Interindividual behavioural variation in response to elevated CO2 predicts mRNA transcript abundance of genes related to acid-base regulation in medaka (Oryzias latipes)
Bibliographic record
Abstract
Rising carbon dioxide (CO2) in aquatic ecosystems due to climate change is a challenge for aquatic ectotherms. We examined whether interindividual variation in behavioural responses to CO2 could predict how a teleost fish would respond to elevated CO2 for multiple phenotypic and molecular traits. To this end, we first quantified behavioural responses of individuals exposed to acute elevated CO2, and used these to assign individuals as either high or low responders relative to the population mean. Subsequently, we exposed both high and low responders to elevated CO2 for 6 weeks and quantified the effect on body condition, behaviour, and mRNA transcript responses of gill and liver genes associated with relevant physiological processes. Generally, we found few relationships between the phenotypic groups and body condition and behaviour following the CO2 exposure period; however, stark differences between the phenotypic groups with respect to gene transcripts from each tissue related to various processes were found, mostly independent of CO2 exposure. The most pronounced changes were in the gill transcripts related to acid-base regulation, suggesting that the observed behavioural variation used to assign fish to phenotypic groups may have an underlying molecular origin. Should the link between behaviour and gene transcripts be shown to have a fitness advantage and be maintained across generations, interindividual variation in behavioural responses to acute CO2 exposure may be a viable and non-invasive tool to predict future population responses to elevated aquatic CO2.
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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".