Global meta‐analysis of wood decomposition rates: a role for trait variation among tree species?
Bibliographic record
Abstract
The carbon flux from woody debris, a crucial uncertainty within global carbon-climate models, is simultaneously affected by climate, site environment and species-based variation in wood quality. In the first global analysis attempting to explicitly tease out the wood quality contribution to decomposition, we found support for our hypothesis that, under a common climate, interspecific differences in wood traits affect woody debris decomposition patterns. A meta-analysis of 36 studies from all forested continents revealed that nitrogen, phosphorus, and C : N ratio correlate with decomposition rates of angiosperms. In addition, gymnosperm wood consistently decomposes slower than angiosperm wood within common sites, a pattern that correlates with clear divergence in wood traits between the two groups. New empirical studies are needed to test whether this difference is due to a direct effect of wood trait variation on decomposer activity or an indirect effect of wood traits on decomposition microsite environment. The wood trait-decomposition results point to an important role for changes in the wood traits of dominant tree species as a driver of carbon cycling, with likely feedback to atmospheric CO(2) particularly where angiosperm species replace gymnosperms regionally. Truly worldwide upscaling of our results will require further site-based multi-species wood trait and decomposition data, particularly from low-latitude ecosystems.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".