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Determining shoal membership using affinity propagation.

Vicenç Quera1, Francesc S Beltran, Inmar E Givoni

  • 1Institute for Brain, Cognition and Behavior (IR3C), Adaptive Behavior and Interaction Research Group (GCAI), Department of Behavioral Science Methods, University of Barcelona, Campus Mundet, Passeig Vall d'Hebron 171, 08035 Barcelona, Spain. vquera@ub.edu

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Summary
This summary is machine-generated.

We developed the Spatio-Temporal Affinity Propagation (STAP) algorithm to track fish shoals that merge and split. STAP accurately detects shoal dynamics, closely matching human observer analyses.

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Area of Science:

  • Computational Biology
  • Animal Behavior
  • Machine Learning

Background:

  • Detecting and tracking collective animal motion, such as fish shoals, is crucial for ecological and behavioral studies.
  • Traditional clustering algorithms struggle with dynamic group structures that undergo fusion and fission over time.

Purpose of the Study:

  • To introduce an extension of the Affinity Propagation (AP) clustering algorithm, named STAP (Spatio-Temporal Affinity Propagation).
  • To enable the detection of dynamic shoal structures that merge and split over time.
  • To evaluate the performance and validity of STAP against human observations.

Main Methods:

  • Developed STAP by incorporating a soft temporal constraint into the AP algorithm to account for cluster dynamics across time steps.
  • Applied STAP to simulated sequences of collective coordinated motion, testing various parameter settings (temporal constraint strength, preferences, distance metric).
  • Validated STAP by comparing its partitioning results with those obtained from human observers in a controlled experiment.

Main Results:

  • STAP effectively handles shoals exhibiting fusion and fission dynamics by maintaining consistency between successive time steps.
  • The performance of STAP was analyzed across different parameter configurations, identifying optimal settings for specific scenarios.
  • In specific conditions, STAP-generated partitions showed high agreement with human observer-based partitioning of the same data.

Conclusions:

  • The STAP algorithm offers a robust and appealing computational approach for analyzing the complex fusion-fission dynamics of collective animal movements.
  • Appropriate parameter tuning is essential for maximizing the accuracy and validity of STAP in real-world applications.
  • STAP demonstrates potential as a tool for quantitative analysis in fields studying animal behavior and collective motion.