Genetic connectivity among swarming sites in the wide ranging and recently declining little brown bat (<i><scp>M</scp>yotis lucifugus</i>)
Bibliographic record
Abstract
Characterizing movement dynamics and spatial aspects of gene flow within a species permits inference on population structuring. As patterns of structuring are products of historical and current demographics and gene flow, assessment of structure through time can yield an understanding of evolutionary dynamics acting on populations that are necessary to inform management. Recent dramatic population declines in hibernating bats in eastern North America from white-nose syndrome have prompted the need for information on movement dynamics for multiple bat species. We characterized population genetic structure of the little brown bat, Myotis lucifugus, at swarming sites in southeastern Canada using 9 nuclear microsatellites and a 292-bp region of the mitochondrial genome. Analyses of F ST, ΦST, and Bayesian clustering (STRUCTURE) found weak levels of genetic structure among swarming sites for the nuclear and mitochondrial genome (Global F ST = 0.001, P < 0.05, Global ΦST = 0.045, P < 0.01, STRUCTURE K = 1) suggesting high contemporary gene flow. Hierarchical AMOVA also suggests little structuring at a regional (provincial) level. Metrics of nuclear genetic structure were not found to differ between males and females suggesting weak asymmetries in gene flow between the sexes. However, a greater degree of mitochondrial structuring does support male-biased dispersal long term. Demographic analyses were consistent with past population growth and suggest a population expansion occurred from approximately 1250 to 12,500 BP, following Pleistocene deglaciation in the region. Our study suggests high gene flow and thus a high degree of connectivity among bats that visit swarming sites whereby mainland areas of the region may be best considered as one large gene pool for management and conservation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".