<i>Ligustrum lucidum</i> invasion decreases abundance and relative contribution of soil fauna to litter decomposition but increases decomposition rate in a subtropical montane forest of northwestern Argentina
Bibliographic record
Abstract
Invasive plant species can alter litter decomposition rates through changes in litter quality, environment conditions, and decomposer organisms (microflora and soil fauna), but limited research has examined their direct impact on soil fauna. We assessed the abundance and relative contribution of soil meso- and macrofauna to litter decomposition in a forest invaded by Ligustrum lucidum W.T. Aiton and in a noninvaded forest in a subtropical mountain forest in northwestern Argentina, using litterbags (0.01, 2, and 6 mm mesh size). Additionally, we analyzed the litter quality and soil properties of both forest types. Soil fauna abundance was lower in the invaded forest than in the noninvaded forest. The contribution of soil macrofauna to litter decomposition was important in both forest types, but soil mesofauna contribution was significant only in noninvaded forest. Litter decomposition was significantly faster in the invaded forest, consistent with its higher quality litter compared with the uninvaded forest. Invaded forest had significantly lower litter accumulation, lower soil moisture, and greater soil pH than noninvaded forest. Our results showed that although soil fauna was less abundant and played a less pronounced role in litter decomposition in invaded forest, these changes did not translate into a reduced litter decomposition rate due to the higher quality of litter produced in the invaded forest.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".