Abstract
Understanding the catch composition of multispecies fisheries is fundamental to effective spatial fishery management. In the Equatorial Western and Central Pacific Ocean (EWCPO), the main catches of the tuna purse-seine fishery include skipjack tuna (Katsuwonus pelamis), yellowfin tuna (Thunnus albacares), and bigeye tuna (Thunnus obesus). Studying the spatiotemporal distribution of the catch composition in the context of climate change contributes to the sustainable development of this fishery. Our study analyzed purse seine fishery data and environmental data from 1997 to 2019, using a random forest model to explore the changing mechanisms of catch composition under different El Niño-Southern Oscillation (ENSO) episodes with catch mean trophic level (CMTL) as the response variable. Emerging hot spot analysis was used to identify significant spatiotemporal hot (cold) spot areas. The results revealed two hot spot areas, namely the western hotspot area (WHA) and the eastern hotspot area (EHA), and two cold spot areas, namely the northern cold spot area (NCA) and the southern cold spot area (SCA). EHA spans the entire central Pacific east of 170°E among different ENSO episodes, expanding and contracting in tandem with the 28 °C isotherm. WHA is mainly influenced by surface organic matter and the Western Boundary Currents and remains among different ENSO episodes. NCA is formed by the westerly anomalies and positive wind stress curl anomalies and exists only under La Niña episodes. SCA persists within the unproductive South Equatorial Current (SEC) and remains stable among different ENSO episodes. Our study contributes to revealing the spatiotemporal dynamics in tuna catch composition and their relationships with environmental factors and interspecies competition, providing valuable insights for ecosystem-based dynamic fishery management.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Allen G R. 2008. Conservation hotspots of biodiversity and endemism for Indo-Pacific coral reef fishes. Aquatic Conservation: Marine and Freshwater Ecosystems, 18(5): 541–556, doi: https://doi.org/10.1002/aqc.880
Arrizabalaga H, Dufour F, Kell L, et al. 2015. Global habitat preferences of commercially valuable tuna. Deep Sea Research Part II: Topical Studies in Oceanography, 113: 102–112, doi: https://doi.org/10.1016/j.dsr2.2014.07.001
Artetxe-Arrate I, Fraile I, Marsac F, et al. 2021. Chapter One-A review of the fisheries, life history and stock structure of tropical tuna (skipjack Katsuwonus pelamis, yellowfin Thunnus albacares and bigeye Thunnus obesus) in the Indian Ocean. Advances in Marine Biology, 88: 39–89
Barrier N, Lengaigne M, Rault J, et al. 2023. Mechanisms underlying the epipelagic ecosystem response to ENSO in the equatorial Pacific ocean. Progress in Oceanography, 213: 103002, doi: https://doi.org/10.1016/j.pocean.2023.103002
Branch T A, Watson R, Fulton E A, et al. 2010. The trophic fingerprint of marine fisheries. Nature, 468(7322): 431–435, doi: https://doi.org/10.1038/nature09528
Chelton D B, Schlax M G, Samelson R M, et al. 2007. Global observations of large oceanic eddies. Geophysical Research Letters, 34(15): 2007GL030812, doi: https://doi.org/10.1029/2007GL030812
Chen Mingcheng, Li T. 2021. ENSO evolution asymmetry: EP versus CP El Nino. Climate Dynamics, 56(11–12): 3569–3579, doi: https://doi.org/10.1007/s00382-021-05654-7
