Thanks to repetition, dustbathing detection can be automated combining accelerometry and wavelet analysis

Dustbathing is performed by many groups of birds, including Galliformes. It consists of a well-defined orderly sequence of movements. Repetitive changes in body position during dustbathing can be automatically detected through data processing of body mounted accelerometer recordings, specifically the complex Morlet continuous wavelet transform. The approach was tested in 13 adult male Japanese quail (Coturnix japonica) fitted with a backpack containing a triaxial accelerometer and video-recorded during at least 6h. Rhythmicity (period 25-60s) in the y-axis acceleration vector is reflected as large power values, and is associated almost exclusively to dustbathing events. Thus, by implementing a threshold value we detected events automatically with an accuracy of 80% (range 66-100%). We show potential uses for characterizing temporal dynamics (e.g. daily rhythms) of dustbathing and for the assessment of intra- and inter-individual variability over long-term studies, even within large complex environments (e.g. natural environments or breeding facilities). Summary statementWe propose a method for automatically detecting dustbathing (i.e a behavior performed by many groups of birds, including Galliformes) from triaxial accerometer recoding using a wavelet technique.