Standard Talk (15 mins) Australian Society for Fish Biology Conference 2022

Enhancing efficiency and robustness for BRUVS monitoring – a spatially balanced approach (#164)

Leanne Currey-Randall 1 , Stacy L Bierwagen 1 , Marcus Stowar 1 , Mike Cappo 1 , Diego Barneche 2 , Ben Radford 2 , Conrad Speed 2 , Matt Birt 2 , Katherine Cure 2 , Dianne McLean 2
  1. Australian Institute of Marine Science, Townsville, QLD, Australia
  2. Australian Institute of Marine Science, Indian Ocean Marine Research Centre, Crawley, WA, Australia

Increased demand for Baited Remote Underwater Video (BRUV) survey use as a monitoring tool has brought forward long-contended questions surrounding their capability to detect change. Lack of sampling standardisation, ecological variance, and temporal concerns are some potential limitations of BRUV survey for repeated sampling. The influence of this variability on the presence and number of fish is recognised, and approaches exist to address some of these issues to optimise sampling for monitoring purposes. The Australian Institute of Marine Science (AIMS) has extensively used BRUV survey across many habitats and regions in Australian waters and carries a strong capability to use legacy data to determine ‘how’ and ‘what’ to best monitor considering the time and costs associated with this method.

We applied a spatially balanced probabilistic sampling design to previously surveyed sites on the Great Barrier Reef to optimise performance in sampling fish communities. Randomised drops were created in MBH Design and weighted with inclusion probabilities using known depth, habitat features (e.g. hard coral), and include a subset of productive legacy sites based on fish abundance and diversity. Trends in distribution and abundance of key species and fish communities were determined with univariate and multivariate representation respectively to show differences in efficiency between previous sampling design and a spatially balanced approach. We used common fish metrics to determine within and between year effects of both natural variation and sampling optimisation. This approach was also compared to other AIMS repeated sampling surveys across sites in Australia. By optimising the sampling to a better-informed design, some of the variability typically linked with BRUV survey is reduced. With these considerations, BRUV surveys remain the optimal method (particularly for monitoring in deep-water offshore areas and predatory species) compared to other available technology and can be considered a viable approach to repeated monitoring.