Does Genetic Variation Maintained by Environmental Heterogeneity Facilitate Adaptation to Novel Selection?
Bibliographic record
Abstract
Environmental heterogeneity helps maintain genetic variation in fitness. Therefore, one might predict that populations living in heterogeneous environments have higher adaptive potential than populations living in homogeneous environments. Such a prediction could be useful in guiding conservation priorities without requiring detailed genetic studies. However, this prediction will be true only if the additional genetic variation maintained by environmental heterogeneity can be used to respond to novel selection. Here we examine the effect of environmental heterogeneity on future adaptability using replicated experimental Drosophila melanogaster populations that had previously evolved for ∼100 generations under one of four selective regimes: constant salt-enriched larvae medium, constant cadmium-enriched larvae medium, and two heterogeneous regimes that vary either temporally or spatially between the two media. Replicates of these experimental populations were subjected to a novel heat stress while being maintained in their original larval diet selection regimes. Adaptation to increased temperature was measured with respect to female productivity and male siring success after ∼20 generations. For female productivity, there was evidence of adaptation overall and heterogeneous populations had a larger adaptive response than homogeneous populations. There was less evidence of adaptation overall for male siring success and no support for faster adaptation in heterogeneous populations.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".