Influence of environmental variables on phytoplankton floristic pattern along the shallow coasts of southwest Bay of Bengal

Thangaradjou, T.; Sethubathi, G. Vijayabaskara; Raja, S.; Poornima, D.; Shanthi, R.; Balasubramanian, T.; Babu, K. Narendra; Shukla, Ashish

doi:10.1016/j.algal.2012.07.005

Cited by 18 publications

(15 citation statements)

References 34 publications

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“…Association of physical and chemical factors with the monsoon The significant peak in chl-a during September was the result of nutrient loading from river influx due to monsoonal precipitation (Sarma et al 2006;Thangaradjou et al 2012). The analysis also revealed significant association of nitrogenous nutrients (NO 2 + NO 3 ) with high chl-a.…”

Section: Discussionmentioning

confidence: 98%

Spatio-temporal distribution of chlorophyll-a in relation to physico-chemical parameters in coastal waters of the northwestern Bay of Bengal

Baliarsingh

Lotliker

Sahu

et al. 2015

Environ Monit Assess

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The present study focuses on understanding the long-term distribution of physico-chemical parameters and their influence on the distribution of chlorophyll-a (chl-a) at a coastal site in the northwestern Bay of Bengal. Chl-a showed large variability (0.12 to 10.05 mg m(-3)) on a spatio-temporal scale during the study period. However, the distribution showed a similar pattern with marginal variability from March 2010 to February 2011 and March 2011 to February 2012. The vertical distribution of salinity, pH, total suspended matter (TSM) and chl-a showed systematic temporal variability. However, dissolved oxygen (DO) and nutrients (nitrite + nitrate, phosphate, silicate) did not show any significant spatio-temporal trend. Chl-a showed bimodal distribution on an annual scale, with the first peak appearing during the pre-monsoon period in March due to a seasonal phytoplankton bloom, whereas the second peak occurring during September as a result of nutrient loading from river influx due to monsoonal precipitation. Factor analysis revealed the association of low salinity and high nutrients with chl-a. This infers that the nutrients brought by the influx of river into the study area were fuelling the growth and abundance of phytoplankton. Cluster analysis resulted in two distinct clusters among all physico-chemical datasets, indicating the presence of two distinct areas separated by the 30 m isobath that were strongly influenced by physico-chemical characteristics associated with the seasonal monsoon.

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Section: Discussionmentioning

confidence: 98%

Spatio-temporal distribution of chlorophyll-a in relation to physico-chemical parameters in coastal waters of the northwestern Bay of Bengal

Baliarsingh

Lotliker

Sahu

et al. 2015

Environ Monit Assess

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“…Given the complexity and dynamism of ecological interactions, especially in wastewater biological treatment systems, studies are increasingly searching for the aid of sophisticated techniques such as multivariate statistics (Balachandran et al, 2008). The multivariate statistics is one of the main tools to relate environmental variables and the density of phytoplankton populations (Thangaradjou et al, 2012).…”

Section: Statistical Analysesmentioning

confidence: 99%

Algae/bacteria consortium in high rate ponds: Influence of solar radiation on the phytoplankton community

Assemany

Calijuri

Couto

et al. 2015

Ecological Engineering

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Using multivariate statistical tools, the composition of the phytoplankton community was related to the characteristics of the domestic sewage used as culture medium in three high rate ponds (HRPs) submitted to different solar radiation levels. A total of 32 genera of phytoplankton were identified in the ponds; the class Chlorophyceae was the most abundant during the entire sampling period, with a larger number of individuals of the genus Desmodesmus in the summer and fall, and of the genus Chlorella in the winter and spring. The lowest occurrence of phytoplankton was observed in the fall, with behavior similar to the evolution of solar radiation throughout the year. Blocking over 30% of the solar radiation allowed for less variability of the phytoplankton community and favored the growth of biomass with higher density of individuals, as well as higher concentrations of chlorophyll-a and dissolved oxygen. On the other hand, the pond with 80% of shading presented the lowest mean density of organisms; from the perspective of wastewater treatment, however, it can be considered the most efficient in terms of organic matter and nutrient removal. According to the regression analysis, the algal biomass in HRPs can be maximized mostly if we consider the positive effect of carbon and phosphorus and the limiting effect of nitrogen and non-biodegradable organic load. For the conditions evaluated in this study, the photoinhibition phenomenon was not observed. Other aspects such as competition with other microorganisms for space and nutrients, or predation by zooplankton, seemed to be more significant for the growth and development of algal biomass. 2015 Elsevier B.V. All rights reserved.

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“…In the annual nutrient cycle, winter winddriven cooling and deep convective mixing results in low surface temperatures and high surface nutrient , and nitrate (f) for premonsoon season levels at the end of monsoon, but the biological utilization of nitrate is minimal due to the low light levels and continual mixing of the water column (Henson et al 2003). Whereas in premonsoon, the nutrients available in the surface layers due to sporadic rain fall and the associated riverine input and coastal upwelling due to strong southwest monsoon winds are utilized by the phytoplankton (Thangaradjou et al 2012). In summer, the increasing light levels and strong stratification due to increased surface heating and reduced wind stress hamper the supply of nutrients to the surface waters which result in lower biological production (Prasannakumar et al 2002).…”

Section: Resultsmentioning

confidence: 98%

Seasonal nitrate algorithms for nitrate retrieval using OCEANSAT-2 and MODIS-AQUA satellite data

Poornima

Sarangi

Shanthi

et al. 2015

Environ Monit Assess

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In situ datasets of nitrate, sea surface temperature (SST), and chlorophyll a (chl a) collected during the monthly coastal samplings and organized cruises along the Tamilnadu and Andhra Pradesh coast between 2009 and 2013 were used to develop seasonal nitrate algorithms. The nitrate algorithms have been built up based on the three-dimensional regressions between SST, chl a, and nitrate in situ data using linear, Gaussian, Lorentzian, and paraboloid function fittings. Among these four functions, paraboloid was found to be better with the highest co-efficient of determination (postmonsoon: R2=0.711, n=357; summer: R2=0.635, n=302; premonsoon: R2=0.829, n=249; and monsoon: R2=0.692, n=272) for all seasons. Based on these fittings, seasonal nitrate images were generated using the concurrent satellite data of SST from Moderate Resolution Imaging Spectroradiometer (MODIS) and chlorophyll (chl) from Ocean Color Monitor (OCM-2) and MODIS. The best retrieval of modeled nitrate (R2=0.527, root mean square error (RMSE)=3.72, and mean normalized bias (MNB)=0.821) was observed for the postmonsoon season due to the better retrieval of both SST MODIS (28 February 2012, R2=0.651, RMSE=2.037, and MNB=0.068) and chl OCM-2 (R2=0.534, RMSE=0.317, and MNB=0.27). Present results confirm that the chl OCM-2 and SST MODIS retrieve nitrate well than the MODIS-derived chl and SST largely due to the better retrieval of chl by OCM-2 than MODIS.

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Influence of environmental variables on phytoplankton floristic pattern along the shallow coasts of southwest Bay of Bengal

Cited by 18 publications

References 34 publications

Spatio-temporal distribution of chlorophyll-a in relation to physico-chemical parameters in coastal waters of the northwestern Bay of Bengal

Spatio-temporal distribution of chlorophyll-a in relation to physico-chemical parameters in coastal waters of the northwestern Bay of Bengal

Algae/bacteria consortium in high rate ponds: Influence of solar radiation on the phytoplankton community

Seasonal nitrate algorithms for nitrate retrieval using OCEANSAT-2 and MODIS-AQUA satellite data

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