Causes and Consequences of Straying into Small Populations of Pacific Salmon
Bibliographic record
Abstract
Abstract Most Pacific salmon Oncorhynchus spp. migrate to their natal sites to spawn. Some, however, stray into nonnatal habitats and interact (e.g., reproduce) with individuals from other populations. Pacific salmon straying has been heavily studied for several decades, particularly from the perspective of the populations that donate the stray migrants. Conservation consequences are experienced primarily by the populations that receive strays, though, and there is recent evidence of significant levels of genetic introgression in small recipient populations, which could contribute to the loss of local adaptations. Straying may also provide the benefit of a demographic rescue effect that could save declining recipient populations from extirpation. We highlight the influence of population abundances on the magnitude of straying into recipient populations and demonstrate this using evidence we collected from a small population of Sockeye Salmon O. nerka in British Columbia, Canada. We also review potential factors that might promote higher donor stray rates and therefore recipient straying. Evidence of factors that affect straying is limited and we identify several knowledge gaps, as well as anthropogenic activities that could promote straying. We encourage further discussion and research on the potential effects of recipient straying and the factors that affect straying rates.
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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 | 0.003 |
| 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 | 0.001 |
| 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".