Effects of climate change on threatened Spanish medicinal and aromatic species: predicting future trends and defining conservation guidelines
Bibliographic record
Abstract
Climate change will impact several ecosystems, and the resilience of the weakest links of the ecological networks may be decisive in maintaining the ecological structure. The assessment of tendencies in the distribution and resilience of endangered medicinal species against global change can be an excellent tool to predict and minimize future negative effects, even more so if we consider that these species may be useful to us. Spain is one of the richest countries in plant diversity along the Mediterranean basin, and many representatives of the Spanish flora are medicinal plants. Under scenarios of climate change, the distribution ranges of many of these species are likely to alter. In this paper we used ecological niche modeling to predict future changes in the distribution of 41 medicinal plants included in the 2013 assessment of threatened species in Spain. We generated climate-based niche models for each medicinal species and projected them for each decade from 2010 until 2080. Our results identified and prioritized the most vulnerable species and areas to future predicted changes. These results should be useful for conservation planning and especially for prioritizing areas for protection.
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 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".