Ammonia oxidizers offset acidification stress via adaptive substrate affinity in aquatic ecosystems

Domain: neuroscience
Published: 2026-01-27
Editorial outcome: accept

Abstract
Ammonia oxidation, a critical nitrogen cycle process, exhibits contradictory responses to aquatic acidification, and the underlying mechanism remains unresolved. Here, through pH manipulation experiments across diverse ecosystems and with a model ammonia-oxidizing archaea species, Nitrosopumilus maritimus strain SCM1, we discover a unifying adaptive mechanism: acidification triggers a compensatory increase in substrate affinity in ammonia-oxidizing microorganisms. This enhancement counteracts the reduction in ammonia availability, with the magnitude of increase being significantly greater in ammonia-oxidizing archaea than in ammonia-oxidizing bacteria. Consequently, in ammonia-oxidizing archaea-dominated systems, this adaptation can sustain or even stimulate oxidation rates under moderate acidification, while ammonia-oxidizing bacteria-dominated systems experience a decline. By incorporating this affinity response into models, we accurately reconcile prior disparate field observations. We thus establish the regulation of substrate affinity as a key determinant of microbial resilience, providing a framework for predicting nitrogen cycle dynamics under future acidification.

Introduction
Aquatic systems globally—including lakes1, estuaries2,3, and the open oceansystems4—are undergoing rapid acidification due to rising atmospheric carbon dioxide (CO2). Projections suggest that continued CO2 uptake by the oceans could lower surface pH by up to 0.5 units by 2100 compared to pre-industrial levels5. In coastal eutrophic waters, acidification is often more pronounced, with pH declines of up to 0.85 units in oxygen-depleted subpycnocline waters due to intensified respiration6. Additional drivers include oxidation of reduced chemicals (e.g., ammonium, manganese(II), hydrogen sulfide, iron(II), methane), organic acid inputs, and atmospheric deposition of sulfur and nitrogen oxides7,8. Understanding the impacts of acidification on biogeochemical processes is thus critical for predicting ecosystem responses to global change9.

Ammonia oxidation (AO), mediated by ammonia-oxidizing archaea (AOA), bacteria (AOB) and complete ammonia oxidizers (comammox), is a linchpin of the nitrogen cycle. This process initiates the formation of nitrate (NO3⁻), drives denitrification—a vital nitrogen loss pathway—and generates the potent greenhouse gas nitrous oxide (N2O). While all three groups oxidize ammonia (NH3) into nitrite (NO2⁻), they occupy distinct niches10. Typically, AOB thrive in nutrient-rich systems (e.g., eutrophic lakes and coastal waters), whereas AOA dominate oligotrophic oceans due to their superior affinity for total ammonium (NH3+NH4⁺)11. In contrast, comammox Nitrospira species are often found in stable, low-turnover habitats such as soils, sediments, and engineered systems (e.g., biofilms and wastewater reactors)12, and are thus expected to play a minor role in the dynamic water columns of reservoirs, estuaries, and the open ocean that are the focus of this study.

Anthropogenic nitrogen inputs and acidification are altering these dynamics13. Conventional models posit that pH decline reduces the availability of NH3, which is the substrate for ammonia-oxidizing microorganisms14–16 (NH3+H+ ⇄ NH4+; pKa = 9.24 at 25°C), thereby decreasing oxidation rates13,17,18. Yet, laboratory culture experiments revealed varying pH sensitivities within the same microbial lineages19–21, and field studies report conflicting results, with acidification sometimes suppressing and other times enhancing rates (Supplementary Fig. 1)13,18,22–25. These inconsistencies suggest NH3 availability alone cannot explain microbial responses.

AOA’s unique adaptations, such as high-affinity ammonium transporters26 and acid tolerant cellular structures27, may enable them to exhibit greater resilience to low pH than AOB. Yet, community-specific responses across environments remain poorly resolved, hindering predictions of nitrogen cycling under acidification.

To address these knowledge gaps surrounding acidification responses, we conducted dual-factor manipulation experiments (substrate concentration and pH) across a broad environmental gradient, from freshwater to oligotrophic oceans (Supplementary Fig. 2), and simultaneously examined the response of AOA Nitrosopumilus maritimus SCM1. Our results demonstrate that ammonia oxidizers can counteract acidification stress by enhancing their substrate affinity, a mechanism that reconciles previously contradictory observations. This adaptive response highlights a previously underappreciated dimension of microbial resilience, deepening our understanding of nitrogen cycle dynamics in a changing aquatic ecosystem.

Results
Diverse acidification response patterns across aquatic ecosystems
Our study captured the acidification response across a wide range of aquatic environments, from freshwater (Shanmei Reservoir, SM; S=0.00) through estuarine waters (Pearl River Estuary, PRE; S=0.14–34.30 and Jiulong River Estuary, JRE; S=4.10–29.05), to oceanic waters (Northwest Pacific, NWP; S=34.84–34.94). These systems exhibited contrasting nitrogen regimes: SM displayed low ammonium (NH4+=350 nM) but high nitrate (NO3⁻=131,000 nM), reflecting efficient nitrification, while JRE showed NH4+ enrichment (up to 58,700 nM), indicative of anthropogenic inputs (Supplementary Table 1).

The ammonia oxidation rates (AOR) at low NH4+ concentration ([NH4+]) addition levels (AORlow, see Methods) varied over 3 orders of magnitude, from 0.9 to 376 nM d−1, across the studied environments (Fig. 1c, e, f, g, k, l). Statistical analysis revealed two primary response patterns to acidification: (I) a monotonic decline, where rates consistently declined with pH reduction (Fig. 1c); and (II) no statistically significant response, where rates remained stable or slightly increased under moderate acidification (Fig. 1e, f, g, k, l). Notably, the second pattern, with rates peaked under moderate acidification before declining, was also observed in SCM1 (Fig. 1m). The absence of significant inhibition in many field samples, coupled with the clear optimization response in SCM1, suggest a capacity for nuanced microbial adaptation to moderate acidification that counteracts a simple inhibitory effect.

The addition of high substrate concentrations partially alleviated substrate limitation and enhanced AOR in both the control and acidification treatment groups (Fig. 1a, c, e, f, g, k, m). However, interestingly, despite variations in salinity and [NH4+] across regions, these distinct response patterns for the AOR groups persisted after NH4+ enrichment (AORhigh, see Methods) (Fig. 1a-j, m), except at NWP1, where high variability obscured the trend (Fig. 1k). Critically, acidification responses were decoupled from NH4+ availability in multiple regions, unlike the reported dual-factor manipulation experiments revealing a [NH4+]-dependent temperature sensitivity of AO28.

Substrate affinity as a key to modulate their acidification responses
Substrate kinetics, which include both substrate (NH3) availability and substrate affinity, play an essential role in understanding the variable responses of ammonia-oxidizing microorganisms to acidification. While (NH4++NH3) is commonly measured and used to derive parameters for substrate kinetics in many studies29,30, we specifically focus on the un-ionized NH3 in our analysis (Supplementary Table 2 and 3). This is because NH3, as the direct substrate for AO, impacts the physiological traits of ammonia-oxidizing microorganisms and their ecological niches.

To examine these dynamics, we designed substrate kinetics experiments to assess how acidification affects the substrate affinity of ammonia-oxidizing communities (see Methods). At a given pH, we observed that oxidation rates increased with (NH4++NH3) concentration ([NH4++NH3]) until saturation, following either the Haldane substrate inhibition model (see Methods and Supplementary Fig. 3a) or the Michaelis–Menten model (see Methods and Supplementary Fig. 3b–g). Results from our field manipulation experiments, AOA pure culture assays, and literature-reported AOB pure cultures14,15,31 focused on Nitrosomonas, the dominant aquatic genus, show that substrate kinetic traits for (NH4++NH3), including the maximum rate (Vmax), the half saturation constant (Km), and the substrate affinity (α), are all affected by acidification (Supplementary Table 4 and 5; Supplementary Fig. 3h–j), although no consistent trends are observed for Km(NH4++NH3) and α(NH4++NH3) under acidification (Supplementary Fig. 3i, j). While Vmax decreases slightly with acidification (−0.7%±0.8% in field studies except at NWP1 stations with high variability and −9.0%±0.1% in pure cultures per 0.1-unit pH drop; Supplementary Fig. 3h), ammonia oxidation communities are still mildly inhibited by acidification under [NH4++NH3] saturated conditions. In addition, at the SM station we observed pronounced substrate inhibition at high ammonium concentrations, which was alleviated under acidified conditions.

