Trading shallow safety for deep sleep: juvenile green turtles select deeper resting sites as they grow

Abstract

To better protect endangered green sea turtles Chelonia mydas, a more thorough understanding of the behaviors of each life stage is needed. Although dive profile analyses obtained using time-depth loggers have provided some insights into habitat use, recent work has shown that more fine-scale monitoring of body movements is needed to elucidate physical activity patterns. We monitored 11 juvenile green sea turtles with tri-axial acceleration data loggers in their foraging grounds in Dry Tortugas National Park, Florida, USA, for periods ranging from 43 to 118 h (mean ± SD: 72.8 ± 27.3 h). Approximately half of the individuals (n = 5) remained in shallow (overall mean depth < 2 m) water throughout the experiment, whereas the remaining individuals (n = 6) made excursions to deeper (4 to 27 m) waters, often at night. Despite these differences in depth use, acceleration data revealed a consistent pattern of diurnal activity and nocturnal resting in most individuals. Nocturnal depth differences thus do not appear to represent differences in behavior, but rather different strategies to achieve the same behavior: rest. We calculated overall dynamic body acceleration (ODBA) to assess the relative energetic cost of each behavioral strategy in an attempt to explain the differences between them. Animals in deeper water experienced longer resting dives, more time resting per hour, and lower mean hourly ODBA. These results suggest that resting in deeper water provides energetic benefits that outweigh the costs of transiting to deep water and a potential increased risk of predation.

Complex movement patterns by foraging loggerhead sea turtles outside the breeding season identified using Argos‐linked Fastloc‐Global Positioning System

“…Use of the same patch during day and night has been linked to an optimum feeding strategy, involving high familiarity of fixed sites so as to increase feeding efficiency (Jadot et al., ), and minimize energy and time expenditure incurred by moving between the resting and feeding areas. Interestingly, of the turtles that exhibited distinct day and night refuges, those in Amvrakikos Gulf moved further offshore at night, similar to that observed for juvenile green turtles in Florida, USA (Hart, White, Iverson, & Whitney, ), although turtles on Zakynthos moved closer to shore at night. The differential use of day and night‐time refuges might be a result of differences in habitat substrate and the bathymetry of the immediate area, reflecting an innate mechanism to seek shelter and avoid predators, as shown for other turtles (Christiansen et al., ; Makowski et al., ; Seminoff et al., ) and wildlife (e.g., daily movement of copepods, Hays, Kennedy, & Frost, ; Roe deer Padié et al., ).…”

Drivers of realized satellite tracking duration in marine turtles

“…We captured turtles using both land-based interception of nesting females after nesting and non-nesting emergences, whereupon we restrained them with a portable corral (96.5 cm wide × 67.3 cm height). We also captured turtles using several in-water methods (i.e., hand captures via snorkeling [ 26 ], rodeo or turtle-jumping [ 27 , 28 ]; trawling, and dipnet [ 24 , 29 ]). Upon capture each new turtle was given a passive integrated transponder (PIT) tag in the shoulder or front flipper (Biomark, Boise, ID; models 12 mm tag = BIO12.B.01 V2 PL.SY and 8 mm tag = BIO8.B.03 V1 PL.SY) and individually numbered Inconel flipper tags (National Band and Tag, Newport, KY; model 681) following established protocols (NMFS-SEFSC 2008).…”

Active acoustic telemetry tracking and tri-axial accelerometers reveal fine-scale movement strategies of a non-obligate ram ventilator

“…We used a total of 10 clusters because it allowed the shapes of major spectra in the acceleration ethograms to become stable, while also ensuring we encompassed all behavioral patterns [25,69]. By visually examining the signal strength amplitude and cycle of resulting clusters, we could visually interpret and assign a cluster as resting behavior because resting produced low-amplitude spectra, indicating the original behavior consisted of low acceleration and the lack of a clear cyclic signal [26,76,77]. The k-means clustering was performed on each individual rather than using the same classification values across all individuals because some individuals only displayed a single resting cluster whereas others displayed more than one [25,26].…”