Non-invasive acoustic detection of wolves

Suter, Stefan M.; Giordano, Marta; Nietlispach, Silvia; Apollonio, Marco; Passilongo, Daniela

doi:10.1080/09524622.2016.1260052

Cited by 60 publications

(36 citation statements)

References 51 publications

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“…The idea of using animal vocalizations to census and locate populations has been widely used for birds (Lambert and McDonald, 2014), anurans (Jones and Ratnam, 2009), elephants (Zeppelzauer et al, 2015), and primates (Spillmann et al, 2015), as well as canids such as jackals (Debnath and Choudhury, 2013) and wolves (Blanco and Cort es, 2011;Suter et al, 2017). Wolf howling is a longrange communication signal and so lends itself well to the detection of animals at a distance (Harrington et al, 2003), and has long been used as an alternative method for surveying wolf presence and population size (Harrington and Mech, 1982;Passilongo et al, 2015;Fuller and Sampson, 1988;Suter et al, 2017;Llaneza et al, 2005). Howling is a narrow band frequency modulated signal used to advertise pack territories, maintain group cohesion, and by dispersing animals to locate potential mates (Mech and Boitani, 2010).…”

Section: Introductionmentioning

confidence: 99%

Tracking cryptic animals using acoustic multilateration: A system for long-range wolf detection

Kershenbaum

Owens²,

Waller

2019

The Journal of the Acoustical Society of America

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The study of animal behavior in the wild requires the ability to locate and observe animals with the minimum disturbance to their natural behavior. This can be challenging for animals that avoid humans, are difficult to detect, or range widely between sightings. Global Positioning System (GPS) collars provide one solution but limited battery life, and the disturbance to the animal caused by capture and collaring can make this impractical in many applications. Wild wolves Canis lupus are an example of a species that is difficult to study in the wild, yet are of considerable conservation and management importance. This manuscript presents a system for accurately locating wolves using differences in the time of arrival of howl vocalizations at multiple recorders (multilateration), synchronized via GPS. This system has been deployed in Yellowstone National Park for two years and has recorded over 1200 instances of howling behavior. As most instances of howling occur at night, or when human observers are not physically present, the system provides location information that would otherwise be unavailable to researchers. The location of a vocalizing animal can, under some circumstances, be determined to within an error of approximately 20 m and at ranges up to 7 km. V

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Section: Introductionmentioning

confidence: 99%

Tracking cryptic animals using acoustic multilateration: A system for long-range wolf detection

Kershenbaum

Owens²,

Waller

2019

The Journal of the Acoustical Society of America

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“…Fieldwork was conducted throughout August 2017, late in the pup rearing season when wolves were more vocally active, but still unable to travel far distances quickly. 8 We estimated wolf pack composition of adults, yearlings, and new pups of the year by the frequency of their howls since larger animals with longer vocal tracts emit lower frequency sounds. 12 We conducted howl surveys from 9:00 p.m.-12:00 a.m. when wolves are most responsive.…”

Section: Fieldworkmentioning

confidence: 99%

“…6 Gray wolf individuals can be identified based on acoustic features of their howls, a type of vocalization characterized by their low frequency and high amplitude that allows them to propagate over long distances in highly forested areas . 7,8 In some mammals, size and social status has been shown to affect the individual's vocalizations. 2 Wolf populations rely on howling as a form of communication within and across packs to signal territorial boundaries and social status.…”

Section: Introductionmentioning

confidence: 99%

“…2 Wolf populations rely on howling as a form of communication within and across packs to signal territorial boundaries and social status. 8 These attributes allow howls to be detected up to 10 km depending on terrain and weather conditions. 10 The characteristics of wolf howls such as their maximum frequency, amplitude, duration, and harmonics are used to identify individuals.…”

Section: Introductionmentioning

confidence: 99%

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Acoustic Identification of Wild Gray Wolves, Canis lupus, Using Low Quality Recordings

Hull¹,

McCombe²,

Dassow³

2020

AJUR

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Invasive trapping and radio-collaring techniques are currently used by conservation biologists to study the population dynamics of gray wolves (Canis lupus). Previous research has found wolf howls can be used to determine individual identity on high quality recordings from captive animals, offering an opportunity for non-invasive monitoring of packs.We recorded wild wolves in Central Wisconsin to determine the effectiveness of these features in determining individuality in low quality recordings. The wolf howls analyzed were from two adult individuals from separate packs. Using a principle component analysis, maximum frequency and end frequency of the calls were determined to be most individualistic. Using these features in a discriminant function analysis, howls were able to be identified from individuals with 100% accuracy. Gray wolves play an important role in ecosystem maintenance, however, the current monitoring techniques are costly and invasive. The creation of an easily accessible, non-invasive technique that can be used by individuals with a variety of technical backgrounds is necessary to address concerns faced by conservation efforts. To address these issues, all analyses performed usedfree or low-cost software, making this method of individual identification a useful alternative for conservation biologists. KEYWORDS: Canis lupus lycaon; Gray Wolf; Acoustic Signatures; Howls; Tracking Method; Conservation; Vocal Individuality

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“…; Suter et al . ), birds (Aide et al . ; Campos‐Cerqueira & Aide ; Frommolt ), bats (MacSwiney et al .…”

Section: Introductionunclassified

Animal Sound Identifier (ASI): software for automated identification of vocal animals

2018

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Automated audio recording offers a powerful tool for acoustic monitoring schemes of bird, bat, frog and other vocal organisms, but the lack of automated species identification methods has made it difficult to fully utilise such data. We developed Animal Sound Identifier (ASI), a MATLAB software that performs probabilistic classification of species occurrences from field recordings. Unlike most previous approaches, ASI locates training data directly from the field recordings and thus avoids the need of pre-defined reference libraries. We apply ASI to a case study on Amazonian birds, in which we classify the vocalisations of 14 species in 194 504 oneminute audio segments using in total two weeks of expert time to construct, parameterise, and validate the classification models. We compare the classification performance of ASI (with training templates extracted automatically from field data) to that of monitoR (with training templates extracted manually from the Xeno-Canto database), the results showing ASI to have substantially higher recall and precision rates.

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Non-invasive acoustic detection of wolves

Cited by 60 publications

References 51 publications

Tracking cryptic animals using acoustic multilateration: A system for long-range wolf detection

Tracking cryptic animals using acoustic multilateration: A system for long-range wolf detection

Acoustic Identification of Wild Gray Wolves, Canis lupus, Using Low Quality Recordings

Animal Sound Identifier (ASI): software for automated identification of vocal animals

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