The principal need is to enhance understanding of greenlip abalone population genetic structure, and the degree to which nearby populations are connected, in order to optimally manage exploitation of commercial greenlip reef systems. Greenlip abalone support valuable fisheries across southern Australia. Total catch is >700 t with a landed value of ~$27M. Most of the catch is harvested in SA.
The majority of abalone-related funding has addressed research needs for blacklip abalone. This research has focussed on stock structure and dynamics, developing assessment and management approaches to overcome spatial complexity, and stock rebuilding strategies. Recent projects (FRDC 2004/019, 2005/024, 2005/029), have clearly demonstrated that (1) blacklip abalone populations are effectively isolated from conspecifics at fine spatial scales (Miller et al. 2009), and (2) each has typically variable life-history parameters (e.g. growth rates) that influence productivity and response to fishing.
Historically little effort has been directed towards understanding variation or interconnectedness among greenlip abalone populations. Connectivity among greenlip abalone populations is expected to be substantially different to that observed for blacklip abalone, due, in part, to environmental differences (current, swell, kelp) in reef systems they inhabit. However, there are few data to support this assertion. If, as expected, patterns of connectivity among greenlip populations differ from blacklip abalone, this will require a different approach and different scales of management and assessment.
Understanding greenlip abalone population structure is clearly a high priority in SA, Tas and WA. Development of improved techniques for assessment, definition of metapopulation boundaries and reducing the spatial scale of management are high research priorities of the SA abalone Management Plan. Investment Platform 3 in the ACA Strategic Plan similarly has developing harvest models that incorporate fine-scale fishery management to guide harvest practices and optimise yield as a research priority.
Miller et al. 2009. Mol Ecol, 18:200-211