Short-term acidification promotes diverse iron acquisition and conservation mechanisms in upwelling-associated phytoplankton

Domain: earth_science
Published: 2023-11-08
Editorial outcome: major_revision

Abstract
Coastal upwelling regions are among the most productive marine ecosystems but may be threatened by amplified ocean acidification. Increased acidification is hypothesized to reduce iron bioavailability for phytoplankton thereby expanding iron limitation and impacting primary production. Here we show from community to molecular levels that phytoplankton in an upwelling region respond to short-term acidification exposure with iron uptake pathways and strategies that reduce cellular iron demand. A combined physiological and multi-omics approach was applied to trace metal clean incubations that introduced 1200 ppm CO2 for up to four days. Although variable, molecular-level responses indicate a prioritization of iron uptake pathways that are less hindered by acidification and reductions in iron utilization. Growth, nutrient uptake, and community compositions remained largely unaffected suggesting that these mechanisms may confer short-term resistance to acidification; however, we speculate that cellular iron demand is only temporarily satisfied, and longer-term acidification exposure without increased iron inputs may result in increased iron stress.

Introduction
Present-day atmospheric carbon dioxide (CO2) concentrations of over 400 ppm have not been observed in the past 800,000 years and are increasing at an average rate that exceeds any observed in the past 20,000 years1–3. Approximately 30% of this released CO2 is absorbed in the surface ocean resulting in a shift in the carbonic acid system and decline in seawater pH known as ocean acidification4. 

Coastal upwelling regions such as the California Current System (CCS) account for a significant proportion of marine primary production and exhibit some of the largest pH variability in the ocean due to the natural acidity of upwelled waters5,6. Some coastal upwelling areas are also expected to experience accelerated or amplified ocean acidification5,7. In the CCS for example, the rate of acidification may be double that of the global average resulting in increased overall exposure to higher acidity for organisms, particularly during periods of strong upwelling5,8,9. 

Phytoplankton, especially diatoms, drive the high levels of primary production in upwelling regions that in turn support highly productive fisheries5; however, their vulnerability to acidification remains uncertain. Most phytoplankton species appear to exhibit relatively little response to acidification although there is variability among taxa10. Field-based experiments with diatom communities suggest that effects from acidification are more frequently positive than negative and coastal communities are less affected than oceanic communities; however, shifts in community composition may also occur11. 

Further uncertainty in phytoplankton responses lies in the relationships between ocean acidification and iron bioavailability. Iron is a critical micronutrient for phytoplankton growth that is often limiting in the ocean including coastal upwelling regions such as the California Current and Peru/Humboldt Current Systems12–15, and its availability in a future more acidic ocean remains controversial16. Reductions in bioavailable iron in response to acidification have been demonstrated in laboratory settings and are attributed to two mechanisms: (1) under acidic conditions, iron is less likely to disassociate from certain organic ligands to its more bioavailable form, labile dissolved inorganic iron (Fe’)17, and (2) the algal inorganic iron uptake protein, phytotransferrin (pTF), is dependent on carbonate ion concentrations ([CO32−]) that decline under acidic conditions18. In the California Current system, [CO32−] is estimated to have declined 35% during the 20th century9. 

The laboratory studies demonstrating these impacts in bioavailable iron may not have captured important dynamics including biotic interactions with siderophore-producing bacteria, a significantly more complex iron pool, and dynamic nutrient availability19. Further acidification experiments with natural seawater and a single diatom species, Thalassiosira weissflogii, did not produce significant differences in iron uptake rates indicating the presence of natural iron-binding ligands that are largely unaffected by pH17, although reductions in Fe’ in the natural environment from increased complexation have also been observed20,21. Other experiments and modeling studies suggest that iron bioavailability may instead increase in response to acidification due to increased solubility of Fe(III)22–24, increased oxidative lifetimes of highly bioavailable Fe(II)5,25, and enhanced dissolution of particulate iron26. 

Previous field-based studies examining the effects of ocean acidification on phytoplankton communities under iron stress or limitation, namely within the iron-limited subarctic North Pacific and Southern Ocean, have shown conflicting results. Some experiments have shown that diatom communities are negatively impacted with reduced abundances, growth rates, and photosynthetic efficiencies27–30. Other experiments observed no changes in growth, macronutrient uptake, and community composition31–33. One experiment in the Southern Ocean observed no differences in growth or macronutrient uptake in the whole phytoplankton community; however, centric diatoms appeared to be favored over pennate diatoms indicating variable responses among more specific taxonomic groups that may not be evident if examining the entire community34. In contrast to these other experiments, Hopkinson, et al.35 made pH adjustments to communities in the Gulf of Alaska resulting in very modest increases to growth and photosynthetic efficiency in conjunction with downregulation of certain photosynthetic proteins. Other ocean acidification studies have been conducted in regions with relatively high ambient iron concentrations, some of which used artificially high levels of strong iron-binding ligands to induce iron stress36–39; however, these ligands also increase the solubility of iron leading to increased overall dissolved iron concentrations40. 

Within upwelling regions, previous experiments also indicate a lack of change in response to acidification with diatom-dominated phytoplankton assemblages, but acidification was not assessed along with iron status even though the phytoplankton communities in these regions often exhibit high iron demand and limitation12–14. In the CCS, several experiments showed no discernible differences in growth, primary productivity, and carbon-to-nitrogen ratios41–43; however, responses were not evaluated with CO2 levels greater than 800 ppm to exceed modern day surface pH variability in upwelling systems (Supplementary Tables 1 and 2). Only Osma, et al.44 examined responses up to 1,600 ppm CO2 in the Peru/Humboldt Current System, but also did not observe differences in macronutrient drawdown, chlorophyll a concentrations, or diatom cell abundances. 

High pH variability in coastal upwelling ecosystems has led to speculation that the native microorganisms in these regions may have the metabolic flexibility to accommodate relatively large pH changes. As evidenced by the response of higher trophic levels (mussels, gastropods, and planktonic copepods) in the Peru/Humboldt Current System, examining ocean acidification relative to local conditions rather than the global mean provides an improved understanding of organisms’ tolerance45. As a result, phytoplankton responses in coastal upwelling regions may be different than those observed in other areas such as chronically iron-limited (high-nitrate low-chlorophyll) regions with low pH variability5,46. 

Furthermore, the aforementioned field-based experiments employed physiological assessments of the whole phytoplankton community that are incapable of discriminating changes only occurring within a specific taxonomic group. At times, taxon-specific pigment analyses and microscopic cell counts were employed showing a lack of change in abundances for more specific taxonomic groups or species, but not offering a window into taxon-specific physiological adjustments28,31,35. As previously mentioned, centric and pennate diatoms exhibited differing trends in their relative abundances in an experiment in the Southern Ocean34 suggesting more taxonomically-specific resolution is needed to evaluate responses to ocean acidification. Transcriptomic and proteomic analyses can provide this greater resolution while also providing insight into the short-term molecular-level changes that may precede physiological changes or differences in community structure. Moreover, molecular-level knowledge of phytoplankton responses to acidification is limited to a small number of taxa under laboratory conditions without considering iron bioavailability or using organisms obtained from upwelling areas47–49. 

Thus, to evaluate potential impacts of acidification on phytoplankton in an upwelling region, ocean acidification was simulated with natural phytoplankton communities in the CCS. Trace metal clean techniques were used throughout enabling interrogation of acidification-driven effects on iron availability without unnatural changes to the iron pool. Air-CO2 gas mixtures up to 1200 ppm CO2 were applied, which exceeds both the projected global mean atmospheric CO2 concentration for the year 2100 under the highest emission Shared Socio-Economic Pathway (SSP5-8.5) and most reported surface seawater pCO2 and pH variability in the CCS (Supplementary Tables 1–3). With a combined physiological and multi-omic approach, these experiments enable probing of mixed phytoplankton assemblages’ sensitivity to short-term acidification exposure from the whole community to taxonomically-specific molecular levels while considering the high importance of iron bioavailability.

Results
Experimental set-up, initial conditions, and assessments of iron limitation
Four incubation experiments were conducted in the CCS. Experiments 1–3 occurred within an upwelling filament, represented by newly upwelled waters at Experiment 1 that progressively aged by Experiment 3, and used communities from the near-surface (Fig. 1a and Supplementary Fig. 1, Table 1). Experiment 4 serves as a farther offshore site with an initial community from a subsurface chlorophyll maximum layer (SCML) at 60 m (Supplementary Fig. 1). 

Initial pH at Experiment 1 was lowest (7.89 ± 0.00) as upwelled water naturally have low pH and then became less acidic (8.06 ± 0.01 by Experiments 3 and 4) from outgassing and primary production (Supplementary Fig. 2 and Table S3)5,8,50,51. In each experiment, triplicate bottles were supplemented with chelexed macronutrients (Table 1) and bubbled with HEPA-filtered air balanced with 400, 800, or 1200 ppm CO2. Samples were then collected at one-day intervals that varied among experiments to attempt to avoid macronutrient depletion with an upper limit of 96 h to also avoid increased bottle effects: 48 and 72 h for Experiment 1, 24 and 48 h for Experiment 2, and 48 and 96 h for Experiments 3 and 4 (Table 1). By the time of sampling, the incubations had an approximate pH of 8.0 ± 0.0, 7.8 ± 0.0, and 7.6 ± 0.0 (Supplementary Fig. 2) and pCO2 concentrations of 413 ± 21, 800 ± 34, and 1197 ± 44 µatm for each respective treatment with some variability in pCO2 concentrations among experiments (Supplementary Table 3). 

As anticipated for the region, initial dissolved iron concentrations ([dFe]) were below 1 nmol L−1 (Fig. 1b)13; only a small fraction of this dissolved iron exists as inorganic Fe(III) with most bound to organic ligands52. Labile particulate iron concentrations were highest in the vicinity of Experiment 1 (>10 nmol L−1), and decreased to generally less than 1.0 nmol L−1 offshore near Experiments 3 and 4 (Supplementary Fig. 3). 

With these iron concentrations, the addition of macronutrients created the potential for iron stress or limitation, particularly at Experiments 3 and 4 with Experiment 2 already showing signs of Fe limitation in situ. Ratios of nitrate to dissolved iron concentrations (NO3:dFe) greater than eight (µmol:nmol) suggest the potential for iron limitation53, and the initial conditions of all experiments exceeded this threshold following the addition of NO3 (Fig. 1c, d). These ratios are also in line with those that naturally occur in the region54. 

Siex is an additional biogeochemical proxy that serves as an indicator of Fe limitation in diatoms53; as Fe-limited diatoms preferentially uptake silicic acid (H4SiO4) relative to NO355, lower than expected H4SiO4 concentrations compared to NO3 concentrations indicates the presence of Fe limitation. Siex reports the difference between H4SiO4 and NO3 concentrations (Methods); therefore, negative values indicate greater H4SiO4 uptake due to iron stress in diatoms. At Experiment 2, negative values were observed from the initial timepoint (T0) throughout the experiment (Fig. 1e). Following macronutrient addition, negative values were also observed in Experiments 3 and 4 while values remained positive in Experiment 1 (Fig. 1e). 

Parallel incubations with unamended control treatments and iron addition treatments (+5 nmol L−1) were performed to further evaluate the presence of iron stress during the experiments. Following 24 h of incubation at Experiment 2 and 48 h at Experiment 3, chlorophyll a concentrations significantly increased with the addition of iron indicating the presence of iron limitation in these communities by the first sampling timepoints (Supplementary Fig. 4). At Experiment 4, nitrate was initially depleted indicating no iron limitation in the initial community (Fig. 2 and Supplementary Fig. 5); however, in incubations initiated 24 h later while following a Lagrangian drifter, significant increases in chlorophyll a were observed only with the addition of both iron and nitrate suggesting co-limitation in this community. As nitrate was added without iron in the community subjected to increased acidification, it was likely iron-limited. Considering low (<1 µmol L−1) nitrate concentrations after 72 h in Experiment 1 (Fig. 2), the phytoplankton community there was not iron-limited, although it may have encountered some degree of iron stress. 

Whole-community physiological responses
Despite differences in pH between treatments at all timepoints (Supplementary Fig. 2), few significant differences (P < 0.05) were observed in chlorophyll a concentrations, particulate carbon-to-nitrogen ratios, and macronutrient concentrations. Short-term iron uptake rates were normalized to both chlorophyll a and particulate organic carbon (POC) with both inorganic iron (FeCl3) and iron bound to the organic ligand desferrioxamine B (Fe-DFB) (Fig. 2 and Supplementary Figs. 6, 7). Although chlorophyll content varies in response to environmental conditions56, light and temperature were consistent during the experiments resulting in comparable chlorophyll-normalized rates that may better reflect photoautotrophic biomass compared to POC. Chlorophyll a has often also been used to normalize Fe uptake rates in phytoplankton communities as POC includes heterotrophic biomass39,57,58. 

When normalized to chlorophyll a, maximum inorganic Fe uptake rates were significantly higher in the 1200 ppm when compared to the 400 ppm treatment at the final timepoint for Experiment 3 (P = 0.016) indicating an increase in cell surface transporters57. All other Fe uptake rates were not significantly different among treatments at each time point and experiment including when normalized to POC. The measurable uptake of Fe-DFB, particularly at Experiments 3 and 4 where rates were similar to those of iron-limited populations and isolates59,60, further indicates iron stress as phytoplankton normally only acquire significant amounts of Fe-DFB when iron-limited57. 

Community growth rates (µ) estimated from chlorophyll a concentrations were also not significantly different between the experiments (Supplementary Fig. 8). In Experiment 1, a relatively rapid average growth rate of 0.87 ± 0.10 d−1 suggests that members of these communities may have performed multiple cell divisions leading to a large increase in chlorophyll a and drawdown of NO3 to <1 µmol L−1. Multiple divisions also may have potentially occurred at Experiment 3 (µ = 0.46 ± 0.04 d−1) and approximately one division may have occurred before the termination of Experiment 4 (µ = 0.27 ± 0.04 d−1) as both experiments were four days long. Collectively, these rates suggest that the total exposure to acidification exceeds that of the community doubling time. In the case of Experiment 2, the shorter duration of the experiment (48 h) and lower average growth rate (µ = 0.32 ± 0.08 d−1) suggest that the community doubling time exceeded the duration of exposure. This lower growth rate was likely driven by iron-limitation in the initial community. 

Microbial community composition
Eukaryotic and prokaryotic community compositions throughout the ocean acidification experiments were inferred via 18S and 16S ribosomal RNA (rRNA) amplicon sequencing (Fig. 3, Supplementary Datasets 1 and 2). Within eukaryotic phytoplankton rRNA, Experiments 1–3 were dominated by centric diatoms, particularly the large chain-forming genera Chaetoceros, Eucampia, and Thalassiosira, with relatively high abundances in Experiment 4 as well (Fig. 3a, Supplementary Fig. 9). Pennate diatoms were also present throughout and largely consisted of Pseudo-nitzschia with a notable presence of Navicula in Experiment 2 (Supplementary Fig. 9). Chaetoceros, Thalassiosira, and Pseudo-nitzschia are also among the most abundant diatoms in both the region and the ocean61,62. Experiment 3 had a high abundance of the prasinophyte Ostreoccocus (Bathycoccaceae) and the pelagophyte Pelagomonas calceolata (Pelagomonadales), both of which are cosmopolitan picoeukaryotes63,64. Pelagomonas calceolata further dominated Experiment 4 in line with their previously observed high abundances in SCMLs65. 

Alphaproteobacteria and Gammaproteobacteria, were prevalent in all four experiments as observed in the global ocean (Fig. 3a)66. In particular, these groups were dominated by the ubiquitous Rhodobacterales order67 and SAR86 clade68 (Supplementary Fig. 10). Cyanobacteria were less than 0.5% of the prokaryotic communities in Experiments 1 and 2 further indicating that the phytoplankton communities were dominated by diatoms. Experiment 3 had a notable presence of Synechococcus (14% of 16S rRNA), and Experiment 4 was dominated by Prochloroccocus (71% of 16S rRNA) indicating a higher presence of cyanobacteria in both of those experiments. 

Like the physiological measurements, increased CO2 largely did not alter the eukaryotic or prokaryotic community compositions. Alpha diversity expressed as the Shannon Index was not significantly different among CO2 treatments in each experiment except for prokaryotes in Experiment 1 where diversity was higher in the 400 ppm treatment (Fig. 3b). Community dissimilarity (Bray-Curtis) was driven more by the distinct initial communities and change in the communities over time with no significant differences among CO2 treatments for both prokaryotes and eukaryotes in each experiment (Fig. 3c and Supplementary Fig. 11, PERMANOVA, P > 0.05). 

Differential abundances of specific taxa as rRNA-derived amplicon sequence variants (ASVs) were evaluated with DESeq2 among the treatments at the final time points (Fig. 3d and Supplementary Fig. 12). Between the 400 ppm and 1200 ppm treatments, DESeq2 estimated that fewer than 1.5% of the eukaryotic ASVs and no more than a single prokaryotic ASV within each experiment were differentially abundant. Five 18S ASVs with significantly decreased abundances at 1200 ppm were haptophytes belonging to the Prymnesiophyceae class or Isochrysidales order without more detailed taxonomic resolution. These taxonomic groups contain calcifying algae that may be more susceptible to acidification as the precipitation of calcium carbonate becomes less energetically favorable, although a wide range of responses for haptophytes has previously been observed and some species are not calcifying10,69,70. Three additional differentially abundant 18S ASVs were metazoan sequences including a hydrozoan and a copepod, which likely stems from uneven distributions of metazoans between treatments rather than an acidification-driven response. 

Differential expression of transcripts and proteins among CO2 treatments
Dominant eukaryotic phytoplankton groups identified from 18S rRNA were abundant in corresponding poly(A)-selected metatranscriptomes and metaproteomes (Fig. 3a). Between 23.8% and 48.2% of orthologous gene groups, or orthogroups, were unique to the detected protein sequences in each experiment’s metatranscriptome assembly highlighting the diverse functional repertoires within each community (Supplementary Fig. 13) in addition to their significant differences in microbial community composition (Fig. 3c). In each experiment, differential expression of KEGG-annotated transcripts was examined between CO2 treatments within these taxonomic groups. The same was conducted for protein abundances in Experiments 1 and 3. Often differentially expressed transcripts were not detected as proteins (Supplementary Table 4), likely due to comparatively lower resolution with mass spectrometry, but some consistent patterns were observed. 

