The genetic implications of translocations on Fisher ( <i>Pekania pennanti</i> ) populations in Montana and Idaho
Bibliographic record
Abstract
Abstract Translocations are the intentional movement of animals across landscapes for conservation purposes and are used as a common method for restoring populations. Fisher (Pekania pennanti) populations have been extensively reintroduced and augmented in the Western United States, often using multiple source populations. To date, little is known about the success of past translocation efforts for Fisher recovery in the West, nor what the effects of using distinct populations for translocations were on contemporary population composition and genetic diversity. To understand the effects of historic translocations on contemporary Fisher population recovery, we analyzed >34 years of genetic data from fishers in Idaho and Montana (n = 315) that received multiple translocations from divergent source populations. We observed high genetic differentiation between fishers descendant from translocations using source populations in the Midwestern United States and translocated to the Cabinet Mountain range (MID) and fishers sampled throughout the rest of Idaho and Montana. Fishers outside of the Cabinet Mountains exhibited little genetic structure and had endemic US Northern Rocky Mountain or British Columbia mitochondrial haplotypes (BC-CLR), consistent with remnant populations that persisted in Montana and Idaho, intermixed with mid-20th century translocations of British Columbia populations. Five fishers had a low probability of nuclear genetic assignment to either the MID or the BC-CLR genetic clusters but showed evidence of recently derived British Columbia genetic ancestry. MID and BC-CLR genetic clusters in the US Northern Rocky Mountains had similar levels of genetic diversity across time and space but exhibited no evidence of gene flow between them. This pattern suggests that while translocation efforts succeeded in creating Fisher populations that persisted on the landscape, they have not created enough propagule pressure to produce movement between populations.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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".