The behavioral context of common dolphin (<i>Delphinus</i> sp.) vocalizations

Henderson, E. Elizabeth; Hildebrand, John A.; Smith, Michael H.; Falcone, Erin A.

doi:10.1111/j.1748-7692.2011.00498.x

Cited by 49 publications

(51 citation statements)

References 65 publications

(79 reference statements)

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“…The ob served patterns in common dolphin foraging corre sponded to behavioral patterns of mesopelagic and small pelagic organisms during cool and warm months, respectively. Echolocation activity was predominantly nocturnal, confirming the findings of previous acoustic studies that showed nighttime being an important foraging time for common dolphins (Henderson et al 2012, Wiggins et al 2013. The acoustic record also indicated reduced echolocation activity between April and June, similar to the low regional abun- (2015).…”

Section: Discussionsupporting

confidence: 85%

“…Recent studies in the same region have shown distinct diel behavioral patterns of common dolphins, where foraging mainly occurs at night and travel and social behavior occurs during the day (Henderson et al 2012, Wiggins et al 2013. The observed diving be havior suggests that the dolphins nocturnally feed on mesopelagic organisms which migrate to the epipelagic zone (surface to 200 m depth) at dusk.…”

Section: Introductionmentioning

confidence: 87%

“…In all of these cases, the association of altered behavior during different lunar periods is attributed to the ability of these predators to access their prey. Dolphins use echolocation to forage and navigate, which creates an opportunity for passive acoustic monitoring to be a useful indicator of odontocete presence and behavior on timescales of hours to years (Henderson et al 2012, Wiggins et al 2013. Dol phin behavior, in addition to instrument sensitivity and environmental conditions, determines the spatial extent of passive acoustic monitoring.…”

Section: Introductionmentioning

confidence: 99%

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Lunar cycles affect common dolphin Delphinus delphis foraging in the Southern California Bight

Simonis¹,

Roch²,

Bailey³

et al. 2017

Mar. Ecol. Prog. Ser.

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In the Southern California Bight, the common dolphin Delphinus delphis is the most abundant dolphin species and preys upon small pelagic fish, mesopelagic fish, and cephalopods. Mesopelagic fish and many cephalopods are available throughout the year, and they form deep scattering layers, some of which characteristically undergo strong diel vertical migrations. The extent of vertical migration depends on the degree of sea surface solar and lunar illumination. At their daytime depth, mesopelagic prey are beyond the range of shallow-diving dolphins. Autonomous acoustic recorders were used to monitor dolphin echolocation at 2 offshore recording locations from 2009 to 2014. Manual and automated classification techniques were used to identify periods of high echolocation activity, indicative of common dolphin foraging. Clear lunar patterns existed in cool months, when echolocation activity was highest during the darkest periods of the night and lunar month, indicating times when dolphins were foraging, possibly on mesopelagic prey. Echolocation was more abundant during warm months, but diel and lunar patterns in echolocation were weaker. Generalized additive mixed models show that the observed patterns in echolocation activity are correlated with lunar day and position of the moon in the night sky. Seasonal patterns may represent geographic shifts in common dolphin populations, shoaling scattering layers, or prey switching behavior during the warm months, whereby dolphins target small pelagic fish not associated with the deep scattering layers. Overall, dolphin foraging activity declined from 2009 to 2014 during warm months, which may be related to a declining abundance of small pelagic fish.

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Section: Discussionsupporting

confidence: 85%

Section: Introductionmentioning

confidence: 87%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Lunar cycles affect common dolphin Delphinus delphis foraging in the Southern California Bight

Simonis¹,

Roch²,

Bailey³

et al. 2017

Mar. Ecol. Prog. Ser.

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“…Furthermore, although variation in acoustic parameters can relate to behavioural state (Azevedo et al, 2010;MayCollado, 2013) and may be useful in predicting surface behaviour (Henderson, Hildebrand, & Smith, 2011;Henderson, Hildebrand, Smith, & Falcone, 2012;Hernandez, Solangi, & Kuczaj, 2010) the available information poorly explains the causative factors of this variability. Esch, Sayigh, Blum, et al's (2009) study is the notable exception in detailing the influence of stress on whistle parameters and production rates.…”

mentioning

confidence: 99%

Changes in bottlenose dolphin whistle parameters related to vessel presence, surface behaviour and group composition

