Neurogenomic diversity enhances collective antipredator performance in <i>Drosophila</i>
Bibliographic record
Abstract
Abstract Collective behavior is a unique social behavior that plays crucial roles in detecting and avoiding predators. Despite a long history of research on the ecological significance, its neural and genetic underpinnings remain elusive. Here we focus on the mesmerizing nature that visual cues from surrounding conspecifics alleviate the fear response to threatening stimuli in Drosophila melanogaster . A large-scale behavioral experiment and genome-wide association analysis utilizing 104 strains with known genomes uncovered the genetic foundation of the emergent behavioral properties of flies. We found genes involved in visual neuron development associated with visual response to conspecifics, and the functional assay confirmed the regulatory significance of lamina neurons. Furthermore, behavioral synchronization combined with interindividual heterogeneity in freezing drove nonadditive, synergistic changes in group performance for predatory avoidance. Our novel approach termed genome-wide higher-level association study (GHAS) identified loci whose within-group genetic diversity potentially contributes to such an emergent effect. Population genetic analysis revealed that selective pressure may favor increased responsiveness to conspecifics, indicating that by-productive genomic diversity within the group leads to a collective phenomenon. This work opens up a new avenue to understand the genomics underpinning the group-level phenotypes and offers an evolutionary perspective on the mechanism of collective behavior.
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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 | 0.000 | 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 | 0.000 |
| 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".