THE EVOLUTION OF TRADE-OFFS: TESTING PREDICTIONS ON RESPONSE TO SELECTION AND ENVIRONMENTAL VARIATION
Bibliographic record
Abstract
The concept of phenotypic trade-offs is a central element in evolutionary theory. In general, phenotypic models assume a fixed trade-off function, whereas quantitative genetic theory predicts that the trade-off function will change as a result of selection. For a linear trade-off function selection will readily change the intercept but will have to be relatively stronger to change the slope. We test these predictions by examining the trade-off between fecundity and flight capability, as measured by dorso-longitudinal muscle mass, in four different populations of the sand cricket, Gryllus firmus. Three populations were recently derived from the wild, and the fourth had been in the laboratory for 19 years. We hypothesized that the laboratory population had most likely undergone more and different selection from the three wild populations and therefore should differ from these in respect to both slope and intercept. Because of geographic variation in selection, we predicted a general difference in intercept among the four populations. We further tested the hypothesis that this intercept will be correlated with proportion macropterous and that this relationship will itself vary with environmental conditions experienced during both the nymphal and adult period. Observed variation in the phenotypic trade-off was consistent with the predictions of the quantitative genetic model. These results point to the importance of modeling trade-offs as dynamic rather than static relationships. We discuss how phenotypic models can incorporate such variation. The phenotypic trade-off between fecundity and dorso-longitudinal muscle mass is determined in part by variation in body size, illustrating the necessity of considering trade-offs to be multi factorial rather than simply bivariate relationships.
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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 | 0.011 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".