WANG Zhihua, YANG Xiaoming, TIAN Siquan. Spatial pattern characteristics of albacore tuna resources at different spatial scales in South Pacific[J]. South China Fisheries Science, 2023, 19(2): 31-41. DOI: 10.12131/20220046
Citation: WANG Zhihua, YANG Xiaoming, TIAN Siquan. Spatial pattern characteristics of albacore tuna resources at different spatial scales in South Pacific[J]. South China Fisheries Science, 2023, 19(2): 31-41. DOI: 10.12131/20220046

Spatial pattern characteristics of albacore tuna resources at different spatial scales in South Pacific

  • Albacore tuna (Thunnus alalunga) is the main target species of longline fishery, accounting for 1/3 of the annual catch of tuna. Since the fishery resources of T. alalunga are rich in South Pacific, to explore the spatial pattern characteristics of its fishery resources in that sea area, and to analyze whether there are differences in the spatial scale are important for the rational utilization and protection of its fishery resources in South Pacific. We aggregated the longline fishing logs of the Chinese mainland from 2015 to 2019 at three spatial scales (1°, 2° and 5°) by season, and calculated their spatial pattern characteristics based on the 5° grid data from Western and Central Pacific Fisheries Commission (WCPFC). The results show that: 1) The spatial pattern of T. alalunga resources in the South Pacific was characterized by great ellipticity, and there was a significant eastwest distribution in the direction of the spatial pattern at all scales. The spatial pattern characteristics of resources (Distribution range, flatness, azimuth, etc.) had similar and obvious seasonal fluctuation characteristics. 2) The spatial pattern of resources obtained from three different scales of the same origin had a small difference, while that from different sources had a great difference. 3) For the same scale of 5° grid data, the resource center of the spatial pattern in China was more southeast and about 10° east in longitude than that of the data from the WCPFC. 4) Data aggregated at larger scales reflect stronger spatial aggregation characteristics of resources. Besides, the hot spots of spatial pattern obtained from different scale data of same origin were different to some extent.
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