Use of an acoustic location system to understand how presence of conspecifics and canopy cover influence Ovenbird (Seiurus aurocapilla) space use near reclaimed wellsites in the boreal forest of Alberta

Wilson, Scott; Bayne, Erin M.

doi:10.5751/ace-01248-130204

Cited by 15 publications

(25 citation statements)

References 36 publications

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“…Thirteen studies used localization to determine animals' individual identities (e.g., Krakauer et al, 2009; Lippold, Fitzsimmons, Foote, Ratcliffe, & Mennill, 2008). Six studies calculated animal abundance, either by direct calculation of number of individuals (Frommolt & Tauchert, 2014; Hedley, Huang, & Yao, 2017; Spillmann et al, Willems, van Noordwijk, Setia, & van Schaik, 2017; Wahlberg et al, 2003; Wilson & Bayne, 2018) or indirectly by calibration of acoustic indices, as described by Stevenson et al (2015) (Thompson et al, 2009). Five studies used localization to infer territory boundaries or habitat use, including assessing animals' relationships with anthropogenic or natural habitat features (Ethier & Wilson, 2019; Hennigar, Ethier, & Wilson, 2019; Kershenbaum et al, 2019; Spillmann et al, 2017; Wilson & Bayne, 2018).…”

Section: Resultsmentioning

confidence: 99%

“…Six studies calculated animal abundance, either by direct calculation of number of individuals (Frommolt & Tauchert, 2014; Hedley, Huang, & Yao, 2017; Spillmann et al, Willems, van Noordwijk, Setia, & van Schaik, 2017; Wahlberg et al, 2003; Wilson & Bayne, 2018) or indirectly by calibration of acoustic indices, as described by Stevenson et al (2015) (Thompson et al, 2009). Five studies used localization to infer territory boundaries or habitat use, including assessing animals' relationships with anthropogenic or natural habitat features (Ethier & Wilson, 2019; Hennigar, Ethier, & Wilson, 2019; Kershenbaum et al, 2019; Spillmann et al, 2017; Wilson & Bayne, 2018). Three studies separated animal sounds from background noise to improve species classification (Kojima, Sugiyama, Hoshiba, Suzuki, & Nakadai, 2017; Kojima, Sugiyama, Suzuki, Nakadai, & Taylor, 2016; Suzuki, Matsubayashi, Nakadai, & Okuno, 2016).…”

Section: Resultsmentioning

confidence: 99%

“…Duration of a study varies from a single recording session to assess bioacoustic traits of a species, to weeks or months of monitoring to map territories or habitat use (e.g., Spillmann et al, 2017). Population monitoring studies often drew on multiple years of data (e.g., Frommolt & Tauchert, 2014; Kershenbaum et al, 2019; Thompson et al, 2009; Wilson & Bayne, 2018).…”

Section: Resultsmentioning

confidence: 99%

“…Many ARUs, such as Wildlife Acoustics recorders and the AudioMoth, can be programmed to record at a certain time of day, which can be useful for selectively recording species when they are highly acoustically active. For instance, Wilson and Bayne (2018) programmed acoustic recorders to record birds between 5:30 a.m. and 8:30 a.m., as birds are particularly active around dawn. Alternatively, we suggest that a recording schedule could be chosen to limit the density of sounds in the soundscape, as a large number of overlapping vocalizations can hinder the performance of localization pipelines (e.g., Hedley et al, 2017; Simmons, Simmons, & Bates, 2008).…”

Section: Resultsmentioning

confidence: 99%

“…This process, also known as acoustic multilateration , gained popularity as a method of studying the behavior and ecology of underwater animals, which are challenging to directly observe (e.g., Spiesberger & Fristrup, 1990; Watkins & Schevill, 1972). Knowing an animal's location broadens the use of ARUs, allowing researchers to generate abundance and density estimates (e.g., Wahlberg, Tougaard, & Møhl, 2003), observe habitat usage (e.g., Wilson & Bayne, 2018), calibrate acoustic indices (e.g., Thompson, Schwager, Payne, & Turkalo, 2009), study animal behavior and communication (e.g., Collier, Blumstein, Girod, & Taylor, 2010), and track animal movements across small and large scales (e.g., Kershenbaum, Owens, & Waller, 2019). Aside from wildlife surveys, applications of localization in conservation include locating poachers by the sounds of gunshots or locating illegal logging by the sounds produced by chainsaws (e.g., Andrei, 2015; Wijers, Loveridge, Macdonald, & Markham, 2019).…”

Section: Introductionmentioning

confidence: 99%

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Acoustic localization of terrestrial wildlife: Current practices and future opportunities

