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精细化监测数据，可精准还原动物的各类运动模式与行为表现。迄今为止，此类数据尚未被广泛用于推断动物的觅食生境信息。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（Alca torda，N=5，采自英国费尔岛）以及普通海鸠（Uria aalge，其中2只采自费尔岛、2只采自英国科隆赛岛）。我们采用聚类算法（Clustering Algorithm）识别水下追击与捕获事件，以及对应追击捕获时长，并以此为指标，从单次潜水、潜水组两个尺度，推断动物在巡游区域内的猎物遭遇情况，以及对猎物可获得性变化的响应。针对单次潜水（普通海鸠共计661次，刀嘴海雀共计6214次），我们以潜水深度、时长及潜水类型（底栖型vs水层型）为关联变量，对追击与捕获事件的数量进行建模分析。针对潜水组（普通海鸠共计58组，刀嘴海雀共计156组），我们以水下总时长为关联变量，对追击与捕获的总时长进行建模分析。刀嘴海雀仅开展水层型潜水，结合追击与捕获事件的垂直分布特征来看，它们大概率以浅水区的猎物为觅食目标。与之相对，普通海鸠的行为更具灵活性，可在底栖型与水层型潜水之间切换。捕获尝试率数据表明，它们会利用深层的猎物集群。本研究凸显了对运动数据开展创新性分析的重要价值：可揭示动物利用觅食斑块的内在机制，为理解不同运动模式背后的行为生态学逻辑，以及阐释动物如何响应猎物分布变化提供了独特契机。