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A comparison of statistical methods for deriving occupancy estimates from machine learning outputs.

Lydia K D Katsis1, Tessa A Rhinehart2, Elizabeth Dorgay3

  • 1School of Geography and Environmental Science, University of Southampton, Southampton, UK. L.K.D.Katsis@soton.ac.uk.

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PubMed
Summary
This summary is machine-generated.

Integrating machine learning with autonomous recording units (ARUs) aids biodiversity monitoring. Classifier-guided listening combined with standard occupancy models offers an accurate and efficient method for estimating species occupancy.

Keywords:
Acoustic monitoringAutonomous recording units (ARUs)Biodiversity monitoringFalse-positive modelsOccupancy modellingYucatán black howler monkey

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Area of Science:

  • Ecology
  • Bioacoustics
  • Computational Biology

Background:

  • Autonomous recording units (ARUs) coupled with machine learning (ML) offer scalable solutions for biodiversity monitoring.
  • Occupancy models are frequently used for analyzing ARU data, but methods for integrating ML outputs require further comparative evaluation.
  • Few studies have directly compared different approaches for integrating ML-derived data into ecological occupancy models.

Purpose of the Study:

  • To evaluate four distinct methods for integrating ARU data and ML outputs into occupancy models.
  • To assess the accuracy of occupancy estimates derived from these integrated methods.
  • To investigate the influence of factors like decision thresholds and data verification on model performance.

Main Methods:

  • Four integration approaches were tested: standard occupancy models with verified data, and false-positive occupancy models using presence-absence data, detection counts, and continuous classifier scores.
  • The Yucatán black howler monkey was used as a case study for evaluating estimator accuracy.
  • Key parameters assessed included decision threshold, temporal subsampling, and verification strategies.

Main Results:

  • Classifier-guided listening with a standard occupancy model yielded accurate occupancy estimates with minimal verification effort.
  • False-positive models produced comparable accuracy under specific conditions but were sensitive to subjective choices, such as the decision threshold.
  • The practical application of false-positive models is limited by the difficulty in establishing stable parameter choices and their increased computational complexity.

Conclusions:

  • For readily detectable species and high-performance classifiers, classifier-guided listening paired with standard occupancy models is a practical and efficient approach for accurate occupancy estimation.
  • This method balances accuracy with reduced verification effort compared to more complex false-positive models.
  • The findings provide guidance for optimizing the integration of acoustic monitoring and machine learning in ecological research.