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Record W4410826490 · doi:10.3897/aca.8.e150995

Sources and sinks of methane and carbon-dioxide in oligotrophic lakes: a case study from Lake Maggiore, Italy

2025· article· en· W4410826490 on OpenAlexaff
Dávid Brankovits, Anne E. Tamalavage, Andrea Lami, Manoj Kamalanathan, Jean‐François Lapierre, Caterina Carabelli, Michela Rogora

Bibliographic record

VenueARPHA Conference Abstracts · 2025
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine and coastal ecosystems
Canadian institutionsUniversité de MontréalUniversité du Québec à Montréal
FundersHorizon 2020 Framework Programme
KeywordsCarbon dioxideMethaneEnvironmental scienceCarbon fibersHydrology (agriculture)Environmental chemistryEcologyChemistryGeologyBiology

Abstract

fetched live from OpenAlex

Lakes are significant sources of greenhouse gases to the atmosphere globally (DelSontro et al. 2018). The magnitude of these fluxes greatly depend on a lake’s trophic status that is sensitive to environmental change. To address considerable gaps in our understanding regarding how forecasted shifts in the environment may affect greenhouse gas dynamics of lake ecosystems, we investigated the underlying biogeochemical mechanisms that control the origins and fate of greenhouse gases methane (CH 4 ) and carbon-dioxide (CO 2 ) in the oligotrophic Lake Maggiore in Italy. Lake Maggiore is a deep oligomictic lake belonging to the LTER Italian and European networks (DEIMS ID: https://deims.org/f30007c4-8a6e-4f11-ab87-569db54638fe). It is located in Northern Italy’s deep subalpine Lake District that includes Lugano, Como, Garda and Iseo lakes. Studies on physical, chemical and biological features of Lake Maggiore have been conducted continuously since the 1980s. These efforts documented how the lake recovered from eutrophication due to remediation measures, and reached the current oligotrophic status. Long-term data also demonstrate how, in the oligotrophication phase, climate change became a significant factor impacting hydrodynamics, oxygen status, and nutrient levels (Rogora et al. 2021). Sampling for this study took place jointly with the regular monitoring schedule during the day in the summer, August 2023 and August 2024, at two locations in Lake Maggiore: Ghiffa, corresponding to the deepest point of the lake (370 m depth), and Pallanza, a semi-pelagic station (100 m depth). In addition to the regularly monitored parameters—such as chlorophyll, dissolved oxygen, and phosphorus, and nitrogen compounds—we measured the concentrations and stable carbon isotope values of dissolved CH 4 and CO 2 , as well as major carbon pools, including dissolved and particulate organic carbon (DOC and POC), across depth profiles. Dissolved CO 2 concentrations varied greatly from 7 µM in the surface layers to as high as 205 µM in the deeper waters, which is consistent with carbon-fixation by photosynthesizing algae or other microorganisms near the surface and/or the accumulation of CO 2 from heterotrophy at depth. Methane concentrations along the depth profile in Ghiffa ranging from 7 nM to 357 nM (Fig. 1) were within the reported range for other oligotrophic lakes and, thus, were considerably lower than source methane concentrations in typical eutrophic lake ecosystems. The slightly elevated CH 4 concentration near the bottom water at 360 m was indistinguishable based on its δ 13 C from the samples collected between 50 m to 300 m depths, demonstrating that methane sourced from the sediments is efficiently oxidized in the aerobic water column. Surprisingly, however, higher CH 4 levels were detected in the surface waters (0-50 m) in both years than in the deeper layers (Fig. 1). The highest CH 4 concentration (2026 nM) with a δ 13 C value as negative as −61.0 ± 0.2‰, which is consistent with a biogenic methane source, was recorded at 7 m depth in Pallanza, in correspondence with the chlorophyll maximum (8.5 µg/l). Collectively, these observations suggest the presence of lateral source(s) in the surface layers above 50 m from (a) methane imported laterally from the shoreline and inlets (Khatun et al. 2024), or (b) production by photosynthesizing microorganisms in oxic conditions (Bižić-Ionescu et al. 2018), or a combination of both. To facilitate a mechanistic understanding of critical cellular and environmental thresholds that drive the production of CH 4 in the oxic surface layer, we conducted microcosm experiments using water samples from both locations (Ghiffa and Pallanza) in conjunction with the regular sampling campaign in August 2024. Methane production significantly reduced within one hour of inorganic phosphorus supplementation in samples collected from the surface chlorophyll maximum layer (8-10 m) at both locations. In contrast, no methane production was observed in deeper samples from below the chlorophyll maximum layers at 50 m. Consistent with the findings of Bižić-Ionescu et al. (2018), our observations indicate that CH 4 levels at the oxic surface layer originate—at least in part—from photosynthetic microorganisms and that oxic methanogenesis is driven by enzymatic processes occurring in response to inorganic phosphorus limitation (high nitrogen to phosphorus ratios). This observation, however, does not exclude the possibility of additional contributions from other near surface sources, such as methane imported laterally from the shoreline. Our first-order observations of dissolved gases in Lake Maggiore add to the growing evidence that CH 4 is produced at the oxic surface layer in aquatic ecosystems—a surprising finding, given CH 4 production has been empirically associated with anoxic conditions. Furthermore, findings from our study demonstrate that oligotrophic lakes provide an opportunity to investigate secondary natural gas sources in aquatic ecosystems that would be difficult or impossible to study in other lake environments. For example, eutrophic lakes are exposed to higher levels of nutrient loads and different nitrogen to phosphorus ratios, which could inhibit oxic methanogenesis observed here. In addition, eutrophic lakes are often associated with overwhelmingly high methane inputs from anoxic sub-habitats, making it difficult to detect, distinguish, and quantify additional contributions even if they are present. Understanding the drivers and delineating the sources of dissolved gases, such as CH 4 and CO 2 , in oligotrophic lakes will allow for their long-term monitoring, better forecasting if trophic changes may occur, and eventually estimating their broader contributions to methane and carbon budgets.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.475
Threshold uncertainty score0.867

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.014
GPT teacher head0.222
Teacher spread0.208 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2025
Admission routes1
Has abstractyes

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