The identification of when, how and where animals feed is essential to estimate the amount of energy they obtain and to study the processes associated with prey search and consumption. We combined the use of animal-borne video cameras and accelerometers to characterise the body and head movements associated to four types of prey capture behaviours in the Magellanic Penguin (Spheniscus magellanicus). In addition, we evaluated how the K-Nearest Neighbour (K-NN) algorithm recognized these behaviours from acceleration data. Finally, we compared the total capture and the capture per unit time (CPUT) derived by identifying prey capture events using the K-NN algorithm to that derived by counting undulations in the dive profile (“wiggles”). During captures, body and head movements were highly variable in the tridimensional space. Energy expenditure (i.e., VeDBA values) during diving periods with prey captures was from three to four times higher than during controls diving periods (i.e., with no capture events). The K-NN classification resulted effective and showed accuracy scores above 90% when considering both head and body related features. In addition, when captures were estimated using the K-NN method, the CPUT was similar or higher to that estimated by counting wiggles. Our study contributes to the knowledge of the trophic ecology of this species and provides an alternative method for estimating prey consumption in the Magellanic Penguin and other diving seabirds.

确定何时，如何以及在何处喂养动物，对于估计它们获取的能量数量以及研究与猎物搜寻和消耗有关的过程至关重要。我们结合使用动物摄像机和加速度计来表征与麦哲伦企鹅（Spheniscus magellanicus）中四种捕食行为相关的身体和头部运动）。此外，我们评估了K最近邻居（K-NN）算法如何从加速度数据中识别这些行为。最后，我们将总捕获量和通过使用K-NN算法识别猎物捕获事件得出的单位时间捕获量（CPUT）与通过对潜水资料中的起伏计数进行计数而得出的捕获量（“摆动”）进行了比较。在捕获期间，身体和头部的运动在三维空间中变化很大。捕获猎物的潜水期间的能量消耗（即，VeDBA值）比对照潜水期（即没有捕获事件）高出三到四倍。当考虑到头部和身体相关特征时，K-NN分类产生了有效的结果，并且显示出90％以上的准确性得分。此外，当使用K-NN方法估算捕获量时，CPUT与通过计算摆动所估计的相似或更高。我们的研究有助于了解该物种的营养生态，并为估算麦哲伦企鹅和其他潜水海鸟的猎物消耗提供了另一种方法。