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Journal article
Behaviour‐specific spatiotemporal patterns of habitat use by sea turtles revealed using biologging and supervised machine learning
The Journal of applied ecology, Early View
2023
Abstract
Conservation of threatened species and anthropogenic threat mitigation commonly rely on spatially managed areas selected according to habitat preference. Since the impact of threats can be behaviour-specific, such information could be incorporated into spatial management to improve conservation outcomes. However, collecting spatially explicit behavioural data is challenging.
Using multi-sensor biologging tags containing high-resolution movement sensors (e.g. accelerometer, magnetometer, GPS) and animal-borne video cameras, combined with supervised machine learning, we developed a method to automatically detect and geolocate typically ambiguous behaviours for the poorly understood flatback turtle Natator depressus. Subsequently, we evaluated behaviour-specific spatiotemporal patterns of habitat use.
Boosted regression trees successfully identified the presence of foraging and resting in 7074 dives (AUC > 0.9), using dive features representing characteristics of locomotory activity, body posture, and three-dimensional dive paths validated by ancillary video data. Foraging was characterised by dives with longer duration, variable depth, tortuous bottom phases; resting was characterised by dives with decreased locomotory activity and longer duration bottom phases.
Foraging and resting showed minimal spatial segregation based on 50% and 95% utilisation distributions. Expected diel patterns of behaviour-specific habitat use were superseded by the extreme tides at the near-shore study site. Turtles rested in areas close to the subtidal and intertidal boundary within larger overlapping foraging areas, allowing efficient access to intertidal food resources upon inundation at high tides when foraging was ~25% more likely.
Synthesis and applications. Using supervised machine learning and biologging tools, we show the potential for dynamic spatial management of flatback turtles to mitigate behaviour-specific threats by prioritising protection of important locations at pertinent times. Although results are a species-specific response to a super-tidal environment, our approach can be generalised to a broad range of taxa and study systems, facilitating a conceptual advance in spatial management.
Details
- Title
- Behaviour‐specific spatiotemporal patterns of habitat use by sea turtles revealed using biologging and supervised machine learning
- Authors/Creators
- Jenna L. Hounslow - Murdoch UniversitySabrina Fossette - Biodiversity and Conservation Science, Department of Biodiversity, Conservation & Attractions Kensington Western Australia AustraliaWei Chong - Murdoch UniversityRanda Bali - Murdoch UniversityAnton D. Tucker - Biodiversity and Conservation Science, Department of Biodiversity, Conservation & Attractions Kensington Western Australia AustraliaScott D. Whiting - Biodiversity and Conservation Science, Department of Biodiversity, Conservation & Attractions Kensington Western Australia AustraliaAdrian C. Gleiss - Murdoch University
- Publication Details
- The Journal of applied ecology, Early View
- Publisher
- John Wiley & Sons Ltd on behalf of British Ecological Society.
- Identifiers
- 991005591509607891
- Copyright
- © 2023 The Authors
- Murdoch Affiliation
- Centre for Sustainable Aquatic Ecosystems; School of Environmental and Conservation Sciences; Harry Butler Institute; Centre for Molecular Medicine and Innovative Therapeutics
- Language
- English
- Resource Type
- Journal article
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- 3 Agriculture, Environment & Ecology
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- Web Of Science research areas
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- Ecology
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