Open‐source workflow approaches to passive acoustic monitoring of bats

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 evaluated against the
performance of an expert manual analyst. Workflow performance depended
strongly on the signal-to-noise ratio and detection algorithm used: the
full deep learning workflow had the best classification accuracy (≤67%)
but was computationally too slow for practical large-scale bat PAM.
Workflows using PAMGuard’s detection module or triggers onboard an SM4BAT
or AudioMoth accurately classified up to 47%, 59% and 34%, respectively,
of calls to species. Not all workflows included noise sampling critical to
estimating changes in detection probability over time, a vital parameter
for abundance estimation. The workflow using PAMGuard’s detection module
was 40 times faster than the full deep learning workflow and missed as few
calls (recall for both ~0.6), thus balancing computational speed and
performance.  We show that complete acoustic detection and
classification workflows for bat PAM data can be efficiently automated
using open-source software such as PAMGuard and exemplify how detection
choices, whether pre- or post-deployment, hardware or software-driven,
affect the performance of deep learning classification and the downstream
ecological information that can be extracted from acoustic recordings. In
particular, understanding, and quantifying detection/classification
accuracy and the probability of detection are key to avoid introducing
biases that may ultimately affect the quality of data for ecological
management.