Species Diversity Induces Idiosyncratic Effects on Litter Decomposition in a Degraded Meadow Steppe
Bibliographic record
Abstract
Litter decomposition is a fundamental path for nutrient cycling in a natural ecosystem. However, it remains unclear how species diversity, including richness and evenness, affects the decomposition dynamics in the context of grassland degradation. Using a litter bag technique, we investigated the litter-mixing effects of two coexisting dominant species ( Leymus chinensis Lc and Phragmites australis Pa), as monocultures and mixtures with evenness (Lc:Pa) from M1 (30:70%), M2 (50:50%), and M3 (70:30%), on decomposition processes over time (60 and 365 days). The litter bags were placed on the soil surface along a degradation gradient [near pristine (NP), lightly degraded (LD), and highly degraded (HD)]. We found that 1) mass loss in mixture compositions was significantly and positively correlated with initial nitrogen (N) and cellulose contents; 2) litter mixing (richness and evenness) influenced decomposition dynamics individually and in interaction with the incubation days and the degradation gradients; 3) in a general linear model (GLM), nonadditive antagonistic effects were more prominent than additive or neutral effects in final litter and nutrients except for carbon (C); and 4) in nutrients (C, N, lignin) and C/N ratio, additive effects shifted to nonadditive with incubation time. We speculated that the occurrence of nonadditive positive or negative effects varied with litter and nutrients mass remaining in each degraded gradient under the mechanism of initial litter quality of monoculture species, soil properties of experimental sites, and incubation time. Our study has important implications for grassland improvement and protection by considering species biodiversity richness, as well as species evenness.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".