Genetics of a reintroduced swift fox population highlights the need for integrated conservation between neighbouring countries
Bibliographic record
Abstract
Abstract The genetic consequences of reintroductions are rarely considered after releases cease, but long‐term viability depends on linked demography and genetic health. Reintroductions of swift foxes Vulpes velox began after 45 years of extirpation from Canada; these have resulted in national down‐listing to ‘threatened’ status, and the re‐establishment of a small contiguous population in Montana, US. Demographic growth has been associated with stable levels of genetic diversity and growing effective population size, but evidence of two genetic clusters, and a recent decline in abundance could be cause for conservation concern depending on underlying mechanisms. We analysed individuals from two time points at 18 microsatellite loci to investigate whether the genetic structure is a consequence of having used two separate release sites and non‐equilibrium population dynamics, but our results suggest that the population is likely at mutation‐ and migration‐drift equilibrium. We examined habitat effects on relative gene flow and found limited evidence for cropland to be a dispersal barrier, but effects of terrain roughness suggest that more rugged landscapes may reduce dispersal capacity. Using parentage analysis we determined maximum dispersal distances of up to 50 km for females and 100 km for males including movements in either direction across the international border, but no mixing of genetic clusters was seen in either country. Greater genetic connectivity among than within respective countries necessitates careful co‐management between Canada, where the species has the highest levels of legislative protection, and contiguous areas of the United States where limited trapping for fur is now permitted. We encourage similar analyses of conservation populations across international borders to determine how optimal genetic management can best mesh with different policies and conservation approaches among countries.
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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 | 0.001 | 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) | 0.002 | 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".