Transcriptional changes were generally lower at 800 versus 400 ppm compared to 1200 versus 400 ppm indicating that transcriptional responses are dependent on CO2 conditions (Fig. 4a and Supplementary Table 5); protein abundances were not examined for the 800 ppm treatments. Dominant taxonomic groups also displayed differing levels of responsiveness with centric diatoms being more responsive than pennate diatoms in terms of both transcripts and proteins. Further variability was observed in the transcripts or proteins that were differentially abundant within taxonomic groups across experiments and time points. 

As acidification is hypothesized to induce iron stress, three indicators of iron stress, the iron starvation-induced genes ISIP1, ISIP2A/pTF, and ISIP3, were examined in both centric and pennate diatoms (Fig. 4b and Supplementary Fig. 14). ISIP1 is linked to endocytosis of siderophore-bound iron71. ISIP2A, or phytotransferrin (pTF), is an inorganic iron uptake protein with a direct dependence on carbonate ions18. As previously mentioned, this carbonate ion dependence may result in negative impacts on iron uptake as carbonate ions decline under acidic conditions. ISIP3 does not have a known function although it has a ferritin-like domain further suggesting a role in iron metabolism72. 

These genes are often highly responsive to iron status in diatoms72–74, and high gene expression relative to others in each sample would not be expected unless the cells were experiencing iron stress or limitation. In Experiments 1 and 2, all three genes were in the top 1% of expressed transcripts by the final time point in all CO2 treatments indicating extremely high expression, iron stress, and iron demand even though macronutrients were depleted at the end of Experiment 1 (Fig. 4b and Supplementary Fig. 14). In Experiments 3 and 4, transcript expression was within the top 5% of expressed genes also indicating iron stress. Importantly, none of the ISIP transcripts were significantly differentially expressed between CO2 treatments at each time point (P > 0.05) suggesting no relative increase or decrease in iron stress as a function of CO2. These patterns were also consistent between centric and pennate diatoms (Fig. 4b and Supplementary Fig. 14). 

ISIP proteins were not universally detected; however, in Experiments 1 and 3, ISIP3 from centric diatoms was in the top 5% of detected proteins at the final time points further suggesting the development of iron stress in these experiments (Supplementary Fig. 15). Other ISIP proteins approached the top 5% in both centric and pennate diatoms. Protein abundances were also not significantly different between CO2 treatments. The exceptions are for ISIP1 and ISIP2A in centric diatoms in Experiment 3 at 48 h where they were not detected in the 1200 ppm treatment; however, both were detected after 96 h where abundances were not significantly different (Supplementary Fig. 15). 

Nutrient stress in Experiments 1 and 3 was further evaluated by examining cellular resource allocations to ribosomal and photosynthetic proteins (Supplementary Fig. 16)75. At Experiment 1, both centric and pennate diatoms displayed significant reductions in their ribosomal protein mass fractions from 48 to 72 h, likely reflecting the onset of nitrogen limitation after 72 h (P < 0.01, Supplementary Fig. 16). At the earlier timepoint, 48 h, pennate diatoms displayed moderate increases in their ribosomal protein mass fractions under higher CO2 (P = 0.11) indicating an increase in ribosomal investment from increased acidity. At Experiment 3, a different response was observed in centric diatoms and Ostreococcus with both significantly reducing their photosynthetic protein mass fractions after the initial timepoint (P < 0.01, Supplementary Fig. 16). This shift is likely a result of increased iron stress as observed in diatoms within the Southern Ocean75. A lack of change to the ribosomal mass fraction in comparison to pennate diatoms at Experiment 1 further highlight the potential differences in responses among taxonomic groups or the interactive effects of ocean acidification under increased Fe stress. 

The total number of significantly differentially expressed transcripts was variable across the experiments with the most found at Experiments 1 and 3 where the exposure to acidification relative to the community growth rates were the longest (Supplementary Fig. 8, Supplementary Tables 4 and 5). Although total differential expression of transcripts within each taxonomic group was less than 1%, those that were differentially expressed highlight the reconfiguration of iron, nitrogen, and carbon uptake proteins as well as cellular iron requirements to increased CO2. Many other differentially expressed genes are of unknown function as anticipated for eukaryotic phytoplankton76. 

Under higher CO2, increased expression of genes related to iron uptake or transport that are distinct from ISIP2a/pTF was observed (Fig. 4c). At times, several of these genes have been observed to be upregulated by diatoms under iron stress in the laboratory and field73,77,78. This response includes an iron permease (FTR1). Diatom FTR1 proteins are related to those found in green algae and yeast where they have been shown to mediate inorganic ferric iron uptake like pTF (Supplementary Fig. 17)72,79. FTR1 transcripts and proteins significantly increased in centric diatoms at 1200 ppm CO2 in Experiment 3 indicating an increase in cell surface transporters. This increase in transporters likely resulted in the significantly higher maximum uptake rates of inorganic iron observed here (Fig. 2d)80. 

Two putative ferrous iron transporters displayed increased transcript expression in Experiment 1 under increased CO2: one in the ZIP family (ZIP1) in centric diatoms and one in the CDF family (FieF-like) in pennate diatoms (Fig. 4c). Diatom homologs of the ZIP1 gene are related to ZIP-family genes in green algae, and have been found to be upregulated under Fe stress for diatoms in the both the field and laboratory73,77,81. The CDF transporter found here is from a distinct clade than one previously described to be upregulated under Fe stress in the model diatom P. tricornutum (Supplementary Fig. 18) and is related to green algal CDF transporters that have been shown to have Mn transport capability82. 

ZIP and CDF-family transporters can transport multiple divalent metal cations79; therefore, if localized to the cell surface, they may serve as low affinity Fe(II) permeases to take advantage of potentially increased Fe(II)25,83. Direct Fe(II) uptake has been demonstrated in eukaryotic microalgae including diatoms supporting this role84. ZIP and CDF-family transporters may also be involved with intracellular transport or release of stored iron potentially indicating an increased reliance on iron recycling or storage79,81,85. 

Putative siderophore-bound iron uptake genes, a FecCD-domain containing gene in centric diatoms and SLC49 family transporters in pennate diatoms also displayed increased transcript expression at 1200 ppm CO2 in Experiment 1 (Fig. 4c). These genes are poorly characterized in diatoms but have been linked to siderophore-based Fe uptake in other organisms. The FecCD domain has been characterized as part of a bacterial operon for a ferric citrate ABC transporter in E. coli86. Similar proteins are present in several centric and pennate diatoms and were likely obtained via horizontal gene transfer from bacteria (Supplementary Fig. 19). Diatom SLC49-family transporters are similar to those found in metazoans where they have been functionally characterized (Supplementary Fig. 20)87. These proteins are related to heme transport and may be responsible for heme uptake as observed in diatom cultures and eukaryotic phytoplankton in the natural environment87,88. 

The increased expression of these transporters is consistent with the expected decrease in bioavailable free inorganic ferric iron that may result from acidification. At Experiment 1, increased transporter expression implies an increased reliance on siderophore-bound iron or ferrous iron that may increase unlike ferric iron. At Experiment 3, the response was instead a transcriptional and proteomic increases of a ferric iron permease (FTR1) that does not have a carbonate-dependence like phytotransferrin. Observed as both transcripts and proteins, this increase suggests that there were a greater number of cell surface transporters for inorganic ferric iron as supported by higher maximum rates of inorganic iron uptake (Fig. 2d)80. 

Some of the ferrous iron transporters with increased expression may be responsible for intracellular transport, these increases may also suggest a greater reliance on iron recycling or releasing stored iron to support growth in the short-term. Also in response to increased CO2 at Experiment 1 centric diatoms increased transcript expression of a CREG-like gene (cellular repressor of E1-A stimulated genes; Fig. 4c) that is upregulated under iron stress in diatoms77,78,90. Diatom CREG proteins are proposed to have a regulatory role in positively inducing endocytosis of iron when iron availability is low suggesting an increased dependence on endocytosis of iron under increased CO291. 

Other differentially expressed genes indicate an adoption of strategies that reduce cellular iron demand. Due to its versatile redox chemistry, iron plays an essential role in photosynthesis-related proteins in diatoms, and reductions in photosynthetic proteins can be related to iron stress83. In centric diatoms, higher CO2 reduced protein abundances of the photosynthetic proteins cytochrome b6 (petB) at Experiment 1 as well as apocytochrome f (petA) and photosystem II D1 (psbA) at Experiment 3 (Fig. 4c and Supplementary Fig. 23). Similarly, these proteins had lower abundances due to increased acidification in two previously mentioned low iron experiments in the Gulf of Alaska35. In pennate diatoms, lower protein abundances for the cytochrome b6 complex protein, petC, and the ferredoxin-NADP+ reductase, petH, were also found at Experiment 1. 

Iron is also critical for mitochondrial electron transport83, and in centric diatoms at Experiment 1, downregulation of an iron-containing mitochondrial protein, mitoNEET (mNT), that may be involved in signaling was observed (Fig. 4c)92. MitoNEET sequences were found to be widely distributed in diatoms where they are related to those in other eukaryotic phytoplankton including green algae (Supplementary Fig. 24). In the model green algae, Chlamydomonas reinhardtii, mNT was downregulated under iron stress indicating that this iron-sparing response may be more widely distributed among phytoplankton93. 

Cellular iron requirements can also be reduced by using iron-free functionally equivalent proteins. In Experiment 3, centric diatoms increased transcript abundances of plastocyanin (petE), a copper-containing functional equivalent for the iron-requiring photosynthetic electron transport chain protein cytochrome c694, and increased protein abundances of the Cu-Zn utilizing superoxide dismutase (SOD; Fig. 4c). Different SOD proteins in diatoms employ different metal cofactors including iron95, and the overrepresentation of Cu-Zn SOD may signify a preference for a non-iron using SOD under high CO2. 

Additionally, nitrogen assimilation proteins are estimated to account for a significant portion of the cellular iron requirement in phytoplankton96, and reducing their transcript or protein abundances has also been associated with iron stress in phytoplankton73. Under higher CO2, pennate diatoms decreased transcript expression of ammonium transporters (AMT) and the ferredoxin-dependent glutamate synthase (Fd-GOGAT) in Experiment 1 and nitrate transporters (NRT) in Experiment 3 (Fig. 4c and Supplementary Fig. 25). Similarly, centric diatoms decreased transcript expression of the ferredoxin-dependent nitrite reductase (Fd-Nir) in Experiment 3 (Supplementary Figs. 23 and 25). 

At the first timepoint of Experiment 3, Ostreococcus decreased transcript expression for the entire assimilatory nitrate reduction pathway including the plastid-localized nitrite transporter (FNT) and the nitrate transporter accessory protein, NAR2 (Fig. 4c). NRT and Fd-Nir also displayed significantly lower protein abundances while NAR2 abundances were not significantly different. Fd-GOGAT transcript expression was also lower at the first timepoint (P = 0.052, Fig. 4c and Supplementary Fig. 25), and by the second timepoint, protein abundances were significantly lower (Supplementary Figs. 25 and 26). Ostreococcus also reduced transcript expression at 800 ppm for these nitrate assimilation genes but not to the same degree as at 1200 ppm further indicating that responses are function of CO2 (Supplementary Fig. 25). Additionally, Ostreococcus responded by increasing transcript expression of a SLC9-family Na+/H+ exchanger at 1200 ppm, potentially to regulate intracellular pH under higher acidity (Fig. 4c)97. 

At the second time point, Ostreococcus displayed further iron-related responses (Supplementary Fig. 26). Like the increased protein abundances of the Cu-Zn SOD for centric diatoms, Ostreococcus increased expression of the Ni-using SOD. Ostreococcus also had reduced protein abundances of the iron storage protein ferritin (FTN) where it may play a role in buffering intracellular iron, a function that may no longer be beneficial under Fe stress98. 

In Experiment 1, centric diatoms displayed remodeling of their carbon uptake pathway with changes in carbonic anhydrase (CA) transcript and protein abundances (Fig. 4c). Carbonic anhydrases (CA) interconvert CO2 and HCO3− as part of the CO2-concentrating mechanism (CCM) in diatoms to overcome relatively low concentrations of ambient CO299. Diatoms possess multiple CAs belonging to evolutionarily distinct families with diverse localizations including extracellular CAs at the cell surface, although there is not consistent conservation between family and location99. 

With increased CO2, transcript expression of α-CA and β-CA in centric diatoms at Experiment 1 significantly increased while expression of δ-CA and ι-CAs significantly decreased (Fig. 4c and Supplementary Fig. 23). These genes were not found to be differentially abundant on the protein level. Conversely, CAs in pennate diatoms at Experiment 1 were not significantly different on the transcript level, but δ-CA proteins significantly decreased with increased CO2 aligning with the lower expression observed in centric diatoms. 

In centric diatoms, α-CA, β-CA, and γ-CA expression was dominated by Chaetoceros whereas other centric diatom genera were expressing δ-CA and ι-CA indicating that these responses may be more genus-specific (Fig. 4c and Supplementary Fig. 23). As zinc is a common cofactor in carbonic anhydrases100 and ZIP-family transporters may also transport zinc79, increased expression of ZIP1, which was also largely from Chaetoceros, may serve to support increased α-CA and β-CA abundances under higher CO2. α-CA and β-CA are believed to be localized to different compartments in the chloroplast in model diatoms99, and expression of α-CA is thought to be constitutive101. γ-CAs and δ-CAshave shown a wider range of localizations99,101 and ι-CA has been localized to the chloroplast in T. pseudonona102. Both δ-CA and ι-CA have been shown to be downregulated under high CO2 as observed here, although for δ-CA, this pattern is not entirely consistent101–104. 

SLC4-family bicarbonate transporters are also part of the CCM105. Despite change in CA expression, SLC4 transporter transcripts were not differentially expressed in any experiment for centric or pennate diatoms (Supplementary Fig. 27). In the protein data, it was only detected at Experiment 1 in centric diatoms after 72 h where it was also not differentially abundant. SLC4 expression has previously been shown to decrease with higher CO2 in the model diatom T. pseudonana103; however, no difference in bicarbonate uptake or certain SLC4 transcripts within a more extensive pH range (7.5–8.2) was observed in the model diatom P. tricornutum suggesting a lack of response over the pH ranges used here105.

Methods
Experimental design
Trace metal clean incubation experiments were conducted at four sites (Table 1) in August 2019 onboard the R/V Atlantis as part of the California Current Ecosystem Long-Term Ecological Research (CCE-LTER) Process Cruise. At each site at approximately 06:00 PST, seawater was collected from near the depth of maximum fluorescence (Supplementary Fig. 1) with a trace metal CTD-rosette fitted with Teflon coated 5L Niskin-X bottles (Ocean Test Equipment) on a non-metallic hydro-line. Niskin bottles were then brought into a positive pressure trace metal clean van where seawater was dispensed into 4 L polycarbonate bottles fitted with perfluoroalkoxy alkane (PFA) tubing and Kynar® PVDF barb fittings for sampling and bubbling. At the end of one inlet line, the bottles also had a plastic aerator (Lee’s Discard-A-Stone) to maximize gas transfer. 

Prior to the incubations, bottles were cleaned using trace metal techniques in a trace metal clean area under positive pressure. First, bottles were treated with acidic detergent (CitraNOX®, Alconox Inc.) for one day then rinsed five times with Milli-Q. Bottles were then filled with 1.2 mol L−1 trace metal grade HCl for two weeks then rinsed again three times with Milli-Q. During each step, detergent, Milli-Q, or HCl was syringed through the aerator, barbs, and tubing with all plastic syringes (NORM-JECT®). 

Bottles were rinsed with seawater three times, then 4 L of seawater was dispensed into each bottle. To attempt to avoid macronutrient depletion, macronutrients (nitrate, phosphate, and silicic acid) that had been prepared with trace metal grade salts and passed over Chelex 100 resin to remove contaminating metals as described by Sunda, et al.113. Macronutrients were added at varying concentrations based on estimated initial concentrations (Table 1). Bottles were then transferred to the laboratory onboard where they were placed in an incubator (Percival Scientific) at 12.5°C with 115 µmol photons m−2 s−1 photosynthetically active radiation (PAR). The incubator was programmed on a light:dark cycle to closely match the timing of sunset and sunrise at the time and location of the incubations (lights on from 06:30 to 19:45). Seawater was then continuously bubbled with commercially prepared gas mixtures of air balanced with 400, 800, or 1200 ppm CO2 (Praxair, Inc.) at a total rate of 0.3 LPM for each treatment. Gas was delivered to each bottle through a 4-port cross connector and fine-tuned with KeckTM tubing clamps to achieve evenly distributed flow of approximately 0.1 LPM in each bottle. Prior to the cross connector, gas was filtered through a 0.2 µm Millex-FG PTFE filter (Millipore), passed through tubing that also cleaned with 1.2 mol L−1 trace metal clean HCl and rinsed three times with Milli-Q, then delivered to the inlet tubing at the bottle through another WhatmanTM HEPA-Vent filter. A HEPA-Vent filter was also was placed on the outlet tubing to prevent contaminating particles from entering the bottles. 

Triplicate bottles were used for all experimental conditions; one replicate from the 800 ppm treatment for experiment 4 (800B) was not included in final analyses due to potential iron contamination. 

Seawater was collected directly from the Niskin bottles and immediately processed for the initial time point. For the later time points, sampling occurred at one-day intervals that varied among experiments: 48 and 72 h for Experiment 1, 24 and 48 h for Experiment 2, and 48 and 96 h for Experiments 3 and 4 (Table 1). These intervals were chosen to attempt to avoid macronutrient depletion with maximum experimental duration of 96 h to also avoid increased bottle effects. To sample these later time points or chlorophyll a at 24-hour intervals, bottles were brought into a HEPA-filtered positive pressure trace metal clean area. Samples were then immediately taken for dissolved inorganic carbon (DIC) followed by subsamples for chlorophyll a, particulate organic carbon and nitrogen, RNA, proteins, Fe uptake, macronutrient concentrations, and total alkalinity (TA) that were dispensed into additional bottles for sampling as described below. 

