CCAMLR

The Secretariat has made changes to the Observers Manual to reflect the current advice from the Scientific Committee and its Working Groups. These changes are presented as an annotated Table of Contents, with a full track-change version of the Manual provided as an appendix. Two additional sections are included at the end of this document which require endorsement from the Working Group. These are:  Proposal for a revised method for recording Krill Feeding Observations, and  Proposal for a revised fish sampling protocol for krill fisheries.

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.