CCAMLR

Picking up the calling of the ATCM XXXIV to the Treaty Parties to intensify their efforts in using remote sensing techniques for improved monitoring of environment and climate changes in the Antarctic, Germany currently carries out a feasibility study on penguin monitoring using such techniques. Additionally, CEP representatives and scientists discuss on an informal level the need and the general possibilities of a penguin monitoring in the Antarctic Treaty area based on remote sensing techniques. In order to stimulate and formalize this discussion, Germany proposes to open an Intersessional Contact Group (ICG) on penguin monitoring via remote sensing on the CEP Discussion Forum on the Secretariat website and invites CCAMLR to participate in this process.

To estimate optimal relative sample size of scientific observer data collected on Antarctic krill commercial fishing vessels, we estimated the relationship between statistical precision and sample size by using variance component analysis. Observer datasets on Japanese krill fishery from 1995–2008 were analysed by using a hierarchical Bayesian model. The models were composed of multistage cluster units (i.e., year, sub-area, vessel, cruise, and haul) based on a state-space model, separating biological process error in the population dynamics from fishery process as observation error. In both krill length and bycatch fish number, the parameters estimated by MCMC hardly show difference among years, sub-areas, and vessels. The potent interaction effect between year and sub-area suggests large spatio-temporal variability in size structure of krill population, which is presumably derived from large variability of recruitment causing difficulty in predicting krill population dynamics. Variances of observer datasets were calculated by the multistage sampling formula with the variance terms derived from the Bayesian model. For both krill length and bycatch fish number, vessel sample size show marked effects on CV, although haul sample size affect CV for only krill length data up to 10% haul coverage. These results suggest that data collection by scientific observers onboard commercial vessels provide an important information for the management of krill resources and Antarctic ecosystem, while we need further discussion about the optimal relative sample size to ensure the statistical precision required for the specific objective of a study with considering the cost of observer deployment.

The authors   analyzed the influence of climate changes on space-time dynamic of Antarctic krill fishery in the Area 48 using the CCAMLR fishery data and time series of ААО induces (Antarctic Oscillation Index) as the indication of climate variability.   Here we used the methods of the integrated ecosystem assessment used by ICES and the modern de-nosing data methods. The influence of climate variability was traced across long-term dynamic of fleet operation including standardized CPUE indices, monthly and daily catches.  The important evidence of ongoing climate variability is the fishery regimes switching observed in long-term fishery. Standardized CPUE index was used as an indicator of probable fishing regime switching.   The peculiarity of such switching in the entire Area 48 and its Subareas 48.2, 48.2 and 48.3 is considered.  It was found that the significant switching of fishery regime occurred in 2006, when fishery transferred to the state «high CPUE» retained during 2006-2010.  This period is characterized by the highest values of CPUE index and AAO index   reached in the Area 48 for the whole 1986-2011 observation period.  It was shown that over 2006-2011years the observed shift of the fishing seasons towards fall-winter months of the Southern hemisphere are accompanied by the shrinking of the fishing season itself. The ongoing climate variability demonstrates the possibility of new fishing regimes switching which can impact on fishery structure in years to come.   Climate changes can become the reason for the changes in abundance, distribution and life cycle of krill affecting space-time variability of fishery strategy and performance. Therefore   to support developing the approaches for management of krill fishery the following factors seem to be urgent: availability of information sufficient to detect and understand regularities and trends in krill distribution in the Area 48; forecasting methods to predict possible fishery regime switching in the years to come.

This paper briefly outlines the key scientific issues that should be resolved for providing sufficient information with the aim of quantitative understanding regularities and trends of krill spatial distribution in the Area 48 being the important information for developing management strategy for krill stock. It is proposed to developing a long-term-strategy for conducting synoptic surveys focused to the tasks outlined here. Some requirements to the design of such synoptic surveys are shown.

Practical implementation of these proposals will contribute to attaining CCAMLR goals in krill stocks management.

We analyzed the 2010/11 krill observer data to confirm the outcome of recent revisions of krill observation scheme. This paper summarizes the status of observer deployment, sampling protocol and data collection in krill fisheries in 2010/11. All the 12 krill fishing vessels that operated in 2010/11 deployed scientific observers in accordance with the agreed systematic coverage. The observer allocation covered all the months and Subareas in which fishing operations were conducted. However, the coverage of krill biological sampling for hauls and the number of krill sampled for biological measurement were quite variable among vessels. Quantitative fish by-catch data were obtained only from five vessels since there was confusion on the sampling and recording protocol for fish by-catch. Given the current state of data collection, mere extension of the observer coverage may not result in the acquisition of necessary information. We should first clarify the objective of observer survey and specify the quantity and accuracy of data needed, and then revise the sampling protocol, priority of sampling, and minimal data requirement taking account of the cost and time budget of scientific observation to give clearer guidance for observers.

We observed whether Antarctic krill escaped from the trawl net or not using an underwater video camera attached on the trawl net of Japanese commercial trawler Fukuei-maru (4,350.62 GT) in 2011. To avoid the influence of camera lighting on krill behavior, the observation was conducted during daytime. Few krill appeared to escape from posterior part of trawl net. When the trawl net caught dense krill swarms, krill escaped from anterior part of trawl net. Krill were observed to swim actively after they escaped from the net, suggesting their escapement mortality may be low. In 2012, we are trying to develop a method using LED lighting system at our laboratory for the purpose of filed observation of krill escapement at night and in the deep depth, where sun light does not reach.

We examined spatio-temporal variability of krill body length and number of bycatch fish as variables of interest based on the scientific observer data for the 2010/2011 fishing season. Both krill length and number of bycatch fish were analyzed by using a hierarchical Bayesian model composed of multistage cluster units (i.e., month, sub-area, fishing gear, flag state, vessel, cruise, and haul) incorporated in a state-space model that separates biological process error from fishery process error and observation error. The parameters of the model were estimated by the Markov chain Monte Carlo (MCMC) method in WinBUGS with statistical software R. Although the posterior distribution adequately converged for the krill length model, some parameters did not converge well in the bycatch fish model. The interaction between month and sub-areahas large effects on krill length. Krill length varies among cruises, but there is no clear difference among cruises within a vessel. The uncertainty in parameter estimation is large in the sub-area effect and the interaction effect between month and sub-area. For the bycatch fish model, there is no obvious influence in biological process and fishery processes except for cruise effect on the number of bycatch fish. Some cruises on which fishing gear TMB was used showed large number of bycatch fish with large its variance, which suggests the necessity of reviewing the procedure of data collection with considering the difference of fishing gear.

Notification of Chile’s intent to conduct krill fishing in 2012/13

Notification of China’s intent to conduct krill fishing in 2012/13

Notification of Germany’s intent to conduct krill fishing in 2012/13