Tagging animals with sensors is a powerful approach to acquire critical information about the behavioural ecology of free-living animals, which ultimately can provide data to inform best practice in conservation efforts. Sensor tags for such deployments need long lifetimes and incorporate multiple sensors, especially location because space use can contextualize behavior. The tag size needs to be minimal so as not to affect the activities of the animal. Aquatic animals in particular present challenges due to lack of wireless communication and water-proofing. Taking these points into consideration we have designed Aquamote: an ultra-low power, tiny sensor tag (20 x 29 mm2) which integrates accelerometer, gyroscope, magnetometer, depth sensor, GPS and BLE. Our poster will showcase the performance of AquaMote and highlight our design decisions to reduce its size and power consumption.

Abstract A new area of biological research is identifying and grouping patterns of behaviour in wild animals by analysing data obtained through the attachment of tri‐axial accelerometers. As these recording devices become smaller and less expensive their use has increased. Currently acceleration data are visualised as 2D time series plots, and analyses are based on summary statistics and the application of Fourier transforms. We develop alternate visualisations of this data so as to analyse, explore and present new patterns of animal behaviour. Our visualisations include interactive spherical scatterplots, spherical histograms, clustering methods, and feature‐based state diagrams of the data. We study the application of these visualisation methods to accelerometry data from animal movement. The reaction of biologists to these visualisations is also reported.