Spatiotemporal range dynamics and conservation optimization for endangered medicinal plants in the Himalaya
Bibliographic record
Abstract
Global climate change threatens the spatiotemporal distribution and resilience of species, especially those in mountainous regions. The Himalaya, a global biodiversity hotspot, harbors one of the richest medicinal plant communities, which are economically significant and contribute to human well-being through their health benefits. However, our understanding of species distribution across space and time under climate change scenarios, as well as effective conservation planning for these medicinal plants in the Himalaya, remains limited. In this study, we used the biomod2 ensemble model to predict the potential habitats of ten medicinal species using 497 occurrence points and 26 environmental variables under the past, present, and two future scenarios (2090; SSP126 and SSP585). We analyzed the spatiotemporal range dynamics of the ten species, performed their threat assessment using the redlistr R package, and developed a systematic conservation plan using Zonation 4.0 software. Our results showed that the habitat of most medicinal plants in the Himalaya have expanded their habitat range from the Last Glacial Maximum to the present, with a substantial contraction projected in both future scenarios. Six species migrated northwards towards high elevation, while four species migrated southwards. Threat assessment results indicated that all the species are endangered. Our conservation planning analysis revealed that northern parts of Pakistan such as Swat, Shangla, Hazara division, Jammu, and Kashmir are the core conservation areas for these ten species. We propose establishing additional protected areas in the Himalaya particularly in the western Himalaya for better management and conservation of endangered medicinal plants. • Western Himalaya are the priority areas for medicinal plants conservation. • Medicinal plants in the Himalaya will shift to higher elevation due to climate change in the future. • Declines in the distribution ranges will threaten the survival of medicinal plants. • Critically endangered plants have a high risk of extinction during the upward shift.
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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 | 0.000 | 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 | 0.000 |
| 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 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".