2023
DOI: 10.3389/fmars.2023.1204664
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Loggerhead turtle oceanic-neritic habitat shift reveals key foraging areas in the Western Indian Ocean

Abstract: Loggerhead sea turtles (Caretta caretta) use both oceanic and neritic habitats depending on their life stage, eventually undertaking an ontogenetic shift. Juveniles likely start foraging in a purely opportunistic manner and later seek resources more actively. In the Indian Ocean, it is still unclear where oceanic-stage individuals go, what they do, and importantly where they forage. Yet, such information is crucial to protect this endangered species from anthropogenic threats such as bycatch in fisheries. To a… Show more

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Cited by 2 publications
(2 citation statements)
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“…Other influences not modelled by either PSY4 or GLORYS12, such as Stokes drift, which is important in subtropical gyres, and tidal effects, which are important in coastal regions, would provide improvements in the retrieval of the active movement of marine animals. In terms of segmentation, the detection of breakpoints would benefit from combining the approach of [ 49 ] with the time series of heading segmentation using the common velocity field presented here to better assert the nature of each detected segment.…”
Section: Discussionmentioning
confidence: 99%
See 1 more Smart Citation
“…Other influences not modelled by either PSY4 or GLORYS12, such as Stokes drift, which is important in subtropical gyres, and tidal effects, which are important in coastal regions, would provide improvements in the retrieval of the active movement of marine animals. In terms of segmentation, the detection of breakpoints would benefit from combining the approach of [ 49 ] with the time series of heading segmentation using the common velocity field presented here to better assert the nature of each detected segment.…”
Section: Discussionmentioning
confidence: 99%
“…Residual errors in the current correction or changes in behaviour along the way may prevent the model from finding a constant heading, resulting in high RMSE for these segments. Monsinjon et al [49] using a hidden Markov model (HMM) on a similar dataset (n = 12 common tracks) showed that approximately 23% of the daily position could be associated with a 'foraging' state. By comparing the proportion of daily positions marked as 'foraging' within each segment with the RMSE, we estimated that a threshold of 30°of RMSE was an acceptable compromise to remove segments (6 out of 47) where the large heading variability indicated foraging rather than directed swimming (see electronic S3.2).…”
Section: Course Correction Breakpointsmentioning
confidence: 99%