Climatic effects on litter decomposition from arctic tundra to tropical rainforest
Bibliographic record
Abstract
Abstract Climatic effects on the decomposition rates of various litter types in different environments must be known to predict how climatic changes would affect key functions of terrestrial ecosystems, such as nutrient and carbon cycling and plant growth. We developed regression models of the climatic effects on the first‐year mass loss of Scots pine needle litter in boreal and temperate forests across Europe (34 sites), and tested the applicability of these models for other litter types in different ecosystems from arctic tundra to tropical rainforest in Canada (average three year mass loss of 11 litter types at 18 sites), the USA and Central America (four litter types at 26 sites). A temperature variable (annual mean temperature, effective temperature sum or its logarithm) combined with a summer drought indicator (precipitation minus potential evapotranspiration between May and September) explained the first‐year mass loss of the Scots pine needle litter across Europe with a higher R2 value than actual evapotranspiration (0.68–0.74 vs. 0.51) and with less systematic error for any sub‐region. The model with temperature sum and the summer drought indicator appeared best suited to the other litter types and environments. It predicted the climatic effects on the decomposition rates in North and Central America with least systematic error and highest R2 values (0.72–0.80). Compared with Europe, the decomposition rate was significantly less sensitive to annual mean temperature in Canada, and to changes in actual evapotranspiration in the USA and Central America. A simple model distinguishing temperature and drought effects was able to explain the majority of climatic effects on the decomposition rates of the various litter types tested in the varying environments over the large geographical areas. Actual evapotranspiration summarizing the temperature and drought effects was not as general climatic predictor of the decomposition rate.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".