# Empirical Approaches to Establishing

Numeric Nutrient Criteria for

Southwest Florida Estuaries

Prepared for:
Tampa Bay Estuary Program

Sarasota Bay Estuary Program

Charlotte Harbor Nati onal Estuary Program

Prepared by: Janicki Environmental, Inc. St. Petersburg, Florida

14 July 20102 

## FOREWORD

This document was authored by Tony Janicki, Ra y Pribble, Mike Wessel, and Keith Hackett of Janicki Environmental, Inc, with funding provid ed by the Tampa Bay Estuary Program and the
Sarasota Bay Estuary Program.3 

## ACKNOWLEDGMENTS

We wish to express our appreciation for the assi stance and input recei ved in developing this document, during and following a meeting at which a draft outline was presented to a group of managers and scientists in the southwest Flor ida community. Meeting attendees and their

affiliations are as follows:

Mr. Mark Alderson, Sarasota Bay Estuary Program Dr. Lisa Beever, Charlotte Harbor National Estuary Program Mr. Rob Brown, Manatee County Natural Resources Department Ms. Veronica Craw, Southwest Florida Water Management District Mr. Bruce DeGrove, Consultant to Mosaic Fertilizer, LLC. Ms. Lizanne Garcia, Southwest Florida Water Management District Mr. David Glicksberg, Hillsborough County Public Works Ms. Kris Kaufman, Southwest Florida Water Management District
Dr. Jay Leverone, Sarasota Bay Estuary Program Ms. Kelli Levy, Pinellas County Departme nt of Environmental Management Ms. Sue Moore, Florida Department of Transportation Ms. Judy Ott, Charlotte Harbor National Estuary Program Mr. John Ryan, Sarasota County Water Resources Mr. Ed Sherwood, Tampa Bay Estuary Program Mr. Jeff Stewart, Mosaic Fertilizer, LLC.4 

## 1.0 Background

The U.S. Environmental Protecti on Agency (USEPA) is developing numeric nutrient water quality standards for Florida waters, including lakes and flo wing waters, and estuaries and coastal waters.
The schedule for estuarine and coastal water crit eria has been recently modified and requires USEPA to propose estuarine and coastal waters nutrient criteria and downstream protective values in Florida by November 14, 2011 to allow for peer review by the Science Advisory Board (SAB) and to allow for public comment, followed by USEPA revision of the proposed numeric nutrient

criteria.

This document presents potential empirical methods for use in developing numeric nutrient criteria for southwest Florida estuaries. The methods discussed below build on research developed by the Tampa Bay, Sarasota Bay, and Charlotte Harbor National Estuary Programs and on previous USEPA work (USEPA, 2009) and reviews by its Science Advisory Board (SAB, 2010).

After review and comment by USEPA and any furthe r peer review, we plan to apply these methods to selected southwest Florida estuaries, a nd make recommendations for appropriate estuarine
numeric nutrient criteria and commensurate Downst ream Protective Values for upstream waters.



### 1.1 Definition of Numeric Nutrient Criteria and Regulatory Framework

Numeric nutrient water quality criteria define levels of nutrients (i.e., nitrogen and phosphorus) protective of the designated uses of water bodies from over-enrichment, as prescribed by the Clean Water Act (CWA). Over-enrichment of water bodies by nitrogen and phosphorus typically stimulates plant and microbial growth, and can re sult in biological and physical responses that adversely affect water quality and aquatic life. The USEPA nutrient crite ria guidance recommends development of criteria for both total nitrogen (TN) and total phosphorus (TP), the primary causal constituents, and for chlorophyll a and water clarity, the primary response constituents, while not precluding the use of alternative causal or response constituents (USEPA, 2009).

The numeric nutrient criteria to be proposed by U SEPA are the result of legal action brought against USEPA. In 2008, Earthjustice, a public interest law firm, filed a lawsuit to require USEPA to promulgate numeric nutrient water quality standards for Florida waters. The lawsuit was filed on behalf of the Florida Wildlife Federation, Sie rra Club, Conservancy of Southwest Florida,
Environmental Confederation of Southwest Florid a, and St. Johns Riverkeeper. On January 14, 2009, USEPA made a determination under section 303(c)(4)(B) of the CWA that numeric nutrient water quality criteria for lakes and flowing waters and for estuaries and coastal waters were necessary for the State of Florida to meet the requirements of the CWA section 303(c), and that Florida’s existing narrative nutrient criteria were insu fficient to ensure protection of the State’s water bodies. This determination recognized that althou gh Florida had expended considerable effort to diagnose and control nutrient pollution, water quality degradation from nutrient pollution was significant and likely to worsen in the State. Th e January 2009 determina tion noted USEPA’s intent to propose numeric nutrient standards for lakes a nd flowing waters within twelve months, and for
estuaries and coastal waters within 24 months.

In August 2009, USEPA entered into a Consen t Decree with Earthjustice to settle the 2008 litigation. USEPA committed to proposing numeric nutrient standards for lakes and flowing waters5 by January 2010, and for estuaries and coastal waters by January 2011. Final standards were to be established for lakes and flowing waters by Octobe r 2010 and for estuaries and coastal waters by October 2011. As noted above, both parties to the USEPA Consent Decree recently agreed to extend the timeline for proposed estuarine and co astal waters nutrient criteria to November 14, 2011, and to August 15, 2012 for finalized criteria.

The State of Florida had responded to the CWA in the late 1990s by developing a method to identify water bodies that exceeded water quality standards. Section 303(d) of the CWA requires
states to submit lists of surface waters that do not meet applicable water quality standards and establish Total Maximum Daily Loads (TMDLs) fo r these waters. TMDLs establish the maximum amount of a pollutant that a water body can assimilate without causing exceedances of water quality standards. Chapter 99-223, Laws of Florid a, known as the Florida Watershed Restoration Act (FWRA), defines a TMDL as “...the sum of the individual wasteload allocations for point sources and the load allocations for nonpoint source s and natural background. Prior to determining
individual wasteload allocations and load allocati ons, the maximum amount of a pollutant that a water body or water segment can assimilate fr om all sources without exceeding water quality
standards must first be calculated.”

The FWRA directed the Florida Department of Environmental Protection (FDEP) to develop a methodology, and adopt it by rule, that clearly de fined those waters that should be included in the state’s 303(d) list of impaired waters (those wate rs that do not meet applicable water quality standards and for which TMDLs must be developed). FDEP formed a Technical Advisory Committee (TAC) to develop a method to define impair ment of lakes, streams, and estuaries. The TAC meetings started in July 19 99, and in 2001 the Environmental Regulation Commission adopted by rule (Chapter 62-303, F.A.C., Identification of Impaired Surface Waters, or IWR) the scientific approach for guiding FDEP’s proc ess for identifying and prioritizin g impaired surface waters in
Florida.

FDEP implemented a detailed, USEPA-approved plan for the development of numeric nutrient criteria and proposed revisions to Chapter 62-302 , F.A.C. (Water Quality Standards) and Chapter 62-303, F.A.C. (Impaired Waters Rule) (IWR) to establish numeric nutrient criteria for lakes and streams. The plan called for adoption of crite ria by the end of 2010, but was accelerated in response to USEPA’s January 2009 determination that criteria are necessary to implement the CWA. The revised schedule following the January 2009 de termination, as noted above, was for proposed criteria for lakes and flowing waters by January 2010, and for estuaries and coastal waters by January 2011.

These numeric nutrient criteria were to be in ad dition to the narrative nutrient standard used for years by Florida, stated in Chapter 62-302.530, F.A.C., “...in no case shall nutrient concentrations of body of water be altered so as to cause an im balance in natural populations of flora or fauna.” FDEP planned to develop numeric nutrient criteri a and use these to assess waters for nutrient impairment, in addition to using the current narrati ve nutrient impairment thresholds in the IWR. Nutrients are present naturally in aquatic systems in many different forms, are necessary for proper functioning of biological communities, and ar e often moderated in their expression by many
natural factors.

The January 2009 determination by USEPA of th e necessity for criteria also included the assumption of responsibility for development of th ese numeric nutrient criteria by USEPA. In a
letter from USEPA Office of Water Assistant Admini strator Grumbles to FDEP Secretary Sole, it was6 noted that section 303(c)(4) of the CWA requires the Administrator to “...promptly prepare and publish proposed regulations setting forth a new or revised water quality standard...”, and that “EPA will move forward to develop federa l proposed regulations setting fo rth numeric nutrient criteria for Florida...” As of this time, the schedule for propos ed criteria for estuaries and coastal waters is November 14, 2011, and for finalized criteria by August 15, 2012.



### 1.2 The Role of Nutrients in Estuaries

Estuaries are semi-enclosed bodies of water that have a free connection with the sea and within which seawater is diluted by freshwater (Hobbie, 2000). Estuaries are the most productive of all aquatic ecosystems. The tidal sheltered waters of estuaries support highly productive and diverse communities of plants and animals that live at th e margins of the sea (USEP A, 1999; Hobbie, 2000). Tides and rivers provide a constant flow of water and nutrients that result in a beneficial environment for primary producers that form the ba se of the maritime food web. Estuaries typically
form transition zones between the land and the se a, and are efficient at retaining dissolved chemicals and pollutants entering the estuarine wate rs prior to discharging to the open sea. One study estimated the retention of nitrogen and phosphorus supplied to the Potomac River at 82% (Bennett, 1983), with most of the retention in the estuarine zone near the mouth of the river. Another study found that nitrogen retention was 87% of input, and phosphorus retention was 72%, for the Patuxent River estuary (Boynton et al., 2008).

Nutrient inputs (TN and TP) from watersheds adj acent to coastal and estuarine waters can have significant impacts on estuarine function. High ra tes of nutrient inputs often stimulate very high rates of primary productivity. Because of this high primary productivity, estuaries provide breeding and nursery grounds for many species of fish and shellfish fisheries. Hundreds of marine organisms, including commercially valuable fish and, depend on estuaries to provide valuable habitat during different stages of their life cy cles (Day et al., 1989; USEPA, 1999; McLusky and Elliott, 2004).

Since the 1970s, many scientists and managers have been studying the deterioration of estuarine ecosystems due to increases in nutrient loads a nd accompanying eutrophication (Paerl et al., 2006; Bricker et al., 2008; Fisher et al., 2006). The targe ting of nutrient inputs from points sources, such as sewage outfalls and industrial effluent, for loading reductions was met with much success, yielding improved water quality following implemen tation of advanced waste water treatment (e.g., Greening and Janicki, 2006). Unfortunately, pop ulation growth and the growing need for agricultural output have led to an increase in nonpoi nt source pollution. It is estimated that human activity has increased the total rate of formati on of reactive nitrogen globally by 33-55% through increases in agriculture via synthetic fertilizer (H owarth, 2008). Increases in reactive nitrogen have
also resulted from increases in th e encouragement of biological nitrogen fixation associated with agriculture and the inadvertent creation of reacti ve nitrogen oxides through fossil fuels combustion (Howarth, 2008; Paerl, 2006). For example, estimated increases of TN loadings to the Chesapeake Bay system are 6-8 times since the pre-colonial period, and TP loadings have increased by 13-24 times (Boynton et al., 1995).

