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patrickbdevaney
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mia9

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West Central BB
Southern BB
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For the segmentation we used similar factor analysis and clustering techniques
as those described by Briceño and Boyer (2009) for Florida Bay. Briefly, raw data was
transformed into z-scores and used as input for factor analysis with VARIMAX rotation
of axes (Minitab 16®
statistical software) to obtain five Principal Components (PC). We
did not apply the Kaiser criterion or the scree test rigorously (Kaiser 1960) for the
selection of PCs, but the magnitude of our retained eigenvalues was above 0.9 and the
contribution to the accounted variance of the five selected PCs was above 10%. These
PCs are linked to specific water quality parameters controlling their structure (Appendix
8). PC1 is mostly a positive function of inorganic N species, NO2
-
, NOx and NH4
+
and is
negatively correlated with salinity. PC2 is linked to TN and TOC perhaps representing
inland contributions, and it is also negatively correlated to salinity. PC3 is strongly
dependent on TP and CHLa, perhaps representing a driver:response pair. PC4
depends upon CHLa and turbidity, perhaps representing a system response. Finally,
PC5 is a function of DO. In total, these five PC account for 79.2% of the total variance.
There is a striking similarity between our PCs and those PCs derived by Caccia
and Boyer (2005) for the whole Biscayne Bay for a shorter period of record. This
similarity in results suggests a highly robust and long-lasting relationship among WQ
variables in Biscayne Bay.
Hierarchical Clustering
The scores from the five PCs were retained and their descriptive statistics
calculated for each sampling station. After trying different combinations of statistics,
grouping was performed with a combination of two parametric statistics (mean and
standard deviation) and two non-parametric statistics (median and MAD) of the scores
for each sampling site as input for hierarchical clustering routines. Our hierarchical
clustering metrics were Euclidean distances and linkage was Ward minimum variance
(Minitab 16®
statistical software). In summary, our final model included ten
biogeochemical parameters (TOC, TN, TON, TP, SRP, CHLA, NOx, NO2-
, NH4
+
,
salinity, turbidity, and DO), four statistics (mean, standard deviation, median and
median absolute deviation) for the five retained Principal Component scores.
Final results of hierarchical clustering are shown in the dendrogram (cluster tree)
of Fig 3.2 for eight water-classes. Using the cluster tree output from MINITAB-16® we
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analyzed the progression of subdivisions by sliding a threshold level along the statistical
distance axis and plotted the resulting clusters as coded maps to visualize such
progression. An example of this process is provided in Figure 3.3, depicting the
progression of BB clustering as statistical difference declines. The 3-cluster
arrangement separates waters from the southernmost bays (Barnes and Manatee;
blue), the most exposed stations to oceanic influence in central bay (light blue), and
those with strongest influence from land sources (brown). Progressive disaggregation to
5, 7 and 8 clusters reveals the persistence of the E-to-W gradient resulting from water
exchange and the development of a N-to-S pattern. This superimposed trends seem to
reflect both, circulation and differential impact from land sources, in turn linked to
changes in urban development (larger in North Bay) to agricultural areas (larger in
South Central) to mostly un-habited wetlands at the southern extreme. The 7-cluster
results for Central Bay and Card South are exactly the same as those of Caccia and
Boyer (2005).
Figure 3.2. Cluster tree showing water classes for Biscayne Bay (NOTE: Observations
along the x-axis are not station numbers)
The purely biogeochemical-statistical classification of Biscayne Bay waters into 8
water classes was partially modified by incorporating additional water circulation and
geomorphological criteria, as well as location and distribution of canal outlets. This
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modification was required for structuring the box-model calculations, which only
included stations in Central and South Biscayne Bay (from Rickenbacker Causeway to
Manatee and Barnes Sound). Modifications for the box-model were as follows: (1)
Station 103 (from cluster 4) was incorporated to the South Central Inshore segment (to
cluster 3); and (2) Station 129 (from cluster 7), just south of Rickenbacker Causeway
was clustered together with Station 126 (from cluster 5) to make a new cluster, NorthCentral Inshore (NCI)
Figure 3.3. Cluster evolution of Biscayne Bay. The 3-cluster arrangement shows the
fundamental subdivision of bay waters. Progressive clustering tracks the development
of two gradients in water quality, E-to-W and N-to-S
The resulting segmentation after taking these changes into consideration is
shown in Fig. 3.4, where the clusters are:
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C3
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