the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 23 Apr 2026
Multiple microscale stable isotope signatures record metabolic processes in ancient deep subsurface barite-pyrite-calcite assemblages
Abstract. Fracture-coating pyrite from deep within the Fennoscandian Shield records the largest 34S-enrichment observed on Earth to date (δ34S = +147 ‰) – likely the result of late-stage Rayleigh distillation during closed-system microbial sulfate reduction (MSR). This implies even heavier sulfur isotope values for the complementary sulfate reservoir during pyrite formation, possibly recorded in coeval sulfate minerals such as barite [BaSO4]. However, barite has been poorly explored as an archive of ancient deep subsurface biosignatures. Here, we compiled published microscale δ34Spyrite, δ18Ocalcite, and δ13Ccalcite data with new secondary ion mass spectrometry (SIMS) δ34Sbarite and δ18Obarite analyses from two localities on the Fennoscandian Shield (Forsmark and Laxemar/Äspö, Southeastern Sweden), aiming to constrain how barite records the history of microbial processes. Comparison between the δ34Spyrite range across both localities (−53.9 to +131.7 ‰) with the δ34Sbarite range (+7.6 to +52.0 ‰) demonstrates that the sulfate reservoir corresponding to extremely 34S-enriched pyrite is not recorded in barite. We identified two groups of barite distinguished by their distinct δ18O/δ34S trends, which is proposed to record different MSR-related processes based on cogenetic δ13Ccalcite data. Although there is overlap between the metabolic processes recorded in both groups, the steeper trending Group 1 was dominantly associated with sulfate-dependent anaerobic oxidation of methane (AOM), whereas the shallower trending Group 2 was dominated by organoclastic sulfate reduction (OSR). The different trends likely resulted from an interplay of MSR pathways (AOM vs. OSR), as well as variations in sulfate reduction rates (SRR) and fractionation-related isotope enrichment (34ε), attributed to paleoenvironmental ratios of sulfate to electron donor abundances. Lower sulfate/electron donor ratios favored methanogenesis and AOM at lower SRR, whereas higher ratios inhibited methanogenesis and favored OSR at higher SRR. A preservation bias against extremely 34S-enriched barite due to undersaturation at high degrees of Rayleigh distillation likely explains its absence in the deep subsurface. Our study highlights the need for microscale multiple stable isotope signatures in fracture-hosted mineral assemblages to understand metabolic processes in the ancient deep biosphere, while stressing that these records are strongly affected by local hydrogeochemical conditions.
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Status: final response (author comments only)
- RC1: 'Comment on egusphere-2026-2220', Anonymous Referee #1, 09 May 2026
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RC2: 'Comment on egusphere-2026-2220', Alexandre Voinot, 11 May 2026
The present manuscript presents a suite of stable isotope data (sulfur and oxygen) obtained from barite samples collected in southeastern Sweden (Forsmark and Laxemar/Äspö). These new data are compared with previously published sulfur isotope data from pyrite and oxygen and carbon isotope data from calcite, with the goal of elucidating microbial processes and associated isotopic fractionation pathways in the deep subsurface.
In principle, this is a valuable and timely study. Integrating multiple isotope systems has the potential to provide important insights into the origin of complex isotopic patterns and, in particular, the unusually large fractionations that have been reported in some closed-system microbial sulfate reduction scenarios. However, in its current form, the manuscript relies on a statistical framework that is not sufficiently robust to support several of the key interpretations. I therefore recommend major revisions before the manuscript can be considered for publication.
General comments:
The introduction accounts for more than a quarter of the total manuscript length and, as a result, feels overly long and at times difficult to follow. While the background material is generally relevant, several sections could be shortened or streamlined, allowing the authors to allocate more space to methods, results, and interpretation.
