Proposed Methodology for the Assessment of Protective 

Numeric Nutrient Criteria for South Florida Estuaries and 

Coastal Waters 

Prepared by: 

Henry Briceño, Joseph Boyer and Peter Harlem 

Florida International University 

Southeast Environmental Research Center 

Miami, Florida 

Delivered to: 

U. S. Environmental Protection Agency 

Miami December 6th, 2010 

This report is contribution # T-501 of the Southeast Environmental Research Center at 

Florida International University
2

OE #148, Florida International University, Miami, Florida 33199

           305‐348‐3095; 305‐348‐4096 fax; http://serc.fiu.edu

Ms Stephanie Sanzone  

Designated Federal Officer(DFO)

EPA Science Advisory Board Staff Office (1400R)

U.S. Environmental Protection Agency

1200 Pennsylvania Avenue, NW

Washington, DC 20460

Re:  EPA SAB Nutrient Criteria Review Panel

Dear Ms. Sanzone

We would like to provide the attached document entitled “Proposed Methodology for

the Assessment of Protective Nutrient Criteria for South Florida Estuaries and Coastal Waters”

to the EPA Science Advisory Board, Nutrient Criteria Review Panel. We speak as members of

South Florida scientific community, principal investigators for many years of a large marine

water quality monitoring network, contributors of statistical and modeling approaches to the

above document, and member (Boyer) of Florida’s Marine Numeric Nutrient Criteria Technical

Advisory Committee (MTAC)

Sincerely,

Dr. Henry O. Briceño, Research Faculty

Southeast Environmental Research Center

Florida International University

Dr. Joseph N. Boyer, Director and Assoc. Professor

Southeast Environmental Research Center

Department of Earth & Environment

Florida International University

Mr. Peter W. Harlem, Research Coordinator

Southeast Environmental Research Center

Florida International University
3

Proposed Methodology for the Assessment of Numeric Nutrient 

Criteria for South Florida Estuaries and Coastal Waters 

Table of Contents 

 Page 

Executive Summary 4 

Introduction 8 

1..a Data 9 

1.b Dataset comparisons 12 

1.c QA/QC 13 

1.d Time-span of datasets (POC and POS) 14 

1.e General Statistics and Time-series 16 

Segmentation Analysis 17 

2.a Purpose 19 

2.b Principal Component Analysis 20 

2.c Cluster analysis 20 

2.d Definition of Segments 21 

2.e Statistical Summary of Segments 25 

Identification of Disturbances 27 

3.a Climate Shift Impact 29 

3.b Hurricane Impact 32 

Threshold Analysis 35 

4.a Description 37 

4.b Examples of Threshold Assessment 42 

4.c Statistical Significance of Thresholds 43 

4.d Limitations 44 

Numeric Nutrient Criteria 45 

5.a Disturbed and undisturbed segments 45 

5.b Long-Term Limit (LT-L) 46 

5.c Upper Limit (UP-L) 47 

Acknowledgements 48 

References 49 
4

Executive Summary 

Water quality of South Florida’s estuaries and coasts is the result of a long-term and 

poorly understood interplay of local, regional and global forcing, drivers, pressures and 

responses. What we have is a series of water quality (WQ) snapshots taken in the last 20 years 

from which to produce not only a motion picture of the past, but a projection of future scenarios 

given a series of regulations are applied and enforced at the local level. South Florida’s coastal 

and estuarine waters have experienced the impact of anthropogenic interventions since the 

early 1900’s, including major disruptions of its hydrology and also sustained urban and 

agricultural development. Furthermore South Florida (SoFlo) waters are influenced by far-field 

sources, such as the Gulf of Mexico and the Mississippi River. Hence, despite their oligotrophic 

nature, SoFlo aquatic ecosystems bear the heritage and signals of such a long and sustained 

influence. 

The Florida Department of Environmental Protection (FDEP) and the US Environmental 

Protection Agency (EPA) are in the process of developing numerical nutrient criteria for 

estuaries and coastal marine waters for Florida. Fortunately several institutions, including the 

National Oceanographic and Atmospheric Administration (NOAA), the National Park Service 

(NPS), the US Geological Survey (USGS), the South Florida Water Management District 

(SFWMD), Florida International University (FIU), individual county programs (Miami-Dade, 

Broward, Monroe and Collier) and diverse research projects linked to the NSF-Florida Coastal 

Everglades Long-Term Ecological Research program (FCE-LTER) have implemented water 

quality (WQ) monitoring for over two decades in some estuaries. We have selected FIU’s 

database as a reference dataset given its spatial-temporal coverage at fixed 353 stations, 

completeness of measured variables (over 725,000 WQ measurements) and its sustained field 

and analytical protocols along the period of record. Other available databases would be used for 

comparison and verification purposes. 

After an initial stage of QA/QC, we redefined the Period of Record (POR) depending 

upon data availability and variable set completeness for the whole set of biogeochemical 

parameters (total and dissolved nutrients, CHL-a, turbidity, temperature, salinity, DO and light 

extinction). We then calculated descriptive statistics and performed long-term trend exploration 

of the redefined POR time-series, to gain insight into patterns of behavior along the POR for all 
5

relevant biogeochemical parameters. This exploration was performed with z-score cumulative 

sum charts (Z-CUSUM). 

A well recognized spatial-temporal variability within estuaries and coastal waters is 

present in our individual SoFlo basins. This fact called for a holistic approach to basin 

segmentation to account for variability not only dictated by a given nutrient concentration level, 

but by the combination of imposed conditions (nutrients, water clarity, climate, weather, extreme 

events, geomorphology, circulation and exchange, management, etc.). Basin segmentation was 

accomplished with an objective classification of station sites combining Principal Component 

Analysis (PCA) and Hierarchical Clustering methods in tandem. Briefly, selected 

biogeochemical variables (8 to 13) for each of the six SoFlo basin (Biscayne Bay, Florida Bay, 

Florida Keys, Whitewater Bay-10,000 Islands, Gulf Shelf and Pine Island-Rookery Bay) were 

used for PCA. Statistics (mean, standard deviation, median and median absolute deviation) of 

retained scores from PCA were input into Hierarchical clustering routines. The rationale behind 

the selection of these statistics was to account not only for level of individual parameter but also 

for their variability. Then, progressive subdivisions were obtained varying the statistical distance 

among groups. Selection of the final number of segments was subjective and knowledge-based. 

Spatial extension and pattern, geomorphology, water circulation and benthic ecosystem 

distribution played a major role on the decision. In summary, we statistically characterized and 

subdivided waters in each basin into what we considered biogeochemically and spatially 

coherent segments. 

A total of 40 waterbody types have been outlined for SoFlo coastal and estuarine waters 

that extend from Biscayne Bay in the east to Dry Tortugas in the south and to Pine Island Sound 

in the northwest. Our preliminary classification rendered 37 segments but re-assessment and 

regrouping focused on the Florida Keys lead to further subdivision of close-to-shore waters on 

the ocean side of the island chain to incorporate variability potentially associated to human 

impact. Additionally, a small waterbody at the mouth of the Caloosahatchee was also 

incorporated. Segment maps were generated placing the separating lines approximately 

midway in between clustered stations, or followed the FATHOM Model subdivision, followed 

previous subdivisions or were arbitrarily drawn following general geomorphologic patterns. In 

summary, border lines are site-specific and not the result of systematic spatial statistical 

analysis. 
6

Non-parametric Kruskal-Wallis tests were used to compare biogeochemical variables 

among segments, and box-and-whisker plots to summarize descriptive statistics. The former 

analysis highlighted statistically significant differences among segments, and the later 

underscored anomalous and probably impacted stations and segments. Sustained high 

concentration levels would suggest sustained impacted and perhaps impaired conditions – not 

meeting designated use type. Anomalous stations isolated from permanent sources of human 

disturbance (urban areas, canal mouths, etc.) 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. 

Substantial changes and climate-driven regime shifts have been documented for water 

quality in SoFlo. The most relevant changes we identified were those occurring in the early-tomid nineties from lower to higher precipitation rates, leading in turn to a generalized region-wide 

decline in water nutrients and chlorophyll a concentrations. Additionally, 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 impact 

combined to render strongly disturbed conditions in northeastern Florida Bay since 2005, we 

discarded data during that disturbed period. The dataset resulting from this selective process 

was used for the remaining calculations. 

Given the scarcity of information on the estuarine effects of nutrient enrichment on SoFlo 

ecosystem components and the well documented relationship between nutrients and CHL-a, we 

approached the derivation of nitrogen (TN) and phosphorus (TP) concentration thresholds for 

each segment by identifying TN and TP concentrations above which significant increases in 

CHL-a would occur. For this purpose we calculated and plotted CHL-a z-scored cumulative sum 

charts (Z-CUSUM) along either TP or TN gradients, mimicking results of nutrient doseexperiments. This allowed us to identify the successive phytoplankton biomass (CHLa) 

reactions to nutrient enrichment, to select the main threshold and to assess the potential health 

status of phytoplankton communities in the water column. The calculated thresholds fix the limit 

for nutrient concentrations above which a NNC would not be considered protective of the actual 

segment conditions with respect to phytoplankton biomass. If a segment is considered to be in 

good ecological condition and supportive of its designated use, the threshold may become the 

proposed long-term NNC. 
7

Numeric nutrient criteria have magnitude, duration, and frequency components. The 

magnitude component is the long-term limit (LT-L) estimated from a single segment or group of 

segments’ threshold values within a basin. The duration and frequency components of the 

criteria are incorporated into an upper limit (UP-L) that must not be exceeded more than once in 

three consecutive years, starting with the year of the assessment. The UP-L was estimated 

from a single segment or a group of segments’ upper 80th confidence interval (CI). The 

selection of the 80th CI and one- in-three years criteria limits Type I error rates to about 10 

percent. 

Descriptive statistics, particularly the 50th and 75th percentiles, combined with the derived 

thresholds and a reasonable understanding of the local ecosystem were used to identify 

similarities and dissimilarities among segments and, more importantly, to identify disturbed and 

undisturbed segments regarding their nutrient levels. Nutrient levels in undisturbed segments 

were associated with natural conditions and were used to estimate the NNC. Natural (temporal 

and spatial) variability of nutrient levels were incorporated in the NNC by (a) averaging limits 

across similar segments to account for spatial variability and (b) by establishing an upper bound 

limit (UP-L) to account for temporal variability. 

