the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 06 Jan 2026
Landscape-scale spatial variability of blue carbon stocks and fluxes in tropical seagrass meadows
Abstract. Seagrass meadows are emerging natural climate solutions for climate change mitigation through their high potential for organic carbon sequestration and storage, also known as blue carbon. However, the variability in current blue carbon stock and flux estimates is high, particularly at landscape scales. This knowledge gap highlights the need for evaluating blue carbon at spatial scales that are both locally robust and globally relevant. We quantified the magnitude of variability in blue carbon stocks and fluxes in tropical intertidal seagrass meadows at the landscape scale. We sampled six intertidal seagrass meadows representing three geomorphic settings, including reef-associated settings, estuaries and lagoons, across Singapore. Across these sites, we measured soil organic carbon (Corg) stocks and greenhouse gas fluxes using the static chamber method. We found that tropical intertidal seagrass meadows stored 132 ± 78 Mg Corg ha−1 (mean ± SD) in the top 100 cm of soil, which varied significantly within sites and geomorphic settings (min–max: 19–303 Mg Corg ha−1), and were positively associated with salinity. Seagrass fluxes averaged 660 ± 695 mg m-2 d-1 of CO2 and 12 ± 484 µg m-2 d-1 of CH4, which, unlike stocks, did not appear to vary significantly across geomorphic settings. However, we identified redox (positive) and bulk density (negative) as independent drivers of CO2, and Corg as an independent, strong predictor of CH4 after accounting for spatial hierarchy and geomorphic setting. Spatially explicit stock assessments and inclusion of greenhouse gas fluxes are important to inform robust coastal carbon budgeting and support the inclusion of seagrass in national climate mitigation frameworks.
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Status: final response (author comments only)
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RC1: 'Comment on egusphere-2025-6519', Anonymous Referee #1, 12 Feb 2026
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AC1: 'Reply on RC1', Naima Iram, 26 Feb 2026
We thank the reviewer for their careful reading of our manuscript and for the constructive, detailed comments. We appreciate the reviewer’s suggestions to improve clarity around spatial scales, sampling design, environmental measurements, and the interpretation of greenhouse gas fluxes. We have utlined our point-by-point responses and planned changes in the attached PDF and we will revise the manuscript accordingly.
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AC1: 'Reply on RC1', Naima Iram, 26 Feb 2026
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RC2: 'Comment on egusphere-2025-6519', Anonymous Referee #2, 08 Mar 2026
The manuscript by Iram et al. samples and estimates organic carbon (Corg) stocks in intertidal seagrass meadows in Singapore across estuarine, reef, and lagoonal settings. The study also includes CO2 and CH4 flux measurements using static chambers. Information on Southeast Asian seagrass ecosystems remains relatively scarce, particularly regarding GHG emissions, so these data will likely be well received by the community.
The main objective of the manuscript is to assess spatial variability in carbon stocks and GHG fluxes across multiple scales. In my view, however, the way spatial variability has been assessed represents the main concern of this review, as it affects both the interpretation and visualization of the results. To evaluate variability across scales, the sampling structure and statistical treatment should reflect the study’s spatial hierarchy (e.g., geomorf. setting, site, plot, and replicate measurements). At present, the number of samples at each level and the nested design are unclear.
The sampling hierarchy could be better described in the Methods. A diagram or table summarizing the nested sampling design would be helpful for readers. For example, the authors could outline the spatial levels considered (e.g., geomorf setting, site, plot, and measurement) and the replication at each level, indicating how many sites were sampled within each setting (estuarine, reef, lagoon), how many plots were established per site, and how many sediment cores or chamber measurements were collected per plot. This would give some structure at how spatial variability was assessed.
In the Results, the current data visualization approach, where all replicates are averaged per plot and per site to build a single depth profile, may not be the most effective way to represent spatial variability. To better convey variability across plots, sites, and settings, I suggest using boxplots within depth segments at each site, and facets to separate panels by setting to reduce clutter.
The Corg stock bar plots in Fig. 3 could be more effectively represented as nested boxplots. I suggest displaying individual plot-level measurements as points overlaid on the boxplots (to improve transparency), grouped by site and colored by setting. This keeps variability within and among sites and settings, while preserving the nested structure of the data. Additionally, significant differences for stocks could be indicated using letters directly on the plot. The same approach could be applied to flux measurements. A single nested plot per variable (stocks or fluxes) should be sufficient, making the separate boxplots for each geomorphic setting (current panel B) unnecessary.
The results, as currently written, read somewhat redundant, because the same text structure is repeated for each variable, starting with the overall mean of all measurements, then reporting a percentage describing intra-site heterogeneity, followed by highlighting the sites with the highest and lowest values, noting differences between geomorphic settings, and mentioning vertical distribution patterns where relevant. While this is descriptive, a layer of analytical depth could be added. For example, the authors could quantify spatial variability using metrics such as the coefficient of variation and variance partitioning (e.g., nested ANOVA or mixed-effects models) to determine how much variability is explained at each spatial scale. Currently, the mixed-effects models are only used to test environmental controls on GHG fluxes and stocks, but they could also be leveraged to assess the nested spatial structure of the data.
