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Use of satellite telemetry and GPS in sea turtle research

The development of satellite telemetry technology in the late 1980s revolutionized the field of wildlife biology by allowing in-depth studies of remotely sensed animal location and movement data (Coyne and Godley, 2005, p. 301). During the last 20 years, the Argos satellite system has become the primary satellite-based wildlife tracking system (Coyne and Godley, 2005, p. 301) and is considered “a standard ecological and conservation tool allowing the movements of many marine and terrestrial species to be documented over a broad range of spatial and temporal scales (Hays et al., 2007, p. 52). Argos satellite-linked transmitters have been used in a variety of terrestrial wildlife tracking studies, such as caribou, gazelles, and wolves, and are used regularly to track marine vertebrates such as cetaceans, pinnipeds, sea turtles, sharks, and commercially and recreationally important fish species (www.argos-system.org). Argos transmitters have also been used extensively in studies of birds, including many raptors, flamingo, gannets, Cory’s shearwater, penguins (www.argos-system.org). The number of animals tracked using the Argos system increased by 500% between 1995 and 2005 (Coyne and Godley, 2005, p. 2) and approximately 4200 animals are currently being tracked by Argos-linked transmitters (www.clsamerica.com/solutions/protecting-wildlife.htm, 2009). The Argos system utilizes polar-orbiting satellites operated by the National Oceanic and Atmospheric Adminitration (NOAA) and the European Organization for the Expoitation of Meteorological Satellites (MetOp)( www.argos-system.org). The Argos system offers two locational platform options: Doppler and GPS. GPS is recommended if the user desires better locational accuracy, more frequent locations or locations at regular intervals (www.argos-system.org). There are eight location classes that are used to describe the accuracy of each position. Class G has an estimated error of < 100m and utilizes GPS. The remaining location classes are all Doppler based. Class 3 has an estimated error of less than 250 m, Class 2 has an estimated error of 250 – 500 m, Class 1 has an estimated error of 500 – 1500 m, and Class 0 has an estimated error greater than 1500 m. Satellites receive four or more messages per pass for Classes 3, 2, 1, and 0. Class A receives 3 messages per satellite pass and no error estimate is provided. Class B locations occur when the satellite receives 2 messages per pass and no error estimate is provided. Class Z locations are invalid (www.argos-system.org). Hays et al. note that improvements have been devised in all components of the Argos system over time, allowing for more types of environmental data to be collected in tandem with location data, as well as for more frequent and accurate collection of location data (2007, p. 52-53). However, tags deployed on marine animals are subject to frequent failure (Hays et al., 2007, p. 53). The more recent development of GPS data loggers, such as the low-power TrackTag manufactured by Navsys, has created a comparable and competitive alternative for acquiring position and ambient temperature data to Argos-linked transmitters. Tools for data use A number of software tools have been developed to facilitate the use of satellite and GPS tracking data. The Animal Movement Analyst extension was developed by the USGS to allow for movement analysis to be conducted within ESRI’s ArcView 3.x GIS program (Hooge et al, 2000, p.1). This extension contains more than fifty functions, including tests of complete spatial randomness, tests for autocorrelation and sample size, random walk models, habitat analyses, animation tools and parametric and non-parametric home range analyses (Hooge et al., 2000, p.1) Satellite Tracking and Analysis Tool (STAT) is a freely available program that uses open source tools and utilities to provide a set of standardized tools and techniques for analysis and management of Argos data (Coyne and Godley, 2005, p.2). STAT is capable of automatically retrieving and parsing Argos data and provides a number of data filtering and editing options, including setting a maximum speed/time/distance between consecutive points, turning angle, location class, and start and end date (Coyne and Godley, 2005, p.4). STAT is also capable of integrating environmental data layers, such as bathymetry (GEBCO and ETOPO2 datasets, sea surface temperature (SST)(NOAA GOES and AVHRR datasets), sea surface heights and currents (CNES/CLS Aviso/Altimetry project data), and chlorophyll concentration (NASA SeaWiFS and MODIS data) with satellite tracks (Coyne and Godley, 2005, pp. 4-6). STAT uses Generic Mapping Tools (GMT) to visualize data (Coyne and Godley, 2005, p.6). Maptool is a free resource (available at www.seaturtle.org) that was developed (by the STAT developers) using GMT v4.3 and perl scripts and allows users to enter or upload sets of latitude/longitude coordinates and map points or tracks against multiple layers of environmental data (www.seaturtle.org).

