CCAMLR

Three Operating Models (OMs) reflecting an optimistic, an intermediate, and a pessimistic current status for the toothfish resource in the Prince Edward Islands are developed. These are planned for use for initial trials of candidate Management Procedures (MPs) to provide future TAC recommendations for this resource. These will aim to provide an appropriate balance between avoiding risk of severe depletion and utilising potential harvest from the resource, in circumstances where the resource status is very uncertain.

A version of the confidence interval calculating program TrawlCI was recompiled to enable it to be run within the DOS emulator of recent versions of the Microsoft Windows operating system. The performance of the recompiled version was compared with that of the original version.
The recompiled version of TrawlCI produced very similar, though not identical, results to the original version. We attribute this to differences in the minimisation routines of the recompiled version. We conclude that the differences evident from these tests are unlikely to significantly influence the estimated long-term yield of Dissostichus eleginoides or Champsocephalus gunnari

Response-biased sampling in the context of regression modelling occurs when sample units are selected with a probability that is a function of the response. In sampling to obtain fish to both age and measure for length there are two potential response-biased sampling processes when length is the response and age is the predictor variable. These sample processes are (i) the actual fishing process involving a particular gear, and (ii) the method of on-deck sub-sampling fish for ageing from the random length frequency sample. When the selectivity of the gear combined with availability of fish to be caught is length-dependent in (i) and when fixed sample sizes per length bin or class are employed in (ii) then both these sampling processes are response-biased. Response-biased sampling and its effect on parameter estimation has been studied for linear and generalized linear, and linear mixed models but since population-average length given age is assumed to follow the von Bertalanffy growth relationship this work extends previous work to general nonlinear models and combines two response-biased sampling processes. Maximum likelihood, naïve least squares, and inverse probability weighted least squares estimation are used to estimate the von Bertalanffy parameters for simulated and real data on the growth of the Patagonian toothfish (Dissostichus eleginoides) given a known selectivity function.

This report outlines the development of a Bayesian sex and age structured population model for the assessment of Antarctic toothfish (Dissostichus mawsoni) in the Ross Sea (Subareas 88.1 and 88.2), and initial progress towards evaluation of spatially explicit models. Three model scenarios were investigated. The first scenario considered the Ross Sea fishery as a single homogeneous area (single-area). The second and third scenarios (2-area and 3-area respectively) split the Ross Sea into either two or three discrete areas, with migrations of fish between areas. The 2-area model appeared to provide a better representation of the some of the observations, although there was a lack of any observations defining migration between areas. The 3-area model, showed many problems in fitting the data adequately. In both of the spatial models, there was a lack of useful data on area splits, migration rates, or migration patterns.
Simulations experiments suggested that reliable, albeit uncertain, estimates of biomass could be obtained from each model when the operating model was the same as the estimation model. However, the single area estimation model was strongly negatively biased when using tag-recapture data simulated from the 2-area model.
All the multi-area models investigated here could do with considerable improvement. The choice of area (i.e., the boundary between areas), selectivity functions (i.e., either domed or logistic), selectivity types (i.e., either age based or length based), and migration ogives need further investigation. Additional tag-recapture data, specifically data that allows movement rates between areas to be quantified, are required to develop more realistic stock structure hypotheses.

Abundance estimates from tag-release and tag-recapture data require that the number and type of errors in data for analysis are minimised. Accurate recording of the sequence numbers on tags at time of release and recapture form an important part of data accuracy. Check digits, as a part of the tag sequence number, can assist in the identification of errors in recorded data.
A computer program to calculate, check, and validate a check digit scheme based on Verhoeff's Dihedral Group D5 check algorithm (Mohr 2005, Verhoeff 1969) is described.

