Patterns of variations in large pelagic fish: A comparative approach between the Indian and the Atlantic Oceans
|Author(s)||Corbineau A.1, Rouyer Tristan2, 3, Fromentin Jean-Marc2, Cazelles B.4, 5, Fonteneau Alain1, Menard Frederic1|
|Affiliation(s)||1 : Ctr Rech Halieut Mediterraneen & Trop, IRD, UMR EME 212, F-34203 Sete, France.
2 : Ctr Rech Halieut Mediterraneen & Trop, IFREMER, UMR EME 212, F-34203 Sete, France.
3 : Univ Oslo, Dept Biol, Ctr Ecol & Evolutionary Synth, N-0316 Oslo, Norway.
4 : ENS, CNRS, UMR 7625, F-75230 Paris 05, France.
5 : IRD, UR GEODES 079, F-93142 Bondy, France.
|Meeting||International Symposium on Climate Impacts on Oceanic Top Predators (CLIOTOP), La Paz, MEXICO, DEC 03-07, 2007|
|Source||Progress In Oceanography (0079-6611) (Pergamon-elsevier Science Ltd), 2010-07 , Vol. 86 , N. 1-2 , P. 276-282|
|WOS© Times Cited||5|
|Abstract||Catch data of large pelagic fish such as tuna, swordfish and billfish are highly variable ranging from short to long term. Based on fisheries data, these time series are noisy and reflect mixed information on exploitation (targeting, strategy, fishing power), population dynamics (recruitment, growth, mortality, migration, etc.), and environmental forcing (local conditions or dominant climate patterns). In this work, we investigated patterns of variation of large pelagic fish (i.e. yellowfin tuna, bigeye tuna, swordfish and blue marlin) in Japanese longliners catch data from 1960 to 2004. We performed wavelet analyses on the yearly time series of each fish species in each biogeographic province of the tropical Indian and Atlantic Oceans. In addition, we carried out cross-wavelet analyses between these biological time series and a large-scale climatic index, i.e. the Southern Oscillation Index (Sol). Results showed that the biogeographic province was the most important factor structuring the patterns of variability of Japanese catch time series. Relationships between the SOI and the fish catches in the Indian and Atlantic Oceans also pointed out the role of climatic variability for structuring patterns of variation of catch time series. This work finally confirmed that Japanese longline CPUE data poorly reflect the underlying population dynamics of tunas. (C) 2010 Elsevier Ltd. All rights reserved.|