Data
Viewed: [[ro.stat.viewed]] Cited: [[ro.stat.cited]] Accessed: [[ro.stat.accessed]]
ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rfr_id=info%3Asid%2FANDS&rft_id=http://e-publications.une.edu.au/1959.11/22240&rft.title=Measures of eastern quoll vocalisation extracted using PRAAT&rft.identifier=http://e-publications.une.edu.au/1959.11/22240&rft.publisher=University of New England, Australia&rft.description=Defining an acoustic repertoire is essential to understanding vocal signalling and communicative interactions within a species. Currently, quantitative and statistical definition is lacking for the vocalisations of many dasyurids, an important group of small to medium-sized marsupials from Australasia that includes the eastern quoll (Dasyurus viverrinus), a species of conservation concern. Beyond generating a better understanding of this species' social interactions, determining an acoustic repertoire will further improve detection rates and inference of vocalisations gathered by automated bioacoustic recorders. Hence, this study investigated eastern quoll vocalisations using objective signal processing techniques to quantitatively analyse spectrograms recorded from 15 different individuals. Recordings were collected from Secret Creek Sanctuary in Lithgow in conjunction with observations of the behaviours associated with each vocalisation to develop an acoustic-based behavioural repertoire for the species. Vocalisation measures were extracted using narrowband spectrograms (FFT method, window length 0.05 sec, dynamic range = 70 dB, time-steps = 1,000, frequency steps = 250, Gaussian window shape) produced in the program PRAAT (5.3.84 DSP Package). Source-related parameters using an autocorrelation method were used to detect the fundamental frequency (F0) contour from which measures of Duration, Median F0, Mean F0, Minimum F0, Maximum F0, Range of F0, Standard deviation of F0, Noise-to-Harmonics ratio, Jitter and Shimmer were extracted. Additionally intensity contours were extracted for each call to measure the Minimum amplitude, Maximum amplitude and Amplitude variation. Analysis of recordings produced a putative classification of five vocalisation types: Bark, Growl, Hiss, Cp-cp, and Chuck. These were most frequently observed during agonistic encounters between conspecifics, most likely as a graded sequence from Hisses occurring in a warning context through to Growls and finally Barks being given prior to, or during, physical confrontations between individuals. Quantitative and statistical methods were used to objectively establish the accuracy of these five putative call types. A multinomial logistic regression indicated a 97.27% correlation with the perceptual classification, demonstrating support for the five different vocalisation types. This putative classification was further supported by hierarchical cluster analysis and silhouette information that determined the optimal number of clusters to be five. Minor disparity between the objective and perceptual classifications was potentially the result of gradation between vocalisations, or subtle differences present within vocalisations not discernible to the human ear. The implication of these different vocalisations and their given context is discussed in relation to the ecology of the species and the potential application of passive acoustic monitoring techniques.&rft.creator=McDonald Paul&rft.date=2017&rft.coverage=Secret Creek Sanctuary in Lithgow, NSW, Australia.&rft.coverage=POLYGON((150.15136328581 -33.465681108231,150.15136328581 -33.465179897285,150.15258637313 -33.465179897285,150.15258637313 -33.465681108231,150.15136328581 -33.465681108231))&rft_rights=This dataset is made available under Creative Commons Attribution licence. https://creativecommons.org/licenses/by/4.0/legalcode&rft_rights=CC BY: Attribution 3.0 AU http://creativecommons.org/licenses/by/3.0/au&rft_subject=Eastern Quolls&rft_subject=Vocalisation&rft_subject=Bioacoustics&rft_subject=Marsupials&rft_subject=Acoustics&rft_subject=Behavioural Ecology&rft_subject=BIOLOGICAL SCIENCES&rft_subject=ECOLOGY&rft_subject=Animal Behaviour&rft_subject=ZOOLOGY&rft_subject=Expanding Knowledge in the Biological Sciences&rft_subject=EXPANDING KNOWLEDGE&rft_subject=EXPANDING KNOWLEDGE&rft_subject=Forest and Woodlands Flora, Fauna and Biodiversity&rft_subject=ENVIRONMENT&rft_subject=FLORA, FAUNA AND BIODIVERSITY&rft_subject=Pure basic research&rft.type=dataset&rft.language=English Access the data

Licence & Rights:

Other view details
Unknown

CC BY: Attribution 3.0 AU
http://creativecommons.org/licenses/by/3.0/au

This dataset is made available under Creative Commons Attribution licence.
https://creativecommons.org/licenses/by/4.0/legalcode

Access:

Other view details

Data is provided under PLoS One's open access requirements.
http://journals.plos.org/plosone/s/data-availability

Contact Information


University of New England

Full description

Defining an acoustic repertoire is essential to understanding vocal signalling and communicative interactions within a species. Currently, quantitative and statistical definition is lacking for the vocalisations of many dasyurids, an important group of small to medium-sized marsupials from Australasia that includes the eastern quoll (Dasyurus viverrinus), a species of conservation concern. Beyond generating a better understanding of this species' social interactions, determining an acoustic repertoire will further improve detection rates and inference of vocalisations gathered by automated bioacoustic recorders. Hence, this study investigated eastern quoll vocalisations using objective signal processing techniques to quantitatively analyse spectrograms recorded from 15 different individuals. Recordings were collected from Secret Creek Sanctuary in Lithgow in conjunction with observations of the behaviours associated with each vocalisation to develop an acoustic-based behavioural repertoire for the species. Vocalisation measures were extracted using narrowband spectrograms (FFT method, window length 0.05 sec, dynamic range = 70 dB, time-steps = 1,000, frequency steps = 250, Gaussian window shape) produced in the program PRAAT (5.3.84 DSP Package). Source-related parameters using an autocorrelation method were used to detect the fundamental frequency (F0) contour from which measures of Duration, Median F0, Mean F0, Minimum F0, Maximum F0, Range of F0, Standard deviation of F0, Noise-to-Harmonics ratio, Jitter and Shimmer were extracted. Additionally intensity contours were extracted for each call to measure the Minimum amplitude, Maximum amplitude and Amplitude variation. Analysis of recordings produced a putative classification of five vocalisation types: Bark, Growl, Hiss, Cp-cp, and Chuck. These were most frequently observed during agonistic encounters between conspecifics, most likely as a graded sequence from Hisses occurring in a warning context through to Growls and finally Barks being given prior to, or during, physical confrontations between individuals. Quantitative and statistical methods were used to objectively establish the accuracy of these five putative call types. A multinomial logistic regression indicated a 97.27% correlation with the perceptual classification, demonstrating support for the five different vocalisation types. This putative classification was further supported by hierarchical cluster analysis and silhouette information that determined the optimal number of clusters to be five. Minor disparity between the objective and perceptual classifications was potentially the result of gradation between vocalisations, or subtle differences present within vocalisations not discernible to the human ear. The implication of these different vocalisations and their given context is discussed in relation to the ecology of the species and the potential application of passive acoustic monitoring techniques.

Data time period: 28 02 2014 to 12 07 2014

Data time period: 2014

This dataset is part of a larger collection

Click to explore relationships graph

Spatial Coverage And Location

text: Secret Creek Sanctuary in Lithgow, NSW, Australia.

text: POLYGON((150.15136328581 -33.465681108231,150.15136328581 -33.465179897285,150.15258637313 -33.465179897285,150.15258637313 -33.465681108231,150.15136328581 -33.465681108231))

Identifiers