A descriptive analysis of the toothfish tagging programme carried out in the Ross Sea since 2001 is presented for the first time. Tag-release and tag-recapture data are presented for both toothfish species for Subareas 88.1 and 88.2 for New Zealand vessels only. This is because data from non-New Zealand vessels were unavailable at the time of the analysis. A total of 4903 Antarctic toothfish have been released and 89 recaptured, and 443 Patagonian toothfish released and 9 recaptured. For the last two years, when tagging has been part of the Conservation Measure, New Zealand vessels have tagged between 1.0 and 1.37 toothfish per tonne of catch. Tagging rates by area over the past three years have been in the same proportion as the catch by area. However, recapture rates have tended to be higher in the northern and eastern SSRUs 88.1C and 88.2E.
The maximum movement of Antarctic toothfish from the New Zealand data set has been about 200 km. However, most (80%) Antarctic toothfish have moved less than 50 km. Consequently, nearly all fish have been recaptured from the same SSRU where they were released. The mean size of tagged Antarctic toothfish has increased since 2001, but is still smaller than the mean size of fish taken in the commercial catch. Larger toothfish (>35 kg) are difficult to tag without significant damage to fish, and there appears to be a trade-off between maximising size of released fish and minimising tagging mortality. Growth rates of Antarctic toothfish that have been at liberty for 2–3 years have averaged 5– 7 cm per year, which is consistent with growth rates predicted from the von Bertalanffy growth curve. The preliminary estimate of tag loss from double tagging experiments is 13% per year.

This paper describes an approach, using CASAL, to undertake operating model/estimation model experiments for Dissostichus spp., and methods for calculating CCAMLR yields using CASAL. We present an example model for the fictitious fishery on a fictitious species Dissostichus spurious (Everson, 2004) and investigate model performance from alternative types of observations. In general, most models were fitted with an expected percent root mean squared error (%RMSE) of less than 20%. The inclusion of additional data (i.e., more observation types) assisted in providing better estimates.
Further research on the expected performance of integrated models is required that investigates a range of alternative “true” states with data that includes bias and variance in observations, as well as the robustness of the estimation model to alternative operating model assumptions. In addition, the expected uncertainty that may arise from an MCMC approach has not been considered here.
In general, operating/estimation modelling experiments provide a means of evaluating alternative approaches to the assessment of stocks, however it should be noted that simulation studies often under-estimate the uncertainty that would be found in a real assessment.

This paper presents a new approach to the stratification of catch-at-length data of Antarctic toothfish (D. mawsoni) in the Ross Sea.
Tree based regression techniques were used to stratify the sampled catch based on the median length of Antarctic toothfish for each set using the observer length frequency data. The median lengths were weighted within the regression by the inverse of the variance, rather than giving equal weights to all tows. Two variables (depth and SSRU) were used by the tree regression model to determine the strata.
The resulting stratification effectively split the fishery into 4 regions, consisting of shallow inshore regions where predominantly smaller fish were found, to deeper offshore regions where only larger fish were found. The paper presents the new estimates of Antarctic toothfish catch-at-length and catch-at-age from the Ross Sea up to the end of the 2003–04 fishing season.

The mark-recapture method of estimating toothfish population size described last year, which uses a modification of the Petersen estimator to take account of mortality and selectivity, is implemented here in Splus code.

This paper investigates the influence of mixing of fish, and the uneven distribution of tag placements and recapture effort, on bias in the Petersen estimate. It does so by constructing a linear model of the South Georgia toothfish fishery, simulating fish movements within this system and overlaying various combinations of tagging and recapture effort to investigate bias. The fishable grounds around South Georgia were divided into 77 very small scale boxes lying along the 1000m contour. The uneven distribution of animals was simulated by adjusting an average movement rate downwards when animals encountered a high CPUE box and upwards in a low CPUE box so that they were retained in high CPUE boxes. The model incorporates the facility for releases by box over a number of years.
The model performed as expected with test situations. It produced a near-perfect estimate of stock size when there was an ideal distribution of tags and/or fishing effort; by ideal we mean that either tagging or fishing effort was in direct proportion to CPUE. When both tagging and fishing effort were non-ideal, eg when effort was concentrated away from tag concentrations, or overly concentrated in them, the Petersen estimator either over-estimated or underestimated (respectively) the true population size. When run on the real tag release data, and using CPUE from 2002-2004 and recapture effort in 2003 and 2004, the model indicated that the Petersen equation produced an under-estimate of true population size. Although we do not advocate using the magnitude of the estimated bias to correct the tagging estimate made last year for 48.3, we do conclude that the particular distribution of tag releases and recapture effort at South Georgia is likely to lead to an under-estimate of the true population size rather than an over-estimate of it.