Variation in spawning time promotes genetic variability in population responses to environmental change in a marine fish
Bibliographic record
Abstract
The level of phenotypic plasticity displayed within a population (i.e. the slope of the reaction norm) reflects the short-term response of a population to environmental change, while variation in reaction norm slopes among populations reflects spatial variation in these responses. Thus far, studies of thermal reaction norm variation have focused on geographically driven adaptation among different latitudes, altitudes or habitats. Yet, thermal variability is a function of both space and time. For organisms that reproduce at different times of year, such variation has the potential to promote adaptive variability in thermal responses for critical early life stages. Using common-garden experiments, we examined the spatial scale of genetic variation in thermal plasticity for early life-history traits among five populations of endangered Atlantic cod (Gadus morhua) that spawn at different times of year. Patterns of plasticity for larval growth and survival suggest that population responses to climate change will differ substantially, with increasing water temperatures posing a considerably greater threat to autumn-spawning cod than to those that spawn in winter or spring. Adaptation to seasonal cooling or warming experienced during the larval stage is suggested as a possible cause. Furthermore, populations that experience relatively cold temperatures during early life might be more sensitive to changes in temperature. Substantial divergence in adaptive traits was evident at a smaller spatial scale than has previously been shown for a marine fish with no apparent physical barriers to gene flow (∼200 km). Our findings highlight the need to consider the impact of intraspecific variation in reproductive timing on thermal adaptation when forecasting the effects of climate change on animal populations.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".