Eutrophication in estuaries, as expressed by in creased phytoplankton production, is linked to increased loadings of nitrogen. Estuarine eutrophi cation is defined as an increase in the rate of supply of organic matter, which includes nitrogen and phosphorus, in estuarine ecosystems which leads to increased rates of primary production (Nix on, 1995; Pinckney et al., 2001). An extensive7 study including information from 63 estuaries around the world was completed, utilizing data concerning phytoplankton production, chlorophyll a , and associated physical and chemical variables (Boynton et al., 1982). The authors found a significant positive relationship between annual phytoplankton production and nitrogen input s, but not between production and phosphorus inputs. The authors also conclude d that nutrient loading rates and recycling rates were more useful than standing stock values for predicting pr oductivity (Boynton et al., 1982). Relationships between nutrient loadings and primary production in estuaries have been used to determine the likely trophic status of estuaries (Painting et al., 2007).

The impacts of eutrophication and how anthropogeni c impacts affect the structure and function of
estuaries continues to be a research thesis for scientists and managers worldwide (Paerl et al., 2006). Eutrophication has resulted in documented cases of reduced biodiversity, habitat degradation, and food web alterations (Nixon, 19 95; Rabalais and Turner, 2001; Paerl et al., 2006; Bricker et al., 2008). Important sources of nutrien ts that support estuarine productivity include nonpoint and point source i nputs from the watershed and atmospheric deposition.

Symptoms of water quality decline ar e typically increased chlorophyll a and macroalgae, low dissolved oxygen, loss of submerged aquatic vegeta tion, and occurrences of harmful algal blooms (HABs) (Bricker et al., 2008). Chlorophyll a , a pigment used in photosynthesis, serves as a measure of biomass (abundance) of phytoplankton in estuarie s. Planktonic algae provide a food source for filter-feeding bivalves (oysters, mussels, scallops, clams) and zooplankton (including the larvae of crustaceans and finfish). Chlorophyll a concentrations can also be used as a measure of overall ecosystem health. High concentrations of chloro phyll a in estuarine waters are a primary indicator of nutrient pollution, as excess nutrient supply fu els the growth of algae. Therefore, high chlorophyll a concentrations can be indicative of ad verse conditions for aquatic life and human

recreation.

Dissolved oxygen (DO) is a critical factor affecting the available ha bitat in estuaries. Dissolved oxygen can be used as an indicator of the he alth of the ecosystem. Cultural eutrophication (nutrient excess leading to overproduction of mi croalgae and associated trophic imbalances) is common in estuaries near human population centers, with evidence of increasing areas impacted by low DO conditions (Hagy et al., 2004; Diaz and Rosenberg, 2008; Rabalais and Turner, 2001). DO can exhibit extreme diel cycles as a result of estuarine eutrophication. Algal photosynthesis elevates DO levels in the water during the day, but at night, when ecosystem respiration is typically greatest, DO can drop dangerously low. Eutrophica tion can lead to periodic or long-term hypoxia (generally considered to be water colu mn DO concentrations less than 2 mg O 2 /l) (Diaz and Rosenberg, 1995) and anoxia (complete absence of DO) in estuarine ecosystems. Fish, crabs, and shrimp can avoid hypoxic conditions, while othe r marine animals demonstrate the ability to withstand prolonged exposure to hypoxic conditions . DO concentrations are often quite variable in estuarine systems due to fluctuations in temp erature, salinity, basin morphology, and overall productivity. Not all low DO conditions are the result of eutrophication, however. Some estuarine systems are susceptible to low DO conditions due solely to physi cal factors (depth, stratification, limited tidal exchange) (Hagy and Murrell, 2007).

Seagrasses are often used as indicators of estuarin e ecosystem health. Estuarine management efforts have been implemented along the southwest Florida coast to increase seagrass acreage by reducing nutrient loads, and have resulted in increasing water clarity and maintenance or increases in seagrass coverage (Corbett and Hale, 2006; Greening and Janicki, 2006). Seagrasses serve several significant functions within estuaries, which ar e degraded if seagrass coverage declines. Human8 activities can harm seagrasses by degrading es tuarine water quality and promoting physical disturbances and algal blooms, o ften attributable to eutrophication and decreased water clarity (Dawes et al., 2004; Iverson and Bittaker, 1986). Seagrasses provide food, shelter, and essential nursery habitats for many recreationally and commerci ally valuable species of fish, crustaceans, and shellfish (Dawes et al., 2004). They also provid e food and habitat for marine mammals, such as manatees, listed species, and waterfowl. Seagrasses also help maintain water clarity by trapping fine sediments and particles with their leaves a nd stabilizing the estuarine sediments with their roots (Fonseca, 1989; Short et a l., 2000). Seagrasses are very effective at removing dissolved
nutrients from water that can enter from land runoff. The removal of sediment and nutrients improves water clarity, thereby improving overall ecosystem health.

Water clarity is a measure of light penetration through the water. Relatively clear waters are indicative of a healthy estuary, although many factors impact water clarity. Excess suspended sediments from runoff and rainfall can negatively imp act water clarity. Nutrients, mainly nitrogen and phosphorus, can fuel the growth of photosynthesizing algae. High chlorophyll a concentrations associated with high algal biom ass can decrease water clarity. Reduced light transmission can decrease seagrass abundance (Boy nton et al., 1996; Stevenson et al., 1993), which can in turn impact the entire food web. Decr eases in seagrass abundance also reduce habitat availability to the hundreds of species which depend on them.



### 1.3 Estuarine Responses to Changes in Nutrient Concentrat ions and Loadings

Increased nutrient loads are normally expected to resu lt in increased algal biomass in an estuary. The response of estuarine water qua lity to varying nutrient loads is dependent on other factors as well, including freshwater inflow, residence time, water clarity, estuarine physiography, and trophic interactions (Howarth and Marino, 2006; Swan ey et al., 2008). Freshwater inflow and tidal exchange determine hydraulic residence time, and he nce the time available for nutrients to react in the estuary (Hagy et al., 2000; Bricker et al., 2008). Freshwater inflow can affect algal abundance and therefore chlorophyll a concentrations via enhanced nutrient supply, changing the location of peak chlorophyll a abundance, or decreasing chlorophyll a abundance because of reduced residence time (Hagy, 1996). When fres hwater inflow is low, the chlorophyll a maximum is typically located further up-estuary than during times of high flow. Low flow also allows a longer residence time for chlorophyll a and nutrients. Longer residence times tend to promote slowergrowing taxa which include dinoflagellates and cyanobacteria (Pinckney et al., 1999), and also allow faster-growing algal taxa to increase eve n more. During high flo w conditions, flushing is more rapid and residence time in rivers and th e estuary is reduced (Sheldon and Alber, 2006; Miller and McPherson, 1991; Hagy et al., 2000). These conditions tend to favor fast growing phytoplankton such as chlorophytes (green algae) and various flagellates (Pinckney et al., 1999). At times, high flows can be excessive, depending on th e morphology of the estuary. Very high flows may not result in higher chlorophyll a abundance due to the relationship between the residence time of water within the system and uptake and growth rates of the phytoplankton community. Changes in flow can also impact the commun ity composition with less desirable species dominating during times of low flow and long er residence times (Bricker et al., 2008).

The dependence of water clarity on colored substances and suspended solids is of particular importance in southwest Florida estuaries, more so than in most other estuarine regions. Dissolved and suspended matter may have more influences on wa ter clarity than does algal biomass (Corbett and Hale, 2006; Christian and Sheng, 2003). The watersheds of southwest Florida estuaries are9 characterized by significant wetland extents that contribute relatively large amounts of colored dissolved organic matter (CDOM) to the estu aries (McPherson and Miller, 1987; Dixon and Kirkpatrick, 1999). CDOM must be considered when developing relationships between incident light and light at depth within southwest Florida estuaries.



## 2.0 Objective

The objective of this document is to identify potential methods for derivation of technically
defensible numeric nutrient criteria for Southw est Florida estuaries (Figure 2-1), based on scientifically sound and robust methods. Input from USEPA and others is encouraged.

To this end, the remainder of this document contains the following:

• Approach and Rationale – A discussion of th e approaches available to derive numeric nutrient criteria, identification of the approp riate approach, and the methods we will employ to use this approach for numeric nutrient criteria derivation.

• Implementation – A discussion of factor s to consider when developing an implementation strategy, including both technical aspects (e.g., temporal and spatial extents, seasonality, natural variability) and regulatory aspects (e.g., time period for compliance evaluation, etc.).10 Figure 2-1. Southwest Florida estuaries – Tampa Bay, Sarasota Bay and Charlotte Harbor.



## 3.0 Approach and Rationale

The USEPA has previously furnished guidance for de riving nutrient criteria in the form of peerreviewed technical approaches and methods (USEPA, 2000a; 2000b; 2001; 2008; 2009). USEPA clearly states their view on development of nutrien t criteria in the recent Science Advisory Board Review Draft (USEPA, 2009).

“USEPA’s view is that the criteria derivation process for the toxic effect of chemical pollutants is not applicable for nutrients be cause effects, while li nked to widespread and significant aquatic degradation, occur through a process of intermediate steps that cannot be easily tested in simple la boratory studies. As a result, nutrient criteria derivation relies in large part on empirical analysis of field data.”11 

### 3.1 Approaches

USEPA and others have identified three analytical approaches for the development of nutrient

criteria:

 the reference condition approach,

 stressor-response analysis, and

 mechanistic modeling.

The approaches are briefly described below.

3.1.1 Reference Condition Approach

As implied by the name, the reference condition a pproach is based on determining criteria based on a group of reference waterbodies. The referenc e waterbodies are selected from among a group of like waterbodies (e.g., the same class of wa terbodies) that represent minimally disturbed conditions (Stoddard e t al., 2006) and have similar characteristi cs (e.g., black-water streams).

Data from the reference waterbodies are assembled and the distributions of either causal or response variables are analyzed (Figure 3-1). Because these reference waterbodies typically are intended to represent minimally disturbed cond itions (or at least an acceptable level of disturbance), USEPA has used specific percentile s derived from these sy stems (USEPA, 2009) to develop nutrient criteria. Genera lly, some percentile of the reference stream distribution is chosen to represent the criterion value but caution should be used in selecting the appropriate value to serve as the benchmark criterion value (Rohm et. al., 2002; Suplee et al., 2007).

Figure 3-1. Example of a Reference Condition approach to set a numeric TN criterion.12 3.1.2 Stressor-Response Approach

The stressor-response approach consists of developing relationships between nutrient concentrations or loads and biological responses. The biological responses should be related to the “designated use of a waterbody (e.g., a biological i ndex or recreational use measure) either directly or indirectly, but ideally quantitatively” (USEPA, 2009). After quantitative relationships have been developed, the nutrient criterion that is protec tive of the specific designated uses can be determined (Figure 3-2).

USEPA (2009) has provided guidance on the development of stressor-response relationships using empirical data analysis approaches and a review of these approaches by the Science Advisory Board (SAB, 2010) has provided additional insights as to how evidence of stressor-response relationships may be used in establishing num eric nutrient criteria. A more comprehensive discussion of stressor-response methods for dete rmining numeric nutrient criteria can be found
below.

