Regularized satellite tracks from: Ocean warming alters the distributional range, migratory timing, and spatial protections of an apex predator, the tiger shark (Galeocerdo cuvier)
Cite this dataset
Hammerschlag, Neil (2021). Regularized satellite tracks from: Ocean warming alters the distributional range, migratory timing, and spatial protections of an apex predator, the tiger shark (Galeocerdo cuvier) [Dataset]. Dryad. https://doi.org/10.5061/dryad.cvdncjt5r
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.
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 < 250 m, 250 m < LC2 < 500 m, and 500 m < LC1 < 1500 m. The error estimates associated with LC A and B are reported to be >1 km and <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 > 10 days between positions or tracks with < 10 positions were not interpolated (Jonsen et al. 2019).
See paper for additional details.