NOTE: Supporting Materials for this Report provided upon request: 

Henry Briceño 

bricenoh@fiu.edu 

 
8

1- Introduction 

The purpose of this document is to provide a scientifically defensible 

methodology to assist the Florida Department of Environmental Protection (FDEP) and 

the US Environmental Protection Agency (EPA) in developing numeric nutrient criteria 

for South Florida estuaries and coastal waters. Because of the diversity of South 

Florida’s (SoFlo) estuaries and coastal areas regarding geomorphologic, climatic, 

circulation and ecosystem structure conditions coupled with differential human 

intervention and management, there is a generalized scientific consensus that uniform 

region-wide nutrient criteria for estuarine and coastal waters are not appropriate, and as 

recommended by EPA (2001), criteria should be designed for particular waterbody 

types. Hence, we will derive numeric nutrient criteria at the sub-basin level in this report. 

Six basins have been previously define in SoFlo, namely Biscayne Bay (BB), 

Florida Bay (FB), Whitewater Bay-Ten Thousand Islands (WWB-TTI), Pine Island 

Sound-Rookery Bay (PIRB), Florida Keys National Marine Sanctuary (FK) and Gulf 

Shelf (SHELF). These are complex systems where neither potential reference 

conditions nor desired cause-and-effect relationships between nutrient enrichment and 

ecosystem responses have been clearly understood. Adding to these difficulties is the 

lack/scarcity of dose-response experimental work on key plant/animal species. 

Fortunately, water-quality (WQ) monitoring has been in place for about 20 years in 

SoFlo and analysis of these historical records may supply the information required to 

assess the status of waterbodies and to gain an adequate insight into biogeochemical 

processes to propose protective nutrient criteria. 

South Florida coastal and estuarine waters have experienced the impact of 

anthropogenic interventions since the early 1900’s, including major disruptions of its 

hydrology (RECOVER 2009) and sustained urban and agricultural development. 

Furthermore SoFlo waters are influenced by far-field sources, such as the Gulf of 

Mexico and the Mississippi River (Oertner et al. 1995; Gilbert et al. 1996; Hu et al. 

2005). Hence, despite their oligotrophic nature, SoFlo aquatic ecosystems bear the heritage 

and signals of such a long and sustained influence. 
9

Several ecological indicators have been developed recently for SoFlo waters, 

including pink shrimp (Browder and Robblee 2009), seagrass (Madden et al. 2009) and 

phytoplankton biomass (CHL-a; Boyer et al. 2009). Chlorophyll a is of special interest 

for our purpose because its value as an index of productivity and trophic status of 

coastal and estuarine waters has been widely documented (Steele 1962, 1981; Boynton 

et al. 1982; Cullen 1982; Butler et al 1995; Cloern and Jasby 2008; ), and for SoFlo the 

relationship nutrient-CHL-a has been empirically established (Zieman et al. 1989; Brand 

2002; CROGEE 2002; Rudnick et al. 2005, 2007; Boyer et al. 2009; Briceño and Boyer 

2010). It is fortunate that in SoFlo there are several WQ monitoring programs that have 

simultaneously measured nutrients and CHL-a. Those datasets are critical for defining 

the nutrient levels at which algal blooms are triggered at each sub-basin. Following this 

line of reasoning we have developed a method to calculate nutrient thresholds (Nutrient 

Threshold Analysis) and to derive nutrient levels supportive of the baseline conditions. 

Furthermore, if these baseline WQ conditions were considered as supportive of a 

waterbody’s designated use, the derived nutrient threshold level could become the 

proposed long-term NNC. A summary of steps that we followed in the development of 

the methodology is shown in the flow diagram of Figure 1. 

1.a Data 

South Florida WQ data has been generated by several institutions, among them, 

the National Oceanographic and Atmospheric Administration (NOAA), the US 

Geological Survey (USGS), the University of Miami Rosenstiel School of Marine and 

Atmospheric Science (RSMAS), the Environmental Protection Agency (EPA), the USNational Park Service (NPS), the Florida Department of Environmental Protection 

(FDEP), Florida International University (FIU), the Miami-Dade County throughout its 

Department of Environmental Research Management (DERM) as well as other 

individual county programs (Broward, Monroe and Collier) and diverse research projects 

linked to the NSF-Florida Coastal Everglades Long-Term Ecological Research program 

(FCE-LTER). 
10

Figure 1. Flow diagram illustrating the steps followed in the derivation of Numeric Nutrient 

Criteria as applied to South Florida coastal and estuarine waters. 

 The most important sources of WQ data for SoFlo are the following: 

i. NOAA/UM/RSMAS: Under the NOAA/AOML/South Florida 

Program surveys are conducted to study the hydrography and 

water quality of South Florida. These cruise surveys collect data for 

temperature, salinity, percent light transmission at l=660nm, 

chlorophyll a fluorescence, and CDOM fluorescence. In addition to 

the underway measurements, there are discrete sampling 

observations taken at stations which are used to calibrate the 

underway instrumentation and calibrate supplementary data. The 

result is a quickly produced, high spatial resolution “snapshot” of 

the temperature, salinity and water quality parameters for SoFlo's 

ecosystem (http://www.aoml.noaa.gov/sfp/data.shtml).
11

ii. DERM. The Biscayne Bay Surface Water Quality Monitoring 

Program is an ongoing routine surface water quality sampling 

program for Biscayne Bay and its watershed canals. The program 

began in 1979 and includes monthly surface water sampling for 

CHLa, DO, color, total ammonia, NOx, salinity, total phosphate, pH, 

light attenuation, extinction coefficient, total suspended solids and 

turbidity at over 105 stations throughout Miami-Dade County. 

Updated data was kindly supplied by Mr. Steve Blair. 

(http://www.gcrc.uga.edu/wqmeta/app/organizations.asp)

iii. USGS. Hydrological and water quality data has been recorded by 

many diverse research programs in the Everglades, Florida Bay 

and Biscayne Bay as a part of the U.S. Geological Survey's South 

Florida Program, including satellite data for Florida Bay. 

http://sofia.usgs.gov/exchange/index.php

iv. EPA. WQ data from a variety of research and monitoring programs 

is available throughout the STORET Data Warehouse, which is a 

repository for water quality, biological, and physical data. 

(http://www.epa.gov/storet/).

v. FIU/SERC. Water quality data has been collected since 1989 and 

has been monitored under the South Florida Water Management 

District and EPA funded Water Quality Monitoring Network since 

1991 for stations in Florida Bay, Whitewater Bay, Ten Thousand 

Islands, Biscayne Bay, the Southwest Florida Shelf (Shelf), the 

Cape Romano-Pine Island Sound area and the Florida Keys 

National Marine Sanctuary. Measured parameters include 

dissolved and total nutrients (N, P, C, SiO2), CHL-a, salinity, pH, 

temperature, DO, alkaline phosphatase activity, and Kd. 

(http://serc.fiu.edu/wqmnetwork/)

vi. FCE-LTER. The FCE LTER Program was established in May of 

2000 in SoFlo to study how hydrology, climate, and human 

activities affect ecosystem and population dynamics in the ecotone 

and, more broadly, in the Florida Coastal Everglades. WQ data is 

available along transects from freshwater sites in the Everglades to 

West Florida Bay. The FCE-LTER website is also an outlet for the 

NPS data. (http://fcelter.fiu.edu/data/).

vii. NPS. Biscayne National Park, Miami-Dade County and United 

States Army Corps of Engineers are involved in monitoring 
12

hydrodynamics and salinity in Biscayne National Park, especially in 

the western portion of central and southern Biscayne Bay. The 

network consists of 36 sampling locations (14 instrumented at 

surface and sea floor, 22 with bottom units only) and records high 

frequency data (every 15 min.), including temperature, salinity, and 

depth. Dr. Sarah Bellmund from Biscayne National Park kindly 

supplied the dataset

1.b Dataset Comparisons 

Following the considerations of EPA’s Guidance for the Data Quality Objectives 

Process (EPA 2004; http://www.epa.gov/quality/qs-docs/g4-final.pdf) the datasets to be 

used in the derivation of NNC were screened for compliance with the following criteria: 

- Long-term and sustained monitoring 

- Regularity in sampling frequency 

- Monitoring at fixed stations over the POR 

- Spatial coverage to render representative and reproducible data 

- Uniformity in field and laboratory protocols 

- High quality results 

- Include causal variables (i.e. nutrients), supporting variables (salinity, turbidity, 

DO, etc.) and ecosystem response variables (i.e. CHL-a) 

Given the differences among datasets with respect to site location, fixed stations 

versus cruise sampling, sampling frequency, measured parameters, and sampling and 

analytical protocols, merging different datasets to fill gaps or to extend the POR into a 

single consolidated dataset was deemed unnecessary. That effort would increase 

variability and decrease its quality. We selected FIU’s database as the reference 

dataset because of its spatial-temporal coverage at fixed stations (353 stations and over 

725,000 determinations), completeness of measured variables and its sustained field 

and analytical protocols along the period of record (POR). Other available databases 

would be used for comparison and verification purposes, especially the NOAA and 

DERM datasets. 
13

The FIU data was organized by basins: Biscayne Bay (BB), Florida Bay (FB), 

Whitewater Bay-Ten Thousand Islands (WWB-TTI) and Pine Island Sound-Rookery Bay 

(PIRB) which were sampled monthly; and the Florida Keys National Marine Sanctuary 

(FK) and Gulf Shelf (SHELF) which were sampled quarterly. Briefly, monitoring included 

field measurements of surface and bottom salinity (practical salinity scale), temperature, 

dissolved oxygen (DO), and turbidity. Unfiltered surface water samples (10 cm depth) 

were analyzed for total organic carbon (TOC), total nitrogen (TN), total phosphorus 

(TP), chlorophyll a (CHL-a), turbidity and light extinction coefficient (Kd, only in FK). 

