Otolith Shape Analyses and Dimensions of the Anchovy Engraulis encrasicolus L. in the Black and Marmara Seas


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Zengin M., SAYGIN S., Polat N.

SAINS MALAYSIANA, vol.44, no.5, pp.657-662, 2015 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 44 Issue: 5
  • Publication Date: 2015
  • Doi Number: 10.17576/jsm-2015-4405-03
  • Journal Name: SAINS MALAYSIANA
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.657-662
  • Keywords: Black Sea, Engraulis encrasicolus, Marmara Sea, otolith dimensions, shape analysis, SALMON SALMO-SALAR, STOCK IDENTIFICATION, SAGITTAL OTOLITH, EUROPEAN ANCHOVY, COMPARATIVE MORPHOLOGY, LENGTH RELATIONSHIPS, FISH POPULATIONS, BODY LENGTH, ATLANTIC, SIZE
  • Ondokuz Mayıs University Affiliated: Yes

Abstract

Otoliths, which can be used for the evaluation of relationships between the environment and organisms, are structures consisting of calcium carbonate. The aim of this study was to realize the shape analysis. In addition, it is to detect the characteristics of otolith biometrics in order to determine the relationship between the fish size of Engraulis encrasicolus L. from the Black and Marmara Seas. The samples were obtained from the Black and Marmara Seas between December 2013 and February 2014. The relationships between the TL (Total length) and OL (Otolith length), TL and OB (Otolith breadth), and TL and OW (Otolith weight) were determined using the linear regression equation. Form factor, roundness, circularity and rectangularity were used for shape analyses. According to the data, there was no difference between localities (p>0.05). Moreover, there was no difference between the left and right otoliths of the individuals sampled from the same locality (p>0.05). According to the regression coefficient for relationships of TL-OL, TL-OB and TL-OW, otolith length was identified as the best index for estimating fish length (r(2)>0.70). It showed that index values were statistically different between two populations (p<0.001).