As air breathing divers, green sea turtles, Chelonia mydas, have to deal with the challenge of moving between the seabed where they rest and forage and the surface to breathe. We sought to characterize their diving behavior and patterns to better understand their ecology and energy budgets during the inter-nesting period. Time depth recorders (TDR) have provided some key insights into sea turtle dive patterns; however, inferring behavior solely from TDR dive profiles can lead to erroneous conclusions. Therefore, we used acceleration data loggers (ADLs) which provide additional information about the dynamic motion of turtles to determine fine-scale activity patterns and diving behaviors. During the 2014 and 2015 nesting seasons we deployed ADLs (TDRX models, Wildlife Computers, Seattle, WA) along with satellite tags (SPOT model, Wildlife Computers, Seattle, WA) on 10 green sea turtles in Dry Tortugas National Park, FL, USA (N = 7) and at Buck Island Reef National Monument, St. Croix, USVI (N = 3). Turtles were intercepted and tagged after nesting events or false crawls.
Results/Conclusions
Individual ADLs recorded for 7-20 days (mean = 11), and turtle inter-nesting periods ranged from 9-13 days. In a total of 103 days of dive data, turtles remained in shallow water (mean = 5.5 m) with a maximum dive depth of 57.5 m We classified dives into one of six dive types, S-dives (N = 101), deep U-dives (N = 526), shallow U-dives (N = 2773), shallow traveling dives (N = 1415), deep traveling dives (N = 478), and shallow active dives (N = 6117). Deep and shallow U-dives had the lowest Overall Dynamic Body Acceleration (ODBA, deep = 0.515, shallow = 0.409), whereas S-dives and shallow traveling dives had the highest ODBA (S-dive = 0.764, shallow traveling = 0.754). Turtle dive duration was positively correlated with dive depth and dive activity level. We also observed diel patterns in diving behavior. Unlike TDR data, ADL dive types are determined by body acceleration which allows for identification of a range of discrete behaviors. By determining the ODBA associated with each dive type we can better understand the energetic “cost” of these behaviors. With these data we can begin answering questions about gravid green sea turtle activity budgets and habitat use.