Additionally, filtered surface water samples were analyzed for dissolved nutrients, 

including nitrate+nitrite (NOx

-

), nitrite (NO2

-

), ammonium (NH4

+

), inorganic nitrogen 

(DIN), soluble reactive phosphate (SRP), and silicate (SiO2). Some parameters were not 

measured directly, but were calculated by difference. Nitrate (NO3

-

) was calculated as 

NOx

-

 - (NO2

-

), and dissolved inorganic nitrogen (DIN) was calculated as NOx

-

 + NH4

+

. All 

values were reported in mg C, N, or P L-1 unless specified. Details of sampling 

methodology and laboratory analysis have been described elsewhere (Boyer et al. 

1999; Boyer and Briceño 2007). 

1.c QA/QC 

Data was directly downloaded from the Southeast Environmental Research 

Center (SERC) website (http://serc.fiu.edu/wqmnetwork/). Downloaded data had been 

subjected to detailed QA/QC procedures by SERC’s NELAC Certified Water Quality 

Laboratory, before posting on the website. Our initial QA/QC procedure involved 

flagging values below Method Detection Limit (MDL), detection of data gaps and 

potential outliers, and correction of miscellaneous typographical errors and formatting. 

Method Detection limits (MDL) posed a special complication because of their variability 

along the POR (Table 1). Values below detection limits were flagged for further 

statistical analysis with Robust Regression on Statistics methods (R-ROS; Helsel 2005). 

Special care was taken on suspected statistical outliers, because ecological data may 

contain extremely high (i.e. CHL-a data) or extremely low (i.e. salinity) values which are 

in fact valid system responses, so their deletion or substitution would impair a correct 

analysis. Criteria for assessing if a value is an outlier is further complicated because 
14

ecological data does not necessarily follow a specific type of statistical distribution. We 

used box-plots as a non-parametric tool to display data without making any 

assumptions of the underlying statistical distribution to isolate potential outliers, and 

applied the golden rule of “no value should be removed from a data set on statistical 

grounds alone” until the final assessment of potential NNC, when some high values 

resulting from natural and/or human impact were selectively removed to test influence of 

deletions on statistics. 

Table 1. Method Detection Limits for FIU/SERC Water Quality Laboratory 

1.d Time-span of datasets (POC and POS) 

The FIU WQ data (Fig 2) were gathered under different programs and funding 

agencies, rendering diverse periods of record, some extending back to 1989 (i.e. Florida 

Bay). From the available dataset (whole period of record, POR) we redefined the 

dataset (Fig 1) to be used in our segmentation analysis (Principal Components and 

Cluster analysis) at each basin, depending upon data availability and variable set 

completeness for the whole set of biogeochemical parameters of interest (total and 

dissolved nutrients, chlorophyll a, turbidity, temperature, salinity, dissolved oxygen). We 

FIU DETECTION LIMITS SUMMARY

Effective date N+N (mg/l) NO2 (mg/l) NH4 (mg/l) TN (mg/l) TP (mg/l) SRP (mg/l) CHLa (ug/l) (ug/l) TOC (mg/l) SILICA (mg/l)

1992 0.0004 0.0001 0.0007 0.004 0.0006 0.0003 0.1 0.12 0.002

1995 0.0004 0.0001 0.0007 0.004 0.0006 0.0003 0.1 0.12 0.002

1996 0.0004 0.0001 0.0007 0.004 0.0006 0.0003 0.1 0.12 0.002

4/30/1997 0.0004 0.0001 0.0007 0.004 0.0006 0.0003 0.1 0.12 0.002

1/13/1998 0.0010 0.0003 0.0008 0.004 0.0003 0.0006 0.1 0.12 0.002

11/12/1998 0.0010 0.0003 0.0008 0.004 0.0003 0.0006 0.1 0.12 0.002

9/27/2000 0.0021 0.0001 0.0014 0.03 0.0003 0.0019 0.1 0.05 0.0011

7/30/2001 0.0021 0.0001 0.0014 0.05 0.0003 0.0019 0.1 0.05 0.0011

8/1/2002 0.0013 0.0003 0.0017 0.05 0.0003 0.0009 0.1 0.06 0.0006

1/26/2004 0.0017 0.0003 0.0036 0.05 0.0006 0.0016 0.1 0.16 0.0008

5/15/2005 0.0020 0.0003 0.0045 0.08 0.0009 0.0016 0.1 0.16 0.0008

9/5/2006 0.0024 0.0003 0.0057 0.03 0.0012 0.0022 0.1 0.16 0.0008

1/7/2008 0.0039 0.0006 0.0049 0.05 0.0019 0.0016 0.1 0.16 0.0110

9/8/2009 0.0027 0.0003 0.0042 0.02 0.0019 0.0019 0.1 0.03 0.0022

Udated compilation as of November 22, 2010 by Henry Briceno
15

called this dataset Period of Segmentation (POS). The summary definition of these two 

datasets is shown in Table 2. 

Table 2. Time-span of South Florida Datasets. POR contains all FIU dataset; POS is 

selected data for Principal Components and Cluster analysis

Florida Keys National Marine Sanctuary (FK); Biscayne Bay (BB); Florida Bay (FB); Whitewater Bay-Ten Thousand 

Islands (WWB-TTI); Pine Island Sound-Rookery Bay (PIRB); and Gulf Shelf (Shelf) 

Figure 2. Spatial coverage of FIU Water Quality Monitoring Network. 

(http://serc.fiu.edu/wqmnetwork/). 

Biscayne Bay Florida Bay Florida Keys

Pine Island

Rookery Bay Gulf Shelf

Whitewater Bay

Ten Thousand

Inslands

POR 9/30/1993 6/27/1989 3/22/1995 1/26/1999 5/31/1995 9/15/1992

3/14/2008 5/3/2008 10/28/2009 3/12/2008 9/13/2007 3/17/2008

POS 6/24/1996 3/1/1991 3/22/1995 9/29/1999 5/31/1995 9/15/1992

9/18/2008 9/16/2008 10/28/2009 9/30/2009 9/13/2007 9/25/2008
16

1.e General statistics and time-series analysis 

Besides descriptive statistics (parametric and non-parametric) we performed 

long-term trend exploration of POR time-series, for selected variables at each station 

(TN, TP, TOC, CHLa, Temperature, DO, Turbidity, DIN, Salinity). The objective of this 

trend analysis was to gain insight into spatial-temporal similarities and differences to 

identify patterns of behavior along the POR for all relevant biogeochemical parameters, 

to finally define periods of “normal” conditions versus those periods when disturbances, 

either natural or anthropogenic, caused observable disruption of the normal status. This 

exploration was performed with box-and-whisker plots (Boyer and Briceño 2008) and zscored cumulative sum chart (Z-CUSUM), a statistical tool which will be widely used in 

this report. Briefly, a Z-CUSUM chart is a plot of the cumulative sum of standardized 

deviations (Sn= Σ[zi-T] for i=1....n) from a target specification (T; i.e. the median), 

against n, the sample number (Ewan, 1963). Cumulative sum (Cusum) control charts 

were first proposed by Page (1954) and have been studied by many authors; in 

particular Page (1961), Ewan (1963), Gann (1991), Lucas (1976), Hawkins (1981, 

1993), Woodall and Adams (1993), Montgomery (2001) and Galeano (2007).

The Z-CUSUM charts and Cusum analysis are standard procedures in the field 

of industrial process control (Duncan 1986; Grant and Leavenworth 1980; Woodall and 

Adams 1993; Montgomery 2001) and are extremely sensitive to small departures from 

the target value. This direct and easy connection between Cusum and process 

performance has recently driven increasing applications of these charts to the earth 

sciences, especially for the analysis of time-series in oceanography, geology and 

ecology (Ibanez et al. 1993; Briceño and Callejon 2000; Manly and MacKenzie 2000; 

Nicholls 2001; Taylor et al 2002; Choe et al. 2003; Scandol 2003; Bonett and Frid 2004; 

De Galan et al. 2004; Adrian et al. 2006; Lavaniegos and Ohman 2007; Molinero et al. 

2008). Z-CUSUM charts were used in the recently posted EPA’s document “Methods 

and Approaches for Deriving Numeric Nutrient Criteria for Nitrogen/Phosphorous 
17

Pollution in Florida’s Estuaries, Coastal waters, and Southern Island Flowing Waters” 

(EPA 2010). Methods and applications of Z-CUSUM charts to trend analysis are 

detailed below. 

2. Segmentation Analysis 

Classification and grouping of SoFlo coastal waters into spatial WQ clusters have 

been performed by Boyer et al. (1997) and Briceño and Boyer (2010) in Florida Bay 

(FB); by Caccia and Boyer (2005), Hunt and Todt (2006), and Briceño et al. (2010) in 

Biscayne Bay (BB); by Boyer (2006) in the Whitewater Bay-Ten Thousand Islands 

(WWB-TTI) region; and by Boyer and Briceño (2009) in the Florida Keys (FK). No 

previous subdivision exists for the Pine Island-Rookery Bay area (PIRB). In all studies 

where subdivisions have been reported, a combination of Principal Component and 

Cluster Analysis was used for grouping FIU’s sampling sites, except in the work by Hunt 

and Todt (2006) where a direct cluster analysis was performed to group a pool of DERM 

and FIU stations. Caccia and Boyer (2005) used FIU WQ data from 1994 to 2003 to 

subdivide BB into five spatial zones, and Hunt and Todt (2006) combined DERM and 

FIU data and also found five geographic domains in BB. Boyer et al. (1997) subdivided 

FB into four classes and Briceño and Boyer (2010) obtained six water classes. 

The process by which the bays were subdivided considered several 

biogeochemical variables (Table 3) and followed an ecological approach, so its results 

mimic very closely those geographical patterns observed in FB and/or BB Bay and/or 

WWB-TTI as follows: water circulation and residence time (Wang et al. 1994, 2003, 

2007; Brand 2001; Cosby et al. 2005;); salinity (Robblee et al. 1989; Boyer et al. 1997; 

Fourqurean and Robblee 1999; Nuttle et al. 2000; Cosby et al. 2005; Bellmund et al 

2009); TP and TN (Fourqurean et al. 1993; Fourqurean and Robblee 1999; Hitchcock et 

al. 2007); phytoplankton biovolumes and type (Phlips and Badylak 1996; Phlips et al. 