Chen Shangfeng, Chen Wen, Wu Renguang, et al. 2025. Atlantic multidecadal variability controls Arctic-ENSO connection. npj Climate and Atmospheric Science, 8: 44, doi: https://doi.org/10.1038/s41612-025-00936-x
Chen Shangfeng, Chen Wen, Zhou Wen, et al. Interdecadal variation in the impact of Arctic sea ice on El Niño–southern oscillation: The role of atmospheric mean flow. Journal of Climate, 2024, 37(21): 5483–5506
Cheung W, Watson R, Morato T, et al. 2007. Intrinsic vulnerability in the global fish catch. Marine Ecology Progress Series, 333: 1–12, doi: https://doi.org/10.3354/meps333001
Chiu C C, Kuo T C, Chang K Y. 2022. Optimising the benefit-cost ratio of fishing grounds for a multi-species fishery in the waters of northern Taiwan. Fisheries Management and Ecology, 29(6): 858–879, doi: https://doi.org/10.1111/fme.12588
Cushing D H. 1971. Upwelling and the Production of Fish. Advances in Marine Biology, 9: 255–334
Deary A L, Moret-Ferguson S, Engels M, et al. 2015. Influence of Central Pacific Oceanographic Conditions on the Potential Vertical Habitat of Four Tropical Tuna Species. Pacific Science, 69(4): 461–475, doi: https://doi.org/10.2984/69.4.3
Díaz-Delgado E, Crespo-Neto O, Martínez-Rincón R O. 2021. Environmental preferences of sharks bycaught by the tuna purse-seine fishery in the Eastern Pacific Ocean. Fisheries Research, 243: 106076, doi: https://doi.org/10.1016/j.fishres.2021.106076
Díaz-Uriarte R, Alvarez De Andrés S. 2006. Gene selection and classification of microarray data using random forest. BMC Bioinformatics, 7(1): 3, doi: https://doi.org/10.1186/1471-2105-7-3
Downing J A, Plante C, Lalonde S. 1990. Fish Production Correlated with Primary Productivity, not the Morphoedaphic Index. Canadian Journal of Fisheries and Aquatic Sciences, 47(10): 1929–1936, doi: https://doi.org/10.1139/f90-217
Environmental Systems Research Institute (ESRI). 2021. Emerging hot spot analysis—Help ∣ ArcGIS for desktop. https://desktop.arcgis.com/en/arcmap/10.3/tools/space-time-pattern-mining-toolbox/emerginghotspots.htm. Accessed 09 June 2024
Eisner L, Hillgruber N, Martinson E, et al. 2013. Pelagic fish and zooplankton species assemblages in relation to water mass characteristics in the northern Bering and southeast Chukchi seas. Polar Biology, 36(1): 87–113, doi: https://doi.org/10.1007/s00300-012-1241-0
Feinberg R. 2010. Marine resource conservation and prospects for environmental sustainability in Anuta, Solomon Islands. Singapore Journal of Tropical Geography, 31(1): 41–54, doi: https://doi.org/10.1111/j.1467-9493.2010.00384.x
Fernandes J A, Cheung W W L, Jennings S, et al. 2013. Modelling the effects of climate change on the distribution and production of marine fishes: accounting for trophic interactions in a dynamic bioclimate envelope model. Global Change Biology, 19(8): 2596–2607, doi: https://doi.org/10.1111/gcb.12231
Fromentin J M, Fonteneau A. 2001. Fishing effects and life history traits: a case study comparing tropical versus temperate tunas. Fisheries Research, 53(2): 133–150, doi: https://doi.org/10.1016/S0165-7836(00)00299-X
Gatalsky P, Andrienko N, Andrienko G. 2004. Interactive analysis of event data using space-time cube. Eighth International Conference on Information Visualisation, 2004. IV 2004. 145–152
Getis A, Ord J K. 1992. The Analysis of Spatial Association by Use of Distance Statistics. Geographical Analysis, 24(3): 189–206, doi: https://doi.org/10.1111/j.1538-4632.1992.tb00261.x
Gökçe G, Saygu I, Eryaşar A. 2016. Catch composition of trawl fisheries in Mersin Bay with emphasis on catch biodiversity. Turkish Journal of Zoology, 40: 522–533, doi: https://doi.org/10.3906/zoo-1505-35
