Does the impact of biodiversity differ between emerging and endemic pathogens? The need to separate the concepts of hazard and risk
Bibliographic record
Abstract
Biodiversity is of critical value to human societies, but recent evidence that biodiversity may mitigate infectious-disease risk has sparked controversy among researchers. The majority of work on this topic has focused on direct assessments of the relationship between biodiversity and endemic-pathogen prevalence, without disentangling intervening mechanisms; thus study outcomes often differ, fuelling more debate. Here, we suggest two critical changes to the approach researchers take to understanding relationships between infectious disease, both endemic and emerging, and biodiversity that may help clarify sources of controversy. First, the distinct concepts of hazards versus risks need to be separated to determine how biodiversity and its drivers may act differently on each. This distinction is particularly important since it illustrates that disease emergence drivers in humans could be quite different to the general relationship between biodiversity and transmission of endemic pathogens. Second, the interactive relationship among biodiversity, anthropogenic change and zoonotic disease risk, including both direct and indirect effects, needs to be recognized and accounted for. By carefully disentangling these interactions between humans' activities and pathogen circulation in wildlife, we suggest that conservation efforts could mitigate disease risks and hazards in novel ways that complement more typical disease control efforts.This article is part of the themed issue 'Conservation, biodiversity and infectious disease: scientific evidence and policy implications'.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".