the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 04 Dec 2025
Microbial role in CO2 fluxes along the river-estuary continuum in a rapidly uplifting catchment of eastern Taiwan
Abstract. The contribution of river metabolisms to carbon cycling is an essential issue, but not well examined in the catchment susceptible to the modulation of active tectonics. This study aims to quantify the rates of autotrophy and heterotrophy, and to identify the community compositions and potential members involved in these microbial processes in the Beinan River in eastern Taiwan. To address this, river water samples were collected in both the wet and dry seasons for incubations amended with 13C-labeled dissolved carbon dioxide and amino acids. The analyses revealed a general pattern pointing to the higher rates in the wet season than in the dry season, and for heterotrophy than for autotrophy. The obtained rates were further scaled up, resulting in the catchment-scale CO2 evasion of ~ 107 mole yr-1, a range constituting several percent of the CO2 flux derived from pyrite-induced weathering, oxidation of petrogenic carbon, and the river-air exchange. The community compositions generally varied with season for most upstream sites and with more abundant sulfur or nitrogen metabolizers in the wet season, as opposed to more abundant phototrophs or heterotrophs in the dry season. This study highlights the complex and dynamic nature of river metabolisms that contribute to carbon evasion in oligotrophic mountainous systems prone to the impacts of rapid uplift and erosion.
- Preprint
(6364 KB) - Metadata XML
-
Supplement
(6178 KB) - BibTeX
- EndNote
Status: final response (author comments only)
-
RC1: 'Comment on egusphere-2025-5904', Anonymous Referee #1, 20 Feb 2026
-
AC1: 'Reply on RC1', Jhennien Chen, 06 Apr 2026
General comments
Comment1: This is a nice and thorough study on metabolic rates in dynamic turbid river systems in a tectonic active area. The use of isotopic labelling of DIC and amino acids in combination with environmental parameter measured in field campaigns and characterisation of the microbial community composition is an impressive achievement. Although at some point it is questionable if all this information in one paper is contributing to the communication of the main message and findings (e.g. are alpha diversity, Shannon index beta diversity needed?). The storyline can be strengthened in the intro and discussion if more focussed hypothesis are formulated.
Reply 1: Thank you for the reviewer’s constructive feedback regarding the focus and storyline of the manuscript. While we agree that the metabolic rates are the primary focus of this study, we have chosen to retain the diversity data (Fig. 4a) as it provides critical evidence for the ecological resilience and metabolic stability of specific sites within this dynamic river system. To address the concern, we have revised the Introduction to provide a better cohesive narrative, specifically investigating whether extreme physical disturbances drive community shifts or if inherent resilience allows for stable carbon transformation. In the revised Discussion (section 4.6), we used these diversity indices to explain two distinct metabolic scenarios: i) seasonal sensitivity at high-sediment MLL01 and LY04 where significant reduction in alpha diversity during the wet season provides the ecological context for more variable metabolic rates; and ii) metabolic versatility at CWL01, where high baseline diversity and a complementary seasonal shift between Cyanobacteria and lithoautotrophs allow for stable carbon transformations despite high turbidity. The revised content (also in the Result, lines 345-348) makes the diversity indices from descriptive statistics into essential mechanistic context for the Beinan River’s carbon cycle.
Comment 2: The objective of this study was to get insights in the role of microbial CO2 fluxes at catchment scale and disentangling the metabolic processes of autotrophic and heterotrophic CO2 exchange in these understudied dynamic mountainous river systems in contrast to the more stable low-land catchments. The sampling strategy and major findings reported (seasonal shifts in metabolic wet-vs dry), although nice, do not seem to fully comply with my view of a study major strength to determine the effect of event-based flushes of groundwater and sediments on CO2 fluxes.
Furthermore, while Wang et al 2024 identified the effects of hot springs on the enrichment of waters with bicarbonate in the same sample locations in the tectonic active Beinan catchment this is not mentioned in this study. Is this not relevant in the total CO2 flux or is it included?
Reply 2: Thank you for the comment on the dynamic nature of the Beinan catchment and the role of groundwater and hot spring inputs. Regarding the sampling strategy, we agree that capturing discrete event-based flushes such as storm-triggered pulses of groundwater and sediment would be an ideal approach when studying CO2 fluxes in mountainous systems. However, the logistical and safety constraints of performing in situ isotope labeling incubations during such extreme flow events is often a challenging consideration to acquire a representative sample. In the mountainous catchments in Taiwan, the high-energy wet season effectively represents a series of frequent event-based flushes that maintain the chronically high turbidity and sediment mobility we observed. Our bi-monthly sampling scheme was designed to establish a systematic seasonal baseline to evaluate metabolic stability across the hydrologic year, capturing the distinct transition between the dry (January - May) and wet (August) seasons of southeastern Taiwan. Notably, the August 2020 sampling coincided with high-turbidity conditions (Fig. S1), providing a representative data point analogous to ‘event-like’ conditions that can be directly contrasted with the lower-flow baseline periods. The sampling strategy has been further clarified in the revised manuscript (lines 120 - 124).
Regarding the potential influence of hot spring inputs, we have carefully evaluated our physicochemical dataset (Table S1) for signatures of hot spring inputs across all sites and seven field campaigns. We observed no localized anomalies in water temperature (T), suggesting that geothermal inputs are primarily limited to the hot spring outcrops, not a prevalent distribution across the whole flow path. While some geochemical signatures (e.g., Cs ions) are sensitive to the detection of hot springs or subsurface high-temperature water-rock interactions in the region (Wang et al., 2024), water chemistry data revealed limited overall contribution of hot spring-sourced ions regardless of seasons. Furthermore, our molecular analyses did not detect a substantial presence of populations specifically originating from hot environments or groundwater. Taken together, geochemical and microbial community data indicate that hot spring or groundwater does not impose a high volumetric contribution to the river water, community composition, metabolism, or even CO2 emission. Their contribution would be more significant at the intersection between hot fluid and river water. Nevertheless, the level of hot spring or groundwater contribution to river metabolism and community composition cannot be quantitatively constrained with the current experimental and sampling framework, and beyond the scope of the current investigation.
