DOI: 10.5061/dryad.t4b8gtjcs
title: Data from: Vertical energy seascapes and diving behavior modulate metabolic scope in a pelagic predator

description: Pelagic fishes must obtain resources in prey-sparse habitats and may be
 considered energy speculators with maximization, gambling high energy
 costs (e.g. metabolism) for a high rate of return (prey capture). As such,
 they may have to carefully use their energy seascape to obtain the
 resources necessary for high growth rates. For diving animals, their
 energy seascape will also have a vertical component in addition to a
 horizontal one, which is rarely considered. Dolphinfish, Coryphaena
 hippurus, embody the maximization strategy as they have high metabolic
 rates and fast growth rates. We coupled biologging on wild individuals
 with lab-based respirometry to estimate dolphinfish swimming metabolic
 rates and vertical energy seascapes. Dolphinfish performed continuous
 yo-yo dives with deeper dives at night but higher activity during the day.
 Dive descents were ~27% less costly than the ascents. Fish modulated their
 behavior so that metabolic costs during the descent/ascent phases of
 deeper dives were less than those for shallow dives. While temperature is
 likely the primary limit of dive depth, the vertical energy seascape may
 be secondary and limit maximum dive depths. Studies of pelagic animal
 energy seascapes should consider the vertical component which will help
 determine their ability to access and utilize prey.
description: We coupled biologging on wild individuals with lab-based
 respirometry to estimate dolphinfish swimming metabolic rates and vertical
 energy seascapes. Wild-captured dolphinfish were used to directly relate
 fish activity to metabolic expenditure by placing ADL-tagged individuals
 into a large annular respirometer to measure oxygen consumption. Following
 the 24-h fasting period, the water level in the holding tank was decreased
 and fish were corralled using a soft, pliable vinyl barrier so that an
 individual fish could be guided into a water-filled sling. The captured
 fish was weighed, measured, and then fitted with a biologging tag package
 that included a multi-sensor ADL (model AGM, 6 cm in length, 2 cm wide,
 1.5 cm high, 25 g in air, Technosmart, Rome, Italy) embedded in a buoyant
 float made of syntactic foam for tag recovery during field deployments.
 The ADL includes an accelerometer, gyroscope, and magnetometer each
 recording in all three axes (x, y, and z) at 25 Hz, as well as a depth and
 temperature sensor measuring at 1 Hz. Once the tag package was securely
 attached, the fish was moved into an 8851 L recirculating annular
 respirometer (3.05 m diameter, 1.22 m height) housed at the ARCC.
 Dissolved oxygen levels in the respirometer were recorded every 30-s using
 two oxygen sensors, one optical-type sensor (YSI Pro ODO, YSI, Yellow
 Springs, OH) attached through a port in the acrylic lid and one
 polarographic-type sensor (YSI Pro Plus Multimeter) in the recirculating
 loop. Routine metabolic rates (RMR; mgO2 kg-1 h-1) were calculated for
 each fish for a minimum of two-hour long segments from the measurement
 phases of the 48-h respirometry trials. The slope of the linear decline in
 oxygen consumption for each segment (R2 &gt; 0.98) was multiplied by
 the volume of the respirometer minus the volume of the fish, divided by
 the mass of the fish and converted to units per hour.
 Following the respirometry trial, acceleration data from the ADL
 were analyzed using Igor Pro v. 9.0.2.4 (WaveMetrics Inc., Lake Oswego,
 OR, USA) with the ‘Ethographer’ extension and R. We corrected for tag
 attachment angle by rotating the raw acceleration data until the x and y
 axis had a mean of zero. We removed the gravitational component of
 acceleration using a 3-s box smoothing window on the raw acceleration
 data. We used a continuous wavelet transformation with Ethographer to
 analyze cyclical patterns in the sway (lateral) axis of the
 accelerometers. This method classified the dominant peak as tailbeat
 cycles that were used to calculate tailbeat frequency (TBF) at 1-s
 intervals. TBF has previously been shown to be a good predictor for
 swimming speed and oxygen consumption of fishes. Mean TBF was calculated
 for each segment of oxygen consumption used to calculate RMR. TBF
 measurements less than 1.0 Hz were removed from analyses to avoid
 overestimating RMRs due to unnatural swimming behavior at low swim speeds
 typically yielding unnaturally high metabolic rates. We
 used the respirometry-based relationship between TBF and RMR to estimate
 swimming metabolic rates (MRSW) of dolphinfish in the wild. Dolphinfish
 were tagged with ADLs off the coast of Baja California, Mexico and the
 Tsushima Strait, Japan due to an abundance of dolphinfish in these areas,
 and local resources to aid in tag deployment and recovery. We prepared the
 acceleration data from the field deployments the same way as the
 acceleration data from the lab calibrations described in the previous
 section. We also analyzed environmental data (depth and temperature) from
 the field deployments. To look at dive behavior, we categorized vertical
 swimming into three phases (ascent, descent, and level) using the fish’s
 vertical velocity (VV, m s-1), calculated as the difference between
 successive depth observations at 1 s intervals. Ascent and descent were
 defined as periods when the fish swam with VV less than or equal to -0.05
 m s-1 or greater than or equal to 0.05 m s-1 for more than 10 s,
 respectively. Because dolphinfish have a high association with the surface
 [16, 17], we identified individual dive excursions as the period of time
 where a dolphinfish was ≥ 10 m in depth for longer than 10
 seconds.
