Habitat Loss and Biotic Velocity Response to Climate Change for Alpine Plant Species in Atlantic Oceanic Islands
Bibliographic record
Abstract
Climate change may shift species outside of their current climatic tolerances, which can be problematic for oceanic islands with limited options for species migration. Their alpine habitats, which represent islands within islands, are a special concern due to high endemism rates. The Canary Islands have two examples for such fragile ecosystems above 2000 m, on La Palma and Tenerife. This study contributes an assessment of the Canary Islands’ climatic habitat for 47 alpine plant species under observed climate change (1959–1989 versus 1990–2019 periods) as well as for future projections (2041–2060 and 2061–2080 periods). We analyzed the contraction of climatic habitats and migration requirements (biotic velocity) for each species to compensate for observed and predicted climate change. Our results suggest that temporary climate refugia exist on Tenerife but are insufficient on La Palma, where habitat loss due to historical climate change has already created an area of significant concern: the Cumbre Vieja Natural Park. On Tenerife, current alpine species can find suitable climate habitats in the caldera area and on the Teide-Pico Viejo volcano. That said, migration paths toward these refugia are long and complex, and human-assisted migration may be required. Species-specific statistics to support such management interventions are provided in this study.
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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 | 0.001 |
| 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".