Functional and phylogenetic diversity promote litter decomposition across terrestrial ecosystems
Bibliographic record
Abstract
Abstract Aim Litter decomposition is a vital process of carbon and nutrient cycling in terrestrial ecosystems. Despite rapid declines in plant diversity worldwide, the effects of plant diversity on litter decomposition, along with the factors driving their directions and magnitudes, remain uncertain. Location Global. Time period 1985–2018. Major taxa studied Plants. Methods By synthesizing 492 paired observations of leaf litter mixtures and monocultures from 110 studies, we conducted a global meta‐analysis of the effects of litter mixtures on litter decomposition rates, which were calculated askcoefficients frommt/m0 = e−kt, wheremtwas the remaining mass of leaf litter at yeartandm0was the initial litter mass at the beginning of experiments. Results Litter mixtures on average increased litter decomposition rates by 5.6% (95% confidence intervals, 3.0–8.1%), and the effects of litter mixtures increased with litter species richness, the functional diversity of chemical traits (leaf C, N, P contents and C : N ratio) and phylogenetic diversity consistently across terrestrial ecosystems. The decomposer abundance and function, including soil fauna abundance, microbial biomass, and extracellular enzyme activities, were positively associated with litter mixture effects on decomposition rates. The structural equation models accounted for 48.6% of the global variation in litter decomposition rates and revealed that the positive effects of litter functional diversity on decomposer abundance and function led to increased litter decomposition rates, while litter phylogenetic diversity had a positive direct effect on litter decomposition rates. Main conclusions As indicators for complementarity effects, functional diversity of the chemical traits and phylogenetic diversity lead to more positive litter mixture effects on decomposition. The positive litter diversity effects on decomposition rates are mechanistically linked with soil fauna abundance, microbial biomass, and extracellular enzyme activities. Our results suggest that plant diversity, especially functional and phylogenetic diversity, increases decomposer abundance and function, and thus plays a key role in carbon and nutrient cycling across terrestrial ecosystems.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".