My primary concern with the manuscript relates to the statistical treatment of the isotope data, specifically the use of Gaussian Mixture Models (GMM) to define discrete groups that form the basis for much of the subsequent discussion. Although the Bayesian Information Criterion (BIC) favours a three-component model, this solution is explicitly rejected with limited justification. This decision appears subjective and is not discussed in sufficient depth. From a statistical standpoint, discarding the preferred model without a clear, objective rationale weakens inferential independence and risks introducing confirmation bias.
If the data robustly support three components, the implications of this outcome should be explored rather than dismissed. Negative isotope slopes, for example, could plausibly reflect multiple generations of mineral growth, mixing of fluids with distinct histories, or other processes independent of the authors’ preferred framework. Presenting and discussing all statistically supported solutions would ultimately strengthen the manuscript. In addition, the observation that barite core analyses dominate one group while rim analyses dominate another may suggest that intra-crystalline processes during barite growth contribute to the overall spread observed in the data. This possibility is potentially important, particularly given the distinct zoning patterns shown in some samples (e.g., Fig. 6d), and deserves more explicit consideration.
Because these group definitions underpin much of the later discussion, the current statistical treatment leaves the interpretations on uncertain footing. Addressing this would significantly improve the manuscript. I encourage the authors to reduce the length of the introduction and use the freed space to present a clearer and more transparent explanation of their clustering approach and its limitations. They could additionally explore other clustering methods. In any case, I would advise approaching the interpretation of the preferred statistical analysis with an unbiased mind, as potentially "unexpected" trends might be the result of significant mechanisms.
Specific comments:
A further concern relates to the strong imbalance between datasets from the two study sites. The sulfur isotope dataset for example is dominated by samples from Laxemar (323 analyses) compared to Forsmark (21 analyses). This imbalance likely affects the performance of the GMM and means that the derived clusters largely reflect the structure of the Laxemar dataset, with Forsmark data contributing relatively little statistical weight. As a result, it is difficult to view this as a truly comparative study of two localities. If additional Forsmark data cannot be obtained, I suggest either analysing sites separately or clearly acknowledging the limited interpretive weight of the Forsmark dataset. Alternatively, removing the Forsmark data altogether may lead to a more internally consistent analysis.
The manuscript generally treats barite as a mineral that undergoes minimal isotope fractionation during precipitation and is subsequently resistant to dissolution. However, several observations appear inconsistent with this assumption, particularly the systematic differences between core and rim compositions and their interpretation in terms of barite dissolution. Potential kinetic isotope effects during precipitation are not considered, despite their relevance to zoning patterns. This raises questions about the assumption that barite isotopes always provide a faithful archive of dissolved sulfate compositions.
Related to this point, core-to-rim sulfur isotope trends in pyrite are attributed primarily to Rayleigh distillation during mineral growth. While this is certainly plausible, a simple quantitative model using literature fractionation factors would help demonstrate whether this mechanism can reproduce the observed magnitudes. Other potential explanations, including precipitation kinetics or diagenetic modification, should also be considered and discussed.
The treatment of carbon isotopes in calcite is similarly simplified. The use of fixed δ¹³C thresholds to infer dominant processes (e.g., -70 to -40‰ for AOM; > -20‰ for OSR) does not fully account for mixed carbon sources, partial methane oxidation, or other processes that can influence calcite δ¹³C values. This oversimplification is particularly problematic where averaged δ¹³C values are used for barite-bearing samples (Section 3.2, line 211). Given the wide range of observed values, such averaging may obscure meaningful variability and potentially bias subsequent interpretations.
The manuscript argues that oligotrophic conditions in the deep subsurface imply low sulfate reduction rates (SRR). However, the interpretations associated with Group 2 require relatively high SRR, which is reconciled by invoking large sulfur isotope fractionation factors (ε³⁴). While this explanation is possible, it remains largely unconstrained and risks becoming flexible enough to accommodate almost any trend. Independent constraints on SRR, or at least a more explicit discussion of uncertainties and alternative parameter combinations, would make this argument more convincing.
