Data from: Experimental evidence that density mediates negative frequency-dependent selection on aggression
Bibliographic record
Abstract
1. Aggression can be beneficial in competitive environments if aggressive individuals are more likely to access resources than non-aggressive individuals. However, variation in aggressive behaviour persists within populations, suggesting that high levels of aggression might not always be favoured. 2. The goal of this study was to experimentally assess the effects of population density and phenotypic frequency on selection on aggression in a competitive environment. 3. We compared survival of two strains of Drosophila melanogaster that differ in aggression across three density treatments and five frequency treatments (single strain groups, equal numbers of each strain, and strains mixed at 3:1 and 1:3 ratios) during a period of limited resources. 4. While there was no difference in survival across single-strain treatments, survival was strongly density-dependent, with declining survival as density increased. Furthermore, at medium and high densities, there was evidence of negative frequency-dependent selection, where rare strains experienced greater survival than common strains. However, there was no evidence of negative frequency-dependent selection at low density. 5. Our results indicate that the benefits of aggression during periods of limited resources can depend on the interaction between the phenotypic composition of populations and population density, both of which are mechanisms that could maintain variation in aggressive behaviours within natural 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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.029 | 0.023 |
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".