Neutral and adaptive drivers of genomic change in introduced brook trout (<i>Salvelinus fontinalis</i>) populations revealed by pooled sequencing
Bibliographic record
Abstract
Abstract Understanding the drivers of successful species invasions is important for conserving native biodiversity and for mitigating the economic impacts of introduced species. However, whole‐genome resolution investigations of the underlying contributions of neutral and adaptive genetic variation in successful introductions are rare. Increased propagule pressure should result in greater neutral genetic variation, while environmental differences should elicit selective pressures on introduced populations, leading to adaptive differentiation. We investigated neutral and adaptive variation among nine introduced brook trout ( Salvelinus fontinalis ) populations using whole‐genome pooled sequencing. The populations inhabit isolated alpine lakes in western Canada and descend from a common source, with an average of ~19 (range of 7–41) generations since introduction. We found some evidence of bottlenecks without recovery, no strong evidence of purifying selection, and little support that varying propagule pressure or differences in local environments shaped observed neutral genetic variation differences. Putative adaptive loci analysis revealed nonconvergent patterns of adaptive differentiation among lakes with minimal putatively adaptive loci (0.001%–0.15%) that did not correspond with tested environmental variables. Our results suggest that (i) introduction success is not always strongly influenced by genetic load; (ii) observed differentiation among introduced populations can be idiosyncratic, population‐specific, or stochastic; and (iii) conservatively, in some introduced species, colonization barriers may be overcome by support through one aspect of propagule pressure or benign environmental conditions.
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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 | 0.000 |
| 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".