Oral Presentation Australian Society for Fish Biology Conference 2025

Efficient risk assessment of bycatch and byproduct species in data-limited, multi-species fisheries (124657)

Grant J Johnson 1 2 , Jonathan J Smart 2 3 , Vinay Udyawer 1 4 , Rik C Buckworth 5 6 , Clive R McMahon 7 , Charlie Huveneers 2
  1. AIMS Darwin, Australian Institute of Marine Science, Brinkin, Northern Territory, Australia
  2. College of Science and Engineering, Flinders University, Adelaide, South Australia, Australia
  3. Centre for Sustainable Tropical Fisheries and Aquaculture and College of Science and Engineering, James Cook University, Townsville, Queensland, Australia
  4. Sharks Pacific, Rarotonga, Cook Islands
  5. Sea Sense Australia Pty Ltd Fisheries Research and Education, Mission Beach, Queensland, Australia
  6. Research Institute for the Environment and Livelihoods, Charles Darwin University, Darwin, Northern Territory, Australia
  7. IMOS Animal Tagging, Sydney Institute of Marine Science, Mosman, New South Wales, Australia

Ecosystem-based fisheries management is a central goal for fisheries globally, but its implementation remains challenging. A key hurdle is assessing the sustainability of bycatch and byproduct species, which are often poorly studied and lack the data needed for traditional stock assessments. This challenge persists even in some of the world’s best-managed fisheries. We have overcome that key hurdle by modifying standard Sustainability Assessment for Fishing Effects (SAFE) framework, and demonstrate how large numbers of diverse species can be efficiently assessed in complex, multi-species fisheries.

Our novel hierarchical approach was applied to 256 teleost and elasmobranch species captured in Australia’s Northern Territory Demersal and Timor Reef Fisheries. The process starts with a rapid screening assessment, using readily available data, to identify potentially at-risk species. Those flagged as higher risk then undergo a more detailed secondary SAFE assessment that incorporates species distribution modelling and refined estimates of fishing footprint through spatial analysis of trawl paths derived from vessel monitoring system data. Monte Carlo simulations were also used to address uncertainty in footprint and capture efficiency estimates.

The initial screening classified 212 species as low-risk. The remaining 44 species, assessed through the enhanced SAFE process, were also ultimately classified as low-risk. While our method introduces additional steps, it enables efficient assessment of large and taxonomically diverse species groups, allowing management efforts to focus on species at greatest risk. This adaptable approach is well-suited to other fisheries, including those with limited catch or effort data, offering a practical pathway toward broader implementation of ecosystem-based management.