Genetic and Environmental Influences on Life History Traits in Lake Trout
Bibliographic record
Abstract
Abstract Lake trout Salvelinus namaycush exhibit substantial life history variation associated with variation in climatic and biotic variables. To assess the environmental and genetic influences on lake trout life history traits, eggs were collected from two lakes in Algonquin Park, Ontario, Lake Opeongo and Louisa Lake, with contrasting ecotypes and forage bases. Eggs from both populations and interpopulation hybrids (Lake Opeongo females × Louisa Lake males) were raised to maturity in a common controlled environment. There was evidence of a genetic basis to life history variation between ecotypes. Hatchery‐reared lake trout from Louisa Lake were found to have a faster prematuration growth rate and earlier age and smaller size at maturation than Lake Opeongo lake trout, whereas Lake Opeongo lake trout had greater postmaturation growth rates. Most of the life history traits of hybrids were intermediate between those of the parent populations. These differences were also consistent with reported differences on these populations in the field. Despite differences in maturation and body size, reproductive investment did not differ significantly among the hatchery‐reared populations. There was also evidence of phenotypic plasticity in growth (juvenile and adult) and age at maturity. For fish originating from each lake, hatchery‐raised fish showed higher prematuration growth rates and younger ages at maturity than did wild fish. Hatchery‐reared fish from Louisa Lake were projected to attain asymptotic sizes nearly 50% larger than those of their wild counterparts. These data provide evidence that wild lake trout populations exhibit a plastic response to resource limitation. The combined results of this study indicate that lake trout life history characteristics are driven by both genetic and environmental influences, which should be incorporated into current life history models and used in making management decisions.
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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 | 0.000 | 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".