The relationship between functional dispersion of mixed‐species leaf litter mixtures and species' interactions during decomposition
Bibliographic record
Abstract
We tested the hypothesis that interactions between plant species during the process of mixed‐species leaf litter decomposition increases with increasing functional diversity of leaves within the mixtures; specifically, there is a positive correlation between functional dispersion and the deviations from Grime's biomass‐ratio hypothesis, with a null intercept. We measured decomposition rates (mg g −1 d −1 ) of mixed‐species leaf litter from two experimental designs: 1) a microcosm experiment with litterbags of species mixtures combining six tree species, alone and in 42 combinations, and 2) an in situ litterbag experiment with all possible mixture combinations of four herb species (from one to four species). Interaction strengths and directions were measured as deviations from community‐weighted means (CWM) of monoculture decomposition values, following the biomass‐ratio hypothesis (BRH). Functional diversity was measured as Laliberté and Legendre's functional dispersion (FDis), using leaf dry matter content (LDMC), leaf nitrogen and carbon contents, and proportions of water soluble compounds, cellulose, hemicellulose and lignin. Correlations between FDis and deviations from BRH varied strongly, depending upon the combination of functional traits, the plant type or the environmental conditions, and the way in which prediction error was expressed (absolute or actual deviation). For tree species, FDis that was based on a combination of water soluble compounds, hemicellulose concentration, and LDMC was negatively correlated with interaction strength but positively with its absolute value. For herbs, interaction strength (absolute or actual) decreased as FDis of the mixtures increased, based on cellulose and lignin contents. There was no positive correlation between functional dispersion and the deviations from Grime's biomass‐ratio hypothesis, with a null intercept. Despite a relationship between litter interactions and functional divergence, this relationship was not generalisable. Other functional traits that were missing in our study might have played an important role.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".