3.1.3 Mechanistic Modeling Approach

The mechanistic modeling approach is used to pred ict specific constituents based on a series of equations and algorithms that represent physical, ch emical, biological, and ecological processes. Mechanistic models include a wide variety of wa ter quality models, some of which were briefly described in previous USEPA nutrient criteria guidance documents (USEPA 2000a, 2000b). A much more in depth discussion of water quality modeling theory and practice can be found in a wealth of references (e.g., Chapra, 1997; Ma rtin and McCutcheon, 1998; Edinger, 2002).

Mechanistic models are valuable tools and where available and useful we intend to explore their use in supporting the development of numeric nut rient criteria for southwest Florida estuaries. Mechanistic models tend to integrate information on the interactions of major ecosystem processes to derive quantitative estimates of effects and mayb e valuable in interpreti ng the stressor-response relationship (SAB, 2010). However, their first-o rder approximations can underestimate the variability and uncertainty in the predictions.

Since reference system approaches tend to be based on subjective decisions on what constitutes the reference condition, we consider them to be the le ast desirable of the three approaches. For cases where limited data exist this may be the only viable option to set site-specific water quality criteria.
The remainder of this document focuses on defining proposed meth ods for developing the stressorresponse approach and implementing the results for developing numeric nutrient criteria in southwest Florida estuaries. However, it is im portant to note that all three methods will be evaluated and potentially utilized to set numeric nut rient criteria for southwest Florida estuaries.



### 3.2 Framework for Establishing Numeric Nutrient Criteria

USEPA has explicitly stated that any of the method s described above may be used individually or some combination of methods may be used to de rive numeric nutrient criteria (USEPA, 2009). For
southwest Florida estuaries where sufficient data are available, the stressor-response approach is preferable for delivering a weight-of-evidence that can be used in the development of appropriate

numeric nutrient criteria.13 As part of its review of the USEPA Empirical Approaches for Nutrient Criteria Derivation , the SAB recommended revisions to the 5-step framework presented in that document. The revised framework provides a very useful means to or ganize our discussion of potential methods for establishing numeric nutrient criteria for southwest Florida estuaries (Figure 3-2).

Step 1 Problem Formulation and Goal Development

Step 2 Conceptual Model Development

Step 3
Data Selection and Evaluation

Step 4 Establish Stressor-Response Approach

Step 5 Model Stressor-Response Relationship

Step 6 Evaluation of Modeled Relationship Consider Other Methods if Necessary

Step 7
Establish Criteria Using a Weight-of-Evidence ApproachIs StressorResponse Appropriate*? Consider
Other
Methods NO

YES

Figure 3-2. Framework for Establishing Nume ric Nutrient Criteria (modified from SAB, 2010).

The use of a weight of evidence approach will be a critical aspect of our intended development of proposed criteria. Aside from the USEPA guidance on empirical approaches to developing numeric nutrient criteria, there are many peer reviewed ar ticles on the development of criteria that will be utilized as guidance in our efforts. What follows is a detailed description of our proposed analytical approach with citations to documents that were instructive in providing guidance in formulating our proposed approach. It is important to note that we have a variety of analy tical tools available to us and that we have demonstrated capability to employ these methods towards this issue.

Our principle analytical software that will be used in this project is Statistical Analysis Systems (SAS V9.2). This software is the gold standard for analys is due to rigorous testing and validation of its statistical procedures, documentation provided by the source code, and log files and output listings14 documenting all steps taken in the analytical process. This allows for the easy replication of results
which will benefit the USEPA in its evaluation of our methods. However, recent advances in sophisticated methods such as recursive par titioning (aka. changepoint analysis) have been popularized in several efforts to develop numeric nutrient criteria and these methods are commonly executed in the R software language. As described in the following sections, we intend to use both SAS and R in the analytical phase of this project. This allows for a full complement of analytical techniques to be employed to develop numeric nutrient criteria for southwest Florida estuarine waters. Where R is used in developing predic tive linear models, the modeling efforts will be replicated in SAS as a quality assurance step. What follows is a detailed description of each step in our intended analytical approach. Methods are described and examples are provided with pertinent literature cited that will allow the reader to have a full understanding of the analytical flow path.



### 3.2.1 Problem Formulati on and Goal Development

To a large extent the goals have been defined by the USEPA - to develop numeric criteria for total nitrogen (TN) and total phosphorus (TP) in sout hwest Florida estuarine waters which fully support their designated aquatic life uses. Therefore, a definition of the appropriate stressor (causal) and response variables is required.

There has been a substantial amount of work in southwest Florida directed towards defining seagrass, water quality, and nutrient loading target s, as well as defining the stressor-response relationships for these estuarine waters (Janicki et al., 1994; Janicki and Wade, 1996; Tomasko et al., 2005; Corbett and Hale, 2006; Greening and Ja nicki, 2006; Janicki et al., 2008; Janicki Environmental, 2009; Janicki et al., 2009; Dixon et al., 2010). We fully intend to utilize existing technically sound information as a basis from whic h to identify the response variables of interest corresponding to designated uses and the anthropog enically influenced stresso r variables that result
in adverse effects to estuarine hea lth. USEPA has employed chlorophyll a concentrations (as a surrogate measure of phytoplankton concentrati on) as a biological response endpoint. This endpoint is also commonly used in a regulatory framework for evaluating water quality and is often used as an index defining the trophic state of waterbodies.

The Tampa Bay Estuary Program and the Sarasota Bay Estuary Program currently have target chlorophyll a concentrations that are used to provide management level indicators of year-to-year variability in estuarine condition. These target chlorophyll a concentrations are related to the light attenuating properties of phytoplankton and subsequent effects on the light available to seagrasses. Higher chlorophyll a concentrations reduce the incident light available to seagrasses for photosynthesis. These targets provide important links to seagrass extent management goals defined by the National Estuary Program Comprehens ive Conservation and Management Plans. Chlorophyll a concentrations are also used by the FDEP as a primary criteria for determining impairment of waterbodies in both freshwaters and estuaries. Chlorophyll a not only affects light penetration through the water column but the de gradation and decay of phytoplankton can also affect water column dissolved oxygen concentrations . FDEP also regulates standards for DO that are used to determine impairment. The value of us ing these parameters as response variables is in their direct linkage to nutrient enrichment in the causal pathway as described above.

Therefore, at a minimum, chlorophyll a and DO concentrations will be used as the primary response variables of interest. The methodology presented in this document is intended to be used15 to develop numeric nutrient criteria for total n itrogen (TN) and total phosphorus (TP) using chlorophyll a and DO concentrations as the biological endpoints. Therefore, these nutrients are
defined as the stressor variable s of interest. Relationships between nutrient enrichment and estuarine eutrophication are well known and accepted ; however, there are many factors such as hydrologic retention times and seasonality that influence the ability of phytoplankton to take up nutrients. Further complications arise when trying to assess stressor-response relationships between nutrients and response when the limiting nutrien t is not considered or when co-limitation may occur over the range of the relationship. Therefore, we intend to consider how covariates such as residence times, seasonality, colored dissolved organic matter and othe r potential confounding factors influence the relationship between nutrie nts and phytoplankton responses in southwest
Florida estuaries. We intend to document th e physical, chemical and biological variables comprising the morphological rela tionships (e.g., habitat, spatial, and temporal) that define the aquatic system of interest, and which may be important in modifying the relationship between nutrient concentrations (both nitrogen and ph osphorus) and observed biological endpoints as recommended by the SAB (2010). Conceptual models are a valuable tool to visualize and refine the conceptual framework under which subs equent data analysis will take place.

The southwest Florida estuaries (Figure 2-1) considered here are all data rich estuaries. Many years of discrete water quality sampling in both the es tuaries and their watersheds, nutrient loading estimates, and many other studies including seagra ss coverage and routine fisheries monitoring are available. The forethought and insight of local sc ientists and resource managers in these estuaries has allowed for analysts to have available to them a wealth of empirical information from which to consider stressor-response relationship as part of the development process for numeric nutrient

criteria.

It has been widely accepted that all three southwes t Florida estuaries display a significant degree of spatial variability that has largely been captured by the delineation of segments within each estuary. This segmentation has also structured much the management strategies that have been employed over the past 15-20 years.

The morphology of Tampa Bay lends itself to division of the bay into distinct segments. Lewis and Whitman (1985) provided a segmentation scheme which defined seven segments of the bay (Figure 3-3), including the four mainstem segments: Old Tampa Bay, Hillsborough Bay, Middle Tampa Bay, and Lower Tampa Bay. This segmentation sc heme has been utilized since the initiation of water quality assessment and pollutant loading devel opment for Tampa Bay. The segments differ in watershed attributes, including size, land use charact eristics, ratio of segment volume to watershed area, circulation and mixing, and distance from th e Gulf of Mexico. Analysis of water quality data supports the segmentation of the bay, with di fferences in water quality obvious between the segments (Appendix 1).

Segment-specific loadings to Tampa Bay (Zarbock et al., 1994, 1996; Janicki Environmental, 2001, 2005, 2009) have been successfully linked to segm ent-specific water quality, for the four mainstem segments of the bay. The TBEP utilized segmen t-specific loading-water quality relationships based on empirical data, as detailed in Janicki and Wade (1996), to set water quality targets consistent with seagrass protection and restoration in the bay. The Tampa Bay CCMP implemented management actions based on these relationships, a nd has been very successful in meeting water quality targets, and as a result has seen increase s in seagrass acreage in Tampa Bay (Greening and Janicki, 2006). The Tampa Bay TMDL was developed from segment-specific loading targets that Tampa Bay resource managers voluntarily committed to maintain (TBEP, 2010).16 Figure 3-3. Tampa Bay segmentation as recognized by the Tampa Bay Estuary Program.

The spatial variability among the lagoonal estuarine segments of Sarasota Bay has also been recognized. Sarasota County’s Ambient Water Qu ality Monitoring Program follows a segmentation scheme developed by Mote Marine Laborator y (Estevez and Palmer, 1990). The design was developed for the Sarasota Bay Estuary Program (S BEP) and is described in the SBEP’s Framework for Action (SBEP, 1992). This segmentation scheme was developed to delineate areas with similar hydrologic features and impacts from the land. The term “Problemsheds” was introduced to represent geographic areas that most likely en compass problems causally related to essential features of the area and it was expressly stated that as more information became available, the segmentation scheme could be adjusted to reflec t additional knowledge gained as a result of
additional data collection efforts. Within e ach segment, sub-segment polygons (strata) were established to distribute sampling effort evenly throughout the segment. Sarasota County Water Planning and Regulatory (County) has been routinely sampling water quality since 1995 using this segmentation scheme at a sampling frequency of one sample per stratum per month. The sampling design is a hierarchical, fixed station design. In itially, the individual monitoring locations within each stratum were randomly gener ated and the locations have sinc e remained fixed at their initial placement.

