CCAMLR

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

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

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

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

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