the N quota may also\nchange due the induction of alkaline phosphatase.\nGreat point. We clarified that this will only hold true in terms of the P quota:\nNow L276: “However, this acclimation signal is not necessarily expected to correspond\nwith a change in cellular N:P ratios due to a changed cellular P quota, if sufficient P can be\nacquired from dissolved organic P for growth.”\nL335: I am not sure I agree with this concluding sentence. As you state several times, this\nrelationship seems rather complex.\nWe revised the sentence to better clarify our conclusion. While the relationship is complex,\nthe lipidome did show sensitivity to the phosphate concentration as we hypothesized\nbased on laboratory cultures. However, this sensitivity was less pronounced between the\n+N-P and +N+P treatments than we had anticipated, and as was seen in other variables.\nThe reason behind this remains too speculative to address further in the manuscript.\nNow L319: “The ratio of N-based betaine lipids to phosphatidylcholine (BL: PC), an\nindicator of P stress in diatoms39, was not significantly different between the +N-P and\n+N+P treatments (Welch’s t-test, p = 0.065, Fig. 3i), suggesting lipid substitution was not a\ndominant P-saving strategy in the diatom communities. However, BL:PC was negatively\ncorrelated with phosphate across all treatment timepoints (Fig. S15; BL: PC Spearman ρ = -\n0.64, p < 0.001) supporting a moderate lipidome response to the decreased P availability\nobserved in the +N treatments.”\nL340: Is this sentence correct? Ie. Higher C:P in the +P treatment?\nThank you for pointing this out, the wording is confusing. The amount of all P-containing\nmolecules was higher in +N+P, but this led to a lower C:P ratio in that treatment. Thus,\nline 342 was incorrect but has been corrected.\nNow L330: “However, when normalized to particulate C, the relative abundance of all P-\ncontaining molecules was higher in the +N+P treatments compared to the +N-P\ntreatments, and the C:P ratios were significantly lower in the +N+P treatments compared to\nthe +N-P treatments (C:P= 249.8 and 503.2, respectively; Welch’s t-test, p = 0.0091).”\nL387: It is only Tyrrell (no et al).\nNow L377: This mistake has been fixed.\nAdam Martiny\nReviewer #4 (Remarks to the Author):\nThis is my second review of this manuscript.\nThe authors have addressed most of my comments from the first review, so the manuscript\nhas been changed quite a bit. The author should be careful about all the statements about\n'adding N or P', especially when those statements are related to what might happen in\nreality. In your experiment, as nutrient enrichment, you only added the inorganic N or P.\nHowever, in the real marine system, especially coastal regions, the terrestrial runoff often\nbrings organic nutrients rather than only inorganic nutrients. This will influence the system\nand food web function in such a mesocosm experiment. see one example study : Guo, J.,\nBrugel, S., Andersson, A. and Cherif, M., 2022. Carbon-nitrogen association influences\nresponse of the microplankton food web to enrichment. Aquatic Microbial Ecology, 88,\npp.187-199. I suggest that you revise your abstract and main conclusion to specify the\ninorganic nutrients you used as the primary nutrient enrichment. This will enhance the\nprecision of the main message.\nThank you for your review. We agreed with your comment and have added “inorganic\nnutrients” where needed to maintain that our focus was on inorganic nutrient enrichment—\nthis was mostly at the beginning and end of the manuscript. While we understand that the\nlanguage “Adding N or P” is not immediately informative on the type of N or P added, it is\nclearly stated in the methods that N was nitrate and ammonium while P was phosphate.\nTherefore, we did not think it was necessary to adjust these phrases throughout the entire\nmanuscript.\nLine 35: “…to evaluate how inorganic nutrient supply…”\nLine 88: “The community was supplied inorganic nutrients once daily…”\nLine 366: “…can be monitored under controlled inorganic nutrient perturbations…”\nLine 371: “…the immediate response to inorganic N addition in our mesocosm\nexperiment…”\nLine 387: “We found that inorganic N supply in the absence of inorganic P supply…”"}]

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