Temporal Analysis of Genetic Structure to Assess Population Dynamics of Reintroduced Swift Foxes
Bibliographic record
Abstract
Reintroductions are increasingly used to reestablish species, but a paucity of long-term postrelease monitoring has limited understanding of whether and when viable populations subsequently persist. We conducted temporal genetic analyses of reintroduced populations of swift foxes (Vulpes velox) in Canada (Alberta and Saskatchewan) and the United States (Montana). We used samples collected 4 years apart, 17 years from the initiation of the reintroduction, and 3 years after the conclusion of releases. To assess program success, we genotyped 304 hair samples, subsampled from the known range in 2000 and 2001, and 2005 and 2006, at 7 microsatellite loci. We compared diversity, effective population size, and genetic connectivity over time in each population. Diversity remained stable over time and there was evidence of increasing effective population size. We determined population structure in both periods after correcting for differences in sample sizes. The geographic distribution of these populations roughly corresponded with the original release locations, which suggests the release sites had residual effects on the population structure. However, given that both reintroduction sites had similar source populations, habitat fragmentation, due to cropland, may be associated with the population structure we found. Although our results indicate growing, stable populations, future connectivity analyses are warranted to ensure both populations are not subject to negative small-population effects. Our results demonstrate the importance of multiple sampling years to fully capture population dynamics of reintroduced populations. Análisis Temporal de la Estructura Genética para Evaluar la Dinámica Poblacional de Zorros (Vulpes velox) Reintroducidos.
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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 | 0.001 | 0.001 |
| 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".