TurtleCam: A “Smart” Autonomous Underwater Vehicle for Investigating Behaviors and Habitats of Sea Turtles

Abstract

Sea turtles inhabiting coastal environments routinely encounter anthropogenic hazards, including fisheries, vessel traffic, pollution, dredging, and drilling. To support mitigation of potential threats, it is important to understand fine-scale sea turtle behaviors in a variety of habitats. Recent advancements in autonomous underwater vehicles (AUVs) now make it possible to directly observe and study the subsurface behaviors and habitats of marine megafauna, including sea turtles. Here, we describe a "smart" AUV capability developed to study free-swimming marine animals, and demonstrate the utility of this technology in a pilot study investigating the behaviors and habitat of leatherback turtles (Dermochelys coriacea). We used a Remote Environmental Monitoring UnitS (REMUS-100) AUV, designated "TurtleCam," that was modified to locate, follow and film tagged turtles for up to 8 h while simultaneously collecting environmental data. The TurtleCam system consists of a 100-m depth rated vehicle outfitted with a circular Ultra-Short BaseLine receiver array for omni-directional tracking of a tagged animal via a custom transponder tag that we attached to the turtle with two suction cups. The AUV collects video with six high-definition cameras (five mounted in the vehicle nose and one mounted aft) and we added a camera to the animal-borne transponder tag to record behavior from the turtle's perspective. Since behavior is likely a response to habitat factors, we collected concurrent in situ oceanographic data (bathymetry, temperature, salinity, chlorophyll-a, turbidity, currents) along the turtle's track. We tested the TurtleCam system during 2016 and 2017 in a densely populated coastal region off Cape Cod, Massachusetts, USA, where foraging leatherbacks overlap with fixed fishing gear and concentrated commercial and recreational vessel traffic. Here we present example data from one leatherback turtle to demonstrate the utility of TurtleCam. The concurrent video, localization, depth and environmental data allowed us to characterize leatherback diving behavior, foraging ecology, and habitat use, and to assess how turtle behavior mediates risk to impacts from anthropogenic activities. Our study demonstrates that an AUV can successfully track and image leatherback turtles feeding in a coastal environment, resulting in novel observations of three-dimensional subsurface behaviors and habitat use, with implications for sea turtle management and conservation.

A global review of green turtle diet: sea surface temperature as a potential driver of omnivory levels

“…Traditional methods include direct observation using snorkel or SCUBA (Reisser et al 2013), the analysis of gut contents from dead turtles (Mortimer 1981) and oesophageal lavage and faecal examination (Seminoff et al 2002), while indirect biochemical approaches include stable isotope analysis (SIA) (Pearson et al 2017). Modern technologies, such as remote videography (Letessier et al 2014), animal-borne cameras (Heithaus et al 2002), autonomous underwater vehicles (Dodge et al 2018) as well as satellite tracking from nesting beach and subsequent in situ validation of diet at foraging locations (Esteban et al 2018), have also been used to document the activities of turtles at their foraging habitats.…”

Mobile robotic platforms for the acoustic tracking of deep-sea demersal fishery resources

“…However, this approach augments considerably the tag's size and, therefore, is not suitable for smaller tags. The tag's size is also an important constraint in (20). In (14), the authors developed a method that used the signal strength to infer the tag's position; nonetheless, its performance and observability studies were not reported.…”

Test of unmanned surface vehicles to conduct remote focal follow studies of a marine predator

“…These technologies can help expand research activities over longer time periods or larger areas, including shallow coastal regions inaccessible by large research vessels (Cokelet et al 2015, Rudnick 2016. Recently, autonomous underwater vehicles (AUVs) have been used for focal follow type studies of fish, sharks, and turtles (Packard et al 2013, Skomal et al 2015, Dodge et al 2018). These studies, which used acoustic transponders on the targeted animal, were temporally limited (1 to < 8 h) and only able to measure oceanographic parameters, not prey abundance (Packard et al 2013, Skomal et al 2015, Dodge et al 2018.…”

“…Recently, autonomous underwater vehicles (AUVs) have been used for focal follow type studies of fish, sharks, and turtles (Packard et al 2013, Skomal et al 2015, Dodge et al 2018). These studies, which used acoustic transponders on the targeted animal, were temporally limited (1 to < 8 h) and only able to measure oceanographic parameters, not prey abundance (Packard et al 2013, Skomal et al 2015, Dodge et al 2018. For focal follow studies with unmanned vehicles to be comparable to traditional survey methods, longer follow durations and direct measurements of prey are needed.…”