Evaluation of Mobile Acoustic Techniques for Bat Population Monitoring

Whitby, Michael D.; Carter, Timothy C.; Britzke, Eric R.; Bergeson, Scott M.

doi:10.3161/150811014x683417

Cited by 31 publications

(40 citation statements)

References 29 publications

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“…It is important for researchers to understand how variations on a standard protocol will influence the data collected. Whitby et al (2014) investigated differences between standard vehicle-based mobile surveys and those conducted on a boat, concluding that boats did not offer advantages in data collection to the car-based mobile surveys. Our study revealed using a bike to complete a mobile survey increased the number of bat passes recorded, but many of these additional recorded passes were of insufficient quality to be identified.…”

Section: Discussionmentioning

confidence: 99%

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Timing and technique impact the effectiveness of road‐based, mobile acoustic surveys of bats

D’Acunto

Pauli

Moy

et al. 2018

Ecology and Evolution

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Mobile acoustic surveys are a common method of surveying bat communities. However, there is a paucity of empirical studies exploring different methods for conducting mobile road surveys of bats. During 2013, we conducted acoustic mobile surveys on three routes in north‐central Indiana, U.S.A., using (1) a standard road survey, (2) a road survey where the vehicle stopped for 1 min at every half mile of the survey route (called a “start‐stop method”), and (3) a road survey with an individual using a bicycle. Linear mixed models with multiple comparison procedures revealed that when all bat passes were analyzed, using a bike to conduct mobile surveys detected significantly more bat passes per unit time compared to other methods. However, incorporating genus‐level comparisons revealed no advantage to using a bike over vehicle‐based methods. We also found that survey method had a significant effect when analyses were limited to those bat passes that could be identified to genus, with the start–stop method generally detecting more identifiable passes than the standard protocol or bike survey. Additionally, we found that significantly more identifiable bat passes (particularly those of the Eptesicus and Lasiurus genera) were detected in surveys conducted immediately following sunset. As governing agencies, particularly in North America, implement vehicle‐based bat monitoring programs, it is important for researchers to understand how variations on protocols influence the inference that can be gained from different monitoring schemes.

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Section: Discussionmentioning

confidence: 99%

“…However, other studies have used walking mobile acoustic surveys to investigate bat habitat selection (Ciechanowski, ), species presence (Bellamy et al., ), and diversity (Berthinussen & Altringham, ). Such measures could reasonably be ascertained from vehicle‐based mobile surveys as well (e.g., Whitby et al., ).…”

Section: Introductionmentioning

confidence: 99%

Timing and technique impact the effectiveness of road‐based, mobile acoustic surveys of bats

D’Acunto

Pauli

Moy

et al. 2018

Ecology and Evolution

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“…Barros, Pessoa & Rui ; Kennedy, Sillett & Szewczak ; Whitby et al . ), particularly to study community diversity, but there has been recent interest in detecting primates (e.g. Heinicke et al .…”

Section: Introductionmentioning

confidence: 99%

“…In terrestrial environments acoustic methods have been used most extensively on bats (e.g. Barros, Pessoa & Rui 2014;Kennedy, Sillett & Szewczak 2014;Whitby et al 2014), particularly to study community diversity, but there has been recent interest in detecting primates (e.g. Heinicke et al 2015;Kalan et al 2015), studying the ecology of, and human impacts on elephants (Wrege et al 2010(Wrege et al , 2012, and for surveys and density estimates of birds (e.g.…”

Section: Introductionmentioning

confidence: 99%

Acoustic monitoring for conservation in tropical forests: examples from forest elephants

