Partitioning the effect of composition and diversity of tree communities on leaf litter decomposition and soil respiration
Bibliographic record
Abstract
The decomposition of plant material is an important ecosystem process influencing both carbon cycling and soil nutrient availability. Quantifying how plant diversity affects decomposition is thus crucial for predicting the effect of the global decline in plant diversity on ecosystem functioning. Plant diversity could affect the decomposition process both directly through the diversity of the litter, and/or indirectly through the diversity of the host plant community and its affect on the decomposition environment. Using a biodiversity experiment with trees in which both functional and taxonomic diversity were explicitly manipulated independently, we tested the effects of the functional diversity and identity of the living trees separately and in combination with the functional diversity and identity of the decomposing litter on rates of litter decomposition and soil respiration. Plant traits, predominantly leaf chemical and physical traits, were correlated with both litter decomposition and soil respiration rates. Surface litter decomposition, quantified by mass loss in litterbags, was best explained by abundance‐weighted mean trait values of tree species from which the litter was assembled (functional identity). In contrast, soil respiration, which includes decomposition of dissolved organic carbon and root respiration, was best explained by the variance in trait values of the host trees (functional diversity). This research provides insight into the effect of loss of tree diversity in forests on soil processes. Such understanding is essential to predicting changes in the global carbon budget brought on by biodiversity loss.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".