Ecology, Environment and Conservation Paper

Special Issue-2014; Page No.(79-86)

STRESSOR RESPONSE MODEL FOR THE SEAGRASS OF PULICAT

T. Lynda Keren and R. Moses Inbaraj

Abstract

Seagrass ecosystems are of great importance to many of the world’s coastal areas because they enhance biodiversity and provide many ecosystem services. Seagrasses are considered as ecosystem engineers. They are also vulnerable ecosystems. There is a documented decrease in the distribution of seagrass worldwide. The objective of this paper is to determine the relationship between various water and sediment quality parameters on the density of seagrass in Pulicat lagoon. Data was collected during the sampling process between December 2011 and August 2012. Correlation analyses were carried out and a linear regression model was developed to determine the relationships between 11 predictor variables and the response variable, density of the seagrass. Results of the correlation analysis are estimated based on Pearson’s r coefficient. Highest positive correlation is found between dissolved oxygen and density. Highest negative correlation is found between depth and density indicating decrease in density with increase in depth. Linear regression model with 11 predictor variables was found to have the best fit with p <0.001 and AIC value 761.193. The results of correlation and regression analyses indicate that each of the predictor variables have a role in the distribution of the seagrass. Parameters like depth, dissolved oxygen, salinity and pH are correlated with density indicating that a variation in these parameters will impact the distribution and health of the seagrass of Pulicat. The regression model can be used to predict the density of seagrass according to the changes in the variables.

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