Unlocking sea turtle diving behaviour from low-temporal resolution time-depth recorders

Abstract

Biologging is a rapidly advancing field providing information on previously unexplored aspects of animal ecology, including the vertical movement dimension. Understanding vertical behaviour through the use of time-depth recorders (TDRs) in marine vertebrates is critical to aid conservation and management decisions. However, using TDRs can be particularly problematic to infer animal behaviour from elusive animals, when tags are difficult to recover and collected data is satellite-relayed at lower temporal frequencies. Here, we present a novel method to process low-resolution TDR data at 5-minute intervals and infer diving behaviour from loggerhead turtles (Caretta caretta) during their elusive pelagic life stage spanning extended periods (> 250 days). Using a Hidden Markov Model (HMM) we identify four behavioural states, associated with resting, foraging, shallow exploration, and deep exploration. Three of the four behavioural states were found to have strong seasonal patterns, corroborating with known sea-turtle biology. The results presented provide a novel way of interpreting low-resolution TDR data and provide a unique insight into sea turtle ecology.

Keywords: Dive analysis; Hidden Markov model; Loggerhead turtle; Seasonal behaviour; Telemetry.

Fig. 1

Depiction of dive profile automatic phase labelling and segmentation. Bottom phase is labelled as depth > 80% maximum depth. If peaks were detected (top panel), and it was possible the turtle could have surfaced given previous ascent speed a single dive the dive was ‘split’, creating two consecutive dives (bottom panel).

Fig. 2

Hidden Markov models suggested four behavioural states. A random selection of 200 dives per state are shown. Surface interval is not depicted.

Fig. 3

Location of each depicting behavioural state throughout single TDR deployment (organism ID 235396; data spanning 214 days): (a) State 1 = Purple, (b) State 2 = Orange, (c) State 3 = Blue and (d) State 4 = Coral. (b) Location of each behavioural state throughout. deployment; Green dot and red triangle showing the beginning and the ending of the track visualized, respectively.

Fig. 4

State allocations across all loggerhead turtles. (a) Proportion of dives in each state across all individuals. Boxplots depict median relative expression levels and the 25 th and 75 th percentiles. Whiskers are 1.5× the interquartile range, data points outside this range are marked as outliers (circles). (b) Proportion of time each turtle spent in each state for the entire deployment duration. Turtle IDs correspond to Argos PTT numbers.

Fig. 5

Move persistence metrics across all deployments. (a) Histograms of move persistence metric for each identified state for all dives across all individuals. (b) Proportions of MP classes within each identified state.

Fig. 6

Plots of best fitting GAMMs to assess monthly variation of the time spent in each of the four behavioural states across loggerhead turtles.