Finally, the absence of isotopically heavy barite is not uniquely constrained by the current discussion. Several alternative explanations could be considered, including sulfate depletion prior to barite saturation, temporal decoupling between pyrite and barite formation, or preferential sampling of late-stage, sulfate-poor conditions. Incorporating barite saturation index calculations or explicitly evaluating these scenarios would strengthen this part of the manuscript.
Minor comments:
Several parts of the manuscript, particularly in the introduction, rely on rather vague statements that would benefit from greater precision. For example, at line 45 the authors refer to a “range” of isotope fractionations without clearly defining what that range is. Providing representative values or at least approximate bounds would greatly improve clarity. Similarly, at line 116, Laxemar is described as being “a couple of kilometers” from the Baltic Sea coastline, while Äspö is located on the coast. Reporting the actual distances between these localities would be more informative and avoid ambiguity.
The manuscript would also benefit from careful attention to language and terminology. There are several grammatical and spelling issues (e.g., line 54: “the slopes (…) is”) that should be corrected. In addition, some statements rely heavily on scientific shorthand or jargon, such as the phrasing at lines 73–74 where AOM and OSR are described as “producing trends.” Rephrasing these statements in clearer, more explicit terms would make the manuscript more accessible to a broader readership.
I have not undertaken a comprehensive review of grammar and spelling, as the structure and wording of the manuscript will likely change substantially in response to the major revisions outlined above. Nonetheless, I strongly recommend that the final revised version be carefully edited for language, clarity, and consistency prior to resubmission.
Finally, regarding QA/QC, I was unable to identify data from standards or reference materials that would substantiate the uncertainty estimates and error propagation described in the methods section. Including these data, or explicitly stating where and how they are reported (e.g., in supplementary materials), would strengthen confidence in the analytical results.
Citation: https://doi.org/10.5194/egusphere-2026-2220-RC2
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- 1
This manuscript presents a valuable microscale isotope dataset from deep fracture-hosted barite-pyrite-calcite assemblages in the Fennoscandian Shield. The authors combine new SIMS δ34S and δ18O analyses of barite with previously published δ34S data from pyrite and δ13C–δ18O data from calcite to explore microbial sulfate reduction, sulfate-dependent anaerobic oxidation of methane, and organoclastic sulfate reduction in the ancient deep biosphere. The analytical work appears careful, the supplementary dataset is extensive, and the topic is well suited to Biogeosciences. The study provides a useful contribution by showing how barite δ18O–δ34S systematics, when considered together with calcite and pyrite isotope records, may help distinguish different microbial sulfur-cycling regimes in deep subsurface fracture systems. I find the manuscript publishable after minor revision, mainly requiring clarification of several interpretive and statistical points.
General comments
The main strength of the manuscript is the integration of multiple microscale isotope systems across barite, pyrite and calcite. This approach has clear potential for improving the interpretation of biosignatures in ancient deep subsurface fracture systems. My main concern is not the quality of the isotope data, but the extent to which some interpretations are developed beyond what the current evidence directly constrains. In particular, the two-group Gaussian Mixture Model classification, the interpretation of Group 1 and Group 2 as dominantly associated with AOM and OSR, the inference of different sulfate reduction rates, and the proposed preservation bias against extremely 34S-enriched barite all need to be presented with greater transparency. The manuscript would be strengthened by distinguishing more clearly between direct observations, model-dependent interpretations and speculative but plausible explanations.
Specific comments
1. Statistical support for the two-group GMM classification should be made more transparent.
The two-group division of barite δ18O–δ34S data is central to the manuscript. However, the Methods state that the Bayesian Information Criterion indicated three components as the optimal GMM solution, whereas the authors selected two components because the three-component model produced negative regression slopes and because two components are consistent with the expected AOM–OSR end-member framework. This choice may be reasonable, but it requires clearer justification.