Satellite data and maps
Satellite-derived data were obtained from the NOAA CoastWatch Browser, and maps were created with matplotlib v2.2.2 for Python v2.7114. Chlorophyll a concentrations on a 0.0125° grid were obtained from MODIS onboard the Aqua (EOS PM). 

Carbonate chemistry
Initial (T0) seawater samples for dissolved inorganic carbon (DIC) and total alkalinity (TA) analysis were collected from the Niskin bottles. Tubing was attached to the Niskin bottle and placed at the bottom of a 250 mL borosilicate glass bottle. The bottle was then inverted to thoroughly rinse the inside of the bottle and then filled from the bottom and allowed to overflow at least one full volume. The tubing was then pinched and withdrawn leaving only a small headspace. 100 µL of a saturated mercuric chloride (HgCl2) solution was added to the sample to prevent biological activity114. Bottles were then sealed with high vacuum grease (Apiezon) on the stopper and fastened using a rubber band and bottle clip. 

For the later timepoints, DIC and TA samples were collected separately. DIC was collected immediately by slowly syringing without introducing bubbles from the incubation bottles into 60- or 120-mL borosilicate glass bottles. Each bottle was gently rinsed with seawater then filled from the bottom using tubing connected to the syringe. Seawater was immediately poisoned with either 25 µL or 50 µL of saturated HgCl2 solution for the 60- or 120-mL bottles respectively. Bottles were then sealed with grease and clipped as done for the T0 samples. DIC was measured with an Automated Infra-Red Inorganic Carbon Analyzer (AIRICA, Marianda) equipped with a LI-COR 7000 CO2/H2O gas analyser as the detector. 

TA was collected from filtrate from the sterivex used for RNA or proteins into 250 mL borosilicate glass bottles. Each bottle was rinsed thoroughly three times before filling. Seawater was poisoned with 100 µL saturated HgCl2 solution and the bottles were sealed as described for the T0 samples. TA was then measured with an open-cell potentiometric acid titration (0.1N HCl) system developed by the Dickson Lab at Scripps Institution of Oceanography115. 

Both seawater DIC and TA measurements were made relative to certified reference materials (CRMs) provided by the laboratory of A. Dickson at Scripps Institution of Oceanography. The accuracy and precision of the measurements from the CRMs were -0.21 ± 1.26 µmol kg−1 (n=24) for DIC and -0.42 ± 1.85 µmol kg−1 (n=17) for TA. The complete seawater carbonic-acid system (i.e., pCO2, pH, CO32−, HCO3− etc) was calculated based on DIC, TA, temperature, salinity, phosphate, and silicate using CO2SYS for MATLAB (v2.1)116. Calculations used the first and second dissociation constants of carbonic acid (K1 and K2) from Mehrbach, et al.117 refit by Dickson and Millero118, the dissociation constants of bisulfate (KHSO4) from Dickson119, and total boron from Uppstrom120. pH values are reported on the total H+ scale. 

Dissolved iron concentrations
Dissolved iron (dFe) was measured via chemiluminescence flow-injection analysis with hydrogen peroxide oxidation (FeLume, Waterville Analytical) as described by Lohan, et al.121. Briefly, seawater was filtered using acid-cleaned 0.2 µm Acropak 200 capsule filters (VWR International) into clean and rinsed low-density polyethylene (Nalgene) bottles in a positive pressure clean van then was acidified to pH ~ 1.8 with HCl (Optima grade, Fisher Scientific). Dissolved iron was oxidized to Fe(III) with hydrogen peroxide (1% v/v optima grade), buffered in-line with an ammonium-acetate mixture, and selectively pre-concentrated on a column packed with resin (Toyopearl 650M chelating resin) at pH ~ 3.5. After the sample was eluted with 0.23 M HCl (Optima grade), the production of radicals catalyses a three-step oxidation reaction with hydrogen peroxide (0.23 M optima grade) and luminol (0.25 mM). Iron was then eluted and oxidized with pH > 9 luminol-ammonia buffer, and the chemiluminescence (425 nm) was measured with a photomultiplier tube. Final dissolved iron concentrations were then quantified via external standard curve. In-house and GEOTRACES consensus reference standards (geotraces.org) were measured in each analytical run to ensure accuracy and precision. 

Labile particulate iron concentrations
Labile particulate iron was calculated as total dissolvable Fe (TDFe) minus dissolved iron (dFe). Unfiltered samples were acidified to pH ~ 1.8 with HCl (Optima grade, Fisher Scientific) and stored for 22–24 months. Immediately prior to analysis, the samples were syringe-filtered using acid-cleaned syringes and 0.2 µm Supor® filters, and then analyzed using the same analytical methods as for dFe samples. The blank from the syringe-filtration was averaged 0.058 nmol L−1, which is at most a 6% correction to the overall measurement of TDFe. Due to the calibration range of the standards, it was necessary to dilute many of the samples, especially nearshore; therefore, offshore filtered Pacific surface seawater (0.22 nmol L−1) was used to dilute samples by a factor of 10 prior to analysis. 

Dissolved macronutrient concentrations
Approximately 30-35 mL of filtrate from the Sterivex filter unit (0.2 µm) was dispensed into 50 mL polypropylene centrifuge tubes and frozen at -20°C until analysis. Each tube was rinsed three times before filling. Nutrients were analysed on a QuAATro continuous segmented flow autoanalyzer (SEAL Analytical) as described by the CalCOFI methods manual. Total oxidized nitrogen (nitrate reduced to nitrite), solely nitrite, and the efficiency of reduction of nitrate to nitrite were assessed to calculate solely the nitrate concentration. Reference materials for nutrients in seawater (KANSO Co., LTD.) were included in the run for quality control. 

Siex proxy for diatom iron limitation
Siex is a biogeochemical proxy that suggests Fe limitation in diatoms due to preferential utilization of silicic acid53,55,122, and was calculated as described by Hogle, et al.53. Briefly, Siex = µmol H4SiO4 L−1 − (µmol NO3 L−1 * RSi:NO3) where RSi:NO3 is the preformed ratio of silicic acid and nitrate from upwelled source waters. Based on averaged values from the upwelling source water densities in the region (σθ=25.8 kg m−3), RSi:NO3 = 0.9953. 

Parallel incubations to assess iron status
Parallel incubations were conducted to evaluate the presence of iron limitations. First, approximately four hours prior to the start of each of the pCO2 experiments, seawater was collected from near the same depth as the pCO2 experiments with the TMC rosette (Supplementary Fig. 1). One litre of seawater was dispensed into trace metal clean polycarbonate bottles. Trace metal clean techniques were used as described above. Triplicate bottles at each site were either unamended (control), supplemented with 5 nmol L−1 iron (+Fe), or supplemented with 100 nmol L−1 of the iron chelator desferrioxamine B (+DFB). 

Bottles were incubated in situ for 24 h with a quasi-Lagrangian drifter as described in Stukel, et al.123. The drifter was equipped with a surface float and holey-sock drogue centered at 15 m. Bottles were attached to the drifter near the depth of collection in mesh bags. Either immediately (T0) or after 24 h of incubation, samples were collected for chlorophyll a and macronutrients. The drifter was followed between the start of these incubations and the acidification incubations to sample from approximately the same water mass. 

One day following the initiation of the fourth pCO2 experiment (E4) and while still following the Lagrangian drifter, an additional trace metal clean incubation experiment was performed using a community collected and incubated on the drifter at 40 m depth. Triplicate 1 L polycarbonate bottles at each site were either unamended (control), supplemented with 5 nmol L−1 (+Fe), supplemented with 10 µmol L−1 (+N), or both iron and nitrate (+FeN). After 48 h, samples were collected for chlorophyll a. 

In addition to the incubations at E3 on the drifter, deckboard incubations in triplicate 2.7 L polycarbonate bottles were performed with neutral density screening for light and flow-through seawater for temperature. These bottles were either unamended (control) or supplemented with 5 nmol L−1 (+Fe). Samples for chlorophyll a were collected immediately (T0) and following 24 and 48 h of incubation. 

Chlorophyll a
Fifty mL of seawater was filtered through a GF/B filter (1.0 µm, 25 mm) for the acidification experiments or a GF/F filter for the parallel incubations under gentle vacuum pressure (<100 mmHg) and frozen at -20°C until analysis. Chlorophyll a extraction was performed using 90% acetone at -20°C for 24 h and measured via in vitro fluorometry on a Turner Designs 10-AU fluorometer using the acidification method. Community growth rates (µ) were derived from the slope of linear regressions with the natural log of chlorophyll a concentrations over time. The last timepoint from Experiment 1 was excluded from the growth rate calculations as macronutrients were depleted. 

Particulate organic carbon and nitrogen
Four hundred mL of seawater was filtered through a pre-combusted Whatman GF/B filter (1.0 µm, 25 mm, 450°C for 6 h) under gentle vacuum pressure (<100 mmHg) and frozen in plastic petri dishes at -20. Filters were dried at 62°C then inorganic carbon was removed by acidifying the filters with HCl vapor followed by drying for another hour at 62°C. Filters were then encapsulated in tin and analysed on a Costech ECS 4010 CHNSO Analyzer following the manufacturer’s instructions. Blank filters were run alongside the samples and subtracted from the sample quantities. 

Short-term 59Fe uptake assays
Iron uptake rates were characterized in subsamples collected from the incubation bottles or directly from the Niskin-X bottles (T0 samples) in acid-cleaned polycarbonate bottles. Additions of either 1 nmol L−1 59FeCl3 or 1 nmol L−1 59Fe-DFB (ferrioxamine B) were made to uptake bottles which were then incubated without light. 59Fe-DFB stocks were equilibrated overnight with a 4:5 ratio of iron to DFB. Four aliquots of each uptake bottle were filtered onto 1 µm polycarbonate filters at one-hour intervals. Each filter was rinsed with an oxalate-EDTA wash solution for 15 min before rinsing with filtered seawater124. Filters were then preserved in Ecolite™ liquid scintillation cocktail (MP Biomedicals), and 59Fe was quantified by liquid scintillation counting. Uptake rates were calculated by the linear regression of particulate 59Fe per litre of seawater against time. Rates were normalized to both chlorophyll a and particulate organic carbon concentrations. 

RNA collection and extraction
Between 525 and 1100 mL of seawater from each bottle or the initial seawater was filtered through a 0.22 µm Sterivex-GP filter unit (Cat. no. SVGP01050, Millipore) which was immediately sealed with a 1.2 mol L−1 HCl and Milli-Q rinsed luer-lock plug and Hemato-SealTM tube sealant, wrapped in aluminum foil, and flash frozen in liquid nitrogen. RNA was extracted using the NucleoMag RNA extraction kit (Machery-Nagel) on an Eppendorf epMotion 5075t as described here: https://doi.org/10.17504/protocols.io.bd9ti96n. RNA quality and quantity were assessed with an Agilent 2200 TapeStation (RNA ScreenTape) and the QuantiTMRibogreen® RNA Assay kit. For RNA at initial timepoint (T0) for Experiment 3, n=2. 

16S and 18S rRNA amplicon sequencing and analysis
From the aforementioned extracted RNA, cDNA was synthesized with the Invitrogen SuperScript™ III First-Strand Synthesis System according to the manufacturer’s instructions. RNA-derived amplicon libraries targeting the V4-V5 hypervariable region of the 16S gene and the V9 hypervariable region of the 18S region were then generated via a one-step PCR using the cDNA with the Azura TruFi DNA Polymerase PCR kit. For 16S, the 515F-Y (GTGYCAGCMGCCGCGGTAA) and 926R (CCGYCAATTYMTTTRAGTTT) primer set was used125. For 18S, the 1389F (TTGTACACACCGCCC) and 1510R (CCTTCYGCAGGTTCACCTAC) primer set was used126. Each reaction was performed with an initial denaturing step at 95°C for 1 min followed by 30 cycles of 95°C for 15 s, 56°C for 15 s, and 72°C for 30 s. 2.5 µL of each PCR reaction was ran on a 1.8% agarose gel confirm amplification. PCR products were purified using Beckman Coulter AMPure XP following the standard PCR clean-up protocol. PCR quantification was performed using Invitrogen Quant-iT PicoGreen dsDNA Assay kit. Samples were then pooled in equal proportions (10 ng DNA per sample) followed by another AMPure XP PCR purification. The final libraries were evaluated on an Agilent 2200 TapeStation (D1000 ScreenTape), quantified with Qubit HS dsDNA kit, and each sequenced at the University of California, Davis Sequencing Core on a single Illumina MiSeq lane with a 15% PhiX spike-in. 

Amplicons were analysed with QIIME2 v2019.10127. Briefly, demultiplexed paired-end reads were trimmed to remove adapter and primer sequences with cutadapt128. Trimmed reads were then denoised with DADA2 to produce amplicon sequence variants (ASVs)129. Taxonomic annotation of ASVs was conducted with the q2-feature-classifier classify-sklearn naïve-bayes classifier130,131 and the SILVA database (Release 138) for 16S132 or the PR2 database (v4.12.0) for 18S133. 16S ASVs with eukaryotic, plastid, and mitochondrial taxonomic assignments were removed. Diversity metrics and significance tests were calculated with the QIIME2 diversity plugin with samples rarefied to 3,756 reads for the 16S data and 82,249 reads for the 18S data (Supplementary Fig. 28)134,135. Differential abundances of ASVs were assessed using the final timepoints of each experiment with DESeq2 v1.22.2136–138. ASVs were considered differentially abundant by those with Benjamini and Hochberg adjusted P-values < 0.05139. 

Metatranscriptome library preparation, sequencing, and analysis
A poly-A selected metatranscriptome sequencing library was constructed with the Illumina TruSeq Stranded mRNA kit according to the manufacturer’s instructions and sequenced on an Illumina NovaSeq 6000 with a S4 flow cell (2x150 bp). Raw reads were quality trimmed with trimmomatic v0.39140 and evaluated with FastQC v0.11.8 and MultiQC v1.9.dev0141. Trimmed reads were assembled into contigs utilizing subsets of samples to generate sub-assemblies with Trinity v2.8.5 with min_contig_length set to 150 bases142. These assemblies were then merged into final assemblies for each experiment with Trans-AbySS v2.0.1143. 

Proteins were predicted from the final assemblies with GenemarkS-T144 which were deduplicated using the nucleotides of the predicted proteins and the dedupe script from BBMap v38.79. Ribosomal RNA sequences were identified with SortMeRNA v2.1 and were also removed145. Reads were mapped to the predicted proteins with Bowtie2 v2.3.5 in local alignment mode146. Only reads with a MAPQ score ≥ 8 were retained. Taxonomic annotation of predicted proteins was performed with DIAMOND (v0.9.31) BLASTP searches against PhyloDB v1.076147,148 with an E-value cutoff of 10−5, minimum query coverage of 60%, and alignment scores in the top 95%. To avoid misannotations from contamination in the reference database, final taxonomic assignment was based on the Lineage Probability Index from the BLASTP hits with the aforementioned cutoffs148,149. Orthogroups across different experiments were identified using the hierarchial phylogenetic approach implemented in OrthoFinder v2.5.4150. For functional annotation, DIAMOND BLASTP searches were performed against the Kyoto Encyclopedia of Genes and Genomes (KEGG; Release 94.1)148,151. KEGG Ortholog (KO) assignment was performed with KofamKOALA which utilizes hmmsearch against KOfam, an HMM database of KOs152. Within taxonomic groups, read counts were summed by KO annotation, and if KO annotation was absent, the KEGG gene annotation was used. Normalization and differential expression of transcripts were then assessed with DESeq2 v1.22.2. Significance in differentially expressed transcripts was determined with Benjamini and Hochberg adjusted P-values (P < 0.05)139. MA plots were generated with the code provided in Cohen, et al.153. 

Protein sample collection, extraction, and preparation
Seawater (300-900 mL) was filtered through a 0.22 µm Sterivex-GP filter unit (Cat. no. SVGP01050, Millipore) which was immediately sealed with a 1.2 mol L−1 HCl and Milli-Q rinsed luer-lock plug and Hemato-SealTM tube sealant, wrapped in aluminum foil, and flash frozen in liquid nitrogen. Proteins were extracted with a detergent-based procedure based on Saito, et al.154. First, filters were removed from the Sterivex casing as described by Cruaud, et al.155 and placed in 750 µL of extraction buffer (2% SDS, 0.1 M Tris/HCl pH 7.5, 5 mM EDTA). Samples were incubated for 10 min at 95°C while mixed at 350 rpm. Sonication was then performed with a Diagenode Biorupter for approximately 8 min (15 s on/off intervals) at 4°C. Next, samples were again incubated at room temperature for 30 min while gently vortexing every 10 min. The filters were then removed from the tubes, and the remaining liquid was centrifuged at 15,000 × g for 30 min at room temperature. The supernatant containing the extracted protein was then removed without disturbing the pellet. Crude protein extracts were quantified with the PierceTM colorimetric BCA protein concentration assay. 

S-TrapTM minicolumns (PROTIFI) were then used to remove SDS extraction buffer from the samples and to digest extracted proteins into mass-spectrometry-compatible peptides. Proteins were first reduced and alkylated using 5 mmol L−1 dithiothreitol and 15 mmol L−1 iodoacetamide respectively, and then denatured using 12% phosphoric acid. Samples were then diluted (1:7) with S-trapTM buffer (pH 7.1 100 mmol L−1 tetraethylammonium bicarbonate in 90% aqueous methanol) and loaded onto the S-trapTM columns. To remove SDS, each sample was washed 10 times with 600 µL S-trapTM buffer using gentle vacuum. Proteins were then digested on-column using trypsin (1 trypsin:25 protein) at 37°C for 16 h. Tryptic peptides were eluted from the S-TrapTM column with a series of 50 mmol L−1 ammonium bicarbonate, 0.2% aqueous formic acid, and 50% acetonitrile + 0.2% formic acid washes. Eluted samples were desalted on pre-conditioned 50 mg C18 columns (HyperSep), dried using a Vacufuge Plus (Eppendorf) and then resuspended in a 1% formic acid, 3% acetonitrile solution prior to liquid chromatography–tandem mass spectrometry analysis. 

