Macro-environment strongly interacts with warming in a global analysis of decomposition
Bibliographic record
Abstract
Abstract Empirical studies worldwide show substantial variability in plant litter decomposition responses to warming, leaving the overall impact of climate change on this process uncertain. We conducted a meta-analysis of 109 experimental warming studies across seven continents, utilizing natural and standardized plant material, to assess the overarching effect of warming on decomposition and identify potential moderating factors. Warming influences decomposition differently across macro-environmental gradients of moisture and temperature. Negative warming effects on decomposition in warmer, low-moisture areas were counterbalanced by the positive, though not significant, warming effects in colder areas, resulting in an overall non-significant effect. We determine that at least 5.2 degrees of warming is required for a significant increase in decomposition. This is particularly relevant given the past decade’s global warmth in higher latitudes, holding a significant proportion of terrestrial carbon. Low-quality plant litter was more sensitive to warming. Therefore, future vegetation changes toward low-quality, temperature-sensitive plants could increase carbon release and reduce the net supply of stored organic matter in the soil by increasing the decomposition of low-quality litter with warming. Our findings emphasize the connection between warming responses, macro-environment, and litter characteristics, refining predictions of climate change’s consequences on key ecosystem processes and its contextual dependencies.
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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 | 0.009 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.005 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".