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
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
2 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
3
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
4 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.
5 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-to- mid 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 dose- experiments. 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.
6 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 [email protected]
7 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.
8 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 US- National 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).
9
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).
10 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
11 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.
12
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
13 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
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
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
14 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
Whitewater Bay Pine Island Biscayne Bay Florida Bay Florida Keys Gulf Shelf Ten Thousand Rookery Bay 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
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/).
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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 z- scored 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
16 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
17 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.
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
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
18
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.).
1 .8
.8 .6 .6 TN
TN .4 .4 .2 .2
0 BB FB FK PIRB SHELF WWB- TTI 0 1 2 3 4 5 6
.08 .02 101 102 103 104 108 109 110 111 112 113 116 121 122 123 124 126 127 128 129 130 131 132 133 134 135
.06 .016 .012 TP TP .04 .008 .02 .004 0 0
BB FB FK PIRB SHELF WWB- TTI 1 2 3 4 5 6 4 10 101 102 103 104 108 109 110 111 112 113 116 121 122 123 124 126 127 128 129 130 131 132 133 134 135 8 3 6 2 CHLA 4 CHLA 1 2 0 0 1 2 3 4 5 6 01 02 03 04 08 09 10 11 12 13 16 21 22 23 24 26 27 28 29 30 31 32 33 34 35 BB FB FK PIRB SHELF WWB-TTI 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
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.
19
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,
20
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.
Basin FB PIRB BB1 BB2 SHELF WWB‐TTI FK Mar‐91 Jan 99 Sep 93 Jun 96 May 95 Sep 92 Mar 95 POC Dec‐07 Sep 09 Sep 08 Sep 08 Sep 07 Sep 08 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 forDODODODODODODO TURB TURB TURB TURB TURB TURB TURB NH4 NH4 NH4 NH4 NH4 NH4 TEMP Factor NO3 NO3 NOx NOx NOx NO2 NO2 NO2 NO2 AnalysisSRPSRPSRPSRP TEMP TON Stations 28 29 21 30 49 47 155 PC6555444 Accounted 79% 81% 76% 73% 63% 75% 66% Variance Segments 4(+2) 6 6(+1) 9 3 8 7
2.d Definition of Segments
Final selection of the number of segments was in part subjective and knowledge- based. 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
21 (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 1CFB 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 6WFB West Florida Bay ROOKERY BAY 4 NPL Naples Bay 1BLK 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 6PD Ponce de Leon 3 OGS Outer Gulf Shelf 7SRM Shark River Mouth 8 WWB Whitewter Bay
22
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 Island- Rookery Bay region .
23
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
24 area maps for each county into a single layer; however, for Miami-Dade County, an in- house 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 PC- cluster 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
25 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.
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
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
26 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.
27
Figure 7. Subdivision of estuarine and coastal waters.
Figure 8. Current circulation patterns in southwest Florida coasts (modified after Lee et al. 2003).
28
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 early- mid 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).
20 Everglades National Park 10 Inflow and Rainfall
0 0 Cusum ‐ Cusum Z ‐ ‐20 ‐10 Z
Flow ‐40 ‐20 Rainfall
‐60 ‐30 1970 1975 1980 1985 1990 1995 2000 2005 2010
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)
29
.12
.1
.08
Pos t- s hif t .06 TP Pr e- s hif t
.04
.02
0 CI GI CL CS EB PD MR NCI SCI IGS CTZ BLK NPL CFB NFB SFB NNB SCB NCO SCO IWW MBS OGS SCM SRM MGS PINE WFB ECFB SNFB WWB MARC 2.25 2 1.75 1.5
1.25 Pos t- s hif t
TN 1 Pr e- s hif t .75 .5 .25 0 CI GI CL CS EB PD MR NCI SCI IGS CTZ BLK NPL CFB NFB SFB NNB SCB NCO SCO IWW MBS OGS SCM SRM MGS PINE WFB ECFB SNFB WWB MARC 16 14 12 10 Pos t- s hif t 8 Pr e- s hif t CHLA 6 4 2 0 CI GI CL CS EB PD MR NCI SCI IGS CTZ BLK NPL CFB NFB SFB NNB SCB NCO SCO IWW MBS OGS SCM SRM MGS PINE WFB ECFB SNFB WWB MARC
Figure 10. Changes in TP, TN and CHLa concentrations nearly-to-mid 1990’s from pre- to post-climate shift as proposed by Briceno and Boyer (2010).
