DOI QR코드

DOI QR Code

한국 연근해 고등어(Scomber japonicus) 자원의 시공간과 환경요인을 고려한 CPUE 표준화

CPUE Standardization Considering Spatio-temporal and Environmental Variables of Chub Mackerel Scomber japonicus in Korean Waters

  • 김무진 (국립수산과학원 수산자원연구부 연근해자원과) ;
  • 윤석진 (국립수산과학원 수산자원연구부 연근해자원과) ;
  • 지환성 (국립수산과학원 수산자원연구부 연근해자원과) ;
  • 방민경 (한국해양과학기술원 해양순환기후연구부) ;
  • 김창신 (국립수산과학원 기후환경연구부 기후변화연구과) ;
  • 강희중 (국립수산과학원 수산자원연구부 연근해자원과)
  • Moo-Jin Kim (Coastal Water Fisheries Resources Research Division, National Institute of Fisheries Science) ;
  • Seokjin Yoon (Coastal Water Fisheries Resources Research Division, National Institute of Fisheries Science) ;
  • Hwan-Sung Ji (Coastal Water Fisheries Resources Research Division, National Institute of Fisheries Science) ;
  • Minkyoung Bang (Ocean Circulation and Climate Research Department, Korea Institute of Ocean Science and Technology) ;
  • Chang Sin Kim (Ocean Climate & Ecology Research Division, National Institute of Fisheries Science) ;
  • Heejoong Kang (Coastal Water Fisheries Resources Research Division, National Institute of Fisheries Science)
  • 투고 : 2024.09.04
  • 심사 : 2024.10.15
  • 발행 : 2024.10.31

초록

The chub mackerel Scomber japonicus is the most important commercial species caught primarily by large purse seine fisheries. The effective management of chub mackerel resources requires a thorough understanding of the current stock status and the factors driving its fluctuations. The catch per unit effort (CPUE) is a crucial index representing the relative abundance of resources, and CPUE standardization was applied using a generalized linear model and generalized linear mixed model (GLMM). This study adopted various explanatory variables including spatiotemporal factors of Year, Month and Area (spatial clustering), and environmental factors of seawater temperature at a depth 50 m ((T50) and Tsushima Warm Current transport (TWC) and catch ratio of chub mackerel (Ratio). The GLMM, which incorporates random effects, was identified as the optimal model. Ratio had the most significant effect on the CPUE, and environmental and spatio-temporal factors had significant influences. Although the nominal CPUE showed an increasing trend across different areas, the standardized CPUE either decreased or exhibited a decreasing rate of increase. These findings serve as fundamental data for stock assessment and contribute to the spatiotemporal and environmentally informed management of chub mackerel resources in Korean waters.

키워드

과제정보

본 연구는 2024년도 국립수산과학원 수산과학연구사업(R2024006)으로 수행되었습니다.

