Data from: Behaviour-specific spatiotemporal patterns of habitat use by sea turtles revealed using biologging and supervised machine learning
Creators
- 1. Murdoch University
- 2. Department of Parks and Wildlife
Description
- 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 identify 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 Expected diel patterns of behaviour-specific habitat use were superseded by the extreme tides at the near-shore study site.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.
Notes
DF.csv can be opened with any text editor (e.g MS Excel, UltraEdit).
R Files can be read into the R statistical software (version 4.0.3 or later), available at https://www.r-project.org/
Shapefiles can be opened in R, QGIS, ArcGIS etc.
Funding provided by: Department of Biodiversity, Conservation and Attractions
Crossref Funder Registry ID: http://dx.doi.org/10.13039/501100002287
Award Number: MU:18841
Funding provided by: Ecological Society of Australia
Crossref Funder Registry ID: http://dx.doi.org/10.13039/501100008702
Award Number: MU:20447
Funding provided by: Australian Government
Crossref Funder Registry ID: http://dx.doi.org/10.13039/100015539
Award Number: Research Training Program
Funding provided by: Murdoch University
Crossref Funder Registry ID: http://dx.doi.org/10.13039/501100001799
Award Number:
Files
DF.csv
Files
(2.4 MB)
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