Terrestrial-aquatic connectivity structures microbial communities during the formation of thermokarst lakes
Bibliographic record
Abstract
Abstract Rising air temperatures and permafrost degradation drive the erosion of palsas (permafrost mounds mainly composed of frozen peat and ice layers) and lead to the formation of thermokarst ponds and lakes, known for their high greenhouse gas (GHG) emissions. This study investigates the impact of permafrost soil erosion during thermokarst lake formation on microbial community structure and its implications for GHG dynamics in a highly degraded permafrost valley (Nunavik, northern Quebec, Canada). Samples were collected from a palsa, an emerging lake connected to the palsa, surrounding peat and soil pore water, and two mature lakes which are older, stratified, and less connected to the palsa. Analysis of total and potentially active microbial communities, based on 16S rRNA gene amplicon sequence variants revealed significant changes in taxonomic and phylogenetic diversity during thermokarst lake formation. We found distinct assembly processes depending on the stage of formation. Firstly stochastics, they became more deterministic as lakes mature. Distinct methanogens/trophs communities in emerging lake led to lower CO2:CH4 ratio compared to the surface of mature lakes. Which presented a greater diversity of methanogens and distinct methanotrophic communities, with acetogenic, hydrogenotrophic and methylotrophic methanogens along anaerobic an aerobic methanotrophs. Multivariate analyses revealed that selection processes were primarily driven by concentrations of CH4, CO2, and NO3−. The interplay between the nitrogen and carbon cycles appears to be pivotal in these assemblages, with nitrogen playing key roles on community structure. These findings underscore the significance of terrestrial-aquatic connectivity in shaping microbial communities and GHG emissions in thermokarst lakes.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".