An exploratory clustering approach for extracting stride parameters from tracking collars on free-ranging wild animals.

Changes in stride frequency and length with speed are key parameters in animal locomotion research. They are commonly measured in a laboratory on a treadmill or by filming trained captive animals. Here, we show that a clustering approach can be used to extract these variables from data collected by a tracking collar containing a GPS module and tri-axis accelerometers and gyroscopes. The method enables stride parameters to be measured during free-ranging locomotion in natural habitats. As it does not require labelled data, it is particularly suitable for use with difficult to observe animals. The method was tested on large data sets collected from collars on free-ranging lions and African wild dogs and validated using a domestic dog.

Flying in a flock comes at a cost in pigeons.

Flying birds often form flocks, with social, navigational and anti-predator implications. Further, flying in a flock can result in aerodynamic benefits, thus reducing power requirements, as demonstrated by a reduction in heart rate and wingbeat frequency in pelicans flying in a V-formation. But how general is an aerodynamic power reduction due to group-flight? V-formation flocks are limited to moderately steady flight in relatively large birds, and may represent a special case. What are the aerodynamic consequences of flying in the more usual 'cluster' flock? Here we use data from innovative back-mounted Global Positioning System (GPS) and 6-degrees-of-freedom inertial sensors to show that pigeons (1) maintain powered, banked turns like aircraft, imposing dorsal accelerations of up to 2g, effectively doubling body weight and quadrupling induced power requirements; (2) increase flap frequency with increases in all conventional aerodynamic power requirements; and (3) increase flap frequency when flying near, particularly behind, other birds. Therefore, unlike V-formation pelicans, pigeons do not gain an aerodynamic advantage from flying in a flock. Indeed, the increased flap frequency, whether due to direct aerodynamic interactions or requirements for increased stability or control, suggests a considerable energetic cost to flight in a tight cluster flock.

Determining position, velocity and acceleration of free-ranging animals with a low-cost unmanned aerial system.

Unmanned aerial systems (UASs), frequently referred to as 'drones', have become more common and affordable and are a promising tool for collecting data on free-ranging wild animals. We used a Phantom-2 UAS equipped with a gimbal-mounted camera to estimate position, velocity and acceleration of a subject on the ground moving through a grid of GPS surveyed ground control points (area ∼1200 m(2)). We validated the accuracy of the system against a dual frequency survey grade GPS system attached to the subject. When compared with GPS survey data, the estimations of position, velocity and acceleration had a root mean square error of 0.13 m, 0.11 m s(-1) and 2.31 m s(-2), respectively. The system can be used to collect locomotion and localisation data on multiple free-ranging animals simultaneously. It does not require specialist skills to operate, is easily transported to field locations, and is rapidly and easily deployed. It is therefore a useful addition to the range of methods available for field data collection on free-ranging animal locomotion.

Improving the accuracy of estimates of animal path and travel distance using GPS drift-corrected dead reckoning.

Route taken and distance travelled are important parameters for studies of animal locomotion. They are often measured using a collar equipped with GPS. Collar weight restrictions limit battery size, which leads to a compromise between collar operating life and GPS fix rate. In studies that rely on linear interpolation between intermittent GPS fixes, path tortuosity will often lead to inaccurate path and distance travelled estimates. Here, we investigate whether GPS-corrected dead reckoning can improve the accuracy of localization and distance travelled estimates while maximizing collar operating life. Custom-built tracking collars were deployed on nine freely exercising domestic dogs to collect high fix rate GPS data. Simulations were carried out to measure the extent to which combining accelerometer-based speed and magnetometer heading estimates (dead reckoning) with low fix rate GPS drift correction could improve the accuracy of path and distance travelled estimates. In our study, median 2-dimensional root-mean-squared (2D-RMS) position error was between 158 and 463 m (median path length 16.43 km) and distance travelled was underestimated by between 30% and 64% when a GPS position fix was taken every 5 min. Dead reckoning with GPS drift correction (1 GPS fix every 5 min) reduced 2D-RMS position error to between 15 and 38 m and distance travelled to between an underestimation of 2% and an overestimation of 5%. Achieving this accuracy from GPS alone would require approximately 12 fixes every minute and result in a battery life of approximately 11 days; dead reckoning reduces the number of fixes required, enabling a collar life of approximately 10 months. Our results are generally applicable to GPS-based tracking studies of quadrupedal animals and could be applied to studies of energetics, behavioral ecology, and locomotion. This low-cost approach overcomes the limitation of low fix rate GPS and enables the long-term deployment of lightweight GPS collars.