Biodiversity series: The function of biodiversity in the ecology of vector-borne zoonotic diseases
Bibliographic record
Abstract
This is a critical evaluation of the influence of species diversity within communities of vertebrates on the risk of human exposure to vector-borne zoonoses. Vertebrates serve as natural reservoirs of many disease agents (viral, bacterial, protozoal) that are transmitted to humans by blood-feeding arthropod vectors. We describe the natural history of the Lyme disease zoonosis to illustrate interactions among pathogens, vectors, vertebrate hosts, and risk to humans. We then describe how the presence of a diverse assemblage of vertebrates can dilute the impact of the principal reservoir (the white-footed mouse, Peromyscus leucopus) of Lyme disease spirochetes (Borrelia burgdorferi), thereby reducing the disease risk to humans. Exploring the logic of what we call the dilution effect reveals four conditions that are necessary for it to apply generally to vector-borne zoonoses: (1) the feeding habits of the vector are generalized; (2) the pathogen is acquired by the vector from hosts (as opposed to exclusively transovarial transmission); (3) reservoir competence (the ability of a particular host species to infect a vector) varies among host species; and (4) the most competent reservoir host tends to be a community dominant, as defined by the proportion of the tick population fed by that species. When these conditions are met, vertebrate communities with high species diversity will contain a greater proportion of incompetent reservoir hosts that deflect vector meals away from the most competent reservoirs, thereby reducing infection prevalence and disease risk. Incorporating the likelihood that the abundance of competent reservoirs is reduced in more diverse communities, owing to the presence of predators and competitors, reinforces the impact of the dilution effect on the density of infected vectors. A review of the literature reveals the generality, though not the universality, of these conditions, which suggests that the effects of diversity on disease risk may be widespread. Issues in need of further exploration include (i) the relative importance of diversity per se versus fluctuating numbers of particular species; (ii) the relevance of species richness versus evenness to the dilution effect; (iii) whether the dilution effect operates at both local and regional scales; and (iv) the shape of empirically determined curves relating diversity to measures of disease risk. Further studies linking community ecology with epidemiology are warranted.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.053 | 0.011 |
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".