Marine Biology

, Volume 157, Issue 8, pp 1889–1895 | Cite as

The use of polymerase chain reaction for the identification of sciaenid eggs

  • Lucia B. Carreon-Martinez
  • Scott A. Holt
  • B. Scott Nunez
  • Cynthia K. Faulk
  • G. Joan Holt


Traditionally, sciaenid eggs have been identified based on morphological characteristics such as size, number of oil globules and/or pigmentation patterns. Identification of sciaenid eggs by these procedures is time consuming and often inaccurate due to considerable egg size overlap among species. The utilization of molecular techniques for the identification of economically important species has become a fundamental component in ecological studies involving fish eggs and larvae. This study reports the development of a series of both multiplex and individual polymerase chain reactions to identify the eggs of 11 sciaenid species commonly found in the Gulf of Mexico and estuaries near Port Aransas and Corpus Christi, TX, USA. Following method development, the discriminatory power of the assay was first determined with samples from adult fish collected from Aransas and Corpus Christi Bays, Galveston Bay and the lower Laguna Madre in northern Mexico. Most (97%) of these fishes were correctly identified to the level of species. To demonstrate the applicability of the assay, wild fish eggs were collected and analyzed from the Aransas Pass tidal inlet from September through December 2005. During this period, the eggs of four target species were positively identified which was in keeping with current knowledge regarding the spawning areas and seasons of these sciaenids based on the presence of mature females, eggs and/or larvae. Future use of this method can provide valuable information to better discriminate spawning sites and seasons of these species.


Multiplex Polymerase Chain Reaction Tidal Inlet Atlantic Croaker Spotted Seatrout Multiplex PCRs 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



We thank the Perry R. Bass Chair (to GJH) and the Sid W. Richardson Foundation for funding. Special thanks to Jason Williams, Cameron Pratt and John Williams (UTMSI) for their technical assistance. We would like to acknowledge Dr. John Hyde, Dr. Russ Vetter and Dr. Roberto Montes de Oca Luna for their opportune advice. The University of Texas at Austin (UT) Institutional Animal Care and Use Committee (IACUC) has approved the described use of animals throughout this study under UT IACUC Protocol # 07043001. This is contribution number 1541 of the University of Texas at Austin Marine Science Institute.


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Copyright information

© Springer-Verlag 2010

Authors and Affiliations

  • Lucia B. Carreon-Martinez
    • 1
  • Scott A. Holt
    • 2
  • B. Scott Nunez
    • 3
  • Cynthia K. Faulk
    • 2
  • G. Joan Holt
    • 2
  1. 1.Great Lakes Institute for Environmental ResearchUniversity of WindsorWindsorCanada
  2. 2.The University of Texas at Austin Marine Science InstitutePort AransasUSA
  3. 3.Lee CollegeBaytownUSA

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