Fish and Fisheries. 2020;21:621-638. | 621 wileyonlinelibrary.com/journal/faf | INTRODUC TI ONAll fishing is fundamentally selective. Depending on what fishers want to catch, they choose gear type (e.g., longlines, set nets, trawls), gear specifications (e.g., mesh size, hook size), and the time and place to deploy these gears. These choices are influenced by the regulatory framework, that is gear regulations, limitations on the catch size and composition, and seasonal and temporal restrictions.Fisheries selectivity relates to desirable species (species selectivity) or sizes (size selectivity). There are three types of size selectivity: AbstractFisheries management typically aims at controlling exploitation rate (e.g., Fbar) to ensure sustainable levels of stock size in accordance with established reference points (e.g., F MSY , B MSY ). Population selectivity ("selectivity" hereafter), that is the distribution of fishing mortality over the different demographic components of an exploited fish stock, is also important because it affects both Maximum Sustainable Yield (MSY) and F MSY , as well as stock resilience to overfishing. The development of an appropriate metric could make selectivity operational as an additional lever for fisheries managers to achieve desirable outcomes. Additionally, such a selectivity metric could inform managers on the uptake by fleets and effects on stocks of various technical measures. Here, we introduce three criteria for selectivity metrics:(a) sensitivity to selectivity changes, (b) robustness to recruitment variability and (c) robustness to changes in Fbar. Subsequently, we test a range of different selectivity metrics against these three criteria to identify the optimal metric. First, we simulate changes in selectivity, recruitment and Fbar on a virtual fish stock to study the metrics under controlled conditions. We then apply two shortlisted selectivity metrics to six European fish stocks with a known history of technical measures to explore the metrics' response in real-world situations. This process identified the ratio of F of the first recruited age-class to Fbar (Frec/Fbar) as an informative selectivity metric for fisheries management and advice.

The natural mortality rate (M) of a fish stock is typically highly influential on the outcome of age-structured stock assessment models, but at the same time extremely difficult to estimate. In data-limited stock assessments, M usually relies on a range of empirically or theoretically derived M estimates, which can vary vastly. This article aims at evaluating the impact of this variability in M using seven Mediterranean stocks as case studies of statistical catch-at-age assessments for information-limited fisheries. The two main bodies carrying out stock assessments in the Mediterranean and Black Seas are European Union’s Scientific Technical Economic Committee for Fisheries (STECF) and Food and Agriculture Organization’s General Fisheries Commission for the Mediterranean (GFCM). Current advice in terms of fishing mortality levels is based on a single “best” M assumption which is agreed by stock assessment expert working groups, but uncertainty about M is not taken into consideration. Our results demonstrate that not accounting for the uncertainty surrounding M during the assessment process can lead to strong underestimation or overestimation of fishing mortality, potentially biasing the management process. We recommend carrying out relevant sensitivity analyses to improve stock assessment and fisheries management in data-limited areas such as the Mediterranean basin.

Mark-recapture experiments can be used to estimate the exploitation rate of a fishery; however, the estimate is influenced by the tag reporting-rate by the fishers. We present two methods to estimate the reporting rates in high/low reward ($100 and $10 CAD respectively) long-term cod tagging experiments. We fit two binomial logistic mixed-effect models, one with temporal auto-correlation in the reporting-rate yeareffects and one with independent year-effects. We estimate reporting-rates separately for recreational and commercial fishers, and test for spatial variation using fixed-effects for spatial regions. Due to the complexity of the fishery, our models account for factors such as recapture-fishery type, fish-size and time-at-liberty. Our results indicate that the recreational fishers reporting-rate was constant at 0.51 across all regions and years. The commercial fishery showed more spatial and temporal variation, with the reportingrates estimates lying between 0.67 and 0.87 for the independent year-effect model, and between 0.57 and 0.84 for the random walk model. Furthermore, we assessed the Handling Editor: Bryan F. J. Manly.The work of Konrad and Cadigan was supported by a Research and Development Corporation (RDC) of Newfoundland and Labrador Grant to N. Cadigan. We thank the technical staff of DFO Science NL Region for tagging and releasing cod, and numerous fish harvesters for providing recapture information. Electronic supplementary material The online version of this article (123 Environ Ecol Stat model performance as well as the coverage probability of nominal 95 % confidence intervals using simulations. We found that the models performed adequately; however, the nominal 95 % confidence intervals tended to be too narrow.