The Use of Image‐Based Data and Abundance Modelling Approaches for Predicting the Location of Vulnerable Marine Ecosystems in the South Pacific Ocean - Matthew Bennion; Ashley A. Rowden; Owen F. Anderson; David A. Bowden; Malcolm R. Clark; Franziska Althaus; Alan Williams; Shane W. Geange; Jordi Tablada; Fabrice Stephenson (Fisheries Management and Ecology, 2025)
Vulnerable marine ecosystems (VMEs) are typically fragile and slow to recover, thereby making them susceptible to disturbance, including fishing. In the high seas, the United Nations General Assembly (UNGA) requested regional fishery management organisations (RFMOs) to implement measures to prevent significant adverse impacts on VMEs. Here, we predict spatial abundances of 15 taxa, 13 VME indicator taxa, in the South Pacific RFMO (SPRFMO) area. Models used seafloor imagery data, an important advance on previously developed presence‐only predictions, to provide information on spatial variation in taxa abundance that is crucial for better inferring likely location of VMEs, rather than just distribution of VME indicator taxa. Abundance models varied in predictive power (mean R 2 ranged 0.02–0.40). Uncertainty estimates of model predictions were developed to inform future spatial planning processes for conservation and management of VMEs. Using the VME index concept, abundance model outputs and previously published presence‐only model predictions were weighted using vulnerability scores, to explore how modelled outputs could provide spatial estimates of likely VME distribution. Spatial predictions of abundance improved on previous modelling to provide an almost complete suite of abundance models for VME indicator taxa in the western portion of the SPRFMO Convention area. Nevertheless, to improve utility of modelled outputs, we recommend more high‐quality seafloor imagery data be gathered within the SPRFMO Convention area to (1) validate abundance models developed here with independent data from the model area, (2) update models, if necessary, (3) link abundance information to ecosystem function and (4) explore validity of the adapted VME index approach used here.