Patterns and causes of life history variation in lake trout, Salvelinus namaycush
Bibliographic record
Abstract
Lake trout (Salvelinus namaycush) exhibit substantial life history variation across their range, but considerable local variation also occurs. Life history traits play a critical role in shaping population dynamics. As such, the observed life history variation makes it difficult to build conservation and management models. Populations should respond to varied environmental conditions by selecting the most favourable combination of life history traits. However, life history traits may alter as a result of phenotypic plasticity and/or genetic response to environmental conditions. This thesis examines the degree and mechanisms behind life history variation in lake trout. Variability is examined over the species range and within small geographic areas. Results indicate a strong association between enviromnental and lake trout life history variability. In particular, climate variables such as net thermal input and winter length and severity are correlated with a number of lake trout life history characteristics across the range. Lake morphology was associated with asymptotic sizes of lake trout, and shows contrasting associations with longevity and size at maturity in northern versus southern ranges. On a local scale, divergence in life history traits represented a combination of plastic responses and genetic adaptations to local conditions. The majority of life history traits examined in this thesis appeared to have a genetic basis; however stunted populations showed a plastic response to lack of forage fish. Yet, not all stunted populations within a region showed similar adaptive responses in early life history characteristics and we propose that the different responses may be predator mediated. Finally, the phenotypic divergence in life history traits among populations introduced from a common source approximately a century ago exhibited rapid plastic and genetic responses to novel environments. This thesis identifies that both plastic and genetic responses are common in lake trout, that these responses can occur quite rapidly, and that a significant number of environmental variables need to be considered before we can predict life history variation. These findings may be even more critical as populations continue to face stresses from climate change, overexploitation, habitat destruction, and introduced species.
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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 | 0.001 |
| 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".