Variability of the inter-pulse interval in sperm whale clicks with implications for size estimation and individual identification

Bøttcher, Anne; Gero, Shane; Beedholm, Kristian; Whitehead, Hal; Madsen, Peter T.

doi:10.1121/1.5047657

Cited by 17 publications

(26 citation statements)

References 37 publications

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“…The IPI estmates of clicks difer between and within dives, suggestng that the recorded clicks came from diferent individuals. In dive I and IV, the median IPI was 3.06 ms (IQR: 2.92-3.18 ms) and 2.88 ms (IQR: 2.19-3.02 ms), which is similar to the IPI estmates of adult sperm whales in Dominica (Bøtcher et al 2018). These IPIs indicate a body length of 9.3 and 9.0 m (Gordon 1991), which is the typical length of sexually mature female sperm whales (Lockyer 1981 : 12.8, 12.3, 9.6, and 10.5 dB for dive I, II, II, and IV).…”

Section: Echolocationmentioning

confidence: 53%

“…This cut-of value was chosen, as echolocaton clicks judged to be from the tagged calves had IPIs shorter than 2 ms for all three calves. An IPI of 2 ms corresponds to a body length of 7.7 m (Gordon 1991), slightly greater than the average body length of 6 m reported for frst year calves (Lockyer 1981) while the IPI of echolocaton clicks from adults of Dominica range between 2.73 and 3.34 ms (Bøtcher et al 2018).…”

Section: Codasmentioning

confidence: 71%

“…The IPIs of the accepted clicks were then obtained from inspectons of the envelope of the click waveform, computed as the absolute value of the Hilbert transform. The two highest peaks of the envelope were identfed, corresponding to the frst and second pulse of the click, and the tme diference between these was taken as the IPI (based on Bøtcher et al 2018). We set an upper limit of 5 ms for the IPI corresponding to a maximum body length of roughly 12 m (Gordon 1991), as the average length of mature female sperm whales of Dominica is 9.2 m (Bøtcher et al 2018).…”

Section: Echolocationmentioning

confidence: 99%

“…To perform that estmaton a minimum and maximum M b , calf of 1 and 2 ton were chosen for neonate and frst year sperm whale calves (Lockyer 1981). The average M b , adult was set to 7.2 ton (Lockyer 1981) corresponding to the median body length of 9.2 m for adult females in the area (based on Bøtcher et al 2018). Hence, assuming the median adult dive duraton of 48 min approximates their ADL (Watwood et al 2006), the estmated ADL of the calves ranges from 28 to 34 min.…”

Section: Dive Behaviormentioning

confidence: 99%

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First-year sperm whale calves echolocate and perform long, deep dives

Tønnesen

Gero

Ladegaard

et al. 2018

Behav Ecol Sociobiol

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Deep diving sperm whales have a complex social structure and the biggest brains on the planet, but very litle is known about the ontogeny of their diving, foraging, echolocaton, and communicaton skills. In large brained terrestrial species, social skills develop earlier than locomotor abilitess but this may not be feasible for sperm whales, which require locomotor skills from birth to breathe, swim, and suckle. Here we show the frst evidence in any wild toothed whale for the relatve development of social and locomotor capabilites. Sound and movement recording tags deployed on three frst-year sperm whale calves for a total of 15 hours revealed that these calves rarely produced codas for communicaton with adult whales, but likely tracked the ample passive acoustc cues emited by clicking adults. The calves' diving capabilites were well developed (maximum dive depth: 285, 337, and 662 m, maximum dive tme: 11, 31, and 44 min) and they all produced clicks in a way that is consistent with echolocaton. The calf performing the longest and deepest dives additonally emited two echolocaton buzzes, suggestng that it atempted to forage. Thus, sperm whales calves may supplement their milk diet with food caught independently at depth much earlier than previously believed. Contrary to terrestrial mammals, we propose that the maturaton of locomotor, diving, and echolocaton skills are favored over investment in developing social communicaton skills at an early age in sperm whales. 2 Significance statement The life of deep diving toothed whales have up untl recently been a mystery and the understanding of their behavior has been limited to surface observatons and rare captve studies. Fortunately, the rapid development of animal-borne bio-logging devices has markedly improved our knowledge of the behavior of adult whales. The behavior and development of young calves are, however, stll largely unknown. Sperm whale calves are challenged by being air breathing marine mammals, which must learn to hunt prey at great depths. Using Dtags, we here show that sperm whale calves have a much more pronounced diving capabilites than previously thought. The onset of independent foraging and foraging efort seem linked to the diving capability of the calf. These results show that young members of this otherwise slowly maturing species of apex predators do learn to dive and may hunt much earlier than previously believed.

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Section: Echolocationmentioning

confidence: 53%

Section: Codasmentioning

confidence: 71%

Section: Echolocationmentioning

confidence: 99%

Section: Dive Behaviormentioning

confidence: 99%

See 2 more Smart Citations

First-year sperm whale calves echolocate and perform long, deep dives

Tønnesen

Gero

Ladegaard

et al. 2018

Behav Ecol Sociobiol

Self Cite

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“…Furthermore, we analysed 60 sequences of echolocation clicks from a single deep dive of the female sperm whale Sophocles, recorded by the Dominican sperm whale project on 24. April 2014 (for details on study site and recordings see [31,32]). We extracted trains manually with the software CoolEdit 2000.…”

Section: Labeling Of Elements and Datasetsmentioning

confidence: 99%

Comparison of methods for rhythm analysis of complex animals’ acoustic signals

Burchardt

Knörnschild

2020

PLoS Comput Biol

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Analyzing the rhythm of animals' acoustic signals is of interest to a growing number of researchers: evolutionary biologists want to disentangle how these structures evolved and what patterns can be found, and ecologists and conservation biologists aim to discriminate cryptic species on the basis of parameters of acoustic signals such as temporal structures. Temporal structures are also relevant for research on vocal production learning, a part of which is for the animal to learn a temporal structure. These structures, in other words, these rhythms, are the topic of this paper. How can they be investigated in a meaningful, comparable and universal way? Several approaches exist. Here we used five methods to compare their suitability and interpretability for different questions and datasets and test how they support the reproducibility of results and bypass biases. Three very different datasets with regards to recording situation, length and context were analyzed: two social vocalizations of Neotropical bats (multisyllabic, medium long isolation calls of Saccopteryx bilineata, and monosyllabic, very short isolation calls of Carollia perspicillata) and click trains of sperm whales, Physeter macrocephalus. Techniques to be compared included Fourier analysis with a newly developed goodness-of-fit value, a generate-and-test approach where data was overlaid with varying artificial beats, and the analysis of inter-onset-intervals and calculations of a normalized Pairwise Variability Index (nPVI). We discuss the advantages and disadvantages of the methods and we also show suggestions on how to best visualize rhythm analysis results. Furthermore, we developed a decision tree that will enable researchers to select a suitable and comparable method on the basis of their data. Author summaryIn the analysis of animal communication more and more interest is shown in rhythm of animal communication and what information this might convey. In this paper, we establish a workflow to analyze the temporal structure-namely the rhythm-of any particular animals'acoustic signal with methods that are applicable for a wide range of signals and results that are easily comparable and interpretable. This workflow will enhance the understanding of rhythmicality in animals' acoustic signals as well as facilitate comparison between species. Methods we conducted ranged from simple distributional and visual PLOS COMPUTATIONAL BIOLOGY PLOS Computational Biology | https://doi.

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