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

## TL;DR

This paper introduces a new method to analyze low-resolution depth data from sea turtles to understand their diving behavior during long, hard-to-track periods in the ocean.

## Contribution

A novel Hidden Markov Model approach to infer sea turtle diving behavior from low-frequency time-depth recorder data.

## Key findings

- Four distinct behavioral states were identified in loggerhead turtles using the HMM method.
- Three of the behavioral states showed strong seasonal patterns, aligning with known sea turtle biology.
- The method provides a new way to interpret low-resolution TDR data for marine animal ecology.

## 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.

The online version contains supplementary material available at 10.1038/s41598-025-05336-y.