Standard Presentation Australian Marine Sciences Association 2026 Conference

Identifying deep-sea vulnerable marine ecosystems from still images in the Tasman Sea (139539)

Katrina Goddard 1 , Savannah Goode 2 , Ellie Hooper 3
  1. Department of Applied and Environmental Sciences, NorthTec (Tai Tokerau Wānanga), Whangārei , New Zealand
  2. Department of Earth, Ocean and Atmospheric Science, Florida State University
  3. Greenpeace Aotearoa, Auckland, New Zealand

In March 2025, Greenpeace and collaborating scientists undertook a deep-sea expedition in the Tasman Sea, an area that has historically been heavily bottom trawled. More recently, there have been several coral bycatch events, raising concerns about the potential impacts on deep-sea vulnerable marine ecosystems (VMEs).

Accurately mapping and designating VMEs is crucial for informing spatial management and conservation planning. In this region, VME designation has primarily relied on density-based metrics, but such approaches risk underestimating VMEs as they fail to capture other VME characteristics, such as uniqueness or rarity, functional significance, fragility, and life-history traits.

Baco et al. (2023) is an expert-based decision framework to identify VMEs from still images which applies all five FAO Deep-sea VME criteria. We present results applying the Baco et al. approach to two seamount areas open to bottom trawling – Central Lord Howe Rise and Northwest Challenger. Of the 260 still images selected for quantitative analysis, a subset of the high-resolution towed camera footage, 45 to 64% were classified as VMEs (respectively).

Our findings show these areas warrant closure to protect identified deep-sea fragile ecosystems and demonstrates that the Baco et al. approach represents a standardised repeatable transparent process for VME identification from deep-sea imagery.

  1. Baco, A. R., Ross, R. E., Althaus, F., Amon, D. J., Bridges, A. E. H., Brix, S., … Yesson, C. (2023). Towards a scientific community consensus on designating vulnerable marine ecosystems from imagery. PeerJ, 11, e16024. https://doi.org/10.7717/peerj.16024