1999; Steidinger et al. 2001; Hunt and Nuttle 2007); seagrass distribution (Zieman et al., 

1989,1991; Roblee et al. 1991; Fourqurean et al. 2003); and bottom composition 

(Wanless et al. 1984). Following these previous findings, basin segmentation for this 
18

report was also accomplished with an objective classification of sampling stations (Fig 

1) combining Principal Component Analysis (PCA) and Hierarchical clustering methods 

in tandem. 

Table 3. Number of analysis summary for FIU dataset selected for segmentation. 

BISCAYNE BAY: CS=Card Sound; MBS= Manatee-Barnes Sound; NCI= North Central Inshore; North Central Outer; NNB= 

Northern North Bay; SCI= South Central Inshore; SCM= South Central Mid Bay; SCO=South Central Outer; Southern North Bay; 

FLORIDA BAY: CFB=Central; ECFB= Eastern Central; NB=Northern; NL= Northern Lakes; SFB= South; WFB= West 

PINE ISLAND-ROOKERY BAY: CI= Collier Inshore; EB= Estero Bay; MARC= Marco Island; NPL= Naples; PINE= Pine Island; 

SCB= San Carlos Bay 

SHELF: IGS= Inner Shelf; MGS= Mid Shelf; OGS= Outer Shelf 

WHITEWATER BAY-10,000 ISLANDS: BLK= Black River; CTZ= Coastal Transition Zone; GI= Gulf Islands; IWW=Internal 

Waterways; MR= Mangrove Rivers; PD= Ponce de Leon; SRM= Shark River Mouth; WWB= Whitewater Bay 

FLORIDA KEYS: BKB= Back bay; BKS= Back Shelf; LK= Lower Keys; MK= Middle Keys; UK= Upper Keys; MAR= Marquesas; 

Offshore 

Basin Segment NOX NH4 TN DIN TP CHLa TOC SAL DO TURB Sub‐totals

BB CS 327 325 326 325 327 323 326 327 327 326         3,259

MBS 1284 1287 1287 1284 1287 1293 1281 1302 1293 1272       12,870

NCI 294 294 294 294 293 290 292 294 294 294         2,933

NCO 282 281 282 281 282 277 281 281 281 282         2,810

NNB 294 294 292 294 294 290 293 294 294 294         2,933

SCI 528 527 528 527 528 522 528 528 526 527         5,269

SCM 539 539 537 538 539 534 538 540 539 539         5,382

SCO 1391 1382 1390 1382 1391 1374 1391 1391 1389 1390       13,871

SNB 429 428 427 428 426 423 429 429 429 429         4,277

FB CFB 851 849 858 847 857 861 853 873 864 842         8,555

CL 179 178 178 178 178 178 177 179 177 178         1,780

ECFB 650 652 649 650 649 674 649 682 676 633         6,564

NFB 855 854 856 853 855 851 851 856 851 848         8,530

SFB 866 868 869 865 869 854 864 869 855 844         8,623

WFB 1457 1458 1466 1451 1462 1466 1458 1498 1482 1413       14,611

FK BKB 1550 1545 1551 1549 1552 1552 1538 1512 1510 1524       15,383

BKS 575 568 576 575 576 576 575 565 564 564         5,714

LK 908 905 908 908 911 910 911 882 879 900         9,022

MAR 635 630 638 636 633 638 637 628 627 624         6,326

MK 459 459 461 460 462 458 462 443 442 460         4,566

Offshore 3838 3834 3863 3850 3853 3861 3856 3814 3793 3811       38,373

UK 870 859 865 870 869 870 870 852 846 863         8,634

PIRB CI 349 348 349 348 347 346 349 349 346 347         3,478

EB 468 465 468 465 468 463 467 468 464 464         4,660

MARC 815 806 815 806 807 807 815 812 806 809         8,098

NPL 662 662 663 661 659 657 663 661 656 658         6,602

PINE 585 583 585 583 581 580 585 585 580 580         5,827

SCB 351 348 350 348 347 348 351 351 348 348         3,490

SHELF IGS 681 679 675 678 681 681 678 658 657 651         6,719

MGS 631 626 629 625 632 632 631 620 618 606         6,250

OGS 1065 1064 1070 1060 1070 1070 1070 1063 1064 1025       10,621

WWB‐TTI BLK 827 823 826 823 815 827 825 827 827 825         8,245

CTZ 1389 1386 1391 1384 1389 1391 1385 1391 1388 1391       13,885

GI 1725 1719 1728 1714 1727 1728 1709 1729 1729 1728       17,236

IWW 657 656 658 655 656 656 657 658 658 658         6,569

MR 1110 1102 1110 1102 1109 1110 1109 1108 1105 1110       11,075

PD 382 376 382 376 380 382 380 382 380 382         3,802

SRM 573 571 573 571 573 573 572 572 570 573         5,721

WWB 1504 1497 1503 1497 1501 1503 1500 1504 1496 1504       15,009

Total 32,835        32,727    32,876    32,741    32,835    32,829    32,806    32,777    32,630    32,516    327,572
19

2.a Purpose 

Coastal and marine ecosystems are not in a steady state, but exhibit continuous 

changes in production, water quality and species composition at different trophic levels 

of the food web (Crossland et al. 2006). This well recognized spatial-temporal variability 

within estuaries and coastal waters, where waterbodies respond differently to nutrient 

inputs (EPA 2001), is present among and within SoFlo basins as shown in the box-plots 

of Figure 3. This fact called for a holistic approach to basin segmentation to account for 

variability not only dictated by a given nutrient concentration level but by the 

combination of imposed conditions (nutrients, water clarity, climate, weather, extreme 

events, geomorphology, circulation and exchange, management, etc.). 

Figure 3. Spatial variability of TN (mg/L), TP (mg/L) and CHL-a (μg/L) in SoFlo basins (left 

panel) and within Florida Bay (right panel). Numbers along right panel x-axis are Station 

IDs. 

0

.2

.4

.6

.8

1

TN

BB FB FK PIRB SHELF WWB-TTI

0

.02

.04

.06

.08

TP

BB FB FK PIRB SHELF WWB-TTI

0

2

4

6

8

10

CHLA

BB FB FK PIRB SHELF WWB-TTI

0

.004

.008

.012

.016

.02

TP

123456

101102103104108109110111112113116121122123124126127128129130131132133134135

0

.2

.4

.6

.8

TN

123456

101102103104108109110111112113116121122123124126127128129130131132133134135

0

1

2

3

4

CHLA

123456 101

102103104108109110111112113116121122123124126127128129130131132133134135
20

2.b Principal Component Analysis 

Principal Component Analysis was applied to reduce the number of variables and 

to detect structure in the relationships among variables. To circumvent the problem with 

BDL values, only biogeochemical variables with less than 10% non-detects were 

selected for Factor Analysis of standardized data using Orthotran/Varimax rotation and 

PC extraction (StatView 5.0.1 ®). The number of variables input ranged from 8 to 13, 

depending upon data availability and contribution to accounted variance. We did not 

apply the Kaiser criterion or the scree test rigorously (Kaiser 1960; Cattell 1966), but in 

general the magnitude of our retained eigenvalues was above 0.8 and the contribution 

to the accounted variance was above 5%. Finally, scores were retained and their mean, 

standard deviation, median, and median absolute deviation (MAD) at each sampling 

station were calculated. Two analyses were performed on Biscayne Bay (BB1 and 

BB2) with different spatial coverage, and results were similar for overlapping areas. A 

summary of results from the PCA analysis is shown in Table 4. 

2.c Cluster analysis 

Calculated mean, standard deviation, median, and median absolute deviation of 

retained PC scores at each sampling station were used for hierarchical cluster analysis 

with Ward distance calculations (SYSTAT 8.0®). In this context, the segmentation takes 

into consideration both, magnitude (mean and median) and variability (standard 

deviation and MAD) of each score at each sampling station. Using the cluster tree 

output from SYSTAT® we 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 

Figures 4 and 5, 
21

Table 4. Summary of inputs and results from segmentation analysis. Values within 

parenthesis are additional segments made of stations not used in the final clustering 

procedure because of their extreme compositional deviation. For Biscayne Bay nine 

segments were selected from results of BB1 and BB2 clustering procedures. 

2.d Definition of Segments 

Final selection of the number of segments was in part subjective and knowledgebased. Spatial extension and pattern, lateral continuity of cluster versus scattered 

pattern, geomorphology and water circulation played a major role on the decision. We 

avoided outlining segments defined by only one station, except when such station was 

significantly different and was geographically isolated from the rest. There are two 

isolated sites with those characteristics. The first is located on the west coast at Wiggins 

Pass Bridge (Sta 467), and the second one in northwest Florida Bay at Coot Bay (Sta 

50). The former, strongly affected by the Cocohatchee River basin, has been assigned 

to cluster COCO; and the later has been taken as representative of a series of isolated 

lakes (Frankovich et al. 2010) paralleling the coast and clustered as Coastal Lakes 

Basin FB PIRB BB1 BB2 SHELF WWB‐TTI FK

POC Mar‐91

Dec‐07

Jan 99

Sep 09

Sep 93

Sep 08

Jun 96

Sep 08

May 95

Sep 07

Sep 92

Sep 08

Mar 95

Oct 09

TN TN TN TN TN TN TN

Input TP TP TP TP TP TP TP

CHLa CHLa CHLa CHLa CHLa CHLa CHLa

Variables TOC TOC TOC TOC TOC TOC TOC

SAL SAL SAL SAL SAL SAL SAL

for DO DO DO DO DO DO DO

TURB TURB TURB TURB TURB TURB TURB

NH4 NH4 NH4 NH4 NH4 NH4 TEMP

Factor NO3 NO3 NOx NOx NOx

NO2 NO2 NO2 NO2

Analysis SRP SRP SRP SRP

TEMP

TON

Stations 28 29 21 30 49 47 155

PC 6 5 5 5 4 4 4

Accounted

Variance 79% 81% 76% 73% 63% 75% 66%

Segments 4(+2) 6 6(+1) 9 3 8 7
22

(CL). Additionally, four sites along the northeastern border of Florida Bay (Stas 7, 8, 10 

and 11) have been set aside as a different segment and named North Florida Bay 

(NFB). All these stations display extreme compositional characteristics, affecting the 

overall statistical grouping and were excluded from the PC-Cluster analysis. In 

summary, a total of 40 segments have been established for SoFlo coastal and estuarine 

waters. Out of that total, 36 segments were obtained from the PC-Cluster method and 

four were later added (Tables 4 and 5). 

