Leaf phenology of three dominant limestone grassland plants matching the disturbance regime
Bibliographic record
Abstract
Abstract Question: Does interspecific variation in leaf phenology among grassland species help to explain the differences in species'performance under contrasting disturbance regimes. Location: Merishausen, northern Switzerland. Methods: Seasonal variations in leaf production and mortality were assessed for three species of nutrient‐poor limestone grasslands: Brachypodium pinnatum, Bromus erectus and Salvia pratensis ; each of these species tends to become dominant under a contrasting form of management. Their phenological characteristics were compared with their performance in plots differently managed for 21 years: (1) mowing in July; (2) mowing in October; (3) controlled burning in February; and (4) no biomass removal. Results: The species‐specific phenological patterns of leaf production and leaf mortality are associated with the abundance of the three species under the different management regimes. B. erectus , with relatively short‐lived leaves and leaf production late in the season dominates plots mown annually in June; it has almost disappeared from plots with winter burning. B. pinnatum , with production maxima of the long‐lived leaves early in the season, does not tolerate June mowing but is most abundant in plots burnt in winter when the species has no living leaves. S. pratensis , a species with long‐lived leaves but fast senescence of all the leaves in autumn, dominates plots mown in October. In unmown plots, all species are equally abundant. Conclusions: The seasonal pattern of leaf production and mortality strongly influence biomass and nutrient loss due to the management, and the growth that can be realized between the disturbances. A species may become dominant if it ‘fits’into the particular management regime, whereas a mismatch between phenological pattern and disturbance regime leads to its elimination from the community.
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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".