Technical Support Document:

Development of Numeric Nutrient Criteria 

for Florida Lakes, Spring Vents and Streams

Prepared by:

Florida Department of Environmental Protection

Standards and Assessment Section

2012
Nutrient Criteria Technical Support Document Table of Contents 

i 2012

Technical Support Document

Development of Numeric Nutrient Criteria for 

Florida Lakes and Streams

Contents

1 Introduction to Nutrient Criteria Development ................................................................... - 1 -

1.1 Background .................................................................................................................. - 1 -

1.2 The Need for Nutrient Criteria ..................................................................................... - 1 -

1.3 Criteria for Both Nitrogen and Phosphorus.................................................................. - 3 -

1.4 Scope of the Numeric Standards .................................................................................. - 4 -

2 The Science of Eutrophication ............................................................................................ - 6 -

2.1 Eutrophication, Nitrogen and Phosphorus ................................................................... - 6 -

2.2 Lake Eutrophication ..................................................................................................... - 7 -

2.3 Stream Eutrophication .................................................................................................. - 8 -

2.4 The Influence of Other Environmental Factors on Eutrophication .............................. - 9 -

2.5 Harmful Algae as a Potential Response Variable for Numeric Nutrient Criteria 

Development ......................................................................................................................... - 10 -

3 Setting Aquatic Life Use Support Thresholds for the Stream Condition Index and Lake 

Vegetation Index, with Discussion of the Stream Diatom Index .............................................. - 12 -

3.1 Introduction ................................................................................................................ - 12 -

3.2 Background ................................................................................................................ - 12 -

3.3 Development of the Stream Condition Index and the Lake Vegetation Index .......... - 15 -

3.4 Establishing Expectations for Aquatic Life Use – Stream Condition Index: ............. - 18 -

3.4.1 Application of the Reference Site Approach ...................................................... - 18 -

3.4.2 Biological Condition Gradient Approach ........................................................... - 19 -

3.4.3 Evaluation of the Reference Site Approach Coupled with BCG ........................ - 22 -

3.4.4 Setting and Evaluating an SCI Threshold that Supports Healthy, Well Balanced 

Communities ..................................................................................................................... - 23 -

3.4.5 Additional Analysis of Rigorously Verified Benchmark Site SCI Data ............. - 26 -
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3.4.6 Evaluation of Benchmark Site Replicate Data: SCI ........................................... - 29 -

3.5 Establishing Expectations for Aquatic Life Use – Lake Vegetation Index: .............. - 31 -

3.5.1 LVI Benchmark Site Approach .......................................................................... - 31 -

3.5.2 Evaluation of Replicate Data: LVI ..................................................................... - 33 -

3.5.3 LVI Biological Condition Gradient Approach ................................................... - 34 -

3.5.4 Setting and Evaluating a LVI Threshold of Aquatic Life Use Support .............. - 37 -

3.5.5 Differences in the Variability in SCI and LVI Scores ........................................ - 38 -

3.6 Role of the LVI in Numeric Nutrient Criteria ............................................................ - 39 -

3.7 Maintenance of Exceptional Biological Communities using the SCI and the LVI ... - 40 -

3.8 Independent Evaluation of Florida’s Bioassessment Program ................................... - 40 -

3.9 SCI and LVI Conclusions .......................................................................................... - 40 -

3.10 Stream Diatom Index Development ....................................................................... - 41 -

3.10.1 Stream Diatom Index Calibration ....................................................................... - 43 -

3.11 Earlier Efforts to Develop Stream Diatom Bioassessment Methods ...................... - 45 -

4 Derivation of the Numeric Criteria for Nitrate – Nitrite in Spring Vents ......................... - 47 -

4.1 Introduction ................................................................................................................ - 47 -

4.2 Recent Changes to Spring Ecosystems ...................................................................... - 48 -

4.3 Why Nitrate-Nitrite Criteria? ..................................................................................... - 53 -

4.4 Criteria Development Methods .................................................................................. - 56 -

4.5 Laboratory Studies ..................................................................................................... - 56 -

4.6 Field Surveys .............................................................................................................. - 57 -

4.7 TMDL Development Activities ................................................................................. - 58 -

4.8 Synthesis of Available Experimental and Observational Data to Derive a Nitrate 

Standard ................................................................................................................................ - 65 -

4.9 Frequency and Duration of the Nitrate Standard ....................................................... - 67 -

4.10 Potential Use of Cyanobacteria and Associated Toxins as Response Variable for 

Springs - 69 -

4.11 Analysis of Nutrient Gradient Study Rapid Periphyton Data................................. - 71 -
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4.12 Summary and Conclusions ..................................................................................... - 71 -

5 Regionalization of Florida’s Numeric Nutrient Criteria for Streams ............................... - 73 -

5.1 Purpose of Developing a Regionalization Scheme .................................................... - 73 -

5.2 Development of Ecoregional and Biological Regionalization Schemes for Florida . - 73 -

5.3 Refinement of Regionalization for Nutrient Criteria Development ........................... - 76 -

6 Stressor-Response Analyses for Florida Streams ............................................................. - 96 -

6.1 Introduction ................................................................................................................ - 96 -

6.2 Macroinvertebrate Exploratory Analysis ................................................................... - 98 -

6.3 Exploratory Analysis of Periphyton Response to Nutrients....................................... - 99 -

6.4 Quantile Regression Analysis of Periphyton Response to Nutrients ....................... - 102 -

6.5 Analysis of Rapid Periphyton Survey Data.............................................................. - 104 -

6.6 Potential Use of Heterotrophic Bacteria as a Response Variable ............................ - 110 -

6.7 Conclusions .............................................................................................................. - 110 -

7 Florida’s Nutrient Benchmark Site Distributional Approach for Rivers and Streams ... - 112 -

7.1 Introduction .............................................................................................................. - 112 -

7.2 Extensive Verification Process for Selection of Benchmark Sites .......................... - 113 -

7.2.1 Landscape Development Intensity Index (LDI) score of ≤ 2 ........................... - 114 -

7.2.2 Screening against the 303(d) List of Impaired Waters ..................................... - 119 -

7.2.3 Screening Against the 0.35 mg/L Proposed Nitrate-nitrite Threshold ............. - 119 -

7.2.4 Verification of Surrounding Land-use by Examining High Resolution Aerial 

Photographs ..................................................................................................................... - 120 -

7.2.5 Obtaining Input from DEP District Biologists.................................................. - 120 -

7.2.6 Field Evaluation Process, Including Watershed Assessment and Biological 

Appraisal of Benchmark Sites......................................................................................... - 121 -

7.3 Results of Benchmark Screening Process ................................................................ - 123 -

7.4 Analysis of Nutrient Benchmark Sites Biological Data ........................................... - 123 -

7.5 Conclusions for Use of the Benchmark Distribution to Establish Nutrient Criteria - 127 -
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7.6 Calculation of Benchmark Derived Nutrient Criteria .............................................. - 128 -

7.6.1 Data Handling ................................................................................................... - 128 -

7.7 Relationship between Stream and River Nutrient Concentrations, their Effects on 

Biological Communities, and Recreational Uses................................................................ - 132 -

7.8 Use of Biological Data in Conjunction with Stream and River Nutrient Thresholds - 133 

-

7.9 Evaluating the Need for Downstream Protection Values......................................... - 134 -

8 Nutrient Longitudinal Study: Downstream Effects of Nutrients in Selected Florida 

Rivers/Estuaries ...................................................................................................................... - 136 -

8.1 Introduction .............................................................................................................. - 136 -

8.1.1 Project Objectives ............................................................................................. - 136 -

8.1.2 Project Description............................................................................................ - 136 -

8.1.3 Sampling Sites .................................................................................................. - 137 -

8.2 Sampling Results ...................................................................................................... - 141 -

8.2.1 Water Quality .................................................................................................... - 141 -

8.2.2 Historical Trends in Nutrient Concentrations ................................................... - 148 -

8.2.3 Freshwater Biological Sampling Results .......................................................... - 152 -

8.2.4 Biological Health in the Steinhatchee and Waccasassa Estuaries .................... - 155 -

8.3 Conclusions .............................................................................................................. - 160 -

9 Basis for the Proposed Lake Chlorophyll a Thresholds in Florida’s Numeric Nutrient 

Criteria Development .............................................................................................................. - 161 -

9.1 Introduction .............................................................................................................. - 161 -

9.2 History of the Trophic State Index ........................................................................... - 161 -

9.3 Other Efforts to Establish Chlorophyll a Thresholds ............................................... - 164 -

9.3.1 Paleolimnological Studies ................................................................................. - 164 -

9.3.2 Expert Opinion .................................................................................................. - 165 -

9.3.3 Biological Responses ........................................................................................ - 165 -

9.3.4 User Perceptions ............................................................................................... - 166 -

9.3.5 Maintaining Existing Conditions ...................................................................... - 166 -

9.3.6 Reference Approach.......................................................................................... - 166 -
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9.4 Investigating Relationships between Cyanobacteria Abundance and Chlorophyll a . - 166 

-

9.5 Conclusions .............................................................................................................. - 169 -

10 Stressor-Response Analyses of Florida Lakes ................................................................ - 173 -

10.1 Introduction .......................................................................................................... - 173 -

10.2 Macrophyte Analyses ........................................................................................... - 173 -

10.3 Chlorophyll a Analyses ........................................................................................ - 177 -

10.3.1 Clear Lakes ....................................................................................................... - 177 -

10.3.2 Colored Lakes ................................................................................................... - 180 -

10.4 Criteria Derivation ................................................................................................ - 186 -

10.5 Establishing Lake Color Levels ............................................................................ - 189 -

10.6 Algal Blooms and Seasonality .............................................................................. - 190 -

11 Magnitude, Frequency, and Duration of Numeric Nutrient Standards ........................... - 193 -

11.1 Yearly Compliance ............................................................................................... - 193 -

11.2 Annual Geometric Mean ...................................................................................... - 199 -

11.3 Consideration of Extreme Events ......................................................................... - 204 -

12 References ....................................................................................................................... - 206 -
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List of Appendices

3-A: Summary of development of the Stream Condition Index (SCI) 

3-B: Summary of development of the Lake Vegetation Index (LVI) 

3-C: Memo from Leska Fore regarding interval and equivalence tests for the SCI

3-D: Site information and taxa lists for stream Benchmark site samples

3-E: Site information and taxa lists for SCI BCG workshop samples 

3-F: Memo from Lester Yaun describing the proportional odds analyses for the SCI and LVI 

BCG workshops 

3-G: Site information and taxa lists for lake Benchmark site samples

3-H: Benchmark Lake Summaries

3-I: Memo from Leska Fore regarding interval and equivalence tests for the LVI

3-J: Site information and taxa lists for LVI BCG workshop samples 

3-K: Site information and taxa lists for Stream Diatom Index BCG workshop samples

6-A: Multiple regression analyses of SCI data and nutrient concentrations, by Xufeng Niu 

6-B: Multiple regression analyses of stream periphyton data and nutrient concentrations, 

Panhandle Region, by Xufeng Niu 

6-C: Multiple regression analyses of stream periphyton data and nutrient concentrations, 

Peninsula Region, by Xufeng Niu 

6-D: Change Point analysis of stream periphyton data and nutrient concentrations, by Xufeng 

Niu

6-E: Quantile regression analysis of stream periphyton data and nutrient concentrations, 

Panhandle region, by Xufeng Niu 

6-F: Quantile regression analysis of stream periphyton data and nutrient concentrations, 

Peninsula region, by Xufeng Niu 

6-G: Analysis of rapid periphyton survey data, by Tetra Tech. 

7-A: Stream Benchmark Summaries

7-B: Comparison of LDI methods, by Tetra Tech. 

8-A: Longitudinal study site photos 

9-A: Literature review of protective thresholds for Chlorophyll a
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Abbreviations Used in this Document

BCG = Biological Condition Gradient 

BPJ = best professional judgment 

EPA = United States Environmental 

Protection Agency 

F.A.C. = Florida Administrative Code 

DEP = (Florida) Department of 

Environmental Protection 

FSI = Florida Springs Initiative

HA = Habitat Assessment

HAB =Harmful Algal Bloom 

HDG = Human Disturbance Gradient 

IWR = Impaired Waters Rule

LDI = Landscape Development Intensity 

Index

LVI = Lake Vegetation Index

MDL = method detection limit

NOx = nitrate+nitrite

NPDES = National Pollutant Discharge 

Elimination System

PCU = platinum cobalt units

ppb = parts per billion 

RPS = Rapid Periphyton Survey 

SAV = submerged aquatic vegetation

SCI = Stream Condition Index 

SDI = Stream Diatom Index

SOP = Standard Operating Procedures 

SSAC = site specific alternative criteria

TAC = Technical Advisory Committee 

TKN = total Kjeldahl nitrogen

TMDL = Total Maximum Daily Load

TN = total nitrogen

TP = total phosphorus 

TSI = Trophic State Index

USGS = United States Geological Survey

WBID = Water Body Identifier

WHO = World Health Organization 
Nutrient Criteria Technical Support Document Chapter 1: Introduction 

- 1 - 2012

1 Introduction to Nutrient Criteria Development

1.1 Background

In response to reports provided by States that nutrients are the leading cause of impairment in 

lakes and coastal waters and the second leading cause of impairment to rivers and streams, the 

United States Environmental Protection Agency (U.S. EPA) published the National Strategy for 

Development of Nutrient Criteria in 1998. That document described the approach that EPA 

would follow for developing nutrient information and working with States and Tribes to adopt 

numeric nutrient criteria as part of State water quality standards. Since that time, the State of 

Florida has conducted an extensive effort to gather and assess the necessary scientific 

information to develop and adopt numeric nutrient standards for lakes, spring vents and streams. 

This document sets forth the scientific and technical basis for Florida’s numeric nutrient 

standards, which were adopted by the Environmental Regulation Commission in December, 

2011. It is envisioned that these standards, in combination with the related bioassessment tools, 

will facilitate the assessment of designated use attainment for its waters and to provide a better 

means to protect state waters from the adverse effects of nutrient over- enrichment. 

1.2 The Need for Nutrient Criteria

The addition of excess nutrients, often associated with human alterations to watersheds, can 

negatively impact waterbody health and interfere with designated uses of waters. Excess 

nutrients can lead to algal blooms (which in turn may produce noxious tastes and odors in 

surface water drinking supplies), nuisance aquatic weeds (which can impact recreational 

activities like swimming and boating), and alteration of the natural community of flora and 

fauna.

Consistent with Florida’s Numeric Nutrient Criteria Development Plan, the Department of 

Environmental Protection (DEP or Department) has developed numeric standards for causal 

variables (phosphorus, nitrogen, and nitrate/nitrite) and/or response variables (Stream Condition 

Index, Lake Vegetation Index, and Chlorophyll a) that interpret the narrative nutrient criterion 

for lakes, spring vents and streams. The standards take into account the hydrologic variability 

(waterbody type) and spatial variability (location within Florida) of the nutrient levels that 

naturally occur in the state’s waters, and the variability in ecosystem response to those nutrient 

concentrations. DEP has performed extensive research and monitoring to evaluate cause/effect 

relationships between nutrients and valued ecological attributes, and to establish nutrient 

standards that ensure that the designated uses of Florida’s waters are maintained.

Florida currently uses a narrative nutrient standard to guide the management and protection of its 

waters. Chapter 62-302.530 (47)(b), Florida Administrative Code (FAC), states that “in no case 

shall nutrient concentrations of body of water be altered so as to cause an imbalance in natural 

populations of aquatic flora or fauna.” 

DEP has relied on this narrative criterion for many years because nutrients are unlike any other 

“pollutant” regulated by the federal Clean Water Act (CWA). Most water quality criteria are 
Nutrient Criteria Technical Support Document Chapter 1: Introduction 

- 2 - 2012

based on a toxicity threshold, evidenced by a dose-response relationship, where higher 

concentrations can be demonstrated to be harmful, and acceptable concentrations can be 

established at a level below which adverse responses are elicited (usually in laboratory toxicity 

tests). In contrast, nutrients are not only present naturally in aquatic systems, they are absolutely 

necessary for the proper functioning of biological communities, and are typically moderated in 

their expression by many natural factors.

By continuing to use the existing narrative nutrient criterion, the Department recognizes that:

• Nutrients are naturally present in Florida aquatic ecosystems at a wide range of 

concentrations and loadings; 

• Due to edaphic factors (related to the geology, soil, and inputs from natural systems such 

as wetlands), background levels of nutrients in many Florida systems, while supporting 

natural populations of flora and fauna, may also be sufficient to contribute to growth of 

algae and other aquatic plants. Sporadic algae growth is considered part of the natural 

population of flora. The reasons that natural, potentially harmful algal blooms develop 

are extremely complex, not always clearly understood by the scientific community, and 

are influenced by non-anthropogenic/natural factors (upwelling, climatological factors, 

natural disruptions in grazing communities, etc.). 

• Aquatic ecosystem responses to nutrients are so complex that the most accurate 

management strategy to ensure support of designated uses (recreation and healthy, well 

balanced aquatic communities) is to determine site-specific relationships between 

anthropogenic inputs of nutrients and environmental responses. The narrative nutrient 

criterion embodies the relationship between nutrient concentrations and environmental 

responses. Absent a general cause and effect relationship that can be used to manage 

nutrient concentrations alone, evaluating both nutrient concentrations and flora and fauna 

(both part of the narrative) provides the most accurate information of attainment and 

nonattainment. 

• The DEP has historically used water quality models to estimate the expected nutrient 

concentrations and biological response when developing water quality-based effluent 

limitations for nutrients for proposed wastewater discharges, and only allows the 

discharges if the permit applicant demonstrates that the discharge will not cause or 

contribute to violations of the narrative nutrient criterion. 

• The U.S. EPA Scientific Advisory Board (SAB) agrees with DEP’s approach for 

establishing nutrient criteria, noting that that the “most scientifically defensible strategy 

for managing nutrients within the range of uncertainty is to verify a biological response 

prior to taking a management action. This risk/performance-based approach to setting 

nutrient criteria is evident not only in Florida’s program, but also in those developed by 

California, Maine, and Ohio (Florida Department of Environmental Protection, 2009; 

Maine Department of Department of Environmental Protection, 2009; McLaughlin and 

Sutula, 2007)” (U.S. EPA SAB 2010). Both Florida’s nutrient Technical Advisory Board 

(TAC) and the SAB recommended that linkages between nutrients and biological 
Nutrient Criteria Technical Support Document Chapter 1: Introduction 

- 3 - 2012

response be incorporated into numeric standards, a concept that clearly supports the 

existing narrative criterion. 

The DEP has been actively working with EPA on the development of numeric nutrient criteria 

for several years. DEP submitted its initial DRAFT Numeric Nutrient Criteria Development 

Plan to EPA Region IV in May 2002, and received mutual agreement on the Numeric Nutrient 

Criteria Development Plan from EPA on July 7, 2004. The DEP revised its plan in September 

2007 to more accurately reflect its evolved strategy and technical approach, and DEP received 

mutual agreement on the 2007 revisions from EPA on September 28, 2007. On January 14, 2009, 

EPA formally determined that numeric nutrient criteria should be established on an expedited 

schedule. On March 3, 2009 DEP submitted its Current Numeric Nutrient Criteria Development 

Plan to EPA Region IV. This revised plan reflects DEP approaches and expedited schedule to 

establish numeric nutrient criteria.

To ensure that Florida’s numeric nutrient criteria are scientifically sound, Florida has been 

guided throughout the development process by recommendations from a Technical Advisory 

Committee (TAC) composed of technical experts from throughout the state. The TAC reviewed 

the technical information collected during the process of establishing the State’s nutrient criteria.

1.3 Criteria for Both Nitrogen and Phosphorus 

Major nutrients required by aquatic organisms include carbon, oxygen, hydrogen, nitrogen and 

phosphorus (Wetzel 2001). There has been a significant amount of scientific research 

concerning which nutrients most often affect the primary productivity in freshwaters, and there is 

a general consensus that it is typically either nitrogen and/or phosphorus (Suplee et al. 2008). 

Since a particular balance of these nutrients is needed by plants and algae, one nutrient is 

typically in excess and the other is considered to be limiting. There has been a great deal of 

recent scientific debate as to whether the emphasis on nutrient control should be on nitrogen, 

phosphorus or both (Schindler et al. 2008). 

The emphasis on controlling eutrophication in freshwater lakes has been focused heavily on 

decreasing inputs of phosphorus, although many studies in lakes (and estuaries) still conclude 

that nitrogen must be controlled as well as, or instead of, phosphorus to reduce eutrophication 

(Schindler et al. 2008; Howarth and Marino 2006). However, recent research has indicated that 

nitrogen may be of equal importance to phosphorus in streams, where nitrogen and phosphorus 

co-limitation appears to be common (Francoeur 2001) and that controls on both nutrients are 

necessary for long-term management of eutrophication along the continuum from freshwater to 

saltwater systems (Pearl 2009). An extensive review by Elser et al. (2007) concluded that all 

fresh and marine ecosystems are limited by both TN and TP to some degree, though marine 

waters were predominantly limited by TN and lakes were predominantly limited by TP. 

