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Defining goals and objectives is a critical component of adaptive management of natural resources because they provide the basis on which management strategies can be designed and evaluated. The aims of this study are: (i) to apply and test a collaborative method to elicit goals and objectives for inshore fisheries and biodiversity in the coastal zone of a regional city in Australia; (ii) to understand the relative importance of management objectives for different community members and stakeholders; and (iii) to understand how diverse perceptions about the importance of management objectives can be used to support multiple-use management in Australia’s iconic Great Barrier Reef. Management goals and objectives were elicited and weighted using the following steps: (i) literature review of management objectives, (ii) development of a hierarchy tree of objectives, and (iii) ranking of management objectives using survey methods. The overarching goals identified by the community group were to: (1) protect and restore inshore environmental assets; (2) improve governance systems; and (3) improve regional (socio-economic) well-being. Interestingly, these goals differ slightly from the usual triple-bottom line objectives (environmental, social and economic) often found in the literature. The objectives were ranked using the Analytical Hierarchical Process, where a total of 141 respondents from industry, government agencies, and community from across Queensland State undertook the survey. The environment goal received the highest scores, followed by governance and lastly well-being. The approach to elicit and rank goals and objectives developed in this study can be used to effectively support coastal resource management by providing opportunities for local communities to participate in the setting of regional objectives.

Population models form the basis for the assessments of species in the tiger prawn component of Australia's northern prawn fishery. Penaeus semisulcatus and P. esculentus are assessed using a size-structured population model. These assessments form the basis for a control rule which predicts future total allowable catches (TACs) for P. semisulcatus and P. esculentus so that the discounted profit from the fishery is maximized. However, there are concerns with this approach: (i) the TAC predictions have consistently overpredicted actual catches and (ii) the assessment for one of the species exhibits a retrospective pattern. A series of analyses was conducted to explore the causes of these observations. Results indicate that catch, effort, and recruitment prediction can be improved substantially by changing the assumed selectivity pattern for one of the surveys, changing how the length frequency data are assembled from the raw data collected, changing the constraints on the minimum amount of effort by target fleet, modifying how the distribution of effort by week is forecasted, and dropping the length frequency data from the most recent recruitment survey. More generally, the analyses illustrate how retrospective analysis can be used to improve how assessments and projections are undertaken when the quantities of interest are known retrospectively.

Biomass, catchability, and natural mortality are key parameters in fish stock assessment. Yet, it is difficult to estimate these quantities, especially natural mortality, when only fishery data are available. Using a method of population depletion analysis, we estimated these population and biological quantities for the white banana prawn (Penaeus merguiensis) in Australia's valuable Northern Prawn Fishery. In addition, we directly included fishing power change over time. The models were implemented in a Bayesian framework by incorporating process error, observation error, and random variability for the underlying parameters. The posterior median initial fishable biomass ranged from ∼2000 to 7000 t year−1, and the median catchability varied from ∼3.8 × 10−4 to 7.3 × 10−4 boat-day−1, resulting in an average fishing power increase of 2.6% per year. An unexpected result is the estimate of exponential natural mortality rate of ∼0.03 week−1. This value is substantially lower than an earlier estimate of 0.05 week−1, which was based on a single year's fishery data in one stock region and has been widely used for over four decades without validation. We attribute this low natural mortality estimate mainly to prawn aggregation behaviour.