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RESEARCH ARTICLE
Contents Vol 75(10)

Tracking 4 years in the life of a female whale shark shows consistent migrations in the Gulf of Mexico and Caribbean

Daniel Daye https://orcid.org/0000-0003-3934-062X A , Rafael de la Parra B , Jeremy Vaudo https://orcid.org/0000-0002-6826-3822 C , Jessica Harvey D , Guy Harvey D , Mahmood Shivji C and Bradley Wetherbee https://orcid.org/0000-0002-3753-8950 A C *
+ Author Affiliations
- Author Affiliations

A University of Rhode Island, Kingston, RI, USA.

B Ch’ooj Ajauil AC, Cancún, QR, Mexico.

C Guy Harvey Research Institute, Halmos College of Arts and Sciences, Nova Southeastern University, Dania Beach, FL, USA.

D Guy Harvey Foundation, Fort Lauderdale, FL, USA.

* Correspondence to: wetherbee@uri.edu

Handling Editor: Colin Simpfendorfer

Marine and Freshwater Research 75, MF23147 https://doi.org/10.1071/MF23147
Submitted: 2 August 2023  Accepted: 18 June 2024  Published: 8 July 2024

© 2024 The Author(s) (or their employer(s)). Published by CSIRO Publishing

Abstract

Context

Satellite telemetry has revolutionised the study of animal movement, particularly for mobile marine animals, whose movements and habitat make consistent, long-term observation difficult.

Aims

Summarise the movements of Rio Lady, a mature female whale shark (Rhincodon typus), to characterise these movements, and to predict expected behaviour throughout the Gulf of Mexico (GOM).

Methods

Rio Lady was tracked using satellite telemetry for over 1600 days, generating over 1400 locations and travelling over 40,000 km. State–space and move persistence modelling enabled characterisation of behaviour, and machine learning (ML) enabled the development of habitat-suitability models to predict habitat utilisation, on the basis of location transmissions and their environmental covariates.

Key results

Rio Lady exhibited annually consistent patterns of movements among three regions within the GOM. Final ML models produced seasonally dynamic predictions of habitat use throughout the GOM.

Conclusions

The application of these methods to long-term location data exemplifies how long-term movement patterns and core areas can be discovered and predicted for marine animals.

Implications

Despite our limited dataset, our integrative approach advances methods to summarise and predict behaviour of mobile species and improve understanding of their ecology.

Keywords: behaviour, conservation, habitat suitability, machine learning, movement ecology, remote sensing, Rhincodon typus, satellite telemetry.

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