Passive acoustic surveys and the BirdNET algorithm reveal detailed spatiotemporal variation in the vocal activity of two anurans

Wood, Connor M.; Kahl, Stefan; Barnes, Stephanie L.; Horne, Rachel Van; Brown, Cathy

doi:10.1080/09524622.2023.2211544

Cited by 11 publications

(12 citation statements)

References 28 publications

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“…BirdNET is a convolutional neural-network-based tool designed for processing acoustic data [ 11 ]. Although BirdNET has rarely been used in scientific studies, the existing evaluations have consistently reported a high accuracy in identifying bird species (reviewed by [ 19 ]) but also anurans and primates [ 21 , 22 ].…”

Section: Discussionmentioning

confidence: 99%

“…On the other hand, if our goal is to study ecological processes, which usually require low-error estimates, such as describing the vocal behavior of a bird species, selecting a higher confidence value may be more appropriate. Although this selection may decrease the number of vocalizations detected, it would provide a more reliable description of the behavior (see [ 21 ]).…”

Section: Discussionmentioning

confidence: 99%

“…We hope that our assessment and the methods we employed, including calculating the optimal confidence score (see [ 22 ]), will encourage researchers and managers to make use of this freely available software (accessible on GitHub) that is user-friendly (e.g., can be run as a GUI from Windows) and ready-to-use (>6500 species already included). Additionally, the continuous development of BirdNET (last update in June 2023) will contribute to further improvements in acoustic sensing and monitoring in both urban and natural environments, including the ability to detect multiple species from various taxa simultaneously [ 19 , 20 , 21 , 22 ].…”

Section: Discussionmentioning

confidence: 99%

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Hearing to the Unseen: AudioMoth and BirdNET as a Cheap and Easy Method for Monitoring Cryptic Bird Species

Bota,

Manzano-Rubio,

Catalán

et al. 2023

Sensors

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The efficient analyses of sound recordings obtained through passive acoustic monitoring (PAM) might be challenging owing to the vast amount of data collected using such technique. The development of species-specific acoustic recognizers (e.g., through deep learning) may alleviate the time required for sound recordings but are often difficult to create. Here, we evaluate the effectiveness of BirdNET, a new machine learning tool freely available for automated recognition and acoustic data processing, for correctly identifying and detecting two cryptic forest bird species. BirdNET precision was high for both the Coal Tit (Peripatus ater) and the Short-toed Treecreeper (Certhia brachydactyla), with mean values of 92.6% and 87.8%, respectively. Using the default values, BirdNET successfully detected the Coal Tit and the Short-toed Treecreeper in 90.5% and 98.4% of the annotated recordings, respectively. We also tested the impact of variable confidence scores on BirdNET performance and estimated the optimal confidence score for each species. Vocal activity patterns of both species, obtained using PAM and BirdNET, reached their peak during the first two hours after sunrise. We hope that our study may encourage researchers and managers to utilize this user-friendly and ready-to-use software, thus contributing to advancements in acoustic sensing and environmental monitoring.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Hearing to the Unseen: AudioMoth and BirdNET as a Cheap and Easy Method for Monitoring Cryptic Bird Species

Bota,

Manzano-Rubio,

Catalán

et al. 2023

Sensors

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“…Unfortunately, implementing some of the state-of-the-art machine learning models can be complex and intimidating for ecologists and managers without an engineering or computing background. Indeed, the difficulty in using sound detection tools is a limiting factor for passive acoustic monitoring surveys (Wood et al 2023a). However, a new generation of user-friendly and ready-to-use machine learning tools have recently emerged and may further improve the effectiveness of automated audio recognition, such as BirdNET and Kaleidoscope Pro (e.g., Manzano-Rubio et al 2022;Bota et al 2023;Wood et al 2023a).…”

Section: Introductionmentioning

confidence: 99%

“…BirdNET is an automated sound classifier that is free, open source, and based on a convolutional neural network architecture for automated identification of over 6000 wildlife species, including birds, anurans, and mammals (Kahl et al 2021;Pérez-Granados 2023;Wood et al 2023aWood et al , 2023b. For each 3 s fragment of an audio file, BirdNET provides a species identification accompanied by a confidence score, allowing researchers to filter the output according to a de- sired confidence level.…”

Section: Introductionmentioning

confidence: 99%

Combining two user-friendly machine learning tools increases species detection from acoustic recordings

Pérez-Granados,

Feldman,

Mazerolle

2024

Can. J. Zool.