Autotrophic biomass was higher in treatments in which both N and P were added than in 

treatments with one nutrient added. The stoichiometry of any given water body can change 

naturally throughout the year or over time and will vary between region (Wetzel 2001), so it 

would be difficult and impractical to estimate the appropriate N:P ratio as well as the appropriate 

magnitude of those nutrients for each water body in Florida. 
Nutrient Criteria Technical Support Document Chapter 1: Introduction 

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DEP and the Nutrient TAC considered whether control of TN, TP, and/or N:P ratios were needed 

for Florida’s waters. Based on the above studies, there is evidence that both nutrients need to be 

controlled, but not any specific N:P ratio. Therefore, the Department’s numeric nutrient 

standards consist of both TP and TN as causal variables. 

1.4 Scope of the Numeric Standards

There are three types of proposed numeric nutrient standards set forth in this document – 

standards applying to Class I and III freshwater streams, standards applying to Class I and III 

spring vents, and standards applying to Class I and III freshwater lakes. For the purpose of these 

standards, a stream is defined as a predominantly fresh surface waterbody with perennial flow in 

a defined channel with banks during typical climatic and hydrologic conditions for its region 

within the state. During periods of drought, portions of a stream channel may exhibit a dry bed, 

but wetted pools are typically still present during these conditions. Streams do not include: 

(a) non-perennial water segments where fluctuating hydrologic conditions, including periods 

of desiccation, typically result in the dominance of wetland and/or terrestrial taxa (and 

corresponding reduction in obligate fluvial or lotic taxa), wetlands, or portions of streams that 

exhibit lake characteristics (e.g., long water residence time, increased width, or predominance of 

biological taxa typically found in non-flowing conditions) or tidally influenced segments that 

fluctuate between predominantly marine and predominantly fresh waters during typical climatic 

and hydrologic conditions; or 

(b) ditches, canals and other conveyances, or segments of conveyances, that are man-made, 

or predominantly channelized or predominantly physically altered and; 

1. are primarily used for water management purposes, such as flood protection, stormwater 

management, irrigation, or water supply; and 

2. have marginal or poor stream habitat or habitat components, such as a lack of habitat or 

substrate that is biologically limited, because the conveyance has cross sections that are 

predominantly trapezoidal, has armored banks, or is maintained primarily for water conveyance. 

Stream numeric nutrient standards do not apply to artificial, predominantly channelized, or 

predominantly physically altered systems because:

• Stream nutrient thresholds were derived from a data distribution of minimally 

disturbed reference sites (benchmark site approach), which did not include canals or 

manmade/altered conveyances; and

• The U.S. EPA Science Advisory Board (U.S. EPA SAB 2011) concluded that a) 

physical characteristics or ongoing maintenance of canals and ditches limits the 

aquatic life found in those waters and limits the appropriateness of traditional 

biological testing for such waters, and b) ecosystem services in canals and ditches are 

controlled primarily by hydrology and habitat quality rather than nutrient levels. As 

recommended by both the SAB and the DEP Nutrient TAC, use of the benchmark site 

approach should include a site specific assessment of biological response to nutrients 

before taking management action. Although there are existing biological assessment

methods for streams to carry out this charge, biological assessment tools specific to

canals or other altered systems are still under development. This is due to the 
Nutrient Criteria Technical Support Document Chapter 1: Introduction 

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complexities associated with attributing biological response to water quality issues 

given the backdrop of hydrological and habitat disturbance in physically altered 

systems, including scheduled maintenance activities designed to promote water 

conveyance, flood control, etc. 

Similarly, stream numeric nutrient standards do not apply to non-perennial water segments 

because non-perennial streams were not included in the reference site data distribution, and 

desiccation is among the most influential stressors affecting the aquatic community. 

Until such time that additional scientific information is available, physically-altered waterbodies

(e.g., ditches, canals) and non-perennial streams will continue to be protected by the existing 

narrative criteria. Wetlands will also continue to be protected through the existing narrative 

criteria (with the exception of the Everglades, for which a numeric nutrient criterion has already 

been established) until sufficient information to develop wetlands nutrient criteria is available. 

Lastly, while the Department does not have numeric standards for these waterbodies themselves, 

the newly adopted narrative downstream protection will apply to them because it is important 

that activities that alter nutrients in those waterbodies do not impact downstream waterbodies. 

For the proposed Class I and III freshwater lake criteria, a lake is defined as “a lentic fresh 

waterbody with a relatively long water residence time and an open water area that is free from 

emergent vegetation under typical hydrologic and climatic conditions. Aquatic plants, as defined 

in subsection 62-340.200(1), F.A.C., may be present in the open water. Lakes do not include 

springs, wetlands, or streams (except portions of streams that exhibit lake-like characteristics, 

such as long water residence time, increased width, or predominance of biological taxa typically 

found in non-flowing conditions).” 

There are no separate numeric nutrient standards for Outstanding Florida Waters (OFWs), but 

these special waters receive additional protection through the permitting programs. Section 

403.061(27), Florida Statutes, grants DEP the power to establish rules that provide for a special 

category of waterbodies within the state, referred to as OFWs, which receive special protection 

because of their natural attributes. OFW is not a designated use category, rather, OFW 

protection is part of the antidegradation component of water quality standards and is 

implemented through the permitting process. 

Projects regulated by DEP or a Water Management District (WMD) that are proposed within an 

OFW must not lower existing ambient water quality, which is defined for purposes of an OFW 

designation as either the water quality at the time of OFW designation or the year before 

applying for a permit, whichever water quality is better. In general, DEP cannot issue permits for 

direct discharges to OFWs that would lower ambient (existing) water quality. In most cases, this 

deters new wastewater discharges directly into an OFW, and requires increased treatment for 

stormwater discharging directly into an OFW. DEP also may not issue permits for indirect 

discharges that would significantly degrade a nearby waterbody designated as an OFW. These 

evaluations are conducted on a permit by permit basis. 
Nutrient Criteria Technical Support Document Chapter 2: The Science of Eutrophication 

- 6 - 2012

2 The Science of Eutrophication

2.1 Eutrophication, Nitrogen and Phosphorus

Nutrients are fundamentally different from most pollutants in that they are essential to aquatic 

life and are not inherently harmful or toxic at natural concentrations (Freeman et al. 2009). In 

fact, aquatic organisms cannot build the proteins and nucleic acids of their cellular structure or 

carry out their basic metabolic processes without the proper concentrations of nitrogen and 

phosphorus. In contrast, some toxicants, such as pesticides or metals, can be toxic to aquatic life 

even in barely detectable concentrations (Suplee et al. 2008). 

Natural aquatic systems can be classified as either oligotrophic (low in nutrients and 

productivity), mesotrophic (intermediate in nutrients and productivity), or eutrophic (high in 

nutrients and productivity). Biologically, oligotrophic systems are generally characterized by 

low amounts of biomass and high species diversity, while eutrophic systems are generally 

characterized by high amounts of biomass and lower species diversity. In oligotrophic and 

mesotrophic systems, macroalgal growth is in balance with grazer biota, and water clarity and 

dissolved oxygen levels are high and support natural populations of fish, shellfish, and 

invertebrates (Bricker et al. 2007). 

Some aquatic ecosystems (particularly lakes and estuaries) may follow a progression, called 

natural eutrophication, in which they evolve from oligotrophic to eutrophic systems, even in the 

absence of human intervention, generally over a long period of time (i.e., centuries). Nutrients 

derived from natural inputs, such as leaf litter or soils, stimulate aquatic productivity (algal and 

aquatic plant growth) over time, and the resulting biological matter can accumulate in sediments. 

Native bacterial populations decompose the accumulated organic matter in the sediments, which 

releases the nutrients back into the water column while consuming dissolved oxygen, thus 

lowering ambient oxygen levels in the aquatic system. Aquatic systems that are following the 

progression from oligotrophy to eutrophy are generally referred to as mesotrophic (Smith 1977; 

Wetzel 2001). The underlying geology, the character and size of the watershed, and other 

natural factors can also determine the trophic status of a waterbody.

The anthropogenic introduction of additional nutrients and particulate matter from atmospheric 

deposition, point source discharges, and agricultural and urban nonpoint sources essentially 

speeds up this natural process. Depending on site-specific factors, these excess nutrients have 

the potential to cause significant increases in the growth of macrophytes, macroalgae, 

heterotrophic bacteria and/or phytoplankton. The increased plant growth may exceed the 

capacity of grazer control, resulting in decreased water clarity and dissolved oxygen levels, with 

associated adverse impacts to natural populations of fish, shellfish and invertebrates (Bricker et 

al. 2007). This accelerated process is generally referred to as cultural eutrophication. 

There are many sources of nitrogen and phosphorus to aquatic systems. Nitrogen is naturally 

introduced into the atmosphere via the nitrogen cycle, but the global rate at which reactive 

nitrogen is introduced into the atmosphere by anthropogenic use of fossil fuels and fertilizer 

production has nearly doubled when compared with the contributions of natural sources alone 
Nutrient Criteria Technical Support Document Chapter 2: The Science of Eutrophication 

- 7 - 2012

(Holland et al. 2005). Point source discharges (e.g., wastewater treatment facilities) have 

generally been considered to be conspicuous sources of nutrients, but for most waterbodies, 

nonpoint sources (storm water runoff) of nutrients are now typically of relatively greater 

importance. This is a result of both improved point source treatment and control (particularly for 

P) and because of increases in the total magnitude of nonpoint sources (particularly for N) over 

the past three decades (Howarth et al. 2002, DEP 2008a). Nonpoint sources of nutrients may be 

from agricultural practices (e.g., excess nutrients from fertilization), soil and stream bank 

erosion, urban development and the associated runoff, loss of wetlands, and the subsequent 

oxidation of the organic soils (Suplee et al. 2008). 

2.2 Lake Eutrophication

The increased productivity resulting from the introduction of excessive levels of nutrients to lake 

ecosystems can lead to increased growth of phytoplankton, and may potentially include 

increased blue-green algae (cyanobacteria) and nuisance aquatic weeds. While eutrophication is 

considered to be a factor contributing to the geographic and temporal expansion of some 

Harmful Algal Bloom (HAB) species (Gilbert et al. 2005, Pitois et al. 2001), DEP analysis of a

large, probabilistically sampled data base of Florida lakes demonstrated no such relationship 

between chlorophyll levels and the probability of HAB species occurrence (see discussion in 

section 9.4 below). Increases in plant biomass results in the organic content of lake sediments to 

increase, and the increased microbial degradation of this organic material leads to lowered 

dissolved oxygen levels, especially in proximity to the lake bottom (Smith 1977; Wetzel 2001). 

Other effects may include decreased water transparency, changes in water color and odor, shifts 

in aquatic macrophyte vegetation, and pH increases (Xavier et al. 2007; Dokulil and Teubner 

2003; Vitousek et al. 1997). 

Subsequently, these changes may result in decreased diversity in benthic macroinvertebrate 

communities to a community more tolerant of nutrient enriched conditions. Increased 

phytoplankton production can also alter the zooplankton community, which in turn can alter the 

availability of forage fish and thus the health of predatory fish (Carpenter and Kitchell 1988). 

Figure 2-1. An oligotrophic lake in Florida. Figure 2-2. A hyper-eutrophic lake in Florida.
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2.3Stream Eutrophication

The first uses of macroinvertebrates as indicators of organic pollution were in streams and rivers 

in Europe (Kolkwitz and Marsson 1908, 1909). The concept that certain species have differing 

levels of tolerance to nutrient pollution has been revisited and revised numerous times over the 

years (Richardson 1929; Cairns and Dickson 1971; Guhl 1987). Concurrent with the 

developments in flowing systems, Thienemann (1925) made similar observations in lakes, and 

benthic macroinvertebrates were used as indicators of changes associated with the 

oligotrophic/eutrophic gradient (Brinkhurst 1974). 

Although some researchers have found that very high levels of nutrients in streams may be 

associated with the disappearance of taxa that are sensitive to organic pollution (Hynes 1960), 

other factors, including canopy cover, water color, flow regime, and grazers, etc., are highly 

influential in moderating the effects of nutrients. Recent studies in New York, Wisconsin, and 

Indiana (Smith et al. 2007; Wang et al. 2007, Gillespie et al. 2008) have refined the linkages 

between nutrients and macroinvertebrate communities. Smith et al. (2007) developed TP and 

NO3

-

thresholds for oligotrophic, mesotrophic, and eutrophic conditions and established optimal 

nutrient regimes for 164 macroinvertebrate taxa. Wang et al. (2007) found significant 

correlations between nitrogen and phosphorus concentrations and percent and number of 

Ephemeroptera, Plecoptera, and Trichotera (EPT), the Hilsenhoff biotic index, and mean 

tolerance values. While Figures 2-3 through 2-5 illustrate a hypothesized transition of an 

invertebrate community from oligotrophic, to mesotrophic, to eutrophic conditions, the actual 

analysis of data collected across a wide nutrient gradient in Florida (see Chapter 6) did not 

produce information that could be used for establishing numeric nutrient standards for streams. 

As described in Chapter 4, eutrophication can also include an increase in dominance by benthic 

filamentous algae (i.e., algae attached to the stream bottom substrates, objects sitting on the 

stream bottom, or detached as floating algal mats). An abundance of aquatic plant biomass 

(which may be natural or exacerbated by anthropogenic nutrients) can also increase the 

magnitude of daily dissolved oxygen and pH oscillations. 
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Figure 2-3. The photo on the left illustrates a diverse array of sensitive taxa (e.g. stoneflies, mayflies, and 

caddisflies). A typical reference stream is seen on the right.

Figure 2-4. The photo on the left shows an increase in organism abundance they may be found at 

intermediate (mesotrophic) levels of nutrients. Note that sensitive taxa, including stoneflies, mayflies, 

caddisflies, and blackfies are well represented. The photo on the right shows intact habitat (snags) and 

riparian zone. 

Figure 2-5. The photo on the left illustrates a depauperate benthic community that may hypothetically be 

associated with eutrophic conditions. As seen in the photo on the right, riparian zone disruption (open 

canopy) and hydrologic modification (no flow) may be highly influential on stream ecology. 

2.4 The Influence of Other Environmental Factors on Eutrophication

Other environmental factors can influence the manifestation of eutrophication in lakes, spring 

vents and streams, including, climate, geochemistry, flow rates, hydraulic retention times, water 
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color, presence of herbivores and grazers, and shading. For instance, within the Florida 

peninsula, variability of lake primary productivity decreases from north to south corresponding 

to latitudinal gradients in climatic regimes (Beaver and Crisman 1991). In addition, 

geochemistry factors such as exposure of surface waters to limerock or the Hawthorne formation 

(ancient marine clays) to can lead to naturally-enriched levels of calcium carbonate, increased 

buffering capacity (Griffith et al. 1994), and higher levels of phosphorus (see Chapter 5). In 

flowing waters, even natural levels of nutrients may produce increased plant biomass during 

stagnant conditions compared to periods of normal stream flow. Similarly, in lakes and 

estuaries, nutrients will exert more effect in systems with a long hydraulic retention time versus 

systems with a short hydraulic retention time. Finally, the expression of productivity by 

nutrients can be repressed by an increase in water color or shading due to canopy cover, both of 

which inhibit light penetration of the water. 

Because these factors moderate the expression of nutrients, site-specific nutrient criteria are 

considered to be the most accurate, and any regionally derived criteria must allow for alternative 

nutrient criteria when the evidence shows that flora and fauna are healthy and fully supportive of 

aquatic life uses. 

2.5 Harmful Algae as a Potential Response Variable for Numeric 

Nutrient Criteria Development

The scientific literature shows that noxious or toxic algae occurred in Florida prior to human 

disturbance, and that anthropogenic nutrient enrichment is NOT solely responsible for modern 

harmful algal bloom events. Paleolimnology, including pigment analyses, shows that 

cyanobacteria were dominant in many Florida lakes prior to human disturbance, in the late 1800s 

(Riedinger-Whitmore et al. 2005). Historic data show that Lyngbya wollei was found in springs 

in 1939 and 1957 (Stevenson et al. 2007). While there is limited historical information 

concerning trends in freshwater harmful algal blooms, there is a more robust database for marine 

systems. 

Significant red tide (Karena brevis) events have been documented to occur prior to significant 

human habitation in the State. Red tides causing massive fish kills in the Gulf of Mexico have 

been reported anecdotally since the 1500s, and written records documenting a K. brevis bloom 

exist since 1844 (Steidinger et al. 1999). For example, during the 1800s, red water or “poisoned 

water” off Florida’s coast was associated with fish, invertebrate, and bird kills; toxic shellfish; 

and a human respiratory irritant (Rounsefell and Nelson 1966). In 1947, naturally occurring 

Karenia blooms caused massive sponge mortality in Tarpons Springs, Key West, and the 

Bahamas (Florida Dept. of State 2010). 

Walsh et al. (2006), outline a compelling case for the underlying cause(s) of Florida’s long 

history of west coast red tides. The authors describe in detail how Saharan dust, rich in iron, 

stimulates the growth of Trichodesmium spp., a genus of cyanobacteria capable of fixing 

atmospheric nitrogen. Trichodesmium on the West Florida Shelf migrate vertically to take 

advantage of fossil phosphorus concentrations in near bottom waters and light and iron in near 

surface waters. K. brevis vertically migrate with Trichodesmium and take advantage of the 
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dissolved organic nitrogen (DON) released by Trichodesmium. The combined bloom moves to 

nearshore waters where the Trichodesmium bloom collapses, releasing large amounts of DON. 

This initiates the K. brevis bloom which can then maintain itself on nutrients released by 

decaying fishes as well as landward sources of nutrients. 

Walsh et al. (2006) also hypothesize that some of the perceived increase in red tide blooms may 

be related to significant increase in the desertification of the Sahara region of Africa, which is 

resulting in the delivery of greater amounts of iron to the Gulf of Mexico. The authors also note 

that increased public awareness and increased state and federal monitoring efforts following a 

large red tide event in 1957 may also have contributed to the perceived increase in bloom 

occurrence and frequency. The Florida Fish and Wildlife Conservation Commission contracted 

with biostatisticians at the University of Florida (UF) to analyze the red tide data for long-term 

trends to determine what valid statistical conclusions could be drawn. UF concluded that the 

nature of the data prevented any valid statistical interpretation concerning trends and human 

influences on K. brevis blooms. A summary of the UF analysis is available at:

http://myfwc.com/research. 

As seen in the fossil record, Pyrodinium blooms have been occurring naturally at levels toxic to 

nearshore Florida fishes and seabirds for 25 million years (Emslie et al. 1996). Reports of 

seafood intoxication West Indies were noted during 1457–1526 by the historian Peter Martyr, 

and the problem was later identified as naturally occurring populations of Gambierdiscus 

toxicus, the alga that causes ciguatera poisoning (Steidinger et al. 1999). Steidinger et al. (1999) 

noted that Pfiesteria (and Pfiesteria-like species) have probably been present in Florida waters 

for many years and misidentified as gymnodinioids. 

While the literature did indicate occurrence of HABs prior to the existence of anthropogenic 

sources, it did not provide any information that supports the derivation of a level for water 

quality standards purposes. The Department considered harmful algal blooms/mats as a potential 

response endpoint for numeric nutrient criteria development (discussed in Sections 4.9 and 9.4), 

and concluded that protection of natural populations of flora and fauna would be effective in 

limiting harmful algal events to frequencies that would have expected to occur under natural 

conditions. 
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3 Setting Aquatic Life Use Support Thresholds for the 

Stream Condition Index and Lake Vegetation Index, with 

Discussion of the Stream Diatom Index 

3.1 Introduction

The ability to measure whether a waterbody’s aquatic community meets the objective of the 

CWA can be critical to informing decisions related to implementation of the State and Federal 

water quality programs. In particular, the establishment of biological assessment measures 

within State water quality standards can be very valuable for making use attainment decisions for 

aquatic life use support, which equates to attainment of the CWA goal regarding biological 

integrity/health. The critical decision is how to develop a quantitative measure, and where to set 

the threshold that indicates attainment or non attainment of the designated use, given the 

complexities of actually measuring biological structure and function. 

This section describes the factors necessary to consider when developing a quantitative measure 

of biological health, and describes the basis for the State’s position for establishing the 

appropriate biologic threshold indicating attainment of the designated use. In turn, these 

biological thresholds are useful for determining particular nutrient concentrations that may 

interfere with designated use attainment.

3.2 Background

The response of biological communities to human point source pollution initially received 

attention in Florida during the late 1950s. In 1958, Bill Beck, a biologist with the Florida State 

Board of Health, wrote a series of “Biological Letters” in which he introduced the concept of 

using invertebrates as biological indicators, especially for demonstrating the effects of excess 

organic matter on streams and lakes (the saprobity index concept). What became known as 

“Beck’s Biotic Index” was developed by sampling invertebrates at control sites located upstream 

of point source discharges and observing which sensitive taxa were eliminated at sites 

downstream of the effluent sources (Beck 1954). Concurrently, there typically was a dramatic 

increase in abundance of tolerant taxa, such as “bloodworms” (certain species of chironomid 

midges) as illustrated in Figure 3-1. 
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Figure 3-1. Typical macroinvertebrate response to organic loading associated with primary wastewater 

treatment typical in the 1950s and 1960s.

