Accounting for local physiological adaptation in bioenergetic models: testing hypotheses for growth rate evolution by virtual transplant experiments
Bibliographic record
Abstract
We constructed bioenergetic models for locally adapted populations of Atlantic silversides, Menidia menidia, from different latitudes (Nova Scotia and South Carolina) to determine how genetic variation in growth physiology affects model parameters and predicted growth and to test two hypotheses on the evolution of countergradient variation in growth rate. Model parameters were estimated simultaneously for each population through a penalized likelihood approach incorporating laboratory measurements of metabolism, specific dynamic action, consumption, and growth. The resulting population-specific parameters differed by an average of 28%. The models were validated by successful (R2 > 0.9) prediction of growth in independent experiments under natural light and temperature conditions and by predicting growth in the field (R2 > 0.95). We then performed virtual reciprocal transplant simulations to test the alternative hypotheses that growth rate along a latitudinal gradient evolves in response to temperature or resource availability. Predictions for each transplanted population deviated significantly from observed growth for each native population, demonstrating the importance of accounting for interpopulation variation in model parameters. Our results indicate that the latitudinal cline in growth rate cannot be explained solely by thermal adaptation but may have arisen owing to the combined effects of temperature and food availability.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".