Comment 3: What about redox situation in the streams influenced by tectonics? Is CH4 exchange not relevant?”
Reply 3: Thank you for the question regarding the redox status and the potential relevance of CH4 exchange. Our unpublished in situ measurements conducted later (2023-2025) indicate that the Beinan River remains in a consistently oxidizing state, with oxidation-reduction potentials (ORP) staying positive across all sites (+18 to +238 mV). This is consistent with the fact that in such high-energy, turbulent mountainous streams, the steep topography facilitates rapid and continuous re-aeration, maintaining aerobic conditions throughout the water column. Methanogenesis typically requires strongly reducing conditions (negative ORP), which are absent in this system due to the lack of stagnant zones and frequent sediment scouring. Furthermore, the chronically low organic substrate availability in this catchment further limits the potential for microbial fermentation. Consequently, we consider CH4 exchange to be a negligible component of the total carbon efflux compared to the dominant CO2 evasion. Overall, the fundamental physical characteristics of the Beinan River - its steep gradient, high turbulence, and frequent sediment scouring - have enabled persistently aerobic conditions through time across the catchment.
Specific comments
Comment 4: Abstract line 18 “several percent” please be more specific here. 107mol yr-1 from microbial origin vs annual total emission across the catchment 2,6 .109 ? (Line 295).”
Reply 4: Thank you for the comment. We have updated the Abstract and the main text (lines 21 and 332-334) to provide the specific percentage as requested. Based on our scaled estimates, the total microbial net emission is 7.89 x 107 mol yr-1 (summed from individual tributaries in Table S5). When compared to the total annual CO2 emission across the catchment calculated in Table S6 (average of 2.08 x 109 mol yr-1 across the five sampling campaigns), the microbial contribution represents approximately 3.8% of the total catchment-scale CO2 evasion. We have replaced ‘several percent’ with ‘approximately 3.8%’ to be more precise.
Comment 5: Line 38 landscape controlled or is it more topographically controlled?”
Reply 5: Thank you for the comment. We have modified the sentence (line 42) to state that CO2 flux is primarily topographically controlled in this study. In the upstream reaches of the Beinan River, the steep topographic relief and rugged channel morphology increase turbulence and gas transfer velocities, leading to the higher CO2 evasion rates observed compared to the lower-gradient downstream reaches.
Comment 6: Line 68 term “individual metabolisms” needs more clarification.
Reply 6: Thank you for the comment. We have clarified the term “individual metabolism” in the revised manuscript (lines 74-77). It refers to the distinct metabolic pathways of carbon transformation quantified in this study, including photoautotrophy (light-dependent DIC fixation), chemoautotrophy (dark DIC fixation), and heterotrophy (mineralization of dissolved organic carbon).
Comment 7: Lines 59-61 The information in this sentence is essential why this study in a rapid uplifting area is so different from the dominant body of literature in this field which is performed on the cratonic continent. While this relation between tectonics and torrential precipitation is a probably obvious for the authors this is not evident for everyone. It would help the storyline if this is relation between tectonics and dynamic, turbid high energy river systems is more explicit.
The expected deviations from the general bentic and hyporheic processes due to the turbid and dynamic river systems can be formulated more explicitely in hypothesis which will give ther reader more guidelines for interpretation in the result section.
Reply 7: Thank you for the comment. We have revised the Introduction (lines 72-74) to explicitly contrast our study system with the cratonic continental systems commonly addressed in the literature. We hypothesized that the intense geological and hydrological impacts of a rapid-uplift area create a physically unstable river environment. In such high-energy systems, the planktonic community constitutes a measurable and vital component of aquatic metabolism. Nevertheless, our incubation experiments only determine the metabolic activities and assemblages of planktonic communities. To constrain benthic and hyporheic processes and metabolic activities, oxygen sensors have to be deployed in the river channel. Once the activities of whole river metabolisms are deduced, the sum of metabolic activities in benthic and hyporheic processes can be constrained by subtracting the activities of planktonic metabolisms (like this study) from the whole activities. The deployment of sensors in turbid environments like those during high water time in Taiwan would be logistically challenging and beyond the scope of the current study.
Comment 8: Line 107-108, The selection of the 5 sample locations along the Beinan rver and tributaries is not explained. Which criteria were used to determine these sample sites? Likewise no argumentation is provided for the selection of sample moments/ timing. As the dynamic nature of the Beinan river is a part of the research objective the regular bi-monthly sampling scheme is surprising. One would expect a focus on events ( hot moments) and baseline moments.
Reply 8: Thank you for the comment. We have revised the text (lines 119-124) to explicitly state that the five stations were chosen to represent individual sub-basins of the catchment and a topographic gradient that captures the transition from metamorphic headwater tributaries to the main stem. This sampling design allows us to observe how shifts in lithology and catchment characteristics influence microbial community structure and metabolic rates.
Regarding the sampling frequency, while we acknowledge the importance of “hot moments” (e.g., extreme storm events), our objective was to establish a systematic seasonal baseline to evaluate metabolic stability across the hydrologic year in such a dynamic system. The bi-monthly scheme was designed to capture the first-order seasonal transition between the dry (January - May) and wet (August) seasons of southeastern Taiwan. As discussed in reply 2, performing in situ isotope labeling incubations during peak storm discharge presents substantial logistical and safety challenges. Notably, the August 2020 sampling coincided with high-turbidity conditions, providing a valuable “event-like” data point to contrast with lower-flow baseline periods (Figure S1). While event-focused sampling would enable us to resolve the highly temporal variations in metabolic activity in response to extreme hydrological and geomorphic processes, our current sampling design and interval offer the characterization of the metabolic activities representative for the investigated sub-basins over a yearly time scale. We are aware that the current dataset represents temporal snapshots of the planktonic metabolisms in a highly dynamic catchment. A long-term plan (probably at a decadal scale) of sampling and analysis would better resolve the variation in ecosystem function and activity and how hydrological and geological processes shape such variations.
