Precisely wrong or vaguely right: simulations of noisy discard data and trends in fishing effort being included in the stock assessment of North Sea plaice

Mark Dickey-Collas Martin A. Pastoors Olvin A. van Keeken ICES stock assessments of North Sea plaice are routinely carried out with eXtended Survivors Analysis (XSA), based on landings and survey data. Recently, the assessments included data on discarded young fish, sampled with high variance. Fishing effort has been declining since the mid-1990s, so conditioning the estimated fishing mortality (F ) on the recent past could introduce bias into the perceived stock size. Simulated populations with North Sea plaice-like characteristics are used to explore the dependence of the perceived stock dynamics on the inclusion of discards data at different sampling noise, using the same methods and XSA settings as ICES. The sensitivities of the results were tested against different trends in fishing effort and recruitment, and different scenarios for shrinkage (i.e. the way in which the past is used to estimate the most recent fishing mortality). Within the bounds of the simulation assumptions, the perception of population trends from an XSA stock assessment can be biased when there are trends in fishing effort: decreasing effort leads to underestimating SSB and overestimating F. When discards are not included, bias in SSB is greatest when effort decreases, and bias in F is greatest when effort increases. Bias in SSB and F were removed by including discard data, but at substantial loss of precision. If effort shows a clear trend and discards are substantial and estimated noisily, the recent trend in the target population may be hard to track with an XSA-type assessment methodology. Introduction North Sea plaice (Pleuronectes platessa) is mainly exploited by a beam trawl fleet that targets both sole (Solea solea) and plaice (Daan, 1997; Piet and Rice, 2004). The legal minimum mesh size in the fleet is heavily geared towards catching the slimmer sole, which leads to substantial discarding of plaice (Van Beek et al., 1990). The differing catching potential of each species with differing relative total allowable catches (TACs), which cannot be accounted for in the management system based on single-species TACs and fixed national quota shares, also leads to increased discarding of young plaice (Daan, 1997; Kraak et al., 2004). This practice of discarding is legal within the EU. However, concern has been raised about the impact of not incorporating discards as a source of mortality on the quality of stock assessments and their associated management advice (ICES, 1986; Alverson et al., 1994; Casey, 1996; Dingsor, 2001; Borges et al., 2005). The annual numbers of young plaice discarded by the North Sea beam trawl fleet varies greatly between years (Van Beek, 1998) and the time-series of empirical estimates of discarding is patchy (ICES, 2005). For the most recent years (1999 2005), shipboard observer estimates of the numbers-at-age discarded may be raised to the total fleet by the ratio of sampled effort to total fleet effort, but for the period before 1999, reconstructions of annual discards using modelled estimates of growth, spatial distribution, and (mesh and sorting) selectivity ogives have been made (Van Keeken et al., 2003; ICES, 2005; also see recommendation in ICES, 1986). From 2004 on, these discard estimates have been included in the stock assessment of North Sea plaice and in the catch predictions (ICES, 2005). However, it is not clear how the inclusion of noisy estimates of discarding has affected the stock assessment (ICES, 2006a). Compared with port samples of landings, samples of discarded fish are few. This is due to the necessity of using onboard observers to estimate discards (Stratoudakis et al., 1998; Tamsett et al., 1999; Allen et al., 2002). As a consequence, estimates of discards are less precise than estimates of landings (Stratoudakis et al., 1999; Cotter et al., 2002; Rochet et al., 2002; Punt et al., 2006), although bias can of course arise also from trends in the misreporting of catch. This has opened up a debate as to the usefulness of including discard estimates in the assessment (Cotter et al., 2004; ICES, 2004; STECF, 2005). This debate has centred on bias vs. precision: excluding discards obviously introduces bias, whereas including discards could increase variance and decrease precision. If the bias was consistent (e.g. similar proportions of discards by year), the interpretation of trends would be easier than when noisy discard estimates are added to remove the bias (ICES, 1986), because the signal would be lost in the noise. The beam trawl fleets fishing for flatfish in the North Sea have shown a substantial decrease in number of fishing days since 1995 (ICES, 2006a). The eXtended Survivors Analysis (XSA) model used to assess the North Sea plaice stock (Darby and Flatman, 1994; Shepherd, 1999) has conservative features: the survivor population is partly driven by the mean fishing mortality of the recent past (ICES, 1983, 1987), which has became known as the shrinkage option. Shrinkage in XSA is aimed at balancing bias and precision: including shrinkage may introduce bias, whereas excluding it may increase the variance. The use of XSA has been criticized precisely for its over-reliance on the shrinkage option. However, these criticisms are largely anecdotal and virtually no study has investigated and documented the influence of conservative measures such as shrinkage. Cotter et al. (2004) raised concerns about using XSA when catch-at-age data for landings and discards were combined, because such datasets are often not collected independently or raised. We address some of these issues by evaluating the quality of XSA assessment of North Sea plaice using catch data that include or exclude discard data. We do not consider the pertinence of input data and assessment method to the overall management of the stock (Kell et al., 1999; Ulrich et al., 2002; Kraak et al., 2004), but only how trends in effort and the quality of discard estimates affect our ability to assess the state of a stock. This analysis cannot be carried out on real data, because the underlying dynamics of the one and only realization represented by the available data remain unknown (Rosenberg and Restrepo, 1994). Therefore, simulations are based on an operating model representing the true dynamics and allowing observations to be generated, which are then used in an XSA to evaluate the perceived dynamics (NRC, 1998; Restrepo, 1998; Mohn, 1999; ICES, 2003, 2004). Instead of using the same model to create and assess the population, the true population was constructed by a separable model, and XSA was used for the assessment. This should introduce more noise into the assessment and enhance the realism of the simulations, because assumptions within any assessment model are unlikely to match completely the processes governing the true population. A range of different scenarios for increasing and decreasing trends in fishing effort, different trends in recruitment, and different noise as (...truncated)