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Effects of iron on growth and reflectance spectrum of the bloom‐forming cyanobacterium M icrocystis viridis
Author(s) -
Chi Guangyu,
Huang Bin,
Ma Jian,
Shi Yi,
Chen Xin
Publication year - 2015
Publication title -
phycological research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.438
H-Index - 44
eISSN - 1440-1835
pISSN - 1322-0829
DOI - 10.1111/pre.12100
Subject(s) - phycocyanin , pigment , biology , cyanobacteria , carotenoid , chlorophyll , photosynthetic pigment , chlorophyll a , photosynthesis , botany , reflectivity , algal bloom , microcystis , algae , phytoplankton , ecology , chemistry , bacteria , genetics , physics , organic chemistry , nutrient , optics
Summary Iron is an important factor in algal blooms because it is involved in cyanobacterial pigment biosynthesis and therefore has the ability to influence the pigment status of algal cells. This role in pigment biosynthesis offers the opportunity for rapid monitoring of iron availability to cyanobacteria through spectral reflectance characterization. In the present study, the freshwater cyanobacterium M icrocystis viridis was cultured with different levels of iron. Cell density, cellular content of iron and photosynthetic pigments, and spectral reflectivity of M . viridis were determined daily during the course of the culture experiment. The results showed that at the lowest iron concentration (0.01 μM) the growth of M . viridis was seriously limited, and the maximal cell density was only approximately 6.4% of the density observed with an iron concentration of 18 μM. Iron availability dramatically affected chlorophyll a , carotenoid and phycocyanin content, with the greatest impact on chlorophyll a . The iron‐induced changes in content and ratios of pigments were detectable through spectral reflectance. Eleven spectral indices previously developed for the estimation of concentrations and/or ratios of pigments and a newly proposed chlorophyll a /phycocyanin index were found to be suitable for generating sensitive regression models between cellular iron content and spectral parameters. The comprehensive application of key sensitive spectral indices and regression equations should help to support monitoring and diagnosis of iron availability to cyanobacteria via remote sensing.