Comment 9: Line 115. The use of cellulose membranes is not common practice and strongly discouraged in research on carbon dynamics due to the risks of contamination. Especially for DOC determination. The same is true for the use of polypropylene sample containers (risk of DOC contamination).
Reply 9: Thank you for the comments on these technical details. We have clarified the materials for sampling and processing in the revised manuscript (lines 130-134) to correct these typos. The filters used were actually made of Supor® (Hydrophilic Polyethersulfone, PES), not cellulose. PES is a low-protein-binding material widely preferred in aquatic biogeochemistry for its minimal organic leaching and high flow rates. DOC samples were collected and stored in high-density polyethylene (HDPE) bottles, rather than polypropylene. To ensure data quality, all HDPE bottles were pre-cleaned (acid-washed and triple-rinsed with Milli-Q water). We also conducted procedural blanks by processing Milli-Q through the entire filtration and storage assembly. The resulting DOC concentrations in these blanks were consistently below the limit of detection, confirming that our materials did not introduce carbon contamination.
Comment 10: Line 386: is this influenced or correlation based?
Reply 10: Thank you for the comment. The statement regarding the relationship between DIC uptake rates and environmental variables (TSM, POC, δ¹³C-POC, and ammonium) was based on Pearson correlation analysis (p < 0.05). To be more precise, we have rewritten the sentence (line 429) to state that these rates were significantly correlated with these parameters, rather than ‘influenced’ by them.
Comment 11: Figure 3 needs a more elaborate figure caption.
Reply 11: Thank you for the comment. We have significantly expanded the figure caption to clearly define the metabolic pathways represented in each panel: light/dark DIC uptake (autotrophy) and amino acid uptake/catabolism (heterotrophy). We have also added a note explaining that the error bars represent the standard deviation of triplicate measurements.
-
AC1: 'Reply on RC1', Jhennien Chen, 06 Apr 2026
-
RC2: 'Comment on egusphere-2025-5904', Anonymous Referee #2, 16 Mar 2026
The manuscript describes the role of microbial C assimilation and respiration along a Taiwanese river continuum.
The experimental work was conducted at seven timepoints for five locations. For four of them, phototrophy and autotrophy were determined using 13-DIC and two of these also quantified heterotrophy using two 13C-labelled amino acids (1 x summer, 1 x winter). These detailed 13C studies are complemented by environmental parameters and geochemical analyses at all campaigns and 16S rRNA analyses at five. The combination of 13C-labelling and genomic analyses represents highly complementary methodology that can bring new insight into the contribution of the riverine microbial community to CO2 flux from such a system.
Major concerns
The manuscript lacks hypotheses and the introduction and later discussion would benefit from, especially to interlink the different datasets.
The labelling design for the DIC aims to enrich to ~5% of the DIC pool, which is consistent with wider stable isotope probing approaches to provide sufficient enrichment while minimising perturbation of the system by excess increase of the pool. However, the extent of addition for the amino acids does not follow this. A final concentration of 50 µM when the DOC concentration (which is made up of much more than just free amino acids) is between 20-80 µM represents a significant increase in the pool size. While it is acknowledged that rates determined may be stimulated as a result (line 139-140) and I appreciate that rates were still often below detection limits, this very important caveat is lost later in the manuscript. This is especially important to consider when these values are then scaled up to the catchment level but could hugely overestimate actual metabolic fluxes from organic pools. These limitations need to be made much clearer both in the methodology and when you upscale these values to a catchment level.
Furthermore, there is no consideration of co-added N for the amino acids (C:N 2:1 glycine; 6:1 leucine) which may also influence the diverging processing of the two amino acids (i.e. if being utilised for N and C is largely being respired, glycine is a much more efficient resource to use than leucine). How the underlying biochemistry may control the observed rates is severely lacking, but important rationale given you select leucine to represent a conservative estimate of heterotrophic activity without any rationale other than it is lower (line 221-222). At points (e.g. Line 444-447), it is implied there is direct uptake but there is no evidence to support this. Finally have the authors considered that only one C position was labelled for both amino acid forms?
Minor comments:
The abstract needs to expand briefly on the rationale behind this work.
Line 11: “issue” is vague – is problem or unknown more precise?
Line 15: higher rates of what? Both hetero and autotrophy? Add some values / relative differences of these rates into the abstract.
Line 72: states that the Beinan River has some of the highest sediment exports and weathering rates in Taiwan. It would be beneficial to put this on a larger context beyond country-specific for an international journal.
Line 74: why were leucine and glycine selected?
Line 154: Should be ICP-MS
Line 178: Formatting of equation is strange
Line 183-184: Why was the instream δ13C not measured? This is an important end member.
Line 250: TSM should be defined at first time of use in main text (only defined in footnote of subsequent table).
Line 254: “smaller” to indicate more negative delta values should be replaced by terminology like “more depleted” as is used in other areas of the MS.
Line 255-256: This states that ammonium was higher in the wet season than the dry season however this was not significant based on Table 2 – please clarify. If it is the case it is as some sites, they could be bold in the table rather than just the heading to provide these site-specific differences.
Figure 3: due to some very high values, it is very difficult to see the low values in panel b and c; in caption state replication level and what the error bars represent (standard error of the mean? Standard deviation?). Also need to state limit of detection.
The statistical analyses need to be improved as only applied to river chemistry data.
Line 356: You call this organic matter degradation – but you have only looked at final mineralisation step of these processes. Depolymerisation is generally considered the rate limiting step, so the fluxes quantified only reflect one, generally very rapid, stage of organic matter degradation.
Line 359-370: There are a lot of assumptions or suggestions here just for ammonium concentrations which are point measurements only that is limited to determine in situ production especially when high additions of organic nitrogen may influence ammonification rates. The production rates are more potential rather than true so this caveat must be extended to residence times.
