Prospects for the pink salmon fishery in the Russian waters of the Bering and Okhotsk Seas in 2023 based on analysis of trawl surveys of the juveniles aboard RV TINRO and RV Professor Kaganovsky in autumn 2022

Modern technique of counting pink salmon juveniles in trawl surveys by two research vessels simultaneously makes it possible to cover the surveyed area in a short time that excludes repeated counts of actively migrating fish at adjacent transects. Such survey in the fall of 2022 was conducted jointly by RV TINRO and RV Professor Kaganovsky. The main aggregations of pink salmon in the offshore areas of the Bering and Okhotsk Seas were accounted that guaranteed reliability of the primary materials for prediction of the pink salmon return and catches in 2023. Two regional groups of pink salmon were separated in the mixed aggregation of juveniles in the Okhotsk Sea using the method of EM-clustering; the ratio of «northern» and «southern» complexes of their local stocks was determined as 55 : 45 %. The total number of pink salmon was assessed as 0.94 ∙ 10⁹ ind. in the Bering Sea and 2.70 ∙ 10⁹ ind. in the Okhotsk Sea. Expected return of pink salmon in 2023 was preliminary estimated as 200 ∙ 10⁶ ind. for the Bering Sea and at least 270 ∙ 10⁶ ind. for the Okhotsk Sea.

1. Лаженцев А.Е., Мазникова О.А. Сеголетки горбуши и кеты южной части Охотского моря в позднеморской период (август-октябрь 2012 г.). Распределение, питание, закономерности роста // Изв. ТИНРО. — 2014. — Т. 176. — С. 51–61. DOI: 10.26428/1606-9919-2014-176-51-61.

2. Нектон западной части Берингова моря. Таблицы численности, биомассы и соотношения видов / под ред. В.П. Шунтова и Л.Н. Бочарова. — Владивосток : ТИНРО-центр, 2006. — 416 с.

3. Шевляков Е.А., Сомов А.А., Шевляков В.А. и др. Промысел горбуши в Дальневосточном рыбопромысловом бассейне в 2022 г.: предварительные исследования, прогноз, интерпретация итогов путины // Наст. бюллетень.

4. Шевляков Е.А., Фельдман, М.Г., Шевляков, В.А., Канзепарова, А.Н. К методике разделения мигрирующих популяционных комплексов охотоморской горбуши в прикурильских водах Тихого океана с использованием гонадо-соматического индекса // Изв. ТИНРО. — 2020. — Т. 200, вып. 1. — С. 24–37. DOI: 10.26428/1606-9919-2020-200-24-37.

5. Scrucca L., Fop M., Murphy T.B. and Raftery A.E. mclust 5: clustering, classification and density estimation using Gaussian finite mixture models // R J. — 2016. — Vol. 8, № 1. — P. 289–317.