We specifically calculated NH3 concentrations in each treatment group to more accurately fit the substrate kinetic model (see Methods; Supplementary Table 2 and 3). When NH3 was used as the substrate for regression, the rates still conformed to the Haldane (Fig. 2a) or Michaelis–Menten models at a given pH (Fig. 2b–g). However, Km(NH3) was 1 to 3 orders of magnitude lower than Km(NH4++NH3), and α(NH3) was 1 to 3 orders of magnitude higher than α(NH4++NH3) (Fig. 2i, j; Supplementary Table 4 and 5; Supplementary Fig. 3i, j).

Meanwhile, both Km(NH3) and α(NH3) exhibited significant pH dependence. Km(NH3) decreased by −13%±8% (field), −19%±7% (AOA pure culture) and −3%±3% (AOB pure culture)14,15,31 (Fig. 2i), whereas α(NH3) increased by 28%±23% (field), 94%±7% (AOA pure culture) and 3±4% (AOB pure culture)14,15,31 per 0.1-unit pH drop (Fig. 2j). This trend has also been observed in multiple pure culture studies both AOB15,16,20,21,31 and AOA16. These findings suggest that as acidification intensifies, ammonia-oxidizing communities require less NH3 to maintain the same rate of activity, likely due to an improvement in their substrate utilization efficiency. Notably, in high-salinity cases (JRE1, JRE2, JRE3 and SCM1 culture), the increase in α(NH3) due to acidification was significantly higher than in freshwater or low-salinity areas (SM, JRE0), as well as in AOB pure culture reported in previous studies (AOB culture) (p<0.05; Fig. 2j), indicating that the acidification effect on substrate affinity was amplified for communities adapted to high salinity.

Overall, our results reveal counteractive effects of acidification on AO. Ammonia-oxidizing microorganisms enhance their substrate affinity (α towards NH3) to accommodate acidification-induced NH3 availability reduction. The balance between these factors likely determines the net response of AOR to acidification. Additionally, since the measured AOR reflect the activity of entire microbial communities (comprising both AOA and AOB), shifts in community composition could further influence the response of AO to acidification.

Differential Responses of AOA and AOB to Acidification
The contrasting responses of AOA and AOB to acidification provide further insights into AO dynamics. By selectively suppressing AOB activity using allylthiourea (ATU) (see Methods) and quantifying AOA/AOB gene copy ratios (see Methods), we found that AOB dominated in freshwater and low-salinity regions (SM, PRE1 and JRE0), while AOA became predominant in higher salinity zones (Supplementary Fig. 4). This spatial shift aligned with previous estuarine observations (e.g., the Yangtze Estuary32,33, the PRE28,33, and the Chesapeake Bay34), where AOB prefer low salinity environments and AOA dominate in higher salinities. Our kinetic analysis supports this distinction: in low-salinity areas (SM, JRE0), Km(NH4++NH3) values exceeded 1 μM, while in high salinity regions (JRE1, JRE2, JRE3 and NWP1), Km(NH4++NH3) values were relatively low (<711 nM) (Supplementary Table 4).

Overall, AOA-dominated regions exhibited significantly higher AOA:AOB gene copies ratio and rate ratio relative to AOB-dominated regions (p<0.05) (Fig. 3a, b). Additionally, although acidification did not cause significant differences in the Vmax between AOB- and AOA-dominated systems (Fig. 3c), it exerted a significantly stronger effect on Km(NH3) and α(NH3) in AOA-dominated regions compared to AOB-dominated ones (p<0.05) (Fig. 3d, e). This pattern was consistent with our pure culture results for SCM1 and with published data for AOB (Nitrosomonas europaea) (Fig. 2i, j), together suggesting that the impacts of ocean acidification on substrate affinity vary between AOB and AOA.

To assess the individual and combined effects of acidification on substrate affinity and availability in AOA and AOB, we developed ecological models based on established substrate-level kinetics and applied them within an ecosystem context. We compared three models (see Methods): the substrate availability (S Avail) model (Fig. 4a), the substrate affinity (S Affin) model (Fig. 4b), and the combined S Avail with S Affin (S Avail + S Affin) model, incorporating both factors (Fig. 4c, d). Our findings highlight the importance of incorporating both reduced substrate availability and increased substrate affinity to accurately predict AOR under future ocean acidification scenarios, as the combined S Avail + S Affin model consistently exhibited the strongest concordance with the observation values and the lowest Root Mean Square Error (RMSE) (Supplementary Discussions; Supplementary Fig. 5, 6). However, comparisons between model predictions and observed values showed marked differences in the influence of these two factors on AOR in AOA-dominated versus AOB-dominated regions (Supplementary Fig. 6, 7).

In AOB-dominated regions, RMSE of the S Avail model was smaller than that of the S Affin model (Supplementary Fig. 6a, b), indicating that the reduction in substrate availability driven by acidification was the primary driver of a sustained decline in AOR. Notably, in AOB-dominated stations, the addition of high substrate levels alleviated the negative effects of acidification (Supplementary Fig. 8a, b), but substrate availability remained the dominant limiting factor, resulting in a persistent decrease in AOR (Fig. 1a, c).

Conversely, in AOA-dominated regions and AOA pure culture experiment, the RMSE for the S Affin model was lower than that for the S Avail model, except at the NWP1 station under minor acidification (Supplementary Fig. 6c–g). This suggests that a slight pH decrease enhances substrate affinity, thereby promoting AOR (Fig. 1e, f, g, k, m). In contrast to AOB-dominated regions, the effect of substrate addition on mitigating acidification-induced changes in oxidation rates was not significant in AOA-dominated regions (Supplementary Fig. 8c–g). When substrate saturation was reached, AOR were solely dependent on changes in Vmax, which decreased with acidification (Fig. 2h).

We extended the model to quantify the impact of changes in substrate availability and affinity on AOR under current in-situ substrate levels in both AOA- and AOB-dominated systems. In AOB-dominated regions, the ratio of the effect of increased substrate affinity to reduced substrate availability was below the −0.5:1 line (Fig. 4e, f). Conversely, in most AOA-dominated regions, this ratio was at or above the −0.5:1 line, with some regions even above the −1:1 line (Fig. 4e, f). This confirms that the effect of increased substrate affinity is more significant in AOA-dominated regions compared to AOB-dominated ones. These findings are consistent with results from AOB and AOA pure culture experiments (Figs. 2j and 4e). The contrasting responses between AOA- and AOB-dominated systems (p<0.05) suggest that the mechanisms by which acidification affects AO differ fundamentally between the two groups.

In general, pH reduction decreases substrate availability for both AOA and AOB while increasing substrate affinity in both groups. However, at AOA-dominated sites, the increase in substrate affinity is significantly stronger than at AOB-dominated sites, and in some locations, this effect can even outweigh the reduction in availability, which enables AOA to better adapt to acidified conditions (e.g., low concentration range in Fig. 4d). Our findings help reconcile inconsistencies in field studies on the effects of acidification on AO, where negative effects have been observed overall, but positive responses have been reported at certain AOA-dominated sites (Supplementary Fig. 1).

