Developing empirical management procedures to meet management objectives for data-limited fisheries

Most of the world's fish stocks are considered data-limited and there are insufficient data for complex stock assessment models; nevertheless, scientifically sound management advice is required. Empirical management procedures rely on empirical data and can guide management decisions. The main goals of this project were to develop and test empirical management procedures to improve data-limited fisheries management. Candidate management procedures can be evaluated using management strategy evaluation, which simulates the managed system and management in a feedback loop. Here, many generic operating models were generated covering a wide range of life histories. First, a trend-based empirical management procedure was explored. Simulations revealed that the management performance depended on the individual growth rate of the species, and the method delivered poor performance with high risk of stock depletion for faster-growing species. However, management performance could be improved by applying a genetic algorithm and optimisation towards specified management objectives such as long-term sustainable exploitation and risk limits demanded by stakeholders. An alternative empirical method (harvest rates) was found to be applicable to faster-growing species. Optimised parameterisations of the empirical methods from generic simulations were confirmed for several case study stocks with more available data. These analyses suggested that the generic methods lead to precautionary management, but management performance can be improved through case-specific optimisation. The outcomes of this project showed that the current management practices of data-limited fisheries resources applied by the International Council for the Exploration of the Sea in Europe are insufficient and do not ensure sustainable and precautionary exploitation, even though this is required through international treaties. However, the management procedures evaluated in this study show a way to overcome current management deficiencies and indicate that simple empirical management procedures are a scientifically sound alternative to expensive model-based management approaches.