, Volume 20, Issue 4, pp 743-758

Spatial characterization of water quality in Florida Bay and Whitewater Bay by multivariate analyses: Zones of similar influence

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Abstract

We apply an objective statistical analysis to a 6-yr, multiparameter dataset in an effort to describe the spatial dependence and inherent variation of water quality patterns in the Florida Bay-Whitewater Bay area. Principal component analysis of 16 water quality parameters collected monthly over a 6-yr period resulted in live principal components (PC) that explained 71.8% of the variance of the original variables. The “organic” component (PC1) was composed of TN, TON, APA, and TOC; the “inorganic N” component (PCII) contained NO2, NO3, and NH4 +, the “phytoplankton” component (PCIII) was made up of turbidity, TP, and Chl a; DO and temperature were inversely related (PCIV); and salinity was the only parameter included in PCV. A cluster analysis of mean and SD of PG scores resulted in the spatial aggregation of 50 fixed monitoring stations in Florida Bay and Whitewater Bay into six zones of similar influence (ZSI) defined as Eastern Florida Bay. Core Florida Bay, Western Florida Bay, Coot Bay, the Inner Mangrove Fringe, and the Outer Mangrove Fringe. Marked differences in physical, chemical, and biological characteristics among ZSI were illustrated by this technique. Comparison of medians and variability of parameter values among ZSI allowed large-scale generalizations as to underlying differences in water quality in these regions. For example. Fastern Florida Bay had lower salinity, TON, TOC, TP, and Chl a than the Core Bay as a function of differences in freshwater inputs and water residence time. Comparison of medians and variability within ZSI resulted in new hypotheses as to the processes generating these internal patterns. For example, the Core Bay had very high TON, TOC, and NH4 + concentrations but very low NO3 , leading us to postulate the inhibition of nitrification via CO production by TOC photolysis. We believe that this simple, objective approach to spatial analysis of fixed-station monitoring datasets will aid scientists and managers in the interpretation of factors underlying the observed parameter distribution patterns. We also expect that this approach will be useful in focussing attention on specific spatial areas of concern and in generating new ideas for hypothesis testing.