참고문헌

  1. Bishop J, Venables WN and Wang YG. 2004. Analysing commercial catch and effort data from a penaeid trawl fishery: A comparison of linear models, mixed models, and generalised estimating equations approaches. Fish Res 70, 179-193. https://doi.org/10.1016/j.fishres.2004.08.003.
  2. Chiarini M, Guicciardi S, Angelini S, Tuck ID, Grilli F, Penna P, Penna P, Domenichetti F, Canduci G, Belardinelli A, Santojanni A, Arneri E, Isajlovic I, Vrgoc N and Martinelli M. 2022. Accounting for environmental and fishery management factors when standardizing CPUE data from a scientific survey: A case study for Nephrops norvegicus in the Pomo Pits area (Central Adriatic Sea). PLoS One 17, e0270703. https://doi.org/10.1371/journal.pone.0270703.
  3. Conn PB, Thorson JT and Johnson DS. 2017. Confronting preferential sampling when analysing population distributions: Diagnosis and model-based triage. Methods Ecol Evol 8, 1535-1546. https://doi.org/10.1111/2041-210X.12803.
  4. Cosgrove R, Sheridan M, Minto C and Officer R. 2014. Application of finite mixture models to catch rate standardization better represents data distribution and fleet behavior. Fish Res 153, 83-88. https://doi.org/10.1016/j.fishres.2014.01.005.
  5. Ducharme-Barth ND, Gruss A, Vincent MT, Kiyofuji H, Aoki Y, Pilling G, Hampton J and Thorson JT. 2022. Impacts of fisheries-dependent spatial sampling patterns on catch-per-unit-effort standardization: A simulation study and fishery application. Fish Res 246, 106169. https://doi.org/10.1016/j.fishres.2021.106169.
  6. Hair JF, Black WC, Babin RJ and Anderson RE. 2018. Multivariate Data Analysis, 8th Editon. Cengage Learning EMEA, Andover, England.
  7. HYCOM (Hybrid Coordinate Ocean Model). 2024a. GOFS 3.1: 41-layer HYCOM + NCODA Global 1/12° Reanalysis. Retrieved from https://www.hycom.org/dataserver/gofs-3pt1/reanalysis on Jul 20, 2024.
  8. HYCOM (Hybrid Coordinate Ocean Model). 2024b. GOFS 3.1: 41-layer HYCOM + NCODA Global 1/12° Analysis. Retrieved from https://www.hycom.org/dataserver/gofs3pt1/analysis on Jul 20, 2024.
  9. Hilborn R and Walters CJ. 1992. Choice, dynamics and uncertainty. In: Quantitative Fisheries Stock Assessment. Springer New York, NY, U.S.A. https://doi.org/10.1007/978-1-4615-3598-0.
  10. Hinton MG and Maunder MN. 2004. Methods for standardizing CPUE and how to select among them. Col Vol Sci Pap ICCAT 56, 169-177. https://api.semanticscholar.org/CorpusID:11000905. 1000905
  11. Hoyle SD, Kim DN, Lee SI, Matsumoto T, Satoh K and Yeh YM. 2016. Collaborative study of tropical tuna CPUE from multiple Indian Ocean longline fleets in 2016. In: IO Tropical Tuna Joint CPUE 2016. http://doi.org/10.13140/RG.2.2.22918.16962.
  12. Hsu J, Chang YJ and Ducharme-Barth ND. 2022. Evaluation of the influence of spatial treatments on catch-per-unit-effort standardization: A fishery application and simulation study of Pacific saury in the Northwestern Pacific Ocean. Fish Res 255, 106440. https://doi.org/10.1016/j.fishres.2022.106440.
  13. Ichinokawa M and Brodziak J. 2010. Using adaptive area stratification to standardize catch rates with application to North Pacific swordfish (Xiphias gladius). Fish Res 106, 249-260. https://doi.org/10.1016/j.fishres.2010.08.001.
  14. Kim SR, Kim JJ, Stockhausen WT, Kim CS, Kang S, Cha HK, Ji HS, Jang SH and Baek HJ. 2019. Characteristics of the eggs and larval distribution and transport process in the early life stage of the chub mackerel Scomber japonicus near Korean waters. Korean J Fish Aquat Sci 52, 666-684. https://doi.org/10.5657/KFAS.2019.0666.
  15. KOSIS (Korean Statistical Information Service). 2024. Fishery Production Survey. Retrieved from https://kosis.kr/ on Jun 1, 2024.
  16. KLIC (Korea Law Information Center). 2024. Enforcement Decree of the Fishery Resources Management Act. Retrieved from https://www.law.go.kr/LSW/main.html on Jul 28, 2024.
  17. Kwon YJ, An DH, Lee JB, Zhang CI and Moon DY. 2008. Standardization of CPUE for bigeye (Thunnus obesus) and yellowfin (Thunnus albacares) tunas by the Korean longline fishery in the Indian Ocean. J Korean Soc Fish Ocean Technol 44, 194-206. https://doi.org/10.3796/KSFT.2008.44.3.194.
  18. Lee CH. 2018. The characteristics of fluctuation on fishing condition of chub mackerel (Scomber japonicus) in 2008-2016. M. S. Thesis, Pukyong National University, Busan, Korea.