In the early 1970s and 1980s, benthic invertebrates were routinely sampled via multi-plate 

artificial substrate samplers (Hester-Dendys). Hester-Dendy samplers are placed in the receiving

waters for 28 days, which is a minimum period of time for colonization by a representative 

benthic community (Figure 3-2). The Hester-Dendy data were summarized using the ShannonWeaver diversity index, a biological metric derived from information theory that became a 

popular method to communicate complicated biological results. The Shannon-Weaver diversity 

index is based upon a combination of the taxa richness at a site and the evenness of the 

distribution of abundance of individuals. Low diversity scores represent conditions where a few 

pollution tolerant organisms are very abundant, to the exclusion of other taxa. This index is 

specified in the Florida’s water quality standards as a measure of biological integrity (Rule 62-

302.530, F.A.C.). It generally has been applied by comparing site-specific control sites to nearby 

test sites. 
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Figure 3-2. Photo of Hester-Dendy samplers used for determining the Shannon-Weaver diversity index.

In 1992, EPA promulgated the concept of “rapid bioassessment,” and Florida embraced the 

concept for establishing biological criteria, starting with the Stream Condition Index (Barbour et 

al. 1996a). Regional expectations (generally eco-regions) for biological communities were 

established by sampling reference sites, determined via a Best Professional Judgment approach. 

Metrics, defined as measures of biological health that respond in a predictable manner to human 

disturbance, were calculated from the raw reference site data. A distribution of the reference site 

metric values was calculated, and scores selected to represent the expectations for that metric 

from a reference site population. A variety of metrics were then combined into a dimensionless 

index, by assigning points to individual metrics based on their relative similarity to the reference 

condition, and summing the points. 

To successfully use any biological assessment tool, an understanding of the system's biological 

components and sources of variability is critical. The biota respond to a wide variety of 

cumulative factors, both natural and anthropogenic (Figure 3-3). As the organisms integrate 

these factors over time, a characteristic community structure emerges, with a range of natural 

variability. Note that Florida biologists have determined that much of the variability at 

minimally disturbed sites may be explained by random, natural events such as sporadic, 

unpredictable rain and drought, which in turn are associated with the relative abundance of 

inundated substrates available for invertebrate colonization. These natural stressors (e.g., flood, 

drought, natural low substrate diversity, periodic natural low dissolved oxygen, etc.) will affect 

all sites, even those with minimal disturbance from humans. To determine when human actions 

are responsible for adverse effects (causing an impaired or imbalanced community), one must 

reasonably account for these natural factors and assure the biological condition has substantially 

deviated from the reference range (Karr and Chu 1997). 
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Figure 3-3. Many factors affect biological community composition. To conclude that human factors are 

primarily responsible for biological degradation, reasonable knowledge of the influence of natural factors is 

essential.

The DEP’s current Stream Condition Index (SCI) and Lake Vegetation Index (LVI) were built 

on the 1990s concepts. The present indices have utilized a human disturbance gradient (HDG) 

approach to identify effective metrics, and thresholds for “impaired” and for “exceptional” 

conditions were established using a combination of the reference site distribution and the 

Biological Condition Gradient (BCG) approach. The BCG employs a group of experts to 

individually review species level data and determine the site’s ecological status (see below). 

3.3 Development of the Stream Condition Index and the Lake Vegetation 

Index

The current Florida Stream Condition Index (SCI) was developed in 2004, and adjustments were 

made in lab counting procedures to reduce variability of results in 2007 (Fore 2007a). It is a 

multi-metric index that assesses stream health using the benthic macroinvertebrate community. 

The DEP expends great efforts to ensure that data are produced with the highest quality, both in 

the field and in the lab. Samplers and lab technicians follow detailed Standard Operating 

Procedures (SOPs), and additional guidance for sampling and data use is provided through a 

DEP document entitled, “Sampling and Use of the Stream Condition Index (SCI) for Assessing 

Flowing Waters: A Primer (DEP-SAS-001/11)”. Samplers are only approved to conduct the SCI 

after passing a rigorous audit by the DEP, and laboratory taxonomists are regularly tested and 

must maintain >95% identification accuracy. Requirements for training, auditing, sampling, and 

analysis of the SCI are detailed in DEP-SOP-003/11 SCI 1000. 

The SCI is composed of ten metrics, eight of which decrease in response to human disturbance, 

with two metrics (% very tolerant and % dominant) increasing in response to human disturbance. 

Based on reference site community similarity, three stream Bioregions were established in which 
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there are slightly different expectations for the metrics based on natural differences: the 

Panhandle, the Northeast, and the Peninsula (note that the SCI is not calibrated for Ecoregion 76, 

the Southern Florida Coastal Plain, where few natural streams exist) (Griffith et al. 1994; Figure 

3-4). To be scientifically defensible, stream systems being evaluated against the SCI should be 

morphologically identifiable as streams, so that potential human influences can be discerned (the 

reference streams should be compared to streams, reference streams should not be compared to a 

system with lake-like or wetland-like conditions). See Appendix 3-A for a description of the 

development of the SCI.

Figure 3-4. Sub-ecoregions of Florida, which were aggregated into 3 bioregions, based on multivariate 

measures of taxonomic similarity.

Seasonality was analyzed in the recalibration of the SCI in 2007 (Fore 2007) and was determined 

to be a minor source of variation in the overall index. To test the influence of season on the SCI, 

78 sites with 578 visits in summer and winter seasons were compared using a paired t-test. 

Summer was defined as May through October and winter as November through April. Within 

each season, data for repeat visits to each site were averaged. Repeat visits to the same sites 

were used to evaluate the sources of variability for the SCI. SCI values were on average 3.5 

points higher in winter than summer (paired t-test, p = 0.049). The observed difference was 

greater in the northeast (6.1 points) than the panhandle (1.6 points) or peninsula (3.6 points). 

Using the same data, the Department evaluated metrics to determine which contribute to the 

seasonal differences in the SCI. Five metrics had values in the winter indicating better biological 

condition; these were Ephemeroptera taxa richness, % filterers, % Tanytarsini, sensitive taxa 

richness, and % very tolerant. In contrast, long-lived taxa richness had higher values in the 

summer. 

The paired t-test is a very powerful test and can detect very small differences because it 

compares each site with itself. However, most metric differences were small compared with the 

overall range of the metric. For example, the % Tanytarsini had the largest relative change from 
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summer to winter (4.6%), but the possible range was 0 to 26%. As such, the Department did not 

modify metric scoring to correct for seasonal differences.

Chapters 62-302 and 62-303, F.A.C., state that SCIs must be temporally-independent and 

separated by a minimum of three months. The requirement to have two SCI samples that are 

taken at temporally independent timeframes (minimum of three months apart) is to ensure that 

the samples are not highly auto-correlated, that they represent different time periods, and that the 

samples have a reasonable potential to capture a range of community composition and a breadth 

of indicator species. This, coupled with evaluating the annual geometric mean of nutrient 

concentrations, will capture latent effects of nutrients, if any, on the biological communities. To 

reduce uncertainty associated with the average score, Rule 62-303.430(2)(a), F.A.C., also 

requires that a third biological health assessment be conducted if there are only two available and 

the difference between the two scores is more than 20 points.. 

The Lake Vegetation Index (LVI) is a multimetric index of the biological integrity of Florida 

lake plant communities, based on a rapid field sampling method. It was developed by DEP to 

help resource managers identify healthy and impaired lakes, and to prioritize restoration efforts. 

The LVI was developed in 2005 and further validated in 2007 (Fore 2007b), and contains four 

metrics that were shown to be strongly correlated with a human disturbance gradient. Three 

metrics (percent native taxa, percent sensitive taxa, and coefficient of conservatism (C of C) of 

the dominant taxa) decline with increasing human disturbance, while the percent invasive exotics 

metric increases. DEP revised the metric scoring for the LVI in 2011 based on new scientific 

information, and those updates are detailed in DEP (2011). As with the SCI, there are detailed 

SOPs for LVI sampling and taxonomic quality assurance, and additional guidance is provided in 

“Sampling and Use of the Lake Vegetation Index (LVI) for Assessing Lake Plant Communities 

in Florida: A Primer (DEP-SAS-002/11)”. See Appendix 3-B for description of the development 

of the LVI. Requirements for training, auditing, sampling, and analysis of the LVI are detailed 

in DEP-SOP-003/11 LVI 1000. 

The DEP has a rigorous quality control program for the LVI, and sampling teams must 

demonstrate that they can obtain an LVI score within the Minimum Detectible Difference 

(MDD) of the index, when compared to other expert teams. The acceptance criterion for this 

proficiency demonstration is ±12 points, which is based on the 90% confidence interval for the 

variance between multiple assessments of the same lake over time (Fore et al. 2007). Teams 

perform the proficiency demonstration at an assigned lake within a window of time during the 

summer, so it is appropriate to allow for the full variance expected for a given lake over time. 

The total range in passing scores for the proficiency testing that DEP has conducted to date is 

between 13 to 20 points. The acceptance criteria of ±12 points is equivalent to two times the 

standard deviation, which is the commonly accepted range for precision in chemical analyses 

(APHA 1995). Additionally, repeated measures of ± 20% is a commonly accepted precision 

target for analytical laboratories conducting inorganic analyses, such as nutrients. The plus or 

minus 12% associated with the LVI is therefore within a commonly accepted range of precision. 

 
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3.4Establishing Expectations for Aquatic Life Use – Stream Condition 

Index: 

The document titled, “Development of Aquatic Life Use Support Attainment Thresholds for 

Florida’s Stream Condition Index and Lake Vegetation Index,” DEP-SAS-003/11 (DEP 2011), 

details the development of aquatic life use attainment thresholds, summarized below. The DEP, 

in consultation with EPA, has used two lines of evidence to set thresholds for exceptional and 

impaired aquatic life conditions for both the SCI and the LVI. The primary method for 

establishing values protective of the designated use involved an examination of the lower 

distribution of minimally disturbed, rigorously verified reference site scores. The second 

approach included an examination of the results of expert opinion elicited through Biological 

Condition Gradient (BCG) workshops, primarily for the exceptional thresholds, and as a second 

line of evidence for the lowest acceptable aquatic life use thresholds. 

3.4.1 Application of the Reference Site Approach 

In 2007, DEP calibrated the SCI using primarily the Biological Condition Gradient approach (see 

Section 3.4.2), resulting in 35 as the value at which the designated use of a healthy, wellbalanced community is met, and exceptional threshold of 67. Subsequent EPA review resulted 

in the recommendation that Florida use an examination of the lower distribution of reference 

sites as the principal line of evidence for establishing aquatic life use support thresholds, in 

combination with the Biological Condition Gradient approach. 

In response to this request, DEP’s consultant conducted statistical interval and equivalence tests 

with SCI data from 55 reference streams (predominantly consisting of the recently verified 

nutrient benchmark sites with additional data from the Fore et al. (2007a) analysis). This 

analysis was performed to determine the lower bounds of the reference site distribution of SCI 

scores, while balancing type I errors (falsely calling a reference site impaired) and type II errors 

(failing to detect that a site is truly impaired) (see Appendix 3-C for full description of analysis) 

(Table 3-1). Appendix 3-D contains complete taxa data for the samples used in this analysis. 

The examination of the average of the two most recent visits to 55 reference streams showed that 

the 2.5th percentile of reference data was an SCI score of 40 points, within a confidence interval 

that ranged from 35-44 points. Therefore, selection of an average SCI score of 40 as a threshold 

for aquatic life protection balances Type I and Type II errors. 

When calibrating an impairment threshold for an index, the amount of human disturbance 

inherent at the reference sites is a major issue. Some states select reference sites based on the 

“best available condition” (the sites may have substantial disturbance), using a Best Professional 

Judgment approach. Florida has employed a rigorous reference site selection approach, which 

objectively demonstrates the “minimally disturbed” (very limited human influence) nature of 

Florida’s reference sites. When establishing an impairment threshold using a lower distribution 

of reference sites, a rigorous reference site selection process provides greatly increased 

confidence that the reference site population is minimally disturbed, thereby significantly 

reducing Type II errors (i.e., classifying impaired sites as healthy). This increased confidence 

also allows for establishing the impairment threshold at a low level of the reference site 

distribution to minimize Type I errors (classifying healthy sites as impaired).
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Using the SCI, the designated use of a healthy, well-balanced community is achieved if the 

average of two site visits is greater than or equal to 40 and no single sample scores less than 35. 

These thresholds would result in approximately 2.5 % of reference sites (known to be minimally 

disturbed) to be deemed impaired. DEP believes that this threshold is consistent with the CWA 

aquatic life use support goal and complies with Florida law, which requires that DEP not abate 

natural conditions. 

Table 3-1. Results of interval and equivalence tests conducted on reference sites with 2 SCI results. Shown 

are site mean, threshold at which designated use is being met, and range for threshold values defined at the 

2.5th and 5th percentile of reference sites (p < 0.05; N = 55 reference sites with two SCI values for each site). 

Reference site values from Fore et al. (2007a) and comprehensively verified nutrient benchmark sites. 

Impairment threshold 

(description)

Ref site 

mean 

Impairment 

threshold 

(numeric)

Impaired Undetermined Reference

2.5th percentile of reference 65 40 <35 35–44 >44 

5th percentile of reference 65 44 <39 39–47 >47 

3.4.2 Biological Condition Gradient Approach 

The U.S. EPA has outlined a tiered system of aquatic life use designation, along a Biological 

Condition Gradient (BCG), that illustrates how ecological attributes change in response to 

increasing levels of human disturbance. The BCG is a conceptual model that assigns the relative 

health of aquatic communities into one of six categories, from natural to severely changed 

(Figure 3-5). It is based in fundamental ecological principles and has been extensively verified 

by aquatic biologists throughout the U.S. 

Chemistry, habitat, and/or flow 

regime severely altered from 

natural conditions.

Watershed, habitat, flow 

regime and water 

chemistry as naturally 

Structure & function similar to 

natural community with some 

additional taxa & biomass; 

ecosystem level functions are 

fully maintained.

Evident changes in structure 

due to loss of some rare native 

taxa; shifts in relative 

abundance; ecosystem level 

functions fully maintained.

Moderate changes in structure 

due to replacement of sensitive 

ubiquitous taxa by more 

tolerant taxa; ecosystem 

functions largely maintained.

Sensitive taxa markedly 

diminished; conspicuously 

unbalanced distribution 

of major taxonomic groups; 

ecosystem function shows 

reduced complexity & 

redundancy.

Extreme changes in structure 

and ecosystem function; wholesale changes in 

taxonomic composition; 

extreme alterations from 

normal densities.

Natural structural, functional, 

and taxonomic integrity is preserved.

5

6

4

3

2

1

Biological Condition

Level of Exposure to Stressors 
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Figure 3-5. The Biological Condition Gradient Model (from Davies and Jackson 2006).

The BCG utilizes biological attributes of aquatic systems that predictably respond to increasing 

pollution and human disturbance. While these attributes are measurable, some are not routinely 

quantified in monitoring programs (e.g., rate measurements such as productivity), but may be 

inferred via the community composition data (e.g., abundance of taxa indicative of organic 

enrichment).

The biological attributes considered in the BCG are: 

1. Historically documented, sensitive, long-lived or regionally endemic taxa

2. Sensitive and rare taxa

3. Sensitive but ubiquitous taxa 

4. Taxa of intermediate tolerance

5. Tolerant taxa

6. Non-native taxa

7. Organism condition 

8. Ecosystem functions

9. Spatial and temporal extent of detrimental effects

10. Ecosystem connectance

The gradient represented by the BCG has been divided into six levels (tiers) of condition that 

were defined via a consensus process (Davies and Jackson 2006) using experienced aquatic 

biologists from across the U.S., including Florida representatives. The six tiers are:

1) Native structural, functional, and taxonomic integrity is preserved; ecosystem function is 

preserved within range of natural variability;

2) Virtually all native taxa are maintained with some changes in biomass and/or abundance; 

ecosystem functions are fully maintained within range of natural variability;

3) Some changes in structure due to loss of some rare native taxa; shifts in relative 

abundance of taxa but sensitive–ubiquitous taxa are common and abundant; ecosystem 

functions are fully maintained through redundant attributes of the system; 

4) Moderate changes in structure due to replacement of some sensitive–ubiquitous taxa by 

more tolerant taxa, but reproducing populations of some sensitive taxa are maintained; 

overall balanced distribution of all expected major groups; ecosystem functions largely 

maintained through redundant attributes;

5) Sensitive taxa are markedly diminished; conspicuously unbalanced distribution of major 

groups from that expected; organism condition shows signs of physiological stress; 

system function shows reduced complexity and redundancy; increased buildup or export 

of unused materials; and 

6) Extreme changes in structure; wholesale changes in taxonomic composition; extreme 

alterations from normal densities and distributions; organism conditioning is often poor; 

ecosystem functions are severely altered.
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The six levels described above are used to correlate biological index scores with biological 

condition, as part of calibrating the index. Once the correlation is well established, a 

determination is made as to which biological condition represents attainment of the CWA goal 

according to paragraph 101(a)(2) related to aquatic life use support, “protection and propagation 

of fish, shellfish, and wildlife.” 

During the development of the BCG model at National BCG Workshops, each of the break-out 

groups independently reported that the ecological characteristics conceptually described by tiers 

1–4 corresponded to how they interpret attainment of the CWA’s interim goal for protection and 

propagation of aquatic life (Davies and Jackson 2006). As described in subsequent sections, two 

panels of Florida experts (one for the SCI, and one for the Lake Vegetation Index) independently 

arrived at the same conclusions as did the national expert groups. Additionally, the State of 

Maine has adopted a policy that aquatic communities conceptually aligned with BCG Category 4 

meets the CWA’s interim goal for protection and propagation of aquatic life, and this was 

subsequently approved by EPA.

DEP conducted a BCG exercise to calibrate scores for the SCI in 2006. Twenty-two experts 

examined taxa lists from 30 stream sites throughout Florida, 10 in each Ecoregion, that spanned 

the range of SCI scores (Appendix 3-E). Without any knowledge of the SCI scores, they 

reviewed the data and assigned each macroinvertebrate community a BCG score from 1 to 6, 

where 1 represents natural or native condition and 6 represents a condition severely altered in 

structure and function from a natural condition. Experts independently assigned a BCG score to 

each site, and then were able to discuss their scores and rationale, and could opt to change their 

scores based on arguments from other participants. At the conclusion of the workshop, DEP 

regressed the mean BCG score given to each stream against the SCI score for that site (Figure 3-

6).

Figure 3-6. Regression line with 90% confidence interval showing the relationship between the mean BCG 

score and SCI score. The median BCG value the expert group considered meeting a healthy, well balanced 

community corresponded to a BCG tier of 4 and an SCI score of 34 (this subsequently changed based upon a 

proportional odds analysis). The “exceptional” threshold was established at 64 and above, based on the score 

associated with a BCG 2.
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The experts were also asked to identify the lowest BCG level that still provided for the 

propagation and maintenance a healthy, well-balanced aquatic community (the interim goal of 

the Clean Water Act) and the BCG category (and higher) represented exceptional conditions (the 

ultimate goal of the Clean Water Act, also referred to as “biological integrity”). All of 22 

participants thought category 2 SCI scores should be considered exceptional, which corresponds 

to an SCI score of 64. Eleven of 22 participants thought SCI scores associated with category 5 

should be impaired, while nine participants thought category 4 represented an impaired 

ecological condition and two experts thought that category 4 was the lowest acceptable 

condition. 

3.4.3 Evaluation of the Reference Site Approach Coupled with BCG 

As part of the SCI calibration process, experts were asked to classify sites into one of the BCG 

categories based solely on the taxonomic data (not the SCI scores). The relationship between the 

mean BCG score for each site and the SCI score was then determined using a least squares 

regression model (Figure 3-6). Experts were also asked to identify the BCG value they 

considered meeting a healthy, well balanced community. In reaction to this question, the mean 

expert response corresponded to a BCG tier of 4. Based on the relationship between the BCG 

and the SCI, this corresponded to an SCI score of 34. 

EPA noted the variability in the expert responses within each BCG category, and conducted an 

additional analysis of the BCG results to further define an acceptable aquatic life use threshold. 

EPA calculated a proportional odds logistic regression model (Guisan and Harrell, 2000) to 

better describe the relationship between a continuous variable (SCI scores) and a categorical 

variable (BCG categories). See Appendix 3-F for a full report of this analysis by Lester Yaun of 

EPA. This model is based on the cumulative probability of a site being assigned to a given tier 

(e.g., Tier 3) or to any higher quality tier (Tiers 1 and 2). Thus, five parallel models are fit, 

modeling the probability of assignment to Tiers 5 to 1, Tiers 4 to 1, Tiers 3 to 1, Tiers 2 to 1, and 

Tier 1 only. Once these five models are fit, the probability of assignment to any single tier can 

be extracted from the model results.

In Figure 3-7, the mean predictions of the proportional odds logistic regression models are 

plotted as solid lines. Lines are color-coded and labeled by different tiers, and each line can be 

interpreted as the proportion of experts that assigned samples with the indicated SCI value to a 

particular tier. For example, approximately 90% of experts assigned a sample with the lowest 

SCI score to Tier 6 (brown line), while the remaining 10% of experts assigned the sample to Tier 

5 (purple line). In the figure, the solid circles represent the actual expert assignments recorded 

from the workshop for each SCI value. The size of the circle is proportional to the number of 

experts that assigned a sample to a particular tier, and the circles are color-coded by tier. There 

is some variability among experts in their assignment of BCG scores, but there is a clear central 

tendency at any given SCI score.
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Figure 3-7. BCG tier assignments modeled with a proportional odds logistic regression.