30 2.25 2 1.75 1.5
1.25 Pos t- s hif t
TN 1 Pr e- s hif t .75 .5 .25 0 CI GI CL CS EB PD MR NCI SCI IGS CTZ BLK NPL CFB NFB SFB NNB SCB NCO SCO IWW MBS OGS SCM SRM MGS PINE WFB ECFB SNFB WWB MARC .14
.12
.1
.08 Pos t- KRW
TP Pr e- KRW .06
.04
.02
0 CI GI CL CS EB PD MR NCI SCI IGS CTZ BLK NPL CFB NFB SFB NNB SCB NCO SCO IWW MBS OGS SCM SRM MGS PINE WFB ECFB SNFB WWB MARC 18 16 14 12
10 Pos t- KRW 8 Pr e- KRW CHLA 6 4 2 0 CI GI CL CS EB PD MR NCI SCI IGS CTZ BLK NPL CFB NFB SFB NNB SCB NCO SCO IWW MBS OGS SCM SRM MGS PINE WFB ECFB SNFB WWB MARC 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.
31 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 re- suspension 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
32 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
33
Sta 1 TN (mg/l) Sta 1 TN
2 60 50 1.5 40 30 1 20 10 mg/l TN 0.5 TN Z-cusum 0 -10 0 Jan-90 Jan-92 Jan-94 Jan-96 Jan-98 Jan-00 Jan-02 Jan-04 Jan-06 Jan-08 Jan-90 Jan-92 Jan-94 Jan-96 Jan-98 Jan-00 Jan-02 Jan-04 Jan-06 Jan-08 Jan-10
Sta 1 TP mg/l Sta 1 TP
0.07 20 0.06 10
. 0.05 0 0.04 -10 0.03 -20
mg/l TP 0.02 -30 TP Z-Cusum TP Z-Cusum 0.01 -40 0.00 -50 Jan-90 Jan-92 Jan-94 Jan-96 Jan-98 Jan-00 Jan-02 Jan-04 Jan-06 Jan-08 Jan-10 Jan-90 Jan-92 Jan-94 Jan-96 Jan-98 Jan-00 Jan-02 Jan-04 Jan-06 Jan-08 Jan-10
Sta 1 CHLA (ug/l) Sta 1 CHLa
8.00 10 7.00 0 6.00 -10 5.00 -20 4.00 -30 3.00 -40 ug/l CHLa 2.00
1.00 Z-Cusum CHLa -50 0.00 -60 Jan-90 Jan-92 Jan-94 Jan-96 Jan-98 Jan-00 Jan-02 Jan-04 Jan-06 Jan-08 Jan-10 Jan-90 Jan-92 Jan-94 Jan-96 Jan-98 Jan-00 Jan-02 Jan-04 Jan-06 Jan-08 Jan-10
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.
34 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.
100.0 100.0
BB BB FK FK FB 10.0 FB 10.0 Shelf Shelf WWB WWB TTI TTI 1.0 RB 1.0 RB Log CHLa (ug/l) CHLa Log CAL (ug/l) CHLa Log CAL TB TB 0.1 0.1 0.1 1.0 10.0 100.0 10.0 100.0 Log TP (uM/l) Log TN (uM/l)
Figure 13. Relationship between CHLa and TN and TP concentration in South Florida.
35 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).
36 227 Th N (unit) 227 Th N (unit) Z 227 Th P (unit) 227 Th P (unit) Z
3.0 16.0 0.2 10.0 14.0 0.2 2.5 8.0 12.0 0.1 2.0 10.0 0.1 6.0 8.0 0.1 1.5 4.0 6.0 0.1 unit unit 1.0 4.0 0.1 2.0 2.0 0.0
0.5 . (SD) Z-Cusum 0.0 . (SD) Z-Cusum Declining 0.0 0.0 Declining 0.0 -2.0 0.0 -2.0 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
227 Th np (unit) 227 Th np (unit) Z
60.0 12.0
50.0 10.0
8.0 40.0 6.0 30.0 4.0 unit 20.0 2.0
10.0 0.0 Z-Cusum (SD) . Declining 0.0 -2.0 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
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
37 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.
