Abstract

Globally, fisheries are in decline and in many parts of the world illegal fishing is a major cause of these declines. Ecological restoration of fisheries needs to be promoted, inter alia through improved enforcement, but, which method is the most successful at improving fish stocks, as well as having the highest economic returns? We compare one open-loop (without feedback) and three closed-loop (with feedback) benefit–cost models representing different restoration interventions aimed at promoting compliance. The hybrid systems methodology has been utilized, combining system dynamics, systems archetypes, mathematical differential equations and economic benefit–cost methodologies. The model is tested with reference to a case study of abalone (Haliotis midae) biomass restoration in the Table Mountain National Park marine area (Zone E), Cape Town. Stocks in Zone E have dropped to below the government’s management threshold for sustainable fisheries of 20 percent of the pre-fished levels, and urgent action is required to restore the stocks. According to the model, all proposed restoration interventions produce stock recovery to 100 percent of carrying capacity, well in excess of government targets of 40 percent. Also, all four models had a net present value of greater than zero, indicating substantial positive net benefits to restoration. Each model had specific management recommendations associated with it- greater involvement by the state, capital investment in restoration, changing poacher behavior and entrepreneurship. Although the Post Keynesian and Institutional model produced the highest net returns to restoration over 80 years (Net present value = US$12.66 million at a 6 % discount rate, 2021 prices), all the models are essentially co-evolutionary models, and have merit over different time periods, compliance rates and assumptions around discount rates. While the case study is developed for abalone the findings of the model are likely to be applicable in a wide range of fishery restoration contexts.

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