Table 5. Aggregation of SoFlo basins into 40 segments resulting from PC/Cluster 

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
23

Figure 4. Cluster tree output from SYSTAT® for the PIRB basin. Distance thresholds have 

been placed to render 3, 4, 5 or 6 clusters. 

 Figure 5. Geographical rendition of the progressive aggregation of stations into clusters (C1, 

C2, ....) and resulting segmentation as the statistical distance changes in the Pine IslandRookery Bay region . 
24

For the Florida Keys (FK), our initial segmentation (Briceño et al, 2010b) drifted 

away from the strictly biogeochemical distribution indicated in the PC-Cluster results 

(Fig 6). We had clustered together all stations on the ocean side of the island chain as 

the OCEAN segment; also, we clustered stations on the Backcountry as a group, and 

also defined a specific segment for the Dry Tortugas. Reassessment of our preliminary 

subdivision, taking into consideration evidence indicating important gradients offshore 

(Boyer and Briceño 2006) and water circulation patterns (Lee et al 2001a, 2001b, 2003) 

led us to strictly follow the biogeochemical characteristics as shown in Fig 6, and 

partially following geomorphologic and hydrological characteristics as well as 

suggestions from EPA’s personnel. 

Figure 6. Results of PC-Cluster analysis of the Florida Keys region 

(abbreviations as in Table 5). 

Three GIS layers (shape files) were prepared for this study. The base map 

showing the land areas of SoFlo was made by combining the U.S Census Bureau land 
25

area maps for each county into a single layer; however, for Miami-Dade County, an inhouse shoreline derived from Lidar data, more accurate than the Census version, was 

used. Segment borders for the GIS layer were lines drawn approximately midway 

between stations (i.e. Gulf Shelf) or following bathymetric contours (i.e. Biscayne Bayl), 

or following the FATHOM Model subdivisions derived from basin geomorphology 

(Cosby et al. 2005; i.e. Florida Bay and Whitewater Bay-Ten Thousand Islands). In the 

Pine Island-Rookery Bay region borders were drawn according to best estimates of 

physical/hydrological separation, but significant uncertainty remains for Marco Insland 

and Naples segments. In summary, border lines are site-specific and not the result of 

systematic spatial statistical analysis. The final subdivision of SoFlo waters is shown in 

Fig 7 

We recognize that drawing segment boundaries is a partially subjective (and 

possibly political) process, but we have based them on our best estimates using local 

knowledge of many other experts in South Florida. In summary, the objective of this PCcluster step was to statistically characterize and subdivide waters in each basin into 

biogeochemically and spatially coherent groups to explore spatial ecosystem variability 

and dynamics, so potential distinction or regrouping for implementation of Numeric 

Nutrient Criteria may be done on more solid grounds. 

2.e Statistical summary of Segments 

Non-parametric Kruskal-Wallis tests (MINITAB 15®) were used to compare 

biogeochemical variables among segments and to summarize descriptive statistics we 

used box-and-whisker plots (StatView®). The former analysis highlighted statistically 

significant differences among segments guiding the potential aggregation of segments 

(Table 6), and the later underscored anomalous and probably impacted stations and 

segments deserving special attention. 

Calculations were performed on data aggregated by segments. Non-parametric 

Kruskal-Wallis’ tests were performed to assess statistically significant differences in 
26

nutrient concentration among segments for each basin (Table 6). Pairing of segments 

rests exclusively on similarities in their WQ conditions for the specific parameter, but 

these highly variable and transitional systems may render some disparity reflecting 

variability and/or transitions among waterbodies. Examples of apparent disparity are, 

SRM and PD for TP in WWB-TTI and NCI in two separated groups for CHLa in BB. 

Although similarities were already observed among segments, we waited until the final 

assessment of thresholds and limits to explore the aggregation of segments under both, 

statistical and ecologically similar conditions. 

Table 6. Summary of Kruskal-Wallis test results for SoFlo estuaries and coastal waters 

for the POR, showing those potential groups amenable of aggregation for NNC 

purposes. Only pairs with p >=0.05 were considered not statistically different.

Time-series of anomalous nutrient-rich stations and segments were explored to 

identify ephemeral and sustained high values. Permanent anomalously high levels 

would suggest sustained disruption (i.e COCO at Wiggins Pass Bridge (Sta 467) within 

Basin Parameter Potential grouping Basin Parameter Potential grouping

BB TN SNB‐NCI‐NCO‐NNB WWB‐TTI TN MR‐SRM

TP WWB‐SRM‐PD

TP SCI‐CS‐NCI MR‐SRM‐PD‐CTZ

SCO‐SCM CHLA MR‐GI

CHLA NCI‐CS BLK‐CTZ

NCO‐MBS PIRB TN EB‐PINE‐NPL‐MARC

SCI‐NCI TP PINE‐MARC‐NPL

FB TN ECFB‐SFB‐NB CHLA SCB‐PINE

TP none COCO‐NPL‐EB‐MARC

CHLA SFB‐NB SHELF TN none

FK TN MK‐LK TP MGS‐IGS

UK‐MAR CHLA MGS‐IGS

TP MK‐OFFSHORE

MAR‐BKB

CHLA MAR‐BKS

OFFSHORE‐MK
27

Pine Island-Rookery Bay region) or substantially different natural conditions. Anomalous 

stations isolated from permanent sources of disturbance (urban areas, canal mouths, 

etc.) were interpreted as responding to site-specific natural behavior (i.e Coot Bay in 

CL; Frankovitch et al. 20010). On the other hand, high nutrient ephemeral events would 

indicate anomalous conditions and be interpreted as responses to short-lived 

disturbances such as hurricanes (i.e. Manatee-Barnes Sound segment; Rudnick et al. 

2007). 

3. Identification of Disturbances 

The purpose of identifying ecosystem disturbances affecting WQ as nutrient 

enrichment was to separate and eventually discard data collected under conditions 

deviating from a “normal” and healthy setting, and to assess the implications of such 

deletions from the dataset using statistical tools. South Florida coastal and estuarine 

waters have experienced the pressure of anthropogenic interventions since the early 

1900’s, including major disruptions of its hydrology and a sustained urban and 

agricultural development. Furthermore, SoFlo waters are influenced by far-field sources, 

such as the Gulf of Mexico and the Mississippi River (Oertner et al. 1995; Gilbert et al. 

1996; Hu et al. 2005). Complex circulation and exchange patterns characterize SoFlo 

coasts and the Gulf/Florida Current plays a major role as depicted in Figure 8. Hence, it 

is not possible to find “sensu stricto” pristine ecosystems in SoFlo or filtrate data from 

each station to approach “undisturbed” conditions because the system has been 

disturbed long before monitoring began. Despite their exceptional oligotrophic nature, 

and recognizing that natural systems have memory of past events, SoFlo waters bear 

the heritage and signals of such long and sustained pressures. 
28

Figure 7. Subdivision of estuarine and coastal waters. 

Figure 8. Current circulation patterns in southwest Florida 

coasts (modified after Lee et al. 2003). 
29

3.a Climate shift impact 

We have observed that some important decadal WQ changes in SoFlo are 

synchronous region-wide, affecting from inland waterbodies (Everglades) to estuaries 

and coastal waters (Briceño and Boyer 2010). Climate-driven changes and even regime 

shifts have been documented and reported in the literature for WQ in SoFlo (Briceño 

and Boyer 2010). The most relevant climate shift during FIU’s WQ POR is that of earlymid 1990’s, perhaps caused by increased sea surface temperature (SST) in the North 

Atlantic which forced a shift from cool–dry to warm–wet conditions and higher 

precipitation rates in SoFlo (Landsea et al 1996; Kerr 2000; Delworth and Mann 2000; 

Enfield et al. 2001). The early-mid 1990’s shift seems to have cascaded - due to an 

increase in water availability- even into water managed deliveries to Everglades 

National Park (Fig 8). Hence, inland water quality and flow to SoFlo estuaries and 

coastal waters changed since the early-mid 1990’s, leading to a region-wide decline in 

TN concentrations (Briceño and Boyer 2008, 2010) and local increases/declines in TP, 

CHLa and turbidity (Figs 10 and 11). 

Figure 9. Z-CUSUM plots show the closely linked precipitation rate on the Everglades 

(blue) and SFWMD water deliveries (red) to Everglades National Park, highlighting a 

similar shift in the early-mid 1990’s from below average (negative slope) to above 

average (positive slope) in the long-term trend. A similar pattern (not shown) is 

displayed by groundwater level in Everglades National Park (i.e. Stations P34 and 

NPS205) (Graph after Castro and Briceño 2007) 

Everglades National Park

Inflow and Rainfall

‐60

‐40

‐20

0

20

1970 1975 1980 1985 1990 1995 2000 2005 2010

Flow Z‐Cusum

‐30

‐20

‐10

0

10

Rainfall Z‐Cusum
30

Figure 10. Changes in TP, TN and CHLa concentrations nearly-to-mid 1990’s from preto post-climate shift as proposed by Briceno and Boyer (2010). 

0

.02

.04

.06

.08

.1

.12

TP

BLK

CFB

CI

CL

CS

CTZ

EB

ECFB

GI

IGS

IWW

MARC

MBS

MGS

MR

NCI

NCO

NFB

NNB

NPL

OGS

PD

PINE

SCB

SCI

SCM

SCO

SFB

SNFB

SRM

WFB

WWB

Pre-shift

Post-shift

0

.25

.5

.75

1

1.25

1.5

1.75

2

2.25

TN

BLK

CFB

CI

CL

CS

CTZ

EB

ECFB

GI

IGS

IWW

MARC

MBS

MGS

MR

NCI

NCO

NFB

NNB

NPL

OGS

PD

PINE

SCB

SCI

SCM

SCO

SFB

SNFB

SRM

WFB

WWB

Pre-shift

Post-shift

0

2

4

6

8

10

12

14

16

CHLA

BLK

CFB

CI

CL

CS

CTZ

EB

ECFB

GI

IGS

IWW

MARC

MBS

MGS

MR

NCI

NCO

NFB

NNB

NPL

OGS

PD

PINE

SCB

SCI

SCM

SCO

SFB

SNFB

SRM

WFB

WWB

Pre-shift

Post-shift
31

Figure 11. Changes in concentration of TP, TN and CHLa from pre- to post-Hurricane 

Season 2005, when SoFlo suffered the impact of three major hurricanes, Katrina, Rita 

and Wilma. 