Line 401-410: were there any links with the community observed i.e. riverine vs. soil? More primary producers downstream where input from surrounding catchment is relatively less and more processed/recalcitrant therefore rely more on primary producers? Currently only consider the nutrients as a control here but community may also reflect this and provide additional support to this suggestion.
Citation: https://doi.org/10.5194/egusphere-2025-5904-RC2 -
AC2: 'Reply on RC2', Jhennien Chen, 06 Apr 2026
Major comment
Comment 1: The manuscript lacks hypotheses and the introduction and later discussion would benefit from, especially to interlink the different datasets.
Reply 1: Thank you for the comment. We have restructured the final paragraph of the Introduction to integrate testable hypotheses with the more explicit objectives. Specifically, we hypothesized that i) the seasonal transition to the wet season (characterized by higher temperatures, elevated nutrient availability, and increased particulate organic matter loading) would broadly shift planktonic metabolic activity, particularly phototrophic/autotrophic and heterotrophic production and respiration; and ii) these seasonal hydrological changes would be also reflected in shifts in microbial community composition; iii) in this physically unstable, chronically turbid catchment, frequent sediment mobility would drive temporal variability in planktonic metabolic rates and their relative contribution to the riverine CO2 evasion.
Comment 2: The labelling design for the DIC aims to enrich to ~5% of the DIC pool, which is consistent with wider stable isotope probing approaches to provide sufficient enrichment while minimising perturbation of the system by excess increase of the pool. However, the extent of addition for the amino acids does not follow this. A final concentration of 50 µM when the DOC concentration (which is made up of much more than just free amino acids) is between 20-80 µM represents a significant increase in the pool size. While it is acknowledged that rates determined may be stimulated as a result (line 139-140) and I appreciate that rates were still often below detection limits, this very important caveat is lost later in the manuscript. This is especially important to consider when these values are then scaled up to the catchment level but could hugely overestimate actual metabolic fluxes from organic pools. These limitations need to be made much clearer both in the methodology and when you upscale these values to a catchment level.
Reply 2: Thank you for the comment. The relationship between substrate concentration and microbial activity has been formulated by Michaelis-Menten kinetics (Wright and Hobbie 1966) and applied to numerous laboratory and field studies. In this framework, the uptake or metabolic rate (V) is a saturating function of substrate concentration [S]: V = Vmax ∙ [S]/ (Km + [S]). Km is the [S] at which the reaction rate is half of Vmax. When [S] >> Km, V approaches Vmax regardless of further increase in concentration, and the community operates near its maximum metabolic capacity. When [S] << Km, V scales nearly linearly with [S] and better reflects possible rates for the target setting. The critical question, therefore, is whether such a relationship and its embedded parameters can be universally applied or valid for most natural settings.
We set out to examine and compare the previously studied examples with our settings. By searching for the literature, we found out that published Km values for low-molecular-weight organic substrates in freshwater and marine systems are consistently in the sub-µM to low-µM range. For example, Brailsford et al. (2019) reported Km values of 1.41 µM for amino acid mixtures with a corresponding Vmax of 0.0004 µmol ml-1 h-1 (4.8 mg-C m-3 h-1) for oligotrophic river water. Boysen et al. (2022) reported half-saturation constant (Kt+S) of 11-79 nM for glycine betaine in oligotrophic North Pacific communities, with Vmax of 0.36 - 0.56 nM h-1 (0.022 - 0.034 mg-C m-3 h-1). By applying the relationships for these two settings, our added substrate concentrations (50 µM) well exceeded the Km values for both cases. Therefore, the projected rates would all reach respective Vmax values. Our highest uptake rates spanned from 0.49 mg-C m-3 h-1 for glycine to 0.12 mg-C m-3 h-1 for leucine, a range falling between these two reported Vmax values by a great margin. The results suggest that the reported kinetics cannot accurately project our field conditions. Alternatively, our rates were input into the kinetic relationships to derive the possible effective substrate concentrations for the target metabolisms. Since our rates were much greater and less than the Vmax values for marine and freshwater settings, respectively, such computation practice can only be applied to the freshwater kinetics. Our calculation revealed that the potential effective substrate concentrations were 0.16 µM for glycine and 0.036 µM for leucine. Such a range of substrate concentrations was well below our added concentrations, suggesting that our experimental setting may not stimulate the target metabolic activity as generally thought.
Nevertheless, the comparison between our results with previous studies suggests a likelihood that our amendment of amino acids may not necessarily stimulate microbial activity. However, the comparison also highlights that each system or setting may be inherited with its specific kinetic relationships. A dedicated experiment amended with a series of substrate concentrations and rate measurements is required. We therefore cannot confidently assert that our measured rates approximate true in situ fluxes, and we agree with the reviewer that the catchment-scale estimates could be reframed since the experimental constraints could be placed as the maximum potential metabolic rates. That is to say, the relative contribution of water column metabolic rate to the total CO2 evasion would be even less than our original estimates.
We have incorporated these lines of discussion into the revised form in section 4.5 (lines 541-553) to justify the amended concentration and its implications for the contribution of planktonic metabolic rates to the catchment-scale CO2 evasion.
Comment 3: Furthermore, there is no consideration of co-added N for the amino acids (C:N 2:1 glycine; 6:1 leucine) which may also influence the diverging processing of the two amino acids (i.e. if being utilised for N and C is largely being respired, glycine is a much more efficient resource to use than leucine). How the underlying biochemistry may control the observed rates is severely lacking, but important rationale given you select leucine to represent a conservative estimate of heterotrophic activity without any rationale other than it is lower (line 221-222). At points (e.g. Line 444-447), it is implied there is direct uptake but there is no evidence to support this. Finally have the authors considered that only one C position was labelled for both amino acid forms?
