Population genetics reveal patterns of natural colonisation of an ecologically and commercially important invasive fish
Bibliographic record
Abstract
Although historical records of introductions that trigger successful biological invasions are common, subsequent patterns of dispersal and colonisation routes are unclear. We use microsatellites to examine genetic population structuring of established invasive brown trout (Salmo trutta) populations in Newfoundland, Canada, for evidence of “natural” dispersal, human-mediated introductions, and colonisation routes. We also explored ancestry of contemporary populations relative to presumed progenitors. Results analysed using STRUCTURE, DAPC, a NJ tree and FST comparisons support records of historical introductions; current Newfoundland populations are largely descended from Scottish stock, with St. John’s the primary introduction site. Subsequent dispersal of these trout was facilitated principally by anadromy, largely consistent with a classic stepping-stone model, with significant isolation-by-distance. With one exception, dispersal along the north and south coasts of the Avalon peninsula appears to be natural and independent, involving stochastic processes resulting in unique outcomes for population composition. This study is a good example of dispersal patterns during a contemporary invasion underscoring the potential for non-anadromous founders to re-express anadromy, facilitating colonization of distant sites.
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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 | 0.000 |
| 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".