EPA recommended that the threshold be set at an SCI score where there is an approximately 

equally low probability of assignment to Tier 5 (i.e., impaired) and a low probability of 

assignment to Tier 2 (i.e., reference conditions). The resultant threshold of 42 balances the 

probability of mistakenly assessing a degraded site as meeting aquatic life use goals with the 

probability of mistakenly assessing a reference site as impaired. This score is similar to and 

consistent with the minimum acceptable average score of 40 as determined by the reference site 

approach. 

EPA supported the DEP approach, stating, “An SCI score > 40 has been determined to be 

indicative of biologically healthy conditions based on an expert workshop and analyses 

performed by both DEP and EPA. Please refer to the EPA’s January 2010 proposal and the final 

TSD (U.S. EPA 2010a and 2010b) accompanying this final rule for more information on the SCI 

and the selection of the SCI value of 40 as an appropriate threshold to identify biologically 

healthy sites” (Federal Register /Vol. 75, No. 233 /December 6, 2010 /Rules and Regulations , 

page 75775). 

3.4.4 Setting and Evaluating an SCI Threshold that Supports Healthy, Well 

Balanced Communities 

Weighing these multiple lines of evidence, DEP determined that an average SCI score of 40 

indicates that the designated use is being met, while an average score of 39 or lower is impaired. 

The results of the BCG approach placed the lowest acceptable score at a BCG level of 4, which 

corresponds to an SCI score of 35. Setting an acceptable bioassessment expectation at BCG level 

of 4 is consistent with the results of National BCG exercises (Davies and Jackson 2006). During 

the development of the BCG model at National BCG Workshops, each of the break-out groups 

independently reported that the ecological characteristics conceptually described by tiers 1–4 
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corresponded to how they interpreted attainment of the CWA’s goal for protection and 

propagation of aquatic life (Davies and Jackson 2006). Furthermore, tiers 1-4 all represent 

conditions that are reflective of a well balanced natural population of flora and fauna. 

EPA recommended that these results be used in conjunction with an analysis of minimally 

disturbed reference sites. This process is described in detail in Fore et al. (2007a), and is similar 

to the approach of other states. The results of the reference site analysis also yielded a SCI value 

of 35 as the lowest SCI value expected to be obtained from most reference sites (although some 

reference sites would still be below this value). Based on results described above, the lowest 

single sample score that meets the designated use is 35 points. 

The proportional odds analysis provides assurance that stream communities deemed exceptional 

(BCG category 2) will not be considered impaired at a threshold of 40. The DEP evaluated 

recent data for the individual metrics of the SCI to determine what range of macroinvertebrate 

attributes would be considered healthy using this impairment threshold. Since DEP conducted 

the SCI calibration in 2007, the State has collected approximately 700 additional SCI samples 

from a variety of sites, including minimally disturbed reference sites (for nutrient criteria 

development), sites located along a nutrient gradient, and randomly chosen sites for the status 

and trends network. Based upon the relationship described in Figure 3-6, the SCI values from 

this data set were subdivided into increments representing half-step BCG Categories, and the 

individual metrics associated with each half step interval were averaged. The metric data 

bracketing BCG category 2 were averaged to demonstrate metric values associated with 

exceptional conditions. Data within the range of the impairment threshold of 40 were also 

averaged to provide an example of the stream condition that Florida’s SCI biological criterion 

will protect (Table 3-2). Note that although there are moderate differences between metrics

associated with exceptional biological communities and those near the lowest acceptable

threshold, the attributes associated with communities near the threshold are still considered to be 

indicative of healthy, well balanced communities by the majority of the Florida stream experts 

who participated in the BCG exercise.

Table 3-2. Average values for metrics at an SCI score equivalent to a Biological Condition Gradient of 

category 2, and average values for metrics near the SCI threshold at which the designated use is met. Data 

was based upon the DEP’s data collection effort since 2007 (total N = 696 SCI samples). 

SCI Metric

Metric Average 

at BCG 2 

(Exceptional)

Metric Average 

Near Lowest 

Acceptable 

Threshold

Number of Total Taxa 32.0 28.7

Number of Clinger Taxa 5.6 3.3

Number of Long Lived Taxa 1.5 1.1

Percent Suspension Feeders and 

Filterers 22.0 15.8
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Number of Sensitive Taxa 5.4 2.7

Percent Tanytarsini 13.3 9.5

Percent Very Tolerant 6.5 14.3

Number of Ephemeroptera Taxa 3.5 2.3

Number of Trichoptera Taxa 4.5 2.6

Percent Dominant 22.6 26.2

Number of Sites in Average 134 64

0.8 2.4 4.0 5.6 7.2

Human disturbance gradient

0

20

40

60

80

100

SCI

 Median 

 25%-75% 

 Non-Outlier Range

 Outliers

Figure 3-8. Relationship between the SCI (2004 data) and the Human Disturbance Gradient (from Fore et 

al., 2007a).

During the development of the Stream Condition Index, the DEP established a clear relationship 

between the SCI and the Human Disturbance Gradient (Figure 3-8). Note the highest range of 

actual SCI scores were observed in the two groups of lowest human disturbance gradient sites 

(left most boxes in Figure 3-8). This wide range needs to be considered when establishing the 

threshold to limit the probability of falsely identifying unimpacted sites as not attaining an 
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aquatic life use. However, the range of scores in the higher human disturbance gradient sites 

(expected to result in a BCG category 5-6) are low. Therefore, the risk is low (virtually nonexistent for the SCI) in applying the biological assessment tool and falsely identifying impacted 

sites as attaining an aquatic life use. 

This variability of the SCI scores within a given range of the human disturbance gradient is 

generally caused by changes in biological community relative to natural occurrences (droughts, 

floods, etc.), as well as the inherent limitation of the biological assessment methods. Biological 

field observations can be influenced by natural conditions that may have occurred prior to the 

sampling event. Changes in hydrology, particularly high and low flow events that result in 

differential water velocities and habitat availability, will affect the biological community in a 

stream, potentially resulting in lower scores. The variability in low human disturbance gradient 

sites also reflects the fact that the biological communities in these systems are able to rapidly 

recover because the habitat and health of the stream is conducive to recovery. In high HDG 

sites, natural hydrologic events (along with human disturbance) can affect the biology, but any 

recovery is slow due to the human disturbance impacts and lack of recruitment of organisms 

from surrounding areas. Therefore, in high human disturbance gradient sites, SCI scores always 

tend to be low, and the range of values remains small. 

The other factor leading to higher variability in scores for low disturbance sites relates to 

sampling issues. DEP’s SCI collection methods follow EPA rapid bioassessment guidance, but 

do not result in a complete ecological census of all taxa present at a site. Instead, they provide a 

practical level of effort that can be used to distinguish healthy from impaired sites. Therefore, 

the sampling method is inherently conducted in a manner that may result in a high range of 

results where taxa are present and a low range of results where taxa are diminished. In other 

words, when taking a sample, it is possible to fail to catch taxa that exist in the waterbody, but it 

is not possible to catch taxa that do not exist in the waterbody. 

In statistical terms, undisturbed sites have a higher probability of Type I error (falsely concluding 

that the site was impaired). Because the variability in the SCIs decreases as human disturbance 

increases, the disturbed sites fundamentally are subject to much lower occurrence rate of a Type 

II error (falsely concluding that the site was unimpaired) when compared to undisturbed sites. 

From a theoretical standpoint, since the error of the method used to collect representative taxa 

can only fail to capture and count taxa, and only 2 of the 10 metrics result in an improved SCI 

when specific organisms are missed, it is likely that Type I errors are of greater concern (occur 

more frequently) with this methodology. 

3.4.5 Additional Analysis of Rigorously Verified Benchmark Site SCI Data 

The Stream Condition Index (SCI) scores from an early version of DEP’s field-verified nutrient 

benchmark site dataset were also evaluated to determine the range and variability of biological 

condition found in Florida’s minimally-disturbed sites (note that the list of sites presented here is 

slightly different than the final list of benchmark sites from which nutrient criteria were derived, 

as described in Chapter 7). Theoretically, these sites may be expected to have an SCI score 

reflective of a BCG category 2. In reality, as indicated previously, there is more variability in the 

actual scores. This benchmark dataset consists of sites determined by experienced DEP 
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scientists to be influenced by only very low levels of anthropogenic stressors. Additional 

selection criteria included a Landscape Development Intensity index score of <2, absence of 

upstream point source discharges, examination of aerial photographs, direct observations of 

watershed land use and hydrologic conditions during site visits, and habitat assessment (see 

benchmark site discussion, Chapter 7). The dataset included 69 sampling events at a total of 53 

stations across the state (16 stations were sampled twice during the verification process). 

The mean SCI score from all 69 sampling events was 65.1, and the median was 65. The 

standard deviation from the mean was 15.8, and the range of scores was 80, spanning from 100 

to 20. The one nutrient benchmark site that scored below the impairment threshold of 40 

occurred at a Steinhatchee River site (at CR 357), which scored 20 on the SCI on August 12, 

2008, after an extended period of low flow conditions (see Figure 3-11). However, when this 

site was subsequently re-sampled on January 14, 2009 (after a period of higher flows), it scored a 

53. Note that another minimally disturbed Steinhatchee River site located approximately 8 miles 

downstream with slightly more flow (at Canal Road), scored 41 and 62 on the SCI during the 

same time period. Based on direct observations, the flow regime was the dominant factor for the 

variability in the SCI scores. DEP SOPs provide clear guidance regarding appropriate conditions 

during which to sample, including a minimum velocity of 0.05 m/sec. Although the 

Steinhatchee at CR 357 achieved this velocity and was not dry prior to sampling, the sluggish 

flows and less than optimal inundated habitat appeared to be responsible for the low SCI scores, 

not any human disturbance (the upstream basin is almost 100% forested). This is an example of 

the type of hydrologic conditions that occur randomly throughout the state, prompting DEP, in 

an attempt to minimize Type I errors, to select the lower 2.5% distribution of reference sites as 

the impairment threshold.

Figure 3-9. Steinhatchee River at CR 357, August 2008. 
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Figure 3-10. Steinhatchee River at CR 357, January 2009.

Figure 3-11. USGS hydrograph for the Steinhatchee River during the period of the two sampling events. The 

mean discharge rate for the Steinhatchee River near Cross City was 7.4 ft3/sec on 8/12/2008 and 23 ft3/sec on 

1/14/2009.
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3.4.6 Evaluation of Benchmark Site Replicate Data: SCI

The 16 benchmark sites with replicate data were analyzed to determine the variability that can 

occur in SCI scores at the same sampling location. The benchmark sites with replicate data are 

shown below in Table 3-3. The mean difference in SCI scores from this sub-dataset was 17.1, 

with a standard deviation of 13.3. The median difference was 18. The largest difference in 

scores occurred at the St. Marys River at SR 2, which received SCI scores of 50 in June 2008, 

and 100 in November 2008. 

Table 3-3. Minimally disturbed stream benchmark sites with replicate SCI data.

Benchmark Site Date 

sampled

SCI 

score

Difference 

between replicates

Blackwater River 

at Highway 4

3/26/2007 56

14

7/9/2008 70

Cypress Branch

11/3/2008 66

3

12/16/2008 63

Escambia River 

at Highway 4

9/19/2007 57

6

7/10/2008 51

Manatee River 

at 64

5/16/2007 81

17

12/17/2008 64

Orange Creek 

upstream of Highway 

21

2/26/2007 74

8

5/1/2008 82

Peters Creek 

at CR 315

5/28/2008 92

19

10/28/2008 73

Sopchoppy River

6/19/2008 41

23

11/13/2008 64

Steinhatchee River 

at CR 357

8/12/2008 20

33

1/14/2009 53

Steinhatchee River 

at Canal Road

8/12/2008 41

21

1/14/2009 62
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St. Marys River 

at SR 2

6/18/2008 50

50

11/12/2008 100

Telogia Creek 

at CR 1641

6/10/2008 78

20

11/20/2008 58

Suwannee River 

at CR 6

10/10/2006 53

2

12/12/2007 51

Withlacoochee River 

above River Dr.

5/7/2008 44

2

10/8/2008 42

Withlacoochee River 

at Stokes Ferry

2/20/2007 68

21

11/7/2007 47

Yellow River 

at Hwy 2

5/15/2007 54

25

7/9/2008 79

Yon Creek 

at SR 12

6/13/2008 81

7

11/20/2008 74

Differences in SCI scores between replicates can be caused by the natural variability of 

environmental factors such as recent hydrologic conditions resulting in changes in habitat 

availability, as well variability associated with laboratory sub-sampling. Based on field 

observations, it was natural factors (water level and flow), not changes in human disturbance, 

that were the main drivers of the differences in SCI scores between replicates taken at different 

times. Note that sampling visits to the sites with duplicate data were not separated by more than 

fourteen months (most were sampled less than six months apart). 

Another indication that human disturbance was not associated with this variability was that no 

correlation was found between Landscape Development Intensity Index score and SCI score 

within the benchmark site dataset (Figure 3-12). This is in contrast to the strong relationship 

between the LDI and SCI scores across the entire range of human disturbance (in Figure 3-8, the 

LDI is a prominent influence on the HDG). 
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Figure 3-12. Minimally disturbed benchmark sites plotted against the Landscape Development Intensity 

Index (LDI). Direct observations indicated that the LDI reflected current land use and disturbance 

conditions.

3.5Establishing Expectations for Aquatic Life Use – Lake Vegetation 

Index:

In 2007, DEP calibrated the LVI using primarily the Biological Condition Gradient approach. 

Subsequent EPA review resulted in the recommendation that Florida use an examination of the 

lower distribution of reference sites as the principal line of evidence for establishing aquatic life 

use support thresholds, in combination with the Biological Condition Gradient approach. The 

document entitled, “Development of Aquatic Life Use Support Attainment Thresholds for 

Florida’s Stream Condition Index and Lake Vegetation Index,” DEP-SAS-003/11 (DEP 2011), 

details the development of aquatic life use attainment thresholds, summarized below.

3.5.1 LVI Benchmark Site Approach 

DEP evaluated data from existing sites to identify benchmark lakes that could be used to 

determine the appropriate threshold for the LVI. To be considered benchmark, the watershedscale landscape development intensity (LDI) index score had to be less than 3, and the LDI of the 

100-m buffer zone around the lake had to be less than 2. DEP biologists also examined aerial 

photos and conducted an onsite watershed survey to ensure that there were no adverse human 

influences not detected by the LDI, and performed a whole-lake habitat assessment. Candidate 

benchmark lakes were excluded if they had a history of adverse human activity (e.g., aquatic 

plant control, artificial fertilization) or current human activity (e.g., adjacent citrus groves). 

Appendix 3-G contains site information and taxa lists for the 30 benchmark lakes used in this 

analysis, and Appendix 3-H contains maps, photos, and a summary of data for each of the 

verified benchmark lakes.

Benchmark Site LDI vs. SCI R2

 = 0.0044

0

20

40

60

80

100

120

1.00 1.20 1.40 1.60 1.80 2.00 2.20

Landscape Development Intensity Index Score

Stream Condition Index Score
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A recent comprehensive review by EPA of riparian buffer effectiveness support the use of the 

100 meter buffer zone. The review described results of studies in which researchers measured 

the percent nitrate and other nutrients removed by forested, wetland, or grassland buffers of 

various widths (Mayer et al. 2006). Based on the published research, the authors modeled a 

relationship between buffer width and nitrate removal, and estimated that “50%, 75%, and 90% 

removal efficiencies would occur in buffers approximately 3 m, 28 m, and 112 m wide, 

respectively” (Mayer et al. 2006). The buffers in these studies were separating waterbodies from 

land uses of higher intensity than the land uses that would comprise a LDI of 3 used as a limit for 

watershed LDI by DEP. Although the review was of riparian buffers bordering streams and

there are no comparable studies for lake buffers, the conclusions of the review are relevant for 

lakes and support the buffer widths used in the DEP selection of reference sites. Additionally, 

Brown and Vivas (2005) showed that a predictable (increasing) response between LDI and 

nutrient loading did not occur until the LDI exceeded 3. 

DEP’s consultant conducted statistical interval and equivalence tests with LVI data from these 

30 reference lakes to determine the lower bounds of the reference site distribution. As was 

described for the SCI, the intent was to identify a threshold for the LVI that balanced Type I 

(falsely calling a reference site impaired) and Type II (failing to detect that a site is truly 

impaired) errors (see Appendix 3-C for statistical description and Appendix 3-I for full analysis). 

The analysis of the most recent LVIs at all 30 sites showed that the 2.5th percentile of reference 

data was in the range of 33-48 points, while the analysis of the two most recent visits at 15 lakes 

showed that the 2.5th percentile of reference data was in the range of 31-53 points (Table 3-4). 

The middle of this range was 46 points, representing a threshold at which aquatic life use is met

that balances Type I and Type II errors. In the proposed water quality threshold for the LVI, 

impairment will be determined by two site visits, so the threshold of 46 is closely aligned with 

the assessment methods. A threshold at which aquatic life use is met of 46 would limit the 

percentage of reference sites that will be deemed impaired to 2.5%. 

In 2011, adjustments were made to the LVI metrics to: 

1. Include the C of C Scores as revised by the 2011 expert panel; 

2. Use the Florida Exotic Pest Plant Council (FLEPPC) Category I (only) instead of 

including both FLEPPC categories; and 

3. Scale the percent Sensitive, C of C Dominant/Co-dominant, and percent Native metrics 

by region. 

To evaluate how the 2011 scoring procedure affected LVI scores, 227 probabilistically-derived 

lake samples, collected from 2008-2010, were compared using both the old and new calculation 

methods. Overall, there was a change to both the mean and median of -3.7 points between the 

two calculation methods. The LVI scores calculated using the old versus new methods were 

very highly correlated, with a correlation coefficient of 0.97. Additionally, LVI scores for the 

two most recent samples from 30 benchmark lakes were recalculated using the new procedure. 

Additional data were available for some sites, and increased the number of lakes with two or 

more samples from 15 to 20 lakes. With the 2011 LVI adjustments, the 2.5th percentile of the 

reference site distribution shifted from 46.47 to 43.27, meaning that a score of 43 is equivalent to 
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the former minimum acceptable threshold for aquatic life use support. Therefore, a value of 43

is the new lowest acceptable LVI score (see DEP 2011 for a complete discussion on this topic). 

Table 3-4. Results of interval and equivalence tests conducted on reference sites with 2 LVI results. Shown 

are site mean, threshold at which aquatic life use is met, and range for threshold values defined at the 2.5th 

and 5th percentile of reference sites (p < 0.05; N = 15 reference sites with two LVI values for each site). 

Impairment threshold (description) Impairment 

threshold (numeric)

Impaired Undetermined Reference

2.5th percentile of reference 46* <31 31–53 >53

5th percentile of reference 50 <37 37–57 >57

* Subsequently adjusted to 43 based on rescaling of the index to include recent information (see 

text).

3.5.2 Evaluation of Replicate Data: LVI 

The 15 benchmark sites with replicate data were analyzed to determine the variability that can 

occur in SCI scores at the same sampling location (Table 3-5). The mean difference in LVI 

scores from this sub-dataset was 8.2, with a standard deviation of 8.3. The median difference 

was 4.3. 

Table 3-5. Minimally disturbed benchmark lake sites with replicate LVI data.

Station Date LVI Range

Blue Cypress Lake

6/14/2007 60.25

10/1/2008 58.75 1.5

Gore Lake

9/17/2003 66.11

11/13/2006 61.82 4.3

Lake Annie

11/3/2005 77.83

10/8/2008 92.75 14.9

Lake Ashby

6/7/2005 45.14

11/3/2005 42.03 3.1

Lake Harney

10/19/2005 36.93

7/23/2008 66.75 29.8

Lake Norris 10/8/2003 63.03 10.0
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10/29/2008 73

Lake Palestine

10/11/2005 94.25

11/8/2006 90.72 3.5

Merial Lake

6/14/2005 77.52

10/26/2005 82.74 5.2

Ocean Pond

10/11/2005 90.44

11/1/2006 86.89 3.6

Otter Lake

10/13/2005 69.73

10/17/2006 72.84 3.1

Rattlesnake Lake

11/10/2005 82.07

10/31/2006 70.21 11.9

Russell Lake

9/30/2003 68.9

10/2/2008 74 5.1

Sellers Lake

10/26/2005 78.53

10/18/2006 81.71 3.2

Swift Creek Pond

10/11/2005 89.78

7/21/2008 67.75 22.0

Wildcat Lake

10/25/2005 92.37

10/17/2006 90.10 2.3

3.5.3 LVI Biological Condition Gradient Approach 

In a process analogous to that for the SCI BCG calibration, 20 Florida plant ecologists, botanists, 

and field lake managers, all with at least five years of experience, were involved in BCG 

calibration of the LVI. The experts examined taxa lists from 30 lakes throughout Florida that 

spanned the range of LVI scores (see Appendix 3-J for site information and taxa lists). Without 

any knowledge of the LVI scores, they reviewed the plant data and assigned each plant 

community a BCG score from 1 to 6, where 1 represents natural or native condition and 6 

represents a condition severely altered in structure and function from a natural condition. 

