the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 06 Mar 2026
Linking marine benthic biodiversity and ecosystem functions related to carbon cycling in a continental mud depocenter
Abstract. The importance of carbon storage in continental seafloor sediments is increasingly recognized, yet the role of benthic macrofaunal biodiversity in the regulation of these processes remains poorly understood. Benthic macrofauna contributes to organic carbon cycling through respiration and secondary production, while the sediment reworking (bioturbation) and ventilation (bioirrigation) of infauna promote the redistribution and remineralization of organic matter in sediments. Here, we investigated how benthic community structure, functional traits, and the relationship between biodiversity and ecosystem functions related to carbon cycling vary along environmental gradients in muddy sediments of the southeastern North Sea. Based on 171 macrofaunal taxa collected from 50 stations, a cluster analysis revealed a clear spatial structuring of the benthic macrofauna communities across the study region. The community composition was primarily structured by bottom shear stress, salinity, and sediment characteristics. Further, a functional trait analysis showed a clear shift in community composition with water depth. Communities in the deeper sections of the study area were dominated by mobile biodiffusors and subsurface filter feeders, whereas shallower communities were characterized by less mobile, surface-modifying bivalves and polychaetes. These contrasting patterns led to pronounced differences in ecosystem functioning: bioturbation and bioirrigation potentials were significantly higher in deeper communities, whereas community secondary production and respiration were higher in shallow communities. Across all stations, community secondary production and respiration increased with taxonomic and functional diversity, while bioturbation and bioirrigation potentials were negatively related to diversity and community evenness, reflecting a dominance by key bioturbating taxa. Our findings demonstrate that environmental gradients fundamentally shape both benthic community structure and the nature of the link between biodiversity and essential ecosystem functions. These results contribute to our understanding of the role macrofauna can play in processes related to carbon sequestration in marine deposition centers with fine-grained sediment and organic matter in shelf sea systems.
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Status: final response (author comments only)
- RC1: 'Comment on egusphere-2026-966', Anonymous Referee #1, 07 May 2026
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RC2: 'Comment on egusphere-2026-966', Anonymous Referee #2, 28 May 2026
The authors present the results of an extensive study that aimed at (1) sampling benthic macrofauna with a wide range of sedimentary habitats of the southeastern North Sea, and (2) translate these marofauna communitiy data (at both the species and functional trait levels) into several proxies/indices of ecosystem functionning. If the study seems well conducted and the dataset valuable, the article generally suffers from a certain gap between the announced objectives from the title (carbon cycling in a depocenter) and the actual content of the paper that to my point of view more deals with the spatial variability of community strcture and functional trait composition accross a sedimentry/depth gradient. At this light, Introduction could be view simplified, and more strait to the point. The method section need to be extensively reviewed. This stands for the whole data acquisition process as well as the statistical analyses performed that clearly need to be explained in more details and/or justified. Please see below a few detialed comments:
L 18-19 : have you actually quantitatively measured proxy for ecosystem functions? It doesn’t seem to be the case, the sentence has then to be modified
L56-57: I would talk about diversity rather than just “species richness” since you already dealt with the species richness to productivity relationship in the previous paragraph. Moreover, it is important to notice here that environmental gradients can also shape single species trait expression, i.e. the way they could contribute to ecosystem functioning, see for example Needham et al. 2011. (https://doi.org/10.1007/s10021-011-9468-0 ) or more simply that sediment characteristics can modify the burrowing behavior and depth in sevreal key species (see Wiesebron et al. 2021, https://doi.org/10.3389/fmars.2021.707785)
L110-113: This affirmation about a certain lack of studies focusing on biological aspects of depocenters and their links to ecosystem functioning needs to be downplayed. Although not from the North sea a certain amount of studies have focused on these aspects, see for example Bonifacio et al. 2019 (https://doi.org/10.1016/j.seares.2017.08.013), Lamarque et al. 2022, 2025 (https://doi.org/10.1016/j.csr.2022.104833, https://doi.org/10.1016/j.csr.2025.105637), or Kauppi et al. 2017 (https://doi.org/10.3354/meps12171).
L117-118: “To address this knowledge gap, we investigated how structural and functional aspects of benthic macrofaunal diversity regulate these processes linked to carbon sequestration” lt’s see
L229: specify the Anova design used
L229-230: What has been exactly tested using Permanova? If you have tested if the groups defined by your clustering step were different based on the very same multivariate data, this would be somehow a typical circular reasoning.
L232-235: more details about the distm must be given here, type of distance/similarity used for both datasets? Model selection criterion used? Selection procedure? Preliminary collinearity assessment and potential transformations done on environmental variables?
L240: Wouldn’t be clearer to talk about “derived ecosystem functions”, since no function have actually be measured?
L252-254: I’m not sure this makes sense, see my comment above.
L271-282: This paragraph needs more precision (in relation with my comment above). It appears also important to state the order (and so the selection procedure) the variables are entered into the model. As well, it seems that there are some collinearities so it would be essential the authors state why they could have left some of them out. For example, salinity and depth appear correlated, but both included as potential explanatory variables. Therefore, as I read this, it seems that depth is not significant in the model because of the large amount of variance shared with salinity that may have been entered before in the model. But if salinity was not considered as explanatory variable, there might be a large chance that depth would be selected and tested as significant.
Figure 2: please indicate what the ellipses stand for
L395: Highlighting carbon sequestration is a bit misleading since the link between the results presented and carbon sequestration is not that explicit. It needs to be downplayed
L408: More context should be made to give these examples of experimental assessments, e.g. temperature, sediment type… As well, much more authors have measured sediment reworking in these species group, so a wider view of the literature would help in depicting generalities.