More recently, seagrass and water quality “targets” have been developed for th e SBEP (Janicki et al., 2008; Janicki Environmental, 2010) using a variation of this segmentation scheme (Figure 3-4). The only change to the scheme was the combination of 4 segments in Sarasota Bay (i.e., 7, 8, 10, 11) into the “Sarasota Bay” segment for expressing se agrass and water quality targets. Otherwise, the segmentation is identical to that originally pr oposed by Estevez and Palmer (1990). Sarasota Bay proper is a large waterbody and while jurisdic tionally divided by the Manatee–Sarasota County line it was recognized by local scientist and natural resource managers that the benefits of expressing management targets for the entire bay as a single number outweighed the intra-bay differences in water quality.17 The Charlotte Harbor National Estuary Program (CHNEP) study area is expansive and includes a diverse set of estuarine waterbodies from lagoonal systems to open estuarine segments with direct tidal forcing. Several segmentation schemes have b een used for various purposes as described in Janicki Environmental (2009). These segmentation schemes were compared and contrasted to identify a segmentation scheme to use for developm ent and reporting of the status of seagrass and water quality in the study area. Based on outcomes of several meetings with local experts as well as objective analytical work it was decided that while several segmentation schemes have been developed in the CHNEP study area to serve vari ous purposes, the segmentation scheme developed for the Coastal Charlotte Harbor Water Quality Monitoring Network (CCHMN), a probabilistic sampling design, was the best segmentation scheme to use for development and reporting of the status and trends in seagrass and water quality (Figure 3-5).

The CCHMN segmentation scheme was develope d based on logistical, jurisdictional, and analytical considerations as well as professional judgment with the recognition that estuarine water quality is a dynamic gradient and is not cons trained by any management segmentation scheme. There was general consensus among the meetin g participates that the CCHMN segmentation scheme could serve as the foundation for a segm entation scheme for reporting on water clarity and seagrass condition over time though some segments were identified for further evaluation. Analysis of water quality data from segments identified for further evaluation revealed that the water quality constituents thought to limit light in estuaries were similar between many of the sub-segments with some exceptions. Exceptions were the distribution of color values in Estero Bay where the northern portions tended to have higher color than the southern extent and turbidity and in Pine IslandSound where the southern extent tended to have higher turbidity values than the northern subsegment. However, seagrass trends and acreage estimates were provided that showed similar trends between most sub-segments identified.

Figure 3-4. Sarasota Bay Estuary Progra m segments and contributing watersheds

.18 Figure 3-5. Charlotte Harbor Na tional Estuary Program segments.



### 3.2.2 Conceptual Models

Conceptual models provide a framework of u nderstanding for the paradigm considered for subsequent data analysis. We have established conceptual models as a primary step in the analytical flow path to define the paradigm un der which subsequent analyses will take place (Figure 3-3 and Figure 3-4). Specific hypothe ses can be generated based on the paradigms illustrated by these conceptual models.



## TN Load Chlorophyll Light Attenuation

Seagrass Growth & Reproduction Seagrass Light Requirement

Figure 3-3. Conceptual model defining the re lationships between nutrient loading and seagrasses.19 Figure 3-4. Conceptual model of relationship between watershed inputs and estuarine responses

.



### 3.2.3 Data Select ion and Evaluation

There are several principles that we intend to fo llow with respect to data analysis. A primary consideration is that analysis should follow the design used to generate the data. For example, analysis of data collected using a fixed stati on sampling design should be conducted differently than analysis conducted on data from probab ilistic designs. There are advantages and compromises associated with both designs but re cognition of the design allows for the proper analytical tool to be chosen. We intend to ch aracterize the sampling design from which the data were derived for each analytical component in the development of the numeric nutrient criteria and explicitly state the assumptions and limitations of the sampling design as it pertains to any outcomes derived from an analysis. Another importa nt component of the preliminary evaluation is the visualization of the data using exploratory da ta analysis methods. Exploratory data analysis methods will be based on the conceptual models d eveloped in the previous step. Scatterplots will be generated to visually explore relationships among potential stressor and response variables. Maps can be generated that display the spatia l variability for particular stressor and response variables of interest within an estuary. Univaria te summary statistics will be generated as an initial exploratory data analysis step as well as Cumu lative Frequency Diagrams (Cumulative Distribution Function) plots, and time series plots. Inter and in tra-annual box plots are valuable tools to explore the temporal variability in the metric of interes t. Simple correlation metrics such as Pearsons correlation coefficient (Rho), and Spearmans rank correlation coefficient (Rho S) will also be utilized in the exploratory data analysis phase.



### 3.2.4 Model the Stressor -Response Relationship

The next step in the analytical path is to model the stressor-response relationship. Our proposed method of using stressor-response relationships to develop numeric nutrient criteria is conceptualized in the illustration provided in Figure 3-5. At this point the determination has been made that a stressor-response model is the preferred method for assessment. The problem has been formulated and a conceptual “model construc t” has been developed that describes the hypothesized cause and effect relationship. Once the model construct has been established,20 empirical analysis approaches will be develop ed from which analytical outcomes will be generated.

As illustrated in Figure 3-5, analytical outcomes will be generated from the data analysis and these analytical outcomes will be evaluated as to their relevance to the conceptual model construct. If the analytical result does not agree with the mode l construct, then the model construct will be revisited. If the outcome supports the model constr uct, then the outcome will be compared as to its relevance to existing information. Since there is a wealth of information established in the stewardship of these estuarine systems, the analyt ical outcome will be weighed against this existing information. If the analytical outcome suppor ts existing information and the conceptual model construct, then an assessment will be conducted as to the appropriate mechanism to implement the analytical outcome as a potential numeric nutrient criterion. This will be further discussed in the implementation section.

Relevance to

Existing

Information?

Model

Construct Data Analysis Analytical

Outcomes

Relevance to
Model

Construct? No

Yes Develop New

Model Construct

No

Implementation Analysis

Yes Establish Numeric

Nutrient Criteria

Figure 3-5. Analytical flow path to developm ent of Estuarine Numeric Nutrient Criteria.



### 3.2.5 Stressor-Response Modeling

Previous research on the southwest Florida estuaries considered, the USEPA guidance on empirical approaches for nutrient criteria development (USEP A, 2009), and the SAB review (SAB, 2010) have guided the development of the me thods proposed in this document. These documents present a suite of analytical tools that can be used in establishing numeric nutrient criteria given that adequate data are available for the assessment and th at adequate steps are taken to ensure that the stressor-response relationship represents biolog ically relevant comparisons between primary production as measured by chlorophyll a and some function of varia tion in nutrient inputs.

The USEPA guidance on the use of empirical appro aches to establishing numeric nutrient criteria has detailed several analytical tools which we intend to employ as part of our analytical approach. Linear regression, logistic regre ssion, and quantile regression are all useful tools that are commonly used in assessing stressor-response relationshi ps. In the USEPA Guidance document, USEPA21 characterizes these tools as appropriate for use when response thresholds have been previously established. The rate of change estimates provided by the slope coefficient in these relationships can be used to predict the crite rion value results in the established threshold. Changepoint methods are valuable tools to identify thresholds indicating where a rate of change in response to a stressor may change dramatically indicating a non linear threshold value such as the assimilative capacity of a system under study. Changepoint me thods are rapidly evolving from simple data mining tools to predictive models using advanced statistical algorithms to evaluate conditional probabilities in the stressor-response relationships. These methods are therefore useful to evaluate the stressor-response relationship whether or not a threshold value has been established. What follows is a brief description of each method with examples provided as necessary to convey our intended approach.

3.2.5.1 Linear Regression

Linear regression is a parametric statistical tec hnique that is used to explore the relationship
between two or more variables. In univariate or dinary linear regression, the relationship between the dependent variable (y-axis) and independent va riable (x-axis) is developed. This is done by fitting a straight line through the set of points such that the sum of squared residuals of the model is as small as possible. That is to say, the ver tical distances between the individual points and the fitted line are minimized. Ordinary linear regression is a well established method.

In linear regression, it is assumed that the data are independent samples from the population. For example, if one is developing numeric nutrient crite ria for the tributaries of Tampa Bay, the data should come from samples that are representative of the spatial and temporal variability of the system. Another important assumptio n of linear regression is that the error term of the model is normally distributed, with constant variance. Ofte n times, one or more of the variables exhibits a non-linear relationship with the other variables. While there are non-linear regression techniques that can be employed, one should try transforming the data. Ordinary linear regressions often can be developed using transformed data and thes e model will satisfy the assumptions of linear regression.

Diagnostic statistics and plots are commonly used to determine if the regression model meets the assumptions of linear regression. The most commonl y used statistics are the statistical significance of the model coefficients and the coefficient of determination (r 2 ). The statistical significance of the model coefficients tests whether the slope and interc ept of the model are significantly different from
zero. The coefficient of determination is a measure of the variance in the de pendent variable that is explained by the model. A plot of the residuals versus the independent variable can be used to judge if the assumption of constant variance is met. Additional plots of residuals versus other variables can also be instructive. For example, a time-series plot of the residuals can be used to assess whether or not the residuals vary seasonall y. Additional diagnostics can be run to identify outliers and test for leverage or influential points. Data points that are identified by these additional diagnostics should be further investigated to determin e if they are the result of a data entry error or other problem that merits removing them from the analysis.

Linear regression models that meet the above assu mptions and satisfy the diagnostic tests should be validated using independent data or with ot her techniques like bootstrapping. In traditional regression, caution should be used when applying a regression model to the range of data that is
outside of the bounds of the data used to de velop the model. However, recent advances in specifying random effects allow for the generalization of a regression model outside of its domain.22 An example of linear regression is presented belo w. The relationship of TN concentrations to chlorophyll a was investigated. Preliminary analyses between chlorophyll a and various water quality constituents revealed a co rrelation between chlorophyll a and TN concentration. Generally, chlorophyll a concentrations increase with increasing TN concentrations, though the relationship is non-linear at lower TN concentr ations. Therefore, the variables were log transformed and the fit of the model was improved (Figure 3-6). Though the amount of variation explained by the model was less th an 50%, the coefficients were highly significant. Obviously a large portion of the variation in chlorophyll a is explained by other variables that are not included in the model, suggesting the model could be impro ved with the addition of relevant variables and possibly interaction terms.

Figure 3-6. Relationship between chlorophyll a and TN concentrations as defined by a linear
regression.

We have previously applied linear regression techni ques that relate either nitrogen loading or concentrations to chlorophyll a concentrations in a number of s egments within the three southwest Florida estuaries. The following presents some of the specific regressions that we have developed.

In Tampa Bay, a regression approach was used to define the quantitative relationship between chlorophyll a concentrations and nitrogen loading (Janicki and Wade, 1996). The form of the regression was:23 

### ) L ( C

i i t s s

, t s

, t 

# 

  

###   2

0 

###  

where: 

###  s

, t 

### C mean chlorophyll a concentration in month t in segment s



###  s

, t 

###  month- and bay segment-specific intercept



###  s 

###  bay segment-specific regression coefficient



### 

 s

, i t 

### L TN load in month t-i and segment s.

Table 3-1 presents the regression coefficients derived from data for the period 1985-1991.

