Reduction of Species Identification Errors in Surveys of Marine Wildlife Abundance Utilising Unoccupied Aerial Vehicles (UAVs)

Abstract

The advent of unoccupied aerial vehicles (UAVs) has enhanced our capacity to survey wildlife abundance, yet new protocols are still required for collecting, processing, and analysing image-type observations. This paper presents a methodological approach to produce informative priors on species misidentification probabilities based on independent experiments. We performed focal follows of known dolphin species and distributed our imagery amongst 13 trained observers. Then, we investigated the effects of reviewer-related variables and image attributes on the accuracy of species identification and level of certainty in observations. In addition, we assessed the number of reviewers required to produce reliable identification using an agreement-based framework compared with the majority rule approach. Among-reviewer variation was an important predictor of identification accuracy, regardless of previous experience. Image resolution and sea state exhibited the most pronounced effects on the proportion of correct identifications and the reviewers’ mean level of confidence. Agreement-based identification resulted in substantial data losses but retained a broader range of image resolutions and sea states than the majority rule approach and produced considerably higher accuracy. Our findings suggest a strong dependency on reviewer-related variables and image attributes, which, unless considered, may compromise identification accuracy and produce unreliable estimators of abundance.

Keywords

• aerial surveys
• cetaceans
• dolphins
• drones
• false positive detections
• marine mammals