DOI: 10.6076/d1388n
title: Kinematics and energetics of foraging behavior in Rice’s whales of the Gulf of Mexico

description: The endangered Rice’s whale (Balaenoptera ricei) is endemic to the Gulf of
 Mexico (GOM) and has a small population of fewer than 100 individuals.
 Little is known about their foraging ecology. Suction cup tags were
 attached to two Rice’s whales to collect information on their diving
 kinematics and foraging behavior. The tagged whales primarily exhibited
 lunge-feeding near the sea bottom and to a lesser extent in the water
 column and at the sea surface. During foraging dives, the whales typically
 circled the prey before executing one or two feeding lunges. Longer
 duration dives and dives with more feeding lunges were followed by an
 increase in the breathing rate. The average lunge rate of both animals was
 much lower than expected based on comparative research on other
 lunge-feeding baleen whales, which may be related to differences in prey
 or inter-specific differences, or may be an indication of different
 foraging conditions. Overall, these data show that Rice’s whale foraging
 behavior differs from other lunge-feeding rorqual species and may be a
 significant factor in shaping our understanding of their foraging ecology.
 Efforts to mitigate threats to the Rice’s whale will benefit from improved
 understanding of patterns in habitat use and fine-scale ecology within the
 population.
description: <strong>Field
 methods</strong> Rice’s whales were tagged with a
 suction-cup-attached Acousonde tag (Greeneridge Sciences, Inc.) using the
 NOAA Ship <em>Gordon</em>
 <em>Gunter</em> to search for whales and the vessel’s 7-m
 Rigid Hull Inflatable Boat (RHIB) <em>R3</em> to approach the
 free-ranging animals. The Acousonde tag was attached to the whale using a
 hand-held pole deployment method. After tag attachment, the whales were
 tracked by observers on the <em>Gordon</em>
 <em>Gunter</em> when possible, during daylight hours. A VHF
 receiver on the tag allowed it to be detected when it was above the water
 surface and provided direction for recovery after the tag detached from
 the whale. Milky Way was tagged in 2015 between 20 September 09:44 and 23
 September 01:10 (CDT) at 29.261<sup>o</sup> N and
 86.268<sup>o</sup> W, and in 2018, Edna was tagged on 3 July
 17:16 at 28.757<sup>o</sup> N and
 85.705<sup>o</sup> W with the tag remaining attached until 4
 July 18:25 (CDT). The water depth at the location of tagging was
 determined post hoc from online bathymetry
 data<sup>35</sup>.  The Acousonde (Model B003B) tag includes
 temperature and pressure sensors, triaxial magnetometers and
 accelerometers, and a hydrophone. The data sampling differed between the
 two tag deployments. In 2015, all non-acoustic sensors were sampled at 5
 Hz. In 2018, the sampling rates were: temperature at 5 Hz, pressure at 10
 Hz, accelerometer at 800 Hz, and magnetometer at 40 Hz. Acoustic data were
 sampled at 9110 Hz during both deployments. For consistency in analyses,
 the accelerometer and magnetometer data from 2018 were down-sampled to 10
 Hz.  <strong>Tag data
 analysis</strong> The accelerometer, magnetometer, and
 pressure data were calibrated and corrected for changes in tag placement
 using the <em>tagtools</em> package
 (animaltags.org) in <em>Matlab </em>(MATLAB 2016b, Mathworks,
 Natick, MA). Pitch and roll were calculated from the accelerometer data,
 and heading was calculated from the combination of the accelerometer and
 magnetometer data. The tag depth sensor was temperature corrected using
 periods when the whale was at the surface before and after deep dives when
 the tag was temperature equilibrating. The correction was performed using
 the <em>fix_pressure </em>function from the
 <em>tagtools</em> package. Dives were detected automatically
 using the <em>find_dives </em>function from the
 <em>tagtools </em>package, with a minimum dive depth set at 10
 m. The International Geomagnetic Reference
 Field (IGRF-13) was used to estimate the orientation of the earth's
 magnetic field at the location of the tag (2015: declination =
 -3.4986°; inclination = 58.2491°; 2018: declination =
 -3.1925°; inclination = 58.4539° ). A transformation applied to the tag
 magnetic data (x', y', z') yielded the cardinal directions
 north, east, and toward the earth’s center (x, y, z): x' y' z'
 sinθcos∅ sinθsin∅ -cos∅ -sin∅ cos∅ 0 cosθcos∅ cosθsin∅ sin∅ =x y z where theta is the earth's field
 declination and a phi = 90 degrees minus the earth's field
 inclination.   Tag data were manually analyzed using
 the Matlab-based <em>Triton </em>software
 package<sup>36</sup> (version 1.0 2021 09 21,
 https://github.com/MarineBioAcousticsRC/Triton) with a customized add-on
 “remora” software module, <em>MTViewer</em> (inspired by
 Burgess MT Viewer, www.acousounde.com), that synchronizes displays of data
 from acoustic and kinematic (pressure, orientation) sensors and includes a
 tool for annotating events. The following events were manually annotated
 and occurrence times were extracted for further analysis: breaths, depth
 of neutral buoyancy, circling, foraging lunge, descent end, and ascent
 start. Breaths were identified by both minima in depth and the broadband
 sound of exhalation/inhalation on the hydrophone. We subdivided breaths in
 inhalation and exhalation, and only inhalation times were used in further
 analysis.  We used a proxy for estimating neutral
 buoyancy by identifying the depth during both descent and ascent at which
 fluking stopped and gliding downward or upward (respectively) began.
