Prelude to a panzootic: Gene flow and immunogenetic variation in northern little brown myotis vulnerable to bat white-nose syndrome
Bibliographic record
Abstract
The fungus that causes bat white-nose syndrome (WNS) recently leaped from eastern North America to the Pacific Coast. The pathogen’s spread is associated with the genetic population structure of a host ( Myotis lucifugus). To understand the fine-scale neutral and immunogenetic variation among northern populations of M. lucifugus, we sampled 1142 individuals across the species’ northern range. We used genotypes at 11 microsatellite loci to reveal the genetic structure of, and directional gene flow among, populations to predict the likely future spread of the pathogen in the northwest and to estimate effective population size ( N e ). We also pyrosequenced the DRB1-like exon 2 of the class II major histocompatibility complex (MHC) in 160 individuals to explore immunogenetic selection by WNS. We identified three major neutral genetic clusters: Eastern, Montane Cordillera (and adjacent sampling areas), and Haida Gwaii, with admixture at intermediate areas and significant substructure west of the prairies. Estimates of N e were unexpectedly low (289–16 000). Haida Gwaii may provide temporary refuge from WNS, but the western mountain ranges are not barriers to its dispersal in M. lucifugus and are unlikely to slow its spread. Our major histocompatibility complex (MHC) data suggest potential selection by WNS on the MHC, but gene duplication limited the immunogenetic analyses.
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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 | 0.000 | 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".