DOI: 10.5061/dryad.cvdncjt5r
title: Regularized satellite tracks from: Ocean warming alters the distributional range, migratory timing, and spatial protections of an apex predator, the tiger shark (Galeocerdo cuvier)

description: Data are regularized tiger shark satellite tracks used in "Ocean
 warming alters the distributional range, migratory timing, and spatial
 protections of an apex predator, the tiger shark (Galeocerdo cuvier)"
 published in Global Change Biology. Paper abstract below: Given climate
 change threats to ecosystems, it is critical to understand responses of
 species to warming. This is especially important in the case of apex
 predators since they exhibit relatively high extinction risk and changes
 to their distribution could impact predator-prey interactions that can
 initiate trophic cascades. Here we used a combined analysis of animal
 tracking, remotely sensed environmental data, habitat modeling, and
 capture data to evaluate the effects of climate variability and change on
 the distributional range and migratory phenology of an ectothermic apex
 predator, the tiger shark (Galeocerdo cuvier). Tiger sharks satellite
 tracked in the western North Atlantic between 2010 and 2019 revealed
 significant annual variability in the geographic extent and timing of
 their migrations to northern latitudes from ocean warming. Specifically,
 tiger shark migrations have extended farther poleward and arrival times to
 northern latitudes have occurred earlier in the year during periods with
 anomalously high sea-surface temperatures. A complementary analysis of
 nearly 40 years of tiger shark captures in the region revealed
 decadal-scale changes in the distribution and timing of shark captures in
 parallel with long-term ocean warming. Specifically, areas of highest
 catch densities have progressively increased poleward and catches have
 occurred earlier in the year off the North American shelf. During periods
 of anomalously high sea-surface temperatures, movements of tracked sharks
 shifted beyond spatial management zones that had been affording them
 protection from commercial fishing and bycatch. Taken together, these
 study results have implications for fisheries management, human-wildlife
 conflict, and ecosystem functioning.
description: Methods excerpted from the paper: Between May
 2010 and January 2019, tiger sharks were tagged off southeast Florida,
 southwest Florida, and the northern Bahamas with Smart Position and
 Temperature Transmitting tags (SPOT tag, Wildlife Computers) to quantify
 spatial movement patterns. At capture, sharks were sexed and measured for
 total length (TL). SPOT tags were affixed to the first dorsal fin and tags
 were coated with antifouling materials to minimize biofouling. Prior to
 deployment, all SPOT tags were tested and confirmed for location accuracy
 at land-based facilities. The geographic location of
 each tagged shark was determined via Doppler-shift calculation made by the
 Argos Data Collection and Location Service (www.argos-system.org) whenever the shark’s tag broke the water’s surface and transmitted. Location accuracy was dependent on the number of tag transmissions received by Argos satellites. Argos provides location accuracy using location classes (LC) 3, 2, 1, 0, A, B, and Z (in decreasing accuracy), corresponding with the following error estimates: LC3 &lt; 250 m, 250 m &lt; LC2 &lt; 500 m, and 500 m &lt; LC1 &lt; 1500 m. The error estimates associated with LC A and B are reported to be &gt;1 km and &lt;5 km, respectively (Tougaard et al. 2008). LC Z estimates are inaccurate or unreliable and were removed from the dataset prior to any analysis. Due to irregular surfacing of sharks (and thus irregular transmission rates) and variation in satellite coverage at any given time, raw SPOT-derived data are subject to autocorrelation and spatial biases. Therefore, prior to any analyses, positional data were interpolated and regularized to daily estimates using a Bayesian state-space model that also accounts for Argos satellite telemetry precision (Graham et al. 2016) using the R package foieGras (Jonsen et al. 2019) in the R statistical software (v. 4.0.2.; R Core Team 2021). Individual tracks with data gaps &gt; 10 days between positions or tracks with &lt; 10 positions were not interpolated (Jonsen et al. 2019). See paper for additional details.