Thermal tolerance has high heritability in Atlantic salmon, Salmo salar
Bibliographic record
Abstract
Rising temperatures due to anthropogenic climate change pose a threat to wild Atlantic salmon ( Salmo salar ) populations in their natural habitat and to farmed populations during their major growth phase in coastal (seawater) net pens. Given that tremendous gains have been made in farmed salmon production through artificial selection programs for traits such as growth rate and disease resistance, we therefore examined variation among 105 families in post-smolt seawater thermal tolerance to assess whether this trait warrants inclusion in a selective breeding program. We used two established thermal challenge protocols for this: a rapid temperature increase using loss of equilibrium as the endpoint (critical thermal maximum; CTmax [1506 fish]) and a slower increase with mortality or morbidity as the endpoint (incremental thermal maximum; ITmax [936 fish]). High estimated heritability values were obtained for both (h² = 0.47 and 0.40, respectively), suggesting that improved acute and/or chronic high-temperature tolerance may be attainable for farmed salmon through artificial selection. Furthermore, given that farmed salmon are not many generations removed from wild, wild populations may also have some capacity to adapt to increasing temperatures brought about by climate change. However, we found no genetic correlation between CTmax and ITmax. Genetic correlations between these indices and other traits that might influence thermal tolerance (body size, condition factor, ventricle size, and hematocrit) were absent or, at most, weak. • Both critical and incremental thermal tolerance (CTmax and ITmax) have high heritability. • There is no significant genetic correlation between CTmax and ITmax. • Thermal tolerance is not genetically correlated with body size, condition factor, ventricle size, or hematocrit.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".