Datasets:
patrickbdevaney
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mia9

Dataset card Data Studio Files
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Paleoecological studies indicate that Biscayne Bay has become increasingly
marine over the last 100 years (Ishman et al. 1998; Stone et al. 2000; Wingard et al.
2003, 2004), with smaller inter-decadal and decadal salinity fluctuations after 1915.
Among the significant changes in the bay during the last century are declines in
submerged vegetation (SAV) in central BB, and reduced molluscan faunal abundance
and diversity bay-wide (Berkeley and Campos 1984, Serafy et al. 1997, Ault et al.
1999a, b) and changes in the benthic foraminifera communities as reported in core and
sediment samples from South Florida (Ishman 1996, 1997, 1998).
Besides impacting SAV, salinity conditions affect some animal species in
Biscayne Bay whose life cycles are closely linked to salinity conditions ranging from 12
to 28 ppt such as: the American crocodile (Crocodylus acutus), oyster (Crassostria
virginica), spotted seatrout (Cynoscion nebulosus), silver perch (Bairdiella chrysoura),
mojarras (Eucinostomus) and red drum/‖redfish‖ (Sciaenops ocellatus) (Bellmund et al
2004).
BISCAYNE BAY COASTAL WETLANDS
Over a century of water management has caused estuarine habitats in the bay to
shrink, while at the same time expanded areas of low biological productivity within
Biscayne Bay’s watershed. This is particularly evident in areas that have been cut off
from freshwater sources by canals or roads. The dwarf mangrove forest, known as the
―white zone‖ along the coast of south Biscayne Bay is a good example of this inland
impact (Fig 2.9). This zone is expanding and its landward boundary has moved inland
by an average of one and a half kilometers (0.9 miles) since 1940 (CERP 2010). The
low productivity of the white zone is primarily a result of absence of freshwater input
from upstream sources. Perhaps one of the most important projects in the
Comprehensive Everglades Restoration Plan (CERP) is to restore wetlands within
Biscayne Bay (Fig 2.9). The project will divert runoff that currently discharges through
regional canals and redistribute the freshwater through a spreader canal system into the
coastal wetlands adjoining Biscayne Bay to provide a more natural and historic overland
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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
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Turkey Point
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Barnes
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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