0

.25

.5

.75

1

1.25

1.5

1.75

2

2.25

TN

BLK

CFB

CI

CL

CS

CTZ

EB

ECFB

GI

IGS

IWW

MARC

MBS

MGS

MR

NCI

NCO

NFB

NNB

NPL

OGS

PD

PINE

SCB

SCI

SCM

SCO

SFB

SNFB

SRM

WFB

WWB

Pre-shift

Post-shift

0

.02

.04

.06

.08

.1

.12

.14

TP

BLK

CFB

CI

CL

CS

CTZ

EB

ECFB

GI

IGS

IWW

MARC

MBS

MGS

MR

NCI

NCO

NFB

NNB

NPL

OGS

PD

PINE

SCB

SCI

SCM

SCO

SFB

SNFB

SRM

WFB

WWB

Pre-KRW

Post-KRW

0

2

4

6

8

10

12

14

16

18

CHLA

BLK

CFB

CI

CL

CS

CTZ

EB

ECFB

GI

IGS

IWW

MARC

MBS

MGS

MR

NCI

NCO

NFB

NNB

NPL

OGS

PD

PINE

SCB

SCI

SCM

SCO

SFB

SNFB

SRM

WFB

WWB

Pre-KRW

Post-KRW
32

3.b Hurricane Impact

Additionally, we compared nutrients time-series with records of paths, areas 

affected, and reported effects produced by tropical depressions, tropical storms, and 

hurricanes which passed over or near South Florida during 1989-2008. The combined 

impact of storm surge and water retreat, waves and wind cause major sediment resuspension and sedimentation (Castaneda-Moya et al. 2010), seagrass kills (Michot et 

al 2002; Carlson et al. 2009; Anton et al 2009), defoliation of mangrove forest (Smith III 

et al 2009) all contributing to WQ changes, especially nutrient enrichment. Hurricane 

impact results in anomalous and sudden changes in CHL a, TP, TN, and turbidity and 

declines in salinity (Davis et al. 2004; Briceño and Boyer 2009, 2010). Hurricane-driven 

TP increases and ensuing algal bloom development have been reported for FB (Butler 

et al. 1995; Tomas et al. 1999; Boyer and Briceño, 2007; Rudnick et al. 2007; Briceño et 

al. 2009; Briceño and Boyer 2010) (Figs 9). The impact of Hurricanes Katrina, Rita, and 

Wilma (KRW) in 2005 was especially concentrated in eastern FB to southern BB and 

their effects on WQ lasted until 2009 (Madden et al. 2009), while previous recorded 

hurricane impacts on WQ indicate ecosystem recovery times of less than a year 

(Briceño et al. 2009). 

 In summary, hurricanes and climate variability are common to SoFlo and the 

probability of their occurrence in the future is very high. Observing the relatively low 

impact of Irene, and even Andrew in 1992 (Category 5) with the disturbances caused by 

KRW, we may conclude that SoFlo estuaries are adapted to those natural phenomena 

and recover in a matter of month from their effects, but KRW was unprecedented. 

Rudnick et al. (2007) support the hypothesis that this bloom was the system response 

to both natural and anthropogenic drivers which unfortunately coincided in space and 

time, namely, expansion of US 1 north of Key Largo and associated soil disturbance, 

hurricane impact and unusual water deliveries by the SFWMD to avoid urban flooding. 

Seasonal nutrient inputs and phenology may cause seasonal algal blooms, but 

sustained high nutrient levels (TP) as those experienced after hurricanes KRW in 2005 

lead to sustained blooms whose magnitude and duration are beyond those expected 

from seasonal nutrient inputs alone and/or storms without human contribution. Hence it 
33

seems reasonable to exclude data following KRW from and the NNC calculations. We 

discarded post KRW data from Manatee-Barnes Sound segment. This segment covers 

the following potentially nutrient impaired Planning Units according to the Florida 

Department of Environmental Protection: 6002 Manatee Bay, 6003 Barnes Sound, 

6005 Long Sound, 6005A Little Blackwater Sound, and 6005B Blackwater Sound 

 
34

Figure 12. Left panel: TN, TP and CHL-a time-series for Station 1, Manatee-Barnes 

Sound (MBS) segment. Sudden TP and CHL-a increases in 2005 highlight the impact of 

hurricanes Katrina, Rita and Wilma (KRW). Hurricane Irene (1999) caused fewer 

disturbances in CHLa, and Hurricane Gordon (1998) seems to have driven an increase in 

TP, which strengthened in 1999 after Irene. Right panel: Z-CUSUM rendition of the same 

time-series, highlighting the close relationship between TP and CHL-a, and their drastic 

increase (steep positive slope) after KRW (2005). Since early 2007 TP and CHL-a began to 

decline, but pre-hurricane conditions (i.e. similar pre-hurricane slope in Z-CUSUM) had not 

been attained by mid 2008. TN was not significantly affected by these hurricane impacts at 

this locality beyond 2005. Note how easy it is to define periods of time on the Z-CUSUM 

charts, characterized by uniform conditions (straight lines separated by breaks in slope). 

Keep in mind that positive (negative) slopes indicate above (below) average concentration.

Sta 1 TP mg/l

0.00

0.01

0.02

0.03

0.04

0.05

0.06

0.07

Jan-90Jan-92Jan-94Jan-96Jan-98Jan-00Jan-02Jan-04Jan-06Jan-08Jan-10

mg/l TP .

Sta 1 TN (mg/l)

0

0.5

1

1.5

2

Jan-90Jan-92Jan-94Jan-96Jan-98Jan-00Jan-02Jan-04Jan-06Jan-08Jan-10

mg/l TN

Sta 1 CHLA (ug/l)

0.00

1.00

2.00

3.00

4.00

5.00

6.00

7.00

8.00

Jan-90Jan-92Jan-94Jan-96Jan-98Jan-00Jan-02Jan-04Jan-06Jan-08Jan-10

ug/l CHLa 

Sta 1 TP 

-50

-40

-30

-20

-10

0

10

20

Jan-90Jan-92Jan-94Jan-96Jan-98Jan-00Jan-02Jan-04Jan-06Jan-08Jan-10

TP Z-Cusum 

Sta 1 TN

-10

0

10

20

30

40

50

60

Jan-90Jan-92Jan-94Jan-96Jan-98Jan-00Jan-02Jan-04Jan-06Jan-08

TN Z-cusum 

Sta 1 CHLa 

-60

-50

-40

-30

-20

-10

0

10

Jan-90Jan-92Jan-94Jan-96Jan-98Jan-00Jan-02Jan-04Jan-06Jan-08Jan-10

CHLa Z-Cusum 
35

4. Threshold Analysis

Focusing on the final objective of this report, we derived a new set of descriptive 

statistics including percentiles from the POR dataset for two causative (phosphorus and 

nitrogen) and one response (chlorophyll a) variables. The selection of these parameters 

finds support on a preliminary assessment of FIU’s dataset which are summarized in 

Figure13, where additional annualized data from Tampa Bay (TB) and Caloosahatchee 

(CAL) have been included. Both nutrients display a positive correlation with CHLa, 

especially TP, the recognized main limiting nutrient region-wide (Brand 1988; Brand et 

al 1991; Boyer et al 1999; Fourqurean et al 1993; Szmant and Forrester 1996; 

Fourqurean and Robblee 1999; Hoyer et al 2002; Boyer 2006; Boyer et al. 2009). There 

is a clear trend from the very oligotrophic waters of FK and BB northwestward to Tampa 

Bay, especially for phosphorous with an evident break of TP:CHLa at WWB-TTI. 

Internally, individual basins display low nutrient:CHLa correlation, but as a group they 

define a loci along a well defined regional trend. 

Figure 13. Relationship between CHLa and TN and TP concentration in South Florida. 

 

0.1

1.0

10.0

100.0

10.0 100.0

Log TN (uM/l) 

Log CHLa (ug/l)

BB

FK

FB

Shelf

WWB

TTI

RB

CAL

TB

0.1

1.0

10.0

100.0

0.1 1.0 10.0 100.0

Log TP (uM/l) 

Log CHLa (ug/l)

BB

FK

FB

Shelf

WWB

TTI

RB

CAL

TB
36

An ecological threshold is the critical value of an environmental driver for which 

small changes can produce an abrupt shift in ecosystem conditions, where core 

ecosystem functions, structures and processes are essentially changed between 

alternative states (Andersen et al. 2008). The following section describes our approach 

to characterize abrupt changes (thresholds) of CHL-a, the selected ecological indicator 

(Boyer et al 2009), as water nutrient concentration of either TN or TP increases. 

Identification and quantification of such breaks or thresholds may be directly 

approached with dose experiments or indirectly through statistical analysis of paired 

time-series of drivers and ecological indexes. 

There is just a handful of quantitative information on the effects of nutrient 

enrichment on ecosystem components in South FloridaSouth Florida (i.e. dose 

experiments). Brand (1988) and Brand et al (1991) studied phytoplankton assemblage 

response to nutrient enrichment (N and P) in Biscayne Bay. Ferdie and Fourqurean 

(2004), Fourqurean (2008), and Herbert 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 direct correlation between nutrient enrichment and a drift of the N:P ratio 

towards a value of 30 (similar to a Redfield Ratio). Nevertheless, their results are 

inconclusive because any ratio (including 30) may be approached by diverse 

combinations of nutrient variability, including nutrient declines (oligotrophication), as in 

fact occurs with their FK leading example at Sta 227 (Fig 14). 
37

Figure 14. Simultaneous decline in N and P concentration in T. testidinum leaves, as 

shown by scatter plots and Z-CUSUM charts (dome shape), render a drift of the N:P 

ratio towards 30 at Station 227 in the Florida Keys. 

