Effect of Litter Quality on Leaf-Litter Decomposition in the Context of Home-Field Advantage and Non-Additive Effects in Temperate Forests in China
Bibliographic record
Abstract
Litter quality is often considered the main driver of rates of decomposition.Litter decomposes faster in its home environment than in any other environment, which is called the home-field advantage (HFA).However, evidence for this phenomenon has not been universal.In addition, litter mixtures of different species can induce a non-additive effect (NAE) on decomposition processes.However, the direction and magnitude of NAE vary and underlying mechanisms remain unclear.The aim of our study was to assess the effect of litter quality on leaf-litter decomposition in the context of HFA and NAEs in temperate forests in China.Litterbags containing aspen (Populus davidiana), birch (Betula platyphylla), and oak (Quercus liaotungensis) litter were incubated in situ in pure aspen and broadleaved mixed forests in Chinese temperate forests for 360 days.The main results were:1. Aspen litter with a low C/N ratio and high initial N concentration decomposed faster than birch litter, both of which decomposed faster than oak litter, which had the lowest quality.2. The rate of decomposition of oak litter was significantly higher in the broadleaved mixed forest than in pure aspen stands; however, the rate of decomposition of birch litter was not significantly different from pure aspen stands and broadleaved mixed forest.3. Contrary to what was predicted, the mixture of aspen and birch litter decomposed faster than expected.However, both the aspen/oak and birch/oak mixtures had a neutral mixing effect where the rates of decomposition were slightly faster than expected.4. Controlling factors based on linear models show that the order of the relative importance of their effect on litter decomposition was as follows: litter quality, forest floor environment, and litter mixtures.This study indicates that: 1.The various litter species exhibited different litter-environment interactions, such as favoring or contradicting the HFA hypothesis.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".