Experts independently assigned a BCG score to each lake, and then were able to discuss their 

scores and rationale, and could opt to change their scores based on arguments from other 
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participants. At the conclusion of the workshop, DEP regressed the mean BCG score given to 

each lake against the LVI score for that lake (Figure 3-13). 

The experts were also asked to identify the lowest BCG level that still provided for the 

propagation and maintenance a healthy, well-balanced aquatic community (the interim goal of 

the Clean Water Act) and the BCG category (and higher) represented exceptional conditions (the 

ultimate goal of the Clean Water Act, also referred to as “biological integrity”). Thirteen of 19 

participants thought category 2 LVI scores should be considered exceptional and one expert did 

not provide an opinion. Twelve of 20 participants thought LVI scores associated with category 5 

should be impaired, while 5 participants thought category 4 represented an impaired ecological 

condition (see Table 3-6 for summary statistics). Although DEP originally proposed that the 

LVI impairment threshold be established at the BCG line of 4.0, DEP decided, in conjunction 

with EPA, to establish the LVI threshold based primarily on the benchmark distribution. This 

analysis suggested that scores of 45 and below should represent impairment, and scores of 78 

and above should represent exceptional. DEP conducted additional analysis of these BCG data 

to account for adjustments in metric scoring, which resulted in new thresholds of 43 and 75 (see 

DEP 2011 for further information). 

Figure 3-13. The Lake Vegetation Index regressed against the Biological Condition Gradient scores 

developed “blindly” by a panel of lake experts. These data reflect updated LVI calculations from the 2007 

calibration exercise; Fore et al. (2007b) contains a previous analysis of these data. The median BCG value the 

expert group considered meeting a healthy, well balanced community corresponded to a BCG tier of 4 and an 

LVI score of 45. The “exceptional” threshold was established at 78 and above, based on the score associated 

with a BCG 2.

Table 3-6. Biological Condition Gradient (BCG) workshop participants’ judgment of which BCG categories 

should be considered exceptional and minimal use attainment for the LVI.
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Exceptional Minimum Use 

Attainment

Mean 2 4.6

Median 2 5

Range 1-3 3-6

Results from the LVI BCG workshop were also analyzed with a proportional odds logistic 

regression model (Guisan and Harrell 2000) to describe the relationship between a continuous 

variable (LVI scores) and a categorical variable (BCG categories). See Appendix 3-F for a full 

report of this analysis by Lester Yuan of EPA. This model is based on the cumulative 

probability of site being assigned to a given tier (e.g., Tier 3) or to any higher quality tier (Tiers 1 

and 2). Thus, five parallel models are fit, modeling the probability of assignment to Tiers 5 to 1, 

Tiers 4 to 1, Tiers 3 to 1, Tiers 2 to 1, and Tier 1 only. Once these five models are fit, the 

probability of assignment to any single tier can be extracted from the model results. 

Figure 3-14. BCG tier assignments based on the Lake Vegetation Index.

The mean predictions of the proportional odds logistic regression models are shown in Figure 3-

14. Lines are color-coded and labeled by different tiers, and each line can be interpreted as the 

proportion of experts that assigned samples with the indicated LVI value to a particular tier. For 

example, approximately 45% of experts assigned a sample with the lowest LVI score to Tier 6 

(brown line), while the remaining 55% of experts assigned the sample to Tier 5 (purple line). In 

the figure, the solid circles represent the actual expert assignments recorded from the workshop 
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for each LVI value. The size of the circle is proportional to the number of experts that assigned a 

sample to a particular tier, and the circles are color-coded by tier. There is some variability 

among experts in their assignment of BCG scores, but there is a clear central tendency at any 

given LVI score.

The LVI range of approximately 50-58 corresponds with both a low probability of assignment to 

Tier 5 (i.e., impaired) and a low probability of assignment to Tier 2 (i.e., reference conditions). 

Thresholds selected in this range of values balance the probability of mistakenly assessing a 

degraded site as meeting aquatic life use goals with the probability of mistakenly assessing a 

reference site as impaired. DEP has not yet had an opportunity to repeat this analysis to account 

for the minor adjustments in LVI metric scoring.

3.5.4 Setting and Evaluating a LVI Threshold of Aquatic Life Use Support 

Weighing these multiple lines of evidence, and after adjustment for metric scoring (DEP 2011), 

the DEP has determined that an LVI score of 43 indicates that the designated use is being met, 

and a score of 42 does not meet the designated use. This threshold of aquatic life use support is 

supported by the lower distribution of verified reference site scores. 

The DEP also evaluated data for the individual metrics of the LVI to determine what range of 

plant attributes would be considered healthy by this threshold of aquatic life use support. This 

analysis includes 244 LVI samples collected in 2007-2008 from a variety of sites, including sites 

located along a nutrient gradient and randomly chosen sites for the status network. Based upon 

the relationship described in Figure 3-13, the LVI values from this data set were subdivided into 

increments representing half-step BCG Categories, and the individual metrics associated with 

each half step interval were averaged. The metric data bracketing BCG category 2 were 

averaged to demonstrate metric values associated with exceptional conditions. Data within the 

range of the threshold of 46 (prior to rescaling) were also averaged to provide an example of 

the plant community condition that Florida’s LVI biological criterion will protect (Table 3-6). 

Note that although there are moderate differences between metrics associated with exceptional 

biological communities and those near the range of the threshold, the attributes associated with 

communities near the threshold are still considered to be indicative of healthy, well balanced 

communities by the majority of the Florida lake experts who participated in the BCG exercise. 

Table 3- 6. Average values for metrics at an LVI score equivalent to a Biological Condition Gradient of 

category 2, and average values for metrics near the LVI score at which the designated use is being met, 

associated with DEP’s data collection effort since 2007, consisting of 244 LVI samples).

LVI Metric Metric Average 

at BCG 2 

(Exceptional)

Metric Average 

Near Lowest 

Acceptable 

Threshold
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Dominant C of C 5.3 3.7

Percent Sensitive Taxa 21.0 7.4

Percent Invasive Taxa 3.9 14.5

Percent Native Taxa 92.4 78.6

Total Taxa 15.5 19.9

Number of Lakes for 

Average 27 39

3.5.5 Differences in the Variability in SCI and LVI Scores 

The variability in LVI scores within the low HDG categories appear to be less than the 

variability in SCI scores at a similar level of human disturbance (Figure 3-15). DEP believes that 

this is predominantly due to three main factors: 

• Lakes are hydrologically less dynamic than streams, meaning there is considerably less 

magnitude associated in water level fluctuations in response to rain events. This 

translates into a reduction of this natural source of stress to lake plants. The organisms 

collected with the SCI are “rheophyllic”, meaning they require some level of water 

velocity to maintain a healthy community. During droughts and stagnant flow events, 

this natural stressor in streams is a highly influential factor, potentially resulting in 

undisturbed sites to fail. During floods, the SCI method is not capable of capturing 

organisms lower than approximately 0.5 m in the water column, requiring postponement 

of sampling until conditions are appropriate. Additionally, high flood velocities could 

result in “catastrophic drift” to the stream invertebrates, meaning they are scoured from 

substrates and less likely to be collected, potentially also resulting in reduced SCI scores. 

Lake plants are much more resilient in coping with the natural water level fluctuations.
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Figure 3-15. Lake Vegetation Index scores vs. the Human Disturbance Gradient.

 

• The LVI sampling and analysis method is more effective in collecting the true taxonomic 

composition of the system, estimated at 80-95% of the “actual” taxa present. This means 

taxa are less frequently overlooked during collection and a more representative “true” 

taxonomic list is generated. For example, the LVI frodus sampling device allows 

collection of plants deep under water, so they may be collected during short term 

moderate high water events. 

 

• The LVI method, since it is based on visual field identification, can assess a much larger 

surface area (25% of the entire lake), compare to the SCI, which requires laboratory 

microscopy, and occurs in a 100 meter section.

Because of pattern and variability of the LVI response to the HDG, the Type I and Type II errors 

are more equally balanced using this methodology. 

3.6 Role of the LVI in Numeric Nutrient Criteria

Section 10.2 of this document describes DEP’s analyses to relate the LVI to TN and TP. LVI 

scores for clear lakes (< 40 PCU) were significantly related to annual geometric mean TN (R2= 

0.441) and TP (R2 = 0.287), while results for colored lakes were significant but not as strong (R2

< 0.2 for TN and TP). The annual geometric mean chlorophyll a was much more strongly and 

predictably correlated to TN and TP, therefore, DEP used chlorophyll a as the biological 

endpoint with which to relate TN and TP concentrations. 

In Chapter 62-302, F.A.C., a lake does not meet the numeric interpretation of the narrative 

nutrient criteria if chlorophyll a, total nitrogen, or total phosphorus concentrations do not meet 

the values specified in the table in Rule 62-302.531(2)(b)(1), F.A.C. Table values are directly 

related to the derivation of the standards. The LVI is not necessary to demonstrate that a 

particular lake does not achieve the numeric nutrient standards. As a measure of whether a lake 

is biologically healthy, the LVI is used to support establishment of a Type III site specific 

0 1 2 3 4 5 6 7

HDG

0

20

40

60

80

100

LVI_1x (average of 4 reps)

 Median 

 25%-75% 

 Non-Outlier Range 

 Outliers
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alternative criteria (SSAC) for nutrients and to identify impaired lakes (Chapter 62-303, F.A.C). 

A lake that fails to meet an average LVI of 43 will be subject to a stressor identification study to 

determine the cause, which could include pollutants or physical disturbance. If the stressor 

identification study links failures of the LVI to nutrients, a nutrient TMDL will be conducted, 

even if the TN, TP, and chlorophyll a meet the applicable criteria in Rule 62-302.531(2)(b)(1), 

F.A.C.. 

In Chapter 62-302.800(3)(b), F.A.C., the LVI is also used as one line of evidence to demonstrate 

that a lake is biologically healthy during SSAC development. Other lines of evidence for 

obtaining a nutrient SSAC include paleolimnology to establish natural background conditions or 

other indications of a lack of imbalance of flora, as shown by chlorophyll a concentrations and 

the absence of algal blooms or algal mats. 

3.7 Maintenance of Exceptional Biological Communities using the 

SCI and the LVI

It is DEP’s goal to maintain exceptional levels of aquatic life use. Therefore, DEP established 

provisions in Chapter 62-303, F.A.C., that list aquatic systems as impaired if they historically 

achieve scores within the exceptional categories for the SCI and LVI (64 and 75, respectively) 

and then have greater than a 20 point reduction from the historic maximum average. The historic 

average is defined as the highest three consecutive scores on record for a particular site. 

3.8 Independent Evaluation of Florida’s Bioassessment Program

Chris Yoder, Research Director of the Center for Applied Bioassessment and Biocriteria at the 

Midwest Biodiversity Institute and former manager of the Ecological Assessment Section at 

Ohio EPA, was contracted by EPA to conduct an independent review of DEP’s Bioassessment 

Program in 2009 (Yoder 2009). This evaluation consisted of an analysis of the following 

elements: Index Period, Spatial Coverage, Natural Classification, Criteria for Reference Sites, 

Reference Conditions, Taxonomic Resolution, Sample Collection, Sample Processing, Data 

Management, Ecological Attributes, Biological Endpoints and Thresholds, Diagnostic 

Capability, and Professional Review. 

Mr. Yoder awarded the DEP Bioassessment Program full points for almost all review elements, 

for a final score of 95% (Yoder 2009), which is among the top three scores in the nation (Chris 

Yoder, personal communication 2009). As a nationally recognized leader in bioassessment, Mr. 

Yoder’s favorable evaluation of DEP’s bioassessment program demonstrates its high level of 

excellence. 

3.9 SCI and LVI Conclusions

The DEP, in consultation with EPA, has used two lines of evidence to set thresholds for 

exceptional and impaired aquatic life conditions for both the SCI and the LVI. The primary 

method for establishing values protective of the designated use involved an examination of the 

lower distribution of minimally disturbed, rigorously verified reference site scores. The second 
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approach included an examination of the results of expert opinion elicited through Biological 

Condition Gradient (BCG) workshops, primarily for the exceptional thresholds, and as a second 

line of evidence for the lowest acceptable aquatic life use thresholds. For the SCI, the 

exceptional threshold is an average score of 64 and above, while average scores below 40 do not 

meet the designated use of a healthy, well balanced aquatic community (with no single score 

below 35). For the LVI, the exceptional threshold is a score of 75 and above, while scores below 

43 do not meet the designated use of a healthy, well balanced aquatic community. There is 

known variability in these biological assessment methods. If two assessments yield scores that 

are greater than 20 points apart, a third assessment will be needed to produce an average score 

for determination of whether or not that site meets its designated use. 

3.10 Stream Diatom Index Development

In a process similar to that described for the SCI and LVI, DEP attempted to develop a 

periphyton assessment tool, the Stream Diatom Index (SDI), using a combination of the Human 

Disturbance Gradient (HDG) and Biological Condition Gradient (BCG) approaches. 

Unfortunately, the diatoms appear to be very strongly influenced by pH (as well as conductivity 

and color), which confounds the relationship between periphyton community response to human 

disturbance, including nutrient enrichment effects. Figure 3-15 describes the relationship 

between the Landscape Development Intensity Index (LDI) and pH. Note that in the minimally 

disturbed condition (LDI <2), there is a wide range of pH, from about 4 to 8 SU. However, as 

the systems experience more human disturbance, the pH tends to be above 6.5 SU, so that when 

the LDI value is higher than 4, it is unlikely that a site will have a pH below 6.5 SU. 

Figure 3-16. LDI and pH are strongly related for Stream Diatom Index development sites. The blue line 

indicated pH of 6.5, at which sites were divided for SDI development. 
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Figure 3-17. There are strong relationships between the Stream Diatom Index and pH throughout various 

categories of human disturbance.

Figure 3-18. Relationship between the Stream Diatom Index and the Human Disturbance Gradient in low 

pH sites (< 6.5 SU). Note lack of strong association.
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Figure 3-19. Relationship between the Stream Diatom Index (SDI) and the Human Disturbance Gradient 

(HDG) in high pH sites (> 6.5 SU). Note lack of strong association.

Based upon a Non-metric Multidimensional Scaling (NMS) analysis and the relationship 

between LDI and pH (Figure 3-16), Florida streams were divided into two pH categories, < 6.5 

SU and > 6.5 SU, for development of the SDI. The SDI does not appear to clearly or predictably 

respond to objective measures of human disturbance (Figures 3-18 and 3-19), currently making it 

an unreliable tool for assessing adverse human effects on stream systems. This fact is extremely 

significant when considering the results from the BCG Workshop described below, and suggests 

that the expert group may actually be assessing the diatom response to factors other than human 

disturbance, potentially pH/conductivity, likely related to their lack of experience with Florida’s 

unique and variable background water quality conditions. 

3.10.1 Stream Diatom Index Calibration 

The BCG calibration process was also used for the SDI, with 15 nationally recognized 

periphyton experts involved in the calibration workshop. The correlation between the expert’s 

average BCG ranking with low pH SDI scores is shown in Figure 3-20, and the correlation 

between the expert’s average BCG ranking with high pH SDI scores is shown in Figure 3-21. 

Site information and taxa lists for samples evaluated in the SDI BCG workshop are in Appendix 

3-J. Unlike previous BCG exercises, where two questions were asked of the expert group (the 

questions distinguished the CWA interim goal from ultimate biological integrity goal), the 

periphyton expert group was asked only a single question, which was developed by an EPA 

Headquarters and EPA Region IV committee:

“In your opinion as an aquatic scientist, where specifically along the gradient would you see a 

point where critical changes lead to a loss of a balanced natural population of flora and fauna?” 
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The average initial response to this question was Category 3.5. After discussion, the final 

average BCG score for this question was 3.1 (Table 3-7). However, establishing an impairment 

threshold near category 3 would result in 73% (51 out of 70) of the previously described 

minimally disturbed nutrient benchmark site samples to be deemed impaired (an unacceptable 

Type I error). 

While the correlations between the BCG ranking and the SDI scores is statistically significant, 

the fact that there is no significant relationship between the BCG and human disturbance indicate 

the SDI should not be used as a bioassessment tool. The periphyton community appears to 

respond more strongly to pH and conductivity than to independent measures of human 

disturbance, and DEP has much additional work before the periphyton index can be used as a 

reliable bioassessment tool. 

Figure 3-20. Relationship between the Stream Diatom Index and Biological Condition Gradient in low pH 

sites (pH < 6.5 SU) as determined by an expert panel. 

Figure 3-21. Relationship between the Stream Diatom Index and Biological Condition Gradient in high pH 

sites (pH > 6.5 SU) as determined by an expert panel. 
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Table 3-7. Nationally recognized periphyton expert responses to the question, “In your opinion as an aquatic 

scientist, where specifically along the gradient would you see a point where critical changes lead to a loss of a 

balanced natural population of flora and fauna?”

BCG Category Initial Votes Final Votes

Between 0/1 0 0

Between 1/2 0 0

Between 2/3 6 7

Between 3/4 3 7

Between 4/5 6 1

Between 5/6 0 0

3.11 Earlier Efforts to Develop Stream Diatom Bioassessment 

Methods

DEP first tried to establish bioassessment methods for streams using periphyton. DEP evaluated 

work by Stevenson and Wang (2001), which provided exploratory information on potential tools 

to assess the trophic state of Florida’s waters. However, there are a number of issues that make 

the measures of the algal community proposed by Stevenson and Wang (2001) to be of limited 

utility in establishing nutrient thresholds. The primary concern with the periphyton models 

proposed by Stevenson and Wang (2001) was the lack of correlation with human disturbance. 

This study only related periphyton community metrics to existing nutrient levels at a variety of 

sites and made no distinction as to whether nutrients were naturally high or were elevated due to 

humans. As shown in this document, there is a wide range of TP and TN concentrations that 

occur in minimally disturbed Florida streams due to edaphic factors. Many of the metrics 

identified by Stevenson and Wang relied on the use of diatoms with a known preference for 

either high or low nutrient conditions, but these same diatoms were not shown to be correlated 

with measures of human disturbance. Therefore, although the metrics may be used to 

differentiate high versus low nutrient sites, they do not provide information as to whether the 

nutrients were from natural or anthropogenic sources. Without this distinction, use of the metrics 

proposed by Stevenson and Wang (2001) would identify waters with naturally high nutrients to 

be impaired even in the absence of anthropogenic influence. DEP has a legislative mandate to 

refrain from creating criteria that would attempt to abate natural background conditions. No 

nutrient threshold was identified in the study that could reliably be used to identify when a 

waterbody became impaired due anthropogenic nutrient enrichment 

The majority of the data that were used by Stevenson and Wang were collected using artificial 

substrate diatometers (i.e., racks of glass slides), which are deployed on the water surface in 

streams and rivers and have been shown to preferentially collect some types of algae over others. 

Additionally, the algal colonization of the diatometers reflects algal growth potential based on 

nutrient concentrations and light availability at the water’s surface, rather than the periphyton 

expression within the stream. In addition, issues have also been identified with sloughing of 
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algae and predation during the collection period, which also complicates the interpretation of the 

data collected from diatometers. After these initial efforts at diatom biocriteria development, 

DEP biologists began collecting periphyton samples from natural substrates within streams. 

To address the limitations of the Stevenson and Wang study, the Department, working with 

Leska Fore, attempted to develop biocriteria based on a similar diatometer (glass slide) dataset 

with sites characterized along a human disturbance gradient (HDG) that included nutrients but 

also included other evidence of human influence on the stream or river. In this study, there were 

insufficient significant relationships between the HDG and diatom metrics to develop a 

multimetric index (Fore 2005). These studies showed that diatom communities are related to 

nutrient concentrations, but they did not show that community differences were due to humaninduced nutrient inputs. 

Subsequently, DEP collected periphyton across a human disturbance gradient using a natural 

substrate collection technique, as described in the sections above. Fore (2010) again investigated 

diatom metrics that would correlate with the human disturbance gradient using data collected 

from these natural substrates. In this analysis, Fore (2010) found that diatom community metrics 

were much more highly correlated with pH than with human disturbance, and that further work 

would need to be done to identify zones of naturally similar pH within Florida to test the metrics 

within those zones. Therefore, an effective and calibrated multi-metric for periphyton is still 

under development. 

 
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4 Derivation of the Numeric Criteria for Nitrate – Nitrite in 

Spring Vents 

4.1 Introduction

Springs and their associated spring runs are a unique class of aquatic ecosystem, highly treasured 

for their biological, economic, aesthetic, and recreational value. Since their principal water 

source is groundwater, most springs have water that is extremely transparent, and rich with 

dissolved ions due to prolonged contact with subterranean limestone. Globally, the largest 

number of springs (approximately 600 – 700) occur in Florida. Springs are often classified based 

on their flow rate, which ranges from more than 2.8 m3/sec (first magnitude) to less than 0.47 

L/sec (eighth magnitude). Many of the larger spring ecosystems in Florida have likely been in 

existence since the end of the last major ice age, approximately 15,000 to 30,000 years (Martin 

1966; Munch et al. 2006). During this period of time, plant and animal communities have 

evolved to become highly adapted to the unique water quality and conditions found in the 

springs. The productivity of the diverse assemblage of aquatic flora and fauna is primarily 

determined by light availability and secondarily affected by the availability of macro and micro 

nutrients and by the ambient groundwater temperature. 

