CCAMLR

This paper presents a methodological framework to estimate the likely cumulative impact on fragile benthic organisms from bottom fishing activity. The approach has been designed to facilitate standardized application among various gear types and areas to allow comparisons between fisheries employing different bottom fishing methods. New Zealand implemented this approach in its preliminary assessment of bottom fishing impacts for the 2008/09 toothfish longline fishery. This paper illustrates the utility of the standardized approach and provides a methodological template for systematic impact assessment in fisheries using bottom impacting methods, to inform mitigation efforts and as a necessary component of a full ecological risk assessment.

Acoustic and trawl samples obtained in the near-bottom layers covered by bottom trawl surveys in the South Georgia area are analyzed. The high heterogeneity of horizontal and vertical fish distribution in the near-bottom layers within the surveyed area is shown. Influence of this fish distribution heterogeneity on trawl surveys efficiency and reliability is analyzed by simulating the impact on fish biomass estimates of varying headline height of bottom trawl and shifting trawl stations positions. Under example of Russian and UK bottom surveys carried out in the South Georgia area during season’s 2000 and 2002, it is shown that inter-seasons and inter-vessels differences between fish biomass estimates may be to a considerable degree stipulated by heterogeneity of fish distribution in the near-bottom layer relative to the trawl sampling strategy, including fishing gear operation zones (Russian trawl HEK-4M and UK trawl FP120) and the trawl station locations rather than by the variability of the stock state.

The suite of data quality metrics introduced by Middleton & Dunn (2008) is examined to identify those metrics that are most informative with respect to the identification of good tagging data. Trips considered to have “known” good tagging data are identified, based on above-median rates of recapture of tags released by the trip, and above-median tag recapture rates by the trip.A bootstrap analysis indicates that the range of data quality metrics associated with known good tagging data is sensitive to the set of trips considered to have good data. However, a restricted set of data quality metrics may be most powerful in distinguishing the trips considered to have good tagging data. These include metrics for taxonomic resolution in the observer data, goodness of fit of catch data to Benford's Law, and the variation in toothfish catch rates.This reduced set of data quality metrics could be helpful in the identification of trips which have similar data quality to the known “good data” trips, but where, due to heterogeneity in the stock and in the spatial and temporal distribution of fishing effort, it is not possible to establish this directly from tag recaptures.

We present developments towards spatially explicit age-structured population dynamics operating models for Antarctic toothfish in the Ross Sea. The operating models consider both a coarse-scale and fine-scale spatial resolution and consider scenarios where abundance can be present over the entire Ross Sea region or constrained to areas where the fishery has operated. The models represent developments towards plausible operating model constructs that may be used for evaluating assessment biases or other factors to quantify risk and uncertainties for management of the Ross Sea Antarctic toothfish fishery. Further, we outline steps towards using the model as an estimation model to estimate movement and spatial distribution parameters as an aid in evaluating the operating models. The models are implemented in the generalised Bayesian population dynamics model, the Spatial Population Model (SPM). The SPM program allows implementation of an aggregate movement model for use with large numbers of areas as a discrete time-step state-space model that represents a cohort-based population age structure in a spatially explicit manner. Models can be parameterised by both population processes (i.e., ageing, recruitment, and mortality), as well as movement processes defined as the product of a set of preference functions that are based on known attributes of spatial location. The operating models considered were single sex age-structured models that categorised fish as immature, mature, or spawning. Observations can include spatially explicit commercial catch proportions-at-age, proportions mature, and tag-release and tag-recapture observations. Estimates of parameters when the operating models were used as estimation models with observations from the Ross Sea Antarctic toothfish fishery appeared to broadly reflect the hypothesised spatial distribution of Antarctic toothfish, suggesting that younger fish were found predominantly in the southern shelf areas and adult fish distributed along the slope and northern areas of the Ross Sea. Fits to the commercial catch proportions-at-age observations were generally good in most models, although fits to the plus group of the proportions-at-age catch data were less than ideal. Model estimates of proportions-mature appeared to be sensible, with a clear pattern that the proportions mature were a function of location and age. Tag release and recapture data were less well fitted by the models due, in part, to the conflict with assumptions of known abundance in the model and the abundance information inherent in the tag-recapture observations.

