Pronounced Genetic Structure and Site Fidelity among Native Muskellunge Populations in Lake Huron and Georgian Bay
Bibliographic record
Abstract
Abstract Conservation and management issues related to genetic diversity and stock structure of native populations of Muskellunge Esox masquinongy have largely been unexplored. In Lake Huron's North Channel and Georgian Bay, Muskellunge populations have been impacted by historical commercial fisheries, recreational fisheries, loss of spawning habitat, historical water quality issues, and ecosystem changes. To determine the spatial genetic structure of native Muskellunge in Lake Huron and Georgian Bay, spawning adults were sampled from 10 sites in the North Channel and eastern Georgian Bay. Genotyping with 20 microsatellite DNA loci showed substantial spatial genetic structure, with significant pairwise divergences among spawning sites. Individual‐ and population‐based analyses revealed hierarchical population structuring, with strong patterns of spawning site fidelity and isolation by distance; very low levels of dispersal and gene flow over historical and contemporary timescales were indicated. Estimation of effective population sizes highlighted the limited genetic resources that are present in these localized populations. The present results show that Muskellunge in Lake Huron and Georgian Bay consist of multiple small populations with limited ranges and high site fidelity and should be managed accordingly. Received February 28, 2016; accepted July 1, 2016 Published online October 11, 2016
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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 | 0.000 |
| 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 | 0.000 |
| 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".