Graham J B, Dickson K A. 2004. Tuna comparative physiology. Journal of Experimental Biology, 207(23): 4015–4024, doi: https://doi.org/10.1242/jeb.01267
Grazia Pennino M, Thomé-Souza M J F, Carvalho A R, et al. 2016. A spatial multivariate approach to understand what controls species catch composition in small-scale fisheries. Fisheries Research, 175: 132–141, doi: https://doi.org/10.1016/j.fishres.2015.11.028
Griffiths S P, Allain V, Hoyle S D, et al. 2019. Just a FAD? Ecosystem impacts of tuna purse-seine fishing associated with fish aggregating devices in the western Pacific Warm Pool Province. Fisheries Oceanography, 28(1): 94–112, doi: https://doi.org/10.1111/fog.12389
Hamed K H, Ramachandra Rao A. 1998. A modified Mann-Kendall trend test for autocorrelated data. Journal of Hydrology, 204(1–4): 182–196, doi: https://doi.org/10.1016/S0022-1694(97)00125-X
Harris N L, Goldman E, Gabris C, et al. 2017. Using spatial statistics to identify emerging hot spots of forest loss. Environmental Research Letters, 12(2): 24012, doi: https://doi.org/10.1088/1748-9326/aa5a2f
Holland K N, Brill R W, Chang R K C, et al. 1992. Physiological and behavioural thermoregulation in bigeye tuna (Thunnus obesus). Nature, 358(6385): 410–412, doi: https://doi.org/10.1038/358410a0
Hollowed A B, Wilson C D, Stabeno P J, et al. 2007. Effect of ocean conditions on the cross-shelf distribution of walleye pollock (Theragra chalcogramma) and capelin (Mallotus villosus). Fisheries Oceanography, 16(2): 142–154, doi: https://doi.org/10.1111/j.1365-2419.2006.00418.x
Houssard P, Point D, Tremblay-Boyer L, et al. 2019. A Model of Mercury Distribution in Tuna from the Western and Central Pacific Ocean: Influence of Physiology, Ecology and Environmental Factors. Environmental Science & Technology, 53(3): 1422–1431
Hsu C C, Lee Y C, Lu P E, et al. 2017. Social Media Prediction Based on Residual Learning and Random Forest//Proceedings of the 25th ACM international conference on Multimedia. Mountain View California USA: ACM: 1865–1870
Hsu T Y, Chang Yi, Lee M A, et al. 2021. Predicting Skipjack Tuna Fishing Grounds in the Western and Central Pacific Ocean Based on High-Spatial-Temporal-Resolution Satellite Data. Remote Sensing, 13(5): 861, doi: https://doi.org/10.3390/rs13050861
Humphries A T, Gorospe K D, Carvalho P G, et al. 2019. Catch Composition and Selectivity of Fishing Gears in a Multi-Species Indonesian Coral Reef Fishery. Frontiers in Marine Science, 6: 378, doi: https://doi.org/10.3389/fmars.2019.00378
Ichii T, Mahapatra K, Watanabe T, et al. 2002. Occurrence of jumbo flying squid Dosidicus gigas aggregations associated with the countercurrent ridge off the Costa Rica Dome during 1997 El Niño and 1999 La Niña. Marine Ecology Progress Series, 231: 151–166, doi: https://doi.org/10.3354/meps231151
Iwao S. 1977. Analysis of spatial association between two species based on the interspecies mean crowding. Population Ecology, 18(2): 243–260, doi: https://doi.org/10.1007/BF02510851
Jiang Rijin, Yang Fan, Chen Feng, et al. 2022. Assessing trophic interactions among three tuna species in the Solomon Islands based on stomach contents and stable isotopes. Frontiers in Marine Science, 9: 961990, doi: https://doi.org/10.3389/fmars.2022.961990
Jo H S, Ham Y G. 2023. Enhanced joint impact of western hemispheric precursors increases extreme El Niño frequency under greenhouse warming. Nature Communications, 14: 6356, doi: https://doi.org/10.1038/s41467-023-42115-7
