Insight into the significance and kinematics of blue whale surface foraging through drone observations and prey data (Supplementary Information)

AUTHORS: Leigh G. Torres, Dawn R. Barlow, Todd E. Chandler, Jonathan D. Burnett Corresponding author: Leigh Torres Hatfield Marine Science Center 2030 SE Marine Science Drive Newport, OR 97365, U.S.A leigh.torres@oregonstate.edu ABSTRACT: To understand how predators optimize foraging strategies, extensive knowledge of predator behavior and prey distribution is needed. Blue whales employ an energetically demanding lunge feeding method that requires the whales to selectively feed where energetic gain exceeds energetic loss, while also balancing oxygen consumption, breath holding capacity, and surface recuperation time. Hence, blue whale foraging behavior is primarily driven by krill patch density and depth, but many studies have not fully considered surface feeding as a significant foraging strategy in energetic models. We collected predator and prey data on a blue whale (Balaenoptera musculus brevicauda) foraging ground in New Zealand in February 2017 to assess the distributional and behavioral response of blue whales to the distribution and abundance of krill prey aggregations. Krill abundance across the study region was greater toward the surface (upper 20 m), and blue whales were encountered where prey was relatively shallow and more abundant. This relationship was particularly evident where foraging and surface lunge feeding was observed. Furthermore, New Zealand blue whales also had relatively short time dives (2.83 ± 0.27 SE min) as compared to other blue whale populations, which became even shorter at foraging sightings and where surface lunge feeding was observed. Using an unmanned aerial system (UAS; drone) we also captured unique video of a New Zealand blue whale’s surface feeding behavior on well-illuminated krill patches. Video analysis illustrates the whale’s potential use of vision to target prey, make foraging decisions, and orient body mechanics relative to prey patch characteristics. Kinematic analysis of a surface lunge feeding event revealed biomechanical coordination through speed, acceleration, head inclination, roll, and distance from krill patch to maximize prey engulfment. We compared these lunge kinematics to data previously reported from tagged blue whale lunges at depth to demonstrate strong similarities, and provide rare measurements of gape size, and krill response distance and time. These findings elucidate the predator-prey relationship between blue whales and krill, and provide support for the hypothesis that surface feeding by New Zealand blue whales is an important component to their foraging ecology used to optimize their energetic efficiency. Understanding how blue whales make foraging decisions presents logistical challenges, which may cause incomplete sampling and biased ecological knowledge if portions of their foraging behavior are undocumented. We conclude that surface foraging could be an important strategy for blue whales, and integration of UAS with tag-based studies may expand our understanding of their foraging ecology by examining surface feeding events in conjunction with behaviors at depth. DATA AVAILABILITY STATEMENT: The full Unmanned Aerial Systems (UAS) videos of the blue whale surface lunge feeding event and other surface events analyzed in this study can be viewed in this Figshare repository. Please refer to the time stamps in Table 1 of the manuscript to view the four foraging events described. The footage was filmed by Todd Chandler, and ownership of UAS videos belongs to Leigh Torres, Oregon State University. These UAS videos should only be used for scientific purposes and should not be shared on social media or broadly without explicit permission from L. Torres. Krill aggregation data files examined in this study (.csv files) and the Matlab script used to identify aggregations and their attributes is provided in this repository. Additionally, data on krill aggregation characteristics at blue whale sightings and at absence locations, and the R script used to analyze and plot these data, are also provided.