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. 2015 Apr 29;10(4):e0123943.
doi: 10.1371/journal.pone.0123943. eCollection 2015.

Passive Acoustic Monitoring of the Temporal Variability of Odontocete Tonal Sounds From a Long-Term Marine Observatory

Free PMC article

Passive Acoustic Monitoring of the Temporal Variability of Odontocete Tonal Sounds From a Long-Term Marine Observatory

Tzu-Hao Lin et al. PLoS One. .
Free PMC article


The developments of marine observatories and automatic sound detection algorithms have facilitated the long-term monitoring of multiple species of odontocetes. Although classification remains difficult, information on tonal sound in odontocetes (i.e., toothed whales, including dolphins and porpoises) can provide insights into the species composition and group behavior of these species. However, the approach to measure whistle contour parameters for detecting the variability of odontocete vocal behavior may be biased when the signal-to-noise ratio is low. Thus, methods for analyzing the whistle usage of an entire group are necessary. In this study, a local-max detector was used to detect burst pulses and representative frequencies of whistles within 4.5-48 kHz. Whistle contours were extracted and classified using an unsupervised method. Whistle characteristics and usage pattern were quantified based on the distribution of representative frequencies and the composition of whistle repertoires. Based on the one year recordings collected from the Marine Cable Hosted Observatory off northeastern Taiwan, odontocete burst pulses and whistles were primarily detected during the nighttime, especially after sunset. Whistle usage during the nighttime was more complex, and whistles with higher frequency were mainly detected during summer and fall. According to the multivariate analysis, the diurnal variation of whistle usage was primarily related to the change of mode frequency, diversity of representative frequency, and sequence complexity. The seasonal variation of whistle usage involved the previous three parameters, in addition to the diversity of whistle clusters. Our results indicated that the species and behavioral composition of the local odontocete community may vary among seasonal and diurnal cycles. The current monitoring platform facilitates the evaluation of whistle usage based on group behavior and provides feature vectors for species and behavioral classification in future studies.

Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.


Fig 1
Fig 1. Location of the marine cable hosted observatory.
The MACHO is connected with a land station at Toucheng Town through a 45-km-long submarine cable (dashed line).
Fig 2
Fig 2. Example of automatic tonal sound detection and unsupervised classification.
(a) Spectrograms produced from MACHO recording using fast Fourier transform with a Hamming window. (b) Burst pulses (red dots), harmonics (blue dots), and representative frequencies (black dots) obtained by the local-max detector. (c) Whistle contours were extracted using the pitch-tracking algorithm; different contours were labeled with different numbers. (d–g) The four whistle types were classified using the unsupervised method.
Fig 3
Fig 3. Seasonal and diurnal variation of detected durations in burst pulses (a) and whistles (b).
The number of detected seconds was calculated hourly and is presented in log scale (colorbar). The black dashed line represents the time of sunset, and the white dashed line represents the time of sunrise.
Fig 4
Fig 4. Seasonal and diurnal variation of whistle usage in mode frequency (a), diversity of representative frequencies (b), diversity of whistle clusters (c), and complexity of whistle sequence (d).
The box plot shows the median (center point), interquartile range (box), minimum to maximum (error bar), and outliers (empty circles).
Fig 5
Fig 5. Component loadings for each whistle usage parameter.
The black points represent the vectors of four whistle usage parameters on the two component factors. Factor 1 explained 45.51% of the variation of whistle usage. Factor 2 explained 28.13% of the variation of whistle usage.
Fig 6
Fig 6. Seasonal and diurnal variation of whistle usage summarized based on the two component factors.
Each data point represents one of two diurnal periods in each season after principle component analysis (a–d). The circled areas represent the 50% central areas of the two diurnal periods in each season estimated using the kernel density method (e–h).

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Grant support

This study was supported by the Ministry of Science and Technology of Taiwan (R.O.C.) under the project of NSC 101-2221-E-002-028-MY2 and NSC 102-3113-P-002-032. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.