Jupiter S, McCarter J, Albert S, et al. 2019. Chapter 39-Solomon Islands: Coastal and Marine Ecosystems. World Seas: an Environmental Evaluation (Second Edition). Volume II: the Indian Ocean to the Pacific: 855–874
Kim J, Na H, Park Y G, et al. 2020. Potential predictability of skipjack tuna (Katsuwonus pelamis) catches in the Western Central Pacific. Scientific Reports, 10: 3193, doi: https://doi.org/10.1038/s41598-020-59947-8
Korsmeyer K E, Dewar H, Lai N C, et al. 1996. The Aerobic Capacity of Tunas: Adaptation for Multiple Metabolic Demands. Comparative Biochemistry and Physiology–Part A: Physiology, 1 Part A Physiology, 113(1): 17–24
Kuhn M. 2008. Building predictive models in R using the caret package. Journal of statistical software, 28: 1–26, doi: https://doi.org/10.18637/jss.v028.i05
Kveladze I, Kraak M J, Van Elzakker C P J M. 2015. The space-time cube as part of a GeoVisual analytics environment to support the understanding of movement data. International Journal of Geographical Information Science, 29(11): 2001–2016, doi: https://doi.org/10.1080/13658816.2015.1058386
Lan Kuowei, Shimada T, Lee M A, et al. 2017. Using Remote-Sensing Environmental and Fishery Data to Map Potential Yellowfin Tuna Habitats in the Tropical Pacific Ocean. Remote Sensing, 9(5): 444, doi: https://doi.org/10.3390/rs9050444
Landergren P, Vallin L. 1998. Spawning of sea trout, Salmo trutta L., in brackish waters—lost effort or successful strategy? Fisheries Research, 35(3): 229–236
Lehodey P. 2001. The pelagic ecosystem of the tropical Pacific Ocean: dynamic spatial modelling and biological consequences of ENSO. Progress in Oceanography, 49(1–4): 439–468, doi: https://doi.org/10.1016/S0079-6611(01)00035-0
Lehodey P, Senina I, Murtugudde R. 2008. A spatial ecosystem and populations dynamics model (SEAPODYM) – Modeling of tuna and tuna-like populations. Progress in Oceanography, 78(4): 304–318, doi: https://doi.org/10.1016/j.pocean.2008.06.004
Leung S, Thompson L, McPhaden M J, et al. 2019. ENSO drives near-surface oxygen and vertical habitat variability in the tropical Pacific. Environmental Research Letters, 14(6): 064020, doi: https://doi.org/10.1088/1748-9326/ab1c13
Liang Tingyu, Lan Kuowei, Naimullah M, et al. 2025. Abundance Variability of Predators: Asynchronous Fluctuation of Tuna Species in the Atlantic Ocean due to Predation Strategies and Climatic Effects. Fisheries Oceanography, 34(2): e12713, doi: https://doi.org/10.1111/fog.12713
Liaw A, Wiener M. 2002. Classification and regression by randomForest. R news, 2(3): 18–22
Lu H J, Lee K T, Lin H L, et al. 2001. Spatio-temporal distribution of yellowfin tuna Thunnus albacares and bigeye tuna Thunnus obesus in the Tropical Pacific Ocean in relation to large-scale temperature fluctuation during ENSO episodes. Fisheries Science, 67(6): 1046–1052, doi: https://doi.org/10.1046/j.1444-2906.2001.00360.x
Luan Jing, Zhang Chongliang, Xu Binduo, et al. 2020. The predictive performances of random forest models with limited sample size and different species traits. Fisheries Research, 227: 105534, doi: https://doi.org/10.1016/j.fishres.2020.105534
Luo Jiangang, Ortner P B, Forcucci D, et al. 2000. Diel vertical migration of zooplankton and mesopelagic fish in the Arabian Sea. Deep Sea Research Part II: Topical Studies in Oceanography, 47(7–8): 1451–1473, doi: https://doi.org/10.1016/S0967-0645(99)00150-2
