Environment and climate, rather than host diversity, drives disease in Galápagos birds
Bibliographic record
Abstract
Viral pathogens are increasingly recognized as important threats to human health and biodiversity. Once a pathogen is established in a population, disease prevalence may vary over space and time due to abiotic and biotic factors influencing transmission and host susceptibility. Identifying the processes that drive outbreaks is particularly challenging for pathogens that have diverse transmission routes. In this study, we investigated the abiotic and biotic drivers of avian pox dynamics in the Galápagos Islands, where the disease has been present for just over a century. We deeply sampled the passerine community at 18 different sites across Santa Cruz Island, capturing variation across seasons, elevations, habitat types and levels of anthropogenic disturbance. Prevalence was not correlated with host abundance, suggesting that transmission is not strongly density dependent. Instead, we found that environment was the strongest driver of disease. Pox prevalence declined with elevation and rose temporally with seasonal increases in temperature. Notably, climate effects appeared over a shorter time scale than typically observed for vector-borne diseases, suggesting environmental transmission may be a potentially underappreciated route of disease spread in this system. Nearly all passerine species in the community were susceptible to infection; however, species varied significantly in their disease burden. Coupled with weak support for the dilution hypothesis, this pattern suggests that certain species may serve as either reservoirs or sinks for infection. These results highlight climate as a key driver of outbreaks of poxviruses, a family of enduring significance for both humans and wildlife.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".