GPS Technology for Semi-Aquatic Turtle Research

Abstract

Global positioning system (GPS) telemetry units are now small enough to be deployed on terrestrial and semi-aquatic turtles. Many of these GPS units use snapshot technology which collects raw satellite and timestamp data during brief periods of data recording to minimize size. We evaluated locations from snapshot GPS units in stationary tests and on wood turtles (Glyptemys insculpta) in northeastern Minnesota. Stationary GPS units were placed in wood turtle habitat to evaluate location accuracy, fix success rate, and directional bias. The GPS fix success rate and accuracy were reduced in closed canopy conditions and when the stationary GPS unit was placed under a log to simulate wood turtle hiding behavior. We removed GPS location outliers and used a moving average calculation to reduce mean location error in stationary tests from 27 m (SD = 38) to 10 m (SD = 8). We then deployed GPS units and temperature loggers on wood turtles and collected 122,657 GPS locations and 242,781 temperature readings from 26 turtles from May to September 2015 and 2016. Location outliers accounted for 12% of locations when the GPS receiver was on a turtle. We classified each wood turtle location based on the GPS location and by comparing temperature profiles from river, sun, and shaded locations to the temperature logger on the turtle. We estimated that wood turtles were on land 68% (SD = 12) of the time from May to September. The fix success rate for land locations was 38% (SD = 9), indicating that wood turtles often use habitats with obstructed views of the sky. Mean net daily movement was 55 m (SD = 192). Our results demonstrate that snapshot GPS units and temperature loggers provide fine-scale GPS data useful in describing spatial ecology and habitat use of semi-aquatic turtles.

A Surfeit of Studies: What Have We Learned from All the Box Turtle (Terrapene carolina and T. ornata) Home Range Studies?

“…did affect HR. We note that we were unable to test whether the study length or number of locations/individual affected HR size; however, we suggest that the raw data from an unusually long-term study with very large sample sizes (e.g., [21,45]) or novel studies using current GPS-tracking technology create samples that vary frequently and with high accuracy (e.g., [75,76]) could be sub-sampled to address these questions, as well as to address important issues of spatial autocorrelation. As shown in this study, meta-analysis is a powerful tool that can detect large-scale patterns across studies and provide evidence for or against conclusions made about species within individual studies.…”

Assessing the accuracy of altitude estimates in avian biologging devices

“…We found an overall improvement in altitude estimates from stationary to moving field trials in both the horizontal and vertical movement trials for the OrniTrack-25 device; mean error decreased from approximately 20.97m during higher flight heights in stationary trials to 6.5m and 3.2m during horizontal and vertical trials, respectively. This was likely due to a reduction in accuracy when a GPS unit is stationary compared to when it is moving [ 32 , 33 ]. This underscores the importance of assessing location accuracy using data from moving GPS units, since stationary units may result in an inappropriate representation of device accuracy and precision.…”

“…2) Estimates during horizontal movements where the UAS flew continuously at a 10m height with movement in the horizontal plane only. This trial was designed to capture the accuracy of each device’s flight altitude estimates based on the notion that GPS devices tend to increase in both accuracy and precision while moving [ 32 , 33 ]. 3) Estimates during vertical movements where the UAS flew with continuous vertical movements (ascensions and descensions, representing movement in the vertical plane only) from 2-60m above ground.…”

Circles within spirals, wheels within wheels; Body rotation facilitates critical insights into animal behavioural ecology

“…Many factors affect GPS performance including habitat type and heterogeneity (Janeau et al 2004;Swanepoel, Dalerum & Van Hoven 2010;Smith et al 2018;Cochrane, Brown & Moen 2019), topography of the terrain (Cain et al 2005;, view of the sky availability (Adams et al 2013), weather conditions (Swanepoel, Dalerum & Van Hoven 2010), submersion in water (Quaglietta et al 2012;Justicia, Rosell & Mayer 2018), time of day (Heard, Ciarniello & Seip 2008), vegetation cover/type (Cain et al 2005;Hansen & Riggs 2008), GPS orientation 2018), in addition to the number of available satellites and their orbiting geometry with respect to one another Vance et al 2017). All these elements affect the propagation of signal quality and/or receiver reception capability and thus increase triangulation error (see for review), with the latter two factors often assessed via the dilution of precision (DOP) values (Dussault et al 2001;Vance et al 2017).…”

“…, underpins the species interaction with its environment and consequently the dual proficiency of signal propagation and reception between satellites and receiver. Resting is the most common behaviour for most terrestrial animals (particularly carnivores) and critically affects fix accuracy, because resting is typically associated with a change of body position (e.g., resting on the collar) and/or coverage within/near 'signal obstructing' environmental features (e.g., sleeping under trees or in caves/ burrows), thus decreasing the available sky for the GPS receiver (Cain et al 2005;Vance et al 2017;Smith et al 2018;Cochrane, Brown & Moen 2019). This issue is compounded for collar-mounted GPS devices, because behaviours variously affect the position of the GPS antenna even though many collars are designed to be bottom-weighted to minimise this problem (Cain et al 2005;Jiang et al 2008;.…”