the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 02 Jul 2026
Linking the environmental factors and heterotrophic bacteria to the variability of carbon isotope (δ13C) across crustacean zooplankton in shallow freshwater ecosystems
Abstract. Planktonic crustaceans are important part of carbon cycling in in shallow, temperate freshwater habitats. The objective of the study was to examine the link between selected environmental variables, densities of various groups of bacteria and δ13C of calanoid copepods, cyclopoid copepods and Daphnia sp. Generalized Linear Models (GLMs) with stepwise-selected environmental predictors showed that δ13C of calanoid copepods was positively correlated with the Day of the year and Other bacteria density and negatively correlated with NH₄⁺ and NO₃⁻ concentrations. Cyclopoid copepods’ δ13C was correlated exclusively with water temperature, which showed a positive effect on the stable carbon isotopes ratio. GLM for Daphnia sp. was the most complex and retained the Day of the year, Other bacteria density and PO43- as positively correlated predictors, while β-proteobacteria NH₄⁺ and NO₃⁻ were included in the model as negatively correlated predictors for δ13C. Our study highlights the complex impact of environmental conditions, microbial processes and functional trait-driven responses on δ13C values in planktonic crustaceans. Thanks to integration of plankton ecology, microbiology and the analysis of stable isotopes the study improves the understanding of carbon flow in shallow freshwater ecosystems.
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Status: open (until 13 Aug 2026)
- RC1: 'Comment on egusphere-2026-3444', Anonymous Referee #1, 17 Jul 2026 reply
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RC2: 'Comment on egusphere-2026-3444', Anonymous Referee #2, 21 Jul 2026
reply
Reviewer comments for Krzton et al. 2026, submitted to Biogeosciences
Krzton et al. attempt to understand the parameters affecting the carbon isotope composition (δ13C) of planktonic crustaceans. If I understand their introduction correctly, they are motivated by the larger topic of climate change impacting food webs and carbon flow within freshwater ecosystems. By correlating a variety of environmental parameters (e.g., pH, temperature, ammonium and nitrate, abundance of bacterial groups, etc.) with the δ13C of planktonic crustaceans using generalized linear models, they attempt to derive a mechanistic understanding of planktonic δ13C. This, in turn, would allow their results to be applied to freshwater ecosystems more broadly and not just their study sites. This is a topic of interest to many groups, as the δ13C of planktonic crustaceans (as well as other freshwater macro- and microorganisms) have been measured to disentangle the complex food webs of marine and freshwater ecosystems, and assess potential impacts of climate change. Therefore, the manuscript is of interest in that regard.
However, I have significant concerns about the manuscript as it is currently presented. My first concern regards readability; at times, it was difficult for me to understand what exactly the authors were trying to say. My second concern regards data availability. I was not able to fully independently evaluate the manuscript as the raw data measured by the authors was never presented, either in the main text or as a supplementary table. Instead, the authors only present averages of their data, or model outputs. For example, Figures 2-4 are just graphical renderings of Table 3, which are their modeled outputs. They also do not give the sequences used for their FISH probes. For the probes, this is extremely important as some FISH probes are outdated and will incorrectly classify certain bacteria in certain clades, largely because our knowledge of bacterial taxonomy & classification is rapidly changing as more genome-based approaches are used, and as sequence data repositories steadily grow. Instead, the authors state that “The dataset used within this study will be made available on a reasonable request” – I do not think this is sufficient (is it reasonable to expect a reviewer to ask for your data, when you send it in for review?) and wonder if it even violates the journal’s own policies regarding data availability (see more detail below). I also note that this is to the detriment to the authors – fewer people will cite your paper if the data is not readily available and they have to contact you (e.g. go through the trouble of finding your current institution, finding updated contact information, waiting for your response, potentially negotiating with you for what is a ‘reasonable’ request…). More often than not, people may just find a similar study and cite that instead.
Given all that, I had to take the authors at their word regarding what they found. In this regard, it does not appear that they can draw strong conclusions. They do not find significant differences in δ13C among the three planktonic crustacean groups they examined (Supplemental Table S2). Perhaps not surprisingly, they can therefore only conclude that there is a “complex interplay of environmental conditions, microbial processes and functional trait-driven response of δ13C values in planktonic crustaceans” (Line 315). They also note, correctly, that one of the weaknesses of their paper is that they don’t measure the δ13C of the actual dietary sources – this would be a much stronger paper if they had instead waited to publish after getting that data. Instead, this paper relies on correlation of many parameters with few data points. For example, only 12 Daphnia samples were measured for δ13C and the fitted model for Daphnia contains 11 predictors – the potential for model overfitting, and potential over-interpretation, is extremely high. And, as I noted above, since they do not include any of their actual measured data… I cannot independently confirm whether their data is indeed overfitted or not.