Metaproteomic processing and analysis
Samples were analyzed on a Dionex UltiMate™ 3000 RSLC-nano LC system (Thermo Scientific) coupled to a Q-Exactive hybrid quadrupole-Orbitrap mass spectrometer (Thermo Scientific). Each sample was analyzed in duplicate, and a blank was run between each set of biological replicates. The peptide matrix was separated using one-dimensional liquid chromatography for 125 min under a non-linear gradient (Supplementary Table 6). The mass spectrometer was operated under positive polarity, data-dependent acquisition mode. Detailed mass spectrometer settings are shown in (Supplementary Table 7). 

Raw mass spectrometry files were converted to mzML format using ThermoRawFileParser156. Two annotated databases were compiled using experiment-specific metatranscriptome sequences (see Metatranscriptome library preparation, sequencing, and analysis) and appended with proteins from the common Repository of Adventitious Proteins to detect contaminants. Databases were then filtered to remove redundant sequences, and database searching was conducted using OpenMS MSGF+ with a 1% false discovery rate157,158. Post translational modification parameters included cysteine carbamidomethylation as a fixed modification, and methionine oxidation and N-terminal glutamate to pyroglutamate conversion as variable modifications. 

Peptides were quantified using MS1 ion intensities, and FeatureFinderIdentification was used to identify unknown features based on identified MS2 spectra in replicate injections159. Individual peptide abundances in each injection were normalized to the sum of all peptide intensities in that injection (total sum scaling), excluding peptides matching to the common contaminants database. Unique peptides were assigned to a taxonomic and functional annotation based on the metatranscriptome assembly and annotation. Non-unique peptides, i.e., peptides matching to more than one taxonomic group or functional annotation, were considered to be ambiguous. As with the transcripts, peptides were grouped based on KEGG Ortholog (KO) and gene annotations within each taxonomic group. Significant differences in protein group abundance for each taxonomic group were assessed within experiments using ion intensities with DESeq2 and Benjamini and Hochberg adjusted P-values (P < 0.05)136,139. Although DESeq2 is designed for RNA-seq data, here it was used for consistency between the transcript and protein analyses, and it has been found to be effective at determining significance in differentially expressed proteins160. 

Ribosomal and photosynthetic protein mass fractions were calculated as described by McCain, et al.75. Specifically, KO gene definitions were searched against character strings for each function. The photosynthesis group included all proteins with “photosyn*”, “light-harvesting complex”, “flavodoxin”, or “plastocyanin” in their annotation where “*” represents a wildcard character. The ribosome group included all proteins with “ribosom*” and excluded proteins responsible for ribosomal synthesis. To be included, each taxonomic group of interest was required to have more than 50 unique peptides for each protein group to avoid known biases75. Mass fractions were then calculated by summing the peptide abundances for each function within each taxonomic group and then dividing by the total peptide abundance for the taxonomic group. Differences in proteomic mass fractions were evaluated via ANOVAs followed by Tukey’s honest significant difference test in R. 

Phylogentic analyses
Taxonomically representative sets of homologs of diatom FTR, CDF-family, FecCD-domain, SLC49-family, and mitoNEET genes with an emphasis on unicellular eukaryotic algae were identified from the NCBI nr and MMETSP databases via BLASTP searches (E-value ≤ 10−3)161,162. To reduce the complexity stemming from the transcriptomes in MMETSP, sequences were clustered at the 95% similarity level with USEARCH163. Seed sequences were then aligned using MAFFT v7164. Poorly aligned regions and regions rich in gaps were identified and removed using trimAI in gappyout mode165. Alignments were then visualized and inspected in SeaView 4166. Maximum likelihood phylogeny was computed in IQTree 2 under the model that produced the best fit using ModelFinder167,168. Ultra-fast bootstrap approximation was estimated from 10,000 replicates. 