Table 3-1. Linear regression coeffici ents for relationships between chlorophyll a and TN loading in Tampa Bay (from Janicki and Wade, 1996).

Parameter Value Coefficient
Intercept 2.226599244
Month

1 -0.192338320

2 0.279799163

3 0.768803604

4 1.270570938

5 2.452550666

6 3.167990897

7 5.171125452

8 5.378311443

9 6.114218870

10 4.191362957

11 2.573604841

12 0.000000000 Bay Segment 1 1.558367876

2 0.320830622

3 0.967240508

4 0.000000000 3 month cumulative TN load*Bay Segment Interaction

1 0.024139861

2 0.023465884

3 0.010144687

4 -0.008584323

The model was fit with 256 observations and resulted in an R 2 value of 0.73. The regression was highly significant with a probability of a greater | F| value of < 0.0001. A plot of predicted versus observed chlorophyll a concentrations is presented in Figure 3-7.24 

### 0 5 10 15 20 25 30 35

0 5 10 15 20 25 30 35PredictedChlorophyll‐a(μg/l)



## Observed Chlorophyll

‐ a (μg/l) Old Tampa Bay

Hillsborough Bay

Middle Tampa Bay

Lower Tampa Bay

Figure 3-7. Predicted versus observed chlorophyll a concentrations in Tampa Bay.

In Sarasota Bay, we have been investigating th e relationships between TN concentrations and loadings with chlorophyll a concentrations in each of the five bay segments (Janicki et al., 2010). A regression model between chlorophyll a and TN concentrations was developed for Blackburn Bay. Residual analysis revealed a seasonal difference in residuals. Specifically, given the same TN concentrations, chlorophyll a concentrations can be expected to be higher in Blackburn Bay during the wetter, warmer summer months (July-October) than during the remainder of the year. Therefore, a seasonal term was added to the regression equation. The season term is a dummy variable which equals one during July-October and zero other months of the year. The final regression equation is:



### ) Seaso n

. ( ]) T

N [

. (

. C

t 

###    

  73 1 72 23 55 3

where: 

###  t 

### C mean chlorophyll a concentration in month t

The model was fit with 133 observations and resulted in an R 2 value of 0.73. The regression was highly significant with a probability of a greater |F| value of < 0.0001. The slope and parameter coefficients were also highly significant. A plot of predic ted versus observed chlorophyll a concentrations is presented in Figure 3-8.25 Figure 3-8. Predicted versus observed chlorophyll a concentrations in Blackburn Bay (R 2 =0.73).

3.2.5.2 Quantile Regression

Ordinary least squares regression is used to model the relationship between one or more independent variables and the conditional mean of a response (dependent) variable. Unlike ordinary least squares regression, which models the relationship between an independent variable and the mean of the response variable, quantile regression models the relationship between the independent variable and the conditional quantiles of the dependent variab le (Koenker and Bassett, 1978). Quantile regression is particularly usef ul when the rate of change in the conditional quantile, expressed by the regression coefficient s, depends on the quantile (Chen, 2005). Therefore, quantile regression can sometimes be useful for informing criterion selection for responses that do not satisfy the assumptions of ordinary least squares regression (USEPA, 2009). Quantile regression makes no distributional assump tion about the error term in the model.

As an example, quantile regression was used to develop relationships between log (chlorophyll a ) and log (TN) for an estuary. This is the same data set that was used in the lin ear regression example. The median prediction interval from the quantile regression was similar to the mean response predicted by ordinary least-squares; however, it can be seen that the slope of the 90 th percentile is
somewhat shallower than that of the 50 th percentile (Figure 3-9).26 Figure 3-9. Relationship between chlorophyll a and TN concentrations as defined by a quantile regression.

3.2.5.3 Logistic Regression

Logistic regression models are used to predict the probability of an event o ccurring as a function of one or more independent variables. Because th e dependent variable in logistic regression is dichotomous, this regression technique lends itse lf to situations where a water quality measurement results in an exceedance or impairment (e.g., di ssolved oxygen < 4 mg/l). In most other respects, logistic regression is similar to ordinary least s quares regression. The model can be fit to observed data using maximum likelihood estimation and sta tistical tests are used to determine if model coefficients differ significantly from zero (USEPA, 2009). Generally, logistic regression is most beneficial when the probability of an event ranges between near zero and near 1 over the range of some independent variable.

Further discussion of logistic regression approach es and an example are provide below in Section

3.2.7.

3.2.5.4 Changepoint Analysis

Changepoint methods are rapidly evolving from si mple data mining tools to predictive models using advanced statistical algorithms to evalu ate conditional probabilities in the stressor-response27 relationships over the range of the relationship. These methods are therefore useful to evaluate the stressor-response relationship whether or not a thre shold value has been established. Collectively referred to as “Decision Trees”, this methodol ogy provides an intuitive and easily conveyed approach to identify threshold responses to envi ronmental stressors that may be used in the development of numeric nutrient criteria. We intend to use a conditional inference tree methodology (Hothorn et al., 2006 ) as one line of evidence for evaluating stressor-response relationships. Conditional inference trees are a form of regression tree analysis (RTA) that has been successfully used to assist in the development of numeric nutrient criteria (e.g., Soranno et al., 2008). The approach is based on recursive pa rtitioning. The partitioni ng process iteratively searches for a point in the stressors variable wh ich maximizes the difference in the response values between two groups of response data. No a priori threshold is specified. The regression tree approach defines the breakpoint as that whic h maximizes the difference between groups by minimizing the p value associated with some statis tical test. The point in the stressor variable at which the p value is minimized, after adjustment for multiple comparisons, is assigned as the breakpoint defining the split of the of the response variable into 2 groups. Once the first split is made the process continues to te st for subsequent splits that are conditional on the first split. Hence, the term “conditional inference” or “conditional probability analysis” that has been popularized recently by the USEPA as a potential approach for establishing numeric nutrient criteria. Soranno et al. (2008) utilized this appr oach in establishing a framework for developing ecosystem specific numeric nutrient criteria in Michigan lakes and developed a bootstrap methodology with this approach to quantify the unc ertainty associated with a particular detected changepoint. Bootstrapping is a powerful tool to quantify uncertainty and fits well with the analytical approach recommended by the SA B in their comment on the USEPA Guidance document regarding characterizing uncert ainty in the assessment process.

Conditional inference trees embed tree-structured r egression models into a well defined theory of conditional inference procedures (Hothorn et al., 2006) . This class of regression trees is applicable to all kinds of regression problems, including nominal, ordinal, numeric, censored as well as multivariate response variables and arbitrary measur ement scales of the covariates. These models can be specified to provide information on the st rength of the stressor-response relationship, account for covariates in the relationship and model conditional probabilities. Validation techniques are built in functions in the procedures which can be invoked to estimate the predictive power of the resultant models. Below is an illus trative example of the analytical process using
conditional inference trees.

Following the example conceptual model provided in Figure 3-5, the hypothesis is established that chlorophyll a concentrations are a function of TN and TP concentrations. The conditional inference tree is used to search for statistically significant changepoints in the relationship between TN and chlorophyll a . In Figure 3-10, the first split occurs at a monthly TN average value of 0.472 mg/l (node 1). Successive splits are performed cond itional that the monthly average TN values are below 0.472 mg/l. The distribution of the response variable is pr ovided in the box plots at the bottom of the figure for each of the terminal nod es. It is easy to see that there is a positive relationship between TN concentrations and chlorophyll a responses. That a nonlinear increase in chlorophyll a levels appears to occur when the TN conc entrations are above 0.472 is information that may be used in a weight-of-evidence approa ch to develop potential criterion value(s) for TN.28 Figure 3-10. Decision tree identifying potential breakpoints between TN and chlorophyll a
concentrations.

To further examine the relationship between TN and ch lorophyll a as part of the weight of evidence approach, it is necessary to consider potential confounding variables as well as other mediating influences in the relationship between TN and chlorophyll a . Subsequent to the analysis above, we postulate that seasonality is likely to influence the relationship between TN and chlorophyll a. Hydrologic conditions in southwest Florida are high ly seasonal and therefore discharges from the pollutant loadings model were used as a covariate in this example. The hypothesis also considered the potential for co-limitation of TN and TP at some point in the relationship between TN and chlorophyll a . Therefore, when phytoplankton populati ons increase there is the potential for phosphorus to become limiting. For this analysis we used a variant of the conditional inference
model that allows for linear model formulation wi thin the terminal nodes of the decision tree. This
method is referred to as model based recursive partitioning (MOB). Since a parametric model framework is specified for this assessment the vari ables were natural log tr ansformed for analysis. In Figure 3-11, the results of the MOB tree are presente d. In this case, discharge is the variable that the response is being partitioned on and the relationship be tween chlorophyll a and TN and TP is
defined within each of the terminal nodes. Thi s illustration suggests that at discharges above 26 million cubic feet per month a statistically si gnificant relationship was observed and below 26 million cubic feet, there was no relationship. The coefficients for the linear regressions are provided for each terminal node as part of the model output. The coefficients for this example are29 provided in Table 3-2. Interestingly, at low vo lumes there appears to be no relationship while at the highest hydrologic volumes there is some evidence of co-limitation of TN and TP.

Discharge p < 0.001 1

 17.082  17.082

Node 2 (n = 23)

-3.3 0.3 -1 3

-3 -0.7 -1 3 Discharge p = 0.019 3

 18.451  18.451

Node 4 (n = 67)

-3.3 0.3 -1 3

-3 -0.7 -1 3 Node 5 (n = 32)

-3.3 0.3 -1 3

-3 -0.7 -1 3

Figure 3-11. Model based recursive partitioning of chlorophyll a based on natural log transformed discharge, total nitrogen (top row of scatterp lots) and total phosphorus concentrations (bottom row of scatterplots).

It is anticipated that decision tree methods will provide valuable information for establishing

numeric nutrient criteria in some cases and not in others. Prin cipally, the results will depend on our ability to account for the many potential conf ounding variables in the relationship between nutrients and phytoplankton responses. The study area for this project includes a diverse set of estuarine water bodies from lagoonal systems with high residence times and little freshwater input Table 3-2. Linear regression coe fficients for relationships between chlorophyll a , TN and TP partitioned by discharge.

ln(Discharge) Node Intercept ln (TN) ln(TP)

<17 2 1.444 -0.395 NS 0.852 NS

17<18.5 4 2.322 0.999 ** -0.001 NS

>18.5 5 4.109 0.983 ** 0.703**30 relative to the estuary size to small estuarine wate rbodies with large hydrologic inputs and direct influence of tidal passes. Decision tree approach es provide valuable tool s for identifying potential stressor-response relationships that can be used to develop numeric nutrient criteria and we intend to fully explore their use in esta blishing criteria for southwest Florida estuaries. Bayesian methods, bootstrap aggregation and validation techniques are available as part of these methods which we intend to explore to minimize the potential for mi sspecification of the true relationship between these variable and provide weight of evidence towa rd the establishment of biologically relevant and meaningful criteria that can serve to protect th e designated uses of these estuarine waterbodies.

