The Use of Acceleration to Code for Animal Behaviours; A Case Study in Free-Ranging Eurasian Beavers Castor fiber

Abstract

Recent technological innovations have led to the development of miniature, accelerometer-containing electronic loggers which can be attached to free-living animals. Accelerometers provide information on both body posture and dynamism which can be used as descriptors to define behaviour. We deployed tri-axial accelerometer loggers on 12 free-ranging Eurasian beavers Castor fiber in the county of Telemark, Norway, and on four captive beavers (two Eurasian beavers and two North American beavers C. canadensis) to corroborate acceleration signals with observed behaviours. By using random forests for classifying behavioural patterns of beavers from accelerometry data, we were able to distinguish seven behaviours; standing, walking, swimming, feeding, grooming, diving and sleeping. We show how to apply the use of acceleration to determine behaviour, and emphasise the ease with which this non-invasive method can be implemented. Furthermore, we discuss the strengths and weaknesses of this, and the implementation of accelerometry on animals, illustrating limitations, suggestions and solutions. Ultimately, this approach may also serve as a template facilitating studies on other animals with similar locomotor modes and deliver new insights into hitherto unknown aspects of behavioural ecology.

Short-term effects of tagging on activity and movement patterns of Eurasian beavers (Castor fiber)

“…Higher mean ODBA levels in the middle of the night suggest that this is the peak activity time for beavers. The mean ODBA value for standing (or being inactive) is 0.06 g (σ = 0.02 g) (Graf et al 2015) and none of our mean ODBA values was below 0.1 g, which suggests that the animals were active during all seven nights after the tagging event.…”

“…Mean ODBA at 8 Hz was calculated by summing up the dynamic acceleration logged in all three axes ) and averaged over 15 min to investigate movement-based activity levels for each individual. The beavers' principal activity periods were determined visually by identifying movement patterns indicating when the beavers left their lodges (indicated by the first dive in the evening) and returned to the lodges again (the last dive in the morning followed by a grooming session; for information on behavioural identification using accelerometry, see Graf et al (2015)). The first night was excluded from this analysis since we captured beavers that night and thus did not have a full principal activity period.…”

“…Campbell et al 2013;Cross et al 2014;Tinnesand et al 2013). Since 1999, beavers have also been tagged with tracking devices such as VHF transmitters (Herr and Rosell 2004;Ranheim et al 2004), GPS systems (Steyaert et al 2015) and tri-axial accelerometers (Graf et al 2015). Consequently, information on age, sex, social status (dominant, subordinate; see Campbell et al 2012 for details), territory sizes, group sizes, reproduction and morphometric parameters (e.g.…”

“…1). This procedure only affected the guard hairs, with the under-fur remaining unaffected (Graf et al 2015).…”

Jumping in the Night: An Investigation of the Leaping Activity of the Western Tarsier (Cephalopachus bancanus borneanus) Using Accelerometers

“…VeDBA and ODBA values may also provide information about energy expenditure [e.g., Qasem et al, 2012;Jeanniard-du-Dot et al, 2017]. Accelerometer data can be used to derive daily activity budgets [e.g., when an animal moves; Yoda et al, 1999;Lagarde et al, 2008;Grü newä lder et al, 2012;Williams et al, 2014] and, if accelerometer and viewer-observed behavioural data are collected simultaneously, accelerometer data can also be used to characterise behaviours (e.g., walking, running, and leaping) [Sakamoto et al, 2009;Nathan et al, 2012;Graf et al, 2015] and to estimate the energy expenditure associated with different behaviours [Wilson et al, 2006;Qasem et al, 2012;Jeanniard-du-Dot et al, 2017]. Until now, accelerometers have primarily been deployed on large-bodied species [ Fig.…”