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Passive acoustic monitoring usually generates large datasets that require machine learning algorithms to scan sound files, although the complexity of developing machine learning algorithms can be a barrier. We assessed the ability and speed of two user-friendly machine learning tools, Kaleidoscope Pro and BirdNET, for detecting the American toad (Anaxyrus americanus, (Holbrook, 1836)) in sound recordings. We developed a two-step approach combining both tools to maximize species detection while minimizing the time needed for output verification. When considered separately, Kaleidoscope Pro successfully detected the American toad in 85.9% of recordings in the validation dataset, while BirdNET detected the species in 58.4% of recordings. Combining the two tools in the two-step approach increased the detection rate to 93.3%. We applied the two-step approach to a large acoustic dataset (n = 6,194 recordings). We started by scanning the dataset using Kaleidoscope Pro (species detected in 417 recordings), then we used BirdNET on the remaining recordings without confirmed presence. The two-step approach reduced the scanning time, the time needed for output verification, and added 37 additional species detections in 45 minutes. Our findings highlight that combining machine learning tools can improve species detectability while minimizing time and effort.

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Performance of unmarked abundance models with data from machine‐learning classification of passive acoustic recordings

Fiss,

Lapp,

Cohen

et al. 2024

Ecosphere

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The ability to conduct cost‐effective wildlife monitoring at scale is rapidly increasing due to the availability of inexpensive autonomous recording units (ARUs) and automated species recognition, presenting a variety of advantages over human‐based surveys. However, estimating abundance with such data collection techniques remains challenging because most abundance models require data that are difficult for low‐cost monoaural ARUs to gather (e.g., counts of individuals, distance to individuals), especially when using the output of automated species recognition. Statistical models that do not require counting or measuring distances to target individuals in combination with low‐cost ARUs provide a promising way of obtaining abundance estimates for large‐scale wildlife monitoring projects but remain untested. We present a case study using avian field data collected in the forests of Pennsylvania during the spring of 2020 and 2021 using both traditional point counts and passive acoustic monitoring at the same locations. We tested the ability of the Royle–Nichols and time‐to‐detection models to estimate the abundance of two species from detection histories generated by applying a machine‐learning classifier to ARU‐gathered data. We compared abundance estimates from these models with estimates from the same models fit using point‐count data and to two additional models appropriate for point counts, the N‐mixture model and distance models. We found that the Royle–Nichols and time‐to‐detection models can be used with ARU data to produce abundance estimates similar to those generated by a point‐count‐based study but with greater precision. ARU‐based models produced confidence or credible intervals that were on average 31.9% (±11.9 SE) smaller than their point‐count counterpart. Our findings were consistent across two species with differing relative abundance and habitat use patterns. The higher precision of models fit using ARU data is likely due to higher cumulative detection probability, which itself may be the result of greater survey effort using ARUs and machine‐learning classifiers to sample significantly more time for focal species at any given point. Our results provide preliminary support for the use of ARUs in abundance‐based study applications, and thus may afford researchers a better understanding of habitat quality and population trends, while allowing them to make more informed conservation recommendations and actions.

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Passive acoustic surveys and the BirdNET algorithm reveal detailed spatiotemporal variation in the vocal activity of two anurans

Cited by 11 publications

References 28 publications

Hearing to the Unseen: AudioMoth and BirdNET as a Cheap and Easy Method for Monitoring Cryptic Bird Species

Hearing to the Unseen: AudioMoth and BirdNET as a Cheap and Easy Method for Monitoring Cryptic Bird Species

Combining two user-friendly machine learning tools increases species detection from acoustic recordings

Performance of unmarked abundance models with data from machine‐learning classification of passive acoustic recordings

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