Multivariate analysis of biologging data reveals the environmental determinants of diving behaviour in a marine reptile

Swimming through sand: using accelerometers to observe the cryptic, pre-emergence life-stage of sea turtle hatchlings

Animals that hatch within a subterranean nest, such as turtle hatchlings, expend some of their limited energy reserves digging out through sand or soil to reach the surface. In sea turtles, this emergence process can take the hatchlings 3-7 days. However, we have a poor understanding of this process as it is difficult to observe what is occurring underground. Here, we utilize a novel method to characterize digging-out behaviour: affixing an accelerometer directly to newly hatched green turtles (Chelonia mydas) to record movement until nest emergence. Our data revealed that buried hatchlings maintained a head-up orientation but did not move in the expected left and right swaying motion associated with alternating limb crawling. Rather, they moved using dorsal-ventral heaving and pitching as if swimming vertically through the sand to the surface. Movement activity was irregular and brief, interspersed by many short periods of inactivity, mostly lasting less than 10 min. The first 24 h of head-up activity displayed no diel patterns, but the last 24 h prior to emergence involved more intense movement during night-time hours compared with daytime hours. Thus, our results add valuable new insight, and in some cases change previous assumptions, regarding the digging behaviours during the egg-to-emergence life stage in sea turtles.

Predictive model of sperm whale prey capture attempts from time-depth data

BACKGROUND

High-resolution sound and movement recording tags offer unprecedented insights into the fine-scale foraging behaviour of cetaceans, especially echolocating odontocetes, enabling the estimation of a series of foraging metrics. However, these tags are expensive, making them inaccessible to most researchers. Time-Depth Recorders (TDRs), which have been widely used to study diving and foraging behaviour of marine mammals, offer a more affordable alternative. Unfortunately, data collected by TDRs are bi-dimensional (time and depth only), so quantifying foraging effort from those data is challenging.

METHODS

A predictive model of the foraging effort of sperm whales (Physeter macrocephalus) was developed to identify prey capture attempts (PCAs) from time-depth data. Data from high-resolution acoustic and movement recording tags deployed on 12 sperm whales were downsampled to 1 Hz to match the typical TDR sampling resolution and used to predict the number of buzzes (i.e., rapid series of echolocation clicks indicative of PCAs). Generalized linear mixed models were built for dive segments of different durations (30, 60, 180 and 300 s) using multiple dive metrics as potential predictors of PCAs.

RESULTS

Average depth, variance of depth and variance of vertical velocity were the best predictors of the number of buzzes. Sensitivity analysis showed that models with segments of 180 s had the best overall predictive performance, with a good area under the curve value (0.78 ± 0.05), high sensitivity (0.93 ± 0.06) and high specificity (0.64 ± 0.14). Models using 180 s segments had a small difference between observed and predicted number of buzzes per dive, with a median of 4 buzzes, representing a difference in predicted buzzes of 30%.

CONCLUSIONS

These results demonstrate that it is possible to obtain a fine-scale, accurate index of sperm whale PCAs from time-depth data alone. This work helps leveraging the potential of time-depth data for studying the foraging ecology of sperm whales and the possibility of applying this approach to a wide range of echolocating cetaceans. The development of accurate foraging indices from low-cost, easily accessible TDR data would contribute to democratize this type of research, promote long-term studies of various species in several locations, and enable analyses of historical datasets to investigate changes in cetacean foraging activity.

Multivariate analysis of biologging data reveals the environmental determinants of diving behaviour in a marine reptile

Diving behaviour of 'surfacers' such as sea snakes, cetaceans and turtles is complex and multi-dimensional, thus may be better captured by multi-sensor biologging data. However, analysing these large multi-faceted datasets remains challenging, though a high priority. We used high-resolution multi-sensor biologging data to provide the first detailed description of the environmental influences on flatback turtle (Natator depressus) diving behaviour, during its foraging life-history stage. We developed an analytical method to investigate seasonal, diel and tidal effects on diving behaviour for 24 adult flatback turtles tagged with biologgers. We extracted 16 dive variables associated with three-dimensional and kinematic characteristics for 4128 dives. K-means and hierarchical cluster analyses failed to identify distinct dive types. Instead, principal component analysis objectively condensed the dive variables, removing collinearity and highlighting the main features of diving behaviour. Generalized additive mixed models of the main principal components identified significant seasonal, diel and tidal effects on flatback turtle diving behaviour. Flatback turtles altered their diving behaviour in response to extreme tidal and water temperature ranges, displaying thermoregulation and predator avoidance strategies while likely optimizing foraging in this challenging environment. This study demonstrates an alternative statistical technique for objectively interpreting diving behaviour from multivariate collinear data derived from biologgers.