Integrative use of spatial, genetic, and demographic analyses for investigating genetic connectivity between migratory, montane, and sedentary caribou herds
Bibliographic record
Abstract
Genetic differentiation is generally assumed to be low in highly mobile species, but this simplistic view may obscure the complex conditions and mechanisms allowing genetic exchanges between specific populations. Here, we combined data from satellite-tracked migratory caribou (Rangifer tarandus), microsatellite markers, and demographic simulations to investigate gene flow mechanisms between seven caribou herds of eastern Canada. Our study included one montane, two migratory, and four sedentary herds. Satellite-tracking data indicated possibilities of high gene flow between migratory herds: overlap of their rutting ranges averaged 10% across years and 9.4% of females switched calving sites at least once in their lifetime. Some migratory individuals moved into the range of the sedentary herds, suggesting possibilities of gene flow between these herds. Genetic differentiation between herds was weak but significant (FST=0.015): migratory and montane herds were not significantly distinct (FST all 9) than vice-versa (4Nm all<5), which suggests migratory herds had a demographic impact on sedentary herds. Demographic simulations showed that an effective immigration rate of 0.0005 was sufficient to obtain the empirical FST of 0.015, while a null immigration rate increased the simulated FST to >0.6. In conclusion, the weak genetic differentiation between herds cannot be obtained without some genetic exchanges among herds, as demonstrated by genetic and spatial data.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".