et al. 2017

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Summary The accelerating loss of biodiversity worldwide demands effective tools for monitoring animal populations and informing conservation action. In habitats where direct observation is difficult (rain forests, oceans), or for cryptic species (shy, nocturnal), passive acoustic monitoring (PAM) provides cost‐effective, unbiased data collection. PAM has broad applicability in terrestrial environments, particularly tropical rain forests. Using examples from studies of forest elephants in Central African rain forest, we show how PAM can be used to investigate cryptic behaviour, mechanisms of communication, estimate population size, quantify threats, and assess the efficacy of conservation strategies. We discuss the methodologies, requirements, and challenges of obtaining these data using acoustics. Where applicable, we compare these methods to more traditional approaches. While PAM methods and associated analysis are maturing rapidly, mechanisms are needed for processing the dense raw data efficiently with standard computer hardware, speeding development of detection algorithms, and harnessing communication networks to move data from the field to research facilities. Passive acoustic monitoring is a viable and cost‐effective tool for conservation and should be incorporated in monitoring schemes much more broadly. The capability to quickly assess changes in behaviour, population size, and landscape use, simultaneously over large geographical areas, makes this approach attractive for detecting human‐induced impacts and for assessing the success of conservation strategies.

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“…bat abundance and occurrence in such datasets. Road-transect acoustic monitoring is a useful technique to assess bat populations over large areas and programmes have now been established by government and non-government agencies in many different countries (e.g., Roche et al 2011;Jones et al 2013;Whitby et al 2014;Loeb et al 2015;Azam et al 2016). Noisy sound environments are also likely to be a problem for other acoustic bat monitoring programmes.…”

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Bat Detective - Deep Learning Tools for Bat Acoustic Signal Detection

Aodha

Gibb

Barlow

et al. 2017

Preprint

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Summary1. Passive acoustic sensing has emerged as a powerful tool for quantifying anthropogenic impacts on biodiversity, especially for echolocating bat species.To better assess bat population trends there is a critical need for accurate, reliable, and open source tools that allow the detection and classification of bat calls in large collections of audio recordings. The majority of existing tools are commercial or have focused on the species classification task, neglecting the important problem of first localizing echolocation calls in audio which is particularly problematic in noisy recordings.2. We developed a convolutional neural network (CNN) based open-source pipeline for detecting ultrasonic, full-spectrum, search-phase calls produced by echolocating bats (BatDetect). Our deep learning algorithms (CNNFULL and CNNFAST) were trained on full-spectrum ultrasonic audio collected along roadtransects across Romania and Bulgaria by citizen scientists as part of the iBats programme and labelled by users of www.batdetective.org. We compared the performance of our system to other algorithms and commercial systems on expert verified test datasets recorded from different sensors and countries. As an example application, we ran our detection pipeline on iBats monitoring data collected over five years from Jersey (UK), and compared results to a widelyused commercial system.3. Here, we show that both CNNFULL and CNNFAST deep learning algorithms have a higher detection performance (average precision, and recall) of search-phase echolocation calls with our test sets, when compared to other existing algorithms peer-reviewed) is the author/funder. All rights reserved. No reuse allowed without permission.The copyright holder for this preprint (which was not . http://dx.doi.org/10.1101/156869 doi: bioRxiv preprint first posted online Jun. 29, 2017; Deep Learning Tools for Bat Acoustic Signal Detection -Mac Aodha et al. 4and commercial systems tested. Precision scores for commercial systems were reasonably good across all test datasets (>0.7), but this was at the expense of recall rates. In particular, our deep learning approaches were better at detecting calls in road-transect data, which contained more noisy recordings. Our comparison of CNNFULL and CNNFAST algorithms was favourable, although CNNFAST had a slightly poorer performance, displaying a trade-off between speed and accuracy. Our example monitoring application demonstrated that our opensource, fully automatic, BatDetect CNNFAST pipeline does as well or better compared to a commercial system with manual verification previously used to analyse monitoring data.4. We show that it is possible to both accurately and automatically detect bat search-phase echolocation calls, particularly from noisy audio recordings. Our detection pipeline enables the automatic detection and monitoring of bat populations, and further facilitates their use as indicator species on a large scale, particularly when combined with automatic species identification. We release our system and datas...

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Evaluation of Mobile Acoustic Techniques for Bat Population Monitoring

Cited by 31 publications

References 29 publications

Timing and technique impact the effectiveness of road‐based, mobile acoustic surveys of bats

Timing and technique impact the effectiveness of road‐based, mobile acoustic surveys of bats

Acoustic monitoring for conservation in tropical forests: examples from forest elephants

Bat Detective - Deep Learning Tools for Bat Acoustic Signal Detection

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