L437-439: see my comment above. It must be better precise in the mat met and results sections how this result arose
L441-446: Also, it must be stated that flow velocity, as well as suspended particles, can induce a switch between deposit and suspension feeding in some species, including A. filiformis (see Loo et al 1996, MEPS), that can have consequences on bioturbation intensity
L445-446: ok but bottom sheer stress also strongly influence grain size and especially its variability as in figure 2 with the opposition of shear stress and kurtosis, so the structure of sediment might matter too?
Citation: https://doi.org/10.5194/egusphere-2026-966-RC2
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- 1
The authors have sampled macrofauna on an area of rather large spatial coverage to estimate the contribution of macrofauna biodiversity and ecosystem functions to carbon cycling on an environmental gradient from sand to mud. The questions presented are important but some clarifications are needed especially to the statistical tests in order to assess the accuracy of the methods and the estimations.
A lot of calculations were done based on one single sample of macrofauna from the same site. Good that the authors in the end do admit that direct measurements are still missing.
The abstract is well written and concise. However there was no methodological mention of how things were done (lab experiment? Observation? Calculation/model?).
Row 22: “…community FUNCTIONAL composition…”
Row 25: “…led to pronounced differences in ecosystem functioning…”
You did actually not measure ecosystem functioning, you calculated the potential of the fauna to modify the sediment structure and ventilate their burrows and you calculated a theoretical secondary production and respiration rate; this should be clear also from the abstract
Introduction
Row 48: “…suggested that community evenness rather than species richness controlS…”
Starting row 65; the role of macrofauna in mediating biogeochemical processes. It is true that they play a pivotal role, but the proportional contribution is dependent on sediment characteristics and hydrodynamical forcing (see Bernard et al. 2019)
Row 78-79: not sure what the which is referring to, if it refers to the whole sentence “biological traits structure reflects local-scale environmental conditions”, the verbs thereafter should be singular. If it refers to the environmental conditions, then for what does it provide better ecological insight?
Materials and methods
Because only one replicate sample of fauna was taken, the comparison between sites includes a large amount of uncertainty. Abundance and biomass and the identity of taxa affect your calculation of bioturbation potential. Very well understanding the amount of work that goes into sieving and going through macrofauna samples I also know that the variation between replicates can be huge. It is estimated that taking five replicate samples gives you 90 % of the taxa present in a sampling area, three replicates gives 70 % so how do you incorporate this uncertainty into your results? Some areas can also represent a much more patchy distribution than others which further complicates the comparison between stations. Possibility to miss rarer, more patchily occurring taxa -> effect on diversity measures?
Row 130: “…permanently mixES…”
Row 132: “..and generally driveS…”
Row 148-149: Was this wet weight, dry weight or ash-free dry weight?
Why did you choose to use summer values of salinity?
What value of temperature was used for the production and respiration calculations? There was no mention of where these data came from, was it a single measurement at the time of sampling or an annual mean/min/max? If a single measurement value is used, this should be mentioned and its effects on the calculated production and respiration discussed.
Rows 184 and 185: Are there many taxa that occur throughout the environmental gradient and if there are, do they create similar burrow or affect the sediment in a similar way in changing environmental conditions? E.g. burrow depth in muddy vs sandy sediment.
Row 193 to 194: Referring to the comment above and the uncertainty coming from the sampling design, isn’t the same issue regarding comparison also affecting this comparison because of the underlying variables used in the calculation of these potentials?
2.4. Statistical analyses
Row 227 > “A Bray-Curtis dissimilarity matrix was calculated based on the transformed data and analyzed using hierarchical clustering…”
Row 229 > “The clusters were further tested by permutational multivariate analysis of variance (PERMANOVA)” To test what exactly was the PERMANOVA run?
Row 240-241: compared among clusters or between clusters?
Row 291 > you cannot compare NMDS plots based on different species/functional traits saying that some stations were always on the right and some on the left and that the pattern is same across all the studied functions. If you are interested in the distribution of the functions across the study area, you should have them all in the same plot. Or do a PCO of the environmental characteristics and overlay the functional groups as vectors on that to see which functional groups actually correlate with what type of environment. Or am I missing something on the methods part, in that case please clarify the statistics behind.
Row 360 and 361: “Summary of the correlation coefficientS..:” and “Bold STYLE indicates STATISTICALLY significant result / STATISTICAL SIGNIFICANCE”)
Were the correlations between diversity indices and the bioturbation and bioirrigation potentials done for the whole dataset together? Given that there was such a clear clustering of the communities depending on environmental factors, wouldn’t it had made sense to also run these cluster-wise to see the relative contribution of those withing respective cluster?
Discussion
4.1 Community structure and functional trait composition:
Row 413 and 414: is this the same reference than in the previous sentence (Vopel et al. 2003?). In that case, move the reference to the end of the second sentence.
Row 459: “supported sustained greater macrofaunal biomass” One verb too many here?
You could be missing the rarer species, how would that affect the functional richness and redundancy?
The introduction especially at the end focused quite a lot on carbon dynamics but this is a bit lacking in the discussion. There is a sentence in the conclusion stating that “…a direct measurement that would allow for linking macrofauna community to carbon sequestration is still missing” and that is good and it is true that you did not directly measure this. But there could be a concluding sentence of which compartment, according to your calculations, had more respiration and which one had more production etc.