Please report the BIC values for one-, two-, three- and preferably four-component models. The rejected three-component solution should be shown in the Supplement, together with a short explanation of why negative slopes are considered geochemically implausible rather than reflecting mixed growth generations, local fluid mixing, non-coeval sulfate reservoirs, or sampling heterogeneity. I also recommend a sensitivity test showing whether the two groups remain robust when Forsmark and Laxemar are treated separately, or when individual crystals/samples rather than individual SIMS spots are used as the statistical unit. Because multiple SIMS spots may come from the same crystal or fracture, they are not fully independent observations; this should be considered when evaluating the robustness of the clustering and regression results.
2. Quantify the basis for the AOM- and OSR-dominated interpretations.
The wording that Group 1 was “dominantly associated” with AOM and Group 2 was “dominated” by OSR is reasonable. However, because Group 1 also includes barite associated with less strongly 13C-depleted calcite, and because OSR-related barite appears to occur in both groups, the basis for “dominantly” should be stated more explicitly.
I suggest adding a short summary table or a few sentences indicating how many samples or analyses in each group are associated with strongly 13C-depleted calcite, moderately depleted calcite, and non-depleted calcite. It would also be useful to distinguish cases where the calcite is petrographically co-genetic with barite from cases where average δ13Ccalcite values from the barite-bearing sample were used. This would strengthen the process-level interpretation without requiring any change to the main conclusion.
3. Present the preservation-bias interpretation more cautiously or support it with a simple calculation.
The proposed explanation that extremely 34S-enriched barite is absent because barite becomes undersaturated during advanced Rayleigh distillation is interesting. However, the current discussion is mostly qualitative. I encourage the authors either to provide a simple first-order calculation showing how sulfate concentration, Ba²⁺ availability and barite saturation state may evolve during Rayleigh distillation, or to slightly soften the wording of this conclusion.
In particular, the maximum observed δ34Sbarite value of approximately +52‰ should be presented as the highest value recorded in the dataset rather than as a demonstrated saturation threshold. If no quantitative saturation calculation is added, the manuscript should more clearly acknowledge alternative explanations, such as temporal decoupling between pyrite and barite formation, limited Ba availability during late-stage sulfate reduction, post-formation dissolution, open-system mixing, or sampling bias.
4. Clarify the inference of sulfate reduction rates and sulfate/electron-donor controls.
The interpretation that differences in δ18O/δ34S slopes reflect variations in sulfate reduction rates, fractionation and sulfate/electron-donor ratios is reasonable and consistent with previous conceptual models. However, the manuscript would benefit from a clearer distinction between directly observed isotope patterns and inferred microbial or hydrogeochemical controls.
For example, higher sulfate availability does not necessarily imply higher sulfate reduction rates if electron donors are limited. Similarly, apparent 34ε, sulfate concentration and cell-specific sulfate reduction rate are related but not interchangeable. I suggest revising this section to state more explicitly that SRR differences are inferred from isotope systematics rather than independently measured. This small clarification would improve the logical flow of the discussion.
5. The comparison with Antler et al. and marine sedimentary systems should be expanded.
The manuscript notes an important discrepancy: the AOM-influenced Group 1 has a steeper δ18O/δ34S trend than the OSR-dominated Group 2, and the Group 2 trend is shallower than OSR-related trends reported from marine sedimentary systems. This difference is important because it supports one of the manuscript’s broader implications, namely that isotope interpretation frameworks developed for marine barite cannot be directly transferred to deep subsurface fracture-hosted barite.
This point deserves a more mechanistic discussion. The authors should compare the deep fracture system with marine SMTZ settings in terms of sulfate concentration, Ba availability, electron-donor limitation, fluid residence time, openness of the sulfate reservoir, temperature, and barite saturation state. A short comparative table would be useful. Such a comparison would help readers understand whether the observed δ18O–δ34S trends are a predictable consequence of low-sulfate deep subsurface environments, or whether they require modification of existing conceptual models for sulfate oxygen and sulfur isotope evolution during microbial sulfate reduction.
Minor comments