Open‐source workflow approaches to passive acoustic monitoring of bats ...

• Main Authors: Brinkløv, Signe M. M., Macaulay, Jamie, Bergler, Christian, Tougaard, Jakob, Beedholm, Kristian, Elmeros, Morten, Madsen, Peter Teglberg
• Format: Dataset
• Language: English
• Published: Dryad 2023
• Subjects: Bats; Passive Acoustic Monitoring PAM; Deep Learning Classifier; Automated Species Detection; FOS Biological sciences; ecological modeling; Ecology, Evolution, Behavior and Systematics; toothed whales
• Online Access: https://dx.doi.org/10.5061/dryad.4xgxd25fh; https://datadryad.org/stash/dataset/doi:10.5061/dryad.4xgxd25fh

The affordability, storage, and power capacity of compact modern recording hardware has evolved passive acoustic monitoring (PAM) of animals and soundscapes into a non-invasive, cost-effective tool for research and ecological management and is particularly effective for bats and toothed whales that consistently echolocate. The use of PAM at large scales hinges on effective automated detectors and species classifiers which, combined with distance sampling approaches, have enabled species abundance estimation of toothed whales. But standardized, user-friendly, and open-access automated detection and classification workflows are in demand for this key conservation metric to be realized for bats. We used the PAMGuard toolbox including its new deep learning classification module to test the performance of four open-source workflows for automated analyses of acoustic datasets from bats. Each workflow used a different initial detection algorithm followed by the same deep learning classification algorithm and was ... : Brief Overview: These data include raw and processed acoustic recordings of bats at woodland edges at different locations in Denmark, made with two common types of acoustic devices: the proprietary SM4BAT FS running firmware version 2.2.7 [Wildlife Acoustics Inc., Maynard, MA, USA], and the open-source AudioMoth running firmware version 1.4.4 (Hill et al., 2018, openacousticdevices.org). Study Context: The study focus was to test how different setups for detection of bat calls affect the performance of complete acoustic detection and classification workflows for bat PAM data and to demonstrate such full, open-source workflows using PAMGuard (www.pamguard.org)Each of the four workflows depended on a different initial detection algorithm: 1) A deep learning detection model based on ANIMAL-SPOT and integrated in PAMGuard, 2) PAMGuard's click detector module, 3) a device-based static trigger on the open source AudioMoth recorder, and 4) a device-based dynamic trigger on the proprietary SM4BAT FS recorder. Data ...