Evolution and determination of phenotype in salmonid fishes: Inferences from molecular markers in the absence of pedigree
Bibliographic record
Abstract
Molecular markers can be used to scrutinize both pedigree and population genetic structure, providing important insights into phenotypic evolution and determination in natural populations. Here, microsatellite markers were used to investigate the determination of size, growth, and maturation traits in salmonid fishes. Two marker-assisted methodologies for estimating quantitative genetic parameters were compared in an aquaculture population of rainbow trout ('Oncorhynchus mykiss'). A regression-based model employing estimates of pairwise relatedness effectively detected significant components of genetic variance and covariance for size and spawning time traits. However, numerical estimates of parameters were unreliable. A Markov Chain Monte Carlo (MCMC) procedure to reconstruct families resulted in estimates that did not differ significantly from those obtained with known pedigree, and was therefore deemed the superior method. This method was then used to investigate constraints on the evolution of larger body size in two stunted populations (designated Lower and Upper) of brook charr ('Salvelinus fontinalis') from Freshwater River, Cape Race, Newfoundland, Canada. Population structure of the system was investigated using ecological and genetic methods. Over a 5-year period, mark-recapture data revealed limited movement, with the proportion of recaptured fish migrating from one population area to another being low (0-3.8%). Despite this, microsatellite analysis based on sixteen polymorphic loci provided no evidence of genetic differentiation. These results indicate that while gene flow occurs between the populations, they are relatively isolated on an ecological time frame. Selective and genetic constraints on the evolution of larger body size were investigated by measuring viability selection acting on length-at-age traits, and by estimating quantitative genetic parameters 'in situ' (following reconstruction of sibships using the MCMC procedure). In the Lower population, significant heritabilities were found for several traits, while selection was found to act on length-at-age traits in both populations. However selection does not in general favor larger adult size, and while such size selective mortality does operate on juveniles there is no evidence for a significant heritability of juvenile size in either population. Thus within each population, selection differentials and estimates of heritabilities for length-at-age traits, suggest that evolution of larger size is prevented by both selective and genetic constraints.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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".