Long‐term effects of the abandonment of grazing on steppe‐like grasslands
Bibliographic record
Abstract
Abstract Question: What are the consequences of grazing abandonment on the Stipa lessingiana dominated steppe‐like grasslands? What is the relative importance of management and environmental factors in causing variation in species composition and abundance in the continuously grazed and abandoned grassland stands? Location: Transylvanian Lowland, Romania. Methods: Repeated vegetation mapping of a grassland stand, where grazing was abandoned 35 years ago; re‐sampling six grassland stands surveyed 29–57 years ago. For revealing long‐term changes in species composition and rank abundance PCoA ordination was applied. The relative importance of management and environmental factors in structuring vegetation were explored by CCA ordination. Diversity, evenness and the relative number and abundance of red‐listed species were compared between managed and abandoned stands. Results: Our results pointed out that grasslands which were formerly grazed and dominated by S. lessingiana, in the long‐term absence of grazing, have been transformed into a S. pulcherrima dominated type. Management, probably by creating bare surfaces and preventing litter accumulation, had the strongest effect on the species composition and abundance in the grasslands. Abandoned grassland stands had lower diversity and evenness compared to continuously grazed stands. While at the same time, the relative number of threatened, rare species did not differ between managed and abandoned sites. Conclusion: Maintaining extensively grazed, as well as un‐managed, Stipa dominated grasslands would be important in order to create various habitat conditions for plant species, especially threatened and rare species, and promote diversity on the landscape scale.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".