Therefore, I recommend that the manuscript should be rejected.
Major comments
Issues with manuscript readability
I would strongly recommend that the authors work with the editor or other English speaker to edit the writing of the final manuscript. Alternatively, it appears that Biogeosciences allows use of AI tools in manuscript writing, as long as the usage is either described in the Methods section or Acknowledgments: https://www.biogeosciences.net/submission.html
My concern is twofold. First, there are numerous small errors – e.g. grammar, typos – throughout the manuscript that need to be fixed. For example, just in the abstract: i) in the first sentence of the abstract, the word “in” appears twice; ii) “Day” and “Other” are capitalized in the abstract in Line 21, but should not be; iii) The last sentence of the abstract is missing a period at the end. These small errors make it difficult to read the manuscript smoothly. There are also numerous articles missing – for example, Line 46 should say “are an important part of…” instead of “are important part of…”
I note, also, that the quality of writing noticeably changes throughout the manuscript. For example, the writing is much clearer in some sections, like “2.5 Statical analyses” – there are few missing articles (e.g., “the”). But then the writing changes right afterward, in section “3.1. Environmental variables” – for example, the grammar is slightly off in Line 153 articles and should instead be written as “Water temperatures in the studied lakes ranged from 10.8 to 25.6 C, reaching the highest values in July and August.” So, I would recommend whoever helped write Section 2.5 to help edit the entire manuscript.
Second, it was difficult for me to understand the meaning of some sentences. For example, when describing the results (Line 185):
“Mean values of δ13C were found in calanoid copepods accounted for -31.8‰ with range between -41.5‰ to -23.7. Mean δ13C values of cyclopoid copepods was -30.6‰ and range: -40‰ to -22.2‰). δ13C measurements of Daphnia sp. showed mean value of -31.2‰ and range between -42.5‰ and –24.4‰)”
There are typos, like the extra right parentheses (this: “)”) that show up. I am also confused by “accounted for” in the first sentence. Are you just trying to say that the mean δ13C value of calanoid copepods was -31.8‰ with a range from -41.5 to -23.7‰?
Discussion
Some additional information on the particulars of the author’s study sites, compared to prior studies, would increase the quality of the manuscript. For example, the authors find that the only factor that positively affected the δ13C of cyclopoid copepods was water temperature (Line 237) but was not a significant predictor for Daphnia sp. δ13C. What’s the seasonal temperature range that these water bodies experience in a year? Do these water bodies freeze over in the wintertime? Would this at all lead you to predict the differing responses of temperature for cyclopoid copepods vs. Daphnia?
In addition, as these water bodies are quite close to Cracow, I would expect high amounts of anthropogenic influence – would this affect your interpretations? For example, the authors counterintuitively find that higher concentrations of ammonium and nitrate to be correlated with slower phytoplankton growth rates – as they note, this is “opposite to what would be expected… but seasonal dynamics and declining water temperatures effects might be involved.” However, the classic understanding of phytoplankton growth rates increasing with reactive nitrogen availability is based on the assumption that these nutrients are typically rare, like in the open oceans. For shallow, freshwater and even artificial lakes – like the ones studied here – would reactive nitrogen species just be high all the time? Would the authors be able to provide additional data on the chemistry of these lakes?
In Line 288, the authors discuss the positive effect of “Other bacteria” on δ13C values. I’m not sure what exactly the authors can do to make this part stronger, but I found it to be speculative as the measurement of community diversity the authors used – alpha vs. beta vs. gamma vs. delta protobacteria – is quite coarse and does not reflect the current genome-based taxonomy used to classify bacteria today. For example, using marker genes beyond rRNA and those targeted in FISH, it has been proposed that delta proteobacteria be reclassified into clades that better reflect the diverse range of chemistry that different subclades can carry out, and which are masked by the broad paintbrush of ‘delta proteobacteria’ ( https://doi.org/10.1099/ijsem.0.004213).
Therefore, inferring mechanism from these coarse groups of bacteria is inherently speculative because each species of bacteria within these groups can perform a wide range of chemistry. As the authors note themselves, methane oxidation can be accomplished by bacteria from the beta, alpha, and gamma clades. Unless the authors can determine this at a finer resolution – i.e., they detect a phylum like Cyanobacteriota – I would suggest they walk back some of their claims. (I also note that, even knowing there is Cyanobacteria present, most studies do not speculate on mechanism as Cyanobacteria themselves can carry out extremely diverse reactions…)
FISH
I can’t figure out the exact sequences of the different probes that the authors used. I would strongly recommend that they have a table in the supplemental with the sequence. Based on the citations, I’m currently concerned that they are using some more outdated FISH probes that may either miss legitimate members of their target group, or hybridize to organisms that are now classified elsewhere. The citations are also confusing because they cite the Lücker et al. 2007 paper, but that is a methods paper that tests a variety of probes – just citing it does not tell me which specific one they used. In addition, the authors used NON338 to evaluate nonspecific fluorescent binding – this is good, as it detects fluorescence caused by nonspecific binding, sticking, etc. But it does not indicate that the taxon-specific probes have complete coverage or perfect phylogenetic specificity. Thus, “less than 5% nonspecific staining” addresses the analytical problem of just using the probes, but not the broader taxonomic problem. This should be discussed as a limitation of their study in the Discussion.