 Given the uncertainties and lack of conclusive dose experiments we focused our 

approach on datasets for two causative (phosphorus and nitrogen) and one response 

(chlorophyll a) variables for obtaining TN and TP thresholds. These thresholds would 

separate nutrient concentrations pertaining to baseline (undisturbed) conditions from 

those promoting significantly increasing phytoplankton biomass (CHL-a) in the water 

column. In other words, nutrient concentrations above the threshold would not be 

protective of the average conditions, as recorded during the period under investigation, 

and regarding phytoplankton biomass. If the sub-basin segment were considered to be 

in an acceptable ecological condition (met designated use type), the calculated 

threshold could become the long-term protective NNC. 

4.a Description 

We assessed departures from “normality” in CHL-a (response variable) as 

nutrient concentrations increased (stressor variable) to identify a nutrient level, 

threshold, that if surpassed would trigger “anomalous” above average CHL-a 

-2.0

0.0

2.0

4.0

6.0

8.0

10.0

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2.0

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3.0

199619971998199920002001200220032004200520062007200820092010

Z-Cusum (SD) .

unit

227 Th N (unit) 227 Th N (unit) Z

-2.0

0.0

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4.0

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8.0

10.0

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0.2

199619971998199920002001200220032004200520062007200820092010

Z-Cusum (SD) .

unit

227 Th P (unit) 227 Th P (unit) Z

-2.0

0.0

2.0

4.0

6.0

8.0

10.0

12.0

0.0

10.0

20.0

30.0

40.0

50.0

60.0

199619971998199920002001200220032004200520062007200820092010

Z-Cusum (SD) .

unit

227 Th np (unit) 227 Th np (unit) Z

Declining

Declining

Declining
38

responses. As mentioned above, Z-CUSUM charts display the departure of a given 

process from a target value. In that respect they are equivalent to the well known 

anomaly diagrams or more properly, to cumulative anomaly diagrams (Fig 15). 

Additionally, these cumulative sum (CUSUM) diagrams are extremely resilient to data 

gaps and signal noise so they are an ideal tool for our purpose. 

Figure 15. Graphic tools to represent departures from the dataset’s central tendency 

indicating the transformation performed (xi= original raw data; wi= transformed data, X= 

average; and σ=standard deviation). Anomaly plots simply separate data below from 

above the average value; the Cumulative Anomaly plot represents the running sum of 

anomalies; and the Z-CUSUM plot is a running sum of standardized anomalies. The last 

two plots are proxies for the rate of change, and in both the slopes represent the rate of 

change of x along the horizontal axis gradient. They differ only in their “y” axis units The 

segment “a-b” represents a predominantly below average tendency and fragment “b-c” 

a predominantly above average tendency. 

TN

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

0.45

0.50

-0.6

-0.5

-0.4

-0.3

-0.2

-0.1

0.0

0.1

0.2

Raw data

Cumulativeanomaly

-7.0

-6.0

-5.0

-4.0

-3.0

-2.0

-1.0

0.0

1.0

2.0 Z‐Cusum

Runningsum of anomaly data

(deviationexpressedin original units)

Runningsum of z‐scoreddata

(deviationexpressedas multiplesof σ)

-0.15

-0.10

-0.05

0.00

0.05

0.10

0.15

0.20

0.25

0.30 Below

Above Anomaly plot

wi=wi‐1 + (xi‐X)/σ w ‐ i

=wi‐1 + (xi‐X) ‐

wi=(xi‐X) ‐

a

b

c
39

We derived the TN and TP concentration thresholds for South Florida estuaries 

and coastal waters using Z-scored cumulative CHL-a charts (Z-CUSUM), which 

measure the departure from an average CHL-a concentration along a nutrient gradient. 

These diagrams depict the cumulative deviation from a mean CHL-a concentration as 

TN or TP increase. It is worth noting that in Z-CUSUM charts, a secular increasing trend 

is displayed as a cup- or V-shaped curve, and a secular declining trend as a peak- or 

dome-shaped curve. In both cases, negative slopes indicate below average values and 

positive slopes above average values. Finally, the inflection point of the cup- or Vshaped curve followed by sustained above-average values corresponds to the main 

threshold between below- and above-average CHL-a concentration for the POC. 

Charts were constructed as follows: 

1- Only records with both parameters reported (CHL-a and TN; or CHL-a and TP) 

were selected (paired series). 

2- The paired data series for each segment was ordered by increasing TN (or TP) 

concentrations. 

3- CHL-a data (xi) were transformed into z-scored cumulative sum data (running 

sum of z-scored data) and plotted along the nutrient (TN or TP) gradient (x-axis). 

The threshold was directly read from the chart. 

4- In some instances the presence of extreme CHL-a values caused distortion of 

the Z-CUSUM chart, hindering identification of the precise threshold. In those 

cases, additional improvements to the Z-CUSUM CHL-a line plot were applied by 

bracketing the CHL-a data close to its central tendency before transformation into 

z-scores, as follows; 

a. (wi)= +1 if xi >Median CHLa 

b. (wi)= -1 if xi =<Median CHLa 

Where wi is transformed value to be z-scored. This transformation brackets the 

data very close to the median CHL-a value and eliminates deformations due to 

extremely high or low values, resulting in a better definition of the threshold. 

Similar charts are produced by transforming the data to a natural log scale before 

calculating the running sum instead of bracketing the data, but we preferred the 

bracketing procedure to maintain the original units and to avoid additional data 

transformations. 
40

5- Finally, the TN (or TP) median and 75th percentile for the segments were 

calculated and plotted on the same diagram for comparison purposes. 

Andersen et al. (2008) give an excellent description of driver:response scenarios 

which illustrate the resulting regime shifts as shown in Fig 16. Panel (a) illustrates a 

regime shift in driver linearly mediated to the ecosystem state response, where jumps 

appear only in the time series. Panel (b) shows a regime shift in ecosystem state after 

the driver exceeds a threshold. The jump appears in the time series of the response. 

Panel (c) shows a hysteresis loop linking the response to the environmental driver 

causing jumps between two alternative states. The Z-CUSUM charts along the lower 

row precisely display the location of the threshold in every case. Note that when 

hysteresis occurs an additional threshold appears. 

Figure 15. Selected relations driver:response and their resulting regime shifts:from (a) 

smooth pressure–status relationships, (b) threshold-like state responses and (c) 

bistable systems with hysteresis. Modified after Andersen et al. 2008. Response Z‐Cusum

Driver

Driver

Time

Response

Time

Response

Driver

Driver

Time

Response

Time

Response Z‐Cusum

Driver

Response

Driver

Driver

Time

Response

Time

Response

Driver

Response Z‐Cusum

Driver

a) Driver threshold b) Response threshold c) Driver‐Response hysteresis

Modified after Andersen et al 2008
41

Translating these conceptual regime shift scenarios to the real world, it is highly 

probable that we will find multiple thresholds, and additional effort will be necessary to 

identify the main threshold. Despite the renown water quality in South Florida estuaries 

and coasts, all these ecosystems have suffered certain degree of nutrient enrichment 

along their recent history, so their natural balance between nutrient concentration and 

ecosystem response has been disturbed. Under ideal conditions, where the ecosystem 

response is solely dependent on metabolic processes of a given species, the threshold 

would be unique (as in dose experiments). Higher nutrient levels will just sustain the 

bloom condition changing the driver’s median concentration, but holding the threshold 

constant. This combination would render two breaks in the response variable along the 

driver’s gradient, and both would be displayed in the Z-CUSUM chart, due to the 

manner the driver and threshold data are related in Z-CUSUM charts. Besides the one 

related to metabolic processes, a persistent break (threshold) will appear at the driver 

median concentration if driver and threshold are linearly correlated. 

There are added complications when we move from the lab dose experiment to 

the real world, so derivation of the meaningful threshold (ecologically significant and 

useful for NNC) is not a straightforward task, due to additional factors, natural and 

created by the data transformation. First, the persistence of that break at the driver’s 

median will increase as the dataset growths given the low-pass filtering nature of the ZCUSUM transformation. Additionally, subsidiaries thresholds due to hysteresis cycles, 

changes in phytoplankton species dominance, and “imported” water masses driven by 

winds, currents, upwelling, etc. may also appear. Fortunately, those are generally 

subdued by the constancy of the meaningful threshold responding to prevalent 

conditions along the POR. 

In order to circumvent all these obstacles a rigorous analysis of the Z-CUSUM 

chart is necessary for extracting the correct threshold. First, if the system has been 

brought to higher nutrient levels than pristine conditions (i.e. South Florida), the 

meaningful threshold would occur below the median nutrient concentration. Hence we 

selected the strongest and most persistent threshold below the median nutrient 

concentration as the meaningful threshold. We verified for the occurrence and 
42

persistence of those selected threshold at the station and segment level and confirmed 

their presence, suggesting some relationship sprung from the occurrence of similar 

phytoplankton communities. 

When comparisons are expanded to basin and even across South Florida waters 

as a whole, it is possible to outline a series of common breaks in a stepwise fashion 

from the most oligotrophic systems (FK) to the eutrophic end members (PIRB, WWBTTI). In summary, and given the robustness of the selected nutrient levels as 

thresholds, we propose this methodology as an additional “Line of Evidence” and a tool 

for the derivation of protective Numeric Nutrient Criteria. 

4.b Examples of Threshold Assessment 

In this section we present some examples of threshold derivation for SoFlo water 

types. In Blackwater segment (BLK)(Fig 16) there is no CHLa threshold below the 

median TN concentration. So, its threshold corresponds to the median TN, which in turn 

is located at the below-average CHLa branch of the chart, meaning it is protective. This 

characteristics suggests long-standing conditions (POR) and an equilibrated system 

with respect to TN. On the other hand, TP in Back Bay (BKB) develops its strong 

threshold below the median TP, suggesting that TP concentrations are generally (>50% 

of the time) enriched in excess of balanced conditions. Neither the median nor the 75th

percentile levels would be protective for this segment. 

