Ant nests increase litter decomposition to mitigate the negative effect of warming in an alpine grassland ecosystem
Bibliographic record
Abstract
Warming can decrease feeding activity of soil organisms and affect biogeochemical cycles. The ant Formica manchu is active on the nest surface and prefers a hot, dry environment; therefore, warming may provide a favourable environment for its activities. We hypothesized that F. manchu benefit from warming and mitigate the negative effects of warming on litter decomposition. We examined the effects of ant nests (nest absence versus nest presence) and warming (+1.3 and +2.3°C) on litter decomposition, soil properties and the plant community in alpine grassland. Decomposition stations with two mesh sizes were used to differentiate effects of microorganisms (0.05 mm) and macroinvertebrates (1 cm) on decomposition. Ant nests increased litter decomposition with and without macroinvertebrates accessing the decomposition station when compared to plots without ant nests. Only litter decomposition in ant nests with macroinvertebrates having access to the decomposition station was not affected negatively by warming. Plots with ant nests had greater soil carbon, nutrient contents and plant growth than plots without ant nests, regardless of warming. Our results suggest that ant nests maintain ecosystem processes and functions under warming. Consequently, a management strategy in alpine grasslands should include the protection of these ants and ant nests.
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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".