Studies using satellite telemetry Satellite telemetry and GPS data loggers have been used in a number of studies of sea turtle movement to examine home range size, migratory and local movement patterns, and preferential habitat and microhabitat usage patterns. Data about sea turtle location and movement can be analyzed in conjunction with environmental data to gain new insight into behavioral mechanisms and potentially develop more effective conservation plans. One of the primary goals of migratory and local movement studies has been to determine usage and time spent within current zones of protection (such as national marine parks and protected marine areas) as compared to areas adjacent and in proximity to such zones of protection (Schofield et al., 2007; Witt et al., 2008; Zbinden et al., 2007). Areas utilized by sea turtles for foraging, during internesting periods, and during migration are often areas of economic importance to artisanal and commercial fishing (Kobayashi et al., 2008, p.109; Witt et al., 2008, pp. 298-299; Zbinden et al., 2007, p. 904) Separate small-scale study of loggerhead female movements using Argos-linked transmitters or GPS data loggers within and adjacent to a marine park in which different zones of the park offer different levels of protection in Laganas Bay, Zykanthos Island, Greece during the reproductive season have shown that areas offering the most protection to sea turtles are not preferentially utilized (Schofield et al., 2007, p.64; Zbinden et al., 2007, p.160). Analysis of turtle GPS location data in conjunction with Natura 2000 benthic habitat and bathymetry data indicate that during internesting intervals female loggerheads prefer submerged sand-banks in very shallow water to other benthic substrate options (Schofield et al, 2007, p.65). In a study of female leatherbacks nesting at Mayumba National Park in Gabon, Africa, Witt et al (2008) deployed Argos-linked transmitters on seven turtles during a reproductive season. The individuals tracked spent a mean of 62% of their time outside of the protected area of Mayumba National Park (Witt et al., 2008, p. 298). Only 9% of the habitat utilized by tracked individuals was encompassed within the spatial extent of the park (Witt et al., 2008, p.298). Habitat zones adjacent to the park are open to different levels of seasonal artisanal and industrial fisheries (Witt et al, 2008, p.299). Argos-linked transmitters were used to study the post-nesting migrations of seven loggerhead females observed nesting at Lakanas Bay, Zakynthos, Greece (Zbinden et al, 2008, p.900). Data were downloaded and analyzed using STAT and locations were plotted using MAPTOOL. Satellite tracks of six of the individuals show migrations to separate areas that have been hypothesized to be the primary foraging grounds for this nesting population (Zbinden et al, 2008, p. 902). Environmental Variables and Habitat/Microhabitat Selection Analysis of satellite track data of 186 loggerheads utilizing pelagic habitat in the North Pacific Ocean in conjunction with remotely sensed environmental data indicates that 5 of the 16 environmental variables examined influence habitat selection and usage (Kobayashi et al., 2008, pp. 103-104). Significant correlations were found between sea surface temperature, chlorophyll a concentrations, magnetic force, magnetic inclination, and magnetic declination frequency distributions in total available and preferentially utilized pelagic habitat (Kobayashi et al., 2008, p.104). All five variables exhibit spatial variation, and SST and chlorophyll a also exhibit seasonal and interannual variation that correspond well to seasonal and interannual variability in the satellite tracks (Kobayashi et al., 2008, p.108). Schofield et al. (2008) used GPS loggers and time-depth recorders (TDRs) equipped to record ambient temperature and depth to monitor the movement of a small sample of female loggerheads in Laganas Bay, Zykanthos Island, Greece during reproductive season. Locations and movements of turtles were analyzed using wind direction and water temperature data for potential influence on microhabitat selection. A strong correlation was found between wind direction and sea turtle locations, and analysis of GPS tracks indicated that turtles were not simply located directly downwind but that they moved parallel to the shore and across wind direction to control their position (Schofield et al., 2008, pp. 16-17). Wind direction and variability in sea temperature within the bay were found to be strongly correlated, particularly in the afternoon (Schofiel et al., 2008, p. 16). Comparison of TDR temperature data to mean and maximum near-shore temperature data indicate that sample turtles preferentially positioned themselves in warmer microhabitats, particularly during the early part of the reproductive season (prior to ambient water temperature approaching 26°C)(Schofield et al., 2008, p. 17). Loggerheads lay multiple clutches within a nesting season, and an inverse relationship has previously been shown between internesting intervals and water temperature. Continued use of satellite telemetry and GPS data loggers will hopefully provide new insight into habitat utilization that will allow development of more effective conservation management practices.