Soil CO2 and CH4 response to experimental warming under various tree species compositions in a temperate harwood forest
Bibliographic record
Abstract
Under climate change, some forest ecosystems appear to be transitioning into net source of carbon dioxide (CO2), raising questions about the future role of soil respiration rate (Rs), which depends on hydroclimatic conditions. Conversely, well-drained forest soils could become more significant sinks of methane (CH4) under warming. The main objective of this study was to assess the effects of artificial soil warming on Rs and CH4 fluxes in a sugar maple forest at the northern limit of Quebec temperate deciduous forests in eastern Canada, and to evaluate the effect of species composition on soil response to warming. We measured Rs and CH4 fluxes during the snow-free period of 2021 and 2022 in 32 plots distributed across three forest types, half of which were artificially heated by approximately 2 °C with heating cables. Forest soils were a very consistent sink for CH4 and it did not respond to artificial soil warming nor was it sensitive to variations in soil moisture, ionic activity in soil solution and forest types. However, we observed an increase in Rs in response to warming in the heated plots, but only up to a threshold of about 15 °C, beyond which Rs started to slow down in respect to the control plots. We also observed a weakening of the exponential relationship between Rs and soil temperature beyond this threshold. This trend varied across the forest types, with hardwood-beech stands being more sensitive to warming than mixedwoods and other hardwoods. This greater response of hardwood-beech stands to warming resulted in a more significant downshift of Rs, starting from a colder temperature threshold, around 10–12 °C. This study highlights a potential plateauing of Rs despite rising soil temperature, at least in eastern Canada’s temperate deciduous forest, but this trend could vary from one forest type to another.
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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 | 0.000 | 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) | 0.001 | 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".