CCAMLR

In this document, we revise stock status assessments of D. eleginoides in research blocks 58.4.4b_1 and 58.4.4b_2 using CPUE analogy method, Petersen method and CASAL model. Detailed results using CASAL models in research block 58.4.4b_1 are described in a separated document. The estimated stock size in block 58.4.4b_1 was 597, 336 and 553 tonnes in CPUE analogy method, Petersen method and CASAL OB_1b model (vulnerable biomass in 2014), respectively.

The stock sizes of D. eleginoides in block 58.4.4b_2 is estimated at 648 tonnes only by using CPUE method, as the catch and tagging data in block 58.4.4b_2 was not enough to be applied to Petersen method and CASAL models.

We propose to continue the current research operation for the next fishing season with the same survey design and total sample size of 60 tonnes in order to further strengthen the stock assessments in the area.

This paper reports analysis of CPUE data on cod in the Barents Sea.  The potential of fleet effort standardisation is explored based on vessel calibration factors, which are established when vessel trawling operations take place at acceptable proximity in time and space. The methodological approach might be of interest for the longline fishery for tothfish as well as for the trawl fishery on krill.

A model for standardisation of fishing power for individual vessels in a commercial fishing fleet is developed. Catch rates of vessels are compared when they are fishing close together in time and space, and their fishing power relative to a standard vessel is calculated. The model is applied to a logbook data base from the Norwegian bottom trawler fleet, and effects of varying model parameters are explored. Relative fishing power from the model is correlated with the vessel's length and engine power. The model seems to be robust when varying catch composition criteria, minimum number of comparisons required and standard vessel.

According to the requirements of Conservation Measure 21-03 (2014), this document provides information on net diagrams and exclusion devices of marine mammals used by the Chilean flag vessels Betanzos and Cabo de Hornos, who present fishing notifications for the 2015/16 krill fishery season, Notification ID 86795, 86796 and 86797.

In Division 58.4.3a, since 2012, research fishing has been conducted in the research block by two vessels using longlines: Shinsei Maru No. 3 (Japan) and the Saint André (France). The catch limit for Dissostichus spp. is 32 tonnes and the total reported catch up in 2013/2014 was 32 tonnes. France notified its intention to continue its exploratory fishery over the coming years in order to achieve a robust stock assessment that would provide advice on a catch limit according to CCAMLR decision rules. This paper aims to present a research plan for 2015/2016 that takes into account the remarks made during the WG-FSA 2014. The objective is to spread fishing effort over across the whole fishable area and to progress on the robustness of the CASAL assessment. The biomass in division 58.4.3a was estimated during WG-FSA 2014 using the Petersen estimator at 386 tonnes. In the absence of an assessment using the CCAMLR decision rules, the re-estimation of the geometric mean of Petersen biomass estimates is used as the estimate of biomass for this division in 2015. Given this analysis estimated biomass at 419 tonnes, which was similar to the 386 tonnes, the catch limit should remain unchanged at 32 tonnes for 2015/16 to maximize the expectation of tag-recapture.

Exploratory fishing for toothfish (Dissostichus spp.) in East Antarctica (Divisions 58.4.1 and 58.4.2) began in 2003. Robust stock assessment and catch limits according to CCAMLR decision rules remain to be determined for these Divisions. Accordingly, a research plan has been developed under Conservation Measure 41–01 to support a proposal for an Australian vessel to participate in exploratory fishing within Divisions 58.4.1 (SSRUs C, E, G) and 58.4.2 (SSRU E). Standardised longline fishing, in conjunction with fish biological measurements, tagging and aging, will be used to develop a stock assessment for these divisions and inform the necessary considerations of spatial structure, biomass and connectivity of toothfish populations. In addition, environmental data from CTD (conductivity, temperature and depth) and video loggers will contribute to models of toothfish habitat use. These models will inform spatial management approaches for toothfish, and the conservation of representative areas of benthic biodiversity. Additional outcomes include mapping of the bathymetry of fishable areas, and improved understanding of the distribution, relative abundance, and life histories of bycatch species.