Statistical analyses
Two-way ANOVAs followed by Tukey’s honest significant difference test were performed on the biological and chemical properties of the seawater using Graphpad PRISM v9.0.0.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Review to ‘Diverse iron acquisifion and conservafion mechanisms support resistance to acidificafion in upwelling-associated phytoplankton’ by Lampe et al.\nGeneral comments\nThis study assessed the effect of ocean acidificafion (OA; 400 vs 800 and 1200 µatm pCO2) via the performance of 4 iron-addifion phytoplankton boftle incubafion experiments along the Californian upwelling system. The lafter enabled to assess OA effects under different degrees of iron limitafion and with differently aged upwelled waters. Newly upwelled waters were invesfigated via experiment 1, experiments 2 and 3 tested progressively aged waters while experiment 4 waters were sampled offsite. In comparison, inifial communifies of experiment 1 and 2 were iron-limited while the other start communifies were not. The durafion of the experiments varied, spanning between 2 (experiment 2), 3 (experiment 1) up to 4 days (experiment 3,4). In my opinion, this study suffers from insufficient incubafion fime for exp. 1 and 2, which caused no apparent physiological, transcripfional, proteomic and community differences. This is in parficular a pity, as in parficular experiments 1 and 2 were the ones that only allowed to assess the effect of iron limitafion next to OA, but they unfortunately also had the shortest incubafion fime. I agree with the authors that the assessment of transcriptomic responses allows already to assess subtle changes within short fimes (2 days), but then sfill the quesfion remains whether these responses will be also reflected in phytoplankton growth and biomass build up (chlorophyll, POC). To see these responses at least a durafion of 4 days is needed. Indeed, the authors point out “there were no observable differences in chlorophyll a producfion and nutrient uptake” (L371) as well as “increased CO2 did not alter the carbon-to-nitrogen rafios of the community suggesfing a lack of CO2 ferfilizafion with no increase in carbon content from high CO2” (L373-374). Unfortunately, I have serious doubts that the overall conclusion drawn from experiments 1 and 2 for upwelled and iron-limited waters is mainly an artefact of the experimental design and as such a result of the short incubafion fime of experiment 1 and 2 (experiment 1 = 3 days, experiment 2 = 2 days).\nI agree with the authors that molecular data on iron uptake are interesfing and provide interesfing insights about the resistance of phytoplankton to OA over a short fime scales (between 2 up to max. 4 days), but I disagree that these data will allow to confer the long-term response, as the experimental durafion of only 2 days did not even allow the phytoplankton cells to adjust their physiological machinery to the applied different environmental condifions (acidificafion, iron). Hence, in my opinion, the presented data for experiment 1 and 2 do not even allow to draw any conclusions on the long-term response. I suggest the authors to mainly focus on the short-term responses idenfified at the molecular level on iron uptake and to avoid to draw any conclusion on ‘chronic long-term exposure’ (L52) because the authors do not even have data to make predicfions on the capacity of phytoplankton on a longer term. Overall, I disagree that the manuscript currently as it is can be published in Nature Communicafions and suggest the authors to rewrite the manuscript by concentrafing solely on the short-term responses at the molecular level on iron and carbon uptake.\nAbstract:\nThere is large emphasis put on the fact that OA was invesfigated in iron-stressed phytoplankton of upwelled waters (L44-46), but as laid out by me before, iron-stressed upwelled phytoplankton was mainly found at the sampling sites of experiment 1 and 2. The lafter two experiments, however, suffered from a too short incubafion fime (2 or 3 days), which avoids to obtain at all potenfial shifts in phytoplankton growth, nutrient uptake and community composifion. From these experiments, however, important conclusions are drawn that “phytoplankton growth, nutrient uptake, and community composifions remained unchanged” (L47-48), which indicates their potenfial resistance to OA. I agree with the authors that ‘molecular-level responses suggest that resistance to acidificafion-driven changes in iron bioavailability is facilitated by iron uptake pathways that are less hindered by acidificafion and strategies that reduce cellular iron demand.” Therefore, I strongly suggest the authors to rewrite this manuscript with a focus solely on the short-term response at the molecular level on the different iron uptake strategies. Please note that the authors even point out by themselves that for experiment 2 “insufficient incubafion fime may not have led to community differences” (L216-217) detected with DESeq2.\nIntroducfion:\nL93-100: I disagree with the authors that “A relafively small number of field-based studies have examined the effects of ocean acidificafion on iron stress in phytoplankton”. In fact, there are various studies that have performed Fe-enrichment experiments simulafing OA condifions in low Fe regions such as Shi et al. 2010 Science, Feng et al. 2011 Deep Sea Res, Sugie et al. 2013 Biogeosciences, Endo et al. 2013 JEMBE, Yoshimura et al. 2013 J Oceanogr, Yoshimura et al. 2014 Deep-Sea Res, Trimborn et al. 2017 MEPS, Pausch et al. 2022 Fronfiers Mar Sci. Moreover, these studies did not add any chelafing agent to induce Fe stress, therefore this secfion needs to be rephrased.\nL113-115: The authors point out that “physiological measurements may not be sensifive enough to detect more subtle change, parficularly over relafively short fime scales”. To me, it is not really clear why a short-term response is more of relevance than a long-term response. In fact, it is much more of interest to befter understand how well microorganisms are adapted on a long-term (up to weeks) to upwelling and low Fe condifions than on a short-term (1-4 days). Moreover, there are also physiological adjustments by microorganisms that can immediately respond, as an example the photochemical quantum yield, commonly used as an important indicator of the physiological status of phytoplankton, can already change within 24h. Also, carbon uptake characterisfics (CA, CO2 and HCO3- uptake) can adjust very quickly. Hence, in my opinion the statement that molecular tools per se are befter than physiological measurements is simply not appropriate.\nDiscussion:\nL367-381: As menfioned above, the observafions on growth, community composifion and biomass formafion in experiments 1 and 2 cannot be considered due to their too short incubafion fime.\nL388-294: Measurements of internal and external carbonic anhydrase acfivifies for various phytoplankton groups and species exist and it would be good to take them in the discussion into account.\nL398-420: Different Fe uptake strategies were observed for the communifies of experiment 1 and 3, this is somewhat addressed, but not really in detail. This is also due to the fact that the community of experiment 1 was Fe-limited already at the start of the experiment, whereas the community of experiment 3 only became Fe-limited due to the addifion of macronutrients. I think this is important to note, but this fact is completely neglected here in the discussion.", "reviewer_2": "Lampe et al examine the responses of diatoms to coastal upwelled waters that were experimentally acidified by increased CO2 concentrafions. The goal of the study is to see how the phytoplankton community structure and physiology responds. They hypothesize that since coastal upwelling regions experience increased pH variability in comparison to open ocean regions, the microbial communifies may be adapted to handle changes in acidity and associated iron stress. The results fit with this hypothesis and there are largely no significant differences in physiological or growth condifions between the three CO2 condifions (400, 800, 1200 ppm).\nThe paper is well wriften and clearly communicates the results. The science is sound, using well established methods in phytoplankton ecophysiology and molecular methods. The methods are adequate to allow replicafion and verificafion of the study.\nI applaud this paper for being upfront about negative results. For the most part acidificafion leads to few or no differences in phytoplankton physiology or induce iron limitafion due to acidificafion. This is true even though macronutrients were added to all condifions to try and push the system into iron limitafion.\nMy major concern is that it is difficult to comprehend the robustness of their metatranscriptome results across the four different experiments and condifions. The authors state up front that the metatranscriptome results were variable, and chose to present a handful of examples from select organisms within select fime points from select treatments in select experiments. Figure 4 shows this best. To determine the robustness of the transcripfional trends presented in the main text, it would be helpful if the authors provided an index of what proporfion of treatments, condifions, and experiments a parficular gene was found to be significantly differenfially transcribed.\nA few detailed comments:\nThe proteomics data seems like a side note in the manuscript and under ufilized.\nFigures 1 and 2 were cutoff in the pdf.\nFigure 3A and B could use some clarificafion. Is this just all the samples from the four fime points combined into a single plot for each experiment? If so, why show the data this way which obscures the temporal trends in community composifion.\nLn 196. You menfion that SAR86 is the prevalent member of the gamma proteobacteria. For the alphaproteobacteria did you see SAR11 was most abundant? I ask given I was surprised to see that the gammas are so much more abundant than the alphas in fig 3 and I'm wondering if the primer sets chosen are biased against the SAR11 clade members?"}, "author_response": "We thank the reviewer for their detailed comments and highlighting the insights gained from our short-term molecular data. We agree with the reviewer’s assessment that we should refocus more on the short-term molecular-level responses. We have made numerous revisions to our manuscript to emphasize the short-term nature of these experiments, better highlight the short-term molecular-level results, and reduce our conclusions about the long-term effects. For example:\n• We have changed our title to highlight that these are short-term responses and removed the word “resistance,” thereby not including any mention of potential long-term effects in the title. It now reads “Short-term acidification promotes diverse iron acquisition and conservation mechanisms in upwelling-associated phytoplankton.”\n• We specify in the abstract that we are showing how phytoplankton “respond to short-term acidification exposure” (lines 45-46) and that the mechanisms we describe may confer “short-term resistance” only (line 52). This is also repeated in our discussion on:\nLines 546-548: “Within all four experiments however, short-term exposure to pCO2 levels surpassing global surface seawater means predicted by the end of the century and most natural surface water variability in the CCS (Supplementary Tables 1-3) produced no observable differences”\nLines 558-559: “These results may suggest that the natural phytoplankton communities observed here possess short-term resistance to acidification at pH values as low as 7.6 despite iron stress.”\nLines 566-567: “However, these results may have occurred from the relatively short duration of the experiment relative to growth rates, particularly at Experiments 2 and 4”\n• We again highlight the short-term nature of these experiments at the end of the introduction (lines 151-153) by stating that “these experiments enable probing of mixed phytoplankton assemblages’ sensitivity to short-term acidification exposure.”\n• Our discussion has largely been rewritten. Rather than focus on resistance, almost all the first six paragraphs focus on the short-term molecular-level response. Moreover, we directly address the short-term nature of some of our experiments potentially leading to a reduced observable effect on lines 534-536, 566-568, and 572-575.\n• We conclude our paper by stating that the tipping points for the effects of OA may exist beyond the concentrations and short time scales used in this experiment on lines 569-575:\n“Ocean acidification is in itself a multiple stressor as several carbon and iron parameters in seawater are changing simultaneously (Hurd et al. 2020). As a disturbance, it may be representative of a nonlinear transition where the tipping points are not reached until extreme conditions are encountered (Scheffer et al. 2009). In the case of phytoplankton in upwelling regions, the tipping point may exist at atmospheric CO levels beyond those estimated by the end of the century under high emissions scenarios or more prolonged exposure compared to the short time-scales used here (Supplementary Table 2).”\n• We have expanded our analysis of our molecular data by including protein data from additional samples at the second timepoint for Experiment 1. These results are now shown in Figs. 3a, 4a, 4c, and Supplementary Figs. 14, 15, and 22.\n• We have added a new Figure 5 that is a cellular schematic highlighting the potential molecular-level changes from acidification-driven changes to iron bioavailability in diatoms.\nHowever, we still believe that it is worthwhile to discuss and make inferences about the longer-term response based on the short-term molecular-level data. These short-term molecular-level changes, particularly since they indicate an iron stress response, offer the ability to make inferences about the longer-term response. We believing following the revisions, we consider multiple potential outcomes and avoid overstating these conclusions.\nFor example:\n• It is now clearly stated in the abstract that these short-term results are extrapolated (line 52) and that “we speculate” (line 53) about longer-term exposure resulting in increased iron stress.\n• Following our discussion of the short-term molecular-level responses, we discuss potential consequences of using less efficient iron pathways and reducing photosynthetic and nitrogen assimilation proteins as observed in our experiments. Reduced efficiencies for iron uptake, photosynthesis, and nitrogen assimilation would impact growth over longer timescales. However, we further acknowledge that future iron supplies remain unknown; therefore, the potential for increased iron stress remains uncertain. We now state on lines 512-521:\n“Collectively, these alternative uptake pathways or iron-sparing measures may have temporarily satisfied the high iron demand of phytoplankton over the relatively short periods examined here, but longer-term exposure to high CO may have resulted in a more iron-stressed community. Siderophore-bound iron or ferrous iron uptake pathways are likely less efficient than those for inorganic ferric iron uptake and a reliance on iron storage is not sustainable over longer time scales. The increase of iron permeases for ferric iron uptake confers increased energetic costs and reduction of iron-requiring photosynthetic and nitrogen assimilation proteins may result in less efficiency in these critical pathways for growth. However, future iron supplies will likely be altered and the net result remains unknown (Boyd et al. 2014; Capone and Hutchins 2013); increased inputs could offset these reductions in iron bioavailability and prevent increased iron stress.”\n• We further acknowledge that both iron stress or continued resistance are potential outcomes on lines 567-568:\n“longer exposure may have showed either impacts from added iron stress or continued resistance.”\nHere, the reviewer also points out several different aspects of our manuscript that indicate a lack of clarity on our part in explaining our experiments and results. In response, we have made several revisions including the addition of new data to resolve these issues with explanations below. We hope that in considering these additions and explanations, the reviewer is confident that iron limitation in phytoplankton was assessed along with ocean acidification within the timescales that they suggest (4 days) at Experiments 3 and 4. As we further detail, we also believe that it is important to consider the community growth rates relative to the duration acidification exposure. Although Experiment 1 was only three days long, the community growth rates were much more rapid suggesting that members of the community performed multiple divisions over the course of the experiment, and there were additional reasons to keep this experiment one day shorter.\n1. The reviewer states that the “initial communities of experiment 1 and 2 were iron-limited while the other start communities were not,” and that “experiments 1 and 2 were the ones that only allowed to assess the effect of iron limitation next to OA, but they unfortunately also had the shortest incubation time.” Rather, there were signs of iron stress in all four experiments with a clear signal of iron limitation in Experiments 2, 3, and 4. We apologize that our explanation of the iron status during the time of sampling for our experiments was unclear, and we now include additional data to highlight this:\na. Our new Fig. 1e shows Si values throughout the experiments which suggests iron limitation at Experiments 2, 3 and ,4. As stated in the manuscript on Lines 183-188:\n“Si is an additional biogeochemical proxy that suggests Fe limitation in diatoms due to preferential utilization of silicic acid (Hogle et al. 2018). At Experiment 2, negative values indicating diatom iron stress or limitation were also observed throughout. Following macronutrient addition, negative values were also observed in Experiments 3 and 4 while values remained positive in Experiment 1 (Fig. 1e).”\nb. We now show results from a parallel deckboard incubation experiment conducted alongside Experiment 3 (Supplementary Fig. 4d). These results along with the results from the parallel in situ incubations for Experiment 2 clearly show the existence of iron limitation in the communities by the first sampling time points (24 hours at Experiment 2 and 48 hours at Experiment 3). As stated on lines 189-193:\n“Parallel incubations with unamended control treatments and iron addition treatments (+5 nmol L-1) were performed to further evaluate the presence of iron stress during the experiments. Following 24 hours of incubation at Experiment 2 and 48 hours at Experiment 3, chlorophyll a significantly increased with the addition of iron indicating iron limitation in these communities by the first sampling time points (Supplementary Fig. 4).”\nc. We now show results from an additional in situ incubation experiment conducted in the vicinity for Experiment 4. Although initiation of this experiment occurred 24 hours following the initiation of the acidification incubations, the seawater was collected while following a Lagrangian drifter making it likely that we used nearly the same water mass and community. As growth was observed after 48 hours, but only following the addition of both iron and nitrate, this experiment shows the existence of iron and nitrogen colimitation. Since nitrate was added to the acidification incubation, the community was no longer nitrogen limited and thus was iron limited throughout the experiment. As stated on lines 193-199:\n“At Experiment 4, nitratewas initially depleted indicating no iron limitation in the initial community (Fig. 2 and Supplementary Fig. 5); however, in incubations initiated 24 hours later while following a Lagrangian drifter, significant increases in chlorophyll a were observed only with the addition of both iron and nitrate suggesting co-limitation in this community. As nitrate was added without iron in the community subjected to increased acidification, it was likely iron-limited.”\nd. Experiment 1 was not iron-limited as it encountered nitrate depletion after 72 hours; however, we see clear signs of iron stress with diatom molecular indicators (Fig. 4b and Supplementary Fig. 13) In our manuscript, we have refrained from referring to this experiment as iron-limited and directly state that this community encountered macronutrient depletion on lines 199-201, 212-215, 315-318.\ne. We have added an additional proteomic analysis that examines changes in cellular resource allocations to ribosomal and photosynthetic proteins (Supplementary Fig. 15). These data show that at Experiment 3, centric diatoms and Ostreococcus exhibit a significant reduction in photosynthetic protein mass fractions after the initial timepoint which is consistent with iron limitation as observed in diatoms within the Southern Ocean (McCain et al. 2022). In contrast, diatoms reduce their ribosomal protein mass fractions during Experiment 1, likely a result of the onset of nitrogen limitation. We describe these results on lines 314-327 with the methods described on lines 888-898.\n2. The reviewer states that there were no observable differences over the short-time scales for Experiment 1 and 2: “this study suffers from insufficient incubation time for exp. 1 and 2, which caused no apparent physiological, transcriptional, proteomic and community differences,” and “the experimental duration of only 2 days did not even allow the phytoplankton cells to adjust their physiological machinery to the applied different environmental conditions (acidification, iron).” We apologize that our results were not clearly explained. In these revisions:\na. We hope it is now clear that not only were transcriptional and proteomic changes observed within 48 hours at Experiment 1, but also that this is experiment had some of the largest changes. These data are shown in Fig. 4a and 4c and Supplementary Table 5. As we now state on Lines 328-330:\n“The total number of significantly differentially expressed transcripts was variable across the experiments with the most found at Experiments 1 and 3 where the exposure to acidification relative to the community growth rates were the longest.”\nb. We also hope it is now clear that our iron addition experiments show that these communities were able to make physiological adjustments as observed by increased growth in response to altered iron availability after 24 hours at Experiment 2 and 48 hours for Experiments 3 and 4. These results are described above in reference to the clarification of iron status and are shown in Supplementary Fig. 4. As mentioned above, we likely did not observe a response here at Experiment 1 because it was not iron-limited rather than insufficient incubation time.\n3. The reviewer again highlights the short timescale of our incubations as a major point of concern stating “I agree with the authors that the assessment of transcriptomic responses allows already to assess subtle changes within short times (2 days), but then still the question remains whether these responses will be also reflected in phytoplankton growth and biomass build up (chlorophyll, POC). To see these responses at least a duration of 4 days is needed.” and “I agree with the authors that molecular data on iron uptake are interesting and provide interesting insights about the resistance of phytoplankton to OA over a short time scales (between 2 up to max. 4 days), but I disagree that these data will allow to confer the long-term response, as the experimental duration of only 2 days did not even allow the phytoplankton cells to adjust their physiological machinery to the applied different environmental conditions (acidification, iron). Hence, in my opinion, the presented data for experiment 1 and 2 do not even allow to draw any conclusions on the long-term response.”\nWe again thank the reviewer for highlighting our molecular responses over the relatively short time scales. As described above, we agree that our manuscript should focus more on these molecular-level responses, and have made numerous revisions listed above to do so. We disagree with the reviewer that we should not draw any conclusions on the long-term response, particularly since our observation indicates an iron stress response with potential impacts on iron uptake, photosynthesis, and nitrogen assimilation, but as described above, we have been careful to not overstate our conclusions and consider multiple potential outcomes. There are several additional factors in relation to these two statements that we believe are important to consider here:\na. Experiments 3 and 4 were four days long thus meeting the minimum requirement suggested by the reviewer and as described above, both experiments exhibited clear signs of Fe limitation.\nb. We disagree that the duration of the experiments should be considered solely with a subjective timescale of 4 days. Rather, we suggest that the duration of exposure to acidification should be considered in the context of community growth rates. We have now included calculations of community growth rates (µ) estimated from the chlorophyll a concentrations as described in the methods on lines 736-739. These results are shown in Supplementary Fig. 7 and indicate that the communities at Experiments 1 and 3 likely were able to perform multiple cell divisions, whereas there was only one doubling in Experiment 4. Experiment 2 was likely too short to have a doubling in the community which was coupled to an initial community that was iron-limited likely leading to a lower growth rate. The results are now mentioned on lines 211-223.\nWe agree with the reviewer that Experiment 2 was likely too short, but have focused our discussion on the results from the other experiments.\nImportantly, we believe Experiment 1 is still extremely valuable and showed some of the largest transcriptional and proteomic changes that indicate a stress response that may translate to larger physiological effects. Although this experiment was not iron-limited, there are strong indications of iron stress as observed from the gene expression data shown in Fig. 4b.