Group Behaviors Foster Zebrafish Foraging Through Social Interaction
Wang, P.; Chen, M.; Li, B.
Show abstract
Group living enhances foraging efficiency, yet whether this benefit extends across developmental stages and how social coordination versus emotional buffering contribute remain unresolved. Using zebrafish (larvae 10 dpf, adults 80 dpf) in a Y-maze foraging task with high-resolution tracking and mirror experiments, we show that grouping reduces foraging latency in adults, but not larvae. Adults form coordinated groups (high polarization, small inter-individual distances), whereas larvae form loose aggregations. Adults, but not larvae, exhibit reduced freezing (social buffering) when grouped. Mirror-presented visual cues suffice to enhance foraging via emotional calming--reducing freezing and stabilizing speed--yet disrupt real-group coordination. Our results reveal a dual mechanism-- instrumental coordination and emotional buffering--by which group behavior promotes foraging. Both mechanisms are developmentally gated, emerging only after maturation, offering insights into the ontogeny of social competence and practical implications for aquaculture and conservation.
Matching journals
The top 5 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
- Social-like responses are inducible in asocial Mexican cavefish despite the exhibition of strong repetitive behaviour 96%
- Evolution of olfactory sensitivity, preferences and behavioral responses in Mexican cavefish: fish personality matters 94%
- Learning and cognition in highspeed decision making 94%
Similar papers in this journal
- Intersection of motor volumes predicts the outcome of ambush predation of larval zebrafish 95%
- A sensation for inflation: initial swim bladder inflation in larval zebrafish is mediated by the mechanosensory lateral line. 94%
- Larval zebrafish maintain elevation with multisensory control of posture and locomotion 94%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.