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Passive acoustic monitoring: Considerations for recording units, BirdNET settings, and filtering methods for long-term avian population monitoring

This research investigated several aspects of passive acoustic monitoring (PAM) which were previously unexplored or understudied. A comparison of autonomous recording units (ARUs) for use with BirdNET for the purpose of bird monitoring was conducted. Four ARUs were compared, including AudioMoth, SM4, SMMicro, and SwiftOne. We found that, of the performance metrics for which ARU choice made a statistically significant difference (P>0.01), which included sensitivity, specificity, F1 harmonic mean, and Matthews Correlation Coefficient, (but not precision: P = 0.94), AudioMoth had the best performance for all statistically significant performance metrics except for specificity, for which SMMicro had the highest. The same audio was then processed using 18 combinations of Overlap and Sensitivity, including default settings. We found that Overlap and Sensitivity values were highly significant (P>0.001) for all performance metrics: precision, sensitivity, specificity, F1 harmonic mean, and Matthews Correlation Coefficient. No individual Overlap-Sensitivity setting combination performed outperformed others in most of the performance metrics; however, in general, as Overlap or Sensitivity increased, the number of true and false positive species reports increased while the number of false negatives decreased. Four confidence-based threshold types were then used to filter BirdNET output to compare threshold performances, comparing two arbitrary thresholds and two species-specific thresholds which were calculated using manual validation data. Of the thresholds tested, one of the arbitrary threshold types and one of the species-specific threshold types achieved a precision ≥ 0.95. We hope this research will help guide PAM decisions regarding ARU choice, BirdNET settings, and threshold type choice.

Identiferoai:union.ndltd.org:siu.edu/oai:opensiuc.lib.siu.edu:theses-4284
Date01 August 2024
CreatorsCorvus, Shasta
PublisherOpenSIUC
Source SetsSouthern Illinois University Carbondale
Detected LanguageEnglish
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Formatapplication/pdf
SourceTheses

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