Accounting for imperfect detection reveals role of host traits in structuring viral diversity of a wild bat community
Bibliographic record
Abstract
Abstract Understanding how multi-scale host heterogeneity affects viral community assembly can illuminate ecological drivers of infection and host-switching. Yet, such studies are hindered by imperfect viral detection. To address this issue, we used a community occupancy model – refashioned for the hierarchical nature of molecular-detection methods – to account for failed detection when examining how individual-level host traits affect herpesvirus richness in eight species of wild bats. We then used model predictions to examine the role of host sex and species identity on viral diversity at the levels of host individual, population, and community. Results demonstrate that cPCR and viral sequencing failed to perfectly detect viral presence. Nevertheless, model estimates correcting for imperfect detection show that reproductively active bats, especially reproductively active females, have significantly higher viral richness, and host sex and species identity interact to affect viral richness. Further, host sex significantly affects viral turnover across host populations, as females host more heterogeneous viral communities than do males. Results suggest models of viral ecology benefit from integration of multi-scale host factors, with implications for bat conservation and epidemiology. Furthermore, by accounting for imperfect detection in laboratory assays, we demonstrate how statistical models developed for other purposes hold promising possibilities for molecular and epidemiological applications.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".