Plant secondary metabolites: a key driver of litter decomposition and soil nutrient cycling
Bibliographic record
Abstract
Summary A broad and diversified group of compounds, secondary metabolites, are known to govern species interactions in ecosystems. Recent studies have shown that secondary metabolites can also play a major role in ecosystem processes, such as plant succession or in the process of litter decomposition, by governing the interplay between plant matter and soil organisms. We reviewed the ecological role of the three main classes of secondary metabolites and the methodological challenges and novel avenues for their study. We highlight emerging general patterns of the impacts of secondary metabolites on decomposer communities and litter decomposition and argue for the consideration of secondary compounds as key drivers of soil functioning and ecosystem functioning. Synthesis. Gaining a greater understanding of plant–soil organisms relationships and underlying mechanisms, including the role of secondary metabolites, could improve our ability to understand ecosystem processes. We outline some promising directions for future research that would stimulate studies aiming to understand the interactions of secondary metabolites across a range of spatio‐temporal scales. Detailed mechanistic knowledge could help us to develop models for the process of litter decomposition and nutrient cycling in ecosystems and help us to predict future impacts of global changes on ecosystem functioning.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".