Track-a-Forager: a program for the automated analysis of RFID tracking data to reconstruct foraging behaviour
Track-a-Forager: a program for the automated analysis of RFID tracking data to reconstruct foraging behaviour
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DOI：
10.1007/s00040-015-0453-z
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发表时间：
2016-02-01
期刊：
影响因子：
1.3
通讯作者：
Wenseleers, T.
中科院分区：
文献类型：
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作者：
Van Geystelen, A.;Benaets, K.;Wenseleers, T.
行为研究越来越多地使用无源射频识别 (RFID) 技术来长时间监测个体动物的觅食行为和活动模式。事实证明，中心地带的觅食者，如群居昆虫、鸟类和许多啮齿动物特别适合这项技术。然而，迄今为止，还没有标准化的方法来过滤和后处理 RFID 扫描仪产生的数据。在这里，我们展示了一个新的用户友好型、公开可用的 Java 程序，名为“Track-a-Forager”，用于分析和严格过滤 RFID 动物跟踪数据。该程序特别适合并具有分析社会昆虫行为的特殊功能，但它足够通用，可以分析从任何物种获得的数据。实施的过滤算法由几个明确定义的步骤组成，用于对同一个体的多个时间聚类的 RFID 扫描进行聚类，确定离开和进入巢穴和/或喂食器的事件，并重建每个个体的觅食行程。 Track-a-Forager 分析 RFID 数据，独立于所使用的扫描仪系统，适用于觅食行为研究中常见的八种不同类型的标准实验设置。这些设置的不同之处在于是否监控人工喂食器处的觅食以及 RFID 扫描仪在巢穴或喂食器处的具体位置。作为一个现实生活中的例子，我们展示了 Track-a-Forager 如何使人们重建的觅食行程比使用原始数据多 75%。
Behavioural studies make increasingly use of the passive radio-frequency identification (RFID) technology to monitor the foraging behaviour and activity patterns of individual animals over extended periods of time. Central place foragers, such as social insects, birds and many rodents have proved particularly well suited for this technology. As yet, however, there is no standardized methodology to filter and postprocess the data resulting from RFID scanners. Here we present a new user-friendly, publically available Java program named "Track-a-Forager" to analyse and rigorously filter RFID animal tracking data. The program is particularly suited and has special features to analyse social insect behaviour, but it is generic enough to analyse data obtained from any species. The implemented filtering algorithm consists of several well-defined steps to cluster multiple temporally clustered RFID scans of the same individual, determine events of leaving and entering the nest and/or feeder and reconstruct foraging trips for each individual. Track-a-Forager analyses RFID data independent of the used scanner system for eight different types of standard experimental setups that are common in foraging behaviour research. These setups differ with respect to whether or not foraging at an artificial feeder is monitored and the specific placement of the RFID scanners at the nest or feeder. As a real-life example, we show how Track-a-Forager enables one to reconstruct 75 % more foraging trips compared to if one were to use the raw data.