Accurate classification of juvenile weakfish Cynoscion regalis to estuarine nursery areas based on chemical signatures in otoliths

Sciaenidae Chinook wind
DOI: 10.3354/meps173253 Publication Date: 2007-09-05T04:52:20Z
ABSTRACT
MEPS Marine Ecology Progress Series Contact the journal Facebook Twitter RSS Mailing List Subscribe to our mailing list via Mailchimp HomeLatest VolumeAbout JournalEditorsTheme Sections 173:253-265 (1998) - doi:10.3354/meps173253 Accurate classification of juvenile weakfish Cynoscion regalis estuarine nursery areas based on chemical signatures in otoliths Simon R. Thorrold1,*, Cynthia M. Jones1, Peter K. Swart2, Timothy E. Targett3 1Applied Research Laboratory, Old Dominion University, Norfolk, Virginia 23529, USA 2Division Geology and Geophysics, Rosenstiel School Atmospheric Science, University Miami, 4600 Rickenbacker Causeway, Florida 33149, 3Graduate College Studies, Delaware, Lewes, Delaware 19958, *E-mail: sthorrol@odu.edu ABSTRACT: We investigated ability trace element isotopic record (young-of-the-year) from east coast USA. Juvenile C. were captured with otter trawls at multiple sites Doboy Sound (Georgia), Pamlico (North Carolina), Chesapeake Bay (Virginia), (Delaware) Peconic (New York), July September 1996. One sagittal otolith each specimen was assayed for Mg/Ca, Mn/Ca, Sr/Ca Ba/Ca ratios using inductively coupled plasma mass spectrometry (ICP-MS), while δ13C δ18O values other pair determined isotope ratio (IR-MS). A multivariate analysis variance that there significant differences among locations. Bootstrapped 95% confidence ellipses canonical variates indicated all 5 locations significantly isolated discriminant space. On basis these differences, linear function (LDFA) artificial neural network (ANN) models used classify individual fish their natal estuary an overall error rate 37% LDFA 29.6% ANN. Addition ANN derived data resulted around 10%. will, therefore, be able use portion adult accurately estuary. KEY WORDS: Estuarine · Otolith chemistry Trace elements Stable isotopes Neural networks Full text pdf format PreviousNextExport citation Tweet linkedIn Cited by Published Vol. 173. Publication date: November 12, 1998 Print ISSN:0171-8630; Online ISSN:1616-1599 Copyright © Inter-Research.
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