Bibliographic Details
| Title: |
Estimation of phytoplankton biomass using HPLC pigment analysis in the southwestern Black Sea |
| Authors: |
Ediger, D.1 dilek@ims.metu.edu.tr, Soydemir, N.2, Kideys, A.E.1 |
| Source: |
Deep-Sea Research Part II, Topical Studies in Oceanography. Aug2006, Vol. 53 Issue 17-19, p1911-1922. 12p. |
| Subjects: |
Phytoplankton, High performance liquid chromatography, Dinoflagellates, Biomass |
| Abstract: |
Abstract: The phytoplankton population of the southwestern Black Sea in May 2001 was studied by taxonomic analysis using microscopic examination and by pigment analyses using high-performance liquid chromatography (HPLC). Pigment data, which identified phytoplankton assemblages dominated by dinoflagellates, diatoms and coccolithophores in May 2001, were compared to phytoplankton cell counts and biomass. There were significant (p<0.002–0.01, r=0.56–0.67) relationships between the taxon-specific pigment concentrations and the taxon-specific cell numbers during this sampling period. The ratios of chlorophyll-a to the dominant accessory pigments calculated by multiple linear regressions were 1.2 (chlorophyll-a: peridinin) in dinoflagellates, 1.8 (chlorophyll-a: fucoxanthin) in diatoms, and 2.66 (chlorophyll-a: 19′-hexonoyloxyfucoxanthin) in coccolithophores. HPLC-determined chlorophyll-a biomass correlated well with the sum of the group-specific pigment biomass (p<0.001, r 2=0.95). The phytoplankton assemblage as revealed by the microscopic and HPLC analyses was thus made up of common Black Sea groups showing that HPLC pigment analysis can be used to quantify phytoplankton assemblages in the Black Sea based on simple ratios. [Copyright &y& Elsevier] |
|
Copyright of Deep-Sea Research Part II, Topical Studies in Oceanography is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) |
| Database: |
Engineering Source |