Transgenerational effects on body size and survival in Brook charr (<i>Salvelinus fontinalis</i>)
Bibliographic record
Abstract
Abstract Higher temperatures are now observed in several ecosystems and act as new selective agents that shape traits and fitness of individuals. Transgenerational effects may be important in modulating adaptation of future generations and buffering negative impacts of temperature changes. The potential for these effects may be important in freshwater fish species, as temperature is a key abiotic component of their environment. Yet, still, relatively few studies have assessed the presence and importance of transgenerational effects under natural conditions. The purpose of this study was to test how parental thermal conditions influenced offspring growth and survival following stocking in Brook charr (Salvelinus fontinalis). To do so, part of the breeders were exposed to a “cold” treatment while others were exposed to a “warm” treatment during the final steps of gonad maturation (constant 2°C difference between treatments along the seasonal temperature decrease). The impact on offspring of a selection treatment targeting production traits of interest (absence of sexual maturation at 1+, combined with increased growth) in breeders was also evaluated. After 7–8 months of growth in captivity, offspring were stocked in natural lakes. Their growth and survival were assessed about a year later. Offspring from “cold” breeders showed lower survival than those from “warm” breeders and the selection treatment had no effect on survival. However, the selection treatment was linked to lower Fulton's condition index, which, in turn, was positively correlated to survival in lakes. This study highlights the importance of working in ecological/industrial context to fully assess the different impacts of transgenerational effects on traits and survival. Our results also have important implications for stocking practices used to support the sport fishing industry.
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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 | 0.000 | 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) | 0.001 | 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".