Is Niagara Falls a barrier to gene flow in riverine fishes? A test using genome‐wide SNP data from seven native species
Bibliographic record
Abstract
Since the early Holocene, fish population genetics in the Laurentian Great Lakes have been shaped by the dual influences of habitat structure and post-glacial dispersal. Riverscape genetics theory predicts that longitudinal habitat corridors and unidirectional downstream water-flow drive the downstream accumulation of genetic diversity, whereas post-glacial dispersal theory predicts that fish genetic diversity should decrease with increasing distance from glacial refugia. This study examines populations of seven native fish species codistributed above and below the 58 m high Niagara Falls - a hypothesized barrier to gene flow in aquatic species. A better understanding of Niagara Falls' role as a barrier to gene flow and dispersal is needed to identify drivers of Great Lakes genetic diversity and guide strategies to limit exotic species invasions. We used genome-wide SNPs and coalescent models to test whether populations are: (a) genetically distinct, consistent with the Niagara Falls barrier hypothesis; (b) more genetically diverse upstream, consistent with post-glacial expansion theory, or downstream, consistent with the riverscape habitat theory; and (c) have migrated either upstream or downstream past Niagara Falls. We found that genetic diversity is consistently greater below Niagara Falls and the falls are an effective barrier to migration, but two species have probably dispersed upstream past the falls after glacial retreat yet before opening of the Welland Canal. Models restricting migration to after opening of the Welland Canal were generally rejected. These results help explain how river habitat features affect aquatic species' genetic diversity and highlight the need to better understand post-glacial dispersal pathways.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".