 Neutral buoyancy was then calculated as the average of those points.
 Circling was identified by a &gt;180° change in heading. Foraging
 lunges were identified as the point at which a sharp increase in speed
 occurred, followed by a rapid deceleration. Lunges typically also
 contained large changes in pitch and roll, which helped to identify them.
 Descent end and ascent start were the points between which the dive
 leveled to a relatively narrow depth range, near the maximum depth of the
 dive. Following annotation, we extracted timing information for each event
 for further analyses using custom Matlab-based routines. Events were
 assigned to day or night periods based on the time of "civil"
 twilight, when the sun was at 6<sup>o</sup> below
 the horizon (http://users.softlab.ntua.gr).  <strong>Estimation of energy expenditure</strong> To get an estimate of the minimum energy required by Rice’s whale to sustain their foraging behavior, we calculated the energy expended as work done during foraging using the minimum specific acceleration of the animals. We estimated mass as an average body mass of 6000 kg, from a generally accepted length-to-weight conversion for baleen whales, using species-specific constants for the closely related Bryde’s whales (a = 0.012965, b = 2.74)<sup>30</sup>. Length was calculated as the average length (9.2 m) from documented Rice’s whales<sup>1</sup>.  Acceleration was calculated as the minimum specific acceleration based on acceleration measured by the tag accelerometer. Measured acceleration is comprised of acceleration due to propulsion, body rotation, and gravity, where the acceleration due to propulsion by the animal is known as the specific acceleration. The minimum acceleration that is due to specific acceleration, the minimum specific acceleration<sup>37</sup> is calculated by combining the data streams from the magnetometer and accelerometer.  The magnetometer only measures body rotation, which makes it possible to separate the acceleration component that is due to body rotation from the component that is due to propulsion. We estimated velocity as the whale’s swimming speed from the tag jiggle (high-frequency vibrations of the tag on the animal that increase with speed), which was regressed against the tags’ accelerometers’ Orientation-Corrected Depth Rate (OCDR), following Cade et al.<sup>38</sup>. Because the speed estimation was most reliable with a sampling rate of &gt;5 Hz, speed was calculated for 2018 first, against which the speed values of 2015 were checked to see if they were reliable. <strong>Statistics</strong> To compare dive characteristics between individuals, we tested whether the measured variables were normally distributed using a Shapiro-Wilk test of normality. As none of the variables were normally distributed, we used a Wilcoxon rank test to compare measurements between individuals. We investigated the effect of activity (descent, circle, lunge, ascent) and individual on power and energy expenditure with linear models. For power, we used a Gaussian-distributed model with log transformation of power to correct for non-linearity in the data, while for energy expenditure, we used a generalized linear model (GLM) with a Gamma distribution. We investigated the influence of energy expenditure and individual on breathing rate using a GLM with quasi-Poisson distribution. Additionally, we tested for the differences in dive depth of lunges preceded by a circle vs lunges that were not preceded by a circle using a linear model. All models were checked against violations of the model assumptions with model diagnostics, and final models were obtained through dredging (<em>MuMIn</em> package), an automated way of selecting the most parsimonious model that corrects for order in variable selection. All statistical tests were performed in R 4.1.2 (R Core Team 2022).