  19. Lee SJ, Kim JB and Han SH. 2016. Distribution of mackerel, Scomber japonicus eggs and larvae in the coast of Jeju Island, Korea in spring. J Korean Soc Fish Ocean Technol 52, 121-129. https://doi.org/10.3796/KSFT.2016.52.2.121.
  20. Lee SI, Kim DN, Lee MK, Jo HJ, Ku JE and Kim JJ. 2018. CPUE standardization of Pacific bluefin tuna caught by Korean offshore large purse seine fishery (2003-2016). J Korean Soc Fish Ocean Technol 54, 116-123. https://doi.org/10.3796/KSFOT.2018.54.2.116.
  21. Maunder MN and Punt AE. 2004. Standardizing catch and effort data: A review of recent approaches. Fish Res 70, 141-159. https://doi.org/10.1016/j.fishres.2004.08.002.
  22. MOF (Ministry of Oceans and Fisheries). 2022. Marine and Fisheries Statistics System - Statistical News: 2021 Fisheries Production in Korea Increased by 1.2% Compared to the Previous Year. Retrieved from https://www.mof.go.kr/statPortal/main/portalMain.do on Sep 2, 2024.
  23. NIFS (National Institute of Fisheries Science). 2024. Forecastnews.Korea Strait Transport Volume. Retrieved from https://www.nifs.go.kr/main.do on Jun 1, 2024.
  24. Ono K, Punt AE and Hilborn R. 2015. Think outside the grids: An objective approach to define spatial strata for catch and effort analysis. Fish Res 170, 89-101. https://doi.org/10.1016/j.fishres.2015.05.021.
  25. Owiredu SA, Onyango SO, Song EA, Kim KI, Kim BY and Lee KH. 2024. Enhancing chub mackerel catch per unit effort (CPUE) standardization through high-resolution analysis of Korean large purse seine catch and effort using AIS data. Sustainability 16, 1307. https://doi.org/10.3390/su16031307.
  26. Riley RD, Snell KI, Ensor J, Burke DL, Harrell Jr FE, Moons KG and Collins GS. 2019. Minimum sample size for developing a multivariable prediction model: Part I-Continuous outcomes. Stat Med 38, 1262-1275. https://doi.org/10.1002/sim.7993.
  27. Rousseeuw PJ. 1987. Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. J Comput Appl Math 20, 53-65. https://doi.org/10.1016/0377-0427(87)90125-7.
  28. Sassa C and Tsukamoto Y. 2010. Distribution and growth of Scomber japonicus and S. australasicus larvae in the southern East China Sea in response to oceanographic conditions. Mar Ecol Prog Ser 419, 185-199. https://doi.org/10.3354/meps08832.
  29. Shelton AO, Thorson JT, Ward EJ and Feist BE. 2014. Spatial semiparametric models improve estimates of species abundance and distribution. Can J Fish Aquat Sci 71, 1655-1666. https://doi.org/10.1139/cjfas-2013-0508.
  30. Shi Y, Zhang X, Yang S, Dai Y, Cui X, Wu Y,Zhang S, Fan W, Han H, Zhang H and Tang F. 2023. Construction of CPUE standardization model and its simulation testing for chub mackerel (Scomber japonicus) in the Northwest Pacific Ocean. Ecol Indic 155, 111022. https://doi.org/10.1016/j.ecolind.2023.111022.
  31. Shin A, Yoon SC, Lee SI, Park HW and Kim S. 2018. The relationship between fishing characteristics of Pacific bluefin tuna (Thunnus orientalis) and ocean conditions around Jeju Island. Fish Aquat Sci 21, 1-12. https://doi.org/10.1186/s41240-017-0078-4.
  32. Shin HR, Lee JH, Kim CH, Yoon JH, Hirose N, Takikawa T and Cho K. 2022. Long-term variation in volume transport of the Tsushima warm current estimated from ADCP current measurement and sea level differences in the Korea/Tsushima Strait. J Mar Syst 232, 103750. https://doi.org/10.1016/j.jmarsys.2022.103750.
  33. Sugimoto T and Tameishi H. 1992. Warm-core rings, streamers and their role on the fishing ground formation around Japan. Deep Sea Res A Oceanogr Res Pap 39, S183-S201. https://doi.org/10.1016/S0198-0149(11)80011-7.
  34. Thorson JT, Shelton AO, Ward EJ and Skaug HJ. 2015. Geostatistical delta-generalized linear mixed models improve precision for estimated abundance indices for West Coast groundfishes. ICES J Mar Sci 72, 1297-1310. https://doi.org/10.1093/icesjms/fsu243.
  35. Tu CY, Tian Y and Hsieh CH. 2015. Effects of climate on temporal variation in the abundance and distribution of the demersal fish assemblage in the Tsushima Warm Current region of the Japan Sea. Fish Oceanogr 24, 177-189. https://doi.org/10.1111/fog.12101.
  36. Yoo JT, Hwang SJ, An DH, Kim JB and Kim ZG. 2010. Standardization of catch per unit effort (CPUE) for bigeye tuna (Thunnus obesus) by the Korean longline fishery in the Pacific ocean. Korean J Fish Aquat Sci 43, 740-746. https://doi.org/10.5657/kfas.2010.43.6.740.