Potential mechanisms of ammonia-oxidizing archaea adaptation to acidification
The consistent decrease in Km(NH3) under acidification observed in our field studies, AOA pure-culture and prior research16 confirm this is a reproducible phenotypic response across systems. This apparent enhancement in substrate affinity likely stems from a suite of physiological adjustments to low pH, rather than a direct modification of the ammonia monooxygenase (AMO) enzyme itself. As suggested by Jung et al. for other nitrifiers (Ca. Nitrosocosmicus oleophilus MY3 and comammox Nitrospira inopinata)16, factors such as shifts in cytoplasmic pH homeostasis35, membrane potential27, and the intracellular NH3/NH4+ equilibrium could collectively alter substrate diffusion and uptake kinetics. Our whole-cell and community-level measurements thus reflect the integrated outcome of these complex cellular processes.

Nevertheless, substantial molecular evidence indicates that AOA are genetically equipped for a more active adaptation to acid stress. AOA express energy-dependent, high-affinity ammonium transporters26,36, whereas many AOB only encode for rhesus (Rh) proteins, relying on low-affinity channels or passive diffusion37–39. This fundamental difference enables AOA to scavenge and concentrate NH3 more effectively as its external availability declines. Furthermore, AOA demonstrate a coordinated transcriptional response to acidification, including upregulation of V-ATPase to expel protons and maintain a higher intracellular pH35, and increased ATP synthase expression18 to fuel this energetically costly homeostasis. The concurrent downregulation of similar proton-pumping genes in AOB underscores a key physiological distinction between these groups.

Therefore, the enhanced substrate affinity in AOA likely arises from an intrinsic capacity to couple proton extrusion and energy generation with high-affinity transport. While acidification may not alter AMO’s inherent kinetics, its overarching impact on cellular bioenergetics and membrane physiology appears to be a dominant factor shaping the observed kinetic parameters.

Ultimately, our study interprets these kinetic shifts from ecological perspectives. The mechanism of enhanced substrate utilization efficiency provides a powerful, empirically supported framework to reconcile contradictory field observations of nitrification under acidification. We posit that this adaptive response is a key determinant of microbial resilience. While the precise molecular contributors warrant further dissection via integrated transcriptomics, proteomics, and single-cell analyses, our findings establish enhanced affinity as a critical, measurable variable for predicting nitrogen cycle dynamics in the acidifying aquatic ecosystems.

Implications for ammonia oxidation dynamics in a changing ocean
Our study reveals a critical trade-off in ammonia-oxidizing microorganisms. While acidification reduces NH3 availability, it simultaneously enhances substrate affinity—a previously overlooked compensatory mechanism. Under the projected 0.5-unit pH decline by 21005, this trade-off drives contrasting outcomes in AOB- versus AOA-dominated systems. In AOB-dominated environments (e.g., freshwater and estuaries), limited affinity gains fail to offset NH3 scarcity (Fig. 5). This may exacerbate nitrogen accumulation, impairing eutrophication control and coastal resilience. In contrast, in AOA-dominated systems (e.g., oligotrophic ocean), high affinity adaptations (α(NH₃) upregulates by 36.24±22.9% per 0.1-unit pH drop) sustain or even elevate oxidation rates (Fig. 5), potentially accelerating nitrogen turnover in pelagic ecosystems.

Notably, substrate-saturated conditions unmask a universal acidification stress by lowering Vmax in both AOA- and AOB-dominated systems (Fig. 5), suggesting that even adapted communities face energetic constraints under extreme acidification. These shifts could have cumulative effects on nutrient dynamics, eutrophication potential, and overall ecosystem functioning, especially in AOB-dominated systems.

In addition, as atmospheric CO2 concentrations increase, ocean acidification is often coupled with rising temperatures. Since warming elevates NH3 concentrations according to the dissociation constant calculation but reduces substrate affinity according to previous experiments28 (Supplementary Fig. 9), we speculate that rising temperatures may counterbalance both the diminishing and enhancing effects of acidification on AOR. In nutrient-rich coastal regions, warming-induced NH3 release may buffer acidification effects, but only where substrates are abundant. In contrast, in oligotrophic offshore environments where substrate availability is severely limited, warming’s negative impact on affinity compounds acidification stress, potentially reducing nitrification rates by 2.2% per 0.1-unit decrease in pH, a critical feedback for oceanic N2O emissions (See Supplementary discussions for details, Supplementary Fig. 10).

We suggest incorporating affinity-availability dynamics into earth system models to improve nitrogen cycle projections. In addition, we also highlight the need for greater focus on studies of AOA-AOB competition under multifactorial stressors (e.g., pH × temperature × hypoxia). By resolving the affinity-availability paradox, our work transforms the understanding of microbial resilience, offering a framework to predict nitrogen cycle disruptions in a rapidly acidifying ocean.

Methods
Field sampling
Samples were collected across six scientific expeditions spanning various trophic environments: from the eutrophic freshwater Shanmei (SM) Reservoir (January 2021), to the Pearl River Estuary (PRE) aboard the R/V Yuezhanke X (July 2020 and July 2021), and the Jiulong River Estuary (JRE) aboard the R/V Ocean II (March 2023 and March 2024), extending to the oligotrophic tropical Northwest Pacific (NWP) aboard the R/V Dongfanghong III (September-October 2021) (Supplementary Fig. 2). The specific sampling locations, including their coordinates, are shown in Supplementary Table 1 and Supplementary Fig. 2. For SM, water was gently pumped into 10 L acid-washed polycarbonate (PC) bottles using a low-pressure electric bilge pump. Temperature, salinity, and depth at SM were measured using an Idronaut Ocean Seven 316Plus. For the PRE, NWP and JRE cruises, temperature, salinity, and depth were measured using a Seabird SBE 911 CTD sensor package, and discrete seawater samples were collected using 12- or 24-Niskin rosette systems (12 L each).

Water samples from the estuaries and reservoir were filtered through 0.2-μm syringe filters and stored in 125 mL acid-washed, seawater-rinsed, high-density polyethylene (HDPE) bottles at −20°C for subsequent nutrient analyses (NH4+, NO2−, NO3−). Open ocean samples were immediately frozen at −80°C after sampling, and transferred to −20°C for storage.

Ammonia oxidation rate incubation experiments
For the ammonia oxidation rate determination, subsampled water was transferred into 120 ml glass serum bottles (CNW, Germany), pre-combusted at 450°C for 4 h, with ~1 mL headspace to minimize carbonate system re-equilibration. 15N-labeled NH4Cl (98 atom% 15N; Sigma-Aldrich, 299251-1G, Lot#TA2540V) was added to the water samples. Samples were incubated in the dark at temperatures near in-situ conditions (± 2°C), with three incubation time points for most samples, except PRE2, which had two time points. Each incubation was conducted with three replicates per treatment. At each incubation time point (including t0), 15 mL was extracted from the serum bottles, 5 mL was for pH measurement and 10 mL was filtered through a 0.2 μm syringe filter, then stored at −20°C for subsequent NOx− (NO2−+NO3−) concentration and isotope analysis. The final concentrations of 15NH4Cl in the experiments and the incubation time points are presented in Supplementary Table 2 and 3.

Acidification and substrate manipulation experiments
To quantify the effect of acidification and substrate concentrations on ammonia oxidation rates, we conducted experiments manipulating pH and substrate concentrations. Because the studied environments ranged from nutrient-rich to oligotrophic zones, both in-situ [NH4++NH3] and the substrate kinetic parameters of ammonia-oxidizing microorganisms can differ substantially across sites, sometimes spanning several orders of magnitude. Accordingly, varying amounts of 15N-NH4+ were added to achieve a range of substrate conditions for rate measurements, and trace metal-clean HCl solutions (0.05M or 0.1M) were simultaneously added to adjust pH levels, prior to sealing with butyl rubber and aluminum caps (Wheaton, USA). Each station involved 3–5 pH gradients and 1–7 15N-NH4+ manipulated concentrations. The specific pH values and 15N-NH4+ concentrations used in the experiments are listed in Supplementary Tables 2 and 3. Note that our experiments address moderate pH reductions (0.134–1.194) rather than extreme acidity. The pH of samples was measured using a pH Meter (Thermo Scientific Orion 3-Star pH Meters) equipped with a pH electrode (Thermo Scientific Orion 8102BNUW PROSS Ultra). The pH remained very stable during the incubation (Supplementary Fig. 11).

