Longitudinal impacts of habitat fragmentation on <i>Bartonella</i> and hemotropic <i>Mycoplasma</i> dynamics in vampire bats
Bibliographic record
Abstract
Abstract Habitat fragmentation can have negative impacts on wildlife including increased risk of infectious disease. To assess spatiotemporal changes in pathogen dynamics in vampire bats ( Desmodus rotundus ) in response to habitat fragmentation, we used general linear mixed models to investigate the influence of site, year, and tree cover on the prevalence of Bartonella and hemotropic Mycoplasma (hemoplasma) in bats in one large and one small forest fragment in northern Belize across seven years. Bartonella was marginally more prevalent in later years, while year and site differences in hemoplasma infections were driven by a peak in prevalence in the third year of the study in the small fragment. Bartonella prevalence increased with forest loss, but only in the large fragment, whereas hemoplasma prevalence showed a marginal negative response to forest loss. The effects of site, year, and forest loss on infection likelihood varied by pathogen genotype. Neither site nor year affected Bartonella genotypes, but one genotype was positively associated with tree cover. Two hemoplasma genotypes were influenced by year, but with differing trends. One genotype increased with tree cover regardless of site while another increased with forest loss at the small fragment only. Our work demonstrates that the effects of habitat fragmentation on infection prevalence depended on both the pathogen and specific genotype. Our findings complicate expectations of how habitat fragmentation affects infectious disease dynamics in bats. As such, management practices aimed at mitigating the impacts of infectious diseases in fragmented systems should be tailored to specific pathogens of concern.
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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 | 0.001 | 0.001 |
| 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".