Reply 3: Thank you for the comment raising the consideration of difference in C:N ratio between the two amino acids (2:1 for glycine and 6:1 for leucine). While both molecules provide a single nitrogen atom, glycine provides substantially higher nitrogen-to-carbon density. In principle, this stoichiometric advantage makes glycine a more efficient nitrogen source in nitrogen-limited systems, as bacteria can acquire nitrogen while respiring fewer carbon bonds. However, dissolved inorganic nitrogen concentrations in the Beinan River (TIN: 5.7 - 30.6 µM; NH4+: 2.6 - 4.9 µM in average, Table 1) suggest that strict nitrogen limitation is unlikely to be the sole driver of glycine preference in this system. Instead, the observed preference likely reflects the greater metabolic efficiency of glycine-centered pathways: the reductive glycine pathway (rGlyP) supports biomass yields up to 17% higher than other carbon assimilation cycles (Dronsella et al., 2025). Furthermore, the glycine cleavage system (GCS) enables rapid, streamlined energy generation from a simple 2-carbon substrate (Kikuchi et al., 2008). Thus, while favorable C:N stoichiometry may contribute under episodically nitrogen-limited conditions, the metabolic efficiency of glycine metabolism provides a more consistent explanation for the observed preference. We have incorporated this discussion into the manuscript (Discussion section 4.4, lines 508-519).
The contrasting metabolic fate of leucine further supports its use as a conservative baseline for heterotrophic activity. In aquatic microbial ecology, leucine is the standard proxy for bacterial protein synthesis (Kirchman et al., 1985; Kirchman, 2001). As a branched-chain amino acid, leucine catabolism requires at least six enzymatic reactions: transamination to ⍺-ketoisocaproate, oxidative decarboxylation to isovaleryl-CoA, followed by sequential dehydrogenation, carboxylation, hydration, and cleavage steps to ultimately yield acetyl-CoA and acetoacetate for entry into the TCA cycle (Massey et al., 1976; Brosnan and Brosnan, 2006) - requiring substantial enzymatic machinery including biotin-dependent carboxylases and multiple CoA-dependent enzymes. Consequently, bacteria typically prioritize leucine incorporation into proteins rather than respiratory oxidation (Kirchman, 2001). This anabolic preference means that leucine-based rate estimates are inherently conservative relative to glycine. By scaling catchment CO2 evasion estimates using these lower leucine-derived rates, we provided a minimum boundary for the contribution of planktonic metabolism to the total carbon flux, ensuring the implications were not overestimated by transient responses to high substrate availability. The rationale for choosing leucine has been stated in the Discussion (lines 515-519).
Regarding the labeling position, we are well aware that only the C-1 (carboxyl) position was 13C-labeled for both amino acids (indicated in line 202). The C-1 carboxyl group is typically the first carbon released as CO2 during initial catabolic steps, including oxidative deamination and decarboxylation reactions (Massey et al., 1976; Kikuchi et al., 2008). Therefore, our 13C-DIC measurements specifically track the initial oxidative decarboxylation potential of these substrates, representing the immediate respiratory capacity of the microbial community. Our 13C-biomass measurements also track incorporation of the C-1 position into cellular material; however, if cells preferentially remove the carboxyl group before assimilating the remaining carbon skeleton, assimilation rates may represent minimum estimates. Altogether, as glycine is more readily metabolized and carboxyl positioned carbon is efficiently channeled into the production of CO2, the incubations amended with these two amino acids offer to bracket the possible range of metabolic activity for natural communities. We note that the selection of substrates at specific concentrations for incubations very likely deviates the detection from true rates. In fact, static incubations like this were conducted under conditions different from the field variation and setting at various degrees (e.g., constant luminance intensity and exemption of water flowing so the nutrient and substrate concentrations were not fixed). We were limited to mimic every parameters that may precisely fit in situ conditions. We have clarified the implications of C-1 labeling in the Materials and Methods section (lines 151-152).
Minor comment
Comment 4: The abstract needs to expand briefly on the rationale behind this work.
Reply 4: Thank you for the comment. We have expanded the abstract to provide a clearer rationale. We now explicitly state that in high-energy tectonic catchments, physical instability and turbidity likely constrain planktonic metabolic processes, which makes the quantification of contribution of planktonic metabolisms a critical but missing piece of the regional carbon budget.
Comment 5: Line 11: “issue” is vague – is problem or unknown more precise?
Reply 5: Thank you for the comment. We have revised the sentence to remove the vague term “issue”.
Comment 6: Line 15: higher rates of what? Both hetero and autotrophy? Add some values / relative differences of these rates into the abstract.
Reply 6: Thank you for the comment. We have revised the abstract to clarify what “higher rates” were referred to and to include representative values: Autotrophic DIC uptake rates ranged from 0.03 - 1.98 mg-C m-3 h-1, with higher values observed in the wet season. Heterotrophic amino acid assimilation rates were comparatively lower (0.004-0.49 mg-C m-3 h-1), yet heterotrophic catabolic rates were dramatically higher than autotrophic rates by one to two orders of magnitude (4.6 - 154.5 mg-C m-3 h-1), indicating that the riverine microbial community is strongly heterotrophic and driven primarily by respiratory carbon oxidation rather than biosynthesis. We have revised the abstract to state that heterotrophic catabolic rates exceeded autotrophic carbon fixation rates by one to two orders of magnitude, with both processes showing higher activity in the wet season than the dry season.
Comment 7: Line 72: states that the Beinan River has some of the highest sediment exports and weathering rates in Taiwan. It would be beneficial to put this on a larger context beyond country-specific for an international journal.
Reply 7: Thank you for the comment. We have revised the Introduction (lines 80-83) to place the Beinan River in a global context. We highlighted that the sediment yields in this system exceed the global average by one to two orders of magnitude (Hilton and West 2020), characterizing it as a critical global hotspot for land-to-ocean carbon transport.
Comment 8: Line 74: why were leucine and glycine selected?