\nc. There are important advantages in keeping the duration of experiments shorter. We are concerned about bottle effects such as phytoplankton adhering to the walls of the bottle. By lengthening the duration of the experiment, we lose more of the ability to capture the molecular-level changes we are highlighting under conditions that are reasonability reflective of in situ conditions. We are also most interested in evaluating phytoplankton responses while they are responding to nutrient availability and growing rather than depleted of macronutrients and encountering stationary growth which ultimately leads to cell senescence. By doing so, we avoid examining responses to OA alongside cell death. Although macronutrient depletion was encountered at the second timepoint at Experiment 1, it was likely early on. Extension of this experiment would have resulted in the conditions we are attempting to avoid.\nWe hope that in light of these revisions and responses that the reviewer is satisfied that we have largely refocused our manuscript on the short-term molecular-level responses, and that we have assessed ocean acidification along with iron stress within relevant time scales. We largely agree with the reviewer’s comments pertaining to other aspects of our manuscript mentioned below. Please see our responses to their additional comments inline and in blue. References are included at the end of this document.\nAbstract:\nThere is large emphasis put on the fact that OA was investigated in iron-stressed phytoplankton of upwelled waters (L44-46), but as laid out by me before, iron-stressed upwelled phytoplankton was mainly found at the sampling sites of experiment 1 and 2. The latter two experiments, however, suffered from a too short incubation time (2 or 3 days), which avoids to obtain at all potential shifts in phytoplankton growth, nutrient uptake and community composition. From these experiments, however, important conclusions are drawn that “phytoplankton growth, nutrient uptake, and community compositions remained unchanged” (L47-48), which indicates their potential resistance to OA. I agree with the authors that ‘molecular-level responses suggest that resistance to acidification-driven changes in iron bioavailability is facilitated by iron uptake pathways that are less hindered by acidification and strategies that reduce cellular iron demand.” Therefore, I strongly suggest the authors to rewrite this manuscript with a focus solely on the short-term response at the molecular level on the different iron uptake strategies. Please note that the authors even point out by themselves that for experiment 2 “insufficient incubation time may not have led to community differences” (L216-217) detected with DESeq2.\nThe reviewer’s concerns listed here are the same as in their general comments, and we have addressed those above. Please refer to those comments.\nIn relation to our statement highlighted by the reviewer, “insufficient incubation time may not have led to community differences”, the full sentence in the text was specifically referring to the number of differential abundant ASVs in Experiment 2 irrespective of other potential changes. As described above, we acknowledge the relatively short time scale of Experiment 2, and our results largely focus on Experiments 1 and 3. However, as shown in Fig. 2d, there is not a substantial difference in the number of differential abundant ASVs between Experiment 2 and the other experiments, particularly Experiment 1 and 4. As a result, we have removed this sentence as it was misleading. We thank the reviewer for pointing it out.\nIntroduction:\nL93-100: I disagree with the authors that “A relatively small number of field-based studies have examined the effects of ocean acidification on iron stress in phytoplankton”. In fact, there are various studies that have performed Fe-enrichment experiments simulating OA conditions in low Fe regions such as Shi et al. 2010 Science, Feng et al. 2011 Deep Sea Res, Sugie et al. 2013 Biogeosciences, Endo et al. 2013 JEMBE, Yoshimura et al. 2013 J Oceanogr, Yoshimura et al. 2014 Deep-Sea Res, Trimborn et al. 2017 MEPS, Pausch et al. 2022 Frontiers Mar Sci. Moreover, these studies did not add any chelating agent to induce Fe stress, therefore this section needs to be rephrased.\nWe agree with the reviewer that our statement is inaccurate and thank the reviewer for pointing out these studies examining the interactions between ocean acidification and iron-limitation. We have now rephrased the text to be more factual and detailed about previous studies that have examined ocean acidification and iron stress simultaneously. We now state on lines 97-112:\n“Previous field-based studies examining the effects of ocean acidification on phytoplankton communities under iron stress or limitation, namely within the iron-limited subarctic North Pacific and Southern Ocean, have shown conflicting results. Some experiments have shown that diatom communities are negatively impacted with reduced abundances, growth, and photosynthetic efficiencies (Endo et al. 2015; Pausch et al. 2022; Sugie et al. 2013; Trimborn et al. 2017). Other experiments observed no changes in growth, macronutrient uptake, and community composition (Endo et al. 2013; Yoshimura et al. 2014; Yoshimura et al. 2013). One experiment in the Southern Ocean observed no differences in growth or macronutrient uptake in the whole phytoplankton community; however, centric diatoms appeared to be favoured over pennate diatoms indicating variable responses among more specific taxonomic groups that may not be evident if examining the entire community (Feng et al. 2010). In contrast to these other experiments, Hopkinson et al. (2010) made pH adjustments to a community in the Gulf of Alaska resulting in modest increases to growth and photosynthetic efficiency in conjunction with downregulation of certain photosynthetic proteins. Other ocean acidification studies have been conducted in regions with relatively high ambient iron concentrations, some of which used artificially high levels of strong iron-binding ligands to induce iron stress (Chen et al. 2004; Hoppe et al. 2013; Mausz et al. 2020; Segovia et al. 2017); however, these ligands also increase the solubility of iron leading to increased overall dissolved iron concentrations (Borer et al. 2005). ”\nThis new paragraph references all but one of the studies listed by the reviewer above. The reference excluded from this paragraph is Shi et al. (2010) as that study used natural seawater in the laboratory with T. weissflogii rather than being a fully field-based experiment that uses natural communities although we describe the results from those experiments elsewhere (lines 89-92):\n“Further acidification experiments with natural seawater and a single diatom species, Thalassiosira weissflogii, did not produce significant differences in iron uptake rates indicating the presence of natural iron-binding ligands that are unaffected by pH (Shi et al. 2010)”\nL113-115: The authors point out that “physiological measurements may not be sensitive enough to detect more subtle change, particularly over relatively short time scales”. To me, it is not really clear why a short-term response is more of relevance than a long-term response. In fact, it is much more of interest to better understand how well microorganisms are adapted on a long-term (up to weeks) to upwelling and low Fe conditions than on a short-term (1-4 days). Moreover, there are also physiological adjustments by microorganisms that can immediately respond, as an example the photochemical quantum yield, commonly used as an important indicator of the physiological status of phytoplankton, can already change within 24h. Also, carbon uptake characteristics (CA, CO2 and HCO3- uptake) can adjust very quickly. Hence, in my opinion the statement that molecular tools per se are better than physiological measurements is simply not appropriate.\nWe agree with the reviewer that we should not state that molecular tools are better than physiological measurements of the whole community. Rather, we intend to emphasize that these measurements of the entire community are often used in a way that is incapable of discriminating between changes only occurring within subsets of the community, e.g. specific phytoplankton groups or genera. This is particularly important as it is likely and supported by other studies that there are differing trends among different phytoplankton groups. We also hope to emphasize that molecular-level knowledge of phytoplankton responses is somewhat limited. As a result, we have removed the text mentioned by the reviewer and now state on lines 131-143:\n“The aforementioned field-based experiments employed physiological assessments of the whole phytoplankton community that are incapable of discriminating changes only occurring within a specific taxonomic group. At times, taxon-specific pigment analyses and microscopic cell counts were employed showing a lack of change in abundances for more specific taxonomic groups or species, but not offering a window into taxon-specific physiological adjustments (Endo et al. 2015; Endo et al. 2013; Hopkinson et al. 2010). As previously mentioned, centric and pennate diatoms exhibited differing trends in their relative abundances in an experiment in the Southern Ocean (Feng et al. 2010) suggesting more taxonomically-specific resolution is needed to evaluate responses to ocean acidification. Transcriptomic and proteomic analyses can provide this greater resolution while also providing insight into the short-term molecular-level changes that may precede physiological changes or differences in community structure. Moreover, molecular-level knowledge of phytoplankton responses to acidification is limited to a small number of taxa under laboratory conditions without considering iron bioavailability or using organisms obtained from upwelling areas (Beszteri et al. 2018; Jones et al. 2013; Valenzuela et al. 2018).”\nIn relation to the reviewers comment about evaluation short-term vs long-term responses, please see our response to the general comments above where the same concerns were listed.\nThe reviewer further states that it would be of more interest to understand adaptation for up to weeks to upwelling, but we believe it is important to consider that upwelling processes largely do not occur on the timescale of multiple weeks. Previous studies indicate that the complete cycle from upwelling initiation to nutrient depletion often only takes 5-7 days (Dugdale and Wilkerson 1989; MacIsaac et al. 1985; Wilkerson et al. 2006). Furthermore, modelling and experimental studies suggested that nutrient depletion can occur within 72 hours regardless of how much nutrient is upwelled (Wilkerson et al. 2006). Therefore, the time scale of incubation on the order of days as used here is more in line with those of upwelling processes.\nDiscussion:\nL367-381: As mentioned above, the observations on growth, community composition and biomass formation in experiments 1 and 2 cannot be considered due to their too short incubation time.\nThe reviewer stated this concern in their general comments. Please refer to our previous response to those above. Particularly, we would like to emphasize the importance of considering the community growth rate relative to acidification exposure which was highest at Experiment 1. We agree with the reviewer that Experiment 2 was relatively short; however, our analysis largely focuses on Experiments 1 and 3. Furthermore, we directly address the short-term nature of Experiment 2 on lines 534-536:\n“A lack of large observable effects in Experiments 2 and 4 may be a result of reduced exposure relative to growth rates compared to Experiments 1 and 3 where multiple cell divisions may have occurred (Supplementary Fig. 7). Corresponding to this reduced exposure relative to growth rate, there were comparatively fewer transcriptional changes in Experiments 2 and 4 as well (Fig. 4a).”\nL388-294: Measurements of internal and external carbonic anhydrase activities for various phytoplankton groups and species exist and it would be good to take them in the discussion into account.\nIt is difficult to translate our results into a direct connection between internal and external carbonic anhydrases as there is not consistent conservation between family and location (Matsuda et al. 2017). We state this in our manuscript on lines 439-440. However, we agree with the reviewer that added discussion of external carbonic anhydrase activity is beneficial. We now state on lines 523-528:\n“Phytoplankton employ a carbon concentrating mechanism (CCM) that includes carbonic anhydrases (CA) to overcome relatively low concentrations of ambient CO (Matsuda et al. 2017). Extracellular CAs (eCA) can alter the carbonate chemistry in the region immediately around the cell (Chrachri et al. 2018) with activities that span orders of magnitude and scale with cell size (Shen and Hopkinson 2015). This function may potentially overcome impacts from acidification such as maintaining elevated for iron uptake via phytotransferrin.” 2− [CO3 ]\nL398-420: Different Fe uptake strategies were observed for the communities of experiment 1 and 3, this is somewhat addressed, but not really in detail. This is also due to the fact that the community of experiment 1 was Fe-limited already at the start of the experiment, whereas the community of experiment 3 only became Fe-limited due to the addition of macronutrients. I think this is important to note, but this fact is completely neglected here in the discussion.\nWe thank the reviewer for pointing out this critical distinction in the observed Fe uptake strategies between Experiments 1 and 3 and strongly agree that our discussion needs to take note of this difference. We have now included text in our results to state this distinction (lines 368-374):\n“The increased expression of these transporters is consistent with the expected decrease in bioavailable free inorganic ferric iron that may result from acidification. At Experiment 1, the increased transporter expression implies an increased reliance on siderophore-bound iron or ferrous iron which may increase unlike ferric iron. At Experiment 3, the response was an instead an increase of a ferric iron permease (FTR) that does not have a carbonate-dependence like phytotransferrin. This increase may represent an increase in cell surface transporters to maintain uptake rates as previously suggested in the diatom T. weissflogii (Harrison and Morel 1986).”\nWe also have added text in the discussion section to further evaluate this difference. We agree with the reviewer that one potential reason for this difference among others is the iron status between the communities at the different experiments, but as we now explain in response to the previous comments by the reviewer and with revisions in the manuscript, rather it was Experiment 1 that was not Fe-limited whereas Experiment 3 was. We now state on lines 487-505:\n“The difference in iron transporter expression between the two experiments may have occurred for several reasons. First, the iron status of the communities was distinctly different; the community at Experiment 1 was not iron-limited whereas the community at Experiment 3 was, although in Experiment 1, the NO :dFe suggested the potential for iron limitation and high expression of ISIP genes indicates some degree of iron stress (Fig. 4b). Meanwhile, Experiment 1 also had the highest initial dFe concentrations (Fig. 1b). Collectively, this may have allowed the community at Experiment 1 to possess relatively high intracellular and/or stored iron to rely on while subjected to potentially added iron stress from acidification. The observed increased expression of ferrous iron transporters that are potentially intracellular may support this increased reliance on intracellular recycling or release of stored iron to sustain growth over these relatively short timescales (Blaby-Haas and Merchant 2012; Lampe et al. 2018). Second, the iron pools between the experiments were likely distinct although they are largely uncharacterized here. Besides higher dissolved iron concentrations, labile particulate iron concentrations were relatively high at Experiment 1, and dissolution of particulate iron may have supported growth (Fig. 1c and Supplementary Fig. 3). Third, the communities were distinct not only in terms of composition but also functional diversity (Figs. 3c). Only 12.2% and 21.5% of orthologous gene clusters in centric and pennate diatoms respectively were found to be shared between the two experiments (Supplementary Fig. 12). Certainly, there is a great deal of functional diversity within diatoms and their responses may not be equivalent.”\nReviewer #2 (Remarks to the Author):\nLampe et al examine the responses of diatoms to coastal upwelled waters that were experimentally acidified by increased CO2 concentrations. The goal of the study is to see how the phytoplankton community structure and physiology responds. They hypothesize that since coastal upwelling regions experience increased pH variability in comparison to open ocean regions, the microbial communities may be adapted to handle changes in acidity and associated iron stress. The results fit with this hypothesis and there are largely no significant differences in physiological or growth conditions between the three CO2 conditions (400, 800, 1200 ppm).\nThe paper is well written and clearly communicates the results. The science is sound, using well established methods in phytoplankton ecophysiology and molecular methods. The methods are adequate to allow replication and verification of the study.\nI applaud this paper for being upfront about negative results. For the most part acidification leads to few or no differences in phytoplankton physiology or induce iron limitation due to acidification. This is true even though macronutrients were added to all conditions to try and push the system into iron limitation. My major concern is that it is difficult to comprehend the robustness of their metatranscriptome results across the four different experiments and conditions. The authors state up front that the metatranscriptome results were variable, and chose to present a handful of examples from select organisms within select time points from select treatments in select experiments. Figure 4 shows this best. To determine the robustness of the transcriptional trends presented in the main text, it would be helpful if the authors provided an index of what proportion of treatments, conditions, and experiments a particular gene was found to be significantly differentially transcribed.\nWe thank the reviewer for their comments on our manuscript. We agree with the reviewer that our transcriptional and proteomic responses are difficult to parse as each experiment’s results are different. Genes that were differentially expressed at one point in time were often not differentially expressed in other time points or experiments. To increase the clarity of molecular-level changes, we have added a new main figure, Figure 5, that shows a cellular schematic of potential molecular changes in diatoms from acidification-driven changes to iron bioavailability.\nWe believe that the differences in results across experiments occurred for several reasons:\n1. There are distinct differences in the community structure and functional repertoires among the experiments. We highlight the significant differences in community structure in Fig. 3C. As part of the revision, we have also performed orthologous gene detection with Orthofinder (Methods – Lines 818-819). These results show the high degree of functional diversity among the communities in each experiment and are described on lines 270-273 and 501-505:\n“Between 23.8% and 48.2% of orthogroups were unique to the detected protein sequences in each experiment’s metatranscriptome assembly highlighting the diverse functional repertoires within each community (Supplementary Fig. 12).”\n“The communities were distinct not only in terms of composition but also functional diversity (Figs. 3c). Only 12.2% and 21.5% of orthologous gene clusters in centric and pennate diatoms respectively were found to be shared between the two experiments (Supplementary Fig. 12). Certainly, there is a great deal of functional diversity within diatoms and their responses may not be equivalent.”\n2. Each experiment had differences in the duration of exposure to acidification relative to the community growth rates. We have now included calculations of community growth rates (µ) estimated from the chlorophyll a concentrations as described in the methods on lines 736-739. These results are shown in Supplementary Fig. 7 and indicate that the communities at Experiments 1 and 3 likely were able to perform multiple cell divisions, whereas there was only one doubling in Experiment 4. Experiment 2 was likely too short to have a doubling in the community which was coupled to an initial community that was iron-limited likely leading to a lower growth rate. The results are now mentioned on lines 211-223. Corresponding to this reduced exposure to acidification, there was also comparatively less transcriptional change at Experiments 2 and 4 (Fig. 4a). As now stated on lines 534-538:\n“A lack of large observable effects in Experiments 2 and 4 may be a result of reduced exposure relative to community growth rates compared to Experiments 1 and 3 where multiple cell divisions may have occurred (Supplementary Fig. 7). Corresponding to this reduced exposure relative to growth rate, there were comparatively fewer transcriptional changes in Experiments 2 and 4 as well (Fig. 4a).”\n3. Between Experiments 1 and 3 where the most transcriptional changes were observed, there were distinct differences in the iron status of the communities with Experiment 3 being iron-limited whereas Experiment 1 was not. To make this distinction between the different iron statuses clearer, we have revised Fig. 1 to show Si values, a biogeochemical proxy for Fe limitation in diatoms, throughout the experiments (Fig. 1e). We also now include additional parallel incubation data showing that the communities Experiments 3 and 4 were likely iron limited after 48 hours. These results are shown in Supplementary Fig. 5 and described on lines 189-201:\n“Parallel incubations with unamended control treatments and iron addition treatments (+5 nmol L-1) were performed to further evaluate the presence of iron stress during the experiments. Following 24 hours of incubation at Experiment 2 and 48 hours at Experiment 3, chlorophyll a concentrations significantly increased with the addition of iron indicating the presence of iron limitation in these communities by the first sampling time points (Supplementary Fig. 4). At Experiment 4, nitratewas initially depleted indicating no iron limitation in the initial community (Fig. 2 and Supplementary Fig. 5); however, in incubations initiated 24 hours later while following a Lagrangian drifter, significant increases in chlorophyll a were observed only with the addition of both iron and nitrate suggesting co-limitation in this community. As nitrate was added without iron in the community subjected to increased acidification, it was likely iron-limited. Considering low (< 1 µmol L-1) nitrateconcentrations after 72 hours in Experiment 1 (Fig. 2), the phytoplankton community there was not iron-limited, although it may have encountered some degree of iron stress.”\nWe also further discuss how this difference may have contributed to the difference in our observations on lines 488-497:\n“The iron status of the communities was distinctly different; the community at Experiment 1 was not iron-limited whereas the community at Experiment 3 was, although in Experiment 1, the NO :dFe suggested the potential for iron limitation and high expression of ISIP genes indicates some degree of iron stress (Fig. 4b). Meanwhile, Experiment 1 also had the highest initial dFe concentrations (Fig. 1b). Collectively, this may have allowed the community at Experiment 1 to possess relatively high intracellular and/or stored iron to rely on while subjected to potentially added iron stress from acidification. The observed increased expression of ferrous iron transporters that are potentially intracellular may support this increased reliance on intracellular recycling or release of stored iron to sustain growth over these relatively short timescales (Blaby-Haas and Merchant 2012; Lampe et al. 2018).”\n4. Experiments 1 and 3 also likely had distinct iron pools. We now state on lines 497-500:\n“The iron pools between the experiments were likely distinct although they are largely uncharacterized here. Besides higher dissolved iron concentrations, labile particulate iron concentrations were relatively high at Experiment 1, and dissolution of particulate iron may have supported growth (Fig. 1c and Supplementary Fig. 3).”\nDespite these differences, we highlight the consistency between Experiments 1 and 3 in terms of using alternative iron uptake pathways that may be less impacted from acidification compared to phytotransferrin (McQuaid et al. 2018) and reductions in cellular iron requirements by reducing iron-requiring proteins or using iron-free functional equivalents. This consistency is stated at several points in the manuscript including the Abstract (lines 48-50), Results, and Discussion. For example, on lines 472-473, we now state:\n“Within centric and pennate diatoms, these responses were variable, but included changes to myriad of genes related to iron uptake and cellular iron demand (Fig. 5).”\nWe largely agree with the reviewer’s remaining comments, and hope that our revisions and responses have adequately addressed each one. Please see our responses to the additional comments inline and in blue below followed by references at the end of this document.