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Machine learning accurately classifies juvenile lemon shark behaviors using accelerometer data. A voting ensemble model improved classification, revealing prey capture is linked to time of day, tide, and season.

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Area of Science:

  • Animal behavior analysis
  • Machine learning applications in ecology
  • Marine biology

Background:

  • Understanding animal activity budgets is crucial for ecological insights.
  • Accelerometers provide high-resolution data for studying animal behavior.
  • Automated behavioral classification is needed due to large data volumes.

Purpose of the Study:

  • To assess machine learning (ML) classifier performance for discerning five behaviors in juvenile lemon sharks (Negaprion brevirostris).
  • To identify the most effective ML model for automated behavioral classification using accelerometer data.
  • To investigate the biological relevance of ML-classified behaviors, specifically headshaking as a proxy for prey capture, in wild sharks.

Main Methods:

  • Collected accelerometer data from juvenile lemon sharks in a semi-captive environment.
  • Used observed behaviors (chafing, burst swimming, headshaking, resting, swimming) for ground-truthing ML models.
  • Trained and tested logistic regression, artificial neural network, random forest, gradient boosting, and voting ensemble (VE) models.

Main Results:

  • The voting ensemble (VE) model achieved the highest classification performance (F-measure 0.88), outperforming the best base learner, gradient boosting (0.86).
  • Application of the VE model to wild shark data revealed significant relationships between headshaking (prey capture proxy) and time of day, tidal phase, and season.
  • Prey capture events were most frequent in the early evening and least frequent during the dry season and high tides.

Conclusions:

  • Machine learning, particularly the voting ensemble model, provides an effective method for automated behavioral classification in sharks using accelerometer data.
  • The study validates the use of ML-derived behavioral data for ecological research, supporting previous hypotheses on shark predation patterns.
  • Findings highlight the influence of environmental factors (time of day, tide, season) on juvenile lemon shark prey capture.