pamlr: a toolbox for analysing animal behaviour using pressure, acceleration, temperature, magnetic and light data in R

Light-level geolocators have revolutionised the study of animal behaviour. However, lacking precision, they cannot be used to infer behaviour beyond large-scale movements. Recent technological developments have allowed the integration of barometers, magnetometers, accelerometers and thermometers into geolocator tags, offering new insights into the behaviour of species which were previously impossible to tag. Here, we introduce an R toolbox for identifying behavioural patterns from multisensor geolocator tags, with functions specifically designed for data visualisation, calibration, classification and error estimation. Some functions are also tailored for identifying specific behavioural patterns in birds (most common geolocators-tagged species), but are flexible for other applications. Finally, we highlight opportunities for applying this toolbox to other species beyond birds, the behaviours they might identify and their potential applications beyond behavioural analyses. Data archiving Currently, the package is on github and will be submitted to CRAN after review. The supporting code (package manual) for this paper is also on https://kiranlda.github.io/PAMLrManual/index.html , but will later be hosted by the Swiss Ornithological Institute. Summary pamlr: a toolbox for analysing animal behaviour using pressure, acceleration, temperature, magnetic and light data in R