Evolution in natural populations: Molecular marker-based inference of life history and quantitative genetic data
Bibliographic record
Abstract
I develop and apply techniques that use molecular markers to infer critical evolutionary patterns in natural populations in general, and in particular to brook charr, 'Salvelinus fontinalis', inhabiting Freshwater River, Cape Race, Newfoundland. To facilitate quantitative genetic and other evolutionary analyses, I developed techniques to aid in the collection of pedigree information and to facilitate power and sensitivity analyses. Based on estimates of genetic differentiation and direct estimates of individual movement Freshwater River brook charr are uniformly and relatively mobile throughout their life cycle. Using molecular markers to infer the relationship between body size and reproductive success, I obtained qualitatively different predictions of optimal life histories from previous estimates for which such molecular data and techniques were not available. My estimates suggest that early maturity is adaptive in this population, relative to estimates that have not been informed by molecular data to relate body size to reproductive success. My estimates explain some of the variation in age at first maturity in Freshwater River brook charr, and furthermore provide the first example of a qualitative difference between optimal life histories as evaluated with and without the use of molecular markers. Finally, I used molecular markers to provide both pedigree information and fitness information, via the recognition of surviving individuals, to measure natural selection on genetically-based variation in body size in Freshwater River brook charr. These pedigree and survival data allowed me to attempt the first explicit use of estimated genetic covariances to remove bias in estimates of natural selection due to environmentally-induced relationships among phenotypic variation and fitness components. I obtained no evidence of a relationship between body size and viability at either phenotypic or genetic levels. Thus the evolution of body size is at least partially constrained by a lack of selection and the currently observed distribution of growth rates is resolvable with the form of viability selection in this system. However I showed that body size is positively phenotypically related to early maturity, which I have shown to be adaptive in this study system. Thus the lack of evolution of larger body size and of earlier maturation remains unexplained.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".