At WG-SAM-2019, it was highlighted that some of the language in the table used to assess research plans in data-poor fisheries was ambiguous, in particular to delegates that do not have English as their first language. In this paper, we propose some revisions to the table to attempt to reduce this ambiguity. 

The five years of survey and observations of Dissostichus spp. in statistical subarea 48.2 on board the Ukrainian vessel SIMEIZ. Survey was conducted in accordance with the recommendations of the Scientific Committee and Commission. Data on the distribution of Antarctic toothfish and by-catch fish, biological characteristics of Antarctic and Patagonian toothfish are presented. Biomass of Antarctic toothfish is estimated using lowest over the study period CPUE rates in the 2019 season.

It  was  reported  at  WG-SAM-2019  that  the  sea  ice  concentration  (SIC) of RB 48.6-5 corresponded well with the dynamics of monthly sea surface temperature  (SST)  anomaly  in  RB  48.6-5.   However,  in  the  prediction  of SIC  in  RB  48.6-5  using  the  monthly  SST  anomaly,  there  is  a  limitation. Because RB 48.6-5 is covered by ice for almost 100% from April to October, consequently there is very little information on the SIC during this period. However, the SST of RB 48.6-2 can be used to predict the SIC in 48.6- 5.  There  is  not  a  strong  correlation  of  SST  between  48.6-2  and  48.6-5.

However there are similar SST positive spikes both in 48.6-2 and 48.6-5 and it is assumed that these are related.  This paper analyses the possibilities to predict the SIC in 48.6-5 for the following season using the daily SST of 48.6-2.

Photo-identification is a technique that uses photographs of animals to identify individuals through natural markings visible on their body and is central for research on cetaceans. Best gear configuration to conduct photo-identification consists in a SLR camera equipped with a 100-400 mm lens. This paper addresses concerns regarding the complexity and the price of the SLR gear by presenting the first trials of a cost-limited bridge camera as an alternative to no photo-identification. This bridge configuration will not match the SLR configuration, especially when animals are far from the observer but can give good results at shorter distances especially when animals are strongly marked.

Since the correlation of SST between Subarea 48.6 and Division 58.4.2 was reported in Namba et al. (WG-SAM-17/10), correlations of SIC between Subareas 48.6 and 88.1 (Ross Sea), Weddell Sea and the Sea offPeru were re-analyzed in this paper. There is some correlation of SIC between Subareas 48.6 and 88.1 with SST data from 2002-2019. In addition, the correlation of SST and SST shifted forward 6 months was analyzed and there was some negative correlation between Subarea 48.6 and the central part of the Pacific ocean. Wider phenomena such as the El Niño Southern Oscillation must influence the correlation.

In order to achieve the milestone “1.5 Update CASAL model” of the ongoing research plan submitted by Japan and France (WG-SAM19/08), the CASAL models for Dissostichus eleginoides at Division 58.4.4b were revised.

Following WG-SAM-2019 suggestions (para 6.76 in the WG-SAM2019 report), we conducted an exploration of the impact of Age Length Keys (ALK), CPUE standardization, and different scenarios of IUU on the CASAL assessment, especially B0 estimation. Furthermore, we estimated the harvest rate (CAY and MAY) as defined in CASAL manual (Bull et. al. 2012) using each CASAL runs according to WG-SAM-2019 suggestions (para 6.76 in the WG-SAM2019 report).

A single-sex age structured CASAL model was built to assess the stock of D. eleginoides at the research block 1 of Division 58.4.4b. The base model (M1) is an update the M4 model described by Okuda and Massiot-Granier (2019). It includes updated catch at age data based on annual ALKs and data sources were weighted using an automatic procedure (“DataWeighting” package, Francis 2014). In addition of this first model, three other models were tested (M2, M3 and M4), exploring both the influence of CPUE standardization and IUU scenarios on the assessment. Compared to the CASAL results reported in WG-SAM-2019/30, the MPD profiles show improvements under all scenarios to better fit the model.

Comparing the MPD profile between M1 (base model), M2 (no 2007/08 CPUE), and M3 (no season effect on CPUE), unnaturally high standardized CPUE in 2007/08 reduce 25 t B0 and difference of standardized CPUE (with/without “Spawning season” effect) cause 10% difference in B0 estimation at the block 1 of Division 58.4.4b (Table 1). Changing IUU level (26.5 t to 40 t) in 2009/10 fishing season cause increasing of B0

In all scenarios, the estimated MCYs for D. eleginoides were higher than current catch limit 19 tonnes in block 1 at Division 58.4.4b. Furthermore, harvest rates achieving the CCAMLR management target (50% B0) were estimated to be c.a. 6%. This value is higher than current precautionary harvest rate for “data-limited” explanatory/research fisheries (4%) where there is no estimate of B0.

This paper represents the annual report of the multi-member longline survey on Patagonian toothfish (Dissostichus eleginoides) at Division 58.4.4b in the 2018/19 fishing season by Japan and France. The C2 and Observer data sets were provided by the CCAMLR Secretariat on the 7th August, 2019. In this paper, the data set during current fishing season (2018/19) was used for reporting the quantity of data and samples collected. The research operations at Division 58.4.4b have not yet been completed in the 2018/19 fishing season. Also, some data is not reflected in tables and figures because it is still under process.

Division 58.4.4b has been a closed area since 2002 (CM 32-02) and scientific research has been conducted with a research plan submitted under CM 24-01 since 2008 by Japan. France joined this research plan in 2015 and a joint proposal was submitted for the first time in 2016 (WG-SAM-16/06 and WG-FSA-16/33 Rev. 1) for a period of 5 years.

The proposal submitted here has been substantially revised to address the concerns expressed at WG-FSA 2019 and SC-CAMLR-XXXVII (para. 3.158). We followed and improved the revised research plan presented in SC-CAMLR-XXXVII Annex 12 and redefined slightly the research objectives. The survey design has been amended to avoid sea pen hotspots in the eastern part of 58.4.4b_2 (WG-FSA-18/23) and a new French vessel has joined the research plan to increase research survey capacity.

Toothfish population structure and bycatch analyses will be presented at WG-FSA-19, while the main concerns regarding objective 2 (“Provide an assessment of the status and productivity of Patagonian toothfish stock”) and 5 (“Contribute to scientific research programs on killer whales (O. orca) ecology and depredation”) are addressed in two documents submitted at WG-SAM-19.

We updated milestone tables of objective 1 and 2 and related descriptions to address WG-SAM-2019 comments (para 6.76-79 in the WG-SAM-2019 report).

We made significant progress on the stock assessment and clarified objectives and milestones to facilitate the reviewing process of this research plan. A list of dates by which specific milestones will be completed and reported to CCAMLR working groups was provided and a final report will be provided at WG-FSA in 2021.

Spanish and Australian scientists are working on the age and growth estimates of Antarctic toothfish within divisions 58.4.1 and 58.4.2 from 2015 and 2017 respectively. During 2019, Korean scientists have joined this colaborative work.

Age-length frequencies from the three members are shown and "a posteriori" calibration between ESP and KOR has been made.

A descriptive analysis of the Patagonian toothfish (Dissostichus eleginoides) population using data collected from observers onboard  two Spanish fishing vessels, obtained between 2017 and May 2019, in international waters of the South Indian Ocean within the FAO areas 51.7 and 57.4 managed by SIOFA, adjacent to the Commission for the Conservation of Antarctic Marine Resources (CCAMLR) convention area is provided in this paper.

Different approaches have been made to define the TOP population features taking into account the depth, latitude, sex and geographic zone. Some differences between the two study areas have been found but differences in temporary distribution of the three surveys could have influenced the results.