TEMPORAL VARIATION FAVORS THE EVOLUTION OF GENERALISTS IN EXPERIMENTAL POPULATIONS OF<i>DROSOPHILA MELANOGASTER</i>
Bibliographic record
Abstract
In variable environments, selection should favor generalists that maintain fitness across a range of conditions. However, costs of adaptation may generate fitness trade-offs and lead to some compromise between specialization and generalization that maximizes fitness. Here, we evaluate the evolution of specialization and generalization in 20 populations of Drosophila melanogaster experimentally evolved in constant and variable thermal environments for 3 years. We developed genotypes from each population at two temperatures after which we measured fecundity across eight temperatures. We predicted that constant environments would select for thermal specialists and that variable environments would select for thermal generalists. Contrary to our predictions, specialists and generalists did not evolve in constant and spatially variable environments, respectively. However, temporal variation produced a type of generalist that has rarely been considered by theoretical models of developmental plasticity. Specifically, genotypes from the temporally variable selective environment were more fecund across all temperatures than were genotypes from other environments. These patterns suggest certain allelic effects and should inspire new directions for modeling adaptation to fluctuating environments.
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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 | 0.000 | 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 | 0.001 |
| 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".