Geographic variability in the acoustic parameters of striped dolphin's ( Stenella coeruleoalba ) whistles

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The striped dolphin is a cosmopolitan species distributed worldwide. Morphological and genetic studies strongly suggest that the Mediterranean and eastern North Atlantic populations are isolated from each other. The Mediterranean population is considered a distinct conservation unit by International Union for the Conservation of Nature experts, classified as "vulnerable." This study describes the geographical variation of the striped dolphin whistles within the Mediterranean Sea. Recordings were collected from 1996 to 2003 throughout the basin, employing multiple platforms. Thirty-seven independent sightings with acoustic data collection were made, and 599 whistles were extracted and considered for statistical analysis. Whistle analysis enabled the identification of sub-populations of striped dolphins within the Mediterranean Sea. The acoustic diversity observed reflects the genetic differences recently found among striped dolphins inhabiting different Mediterranean regions. The results of this study support the hypothesis that gene flow reduction plays an important role in determining variation in whistle duration and frequency parameters, while ecological and social factors influence parameters of the modulation domains. The ability to acoustically identify distinct geographic sub-populations could provide a useful tool for the management of this protected species.

Section: Discussionmentioning

confidence: 96%

Geographic variation of whistles of the striped dolphin (Stenella coeruleoalba) within the Mediterranean Sea

Azzolin

Papale²,

Lammers³

et al. 2013

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“…Recently, Gannier et al (2010) contributed to this aim, focusing their attention on the identification of whistles produced by species in the western portion of the Mediterranean Sea. However, whistles have been shown to vary geographically (Wang et al, 1995b;Sayigh et al, 1995;Janik, 2000;Morisaka et al, 2005; Baz ua-Dur an and Au, 2004;Azzolin, 2008;Azzolin et al, 2013;Papale et al, 2013), and therefore our analysis of whistles recorded in the whole basin will provide additional insights.…”

Section: Introductionmentioning

confidence: 95%

“…Mediterranean odontocetes are morphologically (Calzada and Aguilar, 1995;Archer, 1997), genetically (Garc ıa-Mart ınez et al, 1995Hoelzel et al, 1998;Natoli et al, 2004;Valsecchi et al, 2004), and acoustically (Azzolin, 2008;Papale et al, 2013) differentiated from Atlantic odontocetes. IUCN classifications for Mediterranean odontocetes range from "vulnerable" to "critically endangered" (Reeves and Notarbartolo di Sciara, 2006).…”

Section: Introductionmentioning

confidence: 97%

Combining whistle acoustic parameters to discriminate Mediterranean odontocetes during passive acoustic monitoring

Azzolin

Gannier²,

Lammers

et al. 2014

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Acoustic observation can complement visual observation to more effectively monitor occurrence and distribution of marine mammals. For effective acoustic censuses, calibration methods must be determined by joint visual and acoustic studies. Research is still needed in the field of acoustic species identification, particularly for smaller odontocetes. From 1994 to 2012, whistles of four odontocete species were recorded in different areas of the Mediterranean Sea to determine how reliably these vocalizations can be classified to species. Recordings were attributed to species by simultaneous visual observation. The results of this study highlight that the frequency parameters, which are linked to physical features of animals, show lower variability than modulation parameters, which are likely to be more dependent on complex eco-ethological contexts. For all the studied species, minimum and maximum frequencies were linearly correlated with body size. DFA and Classification Tree Analysis (CART) show that these parameters were the most important for classifying species; however, both statistical methods highlighted the need for combining them with the number of contour minima and contour maxima for correct classification. Generally, DFA and CART results reflected both phylogenetic distance (especially for common and striped dolphins) and the size of the species.

“…However, delphinids alter the time-frequency characteristics of whistles and the repertoire composition according to various behaviors (Van Parijs and Corkeron, 2001;Hernandez et al, 2010). The time-frequency characteristics of whistles also vary among geographically separated populations (Morisaka et al, 2005;Hawkins, 2010;Papale et al, 2013). The high intraspecific variation of whistle makes the species classification based on individual contours challenging.…”

Section: Introductionmentioning

confidence: 97%

Automatic classification of delphinids based on the representative frequencies of whistles

Lin

Chou

2015

Classification of odontocete species remains a challenging task for passive acoustic monitoring. Classifiers that have been developed use spectral features extracted from echolocation clicks and whistle contours. Most of these contour-based classifiers require complete contours to reduce measurement errors. Therefore, overlapping contours and partially detected contours in an automatic detection algorithm may increase the bias for contour-based classifiers. In this study, classification was conducted on each recording section without extracting individual contours. The local-max detector was used to extract representative frequencies of delphinid whistles and each section was divided into multiple non-overlapping fragments. Three acoustical parameters were measured from the distribution of representative frequencies in each fragment. By using the statistical features of the acoustical parameters and the percentage of overlapping whistles, correct classification rate of 70.3% was reached for the recordings of seven species (Tursiops truncatus, Delphinus delphis, Delphinus capensis, Peponocephala electra, Grampus griseus, Stenella longirostris longirostris, and Stenella attenuata) archived in MobySound.org. In addition, correct classification rate was not dramatically reduced in various simulated noise conditions. This algorithm can be employed in acoustic observatories to classify different delphinid species and facilitate future studies on the community ecology of odontocetes.