Characterizing variation in Northwest Atlantic fish-stock abundance

Abstract Rothschild, B. J., and Jiao, Y. 2012. Characterizing variation in Northwest Atlantic fish-stock abundance. – ICES Journal of Marine Science, 69: 913–922. Catch-per-tow indices obtained by research vessels for the years 1963–2009 from NAFO statistical areas 4W, 4X, 5Y, and 5Z were studied to...

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Bibliographic Details
Published in:ICES Journal of Marine Science
Main Authors: Rothschild, Brian J., Jiao, Yue
Format: Article in Journal/Newspaper
Language:English
Published: Oxford University Press (OUP) 2012
Subjects:
Online Access:http://dx.doi.org/10.1093/icesjms/fsr196
http://academic.oup.com/icesjms/article-pdf/69/5/913/29144116/fsr196.pdf
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Summary:Abstract Rothschild, B. J., and Jiao, Y. 2012. Characterizing variation in Northwest Atlantic fish-stock abundance. – ICES Journal of Marine Science, 69: 913–922. Catch-per-tow indices obtained by research vessels for the years 1963–2009 from NAFO statistical areas 4W, 4X, 5Y, and 5Z were studied to determine how fish “apparent abundance” in the decade 2000–2009 differed from the long-term time-series. Cluster analysis of normalized catch-per-tow data indicated that the abundance and species composition of stocks in each statistical area changed dramatically over the 50-year period. There were decreases in thorny skate, ocean pout, cusk, witch flounder, and monkfish and increases in herring, haddock, northern shrimp, and spiny dogfish. Cluster analysis suggested that these decreases and increases were not gradual, but abrupt, and that these abrupt decreases and increases were concentrated in the decade of the 1980s. Observations of abrupt change were supported by regression-tree analysis of individual stocks. Examination of the interrelationship among abundance indices from different stocks by Bonferroni-adjusted correlation coefficients showed that the abundance trajectories of most stocks were uncorrelated. It appears that the set of population transitions during the decade of the 1980s was a dominant event in the statistical time-series.