Estimating the landscape-scale abundance of an arboreal folivore using thermal imaging drones and binomial N-mixture modelling
Shelby A. Ryan, Darren Southwell, Chad T. Beranek, John Clulow, Neil R. Jordan, Ryan R. Witt
Abstract
Estimating the abundance of wildlife populations at a landscape-scale is vital for conservation, but is often hampered by survey costs, data processing and imperfect detection. In this study, we developed a framework that combines a protocol for validating nocturnal thermal drone detections in real-time with N-mixture modelling to estimate the landscape-scale abundance of arboreal folivores. As a case study, we estimated the abundance of koalas ( Phascolarctos cinereus ) across seven reserves (673 km 2 ) in New South Wales, Australia. We conducted thermal drone surveys of 208, 25-ha sites stratified across vegetation type and fire history, on average, three times over consecutive nights (range 1–12 repeats), between 18:00–04:00 h (May to September). All koala detections were validated by field personnel or in real-time with drones equipped with a thermal camera and searchlight. Koalas were detected on 245 occasions. We fitted N-mixture models to validated repeat count data to quantify the effect of site and observation variables on abundance and detectability. Using our top set of competing models, we estimated that 4357 koalas (95 % CI = 2319–8307) occupy the seven reserves, with a mean detection probability of 0.22 (95 % CI = 0.15–0.31) over all survey occasions. We found detection probability decreased with increases in relative humidity and temperature. Koala abundance was negatively associated with fire severity, elevation, tree height and soil clay content, and positively associated with available water content, forest cover and soil organic carbon. Our framework, which combines real-time field validated drone data while accounting for imperfect detection, improves the accuracy of abundance estimates for arboreal folivores across large-scales.