Longitudinal impacts of habitat fragmentation on <i>Bartonella</i> and hemotropic <i>Mycoplasma</i> dynamics in vampire bats
Bibliographic record
Abstract
Habitat fragmentation can negatively impact wildlife, including increasing infectious disease risk. We assessed spatiotemporal changes in pathogen dynamics in vampire bats (Desmodus rotundus) in response to habitat fragmentation using general linear mixed models to investigate the influence of site, year, and tree cover on Bartonella spp. and hemotropic Mycoplasma spp. (hemoplasmas) infection risk in bats in one large and one small forest fragment in Belize across seven years. While Bartonella infections were more likely in the final years of the study regardless of site, hemoplasma infection likelihood was not significantly different across years or sites. Bartonella infections were associated with increased forest loss in the large fragment only, whereas hemoplasma infections were not associated with forest loss. The effects of site, year, and forest loss on infection likelihood varied by pathogen genotype despite low model explanatory power. Neither site nor year was associated with bartonellae, but one genotype was positively associated with tree cover. Two hemoplasmas were influenced by year with differing trends: one genotype was negatively associated with tree loss across sites, while another was positively associated with forest loss at the small fragment only. Both pathogens were similarly influenced by bat demographics and showed instances of infection status and genotype switching. Our work suggests that the effects of habitat fragmentation on infection risk depend on both the pathogen and specific genotype, complicating expectations of how environmental change affects wildlife disease dynamics. Efforts to mitigate infectious disease impacts 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".