The Spatial Population Model (SPM) is a generalised spatially explicit age-structured population dynamics and movement model. SPM can model population dynamics and movement parameters for an age-structured population using a range of observations, including tagging, relative abundance, and age frequency data. SPM implements an age-structured population within an arbitrary shaped spatial structure, which can have user defined categories (e.g., immature, mature, male, female, etc.), and age range. This manual describes how to use SPM, including how to run SPM, how to set up an input configuration file. Further, we describe the population dynamics and estimation methods, and describe how to specify and interpret output.

The ICESCAPE Software Users Guide (Appendix 1) describes a parametric bootstrap (Davison and Hinkley 1997) model for implementing the general abundance estimator proposed by Southwell (2004) for Antarctic land-breeding predators. The software, which was developed as a suite of routines in the R language for statistical computing (R Development Core Team 2008), aims to adjust raw count data for availability and perception bias, and account for sampling fractions less than unity, in order to standardise estimates of breeding populations derived from count data collected under variable conditions. In Antarctica, adjustment for availability is particularly important, because difficulties in accessing breeding sites often lead to counts of land-breeding predators being taken at suboptimal times in a breeding cycle when the availability fraction is substantially different from unity. Adjustment for availability standardises counts to a common reference point of breeding chronology, and is achieved by applying an adjustment factor based on time series of availability throughout a breeding season. Such time series are typically collected at only a limited number of sites, so a search algorithm is used to determine surrogate availability information for a site when none exists. Importantly, standardisation in this way allows site-specific estimates to be aggregated to achieve region-scale population estimates. ICESCAPE uses a bootstrap resampling framework to propagate uncertainties associated with adjustments for availability bias, perception bias and sampling fraction through to final standardised population estimates.

The current assessment method for the mackerel icefish (Champsocephalus gunnari) in sub-area 48.3 employs the CMIX and GYM packages, deriving population numbers-at-age then projecting these numbers forward under the given harvest control rule to set a two-year TAC. One issue is the accurate identification of cohorts from the survey model, given the differential age structures observed in the population at Shag rocks and around South Georgia. In this paper we propose a length-based approach that removes this issue of determining cohorts, using stratified bootstrap techniques to estimate the length distribution of the population from the survey data which, in conjunction with the bootstrapped survey biomass data, yield an estimate of the population numbers-at-length. A length-based population projection is then used to estimate the two-year TAC, given the relevant harvest control rule.

In this paper a simple biomass dynamic population model is used as a paradigm for the dynamics of toothfish spp. in the CCAMLR area. This model underpins a management strategy evaluation framework where the process of stock assessment, TAC calculation and implementation and potential changes in future toothfish productivity (continuous and regime-based) are all simulated. The robustness of the current harvest control rule employed for assessed stocks of toothfish spp. in the convention area is explored with particular attention given to levels of biomass depletion (initial conditions), assessment precision, time-horizon, implementation error and future changes in productivity. An alternative control rule, using target and limit exploitation rates not biomass depletion levels and shorter time-horizons is explored and subjected to similar simulation testing.

The catch-at-age based CASAL model for toothfish in Subarea 48.3, which was presented in 2007, is developed and extended here to include survey data. Additional adjustments to the model suggested by WG-FSA-2007 are implemented. Fits to the survey length frequencies are good for recent surveys but in the past some cohorts have been missed by the survey. Fits to the survey CPUE are generally not very good. There is evidence for a size-based influence on post-tagging mortality, post-tagging growth retardation period and rate of tag loss. When these factors are included in the model the fits to the tag recaptures at size improve. However, there is still some evidence for a sex-based influence on the fits, particularly the poor fits observed from tags released in 2004 where significantly more males than females were recovered. A sex-disaggregated model was created, providing substantially similar fits and results to the sex-aggregated model, and no significant improvement to the tagging data fits. Estimates of cohort strength are made by the new model, but there is uncertainty about the estimates of recent cohorts made using short runs of catch-at-age data and the catchability being estimated by the survey. Estimates of cohort strength should be incorporated in calculations of TACs. A number of different mechanisms for doing this are proposed which maintain SSB above 50% B0 in both the short and long term.

An attempt to use CPUE data based on data of erussian observers in the assessment of Antarctic toothfish stock in division 58.4.1 is made. Unlike some previous results obtained for this division (e.g. WG-SAM-08/4), the assessment was intended to be independent of estimates of toothfich stock biomass in the Ross Sea. As a result of implementation of a dynamic production model, current biomass in this division is estimated as about 15000 t, while the estimate of virgin biomass B0 is found to be about 32000 t.