For each soil parameter (e.g., stocks, Corg content, Corg density, bulk density, grain size), the results are reported as mean ± SD or SE (it is unclear which) and the range of all measurements, lumped together across plots, sites, and settings, at the start of each Results section for the parameter of interest. Several points here:
- Depth basis: Up to which depth were these means calculated? Were they based on a common depth across all cores? Do the reported values include extrapolations to 1 m? I strongly recommend not reporting values that rely on extrapolated depths, and instead presenting results only to the maximum common depth sampled.
- Spatial variability: If the main objective is to assess spatial variability, it is unclear why measurements from different plots, sites, and settings are lumped together.
Finally, I would reconsider the discussion around some of the bivariate relationships between carbon stocks and temperature or salinity. The ranges of these variables across sites in this study are extremely small, approx 1 deg. C for temperature and less than 3 units of salinity. Given that carbon stocks accumulate over centuries, snap-shot measurements of temperature and salinity are unlikely to explain such large variability in stocks. It is possible that any apparent relationships are coincidental rather than ecologically meaningful. In addition, the role of seagrass species is not considered at all in the study, yet species composition can strongly influence carbon stocks and sediment properties. I recommend being critical of these results and consider whether these relationships are robust or generalizable beyond your study.
Other minor comments the author may want to consider:
L38, L53: The acronym for carbon is used inconsistently (e.g., Corg, C, and carbon). Please select one format and use it consistently throughout the manuscript.
L40: Extrapolating carbon stocks to 1 m depth does not typically lead to underestimation, in most cases it leads to overestimation. Please see Krause et al. (2025) for a discussion of this issue.
L55: regarding CH4 emissions offsetting carbon accumulation. The concept that methane emissions “offset” carbon sequestration is not strictly correct, although it is commonly interpreted this way. Please, see Neubauer (2021) and Neubauer and Megonigal (2015) to understand why this interpretation is incorrect. Ecosystems that accumulate carbon will ultimately produce a negative net radiative balance over sufficiently long-time scales because methane has a much shorter atmospheric lifetime than CO2. Consequently, CH4 emissions do not permanently offset carbon sequestration, this usually only applies to ecosystems that are very short-lived like restored sites.
L93: Please add reference in “(ref)”
L100: If cores were sampled to 1 m or to depth of refusal, shorter cores should not be extrapolated to 1 m. Instead, carbon stocks should be reported to a maximum common depth across sites. Although global stocks have traditionally been standardized to 1 m depth, this practice is being reconsidered because many deposits, particularly in seagrasses, do not extend to that depth (e.g., Krause et al., 2025). Additionally, sampling depth of refusal itself could represent an informative result. For example, the authors could report whether the depth of refusal varies among geomorphological settings and whether certain environments tend to develop thicker seagrass sediment deposits, which could provide useful information about carbon storage potential across settings.
L209: The soil organic carbon density results seem somewhat redundant, as Corg content and dry bulk density have already been reported. Consider moving this section out of the main text, and instead use the space to add more analytical depth to the results, for example, by including variance partitioning plots (bar charts showing the percentage of variance explained at each spatial level).
Table 1: The number of cores for stocks in the upper 15 cm is reported as 58, whereas the number of cores for the upper 1 m (including predicted values) is 62. Does this mean that some cores in this study or stock estimates were shorter than 15 cm? Please clarify.
L266-267: Table 1 is referenced in a statement indicating that carbon stocks in the upper 30 cm are larger in reef and lagoonal meadows compared with those in estuarine settings. However, Table 1 does not show this result.
L287-288: Was tidal range measured or assessed for the study sites? Are there differences in tidal range between sites and geomorphic settings, and could these influence carbon stocks or fluxes?
Methods reporting: More information should be provided either in the Methods section or in the Supplementary. In particular, the manuscript should report site and plot coordinates, as well as the number of sediment cores and flux measurements collected at each plot throughout the study period.
It is unclear how many times flux samples were collected. Were measurements taken only once per site, or were there repeated sampling events? The timing of the measurements should also be specified (e.g., month, time of day and tidal stage, ebb or flood). Was there a fan in the chambers? Please clarify whether an equilibration period was allowed after placing the dark chamber tops, or whether the initial concentration data were excluded from the regression. The abrupt transition from light to dark can create a short transient period as photosynthesis ceases and the system stabilizes. In addition, please report how many flux estimates were retained and discarded per site, so that readers can assess how many measurements met basic quality-control criteria.
Citation: https://doi.org/10.5194/egusphere-2025-6519-RC2
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The manuscript is very descriptive and compares Corg stocks and GHG fluxes among sites and geomorphic settings in intertidal seagrass meadows in Singapore. With these results, the authors interpret variability within sites and at the landscape-scale. Some of the methods used are not clear, as relevant information is not provided. But overall, the results and conclusions (if the relevant info is provided) seem strong. See comments in the attached pdf. In supplemental material, describe better what the red trend line represents and avoid the use of / when reporting units, and use a negative superscript.