The design and evaluation of survey-based management strategies is addressed in this article, using three case-study fisheries: North Sea herring, Bay of Biscay anchovy and North Sea cod, with a brief history and the main management issues with each fishery outlined. A range of operational management procedures for the case study stocks were designed and evaluated using trends that may be derived from survey indices (spawner biomass, year-class strength and total mortality) with an array of simple and more structured observation error regimes simulated. Model-free and model-based indicators of stock status were employed in the management procedures. On the basis of stochastic stock-specific simulations, we identified the following key determinants of successful management procedures: (i) adequate specification of the stock-recruit relationship (model structure, parameter estimates and variability), (ii) knowledge of the magnitude and structure of the variation in the survey indices, and (iii) explication of the particular management objectives, when assessing management performance. More conservative harvesting strategies are required to meet specified targets in the presence of increasing stochasticity, due to both process and observation error. It was seen that survey-based operational management procedures can perform well in the absence of commercial data, and can also inform aspects of survey design with respect to acceptable levels of error or bias in the surveys.

Fisheries libraries in R (FLR) is an open-source software framework based in the R statistical software environment, with the main purpose of allowing for the efficient simulation and evaluation of fisheries management strategies. This paper details a generic (non-spatial) biological operating model used to simulate populations and their reaction to fishing, and an observation error model and methods for simulating structured sampling/observation error and bias in key fisheries assessment indices. It also contains the details of how the candidate harvest control rules, based on survey-derived information, can be characterised and implemented, and how the processes of management strategy evaluation and stock assessment can be included in the simulations.