Inhibition experiments
To differentiate the contribution of AOA and AOB to the ammonia oxidation rate, parallel incubations with 15NH4+ and allylthiourea (ATU) were conducted at selected stations (station SM in Shanmei Reservoir, station PRE1, PRE2, PRE3, PRE4 in the PRE in July 2020, and station JRE0, JRE2 in the JRE in March 2024). ATU was used to inhibit β-proteobacterial AOB at 80 μM40, allowing the measurement of AOA activity in ATU-treated samples. AOB contributions were derived by subtracting AOA rates from control (groups without ATU).

SCM1 Cultivation
Nitrosopumilus maritimus strain SCM1 was cultured in 4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid (HEPES) buffered synthetic Crenarchaeota medium (SCM) at 30°C in the dark11,41. Cultures in the late exponential phase were transferred to fresh SCM medium (1% of inoculum) without the addition of NH4+. In acidification experiments, cells were distributed into 45 centrifuge tubes with varying 15N-NH4+ (98% of 15N atom, Sigma-Aldrich) and trace metal-clean HCl, creating substrate and pH gradients. pH was measured at each time point and each incubation vial to monitor the pH value during the incubation. Samples were filtered through 0.2 μm syringe filters, and filtrate was stored at −20°C for NH4+ concentration, NOx− concentration and NOx− isotope analysis.

Chemical measurements
For NH4+, samples were determined within one month after sampling. At stations PRE1, PRE2, PRE3, SM, JRE0, JRE1, JRE2, and JRE3, NH4+ concentrations were measured using the indophenol blue spectrophotometric method with a detection limit of 0.5 μM42. At station PRE4, PRE5, NWP1, NWP2, and the SCM1 culture experiment, NH4+ concentrations were measured using a fluorometric method with a detection limit of 0.7 nM43. The concentrations of NO2− and NO3− were determined using a four-channel Continuous Flow Technicon AA3 Auto-Analyzer (Bran-Luebbe, GmbH), which has a detection limit of 30 nM for both NO2− and NO3−.

Isotopic analyses of NOx−
The δ15N of NOx− was analyzed using a modified denitrifier method44,45. In this method, the denitrifier strain Pseudomonas aureofaciens (ATCC no. 13,985) reduced NO3− to N2O, subsequently the N2O was measured using a Thermo Finnigan Gasbench system with cryogenic extraction and purification system connected to a Delta V PLUS isotopic ratio mass spectrometer (GC-IRMS). The δ15N of NO3− was calibrated by NO3− isotope standards USGS 34, IAEA N3 and USGS 32. The accuracy of measurements was within ±0.2‰ at an injection level of 20 nmol N and within ±0.4‰ at an injection level of 10 nmol N. For samples from station PRE5, where NO3− concentration was lower than 1 μM, 1 mL 1 μM NO3− was added as a carrier to the 9 mL samples to meet the 10 nmol N injection level requirement.

Ammonia oxidation rate calculation
The rates of ammonia oxidation were determined based on the accumulation of 15N in the product pool (NOx−) relative to the initial pool. The rate was calculated using (Eq. 1)28:
AOR = (Ct × nt − C0 × n0) / (T × f15)
where AOR represents the ammonia oxidation rates (nM d−1); Ct and C0 are the NOx− concentration at the ending and beginning of the incubation (nM); nt and n0 are the at% 15N of the NOx− at the ending and beginning of the incubation; T is the duration of the incubation (d); f15 is at% 15N of the substrate pool at the beginning of the incubation.
Because the study sites span environments ranging from nutrient-rich to oligotrophic waters, both the in-situ NH4+ concentrations and the substrate-kinetic characteristics of ammonia-oxidizing microorganisms vary widely—often by several orders of magnitude. In general, we defined incubations with a final tracer addition of < 1× the in-situ NH4+ concentration as the low-NH4+ treatment (AORlow), whereas higher additions were classified as the high-NH4+ treatment (AORhigh). At SM station, the ambient NH4+ concentration was very low (~350 nM), yet the measured AOR was high, indicating rapid substrate turnover. To avoid rapid isotopic dilution of the added 15NH4+ by the ambient 14NH4+ pool, we applied a relatively high substrate addition (≥ 4990 nM). Consequently, both substrate-addition treatments at SM were categorized as AORhigh.

Fitting of substrate kinetic models
At stations SM, JRE0, JRE1, JRE2, JRE3, NWP1 and SCM1 strain incubation, we conducted substrate kinetics experiments at different pH levels to investigate the effects of acidification on the kinetic traits of ammonia oxidation communities and to more accurately evaluate the effect of acidification on AOR. For stations JRE0, JRE1, JRE2, JRE3, NWP1, and SCM1 strain incubation, we applied the Michaelis-Menten model30 (Eq. 2), while at station SM, where substrate inhibition was observed, we used the Haldane substrate inhibition model30 (Eq. 3) to determine the kinetic parameters.
AOR = (Vmax × S) / (Km + S)
AOR = (Vmax × S) / (Km + S + S2/Ki)
where AOR is the ammonia oxidation rate, Vmax is the maximum reaction rate, S is the total ammonium (NH4++NH3) concentration or NH3 concentration, and Km is the apparent half-saturation constant, representing the substrate concentration at which the reaction rate is half of Vmax. Ki is the substrate concentration at which the reaction rate is reduced to half of its Vmax due to inhibitory effects. A nonlinear least squares regression analysis was used to estimate Ki, Km and Vmax.
The substrate affinity (α) of the ammonia-oxidizing microbial community was calculated using the equation (Eq. 4):
α = Vmax / Km
The α values for NH4++NH3 or NH3 were calculated using the respective Km for NH4++NH3 or NH3.

Conversion from pH NBS scale (pHNBS) to pH total scale (pHT)
Given the significantly higher ion strength of seawater compared to freshwater, we used AquaEnv package (an aquatic acid–base modelling environment in R) to convert the pHNBS values measured by the electrode into pHT46. This approach allowed for a more accurate evaluation of H+ concentration in seawater.

The calculation of NH3 concentration
NH3 concentrations in the incubation experiments were calculated based on NH4+ concentrations, pH in total scale, and pKa values using Eq. 513:
NH3 = NH4+ × 10^(pH - pKa)
The pKa values at the temperature of the incubation were calculated based on the temperature and salinity of the incubation system and summarized in Supplementary Table 2 and 3.

