Sex Determination and Population Genetics of Smallmouth Bass (Micropterus dolomieu) in Eastern Lake Ontario
Bibliographic record
Abstract
Conserving genetic diversity within a population increases both its fitness and adaptability to environmental change; however, conservation management units frequently mismatch underlying genetic structure. Further challenges are introduced when quantifying genetic diversity, as the sex ratio and geographic distribution of samples can bias measures of population differentiation. Smallmouth Bass (SMB: Micropterus dolomieu) are an economically and ecologically valuable species of territorial freshwater fish. Regulated as a single unit across Lake Ontario and the St. Lawrence River, SMB are subject to regular anthropogenic dislocation through recreational angling. Since sex is not readily discernible from external morphology, and the genetic mechanism of sex determination is presently uncharacterized, typical SMB sex identification requires lethal autopsy. For this thesis I investigated the association of genomic markers with sex and geography for Smallmouth Bass in Eastern Lake Ontario, to improve the accuracy of phylogeographic study and subsequent conservation in the area. I hypothesized that Smallmouth Bass have an XY chromosomal system, and that genomic markers alone could identify sex. This hypothesis was tested using next-generation DNA sequencing of MluCI-SphI restriction digest associated loci from tournament-angled SMB mortalities. Although no sex-specific markers were present in males (or females), refuting the XY (and ZW) chromosome hypothesis, a leave-one-out predictive model generated with seven single nucleotide polymorphisms (SNPs) correctly predicted the sex of all 23 SMB and a reference female Florida Bass (Micropterus floridanus). Flanking DNA associated with these SNPs was then BLAST-searched against an assembled SMB transcriptome and NCBI’s swissprot database; however, no homology to sex-determining factors was found. I also hypothesized that genetic structure existed within tournament mortalities, which was confirmed following hierarchical K-selection analysis of 2,138 SNPs, indicating an optimal group number of two. Although some group assignments at higher K-values corresponded to approximate geographic location, the sample size of these subpopulations was too small to draw meaningful conclusions. To address this limitation, DNA was also quantified from 228 SMB angled across Lake Ontario to facilitate future fine-scale population structure analysis. Overall, these findings can be used to better manage the SMB fishery in Eastern Lake Ontario and provide a basis for further study.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".