TN Below Raw data 0.30 Anomaly plot 0.25 Above 0.50 0.45 0.20 0.40 0.15 0.35 0.10 0.30 0.25 0.05 0.20 0.00 0.15 -0.05 0.10 0.05 -0.10 0.00 -0.15 ‐ wi=(xi‐X)
0.2 Cumulative anomaly 2.0 Z‐Cusum 0.1 1.0 a c
0.0 0.0 -1.0 -0.1 -2.0 -0.2 -3.0 -0.3 -4.0 -0.4 -5.0 -0.5 ‐ -6.0 -0.6 w =w + (x ‐X) -7.0 b ‐ i i‐1 i wi=wi‐1 + (xi‐X)/σ Running sum of anomaly data Running sum of z‐scored data (deviation expressed as multiples of σ) (deviation expressed in original units)
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.
38 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 V- shaped 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 = 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. 39 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. a) Driver threshold b) Response threshold c) Driver‐Response hysteresis Driver Driver Driver Time Time Time Response Response Response Time Time Time Response Response Response Driver Driver Driver Cusum Cusum ‐ ‐ Z Z Cusum ‐ Z Response Response Response Driver Driver Driver Modified after Andersen et al 2008 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. 40 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 Z- CUSUM 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 41 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, WWB- TTI). 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 above- average 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. 42 BLKBLK CHLa CHLa BackbayBackbay CHLa CHLa 14.00 18.00 12.00 16.00 y = 67.712x - 0.016 14.00 R² = 0.0965 10.00 y = 1.5547x + 0.0999 R² = 0.0361 12.00 8.00 10.00 6.00 8.00 ug/l ug/l 6.00 4.00 4.00 2.00 2.00 0.00 0.00 0.0000 0.2000 0.4000 0.6000 0.8000 1.0000 1.2000 1.4000 1.6000 0.0000 0.0100 0.0200 0.0300 0.0400 0.0500 0.0600 TN (mg/l) TP (mg/l) BLK CHLa Bracket Z Threshold Median TN 75pctl TN Backbay CHLaMod Threshold Median TP 75pctl TP 50 10 0 0 -10 -20 -30 -50 -40 -50 -100 -60 CHLa Z-Cusum . (SD) Z-Cusum CHLa CHLa Z-Cusum . Z-Cusum (SD) CHLa -70 -150 -80 -90 -200 -100 0.000 0.200 0.400 0.600 0.800 1.000 1.200 1.400 1.600 0.000 0.005 0.010 0.015 0.020 0.025 0.030 TN (mg/l) TP (mg/l) 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. CS CHLa Bracket Z Threshold Median TP 75pctl TP CS CHLa Bracket Z Threshold Median TN 75pctl TN 15 30 10 20 10 5 0 0 -10 -5 -20 CHLa Z-Cusum (SD) . CHLa Z-Cusum (SD) . (SD) Z-Cusum CHLa -10 -30 -15 -40 0.000 0.005 0.010 0.015 0.020 0.025 0.030 0.035 0.000 0.100 0.200 0.300 0.400 0.500 0.600 0.700 0.800 TP (mg/l) TN (mg/l) 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®, 43 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 break- point 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). 44 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 45 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 LT- Ls 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. 46 0.7 0.014 Main TP Threshold Main TN Threshold TP median 0.6 TN median 0.012 TP 75th pctl 0.5 TN 75th pctl 0.010 0.4 0.008 mg/l mg/l 0.3 0.006 TN TP 0.2 0.004 0.1 0.002 0 0.000 CS MBS NCI NCO NNB SCI SCM SCO SNB CS NCI NCO NNB SCI SCM SCO SNB MBS 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 47 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 48 REFERENCES Anton, A., J. 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