DNA extraction and qPCR
For DNA extraction, water samples were filtered through polycarbonate membranes with a pore size of 0.2 μm (25 mm in diameter; Millipore) under a pressure of < 200 mmHg (filtered volumes ranged from 0.5 to 1 L for freshwater and estuary, and 4 L for open ocean). These samples were flash frozen in liquid nitrogen and stored at −80°C until further analysis. DNA was extracted using the PowerSoil DNA Isolation Kit (Mo Bio Laboratories, Carlsbad, CA) following the manufacturer’s instructions. The abundance of ammonia-oxidizing archaea (AOA) and β-proteobacterial ammonia-oxidizing bacteria (AOB) was determined by quantitative PCR (qPCR) targeting the amoA gene. The primers used were Arch-amoAF (STAATGGTCTGGCTTAGACG) and Arch-amoAR (GCGGCCATCCATCTGTATGT)48. β-proteobacterial amoA genes were amplified by using primers sets amoA-1F (GGGGTTTCTACTGGTGGT) and amoA-2R (CCCCTCKGSAAAGCCTTCTTC)49.
The PCR mixture was conducted following the protocol outlined by Hou et al.50. Amplification conditions for archaeal and β-proteobacterial amoA genes were applied as reported by Francis et al.48 and Rotthauwe et al.49, respectively. The amplification products were cloned into the pMD18-T vector (TaKaRa) and then transformed into competent cells of Escherichia coli DH5α. Positive clones were randomly selected for sequencing to confirm correct insertion of the target fragment. Then, equal amounts of the plasmid DNA from three different clones of the β-proteobacterial amoA gene and four different clones of the archaeal amoA gene were pooled separately to construct standard curves for qPCR. Each standard DNA curve was serially diluted ten-fold to generate concentration ranging from 100 to 107 gene copies µL−1. Standards, samples and non-template controls were amplified in triplicate with each primer set.
qPCR amplification was performed as described previously with slight modifications51. The qPCR reaction mixture (25 μL) contained 12.5 μL SYBR® Premix Ex TaqTM II (TaKaRa, Dalian, China), 5 μg Bovine Serum Albumin (BSA), 0.4 μM of each primer and 1 μL template DNA of 1–10 ng. Thermal cycling for archaeal amoA genes consisted of initial denaturation at 95°C for 30 s followed by 40 cycles of 95°C for 30 s, 53°C for 60 s, and 72°C for 45 s. For β-proteobacterial amoA gene, cycling conditions were: 94°C for 15 s, followed by 50 cycles of 94°C for 15 s, 60°C for 30 s, and 72°C for 90 s, with a final extension of 60 s at 78°C to assure stringent product detection.
Amplification efficiencies for both archaeal and β-proteobacterial amoA genes ranged from 85% to 96%, with R2 values > 0.99. The specificity of qPCR reactions was confirmed by melting curve analysis and agarose gel electrophoresis. In addition, previous studies showed that γ-proteobacterial amoA genes, another dominant genotype of AOB, were below the detection limit in the study area50,52. Thus, γ-proteobacteria amoA was neglected in this analysis.

Setting of three models for ammonia oxidation response to acidification
Three theoretical models were established to evaluate the impact of substrate availability and affinity on ammonia oxidation under ocean acidification at station SM, JRE0, JRE1, JRE2, JRE3, NWP1 and pure culture SCM1, with each model corresponding to a different hypothesis.
For S Avail model, we assumed that acidification only affected the concentration of substrates required by ammonia oxidizing microorganisms, that is, acidification converted a large amount of NH3 into NH4+,
S Avail = (Vmax(pH0) × S(pH0)NH3) / (Km(pH0) + S(pH0)NH3)
Where Vmax(pH0) and Km(pH0) were obtained from the control incubation, S(pH0)NH3 was obtained from Eq. 5 by calculating the actual concentration of NH3 in the culture system after pH decrease.
For S Affin model, we assumed that acidification only affected the substrate affinity of ammonia oxidizing microorganisms,
S Affin = (Vmax(pH0) × S(pH0)NH3) / (Km(pH0) + S(pH0)NH3)
Where Vmax(pH0) and Km(pH0) were obtained from the control incubation, S(pH0)NH3 was obtained from Eq. 5 by calculating the actual concentration of NH3 in the culture system before pH decrease.
For S Avail + S Affin model, we assumed that acidification affected not only the concentration of substrates required by ammonia oxidizing microorganisms but also substrate affinity,
S Avail + S Affin = (Vmax(pH0) × S(pH0)NH3) / (Km(pH0) + S(pH0)NH3)
Where Vmax(pH0) and Km(pH0) were obtained from the pH down-regulation group fitting by Eqs. 2 and 3, S(pH0)NH3 was obtained from Eq. 5 by calculating the actual concentration of NH3 in the culture system after pH decrease.

Evaluate the respective substrate availability decrease effect and substrate availability increase effect caused by acidification
We conducted a preliminary quantification of the intensity of the substrate availability decrease effect and substrate availability increase effect based on nutrient concentrations at various stations and three models. The actual acidification effect was calculated using the S Avail + S Affin model. The changes in the substrate affinity increase effect under acidification scenarios were determined using the S Affin model. The substrate availability decrease effect was then obtained by subtracting the effect calculated with the S Affin model from the actual acidification effect.

Temperature-pH coupled model for ammonia oxidation
The temperature-pH coupled model was designed to predict the response of ammonia oxidation rates under the framework of global change in regions dominated by different ammonia-oxidizing microorganisms (Supplementary Fig. 12). In this study, the low salinity station JRE0 was analyzed alongside the low salinity station JLR4 from Zheng et al., both of which were dominated by AOB28. In contrast, the high-salinity station JRE3 was compared with the high-salinity station J1 from Zheng et al., and both were identified as being dominated by AOA28. Based on the results of temperature and substrate concentration dual regulation by Zheng et al., we used the functions for Km(NH4+) and Vmax varying with temperature from their study, incorporating slight modifications to better fit our study. In brief, we used the salinity and temperature values to calculate pKa with Eq. 5. Using the NH4+ concentration and the approximate pH from the study area (pH = 7.00 in JLR4 and pH = 7.90 in J1)53, the concentration of NH3 was derived. Consequently, we can fit the functions of Km(T)(NH3) and Vmax(T) with temperature (Eqs. 9 and 10)
Km(T)(NH3) = 2.301e0.174T
Vmax(T) = 884.9e0.0819T
Using the in-situ temperatures, salinity and pH data from JRE0 and JRE3, we calculate the Km(NH3) and Vmax of these two stations across various temperatures and pH level (Eqs. 11 and 12), as well as the concentration of NH3
Km(T)(NH3) = (Km(T0) / Km(T0)) × K0m(NH3)
Where Km(T)NH3 is the half-saturation constant in T (°C), Km(T0)NH3 is the half-saturation constant in the same temperature as JRE0 or JRE3, K0m(NH3) is the half-saturation constant determined in this study at the given pH,
Vmax = (Vmax(T0) / Vmax(T0)) × V0max
Where Vmax(T) is the maximum reaction rate in T (°C), Vmax(T0) is the maximum reaction rate in the same temperature as JRE0 or JRE3, V0max is the maximum reaction rate determined in this study at the given pH.
Therefore, we can compare the changes in acidification effects at different temperatures and substrate concentrations.