et al. 2016

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Cetacean watching from tour boats has increased in recent years and has been promoted as an ethically viable alternative to cetacean viewing in captive facilities or directed take. However, short-and long-term impacts of this industry on the behaviour and energetic expenditure of cetaceans have been documented. Although multiple studies have investigated the acoustic 1 response of dolphins to marine tourism, there are several covariates that could also explain some of these results and should be considered simultaneously. Here, we investigated whether common bottlenose dolphins, Tursiops truncatus, inhabiting Walvis Bay, Namibia vary their whistle parameters in relation to boat presence, surface behaviour and/or group composition. We detected an upward shift of up to 1.99 kHz in several whistle frequency parameters when dolphins were in the presence of one or more tour boats and the research vessel. No changes were demonstrated in the frequency range, number of inflection points or duration of whistles. A similar, although less pronounced difference was observed in response to engine noise generated by the research vessel when idling, suggesting that noise alone plays an important role in driving this shift in whistle frequency. Additionally, a strong effect of surface behaviour was observed, with the greatest difference in whistle parameters detected between resting and other behavioural states that are associated with higher degrees of emotional arousal. Group composition also contributed to the variation observed, with the impact of boats dependent on whether calves were present or not. Overall these results demonstrate high natural variation in the frequency parameters of whistles utilized by dolphins over varying behavioural states and group composition. Anthropogenic impact in the form of marine tour boats can influence the vocalization parameters of dolphins and such changes could have a long-term impact if they reduce the communication range of whistles or increase energy expenditure. Key wordsbottlenose dolphin, marine tourism, Namibia, Tursiops truncatus, vocal behaviour Wildlife tourism involving cetacean (whale, dolphin and porpoise) watching has experienced rapid growth since the 1990s (Hoyt, 2001;O'Connor, Campbell, Cortez, & Knowles, 2009). 2Globally, boat-based cetacean watching generates an estimated 2.2 billion US dollars annually (IWC, 2014). Revenue can provide a valuable subsidy to fishing communities and in some cases wild cetacean viewing has replaced direct hunting of whales and dolphins (Amir & Jiddawi, 2001;Berggren et al., 2007). Compared with captive facilities, responsible boatbased cetacean watching has been promoted as an ethically acceptable option for observing dolphins, providing a valuable forum for environmental education and promotion of conservation efforts (IFAW, 1997). However, a considerable body of work has shown that boats and boat-based cetacean watching can have multiple negative impacts on the behaviour of the focal individual, population or species (Parsons,...

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“…Methods in most common use range from t-tests, ANOVA and Chi-square tests or non-parametric equivalents to regression models (Deecke et al 2005, Filatova et al 2009, Foote et al 2004, Lemon et al 2006, Luís et al 2014, Oleson et al 2007, Sjare and Smith 1986, from simple linear models to those incorporating smooth terms, random effects and generalized estimating equations (Quick and Janik 2008, DeRuiter et al 2013, Di Iorio and Clark 2010, Stimpert et al 2015, Visser et al 2016. Other authors use randomization-based methods Solow 2008, Miller et al 2009) or machine-learning classification methods such as random forest decision trees [Henderson et al 2012]. While some authors have suggested the use of state-switching models for animal call rate data (e.g.…”

mentioning

confidence: 99%

An analysis of pilot whale vocalization activity using hidden Markov models

Popov

Langrock

DeRuiter

et al. 2017

The Journal of the Acoustical Society of America

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Vocalizations of cetaceans form a key component of their social interactions. Such vocalization activity is driven by the behavioral states of the whales, which are not directly observable, so that latent-state models are natural candidates for modeling empirical data on vocalizations. In this paper, we use hidden Markov models to analyze calling activity of long-finned pilot whales (Globicephala melas) recorded over three years in the Vestfjord basin off Lofoten, Norway. Baseline models are used to motivate the use of three states, while more complex models are fit to study the influence of covariates on the state-switching dynamics. Our analysis demonstrates the potential usefulness of hidden Markov models in concisely yet accurately describing the stochastic patterns found in animal communication data, thereby providing a framework for drawing meaningful biological inference.

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The behavioral context of common dolphin (Delphinus sp.) vocalizations

Cited by 49 publications

References 65 publications

Lunar cycles affect common dolphin Delphinus delphis foraging in the Southern California Bight

Lunar cycles affect common dolphin Delphinus delphis foraging in the Southern California Bight

Changes in bottlenose dolphin whistle parameters related to vessel presence, surface behaviour and group composition

An analysis of pilot whale vocalization activity using hidden Markov models

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