Factors Affecting Shark Detection from Drone Patrols in Southeast Queensland, Eastern Australia

Simple Summary Drones offer the potential for monitoring sharks at beaches to improve public safety. It is necessary to investigate their effectiveness at detecting sharks in conditions specific to Southeast Queensland, because they have not previously been used in this capacity in this region. The Queensland SharkSmart drone trial ran for 12 months at five beaches, in 2020–2021. The trial ran 3369 flights and sighted 174 sharks (including 48 large sharks over 2 m in length). Sharks were sighted on 3% of flights on average, with North Stradbroke Island having the highest shark sighting rate. We found that location, the sighting of other marine life, season and time of day all had an important impact on the likelihood of sighting a shark from the drones. Overall, we demonstrated that drones could operate across a range of weather conditions and detect sharks effectively. Additionally, the drones provided extra safety benefits because they were used to identify swimmers caught in rip currents and locate missing persons. This research highlights the broad value of drones as a public safety tool at beaches, and the results of this study will help refine the operation of drones to further improve their effectiveness in the future. Abstract Drones enable the monitoring for sharks in real-time, enhancing the safety of ocean users with minimal impact on marine life. Yet, the effectiveness of drones for detecting sharks (especially potentially dangerous sharks; i.e., white shark, tiger shark, bull shark) has not yet been tested at Queensland beaches. To determine effectiveness, it is necessary to understand how environmental and operational factors affect the ability of drones to detect sharks. To assess this, we utilised data from the Queensland SharkSmart drone trial, which operated at five southeast Queensland beaches for 12 months in 2020–2021. The trial conducted 3369 flights, covering 1348 km and sighting 174 sharks (48 of which were >2 m in length). Of these, eight bull sharks and one white shark were detected, leading to four beach evacuations. The shark sighting rate was 3% when averaged across all beaches, with North Stradbroke Island (NSI) having the highest sighting rate (17.9%) and Coolum North the lowest (0%). Drone pilots were able to differentiate between key shark species, including white, bull and whaler sharks, and estimate total length of the sharks. Statistical analysis indicated that location, the sighting of other fauna, season and flight number (proxy for time of day) influenced the probability of sighting sharks.

[1]  C. Purcell,et al.  Assessing the ability of deep learning techniques to perform real-time identification of shark species in live streaming video from drones , 2022, Frontiers in Marine Science.

[2]  Belinda G. Curley,et al.  The social dimension to the New South Wales Shark Management Strategy, 2015–2020, Australia: Lessons learned , 2022, Marine Policy.

[3]  M. Blumenstein,et al.  SharkSpotter: Shark Detection with Drones for Human Safety and Environmental Protection , 2021, Humanity Driven AI.

[4]  A. Hearn,et al.  The Use of an Unoccupied Aerial Vehicle (UAV) to Survey Shark Species Over Sand and Rocky-Reef Habitats in a Marine Protected Area. , 2021, Journal of fish biology.

[5]  Amy F. Smoothey,et al.  Intra-specific variation in movement and habitat connectivity of a mobile predator revealed by acoustic telemetry and network analyses , 2021, Marine Biology.

[6]  A. Walsh,et al.  Identifying optimal wavelengths to maximise the detection rates of marine fauna from aerial surveys , 2021 .

[7]  I. Santos,et al.  White shark behaviour altered by stranded whale carcasses: Insights from drones and implications for beach management , 2021 .

[8]  Cormac Purcell,et al.  The Drone Revolution of Shark Science: A Review , 2021 .

[9]  A. Scott,et al.  Habitat use and movement patterns of tiger sharks (Galeocerdo cuvier) in eastern Australian waters , 2020 .

[10]  P. Butcher,et al.  Environmental conditions are poor predictors of immature white shark Carcharodon carcharias occurrences on coastal beaches of eastern Australia , 2020 .

[11]  B. Weiler,et al.  Beach-user perceptions and attitudes towards drone surveillance as a shark-bite mitigation tool , 2020 .

[12]  T. Patterson,et al.  Spatiotemporal distribution patterns of immature Australasian white sharks (Carcharodon carcharias) , 2020, Scientific Reports.

[13]  Wanqing Li,et al.  Sharkeye: Real-Time Autonomous Personal Shark Alerting via Aerial Surveillance , 2020, Drones.

[14]  D. Pagendam,et al.  Assessing White Shark (Carcharodon carcharias) Behavior Along Coastal Beaches for Conservation-Focused Shark Mitigation , 2020, Frontiers in Marine Science.

[15]  Euan J. Provost,et al.  Aerial drone technology can assist compliance of trap fisheries , 2020 .

[16]  D. Johnston,et al.  Shark detection probability from aerial drone surveys within a temperate estuary , 2020 .

[17]  B. Cullis,et al.  Beach safety: can drones provide a platform for sighting sharks? , 2019, Wildlife Research.

[18]  B. Kelaher,et al.  Variation in cownose ray Rhinoptera neglecta abundance and group size on the central east coast of Australia. , 2019, Journal of fish biology.

[19]  G. Schofield,et al.  Drones for research on sea turtles and other marine vertebrates – A review , 2019, Biological Conservation.

[20]  Euan J. Provost,et al.  Drones detect illegal and derelict crab traps in a shallow water estuary , 2019, Fisheries Management and Ecology.

[21]  S. Jaquemet,et al.  An innovative fishing gear to enhance the release of non-target species in coastal shark-control programs: The SMART (shark management alert in real-time) drumline , 2019, Fisheries Research.

[22]  Euan J. Provost,et al.  Using drones to quantify beach users across a range of environmental conditions , 2019, Journal of Coastal Conservation.

[23]  D. Pagendam,et al.  Reliability of marine faunal detections in drone-based monitoring , 2019, Ocean & Coastal Management.

[24]  Michael Blumenstein,et al.  Shark Detection from Aerial Imagery Using Region-Based CNN, a Study , 2018, Australasian Conference on Artificial Intelligence.

[25]  C. Layman,et al.  Using a small, consumer grade drone to identify and count marine megafauna in shallow habitats , 2018, Latin American Journal of Aquatic Research.

[26]  Louise Tosetto,et al.  Drone-Based High-Resolution Tracking of Aquatic Vertebrates , 2018, Drones.

[27]  Michael Blumenstein,et al.  Real-Time Drone Surveillance and Population Estimation of Marine Animals from Aerial Imagery , 2018, 2018 International Conference on Image and Vision Computing New Zealand (IVCNZ).

[28]  L. Torres,et al.  Drone Up! Quantifying Whale Behavior From a New Perspective Improves Observational Capacity , 2018, Front. Mar. Sci..

[29]  Shing Yip Lee,et al.  Rainfall and sea surface temperature: key drivers for occurrence of bull shark, Carcharhinus leucas, in beach areas , 2018, Global Ecology and Conservation.

[30]  D. Mayer,et al.  Habitat features influence catch rates of near-shore bull shark (Carcharhinus leucas) in the Queensland Shark Control Program, Australia 1996-2012 , 2018 .

[31]  I. Tibbetts,et al.  Tiger shark (Galeocerdo cuvier) movement patterns and habitat use determined by satellite tagging in eastern Australian waters , 2014 .

[32]  D. Bates,et al.  Fitting Linear Mixed-Effects Models Using lme4 , 2014, 1406.5823.

[33]  M. Heupel,et al.  Variable response of coastal sharks to severe tropical storms: environmental cues and changes in space use , 2013 .

[34]  J. Ovenden,et al.  Comparison of the reproductive ecology of two sympatric blacktip sharks (Carcharhinus limbatus and Carcharhinus tilstoni) off north-eastern Australia with species identification inferred from vertebral counts. , 2012, Journal of fish biology.

[35]  Stephen M. Taylor,et al.  Fine-scale spatial and seasonal partitioning among large sharks and other elasmobranchs in south-eastern Queensland, Australia , 2011 .

[36]  B. Lane,et al.  Characteristics of the biology and distribution of the spinner shark (Carcharhinus brevipinna) in Queensland, Australia based on data collected from the shark control program. , 2010 .

[37]  R. Connolly,et al.  Land–Ocean Coupling of Carbon and Nitrogen Fluxes on Sandy Beaches , 2009, Ecosystems.

[38]  Shing Yip Lee,et al.  Effect of rainfall as a component of climate change on estuarine fish production in Queensland, Australia , 2006 .

[39]  R. Hueter,et al.  Running before the storm: blacktip sharks respond to falling barometric pressure associated with Tropical Storm Gabrielle , 2003 .

[40]  J. Stevens,et al.  Archival Tagging Of School Shark, Galeorhinus Galeus, in Australia: Initial Results , 2001, Environmental Biology of Fishes.

[41]  N. Loneragan,et al.  River flows and estuarine ecosystems: Implications for coastal fisheries from a review and a case study of the Logan River, southeast Queensland , 1999 .

[42]  D. Nelson,et al.  Patterns of activity and space utilization of lemon sharks, Negaprion brevirostris, in a shallow Bahamian lagoon , 1988 .

[43]  H. Akaike.  A new look at the statistical model identification , 1974 .

[44]  M. Bishop,et al.  Assessing variation in assemblages of large marine fauna off ocean beaches using drones , 2020 .

[45]  B. Kelaher,et al.  The potential for unmanned aerial vehicles (UAVs) to conduct marine fauna surveys in place of manned aircraft , 2018 .

[46]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .