Hueter et al.: Horizontal and vertical movements of Isurus paucus in the northwestern Atlantic Ocean 
103 
was protected with heat shrink tubing (3M, Two Har- 
bors, MN). A conventional Rototag (Dalton ID Systems 
Ltd, Henley-on-Thames, UK) and dart tag (Hallprint 
Pty. Ltd.) also were applied to the shark’s first dorsal 
fin and dorsal musculature, respectively. The condition 
of the tagged shark at release was scored as condition 
4 (>30 s of revival time, limited swimming on release) 
according to the release condition categories of Hueter 
et al. (2006). 
In 2015, when LFM2 was captured, the SST was 
25.7°C and depth to the bottom was approximately 750 
m. The gear targeted pelagic fish species, in particu- 
lar istiophorids and swordfish, and consisted of 8 km 
of mainline with gangions of 20-30 m, both composed 
of 2-mm twisted monofilament. The 66 hooks deployed 
were either 15/0 or 16/0 J hooks baited with salted 
clupeid and cyprinid fish species (e.g., silver carp [Hy- 
pophthalmichthys molitrix\), and suspended approxi- 
mately 40 m below the surface. The fishermen attached 
battery-powered light-emitting devices (known locally 
in Cuba as lampos) to the gangions approximately 3 m 
above each baited hook. During gear retrieval in the 
early morning of 14 February, the hooked shark was 
transferred from the fishing vessel to a research boat, 
secured by tail rope at the stern, and maintained in 
the water during the process of taking measurements 
and tagging. 
This shark (LFM2) was tagged with a MiniPAT 
(Wildlife Computers) that archived measurements of 
temperature, pressure, and light level at 5-s intervals 
and summarized these data into 6-h periods. The tag 
was programmed to detach from the shark after 150 
d, and the time-at-depth and time-at-temperature his- 
tograms of the data were distributed among 12 user- 
defined bins. Unlike the MklO tag, which was deployed 
on LFMl, the MiniPAT sends time-series data of depth 
and temperature by way of the Argos satellite system 
in addition to archiving that data. A clear antifouling 
coating (Propspeed, Propspeed USA, Miami, FL) had 
been applied to the tag, excluding its sensors and re- 
lease pin. At deployment, the tag was inserted into the 
dorsal musculature of the shark just below the first 
dorsal fin by using a plastic anchor (Domeier; 20x14 
mm; Wildlife Computers) attached to a 15-cm tether 
composed of stainless steel wire rope with a 23-kg load 
capacity (type 18-8; McMaster-Carr, Santa Fe Springs, 
CA). A newer version of the RD1800 device was em- 
ployed with an internal pin that breaks under pres- 
sure at a depth of 1800 m, releasing the tag from the 
tether. The tether, excluding the portion of the RD1800 
device, was protected with heat shrink tubing (3M). No 
conventional tag was applied to this shark. The condi- 
tion of the shark at release was classified as condition 
2 (no revival time required, slow but strong swimming 
on release; Hueter et al., 2006). 
For both sharks, species identification was deter- 
mined by the presence of taxonomic characteristics of 
the genus Isurus (Campagno, 2001), together with a 
first dorsal fin well behind the free rear tip of the pec- 
toral fin, dark coloration on the ventral surface of the 
snout, and long pectoral fins, all of which collectively 
distinguish the longfin mako from the shortfin mako 
(Guitart Manday, 1966; Garrick, 1967; Bustamante et 
al., 2009). Maturity of the 2 males was assessed by 
stage of clasper development and its condition. 
Compiled data collected through the Argos system 
were uploaded to the Wildlife Computers Data Portal 
(website) for processing with GPE3 software (Wildlife 
Computers). This statistical processing tool runs ex- 
clusively on the tag manufacturer’s Internet servers. 
The GPE3 software uses tag data and corresponding 
SST (NOAA Optimum Interpolation (01) SST V2 High 
Resolution) and bathymetry (NOAA ETOPOl global re- 
lief model. Bedrock version) reference data as inputs 
into its gridded hidden Markov model to generate the 
most likely animal location for a given time, as well 
as a distribution of likelihoods as an indicator of loca- 
tion quality. This model provides an overall score as 
an indicator of how well the model fits the observed 
data. We ran the model with varying inputs for the 
parameter of animal speed to generate a fit with an 
optimal score and realistic maximum likelihood track 
(MLT). Optimal MLTs for LFMl and LFM2 were gener- 
ated by using animal speed inputs of 4.5 and 2.5 m/s, 
respectively. The total distance of the MLT was calcu- 
lated with GE-Path software (vers. 1.4.5). Likelihood 
surfaces were generated by using the raster and ncdf 
packages for statistical software R, vers. 3.2.3 (R Core 
Team, 2015) and by using the script made available 
by the tag manufacturer. For comparing the MLT with 
SST, we produced imagery in R from the Group for 
High Resolution Sea Surface Temperature (GHRSST) 
global 1-km SST data set (website) using functions in 
the fields, maps and raster packages for R. 
We assigned a diel period to each record (day, night, 
dawn, dusk) in the time-series data sent by the Mini- 
PAT. To approximate the times of sunrise and sunset 
for a given date and location (from the MLT), we con- 
sulted an online calculator (website). On the basis of 
these estimates, dawn was defined as the 30-min period 
before and after sunrise, and dusk was defined as the 
30-min period before and after sunset. To evaluate dif- 
ferences in percent time at depth and percent time at 
temperature between the 2 sharks and to test differ- 
ences between day and night, we performed 2-sample 
Kolmogorov-Smirnov (K-S) tests. Mean depths between 
diel periods were compared with Welch’s unequal vari- 
ances ^-test. These statistical analyses were performed 
by using the stats package for R (R Core Team, 2015). 
To further investigate the environmental drivers 
of behavior, we used the time-series depth data from 
the MiniPAT to calculate vertical speed (as a proxy for 
activity level) for comparison with the corresponding 
temperature at depth. The difference between sequen- 
tial depth data points was used to determine vertical 
velocity. The absolute value of the vertical velocity was 
considered the vertical speed. We then examined the 
relationship between daily mean vertical speed and 
minimum (daytime) and maximum (nighttime) temper- 
atures during that segment of the day, using a linear 