description: # Data from: Vertical energy seascapes and diving behavior modulate
 metabolic scope in a pelagic predator
 [https://doi.org/10.5061/dryad.t4b8gtjcs](https://doi.org/10.5061/dryad.t4b8gtjcs) ## Description of the data and file structure ### Files and variables #### File: DF\_Respirometry\_Data.csv **Description:** Respirometry and accelerometry data used to derive the relationship between tailbeat frequency and metabolic rate. ##### Variables * FishID: Unique Fish ID * MO2: Metabolic rate (mgO2 kg h-1) * TBF: Tailbeat Frequency (Hz) * TBP: Tailbeat Period (s) * TBA: Tailbeat Amplitude (g) * ODBA: Overall Dynamic Body Acceleration (g) * Temp: Temperature (Degrees Celsius) * Mass: Body mass of the fish (kg) * FL: Fork Length of the fish (cm)  #### File: DF\_Energetics\_Manuscript\_Dataframe.csv **Description:** Data from the acceleration dataloggers for the tracks of the dolphinfish tagged in Baja California, Mexico ##### Variables * DateTime: Date and time (YYYY-MM-DD HH:MM:SS) * Depth: Continuous depth (m) measurements * Temp: Continuous temperature (degrees C) measurements * TBFreq: Tailbeat Frequency (Hz) * TBPeriod: Tailbeat Period (s) * TBAmp: Tailbeat Amplitude (g) * FMRmgO2kgh: Swimming Metabolic rate: MRsw (mgO2 kg-1 h-1)  * FMRmgO2kgmin: Swimming Metabolic rate: MRsw (mgO2 kg-1 min-1)  * kJhour: Swimming Metabolic rate: MRsw (kJ h-1)  * kJday: Swimming Metabolic rate: MRsw (kJ day-1)  * ID: Unique fish IDs for fish tagged in Mexico * Vertical Velocity (m/s): Vertical velocity (m/s) based on dive depth * hour: Hour of day * daynight: If the time period was day or night based on the respective sunrise and sunset times in that area during the track * depthbinfull: Binned depth measurements (m) * divephase: To look at dive behavior, we categorized vertical swimming into three phases (ascent, descent, and level) using the fish’s vertical velocity (VV, m s-1), calculated as the difference between successive depth observations at 1 s intervals. Ascent and descent were defined as periods when the fish swam with VV less than or equal to -0.05 m s-1 or greater than or equal to 0.05 m s-1 for more than 10 s, respectively. * FMRmgO2kgsec: Swimming Metabolic rate: MRsw (mg O2 kg-1 s-1)  * ILD: Isothermal layer depth (ILD) for each fish as the depth where a temperature change greater than 0.8°C occurred. * state: Behavioral state (State 1: Low Activity, State 2: High Activity) based on hidden Markov model * divephaseHMM: Dive phase based on hidden Markov model, interpolated NAs in original divephase column when classification based on our parameters was not possible.  * region: Region where the fish were tagged (Baja California, Mexico) ## Code/software Acceleration data from the ADL were analyzed using Igor Pro v. 9.0.2.4 (WaveMetrics Inc., Lake Oswego, OR, USA) with the ‘Ethographer’ extension [23] and R (R Core Team, 2016). We developed a two-state model where behavioral states could correspond to a relatively low or high activity state based on tailbeat frequency, in R using the *momentuHMM* package.