Increased winter soil temperature variability enhances nitrogen cycling and soil biotic activity in temperate heathland and grassland mesocosms
Bibliographic record
Abstract
Abstract. Winter air temperatures are projected to increase in the temperate zone, whereas snow cover is projected to decrease, leading to increased soil temperature variability, and potentially to changes in nutrient cycling. Here, we experimentally evaluated the effects of increased winter soil temperature variability on selected aspects of the N-cycle in mesocosms containing different plant community compositions. The experiment was replicated at two sites, a colder mountainous upland site with high snow accumulation and a warmer and drier lowland site. Increased soil temperature variability enhanced soil biotic activity for both sites during winter, as indicated by 35% higher nitrogen (N) availability in the soil solution, 40% higher belowground decomposition and a 25% increase in the potential activity of the enzyme cellobiohydrolase. The mobilization of N differed between sites, and the 15N signal in leaves was reduced by 31% in response to winter warming pulses, but only at the cold site, with significant reductions occurring for three of four tested plant species at this site. Furthermore, there was a trend of increased N leaching in response to the recurrent winter warming pulses. Overall, projected winter climate change in the temperate zone, with less snow and more variable soil temperatures, appears important for shifts in ecosystem functioning (i.e. nutrient cycling). While the effects of warming pulses on plant N mobilization did not differ among sites, reduced plant 15N incorporation at the colder temperate site suggests that frost damage may reduce plant N uptake in a warmer world, with important implications for nitrogen cycling and nitrogen losses from ecosystems.
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 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) | 0.000 | 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".