No abstract

Large mammals that live in arid/desert environments can cope with seasonal and local variations in rainfall, food and climate 1 by moving long distances, often without reliable water or food en route. An animal's capacity for this long distance travel is substantially dependent on the rate of energy utilisation and hence heat production during locomotion-the cost of transport, COT 2-4. Terrestrial COT is much higher than for flying (7.5 times) and swimming (20 times) 4. Terrestrial migrants are usually large 1,2,3 with anatomical specialisations for economical locomotion 5-9 because COT reduces with increasing size and limb length 5,6,7. Here we used GPS tracking collars 10 with movement and environmental sensors to show that blue wildebeest (Connochaetes taurinus, 220 kg) living in a hot arid environment in Northern Botswana walked up to 80 km over five days without drinking. They predominantly travelled during the day and locomotion appeared unaffected by temperature and humidity although some behavioural thermoregulation was apparent. We measured power and efficiency of work production (mechanical work and heat production) during cyclic contractions of intact muscle biopsies from flexor carpi ulnaris of wildebeest and domestic cows (Bos taurus, 760 kg), a comparable sedentary ruminant. The energetic costs of isometric contraction (activation and force generation) in wildebeest and cows were similar to published values for smaller mammals. Wildebeest muscle was substantially more efficient (62.6%) than the same muscle from substantially larger cows (41.8%) and comparable measurements made in smaller mammals (mouse 34% 11 , rabbit 27%). These are the first direct energetic measurements on intact muscle fibres from large mammals and we use them to model the contribution of high working efficiency of wildebeest muscle to minimising thermoregulatory challenge during their long migrations under hot arid conditions. We set out to test the hypothesis that wildebeest undertake long-range locomotion from grazing sites to water sources and that their muscle is optimised to deliver a low COT. We chose blue wildebeest living in the Makgadikgadi Pans National Park in Botswana because water is sparse and in known locations and grazing limited. Wildebeest were captured by darting from a helicopter and fitted with tracking collars of our own design 10 containing GPS, 3D accelerometer, 3D gyroscope, 3D magnetometer, a humidity sensor and a black globe thermometer 12 (to measure combined effect of solar radiation, air temperature and air velocity for the animal) (Fig. 1a, Methods). Collar mass was 1050 g, 0.5% of body mass. After 18 months collars released automatically, dropoff failures were recovered by re-darting, and 17 of the 20 deployed collars were recovered (to date) Barclay for helping us fabricate the thermocouple elements. Field assistants Naomi Terry and Megan Claase. Anna Wilson for logistical support and editorial contributions. Michael Flyman, Department of Wildlife and National Parks for his support and enthusias...

Here, we demonstrate obstacle and secondary drone avoidance capability by quadcopter drones that can perceive and react to modulation of their self-generated acoustic environment when in proximity to surfaces. A ground truth for the interpretation of self-noise was established by measuring the intrinsic, three-dimensional, acoustic signature of a drone in an anechoic chamber. This was used to design sensor arrangements and machine learning algorithms to estimate the position of external features, obstacles or another drone, within the environment. Our machine learning approach took short segments of recorded sound and their Fourier transforms, fed these into a convolutional neural network, and output the location of an obstacle or secondary drone in the environment. The convolutional layers were constructed with a suitable topology that matched the physical arrangement of the sensors. Our surface detection and avoidance algorithms were refined during tethered flight within an anechoic chamber, followed by an exercise in free flight without obstacle avoidance, and finally free flight obstacle detection and avoidance. Our acoustic sense-and-avoid capability extends to vertical and horizontal planar surfaces and tethered secondary drones.