It is important to note that all of these approach es are likely to result in potential criterion values that are sometimes exceeded. In other words, a changepoint can only be identified if there are values above the changepoint value. This is an important consideration in the implementation of a criterion value which will be further discussed in the implementation section.



### 3.2.6 Limiting Nutrient and the Need to Establish Criteria for Both N and P

In most estuarine ecosystems N is the most limit ing nutrient (a nutrient whose concentration in an organism’s environment that determines the growth and productivity of that organism) (Boynton et al., 1982; Howarth, 1988; Chapra, 1997; Nationa l Research Council, 2000; Pennock et al., 1999). Aquatic ecosystems are commonly characterized by their N:P ratios. Receiv ing waters with ratios less than 10:1 (based on concentrations) are consid ered nitrogen limited, while ratios higher than 10:1 are assumed to be phosphorus limited (FDEP, 2000).

In Tampa Bay, bioassay experiments, empirically-d erived nutrient-response relationships, and water quality modeling simulations indicated that contro lling nitrogen loads to the bay should be the primary watershed management focus to limit phytoplankton production and allow for improvements in bay water clarity (Janicki and Wade, 1996; Wang et al., 1999; Dixon et al., 2009; Johansson, 2009). These early st udies clearly established that nitrogen loads were the limiting nutrient in the Tampa Bay estuary and that phosphorus loadings to the bay from the enriched Bone Valley region were not controlling estuarine production.

In Sarasota Bay, ratios between TN and TP conc entrations are typically well below 10 (Lowrey, 1992), which is consistent with presumed nitro gen limitation. In Roberts Bay, FDEP (2005) calculated a median TN:TP ratio of 2.4, indica ting strong nitrogen limitation of phytoplankton growth. In addition, empirically derived relationships also point to the primary role of nitrogen in the eutrophication processes in Sarasota Bay (i.e ., Tomasko et al., 1992 and 1996), as was found in the earliest studies in Tampa Bay (i.e., Johansson, 1991).

In Charlotte Harbor, the two major freshwater i nputs are the Peace and Myakka rivers. Both of these rivers are distinguished by the major phosphate deposits that lie within their watersheds. As a result, it has been generally held that the most of the CHNEP segments are not phosphorus limited. We intend to further these analyses to verify the TN limitation, if it exists.

Given that TP is not the limiting nutrient in these estuaries, but that USEPA wishes to establish criteria for both nitrogen and phosphorus in a ll waters, a methodology for identifying a TP criterion is needed. An important factor to consider in esta blishing this methodology is that variation in the TN:TP ratio can result in undesirable changes in phytoplankton community structure, and thus ecosystem function. Given a period when desira ble phytoplankton community structure exists, or31 at least the lack of any undesirable blooms, th e ambient water quality conditions (i.e., TN:TP ratios) can serve as an appropriate baseline for use in TP criteria establishment. Following establishment of TN concentration criterion, the TP criterion can be based on the TN:TP ratio observed during the baseline period when desirable phytoplankton community structure existed.

An additional approach to establishing a TP criterion is to follow the approach used to establish the TN criteria in the four Tampa Bay segments. In this case, TP loads from the baseline period (1992- 1994) can be estimated and the resultant TP concentrations can be calculated.

We intend to investigate all of these approaches and will develop a recommendation that is based on full evaluation and comparison of the results from application to the empirical data.



### 3.2.7 Compliance with Dissolved Oxygen Standards

There are two major questions that must be addre ssed with respect to establishing numeric nutrient criteria that are protective of DO in the southwest Florida estuaries:

 Is there a relationship between nutrient lo ads, phytoplankton responses and dissolved oxygen levels in these estuaries?

 Do the proposed numeric nutrient criteri a protect DO conditions (i.e., achieve DO standards) in these estuaries?

To address these questions we intend to inves tigate and quantify the relationship between the existing nutrient loadings and chlorophyll a targets in these estuaries and the frequency of DO
values less than the state standard of an instan taneous 4 mg/l and the 5 mg/l daily average (if data allow).

Initially, we will examine the spatial and temporal va riability in DO concentrations in each of the estuarine segments. This will involve synthesizing available data and identifying the spatial and temporal variability in DO within each segment. Maps will be produced for each month across years to display the variability across years within a month as well as the inter-annual variability in DO. Spatial heterogeneity will be examined usin g the probabilistic dataset and contour plots of bathymetry will be used to the extent that valid bathymetry is available to consider the known relationship between DO levels and depth. The results of this task will be a detailed assessment of the spatial and temporal variation in DO in each estuary.

The relationship between nutrient loads and residence times and chlorophyll a and DO responses in each estuarine segment will be examined. Th ese estuaries are heavily influenced by seasonal variability in rainfall with a wet season between mid June and October delivering the bulk of freshwater inflow and nutrients to the system. Wa ter temperatures are also highest during the wet season and contribute to the reduced capacity of es tuarine waters to contain oxygen. Therefore, there are both natural and anthropogenic stressors that can potentially influence DO concentrations in these estuaries. To investigate the role of th ese stressors, the variability in DO concentrations among them will be partitioned. Specifically, we intend to assess if there are quantifiable relationships between nutrient loads and/or concentrations, chlorophyll a concentrations, and DO. The quantification of these relationships will need to consider the natural influences that affect and may confound the relationship among these variables. We will explore several analytical methods in an attempt to quantify these relationships.32 Initially, simple and multiple regression techniques will be applied to assess if there are quantifiable relationships between nutrient loads and/or concentrations, chlorophyll a concentrations, and DO. Given a defensible relationship, expected DO conc entrations for any given nutrient load and/or concentration and chlorophyll a concentration can be predicted.

Logistic regression techniques can also be used to investigate quantifiable relationships between nutrient loads and/or concentrations, chlorophyll a concentrations, and DO. An example of the use of logistic regression on DO data is presen ted in Figure 3-12. The relationship between DO exceedances and flow was investigated. A general trend of lower DO concentrations at lower flows was documented. However, the ordinary least-squares regression was not sufficient to predict DO exceedances based on flows. Therefore, logistic regression was employed (Figure 3-13) with satisfactory results. Logistic regression can be used with categorical and continuous predictor variables and slope estimates can be translated to describe the change in odds of occurrence per unit change in the predictor variable.

Figure 3-12. Relationship between the probability of DO < 4mg/l and flow as defined by a logistic
regression.

Changepoint methods are rapidly evolving from si mple data mining tools to predictive models using advanced statistical algorithms to evalu ate conditional probabilities in the stressor-response relationships over the range of the relationship. These methods are therefore useful to evaluate the stressor-response relationship whether or not a thre shold value has been established. Collectively referred to as “Decision Trees”, this methodol ogy provides an intuitive and easily conveyed approach to identify threshold responses to envi ronmental stressors that may be used in the development of numeric nutrient criteria. We intend to use a conditional inference tree33 methodology (Hothorn et al., 2006 ) as one line of evidence for evaluating stressor-response relationships. Conditional inference trees are a form of regression tree analysis (RTA) that has been successfully used to assist in the development of numeric nutrient criteria (e.g., Soranno et al., 2008). The approach is based on recursive pa rtitioning. The partitioni ng process iteratively searches for a point in the stressors variable wh ich maximizes the difference in the response values between two groups of response data. No a priori threshold is specified. The regression tree approach defines the breakpoint as that whic h maximizes the difference between groups by minimizing the p value associated with some statis tical test. The point in the stressor variable at which the p value is minimized, after adjustment for multiple comparisons, is assigned as the breakpoint defining the split of the response variable into 2 groups. Once the first split is made the process continues to test for subsequent splits that are conditional on the first split. Hence, the term “conditional inference” or “conditional probability anal ysis” that has been po pularized recently by the USEPA as a potential approach for establishing numeric nutrient criteria. Soranno et al. (2008) utilized this approach in establishing a fram ework for developing ecosystem specific numeric nutrient criteria in Michigan lakes and developed a bootstrap methodology with this approach to quantify the uncertainty associated with a partic ular detected changepoint. Bootstrapping is a powerful tool to quantify uncertainty and fits well with the analytical approach recommended by
the SAB in their comment on the USEPA Guidance document regarding characterizing uncertainty in the assessment process.

We intend to investigate all of these approaches and will develop a recommendation that is based on full evaluation and comparison of the results from application to the empirical data.



### 3.3 Downstream Protection Values (DPVs)

Downstream Protection Values (DPVs) for TN and TP are being developed as part of the numeric nutrient criteria effort by USEPA. USEPA defines DPVs as those water quality criteria in flowing waters that ensure protection of designated uses in downstream estuarine waters as required by the CWA under 40 CFR 131.10(b). USEPA (2010) previously proposed TN DPVs based on proposed protective estuarine TN loads, with the DPVs be ing expressed as concentrations in upstream
reaches.

We will recommend a clear definition of DPV prior to deriving DPVs for the estuarine watersheds;
the definition of a DPV will include:

 the temporal scales (multi-year, annual, seasonal, monthly),

 the spatial scales (drainage area, stream reach/river segment, tidal creek, bayou),

 the metrics used (average, geometric average, percentile range), and

 the units of measure for the DPV.

Derivation of DPVs must include definition of th e boundary between the estuary and the drainage network discharging to the estuary. In its previ ous proposal of DPVs, U SEPA utilized the proposed protective load to the estuary from a drainage ne twork and the model-derived fraction of the load from an upstream basin which reaches the estuary, to derive TN DPVs as concentrations for each stream reach of the model drainage network.

Initially, identification of the terminal segments of the drainage network is necessary prior to developing DPVs. Since some hydrologic and nutrie nt loads enter the estuary directly from storm34 drains and waste water treatment plants, the a ppropriate spatial bounds (how far upstream) and definition of terminal segments must be devel oped. We will provide a working definition of terminal segments for DPV develo pment, as well as maps depicted the terminal segments and the spatial bounds represented in the development of protective watershed and estuarine criteria.

There are several potential methods to develop DPVs for the southwest Florida estuaries.

 If the numeric criterion for the receiving estuar y is expressed as a concentration, then set the DPVs for the terminal segments to the numeric nutrient criteria for the estuary.

 If the criterion is expressed as a load, then the terminal segment DPV could be estimated by taking into account the relative hydrologic contribution from the terminal segment to the overall hydrologic load to the estuary.

 If the numeric criterion for the receiving estu ary is expressed as a concentration, then the terminal segment DPV could be derived fr om the relationship between the nutrient concentrations in the terminal segments and t hose in the receiving estuary. However, this method is dependent upon having water quality data available for both the downstream estuarine segment and the terminal segment, thus this method may not be applicable to all terminal segments.

We will investigate the above potential methods to de rive DPVs for the terminal segments to each

estuary.

USEPA also intends to define DPVs for all upstream stream reaches within the watersheds that drain
to the estuaries in Florida. As for terminal s egment DPVs, the definition of upstream segments will have to be developed prior to deriving upstream segment DPVs. Upstream segments may be defined as stream reaches, subbasins, or some other areal unit. After definition of upstream segments, we will define an approach for translat ing terminal segment DPVs to upstream segment

DPVs.