Rhinehart

Chronister

Devlin

et al. 2020

Ecology and Evolution

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Autonomous acoustic recorders are an increasingly popular method for low‐disturbance, large‐scale monitoring of sound‐producing animals, such as birds, anurans, bats, and other mammals. A specialized use of autonomous recording units (ARUs) is acoustic localization, in which a vocalizing animal is located spatially, usually by quantifying the time delay of arrival of its sound at an array of time‐synchronized microphones. To describe trends in the literature, identify considerations for field biologists who wish to use these systems, and suggest advancements that will improve the field of acoustic localization, we comprehensively review published applications of wildlife localization in terrestrial environments. We describe the wide variety of methods used to complete the five steps of acoustic localization: (1) define the research question, (2) obtain or build a time‐synchronizing microphone array, (3) deploy the array to record sounds in the field, (4) process recordings captured in the field, and (5) determine animal location using position estimation algorithms. We find eight general purposes in ecology and animal behavior for localization systems: assessing individual animals' positions or movements, localizing multiple individuals simultaneously to study their interactions, determining animals' individual identities, quantifying sound amplitude or directionality, selecting subsets of sounds for further acoustic analysis, calculating species abundance, inferring territory boundaries or habitat use, and separating animal sounds from background noise to improve species classification. We find that the labor‐intensive steps of processing recordings and estimating animal positions have not yet been automated. In the near future, we expect that increased availability of recording hardware, development of automated and open‐source localization software, and improvement of automated sound classification algorithms will broaden the use of acoustic localization. With these three advances, ecologists will be better able to embrace acoustic localization, enabling low‐disturbance, large‐scale collection of animal position data.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Acoustic localization of terrestrial wildlife: Current practices and future opportunities

Rhinehart

Chronister

Devlin

et al. 2020

Ecology and Evolution

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Sound level measurements from audio recordings provide objective distance estimates for distance sampling wildlife populations

Yip

Knight

Haave-Audet

et al. 2019

Remote Sens Ecol Conserv

Self Cite

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Distance sampling is widely used to estimate animal population densities by accounting for imperfect detection of individuals with increasing distance from an observer. Distance sampling assumes that distances are measured without error; however, it is often applied to human estimated distances, which are known to be inconsistent, inaccurate, and biased. We present an objective technique for estimating distance to vocalizing individuals that relies on the relative sound level (RSL) of the vocalization extracted from autonomous recording unit (ARU) recordings and show the error is less than human estimated error extracted from a literature case study. RSL predicted distances can be obtained by manual measurement in sound viewing software, or automatically with automated signal recognition software. We built calibration datasets of Ovenbirds (Seiurus aurocapilla) and Common Nighthawks (Chordeiles minor) recorded at known distances and used regression of RSL from those recordings to predict distance. There was no error bias of RSL predicted distances when compared to known distances for Common Nighthawk, minimal error bias for Ovenbird, and error from all RSL predicted distances was less than human estimated error extracted from the literature. We then simulated ARU point count surveys with a known density and estimated that density with distance sampling to test whether RSL distance prediction does not violate the assumption that distances are measured without error. There was no difference in density estimates from known distance and density estimates obtained from RSL predicted distance, while density estimates contaminated with human estimated error were significantly lower than density estimates from known distance. We found that a calibration dataset of approximately 300 vocalizations was suitable to minimize error for both species, and so conclude that RSL distance prediction is an accessible method of improving distance estimates relative to human estimation. We provide general recommendations on how to collect calibration recordings for the application of RSL distance prediction to other species and areas.

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Identifying unknown Indian wolves by their distinctive howls: its potential as a non-invasive survey method

Sadhukhan

Root‐Gutteridge

Habib

2021

Sci Rep

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Previous studies have posited the use of acoustics-based surveys to monitor population size and estimate their density. However, decreasing the bias in population estimations, such as by using Capture–Mark–Recapture, requires the identification of individuals using supervised classification methods, especially for sparsely populated species like the wolf which may otherwise be counted repeatedly. The cryptic behaviour of Indian wolf (Canis lupus pallipes) poses serious challenges to survey efforts, and thus, there is no reliable estimate of their population despite a prominent role in the ecosystem. Like other wolves, Indian wolves produce howls that can be detected over distances of more than 6 km, making them ideal candidates for acoustic surveys. Here, we explore the use of a supervised classifier to identify unknown individuals. We trained a supervised Agglomerative Nesting hierarchical clustering (AGNES) model using 49 howls from five Indian wolves and achieved 98% individual identification accuracy. We tested our model’s predictive power using 20 novel howls from a further four individuals (test dataset) and resulted in 75% accuracy in classifying howls to individuals. The model can reduce bias in population estimations using Capture-Mark-Recapture and track individual wolves non-invasively by their howls. This has potential for studies of wolves’ territory use, pack composition, and reproductive behaviour. Our method can potentially be adapted for other species with individually distinctive vocalisations, representing an advanced tool for individual-level monitoring.

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Use of an acoustic location system to understand how presence of conspecifics and canopy cover influence Ovenbird (Seiurus aurocapilla) space use near reclaimed wellsites in the boreal forest of Alberta

Abstract: Use of an acoustic location system to understand how presence of conspecifics and canopy cover influence Ovenbird (Seiurus aurocapilla) space use near reclaimed wellsites in the boreal forest of Alberta. Avian Conservation and Ecology 13(2):4.

Cited by 15 publications

References 36 publications

Acoustic localization of terrestrial wildlife: Current practices and future opportunities

Acoustic localization of terrestrial wildlife: Current practices and future opportunities

Sound level measurements from audio recordings provide objective distance estimates for distance sampling wildlife populations

Identifying unknown Indian wolves by their distinctive howls: its potential as a non-invasive survey method

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