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<p>This study systematically integrates acoustic methods and machine learning (ML) into marine ecosystem management, developing a comprehensive ML framework that combines passive acoustic monitoring (PAM) data with ecological survey observations to predict key coral reef ecological indicators, including fish abundance, fish species richness, and live coral cover. The framework extracts features from multiple acoustic frequency bands and deploys a complete ML workflow covering seven algorithms across three categories: tree-based models (Random Forest, LightGBM, Gradient Boosting), neural networks (Multilayer Perceptron, Recurrent Neural Networks, Bayesian Neural Networks), and an ensemble strategy (Voting Regressor). Evaluated on ten coral reef sites in Sanya, China, the framework was comprehensively compared in terms of predictive accuracy and computational efficiency. Results indicate that the LightGBM model achieves the highest predictive performance of these biological indicators, providing a more efficient, scalable, and non-invasive solution for marine fish monitoring, which can support decision-making in marine ecosystem management. The proposed machine learning-based framework has the potential to be integrated into decision-support tools for ecosystem management, enabling more efficient monitoring of coral reef ecosystems worldwide.</p>

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Modeling coral reef biological indicators using passive acoustic monitoring and machine learning

  • 期刊论文

  • 发表日期：

    2026年6月16日

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  • Bingjia Huang,
  • Zhi Zhang,
  • Xiaoping Wang

摘要

This study systematically integrates acoustic methods and machine learning (ML) into marine ecosystem management, developing a comprehensive ML framework that combines passive acoustic monitoring (PAM) data with ecological survey observations to predict key coral reef ecological indicators, including fish abundance, fish species richness, and live coral cover. The framework extracts features from multiple acoustic frequency bands and deploys a complete ML workflow covering seven algorithms across three categories: tree-based models (Random Forest, LightGBM, Gradient Boosting), neural networks (Multilayer Perceptron, Recurrent Neural Networks, Bayesian Neural Networks), and an ensemble strategy (Voting Regressor). Evaluated on ten coral reef sites in Sanya, China, the framework was comprehensively compared in terms of predictive accuracy and computational efficiency. Results indicate that the LightGBM model achieves the highest predictive performance of these biological indicators, providing a more efficient, scalable, and non-invasive solution for marine fish monitoring, which can support decision-making in marine ecosystem management. The proposed machine learning-based framework has the potential to be integrated into decision-support tools for ecosystem management, enabling more efficient monitoring of coral reef ecosystems worldwide.