Linking animal-borne video to accelerometers reveals prey capture variability
Linking animal-borne video to accelerometers reveals prey capture variability
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DOI：
10.1073/pnas.1216244110
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发表时间：
2013-01
期刊：
影响因子：
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通讯作者：
Y. Watanabe;A. Takahashi
中科院分区：
文献类型：
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作者：
Y. Watanabe;A. Takahashi
了解觅食在生态学中很重要，因为它决定了动物获得的能量，并最终决定了动物的健康状况。然而，监测猎物对单个动物的捕获是困难的。使用动物传播的视频进行直接观察的记录周期很短，间接信号(例如胃温)从未在现场得到验证。我们采取了一种综合的方法来监控捕食者捕获的猎物，方法是在自由游泳的Adélie企鹅身上部署一个摄像机(持续85分钟)和两个加速计(在头部和背部，持续50小时)。电影显示，企鹅迅速移动头部，在水中捕捉磷虾，并在海冰下捕捉鱼类(Pagothenia Borchgrevinki)。捕获速度非常快(群体中每秒两个磷虾)，而且效率很高(在78-89分钟内捕获244个磷虾或33个磷虾)。接收器工作特性分析表明，头部加速度相对于身体加速度的信号具有很高的灵敏度和特异度(0.83~0.90)，可以检测到猎物。将信号分析扩展到整个行为记录表明，磷虾捕获的空间和时间上比博尔奇格列文基捕获的更具变异性。值得注意的是，磷虾捕获率的频率分布符合幂定律模型，表明企鹅的觅食成功取决于少量非常成功的潜水。这里说明的三个步骤(即视频观察、将视频与行为信号联系起来以及信号分析的扩展)是理解重要生态事件(如觅食)的空间和时间变异性的独特方法。
Understanding foraging is important in ecology, as it determines the energy gains and, ultimately, the fitness of animals. However, monitoring prey captures of individual animals is difficult. Direct observations using animal-borne videos have short recording periods, and indirect signals (e.g., stomach temperature) are never validated in the field. We took an integrated approach to monitor prey captures by a predator by deploying a video camera (lasting for 85 min) and two accelerometers (on the head and back, lasting for 50 h) on free-swimming Adélie penguins. The movies showed that penguins moved the heads rapidly to capture krill in midwater and fish (Pagothenia borchgrevinki) underneath the sea ice. Captures were remarkably fast (two krill per second in swarms) and efficient (244 krill or 33 P. borchgrevinki in 78–89 min). Prey captures were detected by the signal of head acceleration relative to body acceleration with high sensitivity and specificity (0.83–0.90), as shown by receiver-operating characteristic analysis. Extension of signal analysis to the entire behavioral records showed that krill captures were spatially and temporally more variable than P. borchgrevinki captures. Notably, the frequency distribution of krill capture rate closely followed a power-law model, indicating that the foraging success of penguins depends on a small number of very successful dives. The three steps illustrated here (i.e., video observations, linking video to behavioral signals, and extension of signal analysis) are unique approaches to understanding the spatial and temporal variability of ecologically important events such as foraging.