\nA few detailed comments:\nThe proteomics data seems like a side note in the manuscript and under utilized.\nWe agree with the reviewer that our proteomic data should be more prominently featured. As part of these revisions:\n• We now include high-level relative abundances of taxonomic groups from the proteomic results in comparison to the 18S rRNA and mRNA data in our new Fig. 3A.\n• We have analyzed additional protein samples to include the second timepoint of Experiment 1. These results are now shown in Figs. 3a, 4a, 4c and Supplementary Figs. 14, 15, and 22.\n• We have reanalyzed all our proteomics data to account for certain post-translational modifications which has allowed us to annotate additional proteins (Methods – Lines 870-873). We now discuss additional proteins such as the photosynthetic proteins petB, psbA, petC, and petH; the mitrochondrial protein mitoNEET (mNT); the non-iron using superoxide dismutases, Cu-Zn SOD and Ni-SOD; and the iron storage protein, ferritin. For mitoNEET, we also now include an additional phylogenetic tree (Supplementary Fig. 20), providing insight into the evolutionary origins and relatedness of this protein in phytoplankton.\n• We performed an additional proteomic analysis that examines cellular resource allocations to ribosomal and photosynthetic proteins. These results on lines 314-327 and Supplementary Fig. 15 show that both centric and pennate diatoms alter their ribosomal protein mass fractions over time at Experiment 1 and that centric diatoms and Ostreococcus alter their photosynthetic mass fractions over time at Experiment 3. These results further highlight the differences in physiological states from differing nutrient limitation between the Experiments and can further explain the variability in responses as described above. Additionally, we observed a moderate increase the in pennate diatom ribosomal protein mass fraction at Experiment 1 under high CO 2 indicating an increase in ribosomal investment from increased acidity (lines 318-320).\nThe proteomics data may still appear like a side note for two reasons:\n1. The proteomics data are lower resolution so we are not capturing as many distinct proteins as we are with transcripts. In fact, the proteomics data relies on the metatranscriptome assembly for annotation. As now stated on lines 277-279 and 878-881:\n“Often differentially expressed transcripts were not detected as proteins (Supplementary Table 4), likely due to comparatively lower resolution with mass spectrometry, but some consistent patterns were observed.”\n“Unique peptides were assigned to a taxonomic and functional annotation based on the metatranscriptome assembly and annotation. Non-unique peptides, i.e., peptides matching to more than one taxonomic group or functional annotation, were considered to be ambiguous.”\n2. In comparison to the transcriptome data, we also have fewer samples. Specifically, we do not have proteomic samples from any of our 800 ppm treatments and any samples from Experiments 2 and 4. This is due to lack of access to additional time on a mass spectrometer to analyze these remaining samples, and unfortunately, we are unable to include these samples in our analysis. This is described on lines 274-275 and 282-283:\n“In each experiment, differential expression of KEGG-annotated transcripts and proteins was examined between CO treatments within these taxonomic groups. The same was conducted for protein abundances in Experiments 1 and 3…protein abundances were not examined for the 800 ppm treatments.”\nConsidering that the largest transcriptional changes were observed at Experiments 1 and 3 at 1200 ppm vs 400 ppm CO , we believe that focusing on those two experiments and treatments for the proteomic results likely allowed us to quantify more of the molecular effects than with the other experiments, although we acknowledge that there are potentially missed differences from not evaluating the other two experiments. Again unfortunately, we will not be able to include those samples in our analysis, but we hope that the reviewer finds that we have better leveraged the samples added to our analysis at Experiment 1 and the previously existing data.\nFigures 1 and 2 were cutoff in the pdf.\nWe apologize that these two figures were cut off in the merged file. The new figures are no longer cutoff. All figures are also submitted separately and downloadable as individual PDF files separate from the text in their original form.\nFigure 3A and B could use some clarification. Is this just all the samples from the four time points combined into a single plot for each experiment? If so, why show the data this way which obscures the temporal trends in community composition.\nWe agree with these figures from our previous submission are unclear. The reviewer is also correct that the previous Figures 3a and 3b showed the total abundances across all samples within each experiment, and in doing so, there is no indication of any temporal trends. We have removed these figure panels and replaced them with a new panel in Figure 3 (Fig. 3a) showing relative abundances within each time point and treatment across our different data sets.\nLn 196. You mention that SAR86 is the prevalent member of the gamma proteobacteria. For the alphaproteobacteria did you see SAR11 was most abundant? I ask given I was surprised to see that the gammas are so much more abundant than the alphas in fig 3 and I'm wondering if the primer sets chosen are biased against the SAR11 clade members?\nWithin Alphaproteobacteria, SAR11 was present in all of our samples, but Rhodobacterales were more abundant within each experiment. We now describe the high presence of Rhodobacterales on lines 238-240:\n“Alphaproteobacteria and Gammaproteobacteria, were prevalent in all four experiments as observed in the global ocean (Fig. 3a). In particular, these groups were dominated by the ubiquitous Rhodobacterales order and SAR86 clade (Supplementary Fig. 9).”\nWe have also added Supplementary Fig. 9 that shows the relative abundance of different bacterial classes and orders. This figure shows that SAR11 were a relatively more minor constituent of the bacterial communities.\nWith respect to bias against SAR11, it has previously been shown that the primers used here (515F-Y and 926R) are less biased against SAR11 compared to the popular primers used in the Earth Microbiome Project (515F and 806R)(Parada et al. 2016). The primers used here have also been shown to have the best performance in matching sequences from marine metagenomes among commonly used primers (McNichol et al. 2021). As a result, we believe our primer selection is optimal for this study and do not believe there was any measurable bias against SAR11 in our data.\nReferences\nBeszteri, S., S. Thoms, V. Benes, L. Harms, and S. Trimborn. 2018. The response of three Southern Ocean phytoplankton species to ocean acidification and light availability: a transcriptomic study. Protist 169: 958-975.\nBlaby-Haas, C. E., and S. S. Merchant. 2012. The ins and outs of algal metal transport. Biochim Biophys Acta Mol Cell Res 1823: 1531-1552.\nBorer, P. M., B. Sulzberger, P. Reichard, and S. M. Kraemer. 2005. Effect of siderophores on the light-induced dissolution of colloidal iron (III)(hydr) oxides. Mar Chem 93: 179-193.\nBoyd, P. W., S. T. Lennartz, D. M. Glover, and S. C. Doney. 2014. Biological ramifications of climate-change-mediated oceanic multi-stressors. Nat Clim Change 5: 71.\nCapone, D. G., and D. A. Hutchins. 2013. Microbial biogeochemistry of coastal upwelling regimes in a changing ocean. 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Mar Freshw Res 71: 263-274.\nJones, B. M. and others 2013. Responses of the Emiliania huxleyi Proteome to Ocean Acidification. PLOS ONE 8: e61868.\nLampe, R. H. and others 2018. Different iron storage strategies among bloom-forming diatoms. Proc Natl Acad Sci USA 115: E12275-E12284.\nMacIsaac, J. J., R. C. Dugdale, R. T. Barber, D. Blasco, and T. T. Packard. 1985. Primary production cycle in an upwelling center. Deep-Sea Res 32: 503-529.\nMatsuda, Y., B. M. Hopkinson, K. Nakajima, C. L. Dupont, and Y. Tsuji. 2017. Mechanisms of carbon dioxide acquisition and CO sensing in marine diatoms: a gateway to carbon metabolism. Philos 2 Trans R Soc Lond B 372: 20160403.\nMausz, M. A., M. Segovia, A. Larsen, S. A. Berger, J. K. Egge, and G. Pohnert. 2020. High CO 2 concentration and iron availability determine the metabolic inventory in an Emiliania huxleyi-dominated phytoplankton community. Environ Microbiol 22: 3863-3882.\nMcCain, J. S. P., A. E. Allen, and E. M. Bertrand. 2022. 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Early-warning signals for critical transitions. Nature 461: 53-59.\nSegovia, M. and others 2017. Iron availability modulates the effects of future CO2 levels within the marine planktonic food web. Mar Ecol Prog Ser 565: 17-33.\nShen, C., and B. M. Hopkinson. 2015. Size scaling of extracellular carbonic anhydrase activity in centric marine diatoms. Journal of Phycology 51: 255-263.\nShi, D., Y. Xu, B. M. Hopkinson, and F. M. M. Morel. 2010. Effect of Ocean Acidification on Iron Availability to Marine Phytoplankton. Science 327: 676-679.\nSugie, K., H. Endo, K. Suzuki, J. Nishioka, H. Kiyosawa, and T. Yoshimura. 2013. Synergistic effects of pCO and iron availability on nutrient consumption ratio of the Bering Sea phytoplankton 2 community. Biogeosciences 10: 6309-6321.\nTrimborn, S. and others 2017. Iron sources alter the response of Southern Ocean phytoplankton to ocean acidification. Marine Ecology Progress Series 578: 35-50.\nValenzuela, J. J., A. López García de Lomana, A. Lee, E. V. Armbrust, M. V. Orellana, and N. S. Baliga. 2018. Ocean acidification conditions increase resilience of marine diatoms. Nat Comm 9: 2328.\nWilkerson, F. P., A. M. Lassiter, R. C. Dugdale, A. Marchi, and V. E. Hogue. 2006. The phytoplankton bloom response to wind events and upwelled nutrients during the CoOP WEST study. Deep-Sea Res. Part II-Top. Stud. Oceanogr. 53: 3023-3048.\nYoshimura, T., K. Sugie, H. Endo, K. Suzuki, J. Nishioka, and T. Ono. 2014. Organic matter production response to CO2 increase in open subarctic plankton communities: Comparison of six microcosm experiments under iron-limited and -enriched bloom conditions. Deep Sea Research Part I: Oceanographic Research Papers 94: 1-14.\nYoshimura, T. and others 2013. Impacts of elevated CO2 on particulate and dissolved organic matter production: microcosm experiments using iron-deficient plankton communities in open subarctic waters. Journal of Oceanography 69: 601-618."}, {"round": 2, "reviewer_comments": {"reviewer_3": "The revised manuscript \"Short-term acidificafion promotes diverse iron acquisifion and conservafion mechanisms in upwelling-associated phytoplankton\" is an interesfing set of incubafion experiments to address short term acidificafion on coastal ocean communifies. I was not one of the original reviewers, but the revised version generally reads well and it looks like the authors have addressed the comments provided by the past reviewers.\nSome specific comments/suggesfions based on my readings of this revised version:\n(1) I don’t understand why the experiments were done with different durafions – it makes cross comparing the results far more complicated. But since all of the experiments did 48 h just comparing those fime points would have made for a cleaner story. Related, and I might have missed it but only certain fime points were presented in Fig 4C – the supplementary figures should have all of fime points/populafions. Maybe it’s moot now.\n(2) Line 52 – This final sentence doesn’t follow from the previous ones – I think there should be some addifional informafion (e.g. that the short term resistance is somehow not sustainable)\n(3) Line 184: The SiEx term needs to be befter described in the main text. The sentence where it is first used does not make sense (to me at least). Maybe just add some clarifying wording to the that secfion.\n(4) Figure 2A – experiment 4 – the significance bar is between 400 and 800?\n(5) Lines 226-244 and figure 3- This secfion of text and the associated figure is conflafing RNA based acfivity with DNA based abundance. Sure if an RNA is present it means that the organism is there, but RNA transcript numbers (either rRNA or mRNA) cannot be used as a metric of community composifion per se. For example, in experiment 4 (line 244), with this data we don’t know if there is a higher presence of cyanobacteria or if they were just more ‘acfive.’ This secfion and figure needs to be revised to remove references to abundance and community composifion and to focus this solely on 'acfivity' by community members.\nOverall this is a solid contribufion.", "reviewer_4": "22-7-2023\nReview for “Short-term acidificafion promotes diverse iron acquisifion and conservafion mechanisms in upwelling-associated phytoplankton”\nStepping in late in the review of the wonderful study by Lampe and many talented and invested collaborators, I will take a bit unorthodox approach to the review. Rather than outlining many specific points, I’d like to review the paper & study strength and limitafions, and allow the authors and editors take their choices.\nI will first briefly outline the main points and then elaborate on a few. I see these as philosophical & strategical and leave it for the authors and editors to find the best path forward.\nThe main strengths of the paper are:\nA. Carefully conducted and successful set of complicated experiments in natural waters, combining many advanced and high-quality analyses (chemical parameters, physiological, OMICS and more)\nB. B. Focus on Fe transport, storage and use by natural eukaryotes, combining transcript level and uptake rates by highly knowledgeable researchers.\nC. Four experiments varying in community composifion and physiological state, which yielded different set of responses to the nutrient addifion/incubafion and to increasing CO2, thus befter informing us on the range of natural responses.\nD. Brave choice to be upfront about negafive results (important!!). As a whole the paper is very honest and does a great job keeping high level of complexity, while sfill conveying clear messages.\nThe main weaknesses are:\nA. The authors are sfill spending too much effort and space on the link between ocean acidificafion and iron availability / uptake. The last sentence in the abstract is indeed a long-shot… The abstract as a whole is focused only ton he effect of acidificafion, while in fact I think this paper has more to offer (see next)\nB. Complexity – many experiments, many variables, many techniques. It is clear that huge efforts were made to simplify it all and streamline it, but…\nC. Synthesis between uptake rates and upregulafion of uptake transporters (between experiments, throughout the experiment) can be improved (see some ideas next)\nBelow I expand on two issues:\n1. Befter integrafion of uptake rates and Fe gene regulafion\nThis is clearly one of the major goals of the paper and indeed the authors quite adequately discuss it (well done!). I did not find much on rate comparison between the experiments (and perhaps throughout the experiments). This is partly as the paper presents uptake rates normalized to POC and not to Chl, which are more homogenous. I did not find an explanafion, but since we are dealing with phytoplankton, Chl may be more appropriate (at least for comparison among experiments). These rates are more variable and perhaps offer some insights.\nBelow, I present a quick analysis, which is just a suggesfion and a possible direcfion to follow. I ignored acidificafion and compared 59Fe uptake rates per Chl among experiments. Starfing with FeCl3 (higher uptake rates & hence more reliable, and also more likely to represent larger/more dynamic Fe pools that may befter match transcripts of several transporters). In this analysis, my assumpfion is that short term uptake rate can report on the Fe limitafion status of the cells (i.e. higher rates indicate that the cells are more stressed and hence upregulate more transporters).\nIf this is the case, we expect to see a link between measured rate and transporters expression– either between exp, or during fime in each exp. Ranking FeCl3 uptake/chl rates among exp (not including To) we find that E4>E3>E2>E1 This would indicate (as I think stated in the paper based on other data) that cells in E1&2 are less limited that E3&4. However, when FeCL3 uptake rates are normalized to POC the data changes dramafically with E1>E3=E4>E2. (I vote for chl)\nLooking at trend with fime E3 and E4 are stable, while uptake rates drop in E1, E2. This does not go very well with the presumed onset of Fe limitafion in E1, but.. Then FeDFB uptake rates, much discussion but no menfion that in E2 you had uptake rates (normalized for both C & Chl) which are an order of magnitude slower than any other exp. Can you find any agreement with transcript pafterns / changes in species composifion etc?\n2. Emphasizing other highlights/achievements of the study – I think that we are all very data driven, but in fact new scienfific approaches, and parficularly the combinafion of knowledge across techniques, scales, funcfions etc, are good enough reason to get the publicafion to Nature Communicafions (or other high impact). I think we are far from resolving the effect of acidificafion on iron bioavailability to diverse phytoplankton in natural systems, due to gaps in our knowledge on Fe chemistry (what are the major forms of Fe out there?), phytoplankton types (who is there and how acfive), coping mechanisms and more. Every sensible reader (or editor) should not be surprised that the effect is variable and even contradictory (in general in papers so far). Can you try and refine / extract methodological/conceptual goals achieved (even for one case) which are related to the general quesfion of iron stress / storage/ uptake/ availability? Maybe you can strengthen the contrast between E1 and E3, emphasizing the differences in status, composifion and response, but emphasize a bit more the set of tools / knowledge gained that allow you to “finger-print” the Fe status and response of this community during the incubafion.\nGood luck and congratulafions on this important scienfific contribufion."}, "author_response": "Reviewer #3 (Remarks to the Author):\nThe revised manuscript \"Short-term acidification promotes diverse iron acquisition and conservation mechanisms in upwelling-associated phytoplankton\" is an interesting set of incubation experiments to address short term acidification on coastal ocean communities. I was not one of the original reviewers, but the revised version generally reads well and it looks like the authors have addressed the comments provided by the past reviewers.\nWe thank the reviewer for their time and comments on our manuscript. Please see our responses inline and in blue followed by references at the end of this document.\n(1) I don’t understand why the experiments were done with different durations – it makes cross comparing the results far more complicated. But since all of the experiments did 48 h just comparing those time points would have made for a cleaner story. Related, and I might have missed it but only certain time points were presented in Fig 4C – the supplementary figures should have all of time points/populations. Maybe it’s moot now.\nThe experiments were done with different durations to attempt to avoid macronutrient depletion as we are most interested in evaluating phytoplankton while they are responding to nutrient availability and growing rather than encountering stationary growth which ultimately leads to cell senescence. By doing so, we avoid examining responses to acidification alongside cell death. Extension of Experiment 1 would have resulted in the conditions we were attempting to avoid. To clarify these reasons, we now state on lines 169-172:\n“Samples were then collected at one-day intervals that varied among experiments to attempt to avoid macronutrient depletion with an upper limit of 96 hours to also avoid increased bottle effects: 48 and 72 hours for experiment 1, 24 and 48 hours for experiment 2, and 48 and 96 hours for experiments 3 and 4 (Table 1).”\nWe also now explain these reasons in the methods section on lines 654-658:\n“For the later time points, sampling occurred at one-day intervals that varied among experiments: 48 and 72 hours for Experiment 1, 24 and 48 hours for Experiment 2, and 48 and 96 hours for Experiments 3 and 4 (Table 1). These intervals were chosen to attempt to avoid macronutrient depletion with a maximum experimental duration of 96 hours to also avoid increased bottle effects.”\nAlthough the time points vary, direct comparisons where time points overlap is still conflated by several factors. The initial environmental conditions were largely different across the wide range of variables that were assessed (Figs. 1 and 2 and Supplementary Figs. 1-3). We also examined distinct microbial communities not only in terms of composition (16S and 18S rRNA, Fig. 3c) but also in terms of functional diversity (Supplementary Fig. 13). Collectively, these differences were likely also responsible for the observed differing community growth rates (Supplementary Fig. 7); thus, the exposure to acidification relative to growth rate varied among the experiments as well (discussed on lines 228-239, 345-347, and 572-574).\nRather than focus on the 48-hour time points, which would largely exclude time points where there is a longer duration of exposure to acidification, we have opted to showcase samples where we have observed interesting changes in differential expression, and are omitting samples where differential expression was relatively low as shown in Fig. 4a or are largely of unknown function (lines 351-352). We describe the plots in Figure 4c in the caption as such:\n“MA plots for taxonomic groups, experiments (E), and time points (T), where significant differential expression for transcripts (top row) or proteins (bottom row) of interest between the 1200 and 400 ppm treatments was observed.”\nHowever, we certainly agree with the reviewer that we should be presenting more of our data, particularly where there is differential expression and there is paired transcript and protein data. In addition to the data presented in Fig. 4, the previous version of our manuscript included Supplementary Figures showing:\n• Transcript expression of iron-starvation induced proteins 1, 2A, and 3 in pennate diatoms (now Supplementary Figs. 14) as well as corresponding protein expression in both centric and pennate diatoms (now Supplementary Fig. 15)\n• Differential expression of nitrogen assimilation transcripts for pennate diatoms at Experiments 1 and 3, centric diatoms and Experiment 3, and Ostreococcus at Experiment 3 (now Supplementary Fig. 25)\n• Transcript and protein expression for Ostreococcus at the second time point (T96) of Experiment 3 (now Supplementary Fig. 26)\n• SLC4-family bicarbonate transporter expression in centric and pennate diatoms (now Supplementary Fig. 27)\nWe have now added new figures showing:\n• Differential transcript and protein expression for centric diatoms for the time points of Experiments 1 and 3 not shown in Fig. 4c. as Supplementary Fig. 23\n• Ferrichrome-binding protein 1 (FBP1) expression from all diatoms for both transcripts (Supplementary Fig. 21) and proteins (Supplementary Fig. 22). These data are also now discussed in the manuscript on lines 400-403 and 512-515 in response to comments by Reviewer 4 (see below).\n(2) Line 52 – This final sentence doesn’t follow from the previous ones – I think there should be some additional information (e.g. that the short term resistance is somehow not sustainable)\nWe agree and thank the reviewer for pointing out this omission. We have expanded this sentence to now state:\n“By extrapolating these short-term results, we speculate that these mechanisms may only temporarily satisfy cellular iron demand, and longer-term exposure to acidification without increased iron inputs may result in increased iron stress for phytoplankton communities.”\n(3) Line 184: The SiEx term needs to be better described in the main text. The sentence where it is first used does not make sense (to me at least). Maybe just add some clarifying wording to the that section.\nWe agree that this text was unclear and have expanded it to better explain the Si proxy. We ex now state on lines 186-191:\n“Si is an additional biogeochemical proxy that serves as an indicator of Fe limitation in diatoms; as Fe-limited diatoms preferentially uptake silicic acid (H SiO ) relative to NO , 4 4 3 lower than expected H SiO concentrations compared to NO concentrations indicates the 4 4 3 presence of Fe limitation. Si reports the difference between H SiO and NO ex 4 4 3 concentrations (Methods); therefore, negative values indicate greater H SiO uptake due 4 4 to iron stress in diatoms.”\n(4) Figure 2A – experiment 4 – the significance bar is between 400 and 800?\nUpon revisiting these data, we realized that the significant difference in Figure 2A was reported in error, and that the means are not significantly different. We have also revisited all the data and statistical tests in Figure 2 to ensure that the remaining information is accurate. We thank the reviewer for helping us find this error and have updated Figure 2A to remove the indication of significance there. This change does not affect any of the text in the manuscript; therefore, no additional revisions in relation to this comment have been made.\n(5) Lines 226-244 and figure 3- This section of text and the associated figure is conflating RNA based activity with DNA based abundance. Sure if an RNA is present it means that the organism is there, but RNA transcript numbers (either rRNA or mRNA) cannot be used as a metric of community composition per se. For example, in experiment 4 (line 244), with this data we don’t know if there is a higher presence of cyanobacteria or if they were just more ‘active.’ This section and figure needs to be revised to remove references to abundance and community composition and to focus this solely on 'activity' by community members.