It is likely that this approach will follow similar logic that was employed by USEPA in the proposed DPVs (USEPA, 2010). This logic assumed that gi ven an estuarine load limit, the watershed load portion of the limit can be assigned to contribu ting areas in the watershed through a mechanism that considered nutrient inputs from the stream ne twork. Although there were significant issues with the mechanism used by USEPA to assign loads to upstream reaches and the methods for developing the proposed loading limit for the estuar y, we do find the guiding logic used by USEPA to be appropriate.

We have developed a methodology to partition the watershed load to an estuary over its watershed subbasins, so that we have the loads reaching th e estuary from each subbasin. We will assess
whether load attenuation should be considered in developing the upstream DPVs, and if so, how to most appropriately quantify this attenuation. In the case where attenuation is included in the DPV methodology, we will consider whether to utili ze first-order decay, or whether it is more appropriate to utilize mechanistic modeling to de rive factors for load attenuation from upstream segments to the estuary.

We will include an evaluation of relationships between ambient concentrations in the upstream segments and those in the downstr eam terminal segments. It may be that significant relationships may be found between these concentrations, so th at the DPV of the downstream terminal segment35 can be used to derive DPVs for the upstream segments. However, this method is dependent upon having water quality data available from both th e terminal segment and the upstream segment, so this method may not be applicable to all upstream segments.



### 3.4 Designated Uses

The primary objective in the establishment of numeri c nutrient criteria is to assure that designated uses of the estuarine waters are protected. To th is end, the criteria must provide fully aquatic life support in these waters. The two primary res ponses that will be investigated (i.e., chlorophyll a and DO) provide important insight into the extent to which aquatic life support is achieved.

Chlorophyll a and the resultant water clarity are major de terminants of the seagrass success in these estuaries. The protection of seagrasses is a critic al element of assuring the aquatic life support in the southwest Florida estuaries. Seagrasses serv e several significant functions within estuaries, which are degraded if seagrass coverage declines. Seagrasses provide food, shelter, and essential nursery habitats for many recreationally and commerci ally valuable species of fish, crustaceans, and shellfish (Dawes et al., 2004). They also provid e food and habitat for marine mammals, such as manatees, listed species, and waterfowl. Seagrasses also help maintain water clarity by trapping fine sediments and particles with their leaves a nd stabilizing the estuarine sediments with their roots (Fonseca, 1989; Short et a l., 2000). Seagrasses are very effective at removing dissolved
nutrients from water that can enter from land runoff. The removal of sediment and nutrients improves water clarity, thereby improving overall ecosystem health.

The effect of reduced DO (hypoxia) on biota is well-known (Diaz and Rosenberg, 1995; USEPA, 2000c). Therefore, achieving aquatic life support is also significantly influenced by estuarine DO
conditions.

To examine further whether full aquatic life suppor t, and thereby designated uses, is achieved by the proposed numeric nutrient criteria, additional data sources will be investigated. These include:

 Tampa Bay

- Biennial aerial surveys of seagrass co ver (Southwest Florida Water Management District);

- Benthic abundance and community structure data from an annual probabilistic sampling during a late summer index period (TBEP and local counties);

- Fish abundance and community structure from a probabilistic sampling conducted monthly (Florida Fish and Wildlife Conservation Commission); and

- Annual bird count data (Florida Audubon).

 Sarasota Bay

- Biennial aerial surveys of seagrass co ver (Southwest Florida Water Management District);

- Benthic abundance and community stru cture data from previous studies;

- Fish abundance and community structur e from a monthly probabilistic sampling conducted (Florida Fish and Wildlife Conservation Commission);

- Annual bird count data (Florida Audubon);

- Oyster bed surveys (Sarasota County); and

- Macroalgae surveys (Sarasota County).36  Charlotte Harbor

- Biennial aerial surveys of seagrass co ver (Southwest Florida Water Management District, South Florida Water Management District, SCCF);

- Benthic abundance and community stru cture data from previous studies;

- Fish abundance and community structur e from a monthly probabilistic sampling conducted (Florida Fish and Wildlife Conservation Commission);

- Juvenile fish survey (FGCU);

- Annual bird count data (Florida Audubon);

- Oyster bed surveys (Sarasota County, FGCU);

- Macroalgae surveys (Sarasota County);

- Bird Rookery monitoring (FDEP);

- Seagrass Transects (FDEP); and

- Scallop Surveys (Florida Fish a nd Wildlife Conservation Commission).

We intend to provide at least a qualitative asse ssment of these data sources relative to the
recommended numeric nutrient criteria. Where data allow, a more quantitative assessment will be attempted. For example, the abundance of species that are supported by the phytoplankton primary production will be related to the proposed nutrient criteria in the various estuaries.



## 4.0 Implementation

In following the analytical flow path described in Figure 3-2, analytical outcomes that are relevant to the developed conceptual models will be evalu ated against the wealth of existing information
that has been documented for these estuaries. Exis ting information will also be utilized to generate potential numeric nutrient criteria. Once potential criteria are established, there will be several important issues to assess to determine the appr opriate implementation of the potential criteria.
These issues include:

 identifying the appropriate temporal scale for assessment,

 considering the effects of seasonality on the assessment,

 characterizing the uncertainty of potential criterion values, and

 practical considerations.



### 4.1 Temporal Scales and Seasonality

The analysis of stressor-response relationships ma y include data averaged over various temporal scales including; individual samples, monthly a verages, seasonal averages or annual averages, among others. The distribution of samples may also be used for analysis rather than a summary statistic of the distribution. However, it is recogn ized that the regulatory assessment protocol used by the USEPA and FDEP will likely be on an annu al basis. The USEPA has previously developed criteria based on exceedance of annual geometric means (USEPA, 2010). The FDEP evaluation for
exceedance of their DO criterion is based on the proportion of exceedances (i.e., 0.10 or 10%) of a state standard using the binomial distribution. These evaluation methods are very different, the binomial approach accounts for the distribution of va lues within a year, while the annual geometric
mean does not.37 We intend to utilize several methods to determin e a biologically-significant exceedance frequency of the potential criteria for a given waterbody. For example, a changepoint identified for TN is likely to be exceeded some proportion of the time within a year or at least in some years. If the criterion defined as the changepoint is taken for use as an annual average, then it will be important to consider how the waterbody under study will be affected by this decision and whether the exceedance of this changepoint over a particular temporal scale results in an adverse biological response within the waterbody. Our approach to exa mining this issue is described in the section on characterizing uncertainty in the proposed criterion.

Seasonality is an important yet o ften overlooked factor to consid er when estimating water quality relationships using empirical methods. Seasona l variations in temperature and photoperiod influence the ability of phytoplan kton to uptake nutrien ts, grow and reproduce. Hydrologic regimes also generally have a seasonal signal that influences estuarine residence times which in turn affects phytoplankton productivity. In southw est Florida, rainfall and hydrology exhibit highly seasonal patterns with a wet summer season, a mo derate winter season with respect to both temperature and rainfall, and typically a ver y dry spring between March and mid-June.

Empirical assessments of water quality relationships vary in terms of their temporal scale of inference. Annual averages are sometimes used but obfuscate any seasonal signal in the data. More commonly, water quality data are averaged based on monthl y observations. In these cases, accounting for seasonality will likely be beneficial in reducing the error variance in statistical modeling techniques. Seasonality is likely to be ex pressed in statistical modeling either through the direct incorporation of a temperature, photoperi od term (which are often collinear) or more commonly through the use of surro gate categorical terms that estimate the differences in mean response based on the average differences due to th e seasonal patterns. A hypothetical example is given below to illustrate this e ffect. In this example, a theoretical relationship is established between TN and chlorophyll a concentrations. The predictive regression equation is:

i i 

### e

. ) Seaso n

. ( ]) T

N [

. ( y 

     60 3 4 7 1 74 14

In this regression equation the season variable is dummy coded such that Winter is set to 0 and Summer is set to 1. Therefore, when a value is taken during summer, the season effect is a 1.47 μg/l shift in the mean response relative to wint er which captures the e ffects that increased temperatures and photoperiods have on the background chlorophyll a concentrations. Sometimes it is also necessary to incorporate an interaction term not only to estimate the shift in the mean response of chlorophyll a as a function of season but also to account for the rate of change in response of chlorophyll to TN concentrations within a season. We intend to incorporate seasonality into our empirical assessments becau se we know that se asonality influences the relationships between nutr ients, and chlorophyll a and DO responses. The parameter estimates and associated measures of uncertainty can also be incorporated into assessment methodologies to allow for these effects to be incorporated into the criteria evaluation process.38 

### 1.47 μg/l

Figure 4-1. Hypothetical regression between TN and chlorophyll a with a seasonal effect.



### 4.2 Characterizing Uncertainty

The importance of characterizing uncertainty was explicitly stated in the SAB review. We intend to use parametric and nonparametric simulation methods to assess the effects of criterion specification. During the analytical approach step, bootstrap methods will have been explored to estimate the most likely changepoint value and characterize the range of potential changepoint values that best define the true relationship be tween stressor and response. Where parametric models are used during the analytical phase, Mont e Carlo methods will be investigated as a means to evaluate the uncertainty of the parametric model and its effects on potential outcomes. Nonparametric simulation methods based on the em pirical distribution will also be employed to understand how the expected dist ribution of values for a particular waterbody will behave in relationship to the specified criterion value. These efforts will provide valuable information on the applicability of potential criterion to serve as an appropriate standard to judge the waterbodies of

interest.

Misspecification of the numeric nutrient criteria would likely result in undue burdens on local resource managers. Misspecification can result in either falsely declaring a waterbody as not meeting its designated uses (Type I error) or al ternatively, in declaring that the waterbody is meeting its designated uses when it truly is not (Type II error). These simulation methods can be used to minimize the potential for misspecification of the criteria by evaluating scenarios in which the distributional aspects of the data can be adju sted and the effects assessed. An example is provided below to illustrate the use of a simple computer simulation to test the effects of39 establishing a criterion value and implementing an exc eedance rule such as the “one in three” rule that has been proposed for evaluating complia nce with numeric nutrient criteria in Florida
freshwater streams.

Consider the following example of a TP distributi on with a geometric mean of 0.739 mg/l (i.e., the USEPA proposed criterion for TP in the Bone Valley region of west central Florida). We impose a theoretically estimated standard deviation of 0.15 mg /l on this distribution implying that 1) there is heterogeneity in the population of TP values within the Bone Valley region and 2) that there is uncertainty in the exact value that represents the true population mean. USEPA has proposed that the criterion value should be exceeded only once every three years. We conducted a simulation by building a simulated data pool of 10,000 TP obser vations from streams in the Bone Valley region
with a known distribution based on the me an and standard deviation (Figure 4-2).

