DOI: 10.5061/dryad.7wm37pvzb
title: Data from: Behaviour-specific spatiotemporal patterns of habitat use by sea turtles revealed using biologging and supervised machine learning

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 &gt; 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 habitat use were superseded by the extreme tides at the near-shore study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.
description: Flatback turtles at Yawuru Nagulagun Roebuck Bay, Western
 Australia were captured at sea and equipped with a CATS tag (Camera or
 Diary) multisensor biologging tag for up to 1 week, from 2018–2021. Data
 have been pre-processed into dives and their dive features, temporal
 covariates and observed behaviours according to methods in the main
 manuscript and Hounslow JL, Fossette S, Byrnes EE, Whiting SD, Lambourne
 RN, Armstrong NJ, Tucker AD, Richardson AR, Gleiss AC (2022) Multivariate
 analysis of biologging data reveals the environmental determinants of
 diving behaviour in a marine reptile. Royal Society Open Science
 9:211860.
description: 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.