Springs also represent an important resource for human utilization, both by indigenous peoples 

(as supported by archeological evidence) and by present day Floridians and tourists, who utilize 

them for a variety of recreational purposes (Scott et al. 2002). People are interested in and 

fascinated by the intrinsic aesthetics of clear, cool water vigorously emanating from 

underground. A number of the spring boil areas have been modified to facilitate swimming, 

recreation, and even “health spas.” Currently, all of the largest springs in Florida, whether 

privately or publicly owned, are managed as recreational parks, which, in turn, attract a large 

number of visitors and generate many millions of dollars in revenue on an annual basis. Many 

springs have suffered declines (generally) in their condition from the visitation by ever 

increasing numbers of people due to uprooting of vegetation, bank erosion, litter, etc. 

Other more serious factors with the potential to permanently alter Florida’s spring ecosystems 

have been increasingly recognized over the last two decades. The two most significant 

anthropogenic factors that have been linked with adverse changes in spring ecosystems are: 

1) Pollution of groundwater, principally with nitrate-nitrogen, resulting from human land 

use changes, cultural practices, and general population growth; and 

2) The simultaneous reduction in groundwater supply through consumptive human 

withdrawals. 

Human influences, in the form of nonpoint source pollution, are one of the most critical issues 

affecting Florida’s springs. Nutrients (predominantly nitrogen) associated with urban and 

agricultural activities (including fertilization and waste disposal), seep through soils and are 

transported to springs by way of underground pathways. Under natural conditions, nutrients are 

essential to the growth of native plants and wildlife. However, when in excess, nutrients can be 

harmful to the environment, leading to eutrophication and potentially allowing periphyton 
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(algae) and invasive plant species to displace native plants, which results in an ecological 

imbalance. Problematic growths of nuisance algae and noxious plants result in reduced habitat 

and food sources for native wildlife, excess organic carbon production, accelerated 

decomposition, and lowered substrate quality, all of which affect the overall health and aesthetics 

of Florida’s springs (Jacoby et al. 2008). 

4.2 Recent Changes to Spring Ecosystems

Prior to wide-scale development of Florida springs and their springsheds, native submerged 

aquatic vegetation (SAV), primarily Sagittaria kurziana and Vallisneria americana, dominated 

the underwater regions near most spring boils (i.e., the limestone vent where the majority of 

aquifer water is discharged to the surface, sometimes in a turbulent manner). Evidence indicates 

that macroalgae occurred naturally in Florida springs, but not in the excessive abundance 

commonly observed today. Stevenson et al. (2004) found that the majority of the Florida springs 

studied had nuisance growths of algae, primarily Vaucheria and Lyngbya wollei. Vaucheria has 

been reported in spring seeps in areas around the world with very limited human activity, but 

typically not in great abundance. Additionally, historic records of Lyngbya wollei exist from 

Silver Spring (Pinowska et al. 2007a) and Whitford (1957), in his study of algae in Florida 

springs noted that Plectonema wollei Farlow (now called Lyngbya wollei) “forms abundant mats 

in the fresh-water springs.” 

However, within the past 20-30 years, anecdotal observations at several springs suggest that 

nuisance algae species have proliferated, and are now out-competing and replacing SAV. As 

benthic algal mats accumulate, they kill beneficial SAV through direct smothering or indirectly 

via shading (Dennison and Abal 1999; Doyle and Smart 1998). Once the native SAV is 

displaced, other non-native taxa such as Hydrilla verticillata (hydrilla) can re-colonize bare 

substrates, leading to other biological and ecological changes. For example, springs have 

become increasingly inhospitable to certain fish, snails, crayfish, turtles, and other animals that 

depend on the spring habitat. The loss of the native SAV, as well as the increased dominance of 

nuisance filamentous algae results in community metabolism changes and disappearance of 

higher order animals. This chain of events reflects the significant adverse structural and 

functional changes that have occurred in spring ecosystems. 

Numerous biological studies have documented excessive algal growth at many major springs. In 

some of the more extreme examples, such as Silver Springs and Weeki Wachee Springs, algal 

mat accumulations have become several feet thick. The thick benthic algal mats are detrimental 

to aquatic life and cause significant problems for recreational use. The profuse growth of 

macroalgae has been linked to increased nutrient levels in the springs (Florida Springs Task 

Force 2000). In a recent survey of 60 first- and second-magnitude springs in Florida, the most 

commonly observed algal taxa were filamentous mat-forming cyanobacteria of the genus

Lyngbya, and the xanthophyte, Vaucheria (Stevenson et al. 2004; Figure 4-1). When algal cells 

senesce and die, they cause localized depletion of oxygen and the release of ammonia and 

hydrogen sulfide, which can further degrade water quality (Joint Nature Conservation Committee 

2004). Lyngbya wollei proliferation is especially problematic, since more than 70 biologically 

active compounds have been isolated from this species, many of which are toxic and/or 

carcinogenic to humans and can therefore inhibit the recreational use of the resource (Osborne et 
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al. 2001). Lyngbya are a source of lyngbyatoxin and aplysiatoxin, which produce a condition 

known as “swimmer’s itch” (Mynderse et al. 1977; Cardellina et al. 1979). Lyngbya wollei can 

produce a variety of paralytic shellfish poisons (e.g., saxitoxin) and other toxins capable of 

producing dermatitis in humans (Carmichael et al. 1997; Onodera et al. 1997; Teneva et al.

2003; Stewart et al. 2006) and deaths in domestic and wild animals that consume algal mats 

(Edwards et al. 1992; Gugger et al. 2005; Hamill 2001; Saker et al. 1999; Falconer 1999). 

Figure 4-1. Lyngbya wollei (A) and Vaucheria (B). Insets show photomicrographs of the algae. Larger 

pictures show masses growing on spring bottom. Taken from Stevenson (2007).

It is hypothesized that the shift from SAV to blue-green algae in Florida springs is being driven 

by land-use change in springsheds, specifically increased loading of dissolved nutrients, 

especially nitrate, into the Floridian aquifer system from agriculture and urbanization. For 

example, in Ichetucknee Springs, mean annual nitrate concentration increased from 0.35 mg/L in 

1975 to 0.70 mg/L in 2001 (Scott et al. 2004). During that period, anecdotal observations 

indicate that some areas in the river, including reaches below Blue Hole and Mission springs, 

became dominated by Lyngbya. 

Detailed study of two spring ecosystems, Silver Springs and the Rock Creek/Wekiva Spring 

complex, suggests that there is evidence for ecosystem scale effects of nitrate enrichment. Using 

a subsidy stress theory to explain the decline in gross primary production (GPP), it is suggested 

that increases in nitrate act to increase productivity for a time, but then act as a stress to depress 

productivity above a certain threshold (Knight and Notestein 2008). One study presents 

compelling evidence for a decrease in overall productivity in Silver Springs from values 

measured in a 1950 study as compared to today (Knight and Notestein 2008). This declining 

ecosystem productivity documented at Silver Springs was highly correlated with increasing 

nitrate nitrogen concentrations during the 50-year period of available data. Declining spring 

flows, increased shading by riparian trees, and altered fish populations were also observed to be 

correlated with declining ecosystem production at Silver Springs and could offer alternate or 

cumulative explanations of the observed ecosystem changes. 
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The second study conducted in the Wekiva River and Rock Springs Run also found an inverse 

correlation between nutrient (total nitrogen and total phosphorus) concentrations and ecosystem 

metabolism. These spring run ecosystems exhibited other significant environmental stresses 

caused by humans, including decreases in discharge, intensive exotic plant management efforts, 

and disturbance due to recreational activities. Studies of whole ecosystem responses to nutrients 

that would result in direct evidence that increased nutrient levels alone could result in decreased 

ecosystem productivity and/or photosynthetic efficiency were not available (Knight and 

Notestein 2008). 

In addition to increased nitrate concentrations, there are numerous other biotic and abiotic factors 

that have also changed in some, but not all, springs. These changes include increased 

recreational use, decreased water output, decreased dissolved oxygen (O2) concentrations in 

spring discharge, increased need for aquatic weed control, greater abundance of invasive species, 

and increased salinities. By themselves or in combination, these factors may either accentuate or 

mask the effects of nutrients in specific spring systems, and may help explain the observed 

variations in response to increasing nitrate levels observed in Florida’s springs over the last 

several decades. For example, it is believed that aquatic plant control techniques (e.g., herbicide 

applications or mechanical harvesters) that are used to suppress excessive growth of non-native 

plants have the potential to serve as severe disturbances that can further promote succession 

towards algal dominated spring ecosystems. 

The specific mechanisms and interactions between nitrate-nitrite enrichment and these other 

confounding factors that cause the observed changes in Florida springs are not fully understood. 

However, there is justifiable concern for potential negative consequences in Florida spring 

systems associated with increased nitrate concentrations. The potential consequences of nutrient 

enrichment in springs include an increase in opportunistic primary producers, increased organic 

matter deposition, greater number of nuisance algae species and algal biomass, decreased plant 

and animal productivity and diversity, reduced water quality, and not insignificantly, a reduction 

of the aesthetics these ecosystems have long provided (see Figures 4-2 and 4-3). 
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1954 2003

1951 2001

Figure 4-2. Change in biota and increase in algae at Weeki Wachee Spring, Hernando County. Pictures of 

mermaids and underwater hunter in the Weeki Wachee show in the past when no benthic macroalgae are 

visible (1951 & 1954) and during the last 7 years when macroalgae (Lyngbya wollei) are abundant (2001 

& 2003). Taken from Stevenson (2007).
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Figure 4-3. Change in biota and increase in algae at Weeki Wachee Spring, Hernando 

County, 1950s (top photo) and 2001 (bottom photo) (credits: Florida Archives; 

Agnieszka Pinowska).
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4.3 Why Nitrate-Nitrite Criteria?

Nitrogen is one of the essential elements for plant life, and nitrate is a form of nitrogen that is 

readily utilized by aquatic plants and algae. However, nitrate in excess can lead to the 

development of nuisance aquatic plant problems (Rabalais 2002, Chapter 4). As a result of 

human land use changes, cultural practices, and general population growth, there has been an 

increase in the level of pollutants, especially nitrate, in groundwater over the last several 

decades. 

Since there is no geologic store of nitrogen, all of the nitrogen emerging in spring vents 

originates from that deposited on the land surface. Because there are generally limited 

biogeochemical mechanisms or processes to retain or remove nitrate once it has been introduced 

into the ground water below the root zone, it is transported through the groundwater largely as a 

conservative solute. Consequently, a significant portion of the nitrate introduced at the land 

surface, especially when in excess of biological demand, finds its way into groundwater and 

ultimately into the spring system. 

Historically, natural background nitrate concentrations in spring discharges are thought to have 

been 0.05 mg/L or less (Maddox et al. 1992). Increasing human populations have altered the 

global nitrogen cycle and other biogeochemical cycles through land use changes, fertilizer use, 

fossil fuel combustion and other pathways. Population increases and land use changes resulted 

in nutrient enrichment. Florida’s karst region has experienced unprecedented population growth 

and changes in land use over the past several decades, with a consequential transfer of nutrients 

to the relatively unprotected groundwater. Katz et al. (1999) utilized isotopic analyses to show 

that substantial portions of nitrate nitrogen found in the Upper Floridan Aquifer and in spring 

discharges are derived from anthropogenic activities such as fertilizer application for agriculture 

and residential uses, livestock waste, and human waste. 

Figure 4-4 shows the changes in nitrate concentration in Weeki Wachee Spring discharge as 

related to the population increase in Hernando County, Florida. The spring nitrate 

concentrations follow a pattern very similar to the population curve with a 10 to 15 year lag. The 

lag period between changes on the land surface and the subsequent effect on spring discharges is 

expected since measurements of the age of water emerging from springs suggest that, on 

average, it has spent between 10 and 30 years in the subsurface. However, these studies have 

also shown that a significant portion of water (30-70%) has residence times less than 4 years and 

that the relative age contributions varyies significantly between springs, depending on the 

characteristics of the springshed. Heffernan et al. (2010) and Cohen (2010, personal 

communication) hypothesize that “older” spring water is associated with lower dissolved 

oxygen, and that age of spring water is correlated with long term rainfall patterns, potentially 

related to the Atlantic Multidecadal Oscillation.
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Figure 4-4. Changes in nitrate concentration in Weeki Wachee Spring discharge and population of Hernando 

County, Florida. Note: that nitrate concentrations follow a pattern very similar to the population curve with 

a 10 to 15 year lag.

Of 125 spring vents sampled by the Florida Geological Survey in 2001-2002, 52 (42%) had 

nitrate concentrations exceeding 0.50 mg/L and 30 (24%) had concentrations greater than 1.0 

mg/L (Scott et al. 2004). Therefore, over 40% of the springs sampled had at least a ten-fold 

increase in nitrate concentrations above background and approximately one quarter of them 

demonstrated at least a 20-fold increase. Similarly, a recent evaluation of water quality in 13 

first-magnitude springs shows that mean nitrate-nitrite levels have increased from 0.05 mg/L to 

0.9 mg/L between 1970 and 2002 (Scott et al. 2004; Figure 4-5). Overall, data suggests that 

nitrate-nitrite concentrations in many spring discharges have increased from 10 to 350 fold over 

the past 50 years, with the level of increase closely correlated with the anthropogenic activity and 

land-use changes within the springshed. 

As a result of the increased nitrate-nitrite levels in groundwater and spring discharges, 

downstream nitrate-nitrite loads are also increasing rapidly in many watersheds. For example, 

nitrate-nitrite concentrations of several springs in the Suwannee River Basin have increased in 

the last 40 years from less than 0.1 mg/L to more than 5 mg/L (Hornsby and Ceryak 1999, cited 

in Katz et al. 1999) with the nitrate-enriched spring discharge resulting in a two to three-fold 

increase in the level of nitrate exported to the Gulf by the Suwannee River. Consequently, the 

Suwannee Sound has experienced an increase in chlorophyll a, resulting in its placement on the 

list of verified Impaired Waters. 
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Figure 4-5. Increased nitrate concentrations in discharges from 13 selected first-magnitude springs 

(Alexander, Chassahowitzka Main, Fanning, Ichetucknee Main, Jackson Blue, Madison Blue, Manatee, 

Rainbow Group composite, Silver Main, Silver Glen, Volusia Blue, Wakulla, and Wacissa #2 Springs) 

between the 1970s and the early 2000s. Taken from Scott et al. 2004.

As nitrate-nitrite concentrations have increased during the past 20 to 50 years, many Florida 

springs have concurrently undergone a number of adverse environmental and biological changes 

as described previously. There is a general consensus in the scientific community that nitrate is 

an important factor leading to the observed changes in spring ecosystems and their associated 

biological communities. Nitrogen, particularly nitrate-nitrite, appears to be the most problematic 

nutrient problem in Florida’s karst region. 

There are four primary reasons for greater concern about nitrate-nitrite compared to phosphorus. 

First, increases in nitrate-nitrite concentrations are nearly omnipresent in areas where 

anthropogenic loading to the land’s surface has occurred. Second, once in the ground water, 

denitrification is negligible and nitrate-nitrite appears to be transported as a conservative solute. 

Third, although Florida’s geology is naturally rich in phosphorus, there does not appear to be a 

trend of increasing phosphorus concentrations in spring discharges. While nitrate-nitrite 

concentrations have increased significantly in most spring discharges, phosphorus concentrations 

have remained relatively constant over the past 50 years. Fourth, since springs are naturally 

rich in phosphorus, the majority of Florida springs are likely to have been historically nitrogen 

limited (Inglett et al. 2008, Knight and Notestein 2008). In their historical natural state, most 

Florida springs contain levels of bioavailable soluble reactive phosphorus levels of between 30-

60 ppb, which lab studies shown to be sufficient to support the observed growth of algae and 

aquatic plants, if nitrogen is maintained in luxury supply. 

Since nitrate-nitrite may be associated with many of the observed detrimental impacts in spring 

systems, there is a need to reduce nitrate-nitrite concentrations in spring vents and in up-gradient 

groundwater. To restore and preserve springs, activities in springsheds that contribute to the 
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transfer of nitrogen to the groundwater must be properly managed to achieve acceptable levels. 

Due to the relatively long lag times between activities on the land’s surface and the resulting 

effect on the spring discharge, it will likely be many years before the effects of nitrate control 

measures will be seen. The first step in the process is to develop a numeric nitrate-nitrite 

criterion for spring systems that will be protective of this unique resource. A nitrate-nitrite 

criterion will help identify systems at risk of degradation from excessive nitrate pollution, as well 

as those where detrimental changes have already occurred and restoration is needed. DEP’s 

derivation of such a nitrate criterion is discussed below. 

4.4 Criteria Development Methods

DEP has worked to derive response-based thresholds that will definitively link nutrient 

thresholds to biological and environmental risk. DEP has utilized multiple lines of evidence 

taken from the results of different types of research, as well as empirical data available from 

various monitoring programs to develop numeric nitrate standards for spring vents. The 

information that was evaluated include: 

• Results from laboratory dosing studies conducted at various scales; 

• In situ algal monitoring;

• Real-world surveys of biological communities and nutrient levels in Florida springs; and 

• Data regarding nitrate concentrations found in minimally disturbed reference streams. 

Similar to the methods being used to establish numeric interpretations of the narrative nutrient 

criteria for lakes and streams, DEP has utilized multiple lines of evidence taken from the results 

of different types of research as well as empirical data available from various monitoring 

programs to develop nitrate criteria for spring vents. This information and its use in deriving the 

recommended nitrate criteria for spring vents are discussed in greater detail below. 

4.5 Laboratory Studies

The results of laboratory experiments have provided much valuable information about the 

response of algae (particularly algal growth response) to increasing nutrient concentrations under 

specific highly controlled conditions. However, experimental systems usually do not include all 

the complexities and ecological processes that affect the response to nutrients in natural 

waterbodies. The limitations of the small-scale experimental platform must be taken into 

account when applying the results to natural full-scale systems.

Nutrient amendment bioassay work was conducted by Cowell and Dawes (2004) to determine 

the nitrate concentration required to achieve a reduction in biomass of Lyngbya wollei, a 

nuisance blue-green benthic algal species that dominates many spring systems due to elevated 

nitrate concentrations. Using Lyngbya cultures incubated in a series of nitrate amendments, they 

found that both the biomass and growth rates were low in treatment groups with nitrate 

concentration at or below 300 μg/L, while the growth rates and biomass were significantly 

higher in treatments with nitrate concentrations at or greater than 600 μg/L (Cowell and Dawes 
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2004). The experiment also showed that the biomass and growth rate in treatment groups with 

nitrate concentrations from 70 to 300 μg N/L were similar, suggesting that further reduction of 

nitrate concentration below the 300 μg/L level would probably not achieve significant additional 

reductions of L. wollei abundance and growth. They concluded that a nitrate concentration of 

300 μg/L should be sufficient to control L. wollei growth. 

Similarly, Albertin et al. (2007) found that growth of small L. wollei mats in nitrate dosed 

raceways approached maximum levels at nitrate concentrations above 518 to 546 μg N/L. In 

similar studies using Vaucheria, growth rates were low at nitrate concentrations below 69 μg 

N/L and increased substantially from 69 to 644 μg N/L. Further growth rate increases at nitrate 

concentrations above 644 μg N/L were minimal. 

In smaller scale microcentrifuge tube microcosms conducted to evaluate the growth response of 

individual macroalgal filaments to precise levels of nitrate dosing at high phosphate levels, 

Stevenson et al. (2007) found that the growth rate of Lyngbya wollei was minimized at nitrate 

concentrations below 34 μg N/L. Growth rates increased substantially at nitrate concentrations 

from 34 to 230 μg N/L and approached maximum levels at concentrations above 230 μg N/L. 

For unexplained reasons, the growth rate of Vaucheria did not respond to nitrate additions in the 

microcentrifuge tube microcosm experiments. Note that microcosm experiments were 

conducted for 11 days and the mescocosm studies generally lasted for 21 days.

As discussed by Stevenson et al. (2007), the difference in results between the raceway and 

microcentrifuge tube experiments were likely related to the differences in scale of the 

experiments. In the microcentrifuge tube microcosms using individual macroalgal filaments, 

very accurate control of nutrient levels was possible. In the larger scale raceways using small 

algal mats, substantial nutrient depletion was possible and could not be accounted for, which 

resulted in a higher estimate of regulating nitrate concentrations. Recognizing the limitations of 

the laboratory experiments, Stevenson et al. (2007) recommended using the ED90 (nitrate-nitrite 

concentration that produces 90 percent of the maximum growth) determined from the highly 

controlled microcentrifuge tube experiments as a preliminary nitrate criterion that could be 

refined using additional information. The best estimate for the nitrate ED90 s determined from 

the laboratory experiments was 230 μg N/L for Lyngbya wollei and 261 μg N/L for Vaucheria 

sp. 

