Conservation with local people: medicinal plants as cultural keystone species in the Southern Alps
Bibliographic record
Abstract
The concept of “cultural keystone species” (CKS) combines ecological and socioeconomic aspects and has a great potential for improving the overall success of conservation and restoration of ecosystems. In our study, we combined an ecological analysis of traditional medicinal plant species with an explorative analysis of the stakeholder landscape to understand the importance of plants for local communities. We investigate the feasibility of the CKS concept for traditional medicinal plants using the Southern Alps (Northern Italy) as a case study. Based on a comprehensive survey of traditional medicinal plants, we analyzed the habitats where they occur and their significance as CKS candidates. We applied the index of identified cultural influence (ICI). We identified some of the relevant stakeholders and their potential interest in traditional medicinal plants. From a total of 273 native medicinal species, we ranked the 10 most important CKS candidates. These comprised species with different ecology such as the herbs Achillea millefolium agg., Alchemilla xanthochlora, Arnica montana, Hypericum perforatum, Matricaria chamomilla, Peucedanum ostruthium, Urtica dioica, the shrub Juniperus communis, and the tree species Betula pendula. By merging their importance for the local communities with their occurrences in the habitats of South Tyrol, the concept of CKS can stimulate species and habitat conservation, and ecosystem restoration.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".