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Patterns of Vocal Activity of the Chinese Bamboo Partridge Using BirdNET Analyzer.

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Passive acoustic monitoring (PAM) using BirdNET shows limitations for identifying Chinese Bamboo Partridge vocalizations. An improved random forest (RF) model significantly enhances recognition accuracy for this endemic species.

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Area of Science:

  • Ornithology
  • Bioacoustics
  • Artificial Intelligence in Ecology

Background:

  • Passive acoustic monitoring (PAM) offers non-invasive, long-term bird vocal activity tracking.
  • Automatic data recognition in large acoustic datasets presents significant challenges.
  • BirdNET is a widely used, free sound analysis algorithm for species identification.

Purpose of the Study:

  • To evaluate BirdNET's effectiveness in identifying Chinese Bamboo Partridge vocalizations.
  • To develop an improved method for species identification using BirdNET outputs.
  • To describe the diurnal and seasonal calling patterns of the Chinese Bamboo Partridge.

Main Methods:

  • Assessed BirdNET-Analyzer and BirdNET-Analyzer-XHS performance for species identification.
  • Developed and implemented a random forest (RF) model utilizing BirdNET detection results.
  • Analyzed acoustic data to determine diurnal and seasonal vocalization patterns.

Main Results:

  • BirdNET-Analyzer achieved 16.6% recall; BirdNET-Analyzer-XHS achieved 50.8% precision.
  • The RF model demonstrated improved performance with 75.2% recall and 74.4% precision.
  • Chinese Bamboo Partridge exhibited bimodal diurnal (sunrise/sunset peaks) and unimodal seasonal (April/May peak) calling activity.

Conclusions:

  • BirdNET alone has limited accuracy for identifying specific regional species like the Chinese Bamboo Partridge.
  • The proposed RF model significantly enhances the accuracy of automated species identification from acoustic data.
  • This study provides a practical framework for improving regional species vocalization recognition using AI.