Rethinking the influence of hydroelectric development on gene flow in a long-lived fish, the Lake Sturgeon Acipenser fulvescens
Bibliographic record
Abstract
Many hydroelectric dams have been in place for 50 - >100 years, which for most fish species means that enough generations have passed for fragmentation induced divergence to have accumulated. However, for long-lived species such as Lake Sturgeon, Acipenser fulvescens, it should be possible to discriminate between historical population structuring and contemporary gene flow and improve the broader understanding of anthropogenic influence. On the Winnipeg River, Manitoba, two hypotheses were tested: 1) Measureable quantities of former reservoir dwelling Lake Sturgeon now reside downstream of the Slave Falls Generating Station, and 2) genetically differentiated populations of Lake Sturgeon occur upstream and downstream, a result of historical structuring. Genetic methods based on ten microsatellite markers were employed, and simulations were conducted to provide context. With regards to contemporary upstream to downstream contributions, the inclusion of length-at-age data proved informative. Both pairwise relatedness and Bayesian clustering analysis substantiated that fast-growing outliers, apparently entrained after residing in the upstream reservoir for several years, accounted for ~15% of the Lake Sturgeon 525-750 mm fork length captured downstream. With regards to historical structuring, upstream and downstream populations were found to be differentiated (FST = 0.011, and 0.013-0.014 when fast-growing outliers were excluded), and heterozygosity metrics were higher for downstream versus upstream juveniles. Historical asymmetric (downstream) gene flow in the vicinity of the generating station was the most logical explanation for the observed genetic structuring. In this section of the Winnipeg River, construction of a major dam does not appear to have fragmented a previously panmictic Lake Sturgeon population, but alterations to habitat may be influencing upstream to downstream contributions in unexpected ways.
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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 | 0.000 | 0.000 |
| 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) | 0.000 | 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".