Reply 8: Thank you for the question. Glycine and leucine were selected because they represent dissolved free amino acids (DFAA), the most readily bioavailable fraction of the dissolved organic matter (DOM) pool, which can be directly transported across cell membranes without requiring extracellular enzymatic hydrolysis (Kirchman, 2001). Beyond this general rationale, these two amino acids were specifically chosen because they represent contrasting ends of heterotrophic metabolism. Leucine is the established standard proxy for bacterial protein synthesis in aquatic microbial ecology (Kirchman et al., 1985; Kirchman, 2001), and its complex catabolic pathway makes it predominantly anabolic, providing a conservative lower bound for heterotrophic activity. Glycine, as the simplest amino acid with a favorable C:N ratio (2:1 vs. 6:1 for leucine), can be rapidly processed through streamlined pathways including the glycine cleavage system (GCS) and reductive glycine pathway (rGlyP) (Kikuchi et al., 2008; Dronsella et al., 2025), making it representative of the more labile, rapidly cycling fraction of the DFAA pool. Together, the two substrates allow us to bracket the range of heterotrophic strategies - from conservative protein synthesis to rapid catabolic turnover - within the riverine microbial community. We have incorporated part of this argument into the text.
Comment 9: Line 154: Should be ICP-MS
Reply 9: Thank you for this correction. We have corrected the text (line 177) to use the standard abbreviation ICP-MS.
Comment 10: Line 178: Formatting of equation is strange
Reply 10: Thank you for the comment. We have revised the formatting of the equation (line 201) to ensure it follows standard typesetting conventions. The new layout clearly displays the fraction components and the relationship between isotopic enrichment in the various carbon pools, making the calculation of uptake and catabolic rates more intuitive for the reader.
Comment 11: Line 183-184: Why was the instream δ13C not measured? This is an important end member.
Reply 11: Organic degradation imparts limited isotopic fractionation on the products, such as DOC and DIC. Therefore, their isotopic compositions resemble those of parental organic matter. In our system, major sources of organic matter are C3 plants and petrogenic organic carbon. Their isotopic compositions ranged from –29 to –25 permil (Lamb et al., 2006) and from –22 to –20 permil (Lien et al., 2025), respectively. Since petrogenic organic matter is more recalcitrant or polymerized, its degradation and conversion to DOC and DIC would be much slower when compared with soil organic matter. Its export to the river would also take a longer and more strenuous path through subsurface rock fabrics and geological structures in metamorphic terranes like in this study. Therefore, only the DOC pool produced from C3 plants is assumed to be linked to the biologically available pool related to this incubation experiment. Based on this, we consider and assume that the isotopic compositions of the DOC pool in our setting would resemble the typical C3 plant.
Comment 12: Line 250: TSM should be defined at first time of use in main text (only defined in footnote of subsequent table).
Reply 12: Thank you very much for the comment. We have revised the manuscript (line 278) to define TSM (total suspended matter) at its first occurrence in the main text. This ensures the abbreviation is clearly established before its subsequent use in the Results and Discussion sections.
Comment 13: Line 254: “smaller” to indicate more negative delta values should be replaced by terminology like “more depleted” as is used in other areas of the MS.
Reply 13: “Smaller” is referred to the delta values, while “more depleted” is referred to the relative abundances of 13C and 12C in the designated entity. Either way is accepted and suitable in terminology usage. Nevertheless, we have revised this (line 282) to accommodate the convention commonly used by part of the isotope community.
Comment 14: Line 255-256: This states that ammonium was higher in the wet season than the dry season however this was not significant based on Table 2 – please clarify. If it is the case it is as some sites, they could be bold in the table rather than just the heading to provide these site-specific differences.
Reply 14: Thank you very much for identifying this discrepancy. This was an error when formatting Table 2. The ammonium concentrations were indeed significantly different between the wet and dry seasons across the catchment, as confirmed by a Wilcoxon test (p < 0.05). We have revised the header in Table 2 to accurately reflect this statistical significance.
Comment 15: Figure 3: due to some very high values, it is very difficult to see the low values in panel b and c; in caption state replication level and what the error bars represent (standard error of the mean? Standard deviation?). Also need to state limit of detection.
The statistical analyses need to be improved as only applied to river chemistry data.
Reply 15: Thank you for the comments. We have revised Figure 3 accordingly. To better present the low values in panels (b) and (c), we have added insets showing the same data on a logarithmic scale. This allows both the high and low values to be clearly visualized. We also updated the caption to state the replication level (n=3 for all light and amino acid incubations) and to clarify that error bars represent standard deviation. For panel (b), dark incubations were conducted with a single measurement per site per date (n=1). Values below zero were not shown in the figure. The statistical analyses have been conducted for the data, and annotated specifically in the figure. Relevant methodological details, statistical analyses, and interpretation of these patterns have been incorporated in the revised manuscript.
Comment 16: Line 356: You call this organic matter degradation – but you have only looked at final mineralisation step of these processes. Depolymerisation is generally considered the rate limiting step, so the fluxes quantified only reflect one, generally very rapid, stage of organic matter degradation.
Reply 16: Thank you for this clarification. We agree that our incubation-derived rates specifically capture the terminal mineralization of dissolved free amino acids rather than the complete organic matter degradation process including depolymerization. The rates cited at line 356 were used as a proxy to support the broader inference of in situ organic degradation, which is primarily evidenced by the geochemical signatures in river water - particularly the elevated ammonium concentrations in the wet season - rather than being claimed as direct measurements of bulk organic matter degradation. We have revised the text to clarify that these rates reflect amino acid mineralization specifically, representing one component of the overall heterotrophic degradation process, and that the rate-limiting depolymerization step is not directly constrained by our measurements (lines 395-396).
Comment 17: Line 359-370: There are a lot of assumptions or suggestions here just for ammonium concentrations which are point measurements only that is limited to determine in situ production especially when high additions of organic nitrogen may influence ammonification rates. The production rates are more potential rather than true so this caveat must be extended to residence times.
