Key conservation actions for European steppes in the context of the Post-2020 Global Biodiversity Framework
Bibliographic record
Abstract
Abstract The Kunming–Montreal Global Biodiversity Framework (KM–GBF) envisions a world living in harmony with nature by 2050, with 23 intermediate targets to be achieved by 2030. However, aligning international policy and national and local implementation of effective actions can be challenging. Using steppe birds, one of the most threatened vertebrate groups in Europe, as a model system, we identified 36 conservation actions for the achievement of the KM–GBF targets and we singled out—through an expert-based consensus approach—ten priority actions for immediate implementation. Three of these priority actions address at least five of the first eight KM–GBF targets, those related to the direct causes of biodiversity loss, and collectively cover all the targets when implemented concurrently. These actions include (i) effectively protecting priority areas, (ii) implementing on-the-ground habitat management actions, and (iii) improving the quality and integration of monitoring programmes. Our findings provide a blueprint for implementing effective strategies to halt biodiversity loss in steppe-like ecosystems. Our approach can be adapted to other taxonomic groups and ecosystems and has the potential to serve as a catalyst for policy-makers, prompting a transition from political commitment to tangible actions, thereby facilitating the attainment of the KM–GBF targets by 2030.
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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 | 0.014 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".