Mainardi S. 2009. A surplus production model with latent truncation and species targeting, with an application to Papua New Guinean fisheries. Fisheries Research, 95(2–3): 296–308, doi: https://doi.org/10.1016/j.fishres.2008.09.037
Miller K A. 2007. Climate variability and tropical tuna: Management challenges for highly migratory fish stocks. Marine Policy, 31(1): 56–70, doi: https://doi.org/10.1016/j.marpol.2006.05.006
Moteki, M, Arai, M, Tsuchiya K, et al. 2001. Composition of piscine prey in the diet of large pelagic fish in the eastern tropical Pacific Ocean. Fisheries Science, 67(6): 1063–1074, doi: https://doi.org/10.1046/j.1444-2906.2001.00362.x
Mugo R, Saitoh S I, Igarashi H, et al. 2020. Identification of skipjack tuna (Katsuwonus pelamis) pelagic hotspots applying a satellite remote sensing-driven analysis of ecological niche factors: A short-term run. PLOS ONE, 15(8): e0237742, doi: https://doi.org/10.1371/journal.pone.0237742
Murphy J T. 2020. Climate change, interspecific competition, and poleward vs. depth distribution shifts: Spatial analyses of the eastern Bering Sea snow and Tanner crab (Chionoecetes opilio and C. bairdi). Fisheries Research, 223: 105417, doi: https://doi.org/10.1016/j.fishres.2019.105417
O’brien R M. 2007. A Caution Regarding Rules of Thumb for Variance Inflation Factors. Quality & Quantity, 41(5): 673–690
Ohno Y, Sato Y, Iwasaka N. 2004. The mixed layer depth in the North Pacific as detected by the Argo floats. Geophysical Research Letters, 31: L11306
Olson R J, Young J W, Ménard F, et al. 2016. Chapter Four-Bioenergetics, Trophic Ecology, and Niche Separation of Tunas. Advances in Marine Biology, 74: 199–344
Pauly D, Christensen V, Dalsgaard J, et al. 1998. Fishing Down Marine Food Webs. Science, 279(5352): 860–863, doi: https://doi.org/10.1126/science.279.5352.860
Pecoraro C, Zudaire I, Bodin N, et al. 2017. Putting all the pieces together: integrating current knowledge of the biology, ecology, fisheries status, stock structure and management of yellowfin tuna (Thunnus albacares). Reviews in Fish Biology and Fisheries, 27(4): 811–841, doi: https://doi.org/10.1007/s11160-016-9460-z
Pet-Soede C, van Densen W L T, Pet J S, et al. 2001. Impact of Indonesian coral reef fisheries on fish community structure and the resultant catch composition. Fisheries Research, 51(1): 35–51, doi: https://doi.org/10.1016/S0165-7836(00)00236-8
Pew Research Center. 2020. Global tuna Conservation. https://www.pew.org/en/projects/archived-projects/global-tuna-conservation. Accessed 09, June, 2024
Picaut J, Ioualalen M, Menkes C, et al. 1996. Mechanism of the Zonal Displacements of the Pacific Warm Pool: Implications for ENSO. Science, 274(5292): 1486–1489, doi: https://doi.org/10.1126/science.274.5292.1486
R Core Team. 2022. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna
Richardson A J. 2008. In hot water: zooplankton and climate change. ICES Journal of Marine Science, 65(3): 279–295, doi: https://doi.org/10.1093/icesjms/fsn028
Rodriguez-Galiano V, Sanchez-Castillo M, Chica-Olmo M, et al. 2015. Machine learning predictive models for mineral prospectivity: An evaluation of neural networks, random forest, regression trees and support vector machines. Ore Geology Reviews, 71: 804–818, doi: https://doi.org/10.1016/j.oregeorev.2015.01.001
Sibert J, Hampton J, Kleiber P, et al. 2006. Biomass, Size, and Trophic Status of Top Predators in the Pacific Ocean. Science, 314(5806): 1773–1776, doi: https://doi.org/10.1126/science.
