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Autori principali: Harvey-Carroll, Jessica, Menéndez-Blázquez, Javier, Crespo-Picazo, Jose Luis, Sagarminaga, Ricardo, March, David
Natura: Artículo científico
Lingua:en
Pubblicazione: Scientific reports 2025
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Accesso online:https://pubmed.ncbi.nlm.nih.gov/40481176/
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author Harvey-Carroll, Jessica
Menéndez-Blázquez, Javier
Crespo-Picazo, Jose Luis
Sagarminaga, Ricardo
March, David
author_facet Harvey-Carroll, Jessica
Menéndez-Blázquez, Javier
Crespo-Picazo, Jose Luis
Sagarminaga, Ricardo
March, David
Harvey-Carroll, Jessica
Menéndez-Blázquez, Javier
Crespo-Picazo, Jose Luis
Sagarminaga, Ricardo
March, David
collection PubMed - marine biology
contents Unlocking sea turtle diving behaviour from low-temporal resolution time-depth recorders. Harvey-Carroll, Jessica Menéndez-Blázquez, Javier Crespo-Picazo, Jose Luis Sagarminaga, Ricardo March, David Animals Turtles Diving Behavior, Animal Markov Chains 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.
format Artículo científico
id pubmed_40481176
institution PubMed
language en
publishDate 2025
publisher Scientific reports
record_format pubmed
spellingShingle Unlocking sea turtle diving behaviour from low-temporal resolution time-depth recorders.
Harvey-Carroll, Jessica
Menéndez-Blázquez, Javier
Crespo-Picazo, Jose Luis
Sagarminaga, Ricardo
March, David
Animals
Turtles
Diving
Behavior, Animal
Markov Chains
Unlocking sea turtle diving behaviour from low-temporal resolution time-depth recorders. Harvey-Carroll, Jessica Menéndez-Blázquez, Javier Crespo-Picazo, Jose Luis Sagarminaga, Ricardo March, David Animals Turtles Diving Behavior, Animal Markov Chains 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.
title Unlocking sea turtle diving behaviour from low-temporal resolution time-depth recorders.
topic Animals
Turtles
Diving
Behavior, Animal
Markov Chains
url https://pubmed.ncbi.nlm.nih.gov/40481176/