Ecosystem Processes Show Uniform Sensitivity to Winter Soil Temperature Change Across a Gradient from Central to Cold Marginal Stands of a Major Temperate Forest Tree
Bibliographic record
Abstract
Abstract The magnitude and frequency of soil frost events might increase in northern temperate regions in response to climate warming due to reduced insulation caused by declining snow cover. In temperate deciduous forests, increased soil frost severity can hamper tree growth and increase the mortality of fine roots, soil fauna and microorganisms, thus altering carbon and nutrient cycling. From single-site studies, however, it is unclear how the sensitivities of these responses change along continental gradients from regions with low to high snowfall. We conducted a gradient design snow cover and soil temperature manipulation experiment across a range of lowland beech forest sites to assess the site-specific sensitivity of tree growth and biogeochemical cycling to soil cooling. Even mild and inconsistent soil frost affected tree increment, germination, litter decomposition and the retention of added 15N. However, the sensitivity of response (treatment effect size per degree of warming or cooling) was not related to prevailing winter climate and snow cover conditions. Our results support that it may be valid to scale these responses to simulated winter climate change up from local studies to regional scales. This upscaling, however, needs to account for the fact that cold regions with historically high snowfall may experience increasingly harsh soil frost conditions, whereas in warmer regions with historically low snowfall, soil frost may diminish. Thus, despite the uniform biotic sensitivity of response, there may be opposing directions of winter climate change effects on temperate forests along continental temperature gradients due to different trends of winter soil temperature.
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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".