From science to society: implementing effective strategies to improve wild pollinator health
Bibliographic record
Abstract
Abstract Despite a substantial increase in scientific, public and political interest in pollinator health and many practical conservation efforts, incorporating initiatives across a range of scales and sectors, pollinator health continues to decline. We review existing pollinator conservation initiatives and define their common structural elements. We argue that implementing effective action for pollinators requires further scientific understanding in six key areas: (i) status and trends of pollinator populations; (ii) direct and indirect drivers of decline, including their interactions; (iii) risks and co-benefits of pollinator conservation actions for ecosystems; (iv) benefits of pollinator conservation for society; (v) the effectiveness of context-specific, tailored, actionable solutions; and (vi) integrated frameworks that explicitly link benefits and values with actions to reverse declines. We propose use of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES) conceptual framework to link issues and identify critical gaps in both understanding and action for pollinators. This approach reveals the centrality of addressing the recognized indirect drivers of decline, such as patterns of global trade and demography, which are frequently overlooked in current pollinator conservation efforts. Finally, we discuss how existing and new approaches in research can support efforts to move beyond these shortcomings in pollinator conservation initiatives. This article is part of the theme issue ‘Natural processes influencing pollinator health: from chemistry to landscapes’.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".