CCAMLR

Integrated assessments that use catch-at-age or abundance-at-age data for model calibration require an ageing error matrix as input in order to adequately account for uncertainty in the data resulting from the imprecision of age determination using annual ring counts from otoliths. This paper describes the methods and results used to provide an ageing error matrix to the HIMI toothfish integrated assessment using repeat readings by 4 readers of a set of 203 reference otoliths sampled from the HIMI fishery. The methods of sampling, preparing, reading, and modelling random reader error for this reference set of otoliths are described. A total of 933 readings were taken and errors were defined as the nearest integer (NI) value deviations, denoted as integer errors (IE), from the mean age for an individual otolith. Since the true age of the fish is unknown, only imprecision and relative differences between readers could be quantified. Linear mixed model analyses indicated that the mean IE ranged between readers only slightly (+/- 0.27 yr) whereas frequencies of random IEs, treated as classes, between readings were relatively high for +/-1 yr relative to the zero IE frequency, and less so for the +/-2 yr and greater classes. These frequencies depend on the readability score of the otolith and its average age and were modelled in two stages. In the first stage the frequency of the absolute value of IE, the AIE, considered as 0, 1, 2, 3, 4, 5 yr and greater, classes for each of the 4 readability classes and 7 aggregate age classes were modelled using continuation ratios and predicted proportions in each AIE class obtained for a given readability score and age. Proportions of the AIE 1 yr error class decreased relative to the AIE 0 class as readability improved while, in general, it increased as age increased. To model any degree of asymmetry in IEs, a binomial/logistic model of the proportion of non-zero IEs that were negative was fitted for given readability and age. This probability decreased from around 0.7 to 0.4 for ages 5 and 21 yr respectively, but did not depend on readability. The construction of the ageing error matrix is described and combines the modelled probabilities for AIE and negative IE while taking into account logical constraints. This two stage approach makes efficient use of the data since only half the number of combinations of error class by readability by age class are required compared to modelling IE classes directly. This approach differs from other studies of ageing error in that it takes into account the otolith readability score and the integer nature of ring count data.

The Secretariat allocated research hauls for exploratory fisheries in Subareas 48.6 and 58.4 in 2008/09, following the Scientific Committee’s guidelines. Some modifications were required to account for SSRUs where fishing had not been previously reported, and SSRUs where the number of positions reported in the fine-scale catch and effort data were insufficient to allow a bootstrap allocation (without replacement) of 5 research hauls per stratum (fished and lightly fished/unfished) for each vessel. Where possible, start positions for 5 research hauls and 2 alternative positions per stratum were provided to each vessel in each SSRU (position separation distances 7-120 nmiles). A total of 1082 starting positions for research hauls were allocated to 13 vessels and 17 SSRUs in Subarea 48.6 and Divisions 58.4.1, 58.4.2, 58.4.3a and 58.4.3b. So far, four vessels (Banzare, Insung No.1, Insung No.22 and Shinsei Maru No.3) have conducted fishing activities in Subareas 48.6 and 58.4 in 2008/09. These vessels deployed 131 research hauls and each vessel completed 10 research hauls in each SSRU fished, with the exception of Insung No.1 (SSRU 5841C) and Shinsei Maru No.3 (SSRU 5842A) where the setting of research lines was terminated early due to the closure of those SSRUs. In 9 of the 14 sets of research lines deployed, the minimum mean distance between start positions was  2 nmiles from allocated positions. And in 11 of the 14 sets of research lines deployed, the minimum distance between research lines was > 5 nmiles (CM 41-01). Some vessels reported that they had been unable to reach the allocated positions due to sea-ice, and sea-ice had also caused difficulties in positioning alterative research lines.

The level of data quality assessment and ‘certification’ as ‘fit for purpose’ depends upon the different requirements for the data, together with a recognition that, however thorough a validation exercise the data are exposed to, there will always be some data that require some additional confirmation of their validity. In the context of CCAMLR, data may undergo one set of quality testing in order to establish whether they are appropriate for entry into databases, and further testing, as required, to determine data integrity for a specific analysis. Identifying these levels of data validation is important for prioritizing the work of the Secretariat in data quality assessment, and also in ensuring that users of CCAMLR data are fully aware of the integrity procedures that have been applied to the data. CCAMLR data are validated at various levels, from data collection and submission by owners/originators, through the Secretariat’s data processing, to analyses conducted by scientists and Working Groups. Data errors and discrepancies are resolved in consultation with data owners/originators and, where possible, corrections and annotations are made to individual records. WG-SAM-08/13 illustrated inconsistencies and errors in fishery data which originated at the vessel-level, and indicated that some errors were not detected during the Secretariat’s data validation. Further, some data had been inadvertently replicated by the Secretariat following repeated data submissions. The methods reported in that paper are now being modified for implementation as part of the Secretariat’s continued work on improving the quality of CCAMLR data. Further work to improve the assessment of data quality and the use of CCAMLR data includes, inter alia, developing further validation procedures and data quality metrics, and integrating these procedures across related CCAMLR datasets. Consideration may also be given to establishing a formal data review/reconciliation procedure with Members to ensure that CCAMLR data are consistent and current with those of data owners/originators.

In 2008 WG-FSA recommended that the Secretariat undertake to identify the tagging event details for all tags recovered (WG-FSA 2008 paragraph 3.55). The Secretariat has been able to verify the tagging details of a greater proportion of tags than reported last year which has resulted in an improved proportion of the tags that can now be linked to tagging events in exploratory fisheries (Table 1). The recapture linkage rate for rajids has been lower than for toothfish in Subareas 88.1 and 88.2. The linking rate for tags recovered in established fisheries is also variable due to the partial inclusion in the CCAMLR database of some information on tag deployments and recaptures from tagging schemes that are operated by individual Members. WG-FSA has requested Members who have previously deployed tags to provide inventories of these tags to the Secretariat (WG-FSA 08 paragraph 3.56). This request, including a tag inventory template, has been sent to all Members. The Secretariat urges all Members with this type of information to provide these details as soon as possible. A copy of the tag inventory template can be obtained from the Secretariat.

A submersible multifrequency acoustic TS-probe was used for measuring the target strength of Antarctic krill in situ at short range. The experimental methods, procedure for data retrieval and analysis with some results for one selected station are presented. A Simrad EK60 split beam system operating at 38, 120 and 200 kHz is installed in the probe, connected to three pressure resistant transducers with 7o half power beam opening angles. The orientation of the probe was monitored with sensors for pressure, compass, pitch and roll. A stereo camera system was also mounted directly on the transducer platform with the purpose of measuring the orientation of the organisms. All system communication between the ship and the probe was through an optical Ethernet link. Firstly, krill presence was documented through recordings with hull-mounted ship echosounders and verified through net samples. The probe was subsequently lowered to suitable depth from the vessel in fixed position and data were acquired at short pulse duration, high ping rate and corresponding short maximum detection range, often limited to 25 – 50 meters below the transducers. The data analysis could then be performed by selecting single target tracks from the echograms. From synchronized detections at the three frequencies within single target tracks, individually based TS frequency response could be determined with fair accuracy. The results from the target strength measurements can further be compared with theoretical model predictions and further utilized in the biomass evaluation.