Statistical analyses
The comparisons between treatment groups and control groups were examined by using the Student’s t-test. A p-value<0.05 was considered statistically significant. The analyses were performed using Prism 10.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "The authors have done a great deal of rewriting / rephrasing of their manuscript in response to the previous reviewer comments. In my opinion they have addressed all of my previous concerns and have reigned in a lot of the speculation that was too broad based on the presented results. This study now represents a step forward in modeling nitrification across changing ecosystems.", "reviewer_2": "General comments:\nThe Km decrease (NH3) for ammonia oxidizers was observed previously in pure cultures (whole cells). However, the physiological reason for this is unclear. The authors focus only on the substrate affinity to discuss their results. But this is whole community and whole cell data, thus maybe other factors might also play a role. Jung et al 2021 also observed a decreased Km Nh3 for a AOA and comammox strain with decreasing pH (DOI: 10.1038/s41396-021-01064-z). There the authors stated “the change in Km (app) for NH3 across this pH range may be due to cellular effects of the differing pH values unrelated to the direct ammonia oxidation pathway.” The substrate affinity might be enhanced, but this might not directly linked to ammonia oxidation, but other cellular processes as stated by Jung et al 2021. While the authors briefly discuss NH3 transporters (I guess mainly for Nh3 “storage” or assimilation; since the active site of the AMO is postulated to be in periplasm; which is not mentioned in the manuscript), other physiological explanations for the findings are not discussed at all. Given that in these studies whole communities are investigated this makes it very hard to judge why the Km decrease is observed. For me a critical discussion of the results is missing.\nIn addition, the material and method part for the incubations lack vital details (see specific comment). The description like it is now would not be sufficient to repeat the experiment. I realize that the experimental setup with these many samples is complex, but please provide more details (maybe expand Suppl Figure 2 in an excel file and add more info there)\nL50: What about complete ammonia oxidizers? Comammox might not play a dominant role in these systems, but should be at least mentioned in the introduction. https://doi.org/10.1038/s41396-020-0701-8\nL51: I would say that this process initiate the formation of nitrate not “link” it.\nL87: Without any statical test, it is unclear whether there is a dome-shaped response, except for Fig.1e. I don’t see a difference between high ammonia (black) in c and f. it seems for Fig.1g there is significant decline in both conditions. Also for Fig1m low ammonia at least the first two points might be not significantly different.\nL188-189: Sorry, maybe I missed it but how can be substrate availability be a limiting factor in Fig1c when 5mM – 200mM NH4+ were added?\nMaterials and methods:\nL373- 408: not detailed enough. The description of the incubation experiments is lacking important information, like time of incubations, how many sampling time points, when sampled, changes in pH observed during incubations. Knowing the incubation duration is crucial to understand the data; enough time for a shift in community?\nFigure 1: statistical test is missing as mention in comment above. Fig1a: in SM 350nM is the in situ ammonium concentration, why is “low” ammonium for this sample than 4990nM but for JRE3, where the in situ concentration is 2363nM the “low” ammonium is 100nM? Is this explained somewhere?\nFigure 3: If I see this correctly there are 3 datapoints for AOB dominated in these boxplots. This might be misleading. https://www.nature.com/articles/nmeth.2813 “For n < 5 we recommend showing the individual data points.”"}, "author_response": "Response to the reviewer’s comments\nReviewer #1\nThe authors have done a great deal of rewriting / rephrasing of their manuscript in\nresponse to the previous reviewer comments. In my opinion they have addressed all of\nmy previous concerns and have reigned in a lot of the speculation that was too broad\nbased on the presented results. This study now represents a step forward in modeling\nnitrification across changing ecosystems.\nReply: We thank the reviewer for the positive feedback.\nReviewer #2:\nGeneral comments:\nThe Km decrease (NH ) for ammonia oxidizers was observed previously in pure\n3\ncultures (whole cells). However, the physiological reason for this is unclear. The authors\nfocus only on the substrate affinity to discuss their results. But this is whole\ncommunity and whole cell data, thus maybe other factors might also play a role.\nJung et al 2021 also observed a decreased Km NH for a AOA and comammox strain\n3\nwith decreasing pH (DOI: 10.1038/s41396-021-01064-z). There the authors stated “the\nchange in Km (app) for NH across this pH range may be due to cellular effects of the\n3\ndiffering pH values unrelated to the direct ammonia oxidation pathway.” The substrate\naffinity might be enhanced, but this might not directly linked to ammonia\noxidation, but other cellular processes as stated by Jung et al 2021. While the\nauthors briefly discuss NH transporters (I guess mainly for NH “storage” or\n3 3\nassimilation; since the active site of the AMO is postulated to be in periplasm; which is\nnot mentioned in the manuscript), other physiological explanations for the findings are\nnot discussed at all. Given that in these studies whole communities are investigated this\nmakes it very hard to judge why the Km decrease is observed. For me a critical\ndiscussion of the results is missing.\nIn addition, the material and method part for the incubations lack vital details (see\nspecific comment). The description like it is now would not be sufficient to repeat the\nexperiment. I realize that the experimental setup with these many samples is complex,\nbut please provide more details (maybe expand Suppl Figure 2 in an excel file and add\nmore info there).\nReply: We thank the reviewer for this insightful comment, which have helped us\nimprove the manuscript. We address the two primary concerns below.\nRegarding the physiological mechanism, we agree with the reviewer that the\ndecrease in K (NH ) is a whole-cell phenomenon that may involve multiple\nm 3\nphysiological processes beyond the ammonia oxidation pathway itself, as also\nsuggested by Jung et al. (2021). In response, we have expanded the Discussion (Lines\n218–247) to 1) explicitly that the observed K decrease likely results from a\nm\ncombination of factors, including cellular pH homeostasis, mebrane potential, and\nintracellular NH /NH + equalibrium; 2) discuss specific, literature-supported adaptive\n3 4\nmechanisms in AOA (e.g., high-affinity ammonium transporters, upregulation of V-\nATPase and ATP synthase) that could collectively contribute to the enhanced apparent\nsubstrate affinity; 3) reframe our conclusion to state that while the exact molecular\ndrivers require further study, the consistent kinetic change we observe provides a\ncritical, quantifiable ecosystem-scale mechanism to explain variable nitrification\nresponses to acidification in the field\nOur primary aim is to provide an ecological explanation for field observations,\nand we now more carefully distinguish this from a definitive molecular mechanism.\nFinally, in response to the reviewer’s concern regarding reproducibility, we have\nexpanded the Methods section and Supplementary Information to provide complete\ndetails of the incubation experiments. We have added detailed descriptions of sample\ncollection, incubation procedures, and pH manipulation in the\nmain Methods text (Lines 385–428). We added Supplementary Table 1 to provide the\nspecific sampling locations (including their coordinates), the in-situ temperature, pH,\nsalinity, and nutrient conditions at each site, Supplementary Table 2 and Supplementary\nTable 3 to list adjusted pH values, tracer concentrations, incubation time points and\nSupplementary Fig. 11 to show the changes in pH before and after incubation at\ndifferent sampling areas.\nRevised Main text:\n“Mechanisms”\n“Potential mechanisms of ammonia-oxidizing archaea adaptation to acidification\nThe consistent decrease in K (NH ) under acidification observed in our field studies, AOA\nm 3\npure-culture and prior research1 confirms this is a reproducible phenotypic response across systems.\nThis apparent enhancement in substrate affinity likely stems from a suite of physiological\nadjustments to low pH, rather than a direct modification of the ammonia monooxygenase (AMO)\nenzyme itself. As suggested by Jung et al. for other nitrifiers (Ca. Nitrosocosmicus oleophilus MY3\nand comammox Nitrospira inopinata), factors such as shifts in cytoplasmic pH homeostasis,\nmembrane potential2, and the intracellular NH /NH + equilibrium could collectively alter substrate\n3 4\ndiffusion and uptake kinetics. Our whole-cell and community-level measurements thus reflect the\nintegrated outcome of these complex cellular processes.