\nWe respectfully disagree with the reviewer that we cannot use rRNA abundance to assess community composition and further disagree that we should focus on activity for the following reasons:\n1. The use of RNA instead of DNA attempts to avoid measuring abundance from deceased cells (Blazewicz et al. 2013), and therefore, may provide a better assessment of microbial community diversity (Hu et al. 2016; Kong et al. 2023).\n2. DNA-based assessments are biased by high copy number variation (Gong and Marchetti 2019; Větrovský and Baldrian 2013), although as the reviewer notes, we agree that RNA-based abundances are affected by variable rRNA expression. In essence, both approaches have a similar but different underlying bias that creates a disconnect between rDNA or rRNA and true cellular abundance, although it remains possible that rRNA is less biased in this regard. As rRNA is a structural component of the cell’s ribosomes, it remains a potentially useful proxy for biomass.\n3. It has been suggested that rRNA is not a reliable indicator of activity (Blazewicz et al. 2013). There is variability between rRNA concentration and growth rates, and for some organisms, there is no relationship at all. Dormant cells may also possess high numbers of ribosomes and therefore potentially high rRNA copies (Blazewicz et al. 2013).\n4. Although less common than rDNA, likely due to added costs or complexity to an extent, other previously published papers have used RNA-based metabarcoding to examine community composition, and at times, in conjunction with DNA-based metabarcoding. For example:\nCohen, N. R., M. R. McIlvin, D. M. Moran, N. A. Held, J. K. Saunders, N. J. o Hawco, M. Brosnahan, G. R. DiTullio, C. Lamborg, J. P. McCrow, C. L. Dupont, A. E. Allen and M. A. Saito (2021). \"Dinoflagellates alter their carbon and nutrient metabolic strategies across environmental gradients in the central Pacific Ocean.\" Nature Microbiology 6(2): 173-186.\nTraving, S. J., O. Rowe, N. M. Jakobsen, H. Sørensen, J. Dinasquet, C. A. o Stedmon, A. Andersson and L. Riemann (2017). \"The Effect of Increased Loads of Dissolved Organic Matter on Estuarine Microbial Community Composition and Function.\" Frontiers in Microbiology 8: 351.\nHu, S. K., V. Campbell, P. Connell, A. G. Gellene, Z. Liu, R. Terrado and D. o A. Caron (2016). \"Protistan diversity and activity inferred from RNA and DNA at a coastal ocean site in the eastern North Pacific.\" FEMS Microbiology Ecology 92(4): fiw050.\nNeedham, D. M., E. B. Fichot, E. Wang, L. Berdjeb, J. A. Cram, C. G. Fichot o and J. A. Fuhrman (2018). \"Dynamics and interactions of highly resolved marine plankton via automated high-frequency sampling.\" The ISME Journal 12(10): 2417-2432.\nWe are not advocating for rRNA as a more suitable alternative to rDNA. Rather, we are suggesting that both approaches offer a molecular-level assessment of microbial community composition, each with their own caveats. Furthermore, we agree with the reviewer it should be clear to the reader that an rRNA-based approach was used so that it is not conflated with DNA-based abundance. We now:\n• State in the results that “community compositions…were inferred via 18S and 16S ribosomal RNA (rRNA) amplicon sequencing” (lines 242-243), and further specify that we are examining “rRNA” and “rRNA-derived amplicon sequence variants.” (lines 244 and 271 and Fig. 3 caption).\n• State in the methods text (lines 817-820): “From the aforementioned extracted RNA, cDNA was synthesized with the Invitrogen SuperScript™ III First-Strand Synthesis System according to the manufacturer’s instructions. RNA-derived amplicon libraries targeting the V4-V5 hypervariable region of the 16S gene and the V9 hypervariable region of the 18S region were then generated…”\nLastly, with respect to the reviewer’s comment that we are not sure if there were more cyanobacteria present or if they were just active, there is unpublished flow cytometry data throughout the same cruise that supports our assessment of the increased abundance of cyanobacteria in the vicinity of Experiments 3 and 4, but we are unable to include these data in our manuscript.\nOverall this is a solid contribution.\nWe again thank the reviewer for their time and constructive feedback.\nReviewer #4 (Remarks to the Author):\nStepping in late in the review of the wonderful study by Lampe and many talented and invested collaborators, I will take a bit unorthodox approach to the review. Rather than outlining many specific points, I’d like to review the paper & study strength and limitations, and allow the authors and editors take their choices.\nI will first briefly outline the main points and then elaborate on a few. I see these as philosophical & strategical and leave it for the authors and editors to find the best path forward.\nThe main strengths of the paper are:\nA. Carefully conducted and successful set of complicated experiments in natural waters, combining many advanced and high-quality analyses (chemical parameters, physiological, OMICS and more)\nB. Focus on Fe transport, storage and use by natural eukaryotes, combining transcript level and uptake rates by highly knowledgeable researchers.\nC. Four experiments varying in community composition and physiological state, which yielded different set of responses to the nutrient addition/incubation and to increasing CO2, thus better informing us on the range of natural responses.\nD. Brave choice to be upfront about negative results (important!!). As a whole the paper is very honest and does a great job keeping high level of complexity, while still conveying clear messages.\nWe thank the reviewer for their thoughtful comments. Please see our responses inline in blue below followed by references at the end.\nThe main weaknesses are:\nA. The authors are still spending too much effort and space on the link between ocean acidification and iron availability / uptake. The last sentence in the abstract is indeed a long-shot… The abstract as a whole is focused only on the effect of acidification, while in fact I think this paper has more to offer (see next)\nWe thank the reviewer for pointing out that the study has more to offer and agree. As the reviewer suggests and we describe in greater detail below (see comment 3), we have further highlighted the methodologies used here to assess the phytoplankton communities’ responses and strengthened our analysis and discussion to better integrate the Fe uptake rates and transcript/protein abundances. We also further highlight our phylogenetic characterization of several relatively uncharacterized iron-related genes in diatoms that were observed to be part of the acidification-driven response here.\nHowever, we respectfully disagree with the reviewer that we are spending too much time on the connections between ocean acidification and iron bioavailability. As described in our introduction, there is enormous uncertainty surrounding phytoplankton responses to ocean acidification, particularly when considering relationships between ocean acidification and iron bioavailability (lines 71-145). These experiments were designed to specifically address this topic and detected an iron-related response indicative of acidification-driven changes to iron bioavailability. Thus, we prefer to keep the manuscript focused on these connections and associated responses, although we largely agree with the comments detailed below and have made several revisions to address them.\nWith respect to the comment about the last sentence of the abstract, we agree that it is speculative as stated within the sentence. In response to a comment by Reviewer 3, we also have modified this sentence to increase clarity. It now reads:\n“By extrapolating these short-term results, we speculate that these mechanisms may only temporarily satisfy cellular iron demand, and longer-term exposure to acidification without increased iron inputs may result in increased iron stress for phytoplankton communities.”\nWe discuss this topic in more detail in our discussion on lines 547-557 where we suggest that the mechanisms employed in response to increased acidification confer less efficiency for iron uptake, photosynthesis, and nitrogen assimilation or greater energetic costs while acknowledging that future iron supplies remain unknown where decreased iron bioavailability could be offset be increased iron inputs. We further acknowledge that it remains possible that continued resistance could be observed over longer time scales (lines 603-605). We believe it is worthwhile to discuss and make inferences about the longer-term responses based on these short-term molecular data, particularly as the molecular-level responses may precede an observable physiological response or shift in community structure, and our text considers multiple potential outcomes to avoid overstating these conclusions.\nB. Complexity – many experiments, many variables, many techniques. It is clear that huge efforts were made to simplify it all and streamline it, but…\nC. Synthesis between uptake rates and upregulation of uptake transporters (between experiments, throughout the experiment) can be improved (see some ideas next)\nAs the reviewer expands on these comments below, we have addressed those separately.\nBelow I expand on two issues:\n1. Better integration of uptake rates and Fe gene regulation\nThis is clearly one of the major goals of the paper and indeed the authors quite adequately discuss it (well done!). I did not find much on rate comparison between the experiments (and perhaps throughout the experiments). This is partly as the paper presents uptake rates normalized to POC and not to Chl, which are more homogenous. I did not find an explanation, but since we are dealing with phytoplankton, Chl may be more appropriate (at least for comparison among experiments). These rates are more variable and perhaps offer some insights.\nBelow, I present a quick analysis, which is just a suggestion and a possible direction to follow. I ignored acidification and compared 59Fe uptake rates per Chl among experiments. Starting with FeCl3 (higher uptake rates & hence more reliable, and also more likely to represent larger/more dynamic Fe pools that may better match transcripts of several transporters). In this analysis, my assumption is that short term uptake rate can report on the Fe limitation status of the cells (i.e. higher rates indicate that the cells are more stressed and hence upregulate more transporters).\nIf this is the case, we expect to see a link between measured rate and transporters expression– either between exp, or during time in each exp. Ranking FeCl3 uptake/chl rates among exp (not including To) we find that E4>E3>E2>E1 This would indicate (as I think stated in the paper based on other data) that cells in E1&2 are less limited that E3&4. However, when FeCL3 uptake rates are normalized to POC the data changes dramatically with E1>E3=E4>E2. (I vote for chl)\nLooking at trend with time E3 and E4 are stable, while uptake rates drop in E1, E2. This does not go very well with the presumed onset of Fe limitation in E1, but.. Then FeDFB uptake rates, much discussion but no mention that in E2 you had uptake rates (normalized for both C & Chl) which are an order of magnitude slower than any other exp. Can you find any agreement with transcript patterns / changes in species composition etc?\nThe reviewer raises several interesting points within this comment that although related, we summarize and address separately for clarity:\n1. Normalization of Fe uptakes to chlorophyll a rather than particulate organic carbon (POC)\nWe agree with the reviewer that it may be more appropriate to prioritize reporting of the chlorophyll-normalized Fe uptake rates over the POC-normalized rates. As a result, we have updated Figure 2d to show the chlorophyll-normalized data rather than the POC-normalized data. Our initial concern with the chlorophyll-normalized data is chlorophyll content variability that is influenced by light, temperature, and nutritional status (expressed as C:Chl)(Wang et al. 2009), although light and temperature are consistent across the experiments when omitting the initial conditions (T0). As a result, we agree that chlorophyll a is more suitable. Furthermore, we are concerned with contributions from heterotrophic biomass to POC although 1 µm filters were consistently used to avoid measuring free-living bacteria. As the initial conditions are likely confounded by chlorophyll variability, we believe it remains worthwhile to include the POC-normalized data as Supplementary Fig. 6 and in Supplementary Fig 7C-D, but as previously mentioned, we now prioritize showing and discussing the chlorophyll-normalized data.\nThe reviewer is also correct that we did not explain our reasoning in the previous version of the manuscript. To address this, we now state on lines 215-219:\n“Although chlorophyll content varies in response to environmental conditions (Wang et al. 2009), light and temperature were consistent during the experiments resulting in comparable chlorophyll-normalized rates that may better reflect photoautotrophic biomass compared to POC. Chlorophyll a has often also been used to normalize Fe uptake rates in phytoplankton communities as POC includes heterotrophic biomass (Hoppe et al. 2013; Marchetti and Maldonado 2016; Trimborn et al. 2015).”\nRelatedly, the reviewer points out that our Fe-DFB uptake rates are much slower at Experiment 2; however, this observation is only for the POC-normalized data and not the chlorophyll-normalized data (Fig. 2d and Supplementary Fig. 6). This difference is driven by much higher POC concentrations at Experiment 2. As we are now prioritizing the chlorophyll-normalized data that do not show this pattern, and the POC-normalized data is influenced by heterotrophic biomass, we have chosen to not further explore these results in the manuscript.\n2. Comparison of Fe uptakes among and throughout the experiments to report on Fe status\nWe agree with the reviewer that the Fe uptake rates should also be presented to make comparisons more easily over time and among experiments. As mentioned in comment 1, we have now added Supplementary Fig. 7 to show both the chlorophyll-normalized rates and the POC-normalized rates over time and across experiments with equivalent y-axes.\nWe also thank the reviewer for making the extremely important suggestion that we better use our Fe uptake rates to assess the Fe nutritional status of the phytoplankton communities. We agree with the reviewer that the maximum short-term Fe uptake rates provides an indication of the number of transporters and thus, iron stress as iron-stressed cells likely upregulate more transporters. The reviewer is also correct that when using the chlorophyll-normalized data and final time points of each experiment, there is an order in FeCl uptake rates where E4>E3>E2>E1. This trend is the same for the chlorophyll-normalized Fe-DFB uptake rates as well. However as the reviewer also notes, we also observed significant reductions in the FeCl uptake rates over time at Experiments 1 and 2. Alone, these uptake rates may suggest that Experiment 2 is less iron-stressed than Experiments 3 and 4; however, our other biogeochemical, experimental, and molecular data are contradictory. Experiment 2 had the high NO :dFe ratios and lowest Si values (Fig. 1c and 1e). We also experimentally show significant increases in chlorophyll a upon the addition of Fe after 24 hours (Supplementary Fig. 4b) and high expression of diatom iron stress marker genes (Fig. 4c and Supplementary Fig. 14).\nAs we have numerous other lines of evidence including experimental iron additions showing iron limitation in this experiment, we would like to refrain from using the FeCl rates to infer iron status. However, we recognize the value in highlighting that Fe-DFB uptake is also indicative of Fe stress at Experiments 3 and 4 as phytoplankton are believed to only take up significant amounts of Fe-DFB when iron-limited. We now state on lines 224-227:\n“The measurable uptake of Fe-DFB, particularly at Experiments 3 and 4 where rates were similar to those of iron-limited populations and isolates (Maldonado and Price 1999; Maldonado and Price 2001), further indicates iron stress as phytoplankton normally only acquire significant amounts of Fe-DFB when iron-limited (Marchetti and Maldonado 2016).”\n3. Connections between uptake rates and ‘omic data\nWe agree with the reviewer that we should stronger connections between the Fe uptake rates and transcript/protein expression patterns. By now showing the chlorophyll-normalized Fe uptake (see comment 1), we now also describe the significantly higher Fe uptake rates at Experiment 3 between the 1200 and 400 ppm treatments (Fig. 2d). This increase in Fe uptake that indicates an increase in the number of transporters for free inorganic ferric iron is coupled to an observed increase in both iron permease (FTR1) transcripts and proteins in centric diatoms (Fig. 4c). We now discuss these results at several points throughout the manuscript:\n• Lines 220-222: “When normalized to chlorophyll a, maximum inorganic Fe uptake rates were significantly higher in the 1200 ppm when compared to the 400 ppm treatment at the final time point for Experiment 3 (P = 0.016) indicating an increase in cell surface transporters (Marchetti and Maldonado 2016).”\n• Lines 390-394: “At Experiment 3, the response was instead an increase of a ferric iron permease (FTR1) that does not have a carbonate-dependence like phytotransferrin. Observed as both transcripts and proteins, this increase suggests that there were a greater number of cell surface transporters for inorganic ferric iron as supported by higher maximum uptake rates of inorganic iron (Fig. 2d).”\n• Lines 517-521: “In Experiment 3, the response was instead transcriptional and proteomic increases in iron permeases (FTR) in centric diatoms signifying an increase in free inorganic iron transporters as its availability is reduced (Fig. 4c). This increase in transporters is further supported by higher maximal uptake rates of inorganic iron that are also a further sign of increased iron stress (Fig. 2d).”\nWe have also further analyzed our data to describe and show ferrichrome-binding protein 1 (FBP1) expression as it has been directly linked to siderophore uptake including that of Fe-DFB (Supplementary Figs. 21 and 22)(Coale et al. 2019). The lack in change in expression is connected to the similar Fe-DFB uptake rates among treatments:\n• Lines 400-403: Ferrichrome-binding protein 1 (FBP1) acts as a receptor in concert with ferric reductases for siderophore uptake including Fe-DFB, but it was not differentially expressed (Supplementary Fig. 21 and 22) corresponding to the similar Fe-DFB uptake rates among treatments (Fig. 2d and Supplementary Fig. 6).\n• Lines 512-515: Genes responsible for Fe-DFB uptake, FRE and FBP1, were also not differentially expressed except for decreased protein abundances in centric diatoms in Experiment 1, although the ferric reductases expressed here may represent separate homologs that does not serve the same functional role (Fig. 4c and Supplementary Figs. 21 and 22)(Coale et al. 2019).\nOtherwise, the data do not show always show strong connections between expression and uptake rates over time within each experiment. For example, ISIP2A expression increases and FTR expression is stable while FeCl uptake rates decline at Experiments 1 and 2 (Fig 4b and Supplementary Figs. 14 and 15). We do not observe a strong correlation between FBP1 expression and Fe-DFB uptake over time either. As a result, we have not made further revisions in relation to this comment.\n2. Emphasizing other highlights/achievements of the study – I think that we are all very data driven, but in fact new scientific approaches, and particularly the combination of knowledge across techniques, scales, functions etc, are good enough reason to get the publication to Nature Communications (or other high impact). I think we are far from resolving the effect of acidification on iron bioavailability to diverse phytoplankton in natural systems, due to gaps in our knowledge on Fe chemistry (what are the major forms of Fe out there?), phytoplankton types (who is there and how active), coping mechanisms and more. Every sensible reader (or editor) should not be surprised that the effect is variable and even contradictory (in general in papers so far). Can you try and refine / extract methodological/conceptual goals achieved (even for one case) which are related to the general question of iron stress / storage/ uptake/ availability? Maybe you can strengthen the contrast between E1 and E3, emphasizing the differences in status, composition and response, but emphasize a bit more the set of tools / knowledge gained that allow you to “finger-print” the Fe status and response of this community during the incubation.\nWe agree that the text should more clearly describe the methodology and how that enabled us to assess the communities’ iron status and responses. We now:\n• State in our abstract that “A combined physiological and multi-omics approach was applied…” (line 47).\n• Briefly highlight our approach at the end of our introduction (lines 153-156): \"With a combined physiological and multi-omic approach, these experiments enable probing of mixed phytoplankton assemblages’ sensitivity to short-term acidification exposure from the whole community to taxonomically-specific molecular levels while considering the high importance of iron bioavailability.”\n• State in the discussion how the combined approaches allowed us to examine the community and identify these responses (lines 496-502): “Here we examined upwelling-associated phytoplankton communities’ responses while they were simultaneously exposed to iron stress and acidification over short time periods. The iron status and responses of these communities were assessed with a combination of experimental, biogeochemical, physiological, and ‘omics approaches. Although large differences were not observed in the bulk community, molecular-level responses with greater taxonomic resolution of dominant groups were indicative of acidification-driven alterations to iron bioavailability and greater iron stress under higher CO .”\nIn terms of additional goals achieved, we also now further highlight our detection and phylogenetic analysis of several relatively uncharacterized iron-related genes in diatoms that are elucidated to be of diverse evolutionary origins and part of the acidification-driven responses we observed here. We now:\n• State in our abstract (lines 50-52): “Underlying these responses are genes of diverse evolutionary origins highlighting unique adaptations in phytoplankton to grow under low iron availability.\n• Describe the evolutionary origins of the ZIP-family protein detected here (lines 364-365):\n“Diatom homologs of the ZIP1 gene are related to ZIP-family genes in green algae…(Lampe et al. 2018).”\n• Emphasize this finding in our discussion (lines 504-506): “Several of the iron-related genes described here are relatively uncharacterized in diatoms and of diverse evolutionary origins highlighting diatoms’ unique adaptations to grow under low iron availability.”\nGood luck and congratulations on this important scientific contribution.\nWe again thank the reviewer for their time and helpful comments.\nReferences\nBlazewicz, S. J., R. L. Barnard, R. A. Daly, and M. K. Firestone. 2013. Evaluating rRNA as an indicator of microbial activity in environmental communities: limitations and uses. The ISME Journal 7: 2061-2068.\nCoale, T. H., M. Moosburner, A. Horák, M. Oborník, K. A. Barbeau, and A. E. Allen. 2019. Reduction-dependent siderophore assimilation in a model pennate diatom. Proc Natl Acad Sci USA 116: 23609-23617.\nGong, W., and A. Marchetti. 2019. Estimation of 18S Gene Copy Number in Marine Eukaryotic Plankton Using a Next-Generation Sequencing Approach. Frontiers in Marine Science 6.\nHoppe, C. J. M., C. S. Hassler, C. D. Payne, P. D. Tortell, B. Rost, and S. Trimborn. 2013. Iron Limitation Modulates Ocean Acidification Effects on Southern Ocean Phytoplankton Communities. PLOS ONE 8: e79890.\nHu, S. K. and others 2016. Protistan diversity and activity inferred from RNA and DNA at a coastal ocean site in the eastern North Pacific. FEMS Microbiology Ecology 92.\nKong, H. and others 2023. RNA outperforms DNA-based metabarcoding in assessing the diversity and response of microeukaryotes to environmental variables in the Arctic Ocean. Science of The Total Environment 876: 162608.\nLampe, R. H. and others 2018. Different iron storage strategies among bloom-forming diatoms. Proc Natl Acad Sci USA 115: E12275-E12284.\nMaldonado, M. T., and N. M. Price. 1999. Utilization of iron bound to strong organic ligands by plankton communities in the subarctic Pacific Ocean. Deep Sea Research Part II: Topical Studies in Oceanography 46: 2447-2473.\n---. 2001. Reduction and transport of organically bound iron by Thalassiosira Oceanica (Bacillariophyceae). Journal of Phycology 37: 298-310.\nMarchetti, A., and M. T. Maldonado. 2016. Iron, p. 233-279. In M. A. Borowitzka, J. Beardall and J. A. Raven [eds.], The Physiology of Microalgae. Springer International Publishing.\nTrimborn, S., C. J. M. Hoppe, B. B. Taylor, A. Bracher, and C. Hassler. 2015. Physiological characteristics of open ocean and coastal phytoplankton communities of Western Antarctic Peninsula and Drake Passage waters. Deep Sea Research Part I: Oceanographic Research Papers 98: 115-124.\nVětrovský, T., and P. Baldrian. 2013. The Variability of the 16S rRNA Gene in Bacterial Genomes and Its Consequences for Bacterial Community Analyses. PLOS ONE 8: e57923.\nWang, X. J., M. Behrenfeld, R. Le Borgne, R. Murtugudde, and E. Boss. 2009. Regulation of phytoplankton carbon to chlorophyll ratio by light, nutrients and temperature in the Equatorial Pacific Ocean: a basin-scale model. Biogeosciences 6: 391-404."}]

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