Data Availability
I strongly disagree with the authors statement that “The dataset used within this study will be made available on a reasonable request,” as it is a common convention in the field to have data available in a supplement or publicly available dataset upon submission. As noted above, I couldn’t even figure out what sequences the authors used for their FISH. For example, here is a recent preprint in Biogeosciences where the authors give 18S tables in their supplement (I know this is slightly different, but just wanted to give a recent example):
https://egusphere.copernicus.org/preprints/2026/egusphere-2026-4192/
Also, the authors should give their raw, measured values for environmental parameters (like pH, temperature, ammonium, etc.) as well as carbon isotope data. Not just the averaged data (e.g., Table 1). Here is a recent example from Biogeosciences, where they have made this data available in their supplemental:
https://bg.copernicus.org/articles/19/1/2022/
I also think this may violate the journal’s own policies. Although the authors technically have a statement on how their data can be accessed, in line with the journals request, the journal also states: “If the data are not publicly accessible at the time of final publication, the data statement should describe where and when they will appear, and provide information on how readers can obtain the data until then. Nevertheless, authors should make such embargoed data available to reviewers during the review process in order to foster reproducibility. The Copernicus review system allows to define such assets as 'access limited to reviewers' and reviewers must then sign that they will use such data only for the purpose of reviewing without making copies, sharing, or reusing. In rare cases where the data cannot be deposited publicly (e.g., because of commercial constraints), a detailed explanation of why this is the case is required. The data needed to replicate figures in a paper should in any case be publicly available, either in a public database (strongly recommended), or in a supplement to the paper.”
https://www.biogeosciences.net/policies/data_policy.html#data_availability
Figures
The figures are difficult to interpret. As a starter, the text on the figures is far too small and difficult to read. But, more importantly, there are no figures showing the actual data measured in this study besides Figure 1. Figures 2-4 are essentially a graphical rendering of Table 3, with model-based uncertainty bands. It basically does not allow the reader, or reviewer, to judge for themselves if the model is indeed a good fit for the measured data. Also – For Figure 4, the phrase “stepwise-selected significant predictors” is confusing because the manuscript states that the full Daphnia model was retained, while Figure 4 apparently displays only predictors with P<0.05. In addition, only 12 Daphnia samples were measured and the fitted model for Daphnia contains 11 predictors (day of the year, proteobacteria, alpha proteobacteria, etc.), the authors should address potential model overfitting – this could also be addressed by just showing the actual measured data. Again, since the data are not made available, it is difficult to really independently evaluate this manuscript.
Minor comments
Line 19: You should briefly define what d13C is – for example, “densities of various groups of bacteria and the carbon isotope composition (d13C) of calanoid copepods”
Line 39: Do you mean ‘reviewed by’ instead of ‘revised by’?
Line 90: Typo, should be “depths” not “depts”
Supplemental: This looks like raw outputs from R. The table caption should define what the column headers are – e.g., “Df; Degrees of freedom.”
Citation: https://doi.org/10.5194/egusphere-2026-3444-RC2
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General comments:
The manuscript presents a valuable and timely contribution to research on carbon cycling in shallow freshwater ecosystems. The authors address the important issue of identifying the environmental and microbial factors shaping carbon isotope (δ¹³C) variability in the major groups of crustacean zooplankton. A particular strength of the study is the integration of stable isotope analyses with bacterial community characterization and environmental parameters, providing insights into pelagic ecosystem functioning from both ecological and biogeochemical perspectives. The study is based on an extensive field dataset collected seasonally from several shallow eutrophic water bodies. The analytical methods and statistical models are appropriate for addressing the stated research objectives, and the results are presented in a clear and logical manner. The Discussion deserves particular recognition for placing the findings within the context of current knowledge and interpreting them in light of the functional differences among the investigated zooplankton groups. I also appreciate that the authors openly acknowledge the main limitation of the study, namely the lack of δ¹³C measurements of potential food sources. Recognizing this limitation and identifying directions for future research strengthens the credibility of the conclusions and highlights promising avenues for further investigation.
Overall, I believe that the manuscript provides novel insights into the functioning of freshwater food webs and is suitable for publication after minor revisions.
Specific comments:
Technical comments: