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

      , ,
      Biology
      MDPI AG

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          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.

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          • Record: found
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          Fitting Linear Mixed-Effects Models Usinglme4

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            • Record: found
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            A new look at the statistical model identification

            IEEE Transactions on Automatic Control, 19(6), 716-723
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              Running before the storm: blacktip sharks respond to falling barometric pressure associated with Tropical Storm Gabrielle

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                Author and article information

                Contributors
                (View ORCID Profile)
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                Journal
                BBSIBX
                Biology
                Biology
                MDPI AG
                2079-7737
                November 2022
                October 23 2022
                : 11
                : 11
                : 1552
                Article
                10.3390/biology11111552
                67526d25-f228-434b-8bf4-46bbd45f2370
                © 2022

                https://creativecommons.org/licenses/by/4.0/

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