A meta‐analysis exploring associations between habitat degradation and Neotropical bat virus prevalence and seroprevalence
Bibliographic record
Abstract
Habitat degradation can increase zoonotic disease risks by altering infection dynamics in wildlife and increasing wildlife–human interactions. Bats are an important taxonomic group to consider these effects, because they harbour many relevant zoonotic viruses and have species‐ and context‐dependent responses to degradation that could affect zoonotic virus dynamics. Yet our understanding of the associations between habitat degradation and bat virus prevalence and seroprevalence are limited to a small number of studies, which often differ in the bats or viruses sampled, the study region, and methodology. To develop a broad understanding of the associations between bat viruses and habitat degradation, we conducted an initial phylogenetic meta‐analysis that combines published prevalence and seroprevalence (‘(sero)prevalence') with remote‐sensing habitat degradation data. Our dataset includes 588 unique records of (sero)prevalence across 16 studies, 64 bat species, and five virus families. We quantified the overall strength and direction of the relationship between habitat degradation and bat virus outcomes and tested how this relationship is moderated by the time between habitat degradation and bat sampling and by ecological traits of bat hosts while controlling for phylogenetic non‐independence among bat species. We found no effect of degradation on prevalence overall, although a weak effect may exist when forest loss occurs the year prior to bat sampling. In contrast, we detected a negative but weak association between degradation and seroprevalence overall that was strengthened when forest loss occurred the year prior to bat sampling. No bat traits that we investigated interacted with habitat degradation to impact virus outcomes, suggesting observed trends are independent of these traits. Biases in our initial dataset highlight opportunities for future work; prevalence was highly zero‐inflated, and seroprevalence was dominated by Desmodus rotundus and rabies virus. These findings and subsequent analyses will improve our understanding of how global change affects host–pathogen dynamics.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".