Beyond the present: How climate change is relevant to pest risk analysis
Bibliographic record
Abstract
Abstract Climate change is widely recognized as a critical global challenge with far‐reaching consequences. It affects pest species by altering their population dynamics, actual and potential distribution areas, as well as interactions with their hosts and natural enemies. Climate change thus has potentially important implications for multiple areas of the pest risk analysis (PRA) process. The importance of including climate change in PRA may vary depending on the climatic context of the PRA area in relation to the speed of climate change. If climatic changes within the time horizon of interest are minimal, their potential impact on pest risk is reduced accordingly. For PRAs in a changing climate, we need to be concerned with how future climates could alter our assessment of the risks currently posed by each pest species. While climate can influence the distribution and abundance of pests and hosts alike, its significance will vary depending on the situation. The inclusion of climate change within a PRA also presents challenges. The dynamic nature of climate change, with its complex interactions and uncertainties, can make it difficult to predict and assess the future risks posed by pests accurately. Uncertainties related to future predictions may be much greater than the potential effects associated with climate change and species’ responses to it. This paper outlines examples of the effects of climate change on hosts and different groups of pests, including invertebrates, pathogens, weeds and vector species. The aim is to review the opportunities and challenges of incorporating climate change into PRA, offering insights for a variety of stakeholders including policymakers on this topic.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".