THE RESPONSE OF SEAGRASS DISTRIBUTION TO CHANGING
WATER QUALITY: PREDICTIVE MODELS FROM MONITORING DATA
James W. Fourqurean,1,2,5 Joseph N. Boyer,2 Michael J. Durako,3 Lee N. Hefty,4 and Bradley J. Peterson1,2
1Department
of Biological Sciences, Florida International University, Miami, FL 33199
2Southeast
Environmental Research Center, Florida International University, Miami, FL
33199
3Center for Marine Science, University of North Carolina at Wilmington, Wilmington, NC 28403
4 Miami-Dade Department of Environmental Resources Management, 33 S.W. 2nd Ave., Miami, FL, 33130
Abstract.
Extensive data sets on water quality and seagrass distributions in Florida Bay have been assembled under complementary, but independent, monitoring programs. This paper outlines a method for exploring the relationships between two such datasets. Seagrass species occurrence and abundance data were used to define 8 benthic habitat classes from 677 sampling locations in Florida Bay. Water quality data from 28 monitoring stations spread across the bay were used to construct a discriminant function model that assigned a probability of a given benthic habitat class occurring for a given combination of water quality variables. Environmental variables important in the discriminant function were mean salinity, salinity variability, the amount of light reaching the benthos, sediment depth, and mean nutrient concentrations were important predictor variables in the discriminant function model. Using a cross-validated classification scheme, this discriminant function identified the most likely benthic habitat type as the actual habitat type in most cases. The model predicted that the distribution of benthic habitat types in Florida Bay would likely change if water quality and water delivery were changed by human engineering of freshwater discharge from the Everglades. These statistical techniques should prove useful predicting landscape-scale changes in community composition in diverse systems where communities are in quasi-equilibrium with environmental drivers.