Evolutionary genetics of behaviour in the fruit fly Drosophila melanogaster
Bibliographic record
Abstract
Understanding of the origin, maintenance, and importance of variation in genes provides a framework for evolutionary genetic analyses. I attempt to broaden our understanding of the evolutionary genetics of behaviour by studying the fruit fly, Drosophila melanogaster. Allelic variation in foraging (for) underlies a larval foraging behaviour polymorphism. Since the foraging gene has been recently shown to affect similar behaviours in honey bees and nematodes, I explored the possibility of a widespread link between this gene and behaviour. Molecular phylogenies, constructed from the amino acid sequences of protein encoded by for (PKG) from an array of species, suggest that the link may extend into vertebrates. This type of conserved gene function can provide a source of candidate genes since the genes affecting behaviour in one species likely influence similar behaviours in additional species. I outline a candidate gene approach (CGA) in an attempt to motivate the integration of modern genetics with behavioural ecology. I describe how candidate genes can be useful for predicting behaviour, manipulating traits, and providing a basis for comparative analyses. I use quantitative trait locus mapping and the CGA to identify a second naturally varying gene, in addition to for, that affects foraging behaviour. I find that wings up A (wupA) is a negative modifier of for that suppresses one of the natural for alleles (forR, rover) but does not affect the other (forS, sitter). This appears to be accomplished by post-translational interactions between the proteins encoded by for and wupA. I show that negative frequency-dependent selection acting on for can maintain the rover/sitter polymorphism. Under food limitation, I find that the fitness of either of the variants is highest when they are rare in the population. By analyzing the D. melanogaster genome, I challenge the long-standing prediction of sex linkage in the genes affecting sexual selection. I find that the genes are distributed throughout the genome and I suggest that the high prevalence of pleiotropic effects (multiple phenotypes of a gene) may account for the lack of predominant sex linkage. Collectively, my thesis examines several of the fundamental issues in evolutionary genetics through analyses of animal behaviour.
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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 | 0.001 | 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".