Fig 1.
White shark tagging and study location.
Main map (top) shows the location of the VPS array (red dots) just North of Carpinteria, CA, USA, as well as three acoustic monitoring locations off Ventura, CA, USA (blue dots). Inset map shows area magnified view of the study area and VPS array. Basemap and map data were produced in ArcGIS software by ESRI, using map imagery available from USGS (https://apps.nationalmap.gov/viewer/). Image (bottom) shows a juvenile white shark being tagged within the VPS array.
Table 1.
Details of VPS derived cruise speed data for all juvenile white sharks.
Table 2.
UAV derived JWS cruise speeds.
Table 3.
Hypothesis tests for typical swim states.
Fig 2.
Relationship of cruising speed to shark size, diel period, temperature and depth.
(A) Median values of raw cruising speeds (m s-1) for each shark were found to have a weak positive linear relationship to shark body size, described by the equation (SSH = 5.3x10-5(M) + 0.61). Data points show the mass (kg) and cruising speed (m s-1) of each shark included in the study. (B) Standardized to total length, a negative correlation between median cruising speed and body mass (size) was observed, with larger sharks effectively swimming more slowly than smaller sharks. Data points show the mass (kg) and cruising speed standardized to the total length of the animal (UTL s-1) for each shark included in the study. (C) Relationship between cruising speed (m s-1, blue trace) and environmental temperature (green trace°C) for the six sharks equipped with depth sensing tags. Trend line shows conditional smoothed mean values, grey shading indicates 95% confidence intervals. (D) Relationship between cruising speed and depth for the same six sharks. Blue trace indicates mean swim speed (m s-1), red trend line indicates mean swimming depth (m).
Fig 3.
Relationship of juvenile white shark cruising speed to biotic and abiotic variables.
(A) Box and whisker plot of swim speeds (in m s-1, log transformed) associated with depth bins (in meters). A great majority of locations (and thus calculated velocities) occurred within depths from 1–5 meters. No difference was seen in median SST with changes in depth profile. (B) Frequency distribution and marginal histogram plot of diel patterns in JWS cruising speeds. Dot size is relative to frequency (log transformed). Significantly more derived locations occurred by night. SSH patterns at night exhibited a significantly larger range, as well as significantly higher maximum values compared with daytime patterns. Marginal histogram shows distributions of plotted variables. (C) Box and whisker plot of swim speeds (in m s-1, log transformed) associated with shark size-class. (D) Ridgeline plot of SSH distributions by individual shark (see Table 1 for reference).
Fig 4.
Metabolic implications of measured swimming speeds and body size in juvenile white sharks.
(A) Estimated mass-specific mean fRMR of juvenile white sharks in the study. Points and trend-lines show mean estimated oxygen consumption rates of all sharks included in analyses, with respect to body size (mass), based on the general equation furnished by Semmens et al. (2013). Shaded areas delineate 95% confidence intervals associated with conditional smoothed mean values. (B) Whole-body routine metabolic rates (mean 420 ± 51mg O2 h−1) based upon the allometric mass-scaling exponent of 0.79, as described by Payne et al (2015).