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mouths) were interpreted as responding to site-specific natural behavior. On the other
hand, eventual occurrence of high nutrient levels would indicate anomalous conditions
responding to short-lived disturbances.
Besides those precipitation changes occurring in the early-to-mid nineties from
lower to higher precipitation rates which lead to region-wide declines in water nutrients
and chlorophyll a concentrations (Briceño and Boyer 2010), we compared nutrients
time-series with records of storms and hurricanes which affected South Florida during
the period 1989-2008. Separate statistics were calculated for before and after identified
events to test the nature, duration and magnitude of the impact. After identifying that
human and hurricane impacts combined to strongly disturb conditions in northeastern
Florida Bay-Southwestern Biscayne Bay since 2005, we discarded data obtained from
that area (MBS) during the disturbed period (2005-2008) for statistical calculations. The
dataset resulting from this selective process defined the Period of Calculation (POC)
dataset to be used for the remaining calculations.
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Table 6.1: Aggregation of SoFlo basins into 40 segments resulting from PC/Cluster
Figure 6.3: Biogeochemical segmentation of South Florida’s estuarine and coastal
waters.
Region No. Segment Sub-basin Region No. Segment Sub-basin
1 MAR Marquesas 1 CS Card Sound
2 BKB Back Bay 2 NCI North central Inshore
FLORIDA 3 BKS Back Shelf 3 NCO North Central Outer-Bay
KEYS 4 LK Lower Keys BISCAYNE 4 NNB Northern North Bay
(FK) 5 MK Middle Keys BAY 5 SCI South Central Inshore
6 UK Upper Keys (BB) 6 SCM South Central Mid-Bay
7 Offshore Offshore 7 SCO South Central Outer-Bay
1 CFB Central Florida Bay 8 SNB Southern North Bay
FLORIDA BAY 2 ECFB East-Central Florida Bay 9 MBS Manatee-Barnes Sound
(FB) 3 NFB North Florida Bay 1 CI Collier Inshore
4 CL Coastal Lakes 2 EB Estero Bay
5 SFB South Florida Bay PINE ISLAND 3 MARC Marco Island
6 WFB West Florida Bay ROOKERY BAY 4 NPL Naples Bay
1 BLK Black River (PIRB) 5 PINE Pine Island Sound
WHITEWATER 2 CTZ Coastal Transition Zone 6 SCB San Carlos Bay
BAY 3 GI Gulf Islands 7 COCO Cocohatchee
(WWB-TTI) 4 IWW Internal Waterways SHELF 1 IGS Inner Gulf Shelf
5 MR Mangrove Rivers (SHELF) 2 MGS Midddle Gulf Shelf
6 PD Ponce de Leon 3 OGS Outer Gulf Shelf
7 SRM Shark River Mouth
8 WWB Whitewter Bay
OFF
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NUMERIC NUTRIENT CRITERIA
As a contribution to the regulatory process under way for the derivation of
numeric criteria by the USEPA and FDEP, we have: (1) Sub-divided the six major South
Florida basins into forty spatially-coherent water-types (segments). This classification
has been adopted by both agencies with minor modifications; and (2) we have derived a
series of protective nutrient thresholds to be considered in the derivation of protective
numeric nutrient criteria.
The USEPA recommends three types of approaches for setting numeric nutrient
criteria (USEPA 2001, 2010): (1) reference condition approaches; (2) stressor-response
analysis; and (3) mechanistic modeling. The reference conditions approach entails the
existence of undisturbed segments of reference, something not to be found in South
Florida after over a century of continuous human intervention. A suggested alternative is
to select the less impacted station or segment complying with its designated use as
reference, but given the recognized site-specific response to nutrient variability that
characterizes estuaries, there is no guarantee that the extrapolations would be correct.
The stressor-response approach requires cause-effect data (i.e. nutrient-dose
experiments), and that information is mostly absent or very scarce for relevant South
Florida biotic components such as phytoplankton biomass (Brand 1986; Brand et al.
1991) or ambiguous for major benthic primary producers (seagrass, epiphytes,
macroalgae, and benthic microalgae) (Armitage et al. 2005; Ferdie and Fourqurean
2004; Herbert and Fourqurean 2008). Findings from a handful of studies in Florida Bay
indicate that N addition had little effect on any benthic primary producers throughout the
bay, and P-induced alterations of community structure were not uniform and did not
always agree with expected patterns of P-limitation (Armitage et al. 2005). Nutrient-dose
experiments of seagrass meadows suggest that upon enrichment, Halodule wrightii
replaces Thalassia testudinum (Fourqurean et al 1995), and total epiphyte and epiphyte
chlorophyll loads were significantly, but weakly, correlated with phosphorus availability
(Frankovich and Fourqurean 1997). Likewise, the responses of benthic communities to
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N and P enrichment in the coral reefs of the Florida Keys vary appreciably between
nearshore and offshore habitats, and responses were species-specific. Nutrient addition
at nearshore sites increased the relative abundance of macroalgae, epiphytes, and
sediment microalgae.
Ferdie and Fourqurean (2004) and Fourqurean (2008) used in situ nutrient
enrichment experiments in seagrass beds in the Florida Keys and Florida Bay. Results
from their analysis of N and P concentration in T. testudinum leaves lead them to
suggest a preliminary relationship between nutrient enrichment and a drift of the N:P
ratio towards a value of 30 (similar to a Redfield Ratio). Meeder and Boyer (2001)
studied areas within and adjacent to Biscayne National Park and documented a strong
correlation between elevated NH4
+
concentrations with a decrease in Thalassia, an
increase in Halodule and fast growing algae, and an increase in filamentous algae cover
near Black Creek.
Given the uncertainties and lack of conclusive dose experiments, we focused our
approach on water quality monitoring datasets for two causative (phosphorus and
nitrogen) and one response (chlorophyll a) variables. This selection finds support on
abundant literature worldwide documenting nutrient influx as the primary cause of algal
blooms (Sparrow et al. 2007), by either natural events such as river plumes, storms and
upwelling (Fujita et al. 1989, Longhurst 1993, Grimes and Kingsford 1996, Oke and
Middleton 2001, Fitzwater et al. 2003, Moisander et al. 2003, Wieters et al. 2003, Yin
2003, Carstensen et al. 2004, Hodgkiss and Lu 2004, Yin et al. 2004, Beman et al.
2005, Furnas et al. 2005), or urban runoff and sewage (Smith et al. 1981, Hodgkiss and
Lu 2004, Lapointe et al. 2004, Carruthers et al. 2005; Sparrow et al. 2007)
The relationship between nutrients and phytoplankton biomass (i.e. CHLa) in
South Florida waters has been widely reported (Brand 1986; Fourqurean et al 1993;
Rudnick et al. 1999, 2006, 2007; Boyer and Briceño 2007; Briceño and Boyer 2008,
2010; Boyer et al. 2009), so CHLa seems to be a reasonable proxy to assess nutrient