Potential for anthropogenic disturbances to influence evolutionary change in the life history of a threatened salmonid
Bibliographic record
Abstract
Although evolutionary change within most species is thought to occur slowly, recent studies have identified cases where evolutionary change has apparently occurred over a few generations. Anthropogenically altered environments appear particularly open to rapid evolutionary change over comparatively short time scales. Here, we consider a Pacific salmon population that may have experienced life-history evolution, in response to habitat alteration, within a few generations. Historically, juvenile fall Chinook salmon (Oncorhynchus tshawytscha) from the Snake River migrated as subyearlings to the ocean. With changed riverine conditions that resulted from hydropower dam construction, some juveniles now migrate as yearlings, but more interestingly, the yearling migration tactic has made a large contribution to adult returns over the last decade. Optimal life-history models suggest that yearling juvenile migrants currently have a higher fitness than subyearling migrants. Although phenotypic plasticity likely accounts for some of the change in migration tactics, we suggest that evolution also plays a significant role. Evolutionary change prompted by anthropogenic alterations to the environment has general implications for the recovery of endangered species. The case study we present herein illustrates the importance of integrating evolutionary considerations into conservation planning for species at risk.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".