| 24 Aug 2026
Environmental controls on methanotrophic oxidation capacity in the southeastern North Sea
Abstract. Shallow coastal seas contribute disproportionately to marine methane (CH₄) emissions, yet the efficiency of the microbial sink that attenuates them remains poorly constrained. Under first-order kinetics, aerobic methane oxidation (MOx) is the product of ambient CH₄ concentration and the fractional turnover rate k′, the latter representing the oxidation capacity of the methanotrophic community. Here we test whether k′ can be predicted from routinely measured environmental variables, drawing on water samples collected during repeated campaigns between 2010 and 2014 across riverine, estuarine, and marine waters of the Elbe and the adjacent southeastern North Sea. In the estuary and adjacent marine waters, a second-order polynomial model based on temperature, salinity, and nitrate reproduced 80 % of the observed spatiotemporal variability in k′ under five-fold cross-validation, indicating that these predictors captured the main environmental controls on k′ and that nonlinear interaction terms cannot be omitted. Neither ambient CH₄ concentration nor phosphate improved model performance, indicating that k′ is not directly controlled by substrate availability or phosphorus limitation. In riverine waters, by contrast, the same approach systematically underpredicted k′ with large uncertainty, suggesting a distinct regional response regime that is likely linked to river-specific environmental controls. Overall, our results highlight that k′ can be parameterized as a dynamic, environmentally dependent term, opening a route to estimating MOx in coastal systems.
Competing interests: At least one of the (co-)authors is a member of the editorial board of Biogeosciences.
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The authors present a study reconstructing k′, the water-column first-order rate constant usually prescribed as a fixed value in methane oxidation, from routinely measured environmental variables. To do so, the authors compile existing campaign data from the Elbe-estuary-German Bight continuum (which is actually pretty rich for this radiotracer measurement), select a proper statistical approach, and interpret the results in a careful way. In general, the manuscript is clear and well written, and I recommend that after a revision, it can be considered for publication in Biogeosciences. Below I outline the points that would improve the manuscript.
Specific comments:
p1 l19 "not directly controlled by substrate availability or phosphorus limitation". I would say this statement is too strong: the absence of an improvement in model performance does not by itself support a causal conclusion.
p7 l123 "As a subset of GAMs, polynomial regression therefore provides a simple parametric framework...". Did you benchmark against a more flexible model, e.g. a tree-based model or neural network, to quantify how much predictive performance the parametric framework costs?
p10 l166 Please verify the salinity reported for station 8.57, as its standard deviation looks suspicious.
p12 l192 Please state in the Methods that both surface and bottom data were used for model development.
p14 l204-205 Can N:P ratios play a role here and give a better estimation compared to the individual nutrient?
p17 l245 Please clarify the model-selection criterion used to select the final model when several candidate models showed similar predictive performance.
p20 l301-307 The discussion of NH₄⁺ is missing, though it was included in the correlation matrix.
p20 l308 A recent study on methane oxidation by Borges et al. (2026) would strengthen the discussion of the riverine part.
p21 l326-331 The cited study was conducted in a lake and therefore may not be directly comparable.
Supplement p5 l77 How do the observation vs. predicted plots look for these two regimes? Show them either here or elsewhere, as appropriate.
Technical corrections:
p8 l156 "reported by (Kappenberg and Grabemann, 2001)": remove the parentheses.
p11 l167 Replace "correlations analysis" with "correlation analysis".
p14 l204-205 Some correlation values are hard to read. Please improve the readability of these two figures.
p16 l231 The Abstract gives 80 %, whereas here it is 0.82 (Figure 4a also shows 0.82). Please make these consistent.
p28 l506-511 Osudar et al. 2017a and 2017b are the same reference.
Supplement p2 l40 Replace “H2SO4” with H₂SO₄.
Supplement, p3 l53 The detection limit 0.010 ± 0.009 is missing the unit, and the second “< 10 nM” should be “> 10 nM”. Also, nM should be replaced with nmol L⁻¹ here and elsewhere.