Differential Vulnerability to Ship Strikes Between Day and Night for Blue, Fin, and Humpback Whales Based on Dive and Movement Data From Medium Duration Archival Tags

Abstract

We examine the dive and movement behavior of blue, fin, and humpback whales along the US West Coast in regions with high ship traffic where ship strikes have been identified as a major concern. All three species are known to feed in coastal waters near areas of high ship traffic. We analyzed data from 33 archival tag deployments representing over 3,000 h of data that were attached with suction-cups or short darts for periods >20 h and recorded depth (≥ 1 Hz), fast-lock GPS positions and other sensors. There were clear differences among the three species but all showed a distinct diurnal difference in diving behavior. While dive depth varied among animals based on where prey was located, whales spent a high proportion of their time closer to the surface where they would be more vulnerable to ship strikes at night than in the day. This was most pronounced for blue whales where vulnerability was twice as high at night compared to the day. We also found differences in movement patterns of whales between day and night. Movements were more localized to specific areas in the day near prey resources while at night these movements often involved directional movements (though sometimes returning to the same area). We show how in several specific areas like the Santa Barbara Channel, these differences in movements and locations translate to a very different overlap with shipping lanes at night compared to the daytime locations, which is the basis for most sighting data.

Animal-Borne Metrics Enable Acoustic Detection of Blue Whale Migration

“…To investigate individual-level behavior, blue whales (B. musculus) were tagged from 2017-2019 along the shelf break of central and northern California, USA. Individual whales were approached using a 6 m rigid hull inflatable boat and tagged using a 6 m pole with one of two different tag types: (1) short duration, high frequency ($2-36 h deployment) suction cup-attached video and 3D accelerometer tags [45,58] manufactured by Customized Animal Tracking Solutions (CATS); and (2) medium duration, medium frequency ($2-32 day deployment) dart-attached 3D accelerometer tags [47,48] manufactured by Wildlife Computers, Inc. CATS tag accelerometers sampled at 400 Hz and were analyzed at full resolution for call detection (see below). CATS tag magnetometer and gyroscopes sampled at 50 Hz, and pressure, light, temperature, and integrated FastlocÒ GPS sampled at 10 Hz.…”

“…Daily CI was calculated over $4.75 years (August 2015-April 2020) from nearly-continuous acoustic data collected via the Monterey Accelerated Research System (MARS) cabled observatory. To test the individual-level behavioral context of population scale patterns identified via PAM, we turn to whale-borne bio-logging approaches [9,[45][46][47][48].…”

Context-dependent variability in the predicted daily energetic costs of disturbance for blue whales

“…1a ). Details of tag configurations, deployment and field procedures are provided in Supplementary Methods S1 and in Szesciorka et al (2016) and Calambokidis et al (2019) . Raw tag data were processed following Cade et al (2016) to identify feeding events, or lunges.…”

“…Moreover, data on the prey they are targeting (such as its patterns of availability, density and distribution) are essential in order to quantify energy intake, the energetic efficiency of foraging and the opportunity to compensate for interrupted feeding ( Bowen et al, 2002 ; Grémillet et al, 2004 ; Goldbogen et al, 2019 ; Booth, 2020 ; Friedlaender et al, 2020 ). These data are increasingly available as technology improves our ability to monitor animals’ activity over extended periods and to sample the environment in which they move ( Cade and Benoit-Bird, 2014 ; Hazen et al, 2015 ; Calambokidis et al, 2019 ; Irvine et al, 2019 ). However, behavior, body condition, reproductive state, home range and resource availability will vary in space and time and among individuals, resulting in large differences in individual energy budgets.…”

“…Data from experimental exposures were used to build state- and range-specific discrete ER functions, as well as continuous functions for noise received level (RL) and range from the source. Moreover, we derived unprecedented information on baseline behavioral states, activity budgets, feeding rates, feeding bouts and movements over multiple days or weeks from the activity patterns of individuals instrumented with electronic loggers and that were not experimentally exposed to sonar ( Szesciorka et al, 2016 ; Calambokidis et al, 2019 ). Finally, we included measurements of krill densities collected around feeding whales ( Goldbogen et al, 2015 , 2019 ; Hazen et al, 2015 ) to quantify expected energy intake in undisturbed conditions.…”