Incubation time, functional litter diversity, and habitat characteristics predict litter-mixing effects on decomposition
Bibliographic record
Abstract
Plant diversity influences many fundamental ecosystem functions, including carbon and nutrient dynamics, during litter breakdown. Mixing different litter species causes litter mixtures to lose mass at different rates than expected from component species incubated in isolation. Such nonadditive litter-mixing effects on breakdown processes often occur idiosyncratically because their direction and magnitude change with incubation time, litter species composition, and ecosystem characteristics. Taking advantage of results from 18 litter mixture experiments in streams, we examined whether the direction and magnitude of nonadditive mixing effects are randomly determined. Across 171 tested litter mixtures and 510 incubation time-by-mixture combinations, nonadditive effects on breakdown were common and on average resulted in slightly faster decomposition than expected. In addition, we found that the magnitude of nonadditive effects and the relative balance of positive and negative responses in mixtures change predictably over time, and both were related to an index of functional litter diversity and selected environmental characteristics. Based on these, it should be expected that nonadditive effects are stronger for litter mixtures made of functionally dissimilar species especially in smaller streams. Our findings demonstrate that effects of litter diversity on plant mixture breakdown are more predictable than generally thought. We further argue that the consequences of current worldwide homogenization in the composition of plant traits on carbon and nutrient dynamics could be better inferred from long-duration experiments that manipulate both functional litter diversity and ecosystem characteristics in "hotspots of biodiversity effects," such as small streams.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.002 | 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".