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flow. The slower, more natural delivery of fresh water over a broad area is expected to
reduce hypersaline conditions and re-establish appropriate estuarine salinities that are
important to provide nursery habitat for fish and shellfish in tidal wetlands and nearshore
bay habitats. This project is expected to create conditions that would be conducive to
the re-establishment of oysters and other components typical of a healthy estuarine
ecosystem.
Figure 2.9: Satellite image showing the low-productivity dwarf mangrove forest, or
―white zone‖ along the western coast of Card and Barnes sounds, and the location of
the Biscayne Bay Coastal Wetlands Project (modified from CERP 2010)
White
Zone
Turkey Point
Card
Sound
Barnes
Sound
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SECTION 3:
STATISTICAL ANALYSIS
SUMMARY STATISTICS
Each WQ parameter at each sampling station was subjected to preliminary
calculation of its statistics. Those dataset with more than 10% non-detects, were
subjected to additional non-parametric calculations of robust regression on order
statistics (Robust-ROS) and analysis of their probability plot. These calculations were
only required for SRP and DIN for the overall BB dataset for initial characterization and
clustering (Appendix 5), and for SRP, NOx, NO3, DIN and SRP for some individual subbasins. Robust-ROS procedure uses probabilities and applies survival analysis methods
on log transformed and flipped data, without assuming normal distribution of the data.
This procedure computes summary statistics avoiding transformation bias from logs to
original data units (Helsel and Cohn 1988). A detailed discussion of the calculations is
presented elsewhere (Helsel 2005). Detection limits for FIU data were not constant
along the POR (Appendix 7), posing additional complications to the calculation of
statistics for characterization and cluster analysis. Finally, summary statistics for all
variables at each station are presented in (Appendix 5).
FACTOR ANALYSIS AND CLUSTERING
Caccia and Boyer (2005) performed an objective classification analysis on FIU’s
water quality data set from 25 sites collected monthly during 1994–2003. Water quality
parameters used for classification included: TN, TON, DIN, NH4, NO3, NO2, TP, SRP,
TN:TP ratio, TOC, DO, CHLa, turbidity, salinity and temperature. This spatial analysis
extended from North Biscayne Bay and to Card Sound but excluded Barnes and
Manatee Bay. They applied Principal Components, retained the scores and input mean
29
and standard deviations of scores to K-means cluster analysis. They defined five zones
having similar water quality characteristics (Fig 3.1), where a robust DIN gradient was
determinant in the spatial clustering from alongshore to offshore in the main Bay. This
gradient results from the dominating influence of freshwater input from canals which
drain the South Dade agricultural area, Black Point Landfill, and sewage treatment
plant. Their South Bay zone (i.e. Card Sound) was high in dissolved organic
constituents and low in inorganic nutrients, reflecting the little urban development and
the extended mangrove wetlands of its watershed. Lowest nutrient concentrations
occurred in the Main Bay, the area most influenced by water exchange with the Atlantic
Ocean. Caccia and Boyer (2005) concluded that water quality in Biscayne Bay is highly
dependent on the land use in the watershed and on the exchange with oceanic waters.
Hunt & Todt (2006) subdivided Biscayne Bay waters into five geographic
domains based on direct clustering analysis of a set of stations (SAS PROC CLUSTER,
Ward method-Euclidean distance) combining temperature and salinity data from FIU
and DERM programs. Their geographic domains were only based on12 quarterly data
of surface samples (<0.5m). Their cluster analysis explained 81 percent of variability in
the data and the distribution pattern is in general similar to that of Caccia and Boyer
(2005). In both approaches, the characteristics of North Biscayne Bay extend just south
of Rickenbacker Causeway (dark blue dots in Fig 3.1) suggesting an active connection
and exchange. In both classifications an east-west gradient is evident in Central
Biscayne Bay, driven by salinity and nutrient differences, in turn caused by mixing of
oceanic and freshwaters. Card Sound appears as a distinct class in both subdivisions,
defining an additional north-sound gradient.
Factor Analysis
FIU monitoring program began in Manatee and Barnes Sound in 1991 as part of
their Florida Bay program and in Biscayne Bay (including Card Sound) in 1993. Ten of
the initial sites in Biscayne Bay were discontinued and ten new sites were incorporated
after an assessment by the SFWMD and FIU in 1996. Several of the new sites were
located out in North Biscayne Bay. Additionally, reassessment of the monitoring
program in September 2007 dropped 6 additional sites. Finally, the period between
30
June-1996 to September-2007 contains the largest number of stations simultaneously
sampled. Hence, we selected that period and those 21 stations distributed throughout
the bay for our segmentation. To differentiate data from this time interval from the
overall POR, we have called it Selected Period of Record (SPOR).
Figure 3.1: a) Caccia and Boyer (2005) subdivision of Biscayne Bay waters into 5
zones, North Bay, Alongshore, Inshore, Mid Bay and South Bay; and b) Classification of
Biscayne Bay waters by Hunt and Todt (2006) into five geographic domains, using
salinity and temperature from DERM and FIU data sets combined.
The SPOR included fourteen biogeochemical parameters, TOC, TN, TON, TP,
SRP, CHLA, NOx, NO2
-
, NO3
-
, NH4
+
, DO, salinity, turbidity, and temperature, for 4,941
samples and a total of 73,368 analytical determinations. After considering: 1) the high
correlation between TN and TON, and NOx and NO3
-
; 2) the high proportion of nondetects for SRP; and 3) the constancy of temperature across the bay, we included only
ten biogeochemical variables in the model: TOC, TN, TP, CHLA, NOx, NO2-
, NH4
+
,
salinity, turbidity, and DO. The number of determinations was now 39,078.
Northern BB
Intermediate BB
Offshore BB