Tan M K, Mustapha M A. 2023. Application of the random forest algorithm for mapping potential fishing zones of Rastrelliger kanagurta off the east coast of peninsular Malaysia. Regional Studies in Marine Science, 60: 102881, doi: https://doi.org/10.1016/j.rsma.2023.102881
Tranter D, Leech G, Airey D. 1983. Edge enrichment in an ocean eddy. Marine and Freshwater Research, 34(4): 665, doi: https://doi.org/10.1071/MF9830665
Vaihola S, Yemane D, Kininmonth S. 2023. Spatiotemporal Patterns in the Distribution of Albacore, Bigeye, Skipjack, and Yellowfin Tuna Species within the Exclusive Economic Zones of Tonga for the Years 2002 to 2018. Diversity, 15(10): 1091, doi: https://doi.org/10.3390/d15101091
Walker N D, Leben R R, Balasubramanian S. 2005. Hurricane-forced upwelling and chlorophyll a enhancement within cold-core cyclones in the Gulf of Mexico: Hurricane-Forced Upwelling in Cyclones. Geophysical Research Letters, 32(18): 2005GL023716, doi: https://doi.org/10.1029/2005GL023716
Wang Jintao, Chen Xinjun, Staples K W, et al. 2018. The skipjack tuna fishery in the west-central Pacific Ocean: applying neural networks to detect habitat preferences. Fisheries Science, 84(2): 309–321, doi: https://doi.org/10.1007/s12562-017-1161-6
Wang Lu, Li T. 2020. Effect of vertical moist static energy advection on MJO eastward propagation: sensitivity to analysis domain. Climate Dynamics, 54(3): 2029–2039
Wang Xiaoshuang, Gong Guanghong, Li Ni, et al. 2019. Detection Analysis of Epileptic EEG Using a Novel Random Forest Model Combined With Grid Search Optimization. Frontiers in Human Neuroscience, 13: 52, doi: https://doi.org/10.3389/fnhum.2019.00052
Wang Xuefeng, Xu Liuxiong, Chen Yong, et al. 2012. Impacts of fish aggregation devices on size structures of skipjack tuna Katsuwonus pelamis. Aquatic Ecology, 46(3): 343–352, doi: https://doi.org/10.1007/s10452-012-9405-0
Weber J M. 2009. The physiology of long-distance migration: extending the limits of endurance metabolism. Journal of Experimental Biology, 212(5): 593–597, doi: https://doi.org/10.1242/jeb.015024
Western and Central Pacific Fisheries Commission. 2022. Tuna fishery yearbook 2021. Oceanic Fisheries Programme Pacific Community Noumea, New Caledonia
Wu Guoli, Zhai Fangguo, Hu Dunxin. 2016. Interannual variations of North Equatorial Current transport in the Pacific Ocean during two types of El Niño. Chinese Journal of Oceanology and Limnology, 34(3): 585–596, doi: https://doi.org/10.1007/s00343-015-4352-y
Wu Yanlun, Lan Kuowei, Evans K, et al. 2022. Effects of decadal climate variability on spatiotemporal distribution of Indo-Pacific yellowfin tuna population. Scientific Reports, 12: 13715, doi: https://doi.org/10.1038/s41598-022-17882-w
Xu Haikun, Lennert-Cody C E, Maunder M N, et al. 2019. Spatiotemporal dynamics of the dolphin-associated purseseine fishery for yellowfin tuna (Thunnus albacares) in the eastern Pacific Ocean. Fisheries Research, 213: 121–131, doi: https://doi.org/10.1016/j.fishres.2019.01.013
Yen K W, Wang Guihua, Lu H J. 2017. Evaluating habitat suitability and relative abundance of skipjack (Katsuwonus pelamis) in the Western and Central Pacific during various El Nino events. Ocean & Coastal Management, 139: 153–160
Yletyinen J, Bodin O, Weigel B, et al. 2016. Regime shifts in marine communities: a complex systems perspective on food web dynamics. Proceedings of the Royal Society B-Biological Sciences, 283(1825): 20152569, doi: https://doi.org/10.1098/rspb.2015.2569
Zhi Xiefei, Yang Hua, Xu Shuwen, et al. 2019. A Comparative Analysis of Atmospheric and Oceanic Conditions Before the Occurrence of Two Types of El Nno Events. Journal of Tropical Meteorology, 25(1): 34–44
Zhu Guoping, Dai Xiaojie, Xu Liuxiong, et al. 2010. Reproductive biology of Bigeye Tuna, Thunnus obesus, (Scombridae) in the eastern and central tropical Pacific Ocean. Environmental Biology of Fishes, 88(3): 253–260, doi: https://doi.org/10.1007/s10641-010-9636-7
Author information
Authors and Affiliations
Corresponding author
Additional information
Foundation item: The National Key Research and Development Program of China under contract No. 2023YFD2401303.
Electronic Supplementary Material
Rights and permissions
About this article
Cite this article
Jiang, M., Wang, J. & Chen, X. Climate change induced environmental variability affects the tuna catch composition: a perspective of catch mean trophic level. Acta Oceanol. Sin. 44, 76–87 (2025). https://doi.org/10.1007/s13131-025-2488-y
Received:
Accepted:
Published:
Issue date:
DOI: https://doi.org/10.1007/s13131-025-2488-y