Comparing An Automated High-definition Oblique Camera System To Rear-seat-observers In A Wildlife Survey In Tsavo, Kenya: Taking Multi-species Aerial Counts To The Next Level (2019)

Abstract In aerial wildlife counts, human observers often fail to detect animals. We conducted a multi-species sample-count in Tsavo National Park, Kenya, with traditional rear-seat-observers (RSOs) and an automated ‘oblique-camera-count’ (OCC) imaging system to compare estimates of 23 wildlife species derived from these two survey methods. An aerial Total Count of elephant, buffalo and giraffe, conducted a month previously, provided a further comparison. In the Tsavo Core (9560 km2), which harbours 80% of Tsavo’s elephants, the OCC system acquired 81 000 images for interpretation, of which 67 000 were obtained in parallel with RSO-counting along 3004 km of flight line. The Tsavo outer blocks (24 171 km2) were surveyed using the OCC system without RSOs to acquire a further 84 000 images. A random sample of 11 553 images were re-interpreted to derive species-specific probabilities of detection and correction factors. Using ‘Jolly II’, non-parametric and Bayesian analyses, and applying correction factors, we demonstrate that the RSOs did not detect 14% of elephants, 60% of giraffe, 48% of zebra and 66% of the large antelopes. For comparison, the Total Count observers did not detect 27% of elephant, 33% of buffalo, 57% of giraffe and 85% of carcasses. The OCC method raises the elephant population estimate to 16 681 ± 4047 (95% cl) from the 12 722 counted in the Total Count (Z = 1.917, p = .0276). These results suggest that RSO-based methods have significantly undercounted wildlife populations. To align with improved counting methods, previous results need to be re-calibrated.