Data from: Taking movement data to new depths: Inferring prey availability and patch profitability from seabird foraging behavior

Detailed information acquired using tracking technology has the potential to provide accurate pictures of the types of movements and behaviors performed by animals. To date, such data have not been widely exploited to provide inferred information about the foraging habitat. We collected data using multiple sensors (GPS, time depth recorders, and accelerometers) from two species of diving seabirds, razorbills (Alca torda, N = 5, from Fair Isle, UK) and common guillemots (Uria aalge, N = 2 from Fair Isle and N = 2 from Colonsay, UK). We used a clustering algorithm to identify pursuit and catching events and the time spent pursuing and catching underwater, which we then used as indicators for inferring prey encounters throughout the water column and responses to changes in prey availability of the areas visited at two levels: individual dives and groups of dives. For each individual dive (N = 661 for guillemots, 6214 for razorbills), we modeled the number of pursuit and catching events, in relation to dive depth, duration, and type of dive performed (benthic vs. pelagic). For groups of dives (N = 58 for guillemots, 156 for razorbills), we modeled the total time spent pursuing and catching in relation to time spent underwater. Razorbills performed only pelagic dives, most likely exploiting prey available at shallow depths as indicated by the vertical distribution of pursuit and catching events. In contrast, guillemots were more flexible in their behavior, switching between benthic and pelagic dives. Capture attempt rates indicated that they were exploiting deep prey aggregations. The study highlights how novel analysis of movement data can give new insights into how animals exploit food patches, offering a unique opportunity to comprehend the behavioral ecology behind different movement patterns and understand how animals might respond to changes in prey distributions.

借助追踪技术获取的详实数据，可精准呈现动物的各类运动与行为表现。迄今为止，此类数据尚未被广泛用于推导动物觅食生境的相关信息。本研究通过多传感器（GPS、时间深度记录仪（time depth recorder）、加速度计（accelerometer））采集了两种潜水海鸟的相关数据：刀嘴海雀（razorbills，*Alca torda*，N=5，采自英国费尔岛），以及普通海鸠（common guillemots，*Uria aalge*，其中2只采自费尔岛、2只采自英国科隆赛岛）。本研究采用聚类算法（clustering algorithm）识别水下追击与捕获事件及其耗时，并以此作为两类尺度的指示指标——单次潜水尺度与潜水集群尺度——用于推断整个水层的猎物遭遇情况，以及动物对到访区域内猎物可获得性变化的行为响应。针对单次潜水（普通海鸠总计661次，刀嘴海雀总计6214次），本研究以潜水深度、时长及潜水类型（底栖型（benthic）vs.浮游型（pelagic））为影响因子，对追击与捕获事件的数量开展建模分析。针对潜水集群（普通海鸠总计58个集群，刀嘴海雀总计156个集群），本研究以水下总时长为影响因子，对追击与捕获的总耗时开展建模分析。刀嘴海雀仅开展浮游型潜水，结合追击与捕获事件的垂直分布特征可知，其大概率利用浅水区的猎物资源。与之相反，普通海鸠的行为模式更为灵活，可在底栖型与浮游型潜水之间切换。捕获尝试率数据表明，它们会利用深水处的猎物聚集群。本研究阐明了对运动数据开展创新性分析，可为解析动物利用觅食斑块的机制提供全新视角，为理解不同运动模式背后的行为生态学原理，以及阐明动物如何响应猎物分布变化提供了独特契机。