Chimienti Marianna, Cornulier Thomas, Owen Ellie, Bolton Mark, Davies Ian M, Travis Justin M J, Scott Beth E
School of Biological Sciences University of Aberdeen Aberdeen UK.
Marine Scotland Science Marine Laboratory Scottish Government Aberdeen UK.
Ecol Evol. 2017 Oct 25;7(23):10252-10265. doi: 10.1002/ece3.3551. eCollection 2017 Dec.
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 (, = 5, from Fair Isle, UK) and common guillemots (, = 2 from Fair Isle and = 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 (= 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 (= 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.
利用追踪技术获取的详细信息有可能提供动物所表现出的运动和行为类型的准确图景。迄今为止，此类数据尚未被广泛用于提供有关觅食栖息地的推断信息。我们使用多个传感器（全球定位系统、时间深度记录仪和加速度计）从两种海鸟——刀嘴海雀（，= 5只，来自英国费尔岛）和普通海鸠（，2只来自费尔岛，2只来自英国科伦赛岛）收集数据。我们使用一种聚类算法来识别追捕和捕捉事件以及在水下追捕和捕捉所花费的时间，然后将其用作推断整个水柱中猎物遭遇情况以及对两个层面所访问区域猎物可获得性变化的反应的指标：个体潜水和潜水组。对于每一次个体潜水（普通海鸠为661次，刀嘴海雀为6214次），我们对追捕和捕捉事件的数量与潜水深度、持续时间以及所进行潜水的类型（底栖 vs. 远洋）进行建模。对于潜水组（普通海鸠为58组，刀嘴海雀为156组），我们对追捕和捕捉所花费的总时间与水下花费的时间进行建模。刀嘴海雀只进行远洋潜水，追捕和捕捉事件的垂直分布表明它们很可能利用浅深度处可得的猎物。相比之下，普通海鸠的行为更为灵活，在底栖和远洋潜水之间切换。捕获尝试率表明它们在利用深层猎物聚集区。该研究强调了对运动数据的新颖分析如何能够为动物如何利用食物斑块提供新的见解，为理解不同运动模式背后的行为生态学以及了解动物如何应对猎物分布变化提供了独特的机会。