\nNevertheless, substantial molecular evidence indicates that AOA are genetically equipped for\na more active adaptation to acid stress. AOA express energy-dependent, high-affinity ammonium\ntransporters3,4, whereas many AOB only encode for rhesus (Rh) proteins, relying on low-affinity\nchannels or passive diffusion5,6,7. This fundamental difference enables AOA to scavenge and\nconcentrate NH more effectively as its external availability declines. Furthermore, AOA\n3\ndemonstrate a coordinated transcriptional response to acidification, including upregulation of V-\nATPase to expel protons and maintain a higher intracellular pH8, and increased ATP synthase\nexpression9 to fuel this energetically costly homeostasis. The concurrent downregulation of similar\nproton-pumping genes in AOB underscores a key physiological distinction between these groups.\nTherefore, the enhanced substrate affinity in AOA likely arises from an intrinsic capacity to\ncouple proton extrusion and energy generation with high-affinity transport. While acidification may\nnot alter AMO’s inherent kinetics, its overarching impact on cellular bioenergetics and\nmembrane physiology appears to be a dominant factor shaping the observed kinetic parameters.\nUltimately, our study interprets these kinetic shifts from ecological perspectives. The\nmechanism of enhanced substrate utilization efficiency provides a powerful, empirically supported\nframework to reconcile contradictory field observations of nitrification under acidification. We posit\nthat this adaptive response is a key determinant of microbial resilience. While the precise molecular\ncontributors warrant further dissection via integrated transcriptomics, proteomics, and single-cell\nanalyses, our findings establish enhanced affinity as a critical, measurable variable for predicting\nnitrogen cycle dynamics in the acidifying aquatic ecosystems.” (Lines 218–247)\n“Field sampling”\n“Samples were collected across six scientific expeditions spanning various trophic\nenvironments: from the eutrophic freshwater Shanmei (SM) Reservoir (January 2021), to the Pearl\nRiver Estuary (PRE) aboard the R/V Yuezhanke X (July 2020 and July 2021), and the\nJiulong River\nEstuary (JRE) aboard the R/V Haiyang Ⅱ (March 2023 and March 2024), extending to the\noligotrophic tropical Northwest Pacific (NWP) aboard the R/V Dongfanghong Ⅲ (September-\nOctober 2021) (Supplementary Fig. 2). The specific sampling locations, including their coordinates,\nare shown in Supplementary Table 1 and Supplementary Fig. 2. For SM, water was gently pumped\ninto 10 L acid-washed polycarbonate (PC) bottles using a low-pressure electric bilge pump.\nTemperature, salinity, and depth at SM were measured using an Idronaut Ocean Seven 316 Plus.\nFor the PRE, NWP and JRE cruises, temperature, salinity, and depth were measured using a Seabird\nSBE 911 CTD sensor package, and discrete seawater samples were collected using 12- or 24-Niskin\nrosette systems (12 L each).\nWater samples from the estuaries and reservoir were filtered through 0.2-μm syringe filters and\nstored in 125 mL acid-washed, seawater-rinsed, high-density polyethylene (HDPE) bottles at −20°C\nfor subsequent nutrient analyses (NH +, NO −, NO −). Open ocean samples were immediately frozen\n4 2 3\nat −80 °C after sampling, and transferred to −20 °C for storage.\nAmmonia oxidation rate incubation experiments\nFor the ammonia oxidation rate determination, subsampled water was transferred into 120 ml\nglass serum bottles (CNW, Germany), pre-combusted at 450 °C for 4 h, with ~1 mL headspace to\nminimize carbonate system re-equilibration. 15N-labeled NH Cl (98 atom% 15N; Sigma-Aldrich,\n4\n299251-1G, Lot#TA2540V) was added to the water samples. Samples were incubated in the dark at\ntemperatures near in-situ conditions (±2 °C), with three incubation time points for most samples,\nexcept PRE2, which had two time points. Each incubation was conducted with three replicates per\ntreatment. At each incubation time point (including t ), 15 mL was extracted from the serum bottles,\n0\n5 mL was for pH measurement and 10 mL was filtered through a 0.2-μm syringe filter, then stored\nat −20 °C for subsequent NO − (NO − + NO −) concentration and isotope analysis. The final\nX 2 3\nconcentrations of 15NH Cl in the experiments and the incubation time points are presented in\nAcidification and substrate manipulation experiments\nTo quantify the effect of acidification and substrate concentrations on ammonia oxidation rates,\nwe conducted experiments manipulating pH and substrate concentrations. Because the studied\nenvironments ranged from nutrient-rich to oligotrophic zones, both in-situ [NH + + NH ] and the\n4 3\nsubstrate kinetic parameters of ammonia-oxidizing microorganisms can differ substantially across\nsites, sometimes spanning several orders of magnitude. Accordingly, varying amounts of 15N-NH +\n4\nwere added to achieve a range of substrate conditions for rate measurements, and trace metal-clean\nHCl solutions (0.05 M or 0.1 M) were simultaneously added to adjust pH levels, prior to sealing\nwith butyl rubber and aluminum caps (Wheaton, USA). Each station involved 3–5 pH gradients and\n1–7 15N-NH + manipulated concentrations. The specific pH values and 15N-NH + concentrations\n4 4\nused in the experiments are listed in Supplementary Tables 2 and 3. Note that our experiments\naddress moderate pH reductions (0.134–1.194) rather than extreme acidity. The pH of samples was\nmeasured using a pH Meter (Thermo Scientific Orion 3-Star pH Meters) equipped with a pH\nelectrode (Thermo Scientific Orion 8102BNUWP ROSS Ultra). The pH remained very stable during\nthe incubation (Supplementary Fig. 11)”. (Lines 385–428)\nL50: What about complete ammonia oxidizers? Comammox might not play a\ndominant role in these systems, but should be at least mentioned in the\nintroduction. https://doi.org/10.1038/s41396-020-0701-8\nReply: Thank you for this valuable suggestion. We agree that comammox (complete\nammonia oxidizers) represent an important group in the nitrogen cycle and deserve\nmention in our manuscript, even if their role may not be dominant in the studied systems.\nWe have added a brief discussion of comammox in the Introduction, citing Koch et al.\n(2019), to acknowledge their potential importance and to clarify why they were not a\nprimary focus in this study.\nRevised Main text:\n“Ammonia oxidation (AO), mediated by ammonia-oxidizing archaea (AOA), bacteria (AOB)\nand complete ammonia oxidizers (comammox), is a linchpin of the nitrogen cycle. This process\ninitiates the formation of nitrate (NO ⁻), fuels phytoplankton growth and drives denitrification—a\n3\nvital nitrogen loss pathway—and generates the potent greenhouse gas nitrous oxide (N O). While\n2\nall three groups oxidize ammonia (NH ) into nitrite (NO ⁻), they occupy distinct niches10. Typically,\n3 2\nAOB thrive in nutrient-rich systems (e.g., eutrophic lakes and coastal waters), whereas AOA\ndominate oligotrophic oceans due to their superior affinity for total ammonium (NH + NH ⁺)11. In\n3 4\ncontrast, comammox Nitrospira species are often found in stable, low-turnover habitats such as soils,\nsediments, and engineered systems (e.g., biofilms and wastewater reactors)12, and are thus expected\nto play a minor role in the dynamic water columns of reservoirs, estuaries, and the open ocean that\nare the focus of this study.” (Lines 49–59)\nL51: I would say that this process initiate the formation of nitrate not “link” it.\nReply: We thank the reviewer for this insightful suggestion. We have revised the\nmanuscript to replace “links” with “initiates”.\nRevised Main text:\n“This process initiates the formation of nitrate (NO ⁻)” (Line 51)\nresponse, except for Fig.1e. I don’t see a difference between high ammonia (black) in\nc and f. it seems for Fig.1g there is significant decline in both conditions. Also for Fig1m\nlow ammonia at least the first two points might be not significantly different.\nReply: We agree that without statistical testing, the identification of response patterns\nwas subjective. We have now performed two-sided t-tests comparing each acidification\ntreatment to the control for all data in Figure 1. The statistical results are now annotated\nin Fig. 1 and details are in the Supplementary Data file, and the Methods section has\nbeen updated to describe the test procedure. (Lines 290-295, Lines 607-610)\nFurthermore, we have revised the Results text to reflect these statistical outcomes. We\nnow more cautiously describe the patterns, categorizing them as \"monotonic decline,\"\n\"no significant response,\" or a \"dome-shaped response\" (the latter primarily in the\nSCM1 culture), ensuring our interpretation is firmly backed by the new statistical\nanalysis (See Lines 87-96).