Greenhouse gas dynamics across a latitudinal gradient of thermokarst lakes
Bibliographic record
Abstract
The Arctic is warming at a faster rate than the rest of the planet due to climate change. The warmer temperatures are causing, among other effects, the permafrost to thaw. When ice-rich permafrost thaws, thermokarst features form due to subsidence of the ground surface and the creation of dynamic depressions, basins, and lakes. As a result, the hydrological cycle in these latitudes is intensifying, causing an increase of nutrients and organic carbon in surface waters. Such impacts on freshwaters affect microbial community composition, and thus, these systems are good sentinels to study processes in primary ecological succession related to ecosystem processes such as productivity and greenhouse gas (GHG) emissions. This study aims to improve the current understanding of microbial processes leading to release of GHG in thermokarst lakes. To that end, we sampled a total of 12 thermokarst basins in August 2022 along a latitudinal gradient (67ºN - 69ºN) in the Northwestern Territories (Canada). The basins were selected so that half were in the taiga biome and half in the tundra biome. In addition, based on satellite images and in-field observations, half of the lakes sampled were in expansion and the other half were undergoing drainage. Water samples were collected for the analysis of GHGs (CH4, CO2, N2O), major ions, dissolved nutrients (organic C, δ13C-DOC, organic and inorganic -N) and microbial community composition (16S rRNA gene metabarcoding). We used Ar-corrected gas saturation of each GHG as a proxy of net metabolic changes. Our first results show that both expanding and shrinking lakes were strongly oversaturated in CH4 andCO2, slightly saturated in N2O, and slightly undersaturated in O2, pointing out higher respiration activity than primary production. Microbial mineralization of organic matter was used as a proxy for GHG production. We found higher concentrations of dissolved organic C in shrinking lakes compared to expanding ones. Following the latitudinal gradient (i.e. biomes), higher temperatures were found in lakes sampled in the taiga compared to those located in tundra, together with deeper permafrost table depths. In surface waters, pH values and dissolved O2 concentrations were significantly higher in tundra lakes compared to taiga lakes, probably as a result of lateral DOM fluxes in more productive ecosystems (i.e. boreal forests). Differences between microbial communities in both biomes are therefore expected, which we will verify once the ongoing sequencing analyses are available. Our study advances the current knowledge of GHG dynamics in thermokarst lakes and helps to predict future effects of climate change impacts in northern latitudes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".