Allen Ann N, Goldbogen Jeremy A, Friedlaender Ari S, Calambokidis John
Cascadia Research Collective 218 1/2 W. 4th Avenue Olympia Washington 98501.
Department of Biology Hopkins Marine Station Stanford University Pacific Grove California 93950.
Ecol Evol. 2016 Sep 29;6(20):7522-7535. doi: 10.1002/ece3.2386. eCollection 2016 Oct.
The introduction of animal-borne, multisensor tags has opened up many opportunities for ecological research, making previously inaccessible species and behaviors observable. The advancement of tag technology and the increasingly widespread use of bio-logging tags are leading to large volumes of sometimes extremely detailed data. With the increasing quantity and duration of tag deployments, a set of tools needs to be developed to aid in facilitating and standardizing the analysis of movement sensor data. Here, we developed an observation-based decision tree method to detect feeding events in data from multisensor movement tags attached to fin whales ). Fin whales exhibit an energetically costly and kinematically complex foraging behavior called lunge feeding, an intermittent ram filtration mechanism. Using this automated system, we identified feeding lunges in 19 fin whales tagged with multisensor tags, during a total of over 100 h of continuously sampled data. Using movement sensor and hydrophone data, the automated lunge detector correctly identified an average of 92.8% of all lunges, with a false-positive rate of 9.5%. The strong performance of our automated feeding detector demonstrates an effective, straightforward method of activity identification in animal-borne movement tag data. Our method employs a detection algorithm that utilizes a hierarchy of simple thresholds based on knowledge of observed features of feeding behavior, a technique that is readily modifiable to fit a variety of species and behaviors. Using automated methods to detect behavioral events in tag records will significantly decrease data analysis time and aid in standardizing analysis methods, crucial objectives with the rapidly increasing quantity and variety of on-animal tag data. Furthermore, our results have implications for next-generation tag design, especially long-term tags that can be outfitted with on-board processing algorithms that automatically detect kinematic events and transmit ethograms via acoustic or satellite telemetry.
动物携带的多传感器标签的引入为生态研究带来了许多机遇,使以前难以观察到的物种和行为变得可观测。标签技术的进步以及生物记录标签的日益广泛使用,正产生大量有时极其详细的数据。随着标签部署数量和持续时间的增加,需要开发一套工具来协助促进和规范运动传感器数据的分析。在此,我们开发了一种基于观察的决策树方法,用于检测附着在长须鲸身上的多传感器运动标签数据中的进食事件。长须鲸表现出一种能量消耗大且运动学上复杂的觅食行为,称为冲刺式捕食,这是一种间歇性的冲撞过滤机制。利用这个自动化系统,我们在总共超过100小时的连续采样数据中,识别出了19头佩戴多传感器标签的长须鲸的进食冲刺动作。利用运动传感器和水听器数据,自动冲刺检测器正确识别出了所有冲刺动作的平均92.8%,误报率为9.5%。我们的自动进食检测器的强大性能证明了一种在动物携带的运动标签数据中进行活动识别的有效、直接的方法。我们的方法采用了一种检测算法,该算法基于对进食行为观察特征的了解,利用一系列简单阈值,这种技术很容易修改以适应各种物种和行为。使用自动化方法检测标签记录中的行为事件将显著减少数据分析时间,并有助于规范分析方法,这对于动物身上标签数据数量和种类迅速增加的关键目标至关重要。此外,我们的结果对下一代标签设计具有启示意义,特别是对于可以配备自动检测运动事件并通过声学或卫星遥测传输行为图谱的机载处理算法的长期标签。