Finally, CHLa displays a “serrated” declining trend in Card Sound as TP 

increases (Fig 17). The declining trend partially hinders the assignation of a TP 

threshold. When exploring the behavior of CHLa along the TN gradient, a strong 

threshold appears at very low TN concentrations (0.1364 mg/l TN) and the aboveaverage branch of the CHLa Z-CUSUM plot extends up to 0.3884 mg/l TN and then 

declines. What this pattern suggests, besides TN as the most important driver in the 

behavior of CHLa concentration, is that other drivers also have an important role on 

CHLa concentrations, especially above 0.3884 mg/l TN. 
43

Figure 16. Threshold Analysis for TN in Black River (BLK) and TP in Back Bay (BKB) Scatter 

plots “shotgun-shot” appearance hinders any useful conclusion for NNC from them. Z-CUSUM 

charts instead, render excellent patterns for interpretation of nutrient:CHLa relationships. 

Figure 17. Z-CUSUM charts for CHLa behavior along TP and TN gradients. For TP 

there is a prevalent declining trend while for TN the tendency is to increase. 

4.c Statistical Significance of Thresholds 

Statistical significance of thresholds was evaluated with the non-parametric 

Mann-Whitney test (StatView®) and break-point analysis (Change-point Analyzer®, 

-100

-90

-80

-70

-60

-50

-40

-30

-20

-10

0

10

0.0000.0050.0100.0150.0200.0250.030

CHLa Z-Cusum (SD) .

TP (mg/l)

Backbay CHLaMod Threshold Median TP 75pctl TP

y = 67.712x - 0.016

R2 = 0.0965

0.00

2.00

4.00

6.00

8.00

10.00

12.00

14.00

16.00

18.00

0.00000.01000.02000.03000.04000.05000.0600

ug/l

TP (mg/l)

Backbay CHLa Backbay CHLa

-200

-150

-100

-50

0

50

0.0000.2000.4000.6000.8001.0001.2001.4001.600

CHLa Z-Cusum (SD) .

TN (mg/l)

BLK CHLa Bracket Z Threshold Median TN 75pctl TN

y = 1.5547x + 0.0999

R2 = 0.0361

0.00

2.00

4.00

6.00

8.00

10.00

12.00

14.00

0.00000.20000.40000.60000.80001.00001.20001.40001.6000

ug/l

TN (mg/l)

BLK CHLa BLK CHLa

-40

-30

-20

-10

0

10

20

30

0.0000.1000.2000.3000.4000.5000.6000.7000.800

CHLa Z-Cusum (SD) .

TN (mg/l)

CS CHLa Bracket Z Threshold Median TN 75pctl TN

-15

-10

-5

0

5

10

15

0.0000.0050.0100.0150.0200.0250.0300.035

CHLa Z-Cusum (SD) .

TP (mg/l)

CS CHLa Bracket Z Threshold Median TP 75pctl TP
44

Taylor 2000a). The former procedure compares above-threshold with below-threshold 

values, while the later procedure uses an iterative combination of CUSUM charts and 

bootstrapping to detect breaks in the slope (Hinkley 1971; Hinkley and Schechtman 

1987) and provides both confidence levels and confidence intervals for each change. 

4.d Limitations 

Statistical significance of thresholds was not always confirmed with the breakpoint analysis (Change-point Analyzer®, Taylor 2000a), even for well defined V-shaped 

charts, where the Mann-Whitney test confirmed the statistical significance of the 

threshold. Additionally, in some instances, where cycles are present in the CHL-a along 

the nutrient gradient, several breaks are confirmed with the brake-point analyzer 

software although not confirmed with the Mann-Whitney test. 

These discrepancies arise because of differences in methodology to assess a 

break and its statistical significance in each case. The Mann-Whitney test compares all 

raw (not z-scored) data below the selected threshold to all data above the selected 

threshold. The break-point software applies a continuous process on CUSUM data 

(internally calculated) and upon finding suspected breaks compares adjacent data 

before and after the suspected break, but not all before and after the break data. Our 

threshold assessment relies on the visual identification of an increasing trend (V or U 

shape) and the selection of the threshold preferentially at the inflexion point of the chart 

below the median nutrient concentration, preceded by a sustained below average 

portion of the time-series (negative slope in Z-CUSUM chart) and followed by a 

sustained average or above average group of CHL-a values (horizontal to positive slope 

in Z-CUSUM chart). 

 
45

4. Numeric Nutrient Criteria 

We propose the development of nutrient criteria that include only two limits: LT-L 

(Long-Term Limit) and UP-L (Upper Limit). The LT-L is the expected nutrient 

concentration in a waterbody at which no adverse effects would be expected or the 

effects would be insignificant for the type of designed use. The UP-L is a nutrient upper 

bound concentration of the LT-L that accounts for natural variability in nutrient drivers, 

for example annual seasonality and climatic variability. Both limits were evaluated for 

their consistency across segments within a waterbody, which led to nutrient limits for 

single segments and groups of segments. We recommend that (a) segments’ long-term 

geometric means do not exceed the LT-L and (b) segments’ annual geometric means 

meet1 the UP-L, at least, twice in three consecutive years, starting with the year of the 

assessment. For the purpose of this assessment, a segment long-term geometric mean 

is the mean of the natural log transformed data for all stations within a segment’s POR 

expressed in the original units. In the future, the Period of calculation may need 

redefining if significant water quality improvements are introduced into a waterbody. A 

segment’s annual geometric mean is the mean of the natural log transformed data for 

all stations within a segment expressed on the original units. Estimation of geometric 

means are based on a water year, which starts in October and ends in September. 

In the following example, we use the Biscayne Bay data to illustrate the application 

of the threshold methodology for the selection of numeric nutrient criteria. Figure 18 

shows the TN data in the left panel and TP data in the right panel. 

5.a Disturbed and Undisturbed Segments 

Descriptive statistics on the nutrient data, in particular, the 50th and 75th

percentiles were used to assess similarities and dissimilarities among segments. This 

information helped us identify segments with nutrient concentrations representative of 

                                                           

1 Be equal or less than the UP‐L
46

background (undisturbed) conditions and segments with elevated concentrations 

(disturbed). The latter group included segments which appear to be disturbed by 

anthropogenic sources or unusual (different) natural conditions (i.e. long residence 

time). Distinguishing one from the other was, in most cases, straight forward but 

required a certain knowledge and understanding of the local ecosystems. For Biscayne 

Bay, the 50th and 75th percentiles, which are shown in Figure 18 were used to 

differentiate between undisturbed and disturbed segments. The TN values are low and 

very similar for SCM, SCO, NCO, SNB, NNB, CS, and NCI (undisturbed) but higher and 

significantly different for SCI, and MBS (disturbed). The disturbed segment is located in 

the southern portion of the Bay and receives significant TN canal discharges from 

agricultural areas (SCI) (Szmant 1987; O’Hair and Wang 1999; Graves et al. 2004; 

Caccia and Boyer 2005, 2007). The TP values are low and very similar for SCM, CS, 

SCO, and SCI (undisturbed) but higher and substantially different for NCO, SNB, and 

NNB (disturbed). The disturbed group of segments is located in the northern portion of 

MBS,the Bay and is influenced by industrial and urban runoff discharges, which are 

typically TP enriched (Caccia and Boyer 2005, 2007). 

5.b Long-Term Limit (LT-L) 

Threshold values (TRHLD), which are shown as solid blue bars in Figure 18, are 

very consistent across the undisturbed segments, suggesting that a common TRHLD 

may be applicable to all segments within Biscayne Bay. The TRHLD ranges from 

0.133 mg/L to 0.137 for TN and is constant at 0.004 mg/L for TP, for undisturbed 

segments. Considering the consistency of these values and their small range, the LTLs were set to the arithmetic average of the TRHLDs, which for Biscayne Bay are 0.135 

mg/L TN and 0.004 mg/L TP. The LT-Ls are shown as solid black lines in Figure 18. 

The consistency of the TRHLDs across segments is remarkable and emphasizes 

the strength of the threshold approach. Natural temporal and spatial variability inherent 

within a waterbody is accounted for by averaging TRHLD values across segments. 

These averages are considered to be a reasonable expression for the LT-L. 
47

Figure14. Proposed numeric nutrient criteria for Biscayne Bay. Blue bars are 

individual thresholds (TRHLD); black dashed lines are proposed long-term limits (LT-L), 

and solid lines are proposed one-in-three years upper limits (UP-L). 

5.c Upper Limit (UP-L) 

The duration and frequency components of the criteria are incorporated in the 

UP-L. We recommend that the UP-L be met at least twice every 3 consecutive years. 

The UP-L was estimated as the upper 80th confidence interval (CI) for a normal 

distribution with a mean and standard deviation specified for each segment. The data 

was transformed to the log space (natural log) before descriptive statistics were 

estimated. The 80th CI was selected to achieve two objectives: (a) to account for 

natural temporal and spatial variability within the waterbody and (b) limit the rate of Type 

I errors. By selecting the 80th CI and a 1-in-3 frequency, the Type I error is 10.4 

percent. 

The Biscayne Bay UP-L was estimated by averaging the 80th CIs for undisturbed 

segments and is shown as a solid line in Figure 18. The TN 80th CI varied from 0.3 to 

0.42 mg/L and averaged 0.38 mg/L, for undisturbed segments; the TP CI ranged from 

0.008 to 0.009 mg/L and averaged 0.009 mg/L, for undisturbed segments. The 

proposed UP-Ls were set to 0.38 mg/L TN and 0.009 mg/L TP. The limits were 

rounded off to the nearest 0.01 and 0.001 mg/L for TN and TP, respectively. It is worth 

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

CS MBS NCI NCO NNB SCI SCM SCO SNB

TN mg/l

Main TN Threshold

TN median

TN 75th pctl

0.000

0.002

0.004

0.006

0.008

0.010

0.012

0.014

CS NCI NCO NNB SCI SCM SCO SNB MBS

TP mg/l

Main TP Threshold

TPmedian

TP 75th pctl
48

noting that in Biscayne Bay disturbed TP segments and disturbed TN segments may 

not be collocated because the northern bay is more impacted by TP-rich discharges and 

the southern by TN-rich discharges. Also, the proposed UP-Ls for TP did not vary 

significantly whether the NCO disturbed segment was included or excluded from the 

calculations. 

6 Acknowledgements 

The authors thank the NPS and specially Dr. Joffre Castro for the support and invaluable 

cooperation to complete this research. 

This report is contribution # T-501 of the Southeast Environmental research Center at 

Florida International University
49

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