Foraging activity of sperm whales (Physeter macrocephalus) off the east coast of New Zealand

Abstract

The occurrence and distribution of sperm whales in New Zealand waters is mainly known from whaling records or opportunistic sightings by the public and a systematic estimation of the abundance and distribution has never been conducted. In this study, we investigated the foraging activity and occurrence of sperm whales off the Eastern coast of New Zealand using passive acoustic monitoring techniques. Three acoustic recorders were moored to the ocean floor at different locations on the east side of the North and South Island to collect passive acoustic data from June 2016 until August 2017. A total of 53,823 echolocation click trains were recorded and analyzed to understand the spatial and temporal variation of sperm whale foraging activity. No difference in the foraging activity was found between night-time and day-time periods at any of the locations. Click train detections increased toward the south, suggesting increased foraging activity near Kaikoura. At each station, sperm whale foraging activity varied by month.

Modelling the spatial distribution of cetaceans in New Zealand waters

“…Offshore species and migratory species, e.g., minke, sei, sperm, killer, humpback and pilot whales, were more generalist and tended to show preference for depth related environmental variables over temperature related variables. Sperm and pilot whale preferences for deeper offshore waters around New Zealand are indicative of the location of their preferred prey, deep‐water squids and mesopelagic fishes often associated with canyons and trenches (Beatson, O'Shea, & Ogle, ; Gaskin & Cawthorn, ; Giorli & Goetz, ; Guerra et al, ). Minke and sei whales' habitat use patterns are poorly known in New Zealand (Baker et al, ), but it is likely they are migrating through New Zealand waters between southern feeding grounds and warmer water breeding grounds.…”

Monitoring of a Nearshore Small Dolphin Species Using Passive Acoustic Platforms and Supervised Machine Learning Techniques

“…Our improved ability to acquire acoustic data continuously from multiple recording stations over extended periods of time has increased the use of automatic signal identification techniques (Zimmer, 2011) and machine learning approaches (Bianco et al, 2019) for detecting marine mammal vocalizations (Shiu et al, 2020). Several detection and classification algorithms have been developed to identify cetacean sounds, particularly the echolocation clicks of toothed whales (Soldevilla et al, 2008;Roch et al, 2011Roch et al, , 2015Giorli et al, 2016;Caruso et al, 2017;Giorli and Goetz, 2019;Hildebrand et al, 2019). Compared to manual analysis methods (spectrogram analysis), such algorithms have the advantage of being able to quickly and reliably analyze large amounts of data and produce standardized measures of sound characteristics that can be used for statistical analysis (Giorli et al, 2016;Caruso et al, 2017).…”

Acoustically estimated size distribution of sperm whales (Physeter macrocephalus) off the east coast of New Zealand

“…Location of the 3 acoustic recorders (red pin symbols) in the study area. For consistency with Giorli and Goetz (2019), station A will be referred to as 'Kaikoura'; Station B will be referred to as 'Palliser'; Station C will be referred to as 'Castlepoint'.…”

“…A custom-made MATLAB (Mathwork Inc., Natick, Massachusetts, USA) algorithm was developed to detect and classify sperm whale's echolocation click trains (Giorli and Goetz 2019). A sixth order band-pass filter (cut off frequencies of 2 and 20 kHz to include sperm whale's echolocation signals bandwidth (Møhl et al 2003a;Zimmer et al 2005a;Caruso et al 2015)) was applied to the acoustic data.…”

“…After the training, the selected ranges were 4 to 15 kHz for peak frequency; 0.2 to 2 s for ICI; and 500 to 1200 µs for signal duration. Previous research used such an approach of classifying click trains (Giorli et al 2016a(Giorli et al , 2016bGiorli and Goetz 2019). A 40 ms long pressure time series around each echolocation signal in each click train detected was extracted.…”