Past environments modulate response to fluctuating temperatures in a marine fish species
Bibliographic record
Abstract
Abstract The rise in ocean temperatures predicted due to the global warming will impact the survival and structure of various marine organisms, in particular ectothermic organisms. Phenotypic plasticity enables species to cope with environmental changes, providing a vital buffer for evolutionary changes. Yet, the dynamics and the molecular mechanisms underpinning these plastic responses remain largely unexplored. Here, we assessed the impact of acclimation environment on organisms capacity for thermal plasticity. We conducted a genome-wide transcriptomic analysis on the Acadian redfish, S. fasciatus , exposed to four temperatures (2.5, 5.0, 7.5 and 10.0 ℃) over a long-term period (up to 10 months) followed by an acute temperature change (24 hours), simulating natural fluctuation condition the species could encounter. Our results showed a dynamic transcriptional response to temperature involving various genes functions. The rapid response to temperature shifts, coupled with the sustained expression of specific genes over an extended period highlighted the species’ capacity for plastic response to temperature changes. We also detected a significant effect of the interaction between the long and short terms temperature exposure on gene expression, highlighting the influence of the past environment on response to acute temperature changes. Specifically, fish acclimated to higher temperatures demonstrated an increased stress-related response to environmental fluctuations, as evidenced by both the shape of their reaction norms and the implication of stress-related gene functions. This result suggests that temperature conditions predicted for the near future in the Northwest Atlantic will trigger less adaptive plasticity to environmental fluctuations, highlighting the species’ vulnerability to ocean warming.
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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".