\nRevised Main text:\n“The ammonia oxidation rates (AOR) at low NH + concentration ([NH +]) addition levels\n4 4\n(AOR , see Methods) varied over 3 orders of magnitude, from 0.9 to 376 nM d-1, across the studied\nlow\nenvironments (Fig. 1c, e, f, g, k, l). Statistical analysis revealed two primary response patterns to\nacidification: (I) a monotonic decline, where rates consistently declined with pH reduction (Fig. 1c);\nand (II) no statistically significant response, where rates remained stable or slightly increased under\nmoderate acidification (Fig. 1e, f, g, k, l). Notably, the dome-shaped pattern, where rates peaked\nunder moderate acidification before declining, was also observed in SCM1 (Fig. 1m). The absence\nof significant inhibition in many field samples, coupled with the clear optimization response in\nSCM1, suggests a capacity for nuanced microbial adaptation to moderate acidification that\ncounteracts a simple inhibitory effect.” (Lines 87-96)\nL188-189: Sorry, maybe I missed it but how can be substrate availability be a\nlimiting factor in Fig1c when 5mM – 200mM NH + were added?\nsubstrate concentrations used in JRE0 station ranged from 5000 nM to 200,000 nM (i.e.,\n5 μM to 200 μM). We apologize for any confusion.\nDespite these seemingly high concentrations, two lines of evidence confirm the\nsystem was substrate-limited. First, in the control, the rate at 5 μM was significantly\nlower than at 200 μM, proving saturation was not reached. Second, the inhibitory effect\nof acidification was alleviated at higher (200 μM) substrate level, a classic indicator of\nsubstrate limitation. This occurs because AOB-dominated communities have\ncharacteristically high K values.\nm\nL373- 408: not detailed enough. The description of the incubation experiments is\nlacking important information, like time of incubations, how many sampling time\npoints, when sampled, changes in pH observed during incubations. Knowing the\nincubation duration is crucial to understand the data; enough time for a shift in\ncommunity?\nReply: We thank the reviewer for this critical feedback. As noted in the General\nComments, we have entirely rewritten and expanded the Methods section to include\nall requested details: incubation durations, number of time points, sampling procedures,\nand pH stability data (now in Supplementary Fig. 11). We are confident the\nmethodology is now described with sufficient detail for replication. (Lines 385–428)\nFigure 1: statistical test is missing as mention in comment above. Fig1a: in SM\n350nM is the in situ ammonium concentration, why is “low” ammonium for this sample\nthan 4990nM but for JRE3, where the in situ concentration is 2363nM the “low”\nammonium is 100nM? Is this explained somewhere?\nReply:\nAs replied above, we have now added the required two-sided t-tests to Fig. 1.\nRegarding the definition of “low” ammonium concentration, to ensure all stations met\nour definition, we defined “low addition” as an addition lower than the in-situ 14NH +\n4\nconcentration. Therefore, both the low and high additions at JRE3 met our definition.\nHowever, the SM station represents a necessary exception due to its distinct\nbiogeochemical and kinetic characteristics. At the oligotrophic SM station, the in-situ\nammonium concentration was extremely low, while the oxidation rate was high,\nindicating a rapid NH + turnover. Therefore, using only nanomolar-level 15NH +\n4 4\nadditions would have led to rapid dilution by ambient 14NH +, preventing accurate\n4\ndetermination of ammonia oxidation rates. We no longer classify the SM 4990nM group\nas a “low addition” in the text, treating it instead as an exception. Consequently, the SM\nstation includes two high-addition groups. However, despite both being high additions,\nwe can still assess the impact of substrate concentration on acidification by comparing\ntheir relative magnitudes.\nWe have now clarified this rationale in the Methods section and provided all\nconcentrations in Supplementary Tables.\nRevised Main text:\n“Ammonia oxidation rates under low ammonium concentration addition levels (lower than in-\nsitu ammonium concentration) and 4990 nM group of SM are shown as open circles with dashed\nlines, and the corresponding addition concentration is marked in gray next to the lines (see Methods).\nAmmonia oxidation rates under high ammonium concentration addition levels (higher than in-situ\nammonium concentration), except 4990 nM group of SM, are shown as solid circles with solid lines\nand the corresponding addition concentration is marked in black next to the lines.” (Lines 280-286)\n“Because the study sites span environments ranging from nutrient-rich to oligotrophic waters,\nboth the in-situ NH + concentrations and the substrate-kinetic characteristics of ammonia-oxidizing\n4\nmicroorganisms vary widely—often by several orders of magnitude. In general, we defined\nincubations with a final tracer addition of <1× the in-situ NH₄⁺ concentration as the low-NH₄⁺\ntreatment (AOR ), whereas higher additions were classified as the high-NH₄⁺ treatment (AOR ).\nlow high\nAt SM station, the ambient NH₄⁺ concentration was very low (~350 nM), yet the measured AOR\nwas high, indicating rapid substrate turnover. To avoid rapid isotopic dilution of the added 15NH +\n4\nby the ambient 14NH + pool, we applied a relatively high substrate addition (≥ 4990 nM).\nhigh\n479)\nFigure 3: If I see this correctly there are 3 datapoints for AOB dominated in these\nboxplots. This might be misleading. https://www.nature.com/articles/nmeth.2813 “For\nn < 5 we recommend showing the individual data points.”\nReply: Thank you for this important suggestion. We agree that box plots can be\nmisleading for small sample sizes.\nWe have revised Fig. 3 and Fig. 4f, accordingly. For the AOB-dominated groups\n(n=3), we now show individual data points with a median line, as recommended by\nthe Nature Methods guideline you cited. We have also updated the figure legends to\nreflect this and noted the use of log-transformation prior to statistical analysis.\nRevised Main text:\n“Fig. 3: (a) The logarithmic ratio of ammonia oxidation rates [log (AOA rate/AOB rate)]. (b)\nmax m 3\nAOA-dominated regions have been marked in the figure, with the stations where substrate kinetics\nfitting was performed highlighted in color. We used a two-tailed t-test to examine whether there is\na significant difference between AOB-dominated and AOA-dominated regions. For AOB dominated\ncolumns, scatter dot plots with the center line depicting the median are used (n = 3). For AOA\ndominated columns, boxplots depict the 25–75% quantile range, with the center line representing\nthe median (50% quantile); whiskers extend to the minimum and maximum values (panels a–e, n =\n4, 9, 8, 7, 7 respectively).” (Lines 321-330)\n“Fig. 4: For the AOB-dominated column, scatter dot plots with the center line depicting the\nmedian are used (n = 3). For the AOA-dominated column, boxplots depict the 25–75% quantile\nrange, with the center line representing the median (50% quantile); whiskers extend to the minimum\nand maximum values (n = 9).” (Lines 343-346)\nReferences:\n1. Jung, M. Y., et al. Ammonia-oxidizing archaea possess a wide range of\ncellular ammonia affinities. ISME J 16, 272-283 (2022).\n2. Li, P. N., et al. Nutrient transport suggests an evolutionary basis\nfor charged archaeal surface layer proteins. ISME J 12, 2389-2402\n(2018).\n3. Andrade, S. L. A., Dickmanns, A., Ficner, R., Einsle, O. Crystal\nstructure of the archaeal ammonium transporter Amt-1 from\nArchaeoglobus fulgidus. P. Natl. Acad. Sci. Usa. 102, 14994-14999\n(2005).\n4. Wacker, T., Garcia-Celma, J. J., Lewe, P., Andrade, S. L. Direct\nobservation of electrogenic NH+ transport in ammonium transport (Amt)\n4\nproteins. P. Natl. Acad. Sci. Usa. 111, 9995-10000 (2014).\n5. Lehtovirta-Morley, L. E., et al. Identifying Potential Mechanisms\nEnabling Acidophily in the Ammonia-Oxidizing Archaeon \"Candidatus\nNitrosotalea devanaterra\". Appl. Environ. Microb. 82, 2608-2619 (2016).\n6. Williamson, G., et al. A two-lane mechanism for selective biological\nammonium transport. Elife 9, (2020).\n7. Wright, C. L., Lehtovirta-Morley, L. E. Nitrification and beyond:\nmetabolic versatility of ammonia oxidising archaea. ISME J, (2023).\n8. Wang, B., et al. Expansion of Thaumarchaeota habitat range is\ncorrelated with horizontal transfer of ATPase operons. ISME J 13, 3067-\n3079 (2019).\n9. Zhou, J., et al. Effects of acidification on nitrification and\nassociated nitrous oxide emission in estuarine and coastal waters. Nat.\nCommun. 14, 1380 (2023).\n10. Lehtovirta-Morley, L. E. Ammonia oxidation: Ecology, physiology,\nbiochemistry and why they must all come together. Fems Microbiol. Lett.\n365, fny058 (2018).\n11. Martens-Habbena, W., Berube, P. M., Urakawa, H., de la Torre, J. R.,\nStahl, D. A. Ammonia oxidation kinetics determine niche separation of\nnitrifying Archaea and Bacteria. Nature 461, 976-979 (2009).\n12. Koch, H., van Kessel, M., Lucker, S. Complete nitrification: insights\ninto the ecophysiology of comammox Nitrospira. Appl Microbiol\nBiotechnol 103, 177-189 (2019)."}]

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