Reply 17: Thank you for the comment. Ammonium concentrations reported here indeed represent point measurements. It can be produced from the degradation of soils and petrogenic organic matter on hillslopes or within the river water. As the transit of river water from upstream to downstream is rapid, the in situ degradation for the production of ammonium within river water would be likely limited. Instead, its production and accumulation in the pores of soil and fractures of rock are volumetrically advantageous over the riverine degradative metabolisms. During high water periods, high hydraulic gradients and runoff enable more efficient transport and export of the ammonium pool into the river, enhancing riverine ammonium concentrations. It is also likely that higher ammonium abundances may stimulate autotrophic ammonium oxidation. However, we do not have definitive evidence to attribute the observed higher DIC rate to the enhanced nitrification activity. The estimate of residence time required to generate the observed ammonium concentration would be even longer if our utilized degradation rates are lower than our measurements. Nevertheless, our quantitative assessment on the potential residence time of in situ production of ammonium from degradation of suspended particulates was based on parameters we collected in this study. While the likelihood of in situ production of riverine ammonium within the river water is low, we are not able to completely rule out this speculation. We have revised the discussion to acknowledge that the residence time calculations are based on the observed production rates and therefore represent lower-bound estimates rather than definitive constraints on in situ nitrogen accumulation (lines 402-406).
Comment 18: Line 401-410: were there any links with the community observed i.e. riverine vs. soil? More primary producers downstream where input from surrounding catchment is relatively less and more processed/recalcitrant therefore rely more on primary producers? Currently only consider the nutrients as a control here but community may also reflect this and provide additional support to this suggestion.
Reply 18: Thank you for the comment. We agree that riverine organic matter downstream may have experienced multiple processes of degradation along the transit. Therefore, they would become more recalcitrant, precluding themselves from further utilization or respiration. Under this context, heterotrophy downstream could be better sustained with the proliferation of in situ phototrophy in downstream. Examination of the 16S rRNA gene community data (Fig. 4b) reveals that BNE is predominantly characterized by heterotrophic taxa - particularly Bacteroidota, Firmicutes, and Gammaproteobacteria - throughout most sampling periods, rather than showing elevated abundance of primary producers such as Cyanobacteria. Therefore, it would be challenging to directly attribute such a DNA-based community composition to the stimulation of phototrophic activity driven by the limited accessibility of recalcitrant organic matter for heterotrophy. We acknowledge that the potential disconnect between community composition and measured rates at BNE - as noted in Section 4.6 - suggests that numerically abundant community members are not equivalently metabolically expressed, and that RNA-based analyses would be needed to identify the truly active phototrophic populations. Regarding the potential influence of soil-derived vs. riverine communities and upstream inputs from agriculture and human activity, while these are plausible contributors to the nutrient accumulation pattern observed at BNE, our current dataset does not include sufficient spatial resolution of terrestrial inputs to quantitatively constrain these contributions. We noted this as a direction for future investigation.
References
Boysen, A. K., Durham, B. P., Kumler, W., Key, R. S., Heal, K. R., Carlson, L. T., Groussman, R. D., Armbrust, E. V., and Ingalls, A. E.: Glycine betaine uptake and metabolism in marine microbial communities, Environmental Microbiology, 24, 2380–2403, https://doi.org/10.1111/1462-2920.16020, 2022.
Brailsford, F. L., Glanville, H. C., Golyshin, P. N., Johnes, P. J., Yates, C. A., and Jones, D. L.: Microbial uptake kinetics of dissolved organic carbon (DOC) compound groups from river water and sediments, Sci Rep, 9, 11229, https://doi.org/10.1038/s41598-019-47749-6, 2019.
Brosnan, J. T. and Brosnan, M. E.: Branched-Chain Amino Acids: Enzyme and Substrate Regulation, The Journal of Nutrition, 136, 207S-211S, https://doi.org/10.1093/jn/136.1.207S, 2006.
Dronsella, B., Orsi, E., Schulz-Mirbach, H., Benito-Vaquerizo, S., Yilmaz, S., Glatter, T., Bar-Even, A., Erb, T. J., and Claassens, N. J.: One-carbon fixation via the synthetic reductive glycine pathway exceeds yield of the Calvin cycle, Nat Microbiol, 10, 646–653, https://doi.org/10.1038/s41564-025-01941-9, 2025.
Kikuchi, G., Motokawa, Y., Yoshida, T., and Hiraga, K.: Glycine cleavage system: reaction mechanism, physiological significance, and hyperglycinemia, Proc. Jpn. Acad., Ser. B, 84, 246–263, https://doi.org/10.2183/pjab.84.246, 2008.
Kirchman, D.: Measuring bacterial biomass production and growth rates from leucine incorporation in natural aquatic environments, in: Methods in Microbiology, vol. 30, Elsevier, 227–237, https://doi.org/10.1016/S0580-9517(01)30047-8, 2001.
Kirchman, D., K’nees, E., and Hodson, R.: Leucine incorporation and its potential as a measure of protein synthesis by bacteria in natural aquatic systems, Appl Environ Microbiol, 49, 599–607, https://doi.org/10.1128/aem.49.3.599-607.1985, 1985.
Dronsella, B., Orsi, E., Schulz-Mirbach, H., Benito-Vaquerizo, S., Yilmaz, S., Glatter, T., Bar-Even, A., Erb, T. J., and Claassens, N. J.: One-carbon fixation via the synthetic reductive glycine pathway exceeds yield of the Calvin cycle, Nat Microbiol, 10, 646–653, https://doi.org/10.1038/s41564-025-01941-9, 2025.
Hilton, R. G. and West, A. J.: Mountains, erosion and the carbon cycle, Nat. Rev. Earth Environ., 1, 284–299, https://doi.org/10.1038/s43017-020-0058-6, 2020.
Kikuchi, G., Motokawa, Y., Yoshida, T., and Hiraga, K.: Glycine cleavage system: reaction mechanism, physiological significance, and hyperglycinemia, Proc. Jpn. Acad., Ser. B, 84, 246–263, https://doi.org/10.2183/pjab.84.246, 2008.
