Ladds Monique A, Salton Marcus, Hocking David P, McIntosh Rebecca R, Thompson Adam P, Slip David J, Harcourt Robert G
School of Mathematics and Statistics, Victoria University of Wellington, Wellington, New Zealand.
Marine Predator Research Group, Macquarie University, Sydney, New South Wales, Australia.
PeerJ. 2018 Oct 26;6:e5814. doi: 10.7717/peerj.5814. eCollection 2018.
Accurate time-energy budgets summarise an animal's energy expenditure in a given environment, and are potentially a sensitive indicator of how an animal responds to changing resources. Deriving accurate time-energy budgets requires an estimate of time spent in different activities and of the energetic cost of that activity. Bio-loggers (e.g., accelerometers) may provide a solution for monitoring animals such as fur seals that make long-duration foraging trips. Using low resolution to record behaviour may aid in the transmission of data, negating the need to recover the device.
This study used controlled captive experiments and previous energetic research to derive time-energy budgets of juvenile Australian fur seals ( equipped with tri-axial accelerometers. First, captive fur seals and sea lions were equipped with accelerometers recording at high (20 Hz) and low (1 Hz) resolutions, and their behaviour recorded. Using this data, machine learning models were trained to recognise four states-foraging, grooming, travelling and resting. Next, the energetic cost of each behaviour, as a function of location (land or water), season and digestive state (pre- or post-prandial) was estimated. Then, diving and movement data were collected from nine wild juvenile fur seals wearing accelerometers recording at high- and low- resolutions. Models developed from captive seals were applied to accelerometry data from wild juvenile Australian fur seals and, finally, their time-energy budgets were reconstructed.
Behaviour classification models built with low resolution (1 Hz) data correctly classified captive seal behaviours with very high accuracy (up to 90%) and recorded without interruption. Therefore, time-energy budgets of wild fur seals were constructed with these data. The reconstructed time-energy budgets revealed that juvenile fur seals expended the same amount of energy as adults of similar species. No significant differences in daily energy expenditure (DEE) were found across sex or season (winter or summer), but fur seals rested more when their energy expenditure was expected to be higher. Juvenile fur seals used behavioural compensatory techniques to conserve energy during activities that were expected to have high energetic outputs (such as diving).
As low resolution accelerometry (1 Hz) was able to classify behaviour with very high accuracy, future studies may be able to transmit more data at a lower rate, reducing the need for tag recovery. Reconstructed time-energy budgets demonstrated that juvenile fur seals appear to expend the same amount of energy as their adult counterparts. Through pairing estimates of energy expenditure with behaviour this study demonstrates the potential to understand how fur seals expend energy, and where and how behavioural compensations are made to retain constant energy expenditure over a short (dive) and long (season) period.
准确的时间 - 能量预算总结了动物在特定环境中的能量消耗，并且可能是动物如何应对资源变化的敏感指标。得出准确的时间 - 能量预算需要估计在不同活动中花费的时间以及该活动的能量消耗。生物记录器（例如加速度计）可能为监测像海狗这样进行长时间觅食之旅的动物提供一种解决方案。使用低分辨率记录行为可能有助于数据传输，从而无需回收设备。
本研究利用受控的圈养实验和先前的能量学研究来推导澳大利亚幼年海狗（配备三轴加速度计）的时间 - 能量预算。首先，给圈养的海狗和海狮配备以高分辨率（20赫兹）和低分辨率（1赫兹）记录的加速度计，并记录它们的行为。利用这些数据，训练机器学习模型以识别四种状态——觅食、梳理毛发、游动和休息。接下来，估计每种行为的能量消耗，作为位置（陆地或水域）、季节和消化状态（餐前或餐后）的函数。然后，从九只佩戴高分辨率和低分辨率记录加速度计的野生幼年海狗收集潜水和运动数据。将从圈养海狗开发的模型应用于野生澳大利亚幼年海狗的加速度计数据，最后重建它们的时间 - 能量预算。
用低分辨率（1赫兹）数据构建的行为分类模型能够以非常高的准确率（高达90%）正确分类圈养海狗的行为，并且记录没有中断。因此，利用这些数据构建了野生海狗的时间 - 能量预算。重建的时间 - 能量预算表明，幼年海狗消耗的能量与类似物种的成年海狗相同。在性别或季节（冬季或夏季）之间未发现每日能量消耗（DEE）有显著差异，但当能量消耗预计较高时，海狗休息得更多。幼年海狗在预期具有高能量输出的活动（如潜水）期间使用行为补偿技术来节省能量。
由于低分辨率加速度计（1赫兹）能够以非常高的准确率对行为进行分类，未来的研究或许能够以更低的速率传输更多数据，从而减少回收标签的需求。重建的时间 - 能量预算表明，幼年海狗消耗的能量似乎与其成年同类相同。通过将能量消耗估计与行为配对，本研究展示了理解海狗如何消耗能量以及在短（潜水）和长（季节）时间段内何处以及如何进行行为补偿以保持恒定能量消耗的潜力。
