Species Misidentification in Drone-Based Shark Surveillance and Implications for Beach Management

Drone-based shark surveillance has been implemented as a non-lethal mitigation method to minimise the risk of human–shark interactions along beaches of New South Wales (NSW), Australia. However, real-time misidentification remains problematic, often triggering unnecessary countermeasures due to marine animals that pose little to no risk to humans. We investigated shark misidentification in drone surveys by comparing real-time identification with post-flight verification across 900 flights. Post-flight analyses revealed false-positive detection rates of 53%, 79%, and 100% for bull (Carcharhinus leucas), white (Carcharodon carcharias), and tiger (Galeocerdo cuvier) sharks, respectively, which collectively are the ‘target’ sharks of mitigation measures in NSW. Of the 269 flights in which sharks were identified in real time as target sharks, 62% were confirmed post-flight as other sharks (i.e., whaler species, grey nurse, leopard, or wobbegong), sharks that could not be identified (unknown sharks), or non-shark species (i.e., guitarfish). Conversely, 25% of flights with target sharks identified post-flight were recorded in real time as ‘other’ or ‘unknown’ sharks. Overall, real-time classification overestimated the presence of target sharks, with an apparent prevalence approximately twice the true prevalence. Countermeasure activations based on real-time classification of target sharks were accurate in only 36% of instances. Non-shark species (i.e., guitarfish or gamefish) also triggered 39 countermeasures, including 28 water evacuations. Integrating artificial intelligence or other advances (e.g., higher-resolution video on larger screens) may enhance the effectiveness of drone-based surveillance by assisting pilots with real-time shark detection and identification.