4.6 Field Surveys

Numerous surveys of macroalgae and nutrients in springs have been conducted to demonstrate 

the cause-effect relationships between elevated nutrient concentrations and macroalgal growth, 

and to evaluate the nitrogen or phosphorus concentrations associated with proliferations of 

macroalgae. The benefit of using results of field surveys for nutrient criteria development is the 

direct applicability of observed nutrient concentrations and biological responses. 

In a survey of Florida springs, macroalgae were found at 59 of the 60 sampled sites, and an 

average of 50% of the spring bottoms were covered by macroalgae with the thickness of 

macroalgal mats commonly being 0.5 m or more and as thick as 2 m in one spring boil 

(Stevenson et al. 2004). Lyngbya wollei and Vaucheria spp. were the two most common taxa of 

macroalgae that occurred in extensive growths in the studied springs, however 23 different 

macroalgal taxa were observed in the spring survey. 
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During the surveys, the abundance of Vaucheria spp. within the springs was found to be 

positively related to nitrogen concentrations. Non-linear models of Vaucheria percent cover and 

thickness along the TN and nitrate gradients explained substantially more variation than a linear 

model, with a clear threshold in Vaucheria response at 0.454 mg N/L as nitrate (i.e., 0.591 mg 

N/L as TN), respectively. Excessive growth and cover of Vaucheria were found at sites with 

nitrate concentrations at or above the 0.454 mg/L threshold, with Vaucheria abundance being 

significantly less at sites with lower nitrate levels (Stevenson et al. 2007). Note that an 

analogous relationship between nitrate and Lyngbya wollei abundance was not observed. The 

excessive growth of Vaucheria sp. is considered to constitute an imbalance of the natural 

biological communities, and not in compliance with Florida’s narrative nutrient criteria. 

Therefore, to provide for a margin of safety, a protective numeric nitrate criterion would need to 

be below the observed 0.454 mg N/L nitrate Vaucheria threshold. 

4.7 TMDL Development Activities

The Wekiva River and Rock Springs Run are spring-dominated systems that were listed on the 

State’s impaired waters list due to evidence of an imbalance in aquatic flora characterized by 

excessive algal growth and lower ecosystem metabolic activities. There was also evidence that 

the impairment of the Wekiva River and Rock Springs Run was caused by elevated nitrate. The 

mean nitrate concentration in the Wekiva River and Rock Springs Run ranged between 0.60-0.70 

mg N/L, which is significantly higher than levels found at nearby minimally disturbed reference 

sites with similar characteristics (Juniper and Alexander Springs). Additionally, the Wekiva and 

Rock Springs nitrate-nitrite levels were above the threshold nitrate concentration identified by 

Stevenson et al. (2004) to be associated with nuisance Vaucheria growth (Gao 2008). 

During the development of the TMDL for these waterbodies, protective nutrient concentration 

targets were derived using periphyton and water quality data collected from spring-dominated 

portions of the Suwannee River and two tributaries, the Withlacoochee River and Santa Fe River 

(Hornsby et al. 2000). These data were considered applicable to the Wekiva River and Rock 

Springs Run since the Suwannee River is heavily influenced by spring inflow, and in the absence 

of anthropogenic inputs, the algal communities would be expected to be generally similar in 

composition to those in the Wekiva River and Rock Springs Run. 

An evaluation of periphytometer data collected from 1990 through 1998 at 13 sites along the 

Suwannee River showed positive correlations for both periphyton biomass versus nitrate 

concentration and cell density versus nitrate concentration. The functional relationships of cell 

density versus nitrate concentration and periphyton biomass (represented as ash free dry weight, 

or AFDW) versus nitrate concentration are shown in Figures 4-6 and 4-7, respectively. Data 

presented in these figures represent long-term average biomass, cell densities, and nitrate 

concentrations at the stations across the Suwannee River system (Niu and Gao 2007). 
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Figure 4-6. Relationship between mean nitrate concentration and mean periphyton cell density from 

sampling sites on the Suwannee, Santa Fe, and Withlacoochee Rivers (Mattson et al. 2006).

Figure 4-7. Relationship between mean nitrate concentration and mean periphyton biomass from sampling 

sites on the Suwannee, Santa Fe, and Withlacoochee Rivers (Mattson et al. 2006).

Mean NOx vs. Mean Cell Density

0

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As can be seen for both cell density and biomass, periphyton abundance significantly increased 

when nitrate concentration increased above approximately 0.350 mg/L. The data were further 

evaluated using a change point analysis to better define the nitrate concentration that may 

significantly impact the periphyton biomass and cell density. The change point analysis fits a 

step function through observed data by examining the probability of each data point as the 

change point. For both periphyton cell density and periphyton biomass, change point step 

functions were shown to be the best model among the models tested, which supports the use of 

change point analysis. 

For the relationship between cell density and nitrate concentration, the change point step function 

identified two populations of sites. The first set of sites had cell densities near 163,000 cells/cm2

(P = 0.009), which was considered as the baseline condition under which no significant nitrate 

impact was detected. The second group of sites had cell densities near 616,000 cells/cm2 (P = 

0.0001), which was significantly elevated above the baseline condition. The change-point 

analyses also indicated that the critical increase in mean algal cell density occurred as the mean 

nitrate concentration increased from 0.286 to 0.401 mg/L (Niu and Gao 2007). This suggests 

that to prevent the periphyton cell density from increasing to the higher level, the nitrate 

concentration a target concentration should be established below 0.401 mg/L. 

Similarly, the change point analysis of the relationship between periphyton biomass and nitrate 

concentration identified two populations of sites. The first set of sites had a periphyton biomass 

near 1.73 g/m2

 (p< 0.0001), which was considered to be the baseline condition under which no 

significant nitrate impact was detected. The second group of sites had an increased algal 

biomass near 4.15 g/m2 (p = 0.0001), which was significantly elevated above the baseline 

condition. The change point analyses also indicated that the critical increase in mean periphyton 

biomass occurred as the mean nitrate concentration increased from 0.401 to 0.420 mg /L (Niu 

and Gao 2007). This suggests that to prevent the periphyton cell density from increasing to the 

higher level, the nitrate concentration a target concentration should be established below 0.420 

mg /L. 

Since periphyton cell density exhibited a slightly more sensitive response to increasing nitrate 

concentrations, that relationship was used as the basis for the nitrate target concentration. 

Although the nitrate concentration that resulted in the periphyton cell density increase could be 

any at level between 0.286 mg/L and 0.401 mg/L, 0.286 mg/L was chosen to be the TMDL 

nitrate target concentration for the Wekiva River and Rock Springs Run systems. Choosing the 

nitrate target concentration of 0.286 mg /L provided a conservative criterion with an adequate 

margin of safety that is reasonably protective of the biological communities within these systems 

(Gao 2008). 

Following adoption of the TMDL, the change point analysis was repeated using additional data 

collected from 1990 through 2007 for the same 13 sites located along the Suwannee River. To 

account for any long-term temporal changes at a site, the period of record was divided into four 

periods. The average periphyton abundance and nitrate-nitrite data for each period for each site 

were used to repeat the change point analysis. The results were very similar to those obtained 

from the original analyses as described above. A nitrate concentration change point of 0.440 mg 

/L was determined for both periphyton cell density and biomass (Figures 4-8 and 4-9). Since 

these change points represent the lower concentration range for the group of sites with 
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significantly higher periphyton abundance, as compared to the baseline group, a protective 

nitrate criterion should include an appropriate safety margin to assure that sites do not reach this 

level. 

Figure 4-8. Change point analysis for data from the 13 stations At the Suwannee River System (Mean Cell 

Density vs. Mean NOx). Change Point = 0.44 mg N/L. The 95% confidence interval for the change point 

based on 1000 bootstrapping samples is 0.378 to 0.629. 

Mean NOx (Mg/L)

Mean Cell Density (/Square-CM)

0.0 0.2 0.4 0.6 0.8

0 2*10^5 6*10^5 10^6
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Figure 4-9. Change point analysis for data from the 13 stations At the Suwannee River System (Mean 

Biomass vs. Mean NOx). Change Point = 0.44. The 95% confidence interval for the change point based on 

1000 Bootstrapping samples is 0.441 to 0.584 μg N/L. 

Figures 4-10 and 4-11 illustrate the same observed relationships between periphyton cell density 

versus and nitrate and algal biomass versus nitrate concentrations, respectively, with field and 

laboratory results illustrated in red lines. The 0.44 mg/L change point represents the upper 

nitrate-nitrite concentration where the observed biological changes occur. Additionally, the 0.23 

mg/L nitrate-nitrite threshold based on the laboratory studies, which represents the lower bound 

of the range in which biological changes occur, are also shown on the graphs for reference. Both 

graphs clearly indicate that algal abundance is restricted at nitrate concentrations below 

approximately 0.35 mg/L, with the potential for increased algal cell density and biomass 

increasing substantially near the 0.44 mg/L change point. 

Mean NOx (Mg/L)

AFDW (g/Square-Meter)

0.0 0.2 0.4 0.6 0.8

0 2 4 6
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Figure 4-10. Relationship between mean nitrate concentration and mean periphyton abundance for 

sampling sites on the Suwannee, Santa Fe, and Withlacoochee Rivers. Values with nitrate concentrations 

above and below 0.35 mg N/L are shown in different colors. Red dotted lines are at 0.23 mg N/L and 0.44 

mg N/L, representing results from lab and field studies, respectively.

y = 127582e2.351x

R2 = 0.7215

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Nitrate + Nitrite (mg N/L)

Mean Cell Density (#/cm 2) 

Figure 4-11. Relationship between mean nitrate concentration and mean periphyton biomass for sampling 

sites on the Suwannee, Santa Fe, and Withlacoochee Rivers. Values with nitrate concentrations above and 

below 0.35 mg N/L are shown in different colors. Red dotted lines are at 0.23 mg N/L and 0.44 mg N/L, 

representing results from lab and field studies, respectively.

y = 1.307e1.84x

R2

 = 0.6741

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)
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In addition to the observed increases in periphyton cell density and biomass, shifts in the 

structure and taxonomic composition of the periphyton community were also observed across 

this nitrate-nitrite gradient. The abundance of taxa indicative of eutrophic conditions (Van Dam 

et al. 1994) increased significantly with increasing nitrate-nitrite concentrations above 

approximately 0.35 mg/L (Figure 4-12). Similar to the changes in the algal abundance and 

biomass, the abundance of the eutrophic taxa are similar at sites with nitrate-nitrite 

concentrations below 0.35 mg/L, while the abundance of these taxa increases with increasing 

nitrate-nitrite concentrations above 0.35 mg/L. The significant alterations in community 

composition, in combination with the increases in cell density and biomass, clearly demonstrate 

that increased nitrate-nitrite levels in the range between the 0.23 mg/L laboratory threshold and 

the 0.44 mg/L change point based on field observations were associated with an imbalance of 

aquatic flora (Rule 62-302, FAC). Since the 0.23 mg/L laboratory limit is lower than necessary 

to be protective and the 0.44 mg/L change point is not adequately protective, this range must be 

refined to develop an appropriate criterion. 

Figure 4-12. The mean cell density of Van Dam eutrophic indicator diatoms increases with increasing 

nitrate-nitrite concentrations. Red dotted lines are at 0.23 mg N/L and 0.44 mg/L, representing results from 

lab and field studies, respectively.

Based on the analyses performed, multiple lines of evidence indicate that the biological changes 

appear to occur at sites with nitrate-nitrite concentrations above approximately 0.44 mg/L. Since 

the 0.44 mg/L change point represents the upper bound of the range in which the biological 

changes occur, an appropriate safety factor to assure that sites do not reach this level should be 

applied to the change point to derive a protective nitrate-nitrite criterion. 

To define this safety factor, two complimentary analyses, as described in Hallas and Magley 

(2008), were conducted and the results averaged (Figure 4-13). The full confidence interval 

procedure resulted in a protective threshold of 0.33 mg/L, and the upper half of the confidence 

interval method yielded a protective threshold of 0.38 mg/L. The average of these 

complimentary methods resulted in the final protective threshold of 0.35 mg/L. 

y = 2345.1e3.6176x

R2 = 0.6838

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Figure 4-13. An example of the full range confidence interval procedure to develop a nitrate-nitrite criterion. 

The full confidence interval procedure resulted in a protective threshold of 0.33 mg/L. The upper half of 

confidence interval method yielded a protective threshold of 0.38 mg/L. The average these complimentary 

methods resulted in the final protective threshold of 0.35 mg/L.

4.8Synthesis of Available Experimental and Observational Data to 

Derive a Nitrate Standard

As described above, DEP evaluated multiple lines of evidence, including laboratory experiments, 

mesocosm dosing studies, and field studies, during the development of the springs nitrate 

criterion. Given the limitations of the small scale laboratory experiments, the nitrate criteria was 

derived primarily based on actual field scale observations, which included an abundance of field 

data collected over 17 years at 13 sites in the spring dominated Suwannee, Santa Fe, and 

Withlacoochee Rivers. 

The results of laboratory experiments provided information about the response of algae 

(particularly algal growth response) to increasing nutrient concentrations under specific highly 

controlled conditions. However, laboratory experiments such as Stevenson et al. (2007) cannot 

include all the complexities and ecological processes that affect the response to nutrients in 

natural waterbodies. Because the natural environment is often not optimal for algal growth due 

to conditions such as mat thickness, sloughing, suboptimal light and temperature, losses from 

grazing (aquatic species consumption), competition from other submerged aquatic vegetation, 

and other factors, the limitations of the small-scale experimental platform must be taken into 

account when applying the results to natural full-scale systems for nutrient criteria development. 

y = 127582e2.351x

R2

 = 0.7215

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)
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Due to the known limitations, the use of small scale laboratory studies as the basis for nutrient 

criteria could result in overly conservative criteria. Lab conditions obviously do not exist in the 

natural environment. 

On the other hand, it should be noted that Vaucheria sp. did not show a response to nitrate 

additions in the small scale laboratory experiment, but exhibited a clear response to nitrate under 

field conditions (Stevenson et al., 2007). Due to the lack of response observed for Vaucheria 

sp., the reliability of using such a small scale experimental design to predict responses under 

natural stream conditions must be considered. In addition, Lyngbya wollei, is known to have the 

ability to fix nitrogen from the atmosphere, which further complicates the interpretation of the 

experimental results.

There is also no evidence to suggest that the short-term responses observed under controlled lab 

settings equates to impairment of the designated use in conditions experienced in State waters. 

In fact, the more realistic field surveys demonstrated that excess biomass does not become 

persistent in springs until average nitrate exceeds 0.441 mg/L for a three-year period. The 

0.441 mg/L threshold was adjusted to 0.35 mg/L to provide a fully protective criterion with a low 

likelihood of excess algal growth (at the 95% confidence limit). 

Stevenson et al. (2007) found that the percent of spring bottoms covered by Lyngbya wollei in 

surveys of 60 spring sites in 29 first magnitude springs ranged from zero to almost 80%, and that 

mat thickness ranged from zero to 0.25 m, but neither metric was related to TN, TP, or soluble 

nutrient concentrations in spring water. Conversely, non-linear models of Vaucheria % cover 

and thickness along the TN and nitrate gradients explained substantially more variation than a 

linear model, with thresholds in Vaucheria response at 0.454 and 0.591 mg N/L as nitrate and as 

TN, respectively. Average Vaucheria cover was only 2.3 % when nitrate levels were below 

0.454 mg/L. These field studies demonstrated that only % cover and thickness of Vaucheria 

mats (not Lyngbya) were related to nitrogen concentrations in spring water. Stevenson et al.

(2007) recommended that Vaucheria response to nitrate at 0.454 mg/L could be used as a 

benchmark for nitrogen criteria that should prevent nuisance levels of Vaucheria. Due to the 

lack of response of Lyngbya to nitrate, no analogous benchmark for Lyngbya was possible based 

on the study.

Laboratory nutrient addition experiments indicated that when nitrate concentrations were 

manipulated and phosphate was maintained in luxury supply, growth rates of single strands of 

Lyngbya wollei did not respond more than 10% at nitrate concentrations below 0.034 mg/L, 

increased substantially at nitrate concentrations from 0.034-0.230 mg/L, and did not respond 

more than 10% to further nitrate concentration increases above 0.230 mg/L (in microcentrifuge 

microcosms). However, in other experiments using 0.01 g of algae (donut microcosms), 

Lyngbya wollei did not respond more than 10% at nitrate concentrations below 0.327 mg/L, had 

a median growth at 0.519 mg/L, and did not respond more than 10% to further nitrate 

concentration increases above 0.821 mg/L. Heffernan et al. (2010) note that there was no growth 

response in Lyngbya wollei or Vaucheria to nitrate levels of up to 5 mg/L in flow through 

mesocosm studies. This variability, and the general limitation of laboratory experiments to 

predict field responses (e.g., no grazers, no light limitation, and no physical disturbance), 

indicates that experiments alone are insufficient to establish nitrate criteria.
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Stevenson et al (2007) state, “Reducing nutrient loads sufficiently to stop versus slow growth of 

the macroalgae may be unrealistic for many reasons. First, results from our experiments show 

these algae seem to be able to continue to grow, even in very low nutrient concentrations. 

Second, natural phosphate levels in many Florida springs may be too high to stop their growth 

and controlling nitrate sufficiently may be impractical. Evidence indicates these macroalgae 

likely occurred naturally in Florida springs, but not in the excessive abundance as today”. 

Heffernan et al. (2010) and Cohen (personal communication, 2010) provide evidence that 

reductions in DO concentrations since 1972, likely related to the age of the water emerging at 

spring vents (due to changes in rainfall levels as a result of the Atlantic Multidecadal 

Oscillation), is an important factor in reducing grazers and allowing more Lyngbya biomass in 

Florida springs. Heffernan et al. (2010) argue that factors other than nitrate enrichment (e.g., 

naturally lowering DO, reductions in grazers, changes in macrophytes) better explain the 

occurrence of present day algal mats in springs. The authors agree that implementation of nitrate 

reduction is an appropriate precautionary management strategy, but that effective adaptive 

management should involve an approach that addresses a wide range of mechanisms that 

determine ecological condition in springs. After evaluating available scientific studies regarding 

algal growth in springs, the Department concluded that setting the nitrate criteria in a manner that 

prevents excess algae growth in the field (observed Vaucheria sp. and diatom responses) will 

protect both aquatic life and recreational uses.

The frequency and duration components of the springs nitrate criteria were specifically designed 

to guard against conditions that promote excess algae growth that occurred when the long term 

(3-year) average in the waterbody was above 0.441 mg/L. To assess whether a spring attains the 

nutrient standard, DEP also set the minimum sample size to calculate an annual average 

consistent with how the standard was derived. Pursuant to Subsection 62-302.531(6), F.A.C., at 

least four temporally-independent samples per year with at least one sample taken between May 

1 and September 30 and at least one sample taken during the other months of the calendar year 

are required to calculate an annual average nitrate-nitrite concentration (note that the rule 

specifically addresses TN, but nitrate is a component of TN). Additionally, to be treated as 

temporally-independent, samples must be taken at least one week apart.

4.9 Frequency and Duration of the Nitrate Standard

An additional conservative step was taken by setting the frequency component of the standard to 

allow the annual average nitrate concentration to exceed 0.35 mg/L in only one year out of every 

three-year period. DEP evaluated the inter-annual variability of nitrate-nitrite in springs using 

data from IWR Run 44 and calculated that a spring with a long-term average of 0.35 mg/L would 

be expected to have an inter-annual standard deviation of approximately 0.097. Based on the 

binomial distribution and assumption of inter-annual independence (i.e., no or minimal 

autocorrelation between years), it can be expected with 90% confidence that the 80th percentile 

geometric mean concentration will be exceeded no more than once in a three-year period. Given 

the expected level of variability around 0.35 mg/L, the long-term geometric nitrate concentration 

in a spring would need to be 0.27 mg/L to be consistently found in compliance at least 90% of 

the years. Furthermore, because short-term (i.e., 3-year) variability is inherently greater than 

longer term variability, the 3-year average nitrate must be lower to consistently achieve the 
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criterion, with no exceedances of the one-in-three test. The 3-year mean would need to be 

approximately 0.26 mg/L (0.22-0.29 mg/L) to consistently achieve the nitrate criterion. 

Although the mesocosm studies suggested response time frames shorter than one year, the 

inability of lab studies to reproduce field conditions (e.g., lack of grazers or other physical 

disturbance) suggests that lab studies should not be relied upon for the duration component of a 

criterion. The three year average periphyton response to nitrate enrichment found in the analysis 

of data along the Suwannee, Santa Fe, and Withlacoochee Rivers was among the strongest 

evidence for the criterion, suggesting that a three year averaging period would be the most 

appropriate duration for criterion expression. However, to allow State waters to be assessed 

more quickly, DEP ultimately decided on a one-year averaging period. 

Although DEP investigated applying the nitrate criterion as a 10% exceedance frequency of the 

monthly mean, the Department reconsidered use of the monthly mean because the strongest 

evidence of imbalance was shown by a change point analysis of three-year averaged data. 

