NABat ML: Utilizing deep learning to enable crowdsourced development of automated, scalable solutions for documenting North American bat populations

Abstract

Bats play crucial ecological roles and provide valuable ecosystem services, yet many populations face serious threats from various ecological disturbances. The North American Bat Monitoring Program (NABat) aims to use its technology infrastructure to assess status and trends of bat populations, while developing innovative and community‐driven conservation solutions. Here, we present NABat ML, an automated machine‐learning algorithm that improves the scalability and scientific transparency of NABat acoustic monitoring. This model combines signal processing techniques and convolutional neural networks (CNNs) to detect and classify recorded bat echolocation calls. We developed our CNN model with internet‐based computing resources (‘cloud environment’), and trained it on >600,000 spectrogram images. We also incorporated species range maps to improve the robustness and accuracy of the model for future ‘unseen’ data. We evaluated model performance using a comprehensive, independent, holdout dataset. NABat ML successfully distinguished 31 classes (30 species and a noise class) with overall weighted‐average accuracy and precision rates of 92%, and ≥90% classification accuracy for 19 of the bat species. Using a single cloud‐environment computing instance, the entire model training process took <16 h. Synthesis and applications. Our convolutional neural network (CNN)‐based model, NABat ML, classifies 30 North American bat species using their recorded echolocation calls with an overall accuracy of 92%. In addition to providing highly accurate species‐level classification, NABat ML and its outputs are compatible with Bayesian and other statistical techniques for measuring uncertainty in classification. Our model is open‐source and reproducible, enabling future implementations as software on end‐user devices and cloud‐based web applications. These qualities make NABat ML highly suitable for applications ranging from grassroots community science initiatives to big‐data methods developed and implemented by researchers and professional practitioners. We believe the transparency and accessibility of NABat ML will encourage broad‐scale participation in bat monitoring, and enable development of innovative solutions needed to conserve North American bat species.

Automated echolocation classifiers vary in accuracy for northeastern U.S. bat species

“…Using the results presented here, users can also select the best program suited for their species of interest and the type of error they can best tolerate. Recently developed machine learning algorithms (EchoVision [ 54 ]; NABat ML [ 55 ]) show some promise for automated classification of bat echolocation calls. However, EchoVision was trained on and is restricted to processing zero-crossing recordings, and both algorithms were trained and tested on qualitatively identified recordings rather than on recordings from known species.…”

Acoustic monitoring yields informative bat population density estimates

“…As acoustics are incorporated into population monitoring surveys, it will be necessary to implement models accounting for biases associated with sampling cryptic, volant animals with dynamic foraging ranges and no unique signature in their calls. While methods for bat species identification are advancing (e.g., Khalighifar et al., 2022 ), we still cannot identify an individual bat based on its echolocation call (Weller, 2007 ).…”

Towards a General Approach for Bat Echolocation Detection and Classification

“…The first deep learning-based methods applied to bat acoustic monitoring focused on determining the presence of bats versus background noise (Mac Aodha et al, 2018) or the species present (Chen et al, 2020; Kobayashi et al, 2021; Khalighifar et al, 2022) from short audio clips, i.e. typically shorter than 50 milliseconds.…”