No abstract

Risk analysis has been used recently to enhance scientific advice to managers by providing estimates of risk to the fishery of different management strategies. However, little consideration has been given to the accuracy of these estimates. We present a reformulation and generalization of the risk analysis procedure of Francis (1992. Can. J. Fish. Aquat. Sci. 49: 922–930) and use simulation methods to examine the properties of a number of alternative risk estimators for two of New Zealand's main fisheries. It is shown that the choice of estimator can strongly affect the final estimates of risk and that the risk estimators can be alarmingly inaccurate. The accuracy of estimates is also shown to vary according to the type of risk being estimated, so analysts may improve the accuracy of their estimates by choosing the type of risk they estimate.

Cordue, P. L. 2007. A note on non-random error structure in trawl survey abundance indices. – ICES Journal of Marine Science, 64: 1333–1337. Trawl survey time-series are routinely used in stock assessments to provide indices of relative abundance. There is the general assumption, for each year y, that the expected value of the trawl survey index (Xy) is related to the biomass (By) by a single proportionality constant q: E(Xy) = qBy. In reality, the constant q varies due to many factors. An important factor, which is almost always ignored, is the role that non-trawlable ground can play in the variation of q. A general formula is derived for the q of a stratified random trawl survey. When the survey area contains non-trawlable ground, strata-specific data on the proportion of non-trawlable ground and on the preference of the species for trawlable/non-trawlable ground is required to weight stratum estimates correctly. In the absence of the correct stratum weights, a shift in the spatial distribution of the stock can combine with differing proportions of non-trawlable ground to introduce non-random error and possibly spurious trends into biomass indices. Each survey and species/stock should be considered on a case-by-case basis. A stratum-specific assessment of the proportion of non-trawlable ground is a pre-requisite for the production of trawl survey biomass indices.

Cordue, P. L. 2012. Fishing intensity metrics for use in overfishing determination. – ICES Journal of Marine Science, 69: 615–623. The issue of constructing a technically correct fishing intensity metric from the output of a stock assessment model is considered. Four metrics of annual overall “fishing intensity” are defined. The metrics are applicable to age- or length-structured stock assessment models, which may, or may not, include complex spatial and temporal structure in the population and the fisheries. Two of the metrics are termed “direct” as they are calculated from the model output in the given year. Equivalent annual U is a number-based exploitation rate, and equivalent annual F is an average fishing mortality rate. The other two metrics, equilibrium stock depletion (ESD) and spawning potential ratio (SPR), measure fishing intensity in terms of the potential long-term effect on the stock (via 1–ESD and 1–SPR). The use of the metrics for overfishing determination is illustrated with a simple, spatial, two-fishery model. Summary statistics, which are sometimes used as a measure of fishing intensity, such as total catch over a reference biomass, or a number-weighted F are shown to be technically invalid for overfishing determination. The common approach, for virtual population analysis assessments, of using an average F over a specified age range is also discussed.

A least squares method is presented for estimating length to target strength relationships for a target species and associated species using a series of target strength distributions and associated trawl catches. A by-product of the estimation procedure is an objective determination of the correspondence between modal lengths in the trawl catches and the modal lengths in the associated target strength distributions. The method is illustrated by applying it to a data set collected to determine the length to target strength relationship for hoki (Macruronus novaezelandiae).