Additionally, analysis of the monthly mean demonstrated that such an expression would 

unnecessarily require maintenance of long-term average nitrate conditions well below the 

established response threshold. For example, given the typical variance in the data, a stream 

with a long-tem annual average nitrate concentration at 0.35 mg/L would be expected to exceed 

the criterion during approximately 50% of the months, well in excess of a 10% exceedance rate. 

Review of the month-to-month nitrate variability at stream benchmark sites and sites within the 

Suwannee drainage basins suggests that the long-term average concentration in a stream would 

need to be below 0.10 mg/L in order to consistently meet the <10% exceedance requirement of 

0.35 mg/L. 

The exceedance frequency (no more than one exceedance in a three year period) was based on 

EPA’s Technical Support Document for Water Quality-Based Toxics Control (EPA1991), which 

when applied to non-toxic substances, such as nutrients, is inherently protective. The 

exceedance frequency also accounts for actual nutrient variability that does not impact the 

designated use, and, as such, results in preventing excess algae growth that has been 

demonstrated to occur when the three- year average nitrate condition is greater than 0.441 mg/L. 

Furthermore, the exceedance frequency will prevent excess growth of algae, maintain well 

balanced natural populations of flora, and therefore support recreational uses.

DEP also evaluated whether the criteria needed to address seasonal changes in algal abundance. 

Lyngbya and Vaucheria are found globally, with their abundance being regulated by complex 

interactions among a set of diverse biogeochemical and physical environmental variables. 

Seasonal variability in macroalgal cover or abundance has not been widely studied in springs to 

determine the times of year when problems may be greatest or whether seasonal factors could 

help explain macroalgal ecology. Due to the relatively consistent temperature, the abundance of 

Lyngbya and Vaucheria in springs generally does not follow expected seasonal patterns. Field 

observations by Stevenson et al. (2007) during a year-long study suggest that any seasonal 

patterns in algal abundance is site specific and may depend on the dominant algal species, water 

color, and level of physical disturbance. 

At Manatee Spring, which is dominated by Vaucheria, macroalgal coverage area varied from 

near zero in the late spring and early summer to a peak in late winter. Both algal area and 
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thickness appeared to respond strongly to water levels in the Suwannee River. Low algal 

biomass in June and July of 2005 was likely a result of high water levels in the Suwannee River, 

which caused flooding of Manatee Spring by dark tannic waters and thereby reduced sunlight 

available for algal growth. In contrast, the algal coverage in Ichetucknee Spring, dominated by 

Lyngbya, exhibited far less seasonal variation than observed at Manatee Spring. Over the yearlong study there was a gradual decline in algal area, thickness and volume. Due to the limited 

information available concerning the seasonal patterns of macroalgal abundance in Florida 

springs, the apparent differences in seasonal patterns among springs, and the limited seasonal 

variation in some springs, the nitrate standard was developed based on long-term (three year) 

average concentrations and applied as an annual average. 

Ultimately, the criteria need to be expressed consistent with their derivation. The frequency and 

duration components of the springs nitrate criteria were specifically designed to guard against 

conditions that promote excess algae growth that occurred when the long term (3-year) average 

in the waterbody was above 0.441 mg/L. 

4.10 Potential Use of Cyanobacteria and Associated Toxins as 

Response Variable for Springs

As previously described, Lynbya wollei and Vaucheria sp. were the two most abundant 

macroalgae in Florida springs, and both have been found prior to significant human disturbance 

in Florida, when nitrate levels were at natural background levels. Both taxa were observed by 

Whitford (1957) in his study of algae in Florida springs. He noted that Plectonema wollei 

Farlow (now called Lyngbya wollei) “forms abundant mats in the fresh-water springs.” Under 

contract with DEP, Dr. Aga Pinowska found a collection of Lyngbya at Harvard’s Farlow 

Herbarium, which was collected from Silver Springs in 1939. Her examination of that material 

showed that it looked like the alga that we now identify as Lyngbya wollei (Stevenson et al. 

2007). Therefore, it may be concluded that Lyngbya wollei and Vaucheria sp. naturally occurred 

in Florida springs even at historically low nitrate concentrations. 

Concern has been expressed regarding the potential toxicity of Lyngbya wollei to humans. The 

assessment of toxicity in cyanobacteria and associated public health risks is complicated by 

variable toxin production in many species (PBSJ 2007). The mere identification of a 

cyanobacterial species is not sufficient to identify its toxicity because numerous strains of 

differing toxicity may belong to the same species, and both toxic and nontoxic strains may be 

present in the same blooms (PBSJ 2007). Environmental conditions that foster toxin production 

are not well understood, and sophisticated tests are required for determining whether or not a 

bloom contains toxic species (Mur et al. 1999). It has been hypothesized that the random 

reporting of dermatitis and respiratory symptoms by recreational swimmers in Florida waters 

may be partly attributed to various toxin-producing and nontoxin-producing strains of Lyngbya 

wollei (PBSJ 2007).
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To test this hypothesis, a study conducted by PBSJ (2007) focused on the assessment of toxicity 

of Lyngbya spp. in Florida waters and implications for human health via exposure during 

recreational activities. The three main objectives of the study were to: 1) identify the distribution 

of Lyngbya wollei in Florida First Order Magnitude springs and other areas of concern; 2) 

characterize and quantify algal toxins associated with marine and freshwater Lyngbya spp.; and 

3) identify genetic diversity and toxin-producing strains of Lyngbya to isolate and develop a 

molecular-based screening assay to detect harmful Lyngbya species.

A total of sixty-four samples of Lyngbya wollei were collected in 2004 (PBSJ 2007). All 64 

samples were analyzed for cylindrospermopsin, deoxy-cylindrospermopsin, lyngbyatoxin A, and 

debromoaplysiatoxin by the analytical laboratory at the University of Queensland (National 

Research Center for Environmental Toxicology). The results were negative for all 64 samples. 

Ten of the 64 samples were also analyzed for saxitoxin by the National Research Center for 

Environmental Toxicology, however no saxitoxins could be confirmed in these samples. 

Although no toxins were found in L. wollei samples collected in 2004, additional sampling was 

conducted in 2006, targeting waters with high recreational usage where previous reports of 

symptoms of dermatotoxin exposure had occurred (PBSJ 2007). These systems included the 

Ichetucknee River (six separate springs), Alexander Springs, Silver Glen Springs, and Juniper 

Springs. Ichetucknee Springs State Park was revisited on July 25, 2006, however, only a small 

amount of L. wollei was found within the epiphytic and floating mats of filamentous 

cyanobacteria, and no benthic mats of L. wollei were observed at the seven sampling points 

along the spring run (PBSJ 2007). In contrast, mats of L. wollei were prevalent at Silver Glen 

Spring on August 1, 2006, and samples of L. wollei were collected from four discrete locations. 

L. wollei was also observed at two locations in Alexander Spring and at one location in Juniper 

Spring on August 17, 2006. Six L. wollei samples collected in 2006 (four from Silver Glen 

Spring, one from Alexander Spring, and one from Juniper Springs) were analyzed for 

lyngbyatoxin A, debromoaplysiatoxin, and saxitoxin by Greenwater Laboratories in Palatka, FL. 

Lyngbyatoxin A (LT) and debromoaplysiatoxin (DAT) were not observed in any of the 

samples. Although the presence of saxitoxin or saxitoxin-like compounds was suggested by the 

ELISA test, confirmation was not established with the LC/MS/MS for saxitoxin (PBSJ 2007). 

These findings indicate that Lyngbya wollei can naturally be found in low nitrate springs, but that 

toxin production was limited or non-detectable during the periods sampled. The Department 

concluded that there is insufficient understanding of the potential toxic effects of Lyngbya, a 

species that naturally occurs at low nitrate concentrations in Florida springs, to form the basis of 

numeric nutrient criteria.

While the Department and EPA considered recreational uses in deriving numeric criteria in 

springs, there is no quantitative framework to determine at what threshold of algal abundance the 

recreational use is adversely affected, in part due to the large variability in user perception. In 

contrast, change point analysis showed a significant cause-effect relationship between aquatic 

flora (periphyton) and nitrate concentrations, and the nitrate criterion was set a concentration 

designed to prevent a change from well balanced background periphyton levels, above which the 

Department determined to be associated with imbalances in natural population of flora and 

indirectly, fauna. Because the Department focused on the biological indicators that showed the 

greatest sensitivity, the proposed rule protects recreational uses by preventing excess algae 

(preventing an imbalance in natural populations of flora) that would impede recreational uses. 
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The proposed Springs nutrient standard implements the existing narrative criterion for nutrients, 

which states “[i]n no case shall nutrient concentrations of a body of water be altered so as to 

cause an imbalance in natural populations of aquatic flora or fauna,” [Rule 62-302.530(47)(b), 

F.A.C.]. The maintenance of well balanced natural population of aquatic flora and fauna is 

inherently protective of recreational uses from the harm caused by excessive algal growth due to 

nutrients. The sensitive biological indicators considered for springs included the presence of 

submerged aquatic vegetation and periphyton abundance, biomass, and community structure, as 

well as invertebrate responses. The concentrations of nitrogen and phosphorus themselves do not 

inhibit recreation, rather, it is imbalances in aquatic flora caused by nutrient enrichment that will 

inhibit recreational use. It should be noted that some harmful algal blooms or mats occur 

naturally and recreation may be impacted during the blooms, however protection of natural 

populations of aquatic flora and fauna is protective of recreation that would be expected to occur 

under natural conditions.

4.11 Analysis of Nutrient Gradient Study Rapid Periphyton Data

In Chapter 6, an analysis of algal responses (Rapid Periphyton Survey, or RPS) to nutrients and 

other variables is presented. The analyses demonstrated that periphyton thickness was 

significantly correlated with nitrate- nitrite, canopy cover, and water color, but not with TP. It 

should be noted that color is a strong confounding factor in these analyses since it is correlated to 

some extent with canopy cover, nitrate-nitrite concentration, and TP concentration. The analyses 

of the rapid periphyton assessment data are provided in greater detail in Appendix 6-G. Note 

that in streams where color was less than 40 PCU and canopy was relatively open, there was a 

significant correlation between nitrate-nitrate and excess algal growth (defined as >50% of the 

measurements having algal thickness exceeding 2 cm) within a rather large range of uncertainty. 

The area of uncertainty associated with the nitrate-nitrate concentration that elicited the response 

included the 0.35 mg/L threshold found for clear spring systems. Although DEP considered 

applying the 0.35 mg/L nitrate-nitrite limit to all streams with color measurements of less than 40 

PCU, the Department concluded that the uncertainty associated with current data was too large, 

and is continuing to collect more RPS data to better quantify algal responses to nitrate in clear 

streams. 

4.12 Summary and Conclusions

Multiple lines of evidence indicate that reducing nitrate-nitrite concentrations in springs should 

reduce algal growth rates. Control of nitrate-nitrite is expected to result in a reduced frequency, 

intensity, and duration of nuisance macroalgal growth in springs and to prevent biological 

imbalances (i.e., to restrict growth and accumulations of nuisance macroalgae and to preserve the 

native periphyton community structure). 

The most conservative experimental results, those from microcentrifuge tube experiments using 

a single strand of algae, suggest that nitrate concentrations less than 0.230 mg NO3-N/L are 

needed to slow growth of Lyngbya wollei. Similarly, to reduce the growth of Vaucheria under 

laboratory conditions, nitrate concentrations below approximately 0.261 mg NO3-N/L would be 
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required. Note that other experiments conducted with slightly more algal biomass indicated that 

much higher nitrate concentrations would be needed to sustain actual mat growth. 

Results of periphyton field surveys conducted at a large number of spring systems indicated that 

nitrate concentrations would need to be reduced below the observed 0.454 mg N/L threshold to 

reduce the nuisance abundance and cover of Vaucheria spp. in Florida springs (Pinowska et al.

2007a). An analogous relationship with Lyngbya was not observed. Since the 0.454 mg N/L 

threshold represents the lower range of nitrate concentrations for sites with excessive algal 

growth and cover, an appropriate safety factor is needed to turn the threshold into a protective 

criterion.

In addition, nearly two decades of scientific results from periphytometers deployed in the spring 

dominated, low color (generally <40 PCU) Suwannee, Santa Fe, and Withlacoochee (north) 

Rivers clearly indicated significant increases in diatom cell density and biomass along with 

alterations in taxonomic community structure (which are indicative of an imbalance) occur as 

nitrate concentrations approach the 0.441 mg N/L change point. 

A margin of safety, derived by averaging the upper half-range and full range 95 percent 

confidence intervals, was applied to the 0.44 mg N/L change point to derive the final 0.35 mg/L 

nitrate-nitrite criterion. A provision that maintains spring nitrate concentrations < 0.35 mg/L for 

at least two years of every consecutive three year period provides confidence that adverse 

responses will not be observed. This would protect both healthy, well balanced aquatic 

communities and recreation that would be expected under natural conditions. 
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5 Regionalization of Florida’s Numeric Nutrient Criteria for 

Streams 

5.1Purpose of Developing a Regionalization Scheme

As part of the analyses conducted to derive nutrient standards for streams, the Department 

evaluated the available data for regional differences and ultimately developed a regionalization

scheme for the stream nutrient standards. Classification or regionalization of streams provides a 

framework upon which to develop and base protective nutrient criteria. Proper classification 

ensures homogeneous populations of streams with similar and comparable nutrient regimes and 

biological communities. It helps assure that thresholds selected from a benchmark or reference 

approach are truly inclusive of the natural frequency distribution and thus will be inherently 

protective of the natural populations of flora and fauna inhabiting these systems. 

It should be noted that Florida’s geology includes fairly recent sedimentary deposits of marine 

origin. Certain marine clays (e.g., the Hawthorn Formation) and limestone formations that lie 

near the surface are extremely high in phosphorus. Some of these phosphatic deposits are mined, 

making Florida one of the larger producers of phosphate (Florida produces approximately 25% 

of phosphate used throughout the world). Proper spatial classification to capture regional 

differences in natural nutrient concentrations is essential.

5.2 Development of Ecoregional and Biological Regionalization 

Schemes for Florida

DEP initially used Level IV ecological subregions (Griffith et al. 1994, Figure 5-1) from 

Florida’s bioassessment program as a starting point for regionalization efforts necessary to 

establish nutrient criteria. Ecoregions are usually defined by patterns of homogeneity in a 

combination of factors such as climate, physiography, geology, soils, and vegetation (Griffith et 

al. 1994). During development of the bioassessment program, DEP analyzed stream reference 

site macroinvertebrate community patterns in all nine ecological subregions north of Lake 

Okeechobee (Barbour et al. 1996b). The data indicated the presence of four distinct bioregions, 

within which there were similar biological community composition and structure (Figure 5-2). 

These bioregions include the panhandle (regions 65f, 65g, 65h, and the majority of 75a), the 

northeast (region 75e and 75f), the peninsula (regions 75b, 75c, and 75d, and a small part of 

75a), and the Everglades (regions 76a, 76b, 76c, and 76d). Similar patterns of relatively 

homogeneous groupings in the Peninsula versus the Panhandle have been observed in wetlands 

macrophyte, algae, and invertebrate data (Lane et al. 2003). 
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Figure 5-1. Level IV subecoregions for Florida’s small/wadeable streams.

Alabama

Georgia

Gulf of Mexico

Atlantic

Ocean

Southern Pine Plains and Hills (656)

Dougherty/Marianna Plains (657)

Tifton Upland/Tallahassee Hills (658)

Gulf Coast Flatwoods (751)

Southwestern Florida Flatwoods (752)

Central Florida Ridges and Uplands (753)

Eastern Florida Flatwoods (754)

Okefenokee Swamps and Plains (755)

Sea Island Flatwoods (756)

Everglades (761)

Big Cypress (762)

Miami Ridge/Atomic Coastal Strip (763)

Southern Coast and Islands (764)

Level IV Subecoregions for Florida

Small / Wadeable Streams

N

0 50 100 150 200 Miles

Map prepared April 18, 2002 by: the GIS subsection

Bureau of Watershed Management

Division of Water Resource Management

This map is a representation of ground condition and is

Not intended for delineations or analysis of the features shown.

For more information, contact devan.branscum@dep.state.fl.us

Griffith 1994
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Figure 5-2. Stream Bioregions of Florida.
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5.3 Refinement of Regionalization for Nutrient Criteria Development 

DEP used streams bioregions as a starting point for the development of nutrient criteria regions. 

Based upon the observed biological community resemblance within a bioregion, it is logical that 

these biologically-similar regions will have analogously similar responses to nutrient 

concentrations. However, subsequent evaluation of nutrient concentrations in the benchmark 

sites revealed additional spatial patterns; that is, the bioregions were not sufficiently homogenous 

with regards to nutrient concentration. Alternative nutrient regions were developed based on a 

consideration of the bioregions, ecoregions, geological formations (e.g., Bone Valley and Peace 

River, Hawthorn Formation), benchmark nutrient levels, geostatistical analysis, and drainage 

basins. 

Phosphate-bearing sands, clays and carbonate rocks occur at or near land surface across the 

northern tier of the eastern Florida panhandle and southward along the west-central portion of the 

Florida peninsula from the Georgia line to near Port Charlotte in southwest Florida (Figure 5-3). 

These mostly shallow marine sediments comprise the late Oligocene-Miocene-early Pliocene 

Hawthorn Group (approximately 25 to 4 million years old). Commercially-viable phosphate 

deposits occur within this formational group, and are currently mined in Hamilton County in 

North Florida and in Hillsborough, Polk, Manatee, Hardee and De Soto counties in southwest 

Florida. 

DEP recognized there was a sub-region of the Peninsula bioregion with exceptionally high 

natural phosphate levels during initial work to derive reference-based TMDLs for the Northern 

Lake Okeechobee tributaries. As part of the analysis, the DEP utilized an outlier analysis to 

exclude benchmark sites within the Peninsula bioregion with exceptionally high phosphorus 

levels, and the vast majority of the excluded data were from the Bone Valley. This naturally 

high phosphate area is in portions of Hillsborough, Polk, Hardee, and Manatee, DeSoto, Sarasota 

counties (due to natural phosphatic deposits, which occur primarily in the Peace River Formation 

and the Bone Valley Member). The Bone Valley Member (originally the Bone Valley 

Formation of Matson and Clapp 1909), and the Peace River Formation occurs in a limited area 

on the southern part of the Ocala Platform in Hillsborough, Polk and Hardee Counties (Figure 5-

4). 

Throughout its extent, the Bone Valley Member is a clastic unit consisting of sand-sized and 

larger phosphate grains in a matrix of quartz sand, silt and clay. The lithology is highly variable, 

ranging from sandy, silty, phosphatic clays and relatively pure clays to clayey, phosphatic sands 

to sandy, clayey phosphorites (Webb and Crissinger 1983). In general, consolidation is poor and 

colors range from white, light brown and yellowish gray to olive gray and blue green. Mollusks 

are found as reworked, often phosphatized casts. Vertebrate fossils occur in many of the beds 

within the Bone Valley Member. Shark's teeth are often abundant. Silicified corals and wood 

are occasionally present as well. 

The Bone Valley Member is an extremely important, unique phosphate deposit and has provided 

much of the phosphate production in the United States during the twentieth century. Mining of 

phosphate in the outcrop area began in 1888 (Cathcart 1985) and continues to the present. 

Phosphatic pebbles are observed in the streams and rivers within the area of this formation.
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Figure 5-3. Miocene-age phosphate-bearing formations in Florida (green areas) from Scott, 2000.
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Figure 5-4. Location of the Peace River and Bone Valley geologic formations.
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In the eastern panhandle region of Florida between the Apalachicola River to the west and the 

Suwannee/Withlacoochee River valley to the east, the Torreya Formation (part of the Hawthorn 

Group) underlies the northern panhandle “Red Hills” region, also referred to as the Tallahassee 

Hills or Northern Highlands (Figure 5-5). The Torreya Formation is characterized by an upper 

unit comprised of white to olive-gray clayey sands, and a phosphate-bearing lower carbonate 

unit, all overlain by Pliocene-age Miccosukee Formation sands and clays, which are partially 

derived from reworked Torreya and other older sediments. The Miccosukee Formation is a 

prodeltaic deposit which grades westward into the Citronelle Formation in central Gadsden 

County. These formations lie to the north of the Gulf Coastal Plain, and are separated from it by 

the Cody Escarpment, a paleo-shoreline feature created during a past high sea level “stillstand”. 

The Cody Escarpment represents a marked topographical break, with relatively low elevation 

flat, sandy Coastal Plain sediments to the south, and elevated rolling “Red Hills” to the north. 

The Torreya Formation (primarily exposed in creek beds and other mid to low elevation areas of 

the Northern Highlands) and to a lesser extent the overlying Miccosukee & Citronelle 

Formations (exposed at higher elevations within the Northern Highlands) are the most likely 

sources of naturally-derived phosphorus in surface waters of the region. 

Whereas several major rivers and streams (notably the Ochlockonee, St. Marks and Aucilla 

Rivers) flow through the Northern Highlands, across the Cody Escarpment and south to the Gulf 

of Mexico, many streams are captured by karst swallets just north of the Escarpment, recharging 

the Floridian aquifer system. Rivers which cross the Cody Escarpment (or whose headwaters 

are at or near the Escarpment) bring with them possible phosphorus-bearing sediments and 

dissