Abstract
Increasingly animal behaviour studies are enhanced through the use of accelerometry. To allow translation of raw accelerometer data to animal behaviours requires the development of classifiers. Here, we present the “rabc” package to assist researchers with the interactive development of such animal-behaviour classifiers based on datasets consisting out of accelerometer data with their corresponding animal behaviours. Using an accelerometer and a corresponding behavioural dataset collected on white stork (Ciconia ciconia ), we illustrate the workflow of this package, including raw data visualization, feature calculation, feature selection, feature visualization, extreme gradient boost model training, validation, and, finally, a demonstration of the behaviour classification results.
Keywords: animal behaviour classification, accelerometer, XGBoost, data visualization, interactive process
Introduction
Our understandings of animal movement patterns and behaviours continue to rapidly advance with the use of ever smarter and smaller tracking technologies (Ropert-Coudert & Wilson, 2005; Williams et al., 2019). Increasingly, the tracking of animals is also combined with accelerometer (ACC) data collection to study the free-roaming behaviours of animals across a wide range of taxa (Brown, Kays, Wikelski, Wilson, & Klimley, 2013; Shepard et al., 2008). Compared with direct human observation, using ACC to study animal behaviours has the obvious advantage that it reduces the influence of human presence and also allows the recording of behaviours that would otherwise be hard to observe, away from the human eye (Brown et al., 2013). However, these obvious merits of ACC technology can only be achieved when a reliable behaviour classification model is available that can convert raw ACC data into meaningful behaviour types.
Many studies have already conducted behaviour classification from ACC data (e.g., Nathan et al., 2012). In most cases, ACC data with corresponding behavioural field observations are used to train behaviour classification models (e.g., Kölzsch et al., 2016; Kröschel, Reineking, Werwie, Wildi, & Storch, 2017). However, in some instances the thus developed classifiers that translate ACC data into behaviour types yield only low classification accuracy (Fehlmann et al., 2017). As a general remedy, using fewer behaviour classes and aggregating behaviours usually yields better classification performance (Ladds et al., 2017). However, such grouping of behaviours is typically based on biological or ecological considerations and not necessarily also considering the capacity of ACC data to discriminate between behaviours. It is this often iterative process of combining and splitting behaviours within the behaviour set that the here presented rabc package also endeavours to assist with. In this way, the rabc package allows the user to derive optimal and validated behaviour classifiers suited to their specific research system and questions.
To help biologists translate ACC data into behaviours this package uses XGBoost, which is currently one of the most promising supervised machine learning methods for this specific purpose (Hui et al., in prep). Unlike the web-based tool ”AcceleRater” (Resheff, Rotics, Harel, Spiegel, & Nathan, 2014), our rabc package does not focus on providing a ”one-stop service” turning raw ACC data into behaviours. Rather, this package focuses on (1) providing interactive visualization tools to its user to assist in handling and interpreting the ACC input data, (2) decide on appropriate behaviour categories for classification as highlighted in the previous paragraph, and (3) reduce ACC data volume efficiently and effectively (through the calculation and selection of a range of features) without compromising behaviour classification performance. In brief, this package endeavours to open the lid of the machine-learning ”black-box”, allowing the integration of the user’s expert knowledge on their own research system in developing advanced behaviour-classification models.
Rabc workflow
The general workflow of the rabc package to transform ACC data using supervised machine learning methods into behaviours is outlined in Fig. 1. The data flow is composed of the following elements (where the numbering refers to the paragraphs where these are being described in detail): 2.1 ACC dataset preparation with behaviour labels; 2.2 ACC visualization; 2.3 Feature calculation; 2.4 Feature selection; 2.5 Feature visualization; 2.6 Model training and valication; 2.7 Classification result check. The rabc package can be installed in Rstudio by “devtools::install_github(“YuHuiDeakin/rabc”)”.