Data from: Behaviour-specific spatiotemporal patterns of habitat use by sea turtles revealed using biologging and supervised machine learning

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 Expected diel patterns of behaviour-specific habitat use were superseded by the extreme tides at the near-shore study site.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.

受胁物种保护与人为威胁缓解工作通常依赖于基于栖息地偏好筛选的空间管理区域。由于威胁的影响具有行为特异性，可将此类信息纳入空间管理体系，以提升保护成效。然而，获取具备空间明确性的行为数据仍颇具挑战。本研究结合搭载高分辨率运动传感器（如加速度计、磁强计、GPS）和动物搭载式摄像机的多传感器生物记录标签（multi-sensor biologging tag），并结合监督机器学习（supervised machine learning）技术，开发了一种可自动识别并地理定位典型模糊行为的方法，用于研究人们认知较少的平背海龟（flatback turtle, *Natator depressus*）。随后，本研究对不同行为对应的栖息地利用时空模式开展了评估。本研究利用表征运动活动、身体姿态及三维潜水路径特征的潜水参数，并辅以辅助视频数据进行验证，最终通过提升回归树（boosted regression tree）成功在7074次潜水中识别出觅食与休息行为（AUC＞0.9）。觅食行为的特征为潜水时长更长、深度多变且底部阶段轨迹曲折；休息行为则表现为运动活动减弱且底部阶段时长增加的潜水行为。基于50%和95%利用分布（utilization distribution）分析，觅食与休息行为的空间分隔度极低。本研究预期的行为特异性栖息地利用昼夜节律模式，被该近岸研究区域的极端潮汐所打破。海龟会在更大范围的重叠觅食区域内，选择靠近潮下带与潮间带交界的区域休憩，以便在高潮淹没潮间带时能够高效获取潮间带食物资源——此时海龟的觅食概率提升约25%。综合与应用：本研究借助监督机器学习与生物记录工具，证实了通过在关键时段优先保护重要区域，可对平背海龟实施动态空间管理，以缓解行为特异性威胁。尽管本研究结果是该物种对超潮汐环境的特异性响应，但本研究方法可推广至广泛的类群与研究体系，推动空间管理领域的概念性进展。