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SCM SCO SNB
CS MBS NCI NCO NNB SCI SCM SCO SNB
Surface 0.00% 1.65% 0.37% 0.74% 0.37% 1.72% 0.99% 0.09% 0.74%
Bottom 1.11% 3.14% 2.58% 1.48% 2.95% 2.46% 1.23% 1.57% 1.97%
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Finally, we compared similarities (differences) in nutrient concentration among
segments using Kruskal-Wallis test for three parameters of interest for numeric nutrient
criteria development, TN, TP and CHLa. Results are shown in Table 3.2. Differences in
Total Nitrogen distribution are not statistically significant for CS-NCI; NCO-SNB-NNB;
NCI-SNB-NNB; and SNB-NNB. For TP, there are not statistically significant differences
for MBS-NCO; SCI-CS-NCI; SCM-SCI; and CS-NCO-NCI. For CHLa, there are not
statistically significant differences for MBS-NCO; and CS-NCI. In summary, for those
segment pairs (triplets) with no significant differences the same criteria may apply.
Table 3.2: Results of Kruskal-Wallis test comparing TN, TP and CHLa distribution
among segments of Biscayne Bay. Probabilities highlighted in bold (p>0.05) are those
pairs whose differences are not statistically significant.
MBS SCI SCO SCM C S NCO NCI SNB NNB
MBS 1 * * * * * * * *
SCI 0.000 1 * * * * * * *
SCO 0 0 1 * * * * * *
SCM 0 0 0 1 * * * * *
C S 0 0 0 0.012 1 * * * *
NCO 0 0 0.000 0 0.002 1 * * *
NCI 0 0 0 0.001 0.458 0.018 1 * *
SNB 0 0 0 0 0.020 0.285 0.127 1 *
NNB 0 0 0 0 0.050 0.246 0.221 0.846 1
MBS SCI SCO SCM C S NCO NCI SNB NNB
MBS 1 * * * * * * * *
SCI 0.000 1 * * * * * * *
SCO 0 0.000 1 * * * * * *
SCM 0 0.009 0.340 1 * * * * *
C S 0.001 0.795 0.001 0.036 1 * * * *
NCO 0.269 0.053 0 0.000 0.044 1 * * *
NCI 0.000 0.295 0.021 0.193 0.470 0.006 1 * *
SNB 0 0 0 0 0 0 0 1 *
NNB 0 0 0 0 0 0 0 0 1
MBS SCI SCO SCM C S NCO NCI SNB NNB
MBS 1 * * * * * * * *
SCI 0 1 * * * * * * *
SCO 0 0 1 * * * * * *
SCM 0 0.016 0 1 * * * * *
C S 0 0.016 0 0 1 * * * *
NCO 0.661 0 0 0 0 1 * * *
NCI 0 0.075 0 0.000 0.568 0 1 * *
SNB 0 0 0 0 0 0 0 1 *
NNB 0 0 0 0 0 0 0 0.000 1
p-value for Total Nitrogen
p-values for Total Phosphorous
p-values for Chlorophyll-a
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TREND ANALYSIS
Biscayne Bay has been a dynamic environment over the last 500 years,
experiencing marked salinity and benthic habitat variability. Some changes may be
connected to anthropogenic intervention while others seem to respond to regional or
global changes. Salinity has increased, seagrass has declined; open-marine species
have replaced estuarine assemblages and the reduction of managed freshwater
deliveries into BB has been coupled with increases in foraminifera abundance (Archaias
and Articulina) since 1947 and declines in Thalassia after 1950 (Wingard et al 2004).
Since the middle 1980’s significant regional ecosystem shifts have been documented in
South Florida coastal waters (Boesch et al. 1993; Fourqurean and Robblee 1999; Hunt
and Nuttle 2007; Briceño and Boyer 2010). Some of the significant changes were:
seagrass die-off beginning in 1987; large plankton blooms in the western and central
portions of the Florida Bay (Robblee et al. 1991); increased phytoplankton abundance
(Phlips and Badylak 1996); increased turbidity; significant declines in fisheries and
shrimp harvest (Tabb and Roessler 1989; Tilmant 1989); sponge mass mortality in hard
bottom areas of southern Florida Bay (Butler et al. 1995); extended coral bleaching in
the Florida Keys reef tract after the 1980’s; and sustained nutrient concentration decline
in waters leaving the Everglades (Briceño and Boyer 2007, 2008).
Recent changes in BB have been reported by Migliaccio et al. (2008) who
studied biogeochemical trends from years 1996 to 2007, using DERM, NPS and FIU
data. They reported increasing trend for chlorophyll-a likely responding to the increase
in NH4-N, SRP, and TPO4-P, in turn responding to a dramatic population increase and
land use change in the watershed from agricultural land to residential and urbanizing
areas.
Our analysis of time-series components was performed with Akritas Theil/Sen
regression (ATS) for secular trend (Appendix 9) and Zcusum charts for detailed
scrutiny. A Cusum chart is a plot of the cumulative sum of deviations (Sn= Σ[zi-T] for
i=1….n) from a target (T), in our case, the time-series grand mean, against n, the
sample number (or date if regularly sampled) (Ewan, 1963). Cusum charts and Cusum
analysis are standard procedure in the field of industrial process control (Duncan 1974;
Grant and Leavenworth 1980; Montgomery 2001). For a detailed explanation on
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construction, properties and applications of Zcusum charts, the reader is referred to
Appendix 4). Visual inspection of Zcusum charts gives a detailed history of the
parameters variability along the time-line or another variable gradient providing more
information than ATS, ordinary linear regression or Line of Organic Correlation.
Finally, we must always keep in mind that trend analysis is limited to the window
of observation; trends change with continued data collection. In addition, water quality in
South Florida may fluctuate according to disturbance events of longer periodicity, and
trends may even reverse during a given period of record.
Total Nitrogen
As shown in Fig 3.19, TN declined across the Bay displaying two cycles common
to all segments during the POC. From 1996 to 1999 values were in general above
average (positive Zcusum slope), followed by a decline to below average values until
2002, except in SCO where the decline began in 1998. The second cycle extends from
2003 to 2007, with the break from above to below average approximately occurring at
the end of 2004. In MBS the second cycle is less developed. Finally, seasonality is
crudely developed, perhaps reflecting managed water deliveries non tuned to natural
rain cycles and water inflow.
Total Phosphorous