Minke whale acoustic behavior and multi-year seasonal and diel vocalization patterns in Massachusetts Bay, USA

Abstract

Passive acoustic monitoring (PAM) is a rapidly growing field, providing valuable insights in marine ecology. The approach allows for long-term, species-specific monitoring over a range of spatial scales. For many baleen whales fundamental information on seasonal occurrence and distribution is still missing. In this study, pulse trains produced by the North Atlantic minke whale, a highly mobile and cryptic species, are used to examine its seasonality, diel vocalization patterns and spatial distribution throughout the Stellwagen Bank National Marine Sanctuary (SBNMS), USA. Three and a half years (2006, 2007 to 2010) of near continuous passive acoustic data were analyzed using automated detection methods. Random forests and cluster analyses grouped pulse trains into 3 main categories (slow-down, constant and speed-up), with several subtypes. Slow-down pulse trains were the most commonly recorded call category. Minke whale pulse train occurrence was highly seasonal across all years. Detections were made from August to November, with 88% occurring in September and October. No detections were recorded in January and February, and only few from March to June. Minke whale pulse trains showed a distinct diel pattern, with a nighttime peak from approximately 20:00 to 01:00 h Eastern Standard Time (EST). The highest numbers of pulse trains were detected to the east of Stellwagen Bank, suggesting that minke whales travel preferably in deeper waters along the outer edge of the sanctuary. These data show that minke whales consistently use Stellwagen Bank as part of their migration route to and from the feeding grounds. Unlike other baleen whales in this area they do not appear to have a persistent year-round acoustic presence.

Description and seasonal detection of two potential whale calls recorded in the Indian Ocean

“…Across other oceans, minke whales are known to produce a range of sounds including pulses, clicks, downsweeps, and a variety of other frequency-modulated sounds (e.g., boing, "star wars," and "bio-duck" signals) (e.g., Beamish and Mitchell, 1973;Edds-Walton, 2000;Mellinger et al, 2000;Gedamke et al, 2001;Rankin and Barlow, 2005;Oswald et al, 2011;Risch et al, 2013;Risch et al, 2014). It has been suggested that pulses, the star-wars signals, and boings may act as song.…”

Vocalization Source Level Distributions and Pulse Compression Gains of Diverse Baleen Whale Species in the Gulf of Maine

“…This source level distribution SL max has a mean of 177.7 ± 5.4 dB re 1 µPa at 1 m ( Figure 6E). Different calling patterns and pulse train types [14] were not separated during the calculations. Thus, the source level distributions shown here are an average over all minke whale vocalization pulse train types detected in the region.…”

“…The minke whales were identified from their pulse trains ( Figure 1G) comprised of a series of click sequences [12][13][14]. Four hundred thirty one pulse trains, consisting of 5390 clicks, are associated with 16 bearing-time trajectories (Figure 2).…”

“…The four baleen whale species analyzed here are fin whale (Balaenoptera physalus), sei whale (Balaenoptera borealis), minke whale (Balaenoptera acutorostrata) and an Unidentified Baleen Whale Species (UBWS) . Each baleen whale species was identified from its characteristic vocalization type: the fin whales were identified from their short duration 20-Hz center frequency calls [5][6][7][8]; the sei whales from their downsweep chirps occurring singly or as doublets with roughly a 4-s separation or sometimes as triplets [9][10][11]; and the minke whales were identified from their pulse trains [12][13][14] comprised of a series of click sequences (see Figure 1 here and also the Extended Data Figures 1-4 of [1]). The unidentified baleen whale species vocalized downsweep signals in the 30-60 Hz frequency range over a 2-3-second duration (see Figure 1J here and the Extended Data Figures 1B and 3A of [1]).…”

Soundscape cube: A holistic approach to explore and compare acoustic environments

“…These diverse sources respond to various environmental forcing, ecosystem processes, biological factors, and human activities that are contributing their respective imprints on SPL average and variability at diverse frequencies (e.g. Gervaise et al, 2012;Kinda et al, 2013;Risch et al, 2013;Bittencourt et al, 2016;Mathias et al, 2016). Soundscapes are intrinsically characterized by large multi-scale variability over space and time that makes their comparison difficult and dependent of the angle of view of the observer and the comparison metrics (cf.…”