Kirchman, D.: Measuring bacterial biomass production and growth rates from leucine incorporation in natural aquatic environments, in: Methods in Microbiology, vol. 30, Elsevier, 227–237, https://doi.org/10.1016/S0580-9517(01)30047-8, 2001.
Kirchman, D., K’nees, E., and Hodson, R.: Leucine incorporation and its potential as a measure of protein synthesis by bacteria in natural aquatic systems, Appl Environ Microbiol, 49, 599–607, https://doi.org/10.1128/aem.49.3.599-607.1985, 1985.
Lamb, A. L., Wilson, G. P., and Leng, M. J.: A review of coastal palaeoclimate and relative sea-level reconstructions using δ13C and C/N ratios in organic material, Earth-Science Reviews, 75, 29–57, https://doi.org/10.1016/j.earscirev.2005.10.003, 2006.
Lien, W.-Y., Chen, C.-T., Lee, Y.-H., Su, C.-C., Wang, P.-L., and Lin, L.-H.: Two-stage oxidation of petrogenic organic carbon in a rapidly exhuming small mountainous catchment, Commun. Earth Environ., 6, 45, https://doi.org/10.1038/s43247-025-02015-8, 2025.
Massey, L. K., Sokatch, J. R., and Conrad, R. S.: Branched-Chain Amino Acid Catabolism in Bacteria, BACTERIOL. REV., 40, 1976.
Wright, R. T. and Hobbie, J. E.: Use of glucose and acetate by bacteria and algae in aquatic ecosystems. Ecol., 47, 447–464, https://doi.org/10.2307/1932984, 1966.
-
AC2: 'Reply on RC2', Jhennien Chen, 06 Apr 2026
Viewed
| HTML | XML | Total | Supplement | BibTeX | EndNote | |
|---|---|---|---|---|---|---|
| 263 | 185 | 36 | 484 | 36 | 22 | 27 |
- HTML: 263
- PDF: 185
- XML: 36
- Total: 484
- Supplement: 36
- BibTeX: 22
- EndNote: 27
Viewed (geographical distribution)
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
Review Biogeosciences 2025-5904
Microbial role in CO2 fluxes along the river-estuary continuum in a rapidly uplifting catchment of eastern Taiwan.
Chen et al.
This manuscript describes the role of microbial CO2 fluxes along a river-estuary continuum in Taiwan. The specific situation with an uplifting catchment because of active tectonics is identified as a unique feature of this catchment study.
This study can reduce the bias towards river continuum studies in low altitude; temperate catchment as opposed to mountainous catchments. However, apart from better data coverage of CO2 exchange from mountainous catchments it is important to stress that insights this study, performed in a unique tectonic situation and related dynamic turbid river systems can bring new insights to the current field of river metabolism studies.
The study aims to disentangle assimilation rates and respiration rates of different metabolisms with 13C labelling techniques using DIC and amino acids. Furthermore, microbial community compositions were determined based on 16S rRNA gene tags. Sampling took place at different sampling sites in the Beinan river system. Specific metabolic rates were measured during four of seven field campaigns.
The results of this study give an indication of the size of the yearly CO2 evasion of the whole catchment and show seasonal shifts in the dominant metabolism in upstream systems waters between the wet and dry season.
General comments
This is a nice and thorough study on metabolic rates in dynamic turbid river systems in a tectonic active area. The use of isotopic labelling of DIC and amino acids in combination with environmental parameter measured in field campaigns and characterisation of the microbial community composition is an impressive achievement. Although at some point it is questionable if all this information in one paper is contributing to the communication of the main message and findings (e.g. are alpha diversity, Shannon index beta diversity needed?). The storyline can be strengthened in the intro and discussion if more focussed hypothesis are formulated.
The objective of this study was to get insights in the role of microbial CO2 fluxes at catchment scale and disentangling the metabolic processes of autotrophic and heterotrophic CO2 exchange in these understudied dynamic mountainous river systems in contrast to the more stable low-land catchments. The sampling strategy and major findings reported (seasonal shifts in metabolic wet-vs dry), although nice, do not seem to fully comply with my view of a study major strength to determine the effect of event-based flushes of groundwater and sediments on CO2 fluxes.
Furthermore, while Wang et al 2024 identified the effects of hot springs on the enrichment of waters with bicarbonate in the same sample locations in the tectonic active Beinan catchment this is not mentioned in this study. Is this not relevant in the total CO2 flux or is it included?
What about redox situation in the streams influenced by tectonics? Is CH4 exchange not relevant?
Specific comments
Abstract line 18 “several percent” please be more specific here. 107mol yr-1 from microbial origin vs annual total emission across the catchment 2,6 .109 ? (Line 295)
Line 38 landscape controlled or is it more topographically controlled?
Line 68 term “individual metabolisms” needs more clarification.
Lines 59-61 The information in this sentence is essential why this study in a rapid uplifting area is so different from the dominant body of literature in this field which is performed on the cratonic continent. While this relation between tectonics and torrential precipitation is a probably obvious for the authors this is not evident for everyone. It would help the storyline if this is relation between tectonics and dynamic, turbid high energy river systems is more explicit.
The expected deviations from the general bentic and hyporheic processes due to the turbid and dynamic river systems can be formulated more explicitely in hypothesis which will give ther reader more guidelines for interpretation in the result section.
Line 107-108, The selection of the 5 sample locations along the Beinan rver and tributaries is not explained. Which criteria were used to determine these sample sites? Likewise no argumentation is provided for the selection of sample moments/ timing. As the dynamic nature of the Beinan river is a part of the research objective the regular bi-monthly sampling scheme is surprising. One would expect a focus on events ( hot moments) and baseline moments.
Line 115. The use of cellulose membranes is not common practice and strongly discouraged in research on carbon dynamics due to the risks of contamination. Especially for DOC determination. The same is true for the use of polypropylene sample containers (risk of DOC contamination).
Line 386: is this influenced or correlation based?
Technical corrections
Figure 3 needs a more elaborate figure caption.