Vocal signatures affected by population identity and environmental sound levels

Passive acoustic monitoring has improved our understanding of vocalizing organisms in remote habitats and during all weather conditions. Many vocally active species are highly mobile, and their populations overlap. However, distinct vocalizations allow the tracking and discrimination of individuals or populations. Using signature whistles, the individually distinct calls of bottlenose dolphins, we calculated a minimum abundance of individuals, characterized and compared signature whistles from five locations, and determined reoccurrences of individuals throughout the Mid-Atlantic Bight and Chesapeake Bay, USA. We identified 1,888 signature whistles in which the duration, number of extrema, start, end, and minimum frequencies of signature whistles varied significantly by site. All characteristics of signature whistles were deemed important for determining from which site the whistle originated and due to the distinct signature whistle characteristics and lack of spatial mixing of the dolphins detected at the Offshore site, we suspect that these dolphins are of a different population than those at the Coastal and Bay sites. Signature whistles were also found to be shorter when sound levels were higher. Using only the passively recorded vocalizations of this marine top predator, we obtained information about its population and how it is affected by ambient sound levels, which will increase as offshore wind energy is developed. In this rapidly developing area, these calls offer critical management insights for this protected species.

Methods
Acoustic data were collected between 2016 at 2018 at sites 1, 2, and 5 were located 12, 31, and 64 km east of Ocean City, Maryland, USA, respectively. Water depths at these sites ranged from approximately 20 – 42 m, and the acoustic recording instruments were deployed approximately 1 m above the ocean floor using bottom-anchored moorings.

Ambient sound levels were calculated in MATLAB (MathWorks, Natick, Massachusetts, USA) as the relative broadband (up to 24 kHz, given the sampling rate of 48 kHz) root mean square sound pressure level (SPL; dB re 1µPa root-mean-square (rms)) during the recording in which the signature whistle occurred (two or five minutes in duration).

PAMGUARD Whistle and Moan Detector was utilized to determine hours with possible dolphin presence. These hours were then manually searched for signature whistles with high signal-to-noise ratios. Signature whistles were manually identified using the SIGID criteria -in which the same whistle repeated in a pattern of two or more whistles within 1–10 s of one another and with a minimum length of 0.2 s. Whistle contours (shape of the whistle) were obtained using Beluga software (https://synergy.st-andrews.ac.uk/soundanalysis) within MATLAB (Math-Works, Natick, Massachusetts, USA). Whistles with low signal-to-noise ratio or abundant non-linear features that obscured the shape of the whistle could not be included in the analysis.

For each whistle, manual measurements were taken in Raven Pro 2.0 Interactive Sound Analysis Software (Cornell Lab of Ornithology, Center for Conservation Bioacoustics, Ithaca, New York, USA) of the duration, start, end, maximum, minimum, and delta frequencies (maximum minus minimum frequency), and number of local extrema (e.g. local minima and maxima) including the start and end of the whistle.