Kin-recognition and predation shape collective behaviors in the cannibalistic nematode Pristionchus pacificus
Bibliographic record
Abstract
Kin-recognition is observed across diverse species forming an important behavioral adaptation influencing organismal interactions. In many species, the molecular mechanisms involved are difficult to characterize, but in the nematode Pristionchus pacificus molecular components regulating its kin-recognition system have been identified. These determine its predatory behaviors towards other con-specifics which prevents the killing and cannibalization of kin. Importantly, their impact on other interactions including collective behaviors is unknown. Here, we explored a high altitude adapted clade of this species which aggregates abundantly under laboratory conditions, to investigate the influence of the kin-recognition system on their group behaviours. By utilizing pairwise aggregation assays between distinct strains of P. pacificus with differing degrees of genetic relatedness, we observe aggregation between kin but not distantly related strains. In assays between distantly related strains, the aggregation ratio is frequently reduced. Furthermore, abolishing predation behaviors through CRISPR/Cas9 induced mutations in Ppa-nhr-40 result in rival strains successfully aggregating together. Finally, as Caenorhabditis elegans are found naturally occurring with P. pacificus, we also explored aggregation events between these species. Here, aggregates were dominated by P. pacificus with the presence of only a small number of predators proving sufficient to disrupt C. elegans aggregation dynamics. Thus, aggregating strains of P. pacificus preferentially group with kin, revealing competition and nepotism as previously unknown components influencing collective behaviors in nematodes.
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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".