Evolution of sensory systems underlies the emergence of predatory feeding behaviours in nematodes
Bibliographic record
Abstract
Abstract Understanding how animal behaviour evolves remains a major challenge, with few studies linking genetic changes to differences in neural function and behaviour across species. Here, we identify specific sensory adaptations associated with the emergence of predatory feeding behaviours in the nematode Pristionchus pacificus . While Caenorhabditis elegans uses contact-dependent sensing primarily to avoid threats, Pristionchus pacificus has co-opted this modality to support both avoidance and prey detection, enabling context-dependent predatory behaviour. To uncover a potential mechanism underlying the evolution of P. pacificus prey perception, we mutated 27 canonical mechanosensory genes and assessed their function using behavioural assays, automated behavioural tracking, and a machine learning analysis of behavioural states. While several mutants showed mechanosensory defects, Ppa-mec-6 mutants specifically also impaired prey detection, indicating the emergence of a novel mechanosensory module linked to predatory behaviour. Furthermore, disrupting both mechanosensation alongside chemosensation revealed a synergistic influence for these modalities. Crucially, both mechanosensation and chemosensation pathways converge in the same environmentally exposed IL2 neurons, and silencing these cells induced severe predation defects validating their importance for prey sensing. Thus, predation evolved through the co-option of mechanosensory and chemosensory systems that act together to shape the evolution of complex behavioural traits.
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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 | 0.001 |
| 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".