Data from: Functional diversity of decomposers modulates litter decomposition affected by plant invasion along a climate gradient
Bibliographic record
Abstract
1. Litter decomposition is fundamental to carbon (C) and nutrient cycling in ecosystems, which could be altered by plant invasion. The impacts of plant invasion on litter decomposition are generally predicted by traits difference between leaf litters of invasive and non-invasive species. However, plant invasion not only changes litter composition, but might also increase the activity or change the functional diversity of decomposers to alter litter decomposition, which is barely studied, and the effect could be different under varied climate conditions. 2. We studied decomposition of litters from non-invasive and invasive native plants, as affected by litter treatments (in a mixture or alone) and decomposer organisms of different functional groups (by controlling the mesh size of litterbags), in sites with or without an invasive woody grass, Moso bamboo (Phyllostachys edulis), at seven locations across a climate gradient. 3. We show that greater decomposer functional diversity, particularly the presence of macrofauna, accelerated the cycling of litter C and nitrogen (N), increased the climatic sensitivities of decomposition rates, but decreased the N use efficiency of decomposers (represented by litter C to N loss ratio). Litter decomposed in mixtures decomposed faster (by 9.5%) and had more N loss (by 28.9%) than that of in monoculture, regardless of the functional diversity of decomposers. In contrast, the invasion of Moso bamboo slowed decomposition and decreased N use efficiency; this negative effect could be reversed when macrofauna was excluded from the decomposition process, which challenges the nutrient facilitation hypothesis. Bamboo invasion depressed the climatic sensitivity of decomposer functional groups when macrofauna was present but not when macrofauna was excluded. 4. Synthesis. We found that the functional diversity of decomposer organisms modulates and largely determines litter decomposition affected by a woody grass invasion along a climate gradient. These results suggest that, under current and future climate, including the changes in decomposer functional groups, particularly macrofauna, and their interaction with litter traits, would provide a mechanistic and more reliable prediction on ecosystem functions altered by invaders than a functional trait-based framework.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.012 | 0.002 |
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".