Abstract

Southern bluefin tuna (SBT) are presently a quota-managed species in the multi-species eastern Australian tuna and billfish longline fishery (ETBF). Capture of SBT is regulated by quota, as is access to regions likely to contain SBT. A habitat prediction model combining data from an ocean model and pop-up satellite archival tags is used to define habitat zones based on the probability of SBT occurrence. These habitat zones are used by fishery managers to restrict access by ETBF fishers to SBT habitat during a May-November management season. The zones display a distinct seasonal cycle driven by the seasonal southward expansion and northward contraction of the East Australia Current (EAC) and as a result access by fishers to particular ocean regions changes seasonally. This species also overlaps with the commercially valuable yellowfin tuna (YFT), thus, we modified the SBT model to generate YFT habitat predictions in order to investigate habitat overlap between SBT and YFT. There is seasonal variation in the overlap of the core habitat between these two species, with overlap early (May-Jul) in the management season and habitat separation occurring towards the end (Aug-Nov). The EAC is one of the fastest warming ocean regions in the southern hemisphere. To consider the future change in distribution of these two species compared to the present and to explore the potential impact on fishers and managers of the future, we use future ocean predictions from the CSIRO Bluelink ocean model for the year 2064 to generate habitat predictions. As the ocean warms on the east coast of Australia and the EAC extends southward, our model predicts the suitable habitat for SBT and YFT will move further south. There was an increase in the overlap of SBT and YFT habitat throughout the management season, due to regional variation of each species’ habitat. These results illustrate that a management tradeoff exists between restricting fisher access to SBT habitat and allowing access to YFT habitat. We suggest that some options to address this tradeoff are possible by identifying the seasonal variability of the overlap.

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