Figure 4-2. Hypothetical total phosphorus distri bution of 10000 simulated observations with a geometric mean (i.e., 0.739 mg/l) based on EPA Bone Valley criterion for streams and a standard deviation of 0.15 mg/l. Distribution is show as lognormal (Top) and log transformed (Bottom).
Descriptive statistics are inset.40 We then pulled at random 12 observations from this pool and calculated a mean of the natural log transformed values and exponentiated those mean va lues to derive the geometric mean. Each trial outcome then represents the expected geometric me an for a given year. We performed 1000 trials to generate an expected distribution of annual geometr ic means. The true geometric mean is 0.739 mg/l (or a very close approximation depending purely on randomness). Therefore, we can calculate the number of times out of 1000 when the annual geometric mean would exceed the known (or true) geometric mean. Results suggest that when the true mean is 0.739 mg/l, and 12 observations are taken each year, the annual geometric mean will exceed the true mean nearly 50% of the time by chance. This means that the “o ne in three years” rule is likely to result in falsely declaring a stream reach to be impaired when in actuality it meets the criterion value.

Nonparametric confidence intervals can be cons tructed using simulation that would allow for uncertainty to be characterized and used to set mo re meaningful criteria that minimize the chances for Type I errors that might result in undue bur dens on local resource managers and a misallocation of resources toward the proper stewardship of these waterbodies. These methods are robust in simulating a wide array of potential scenario’s and evaluating possible outcomes which informs decision makers about risk; an important consid eration in the implementation process. These methods are similar to those used by FDEP to a ssess compliance with the State DO standard. We
intend to use simulation methods to characterize unc ertainty in the stressor-response relationship, gain insights into how sampling frequencies and sampling designs ma y affect the evaluation of the potential criterion values and establish numeric nutrient criteria that capture the underlying uncertainties inherit in the data collection and evaluation process.



### 4.3 Compliance Assessment

Effective assessment of compliance with the established numeric nutrient criteria will depend upon a firm understanding of the importance of spatia l and temporal variability and uncertainty in the stressor-response relationships. Also, considerati on of the magnitude, frequency, and duration of any deviations from established criteria is critical especially in the context of evaluating biologically-relevant responses to these deviations . The occurrence of highly infrequent, but very significant catastrophic events (e.g., hurricanes, spills) complicates the implementation of the numeric nutrient criteria. Therefore, the methods for compliance assessment must also recognize these issues. An example of an assessment meth odology used within the Tampa Bay estuary that attempts to take these issues into account is described below.



### 4.3.1 Consideration of th e Frequency and Duration of Exceedances of Criteria

TBEP developed an assessment methodology that ta kes the issues discussed above into account. The Technical Advisory Committee (TAC) and Mana gement Board of the TBEP have adopted the Decision Matrix process for track ing the status of chlorophyll a and light attenuation in the four mainstem Tampa Bay segments. Water quality ta rgets have been set by the TBEP commensurate with seagrass protection and recovery in the Bay, and the Decision Matrix process provides a method to determine if the seagrass goals and water quality targets are being achieved on an annual basis (Janicki Environmental, 2000). The Modeling Subcommittee of the TBEP TAC formalized the concepts of magnitude and duration of differences between observations and targets, as these were determined to be important considerations in determining the appropriate management response level to any target exceedances. Various levels of management res ponse were anticipated, based on four potential41 outcomes for both chlorophyll a concentrations and light attenuation. Figure 4.3 provides the decision framework for chlorophyll a assessment, with the various outcomes resulting from combinations of magnitude and duration of ex ceedances. A similar decision framework is employed for evaluation of light attenuation.

Target 

### CHLOROPHYLL

CONCENTRATION



### Outcome 0 

Magnitude 

### >

Duration Large



### Outcome 2 Short Small

Short Duration 

### Outcome 1

Outcome 2 Outcome 3 Long Long Target 

### CHLOROPHYLL

CONCENTRATION



### Outcome 0 

Magnitude 

### >

Duration Large



### Outcome 2 Short Small

Short Duration 

### Outcome 1

Outcome 2 Outcome 3 Long Long

Figure 4-3. TBEP Decision Matrix framework for chlorophyll a (from Janicki Environmental, 2000).

Exceedances of small magnitude were defined as those more than one standard error above the segment-specific targets, while large magnitude exc eedances were those mo re than two standard errors above the targets. Short duration exceedance s where defined as those less than four years in extent, with any exceedances lasting four years or more defined as long duration exceedances (Janicki Environmental, 2000).

A method of combining the annual results from both the chlorophyll a and light attenuation decision frameworks was developed which de termined the level of management activity recommended in response to any specific year’s eva luation results. The Decision Matrix is shown in Figure 4-4, with the table cells color c oded as indications of the degree of response recommended. The recommended management actions resulting from the decision matrix are classified by color into three categories, as follows:

 GREEN - “Stay the course”; partners continue with planned projects to implement the CCMP. Data summary and reporting via the Baywide Environmental Monitoring Report and annual assessment and progress reports.

 YELLOW - TAC and Management Board on caution alert; review monitoring data and loading estimates; attempt to identify causes of target exceedances; TAC report to Management Board on findings and recommended responses if needed.

 RED - TAC, Management and Policy Boards on alert; review and report by TAC to Management Board on recommended types of responses. Management and Policy Boards take appropriate actions to get the program back on track.42 Decision matrix identifying appropriate categories of management actions in response to various

outcomes of the monitoring and assessment of chlorophyll a and light attenuation data.



## CHLOROPHYLL LIGHT ATTENUATION

Outcome 0 Outcome 1 Outcome 2 Outcome 3

Outcome 0 GREEN YELLOW YELLOW YELLOW



## Outcome 1 YELLOW YELLOW YELLOW RED



## Outcome 2 YELLOW YELLOW RED RED



## Outcome 3 YELLOW RED RED RED

Figure 4-4. TBEP Decision Matrix. (from Janicki Environmental, 2000).



### 4.3.2 Consideration of Catastrophic Events and Karenia brevis Blooms

Southwest Florida has historically been subj ect to occasionally extreme events including hurricanes, blooms of the red tide causing organism Karenia brevis, and unintended releases of nutrient rich sources (i.e., spills). The impact of th ese episodic natural an d anthropogenic events will likely impact water quality and should be some how accounted for in the evaluation process for determining impairment of southwest Florida es tuaries. While it is not possible to predict accurately the timing or influence of these events, an evaluation of the frequency of previous events when water quality data were available will lend insi ghts into how these events might impact future water quality in terms of frequency and duration. We intend to explore these effects via empirical analysis and existing literature on the subject.

4.3.2.1 Catastrophic Events

Irrespective of the cause of these impacts, the evaluation process should consider how to handle these events in evaluation of a waterbody. S hort-term water quality exceedances are likely event driven, while long-term exceedances are likely more indicative of chronic conditions. Time series analysis was recommended for consideration by the SAB and we intend to investigate how time series analysis might be used in the impairme nt evaluation process when catastrophic events occur and may produce responses within an estuarine segm ent outside the expected time series trend. If eutrophication is defined as a rate (Nixon, 1995 ), then time series analysis may be a valuable component in determining th e fate of a waterbody with respect to its trophic status. Further, if a waterbody is meeting its designated uses and there is no evidence that the waterbody is degrading based on time series trend analysis, should a nut rient criterion exceedance solely caused by a catastrophic event necessarily result in the same ma nagement response simply based on a nutrient
criterion exceedance?

We also intend to use the available data to assess the responses in various estuarine segments that have been impacted by a catastrophic event. Spec ifically, we will investigate the lag in responses as well as the time to recovery from these events within each estuarine segment. Clearly, these responses will vary across segments and the magn itude of the catastrophic event. Results from these analyses will be useful in making recomme ndations regarding the methodology to be used in43 accounting for the effects of these events in th e overall assessment of compliance with the
established numeric nutrient criteria.

4.3.2.2 Karenia brevis Blooms

Alcock (2007) has provided a significant review of the cause, consequences, and management strategies with respect to red tide blooms (e.g., Karenia brevis) along the West Florida Shelf. He has summarized his work and those of others into several key points. These include:

 The initiation and development of blooms occur offshore and in deeper waters.

 Upon inshore movement via wind and ocean currents, these blooms may benefit from nutrient inputs from the adjacent coastal lands.

 The predominant nutrient sources that initiate and support a bloom likely change over the
course of the bloom.

 It is uncertain whether any particular combin ation of nutrient sources is common to all
blooms.

 Conclusive evidence of a strong linkage between red tide blooms and the land-based nutrient inputs “remains elusive”.

While the precise relationship between land-based nutrient inputs and red tide blooms remains uncertain, Alcock concluded that “caution agai nst dismissing it (coastal pollution) as inconsequential.” However, given the lack of conclusive evidence addressing how red tide blooms that enter the southwest Florida estuaries are spec ifically related to nutrient loading from their watersheds and ambient nutrient conditions, using red tide blooms to support numeric criteria development within the estuary is difficult. Clea rly, the management of nutrient loading, as has been evidenced in Tampa Bay, Sarasota Bay, an d Charlotte Harbor, not only benefits the longerterm conditions in these estuaries but also contri butes to the reduced likelihood of extended bloom conditions when they do occur and move in shore from the Gulf of Mexico.

4.3.2.3 Other Considerations

As discussed above, the implementation of the u ltimately established nutrient criteria will require facing a number of significant hurdles. The pr oposed numeric nutrient criteria should also be considered within the context of expected future da ta collection efforts to en sure that the evaluation process remains relevant and appropriate to the data sources being assessed. Clear and reasonable expectations with regard to future data needs to assess compliance effectively should be expressed. We intend to offer specific re commendations for those data needs for each of the three estuarine

systems.44 

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## APPENDIX 1
Tampa Bay Segment-Spe cific Water Quality Annual TN, TP, and Chlorophyll a Concentrations5354 

## APPENDIX 2 Red Tide Monitoring Program55 

## Red Tide Offshore Monitoring Program

Volunteers collect water samples to help Fi sh and Wildlife Research Institute scientis ts monitor red tides in Florida waters. Learn about the Red Tide Offshore Monitoring Program and how to become a volunteer.



## BACKGROUND AND DESCRIPTION

The Florida Fish and Wildlife Research Inst itute's (FWRI) Red Tide Offshore Monitoring Program (RTOMP) was established in 2000. The initial purpose of the program was to help FWRI scientists monitor and detect harmful algal blooms (HABs) in Florida by asking volunteers to collect offshore wate r samples. The RTOMP has grown over the years and now provides increased coverage of the Gulf of Mexico, early warning of offshore algal blooms, red tide event response, and partnerships with the marine community.

Because of limited state personnel, boats, and
resources, the RTOMP relies

on volunteers of all kinds— charter boat captains, commercial fishermen, private
citizens, divers, and more—to collect water samples from offshore areas by boat.

Volunteers are located in all areas of Florida. Most samples

are collected from the Gulf of Mexico, as shown in this map of sampling activity in 2008.

Once the samples are return ed to FWRI, they are examined under a microscope. Karenia brevis (the cell that causes Florida's red tide) and other HAB species are identified a nd counted. The data are entered into the HAB Historical Database and are reported back to the volunteers. Data collected by volunteers are used in conjunction with statewide monitoring results to determine red tide status. Sampling Activity in 200956 he amount of time volunteers commit to sampling and red tide event response is invaluable. In 2007, volunteers collected more than 500 water samples and spent 197 days co llecting those samples in response to a red tide in the northwest and southwest regions of t he state. In 2008, volunteers collected 479 water samples over 210 days.