A data-driven method for reconstructing and modelling social interactions in moving animal groups

Area of Science:

• Collective behavior in biological systems
• Animal movement ecology
• Mathematical modeling of biological systems

Background:

• Group-living organisms exhibit complex collective movements, from microbial colonies to animal flocks.
• Understanding coordination mechanisms at the individual level is crucial for explaining group dynamics.
• Existing methods for social interaction analysis often lack predictive power and mathematical rigor.

Purpose of the Study:

• To develop a general method for extracting individual interactions driving collective movement coordination.
• To apply this method to characterize social interactions in two fish species (rummy-nose tetra and zebrafish).
• To enable the development of quantitative models for emergent group-level dynamics.

Main Methods:

• Utilized novel tracking techniques for high-precision, large-scale datasets of individual movements.
• Developed a general method to quantify inter-individual interactions from movement data.
• Applied the method to analyze social interactions in shoaling fish (Hemigrammus rhodostomus, Danio rerio).

Main Results:

• Successfully extracted and quantified social interactions governing collective movement in fish.
• Characterized the burst-and-coast motion patterns in rummy-nose tetras and zebrafish.
• Demonstrated the potential to build predictive mathematical models from individual interaction data.

Conclusions:

• The developed method provides a robust framework for analyzing social interactions in collective movement.
• This approach facilitates the creation of accurate models predicting emergent group behaviors.
• The method is broadly applicable to diverse biological and social systems exhibiting collective motion.