Data from: Comprehensive evaluation of genetic population structure for anadromous river herring with single nucleotide polymorphism data
Bibliographic record
Abstract
Anthropogenic activities are placing increasing pressure on many species, particularly those that rely on more than one ecosystem. River herring (alewife, Alosa pseudoharengus and blueback herring, A. aestivalis collectively) are anadromous fishes that reproduce in rivers and streams of eastern North America and migrate to the western Atlantic Ocean. Here, we use data from single nucleotide polymorphisms (SNPs) to provide a comprehensive analysis of population structure for both species of river herring throughout their native ranges. We sampled river herring spawning runs in rivers from Newfoundland to Florida, examining a total of 108 locations, and genotyping over 8000 fish. We identified geographic population groupings (regional genetic groups) in each species, as well as significant genetic differentiation between most populations and rivers. Strong correlations between geographic and genetic distances (i.e., isolation by distance) were found range-wide for both species, although the patterns were less consistent at smaller spatial scales. River herring are caught as bycatch in fisheries and estimating stock proportions in mixed fishery samples is important for management. We assessed the utility of the SNP datasets as reference baselines for genetic stock identification. Results indicated high accuracy of individual assignment (76–95%) to designated regional genetic groups, and some individual populations, as well as highly accurate estimates of mixing proportions for both species. This study is the first to evaluate genetic structure across the entire geographic range of these species and provides an important foundation for conservation and management planning. The SNP reference datasets will facilitate continued multi-lateral monitoring of bycatch, as well as ecological investigation to provide information about ocean dispersal patterns of these species.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".