Long-term natural dynamics of an alpine lichen heath in the Teberda State Biosphere Reserve, northwestern Caucasus
Bibliographic record
Abstract
The alpine vegetation of Europe and the Caucasus has experienced substantial changes due to climate warming and reduced grazing. Exposed ridge communities, such as alpine heaths, are presumed to be less vulnerable to such processes. Herein, we analyze long-term dynamics (over 37 years) of an alpine lichen heath in the Teberda State Biosphere Reserve, Karachaevo-Cherkessian Republic, Russia. We counted the shoots of all vascular plants present on permanent plots. Autocorrelated linear regressions, a non-metric multidimensional scaling ordination, and a fourth-corner analysis were applied to characterize the relationships between shoot numbers, climate variables (temperatures and precipitation), functional traits, and species strategies. Nine species, including dominant Festuca ovina L. and Antennaria dioica (L.) Gaertn., increased their abundances, whereas Carex spp. were observed to decrease. The overall dynamics were mainly driven by increasing mean temperatures during the growing season (July and August). None of the changes observed in the selected traits were correlated with increasing or decreasing numbers of species; however, some traits (plant height, specific root length, specific leaf area, and leaf carbon content) were potentially associated with climate variables. The observed dynamics suggested an overall increase in the abundance of herbaceous plants. Generally, our results support “greening” effects in tundra and alpine biomes.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| 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 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".