Kompas, TomChu, Long2022-04-280165-7836http://hdl.handle.net/1885/264159This paper develops a neural network approach to estimate 'Maximum Economic Yield' (MEY) for a short-lived species. Unlike long-lived species, short-lived species are often relatively resistant to fishing pressure, but overall stock availability and MEY can also vary considerably in these fisheries due to environmental factors, such as changes in ocean currents and the weather. We argue that the common CPUE approach results in economic overfishing (lost profitability) if the price of fish varies with catch and, if so, neural networks can provide a very useful ex-ante prediction for stock availability and estimates of MEY, depending on how environmental factors unfold. Our approach is illustrated using a case study for banana prawn catch in the Australian Northern Prawn Fishery. Results show the superiority in both data-fitness and the predictive capability of the neural network approach, helping to provide a more reliable rainfall-dependent measure of MEY, thus avoiding the risk of economic overfishing in the currently applied catch-rate or trigger strategy. The results enforce our view that the CPUE approach generally results in economic overfishing if fish price varies with the catch.application/pdfen-AU© 2018 The authorsMaximum Economic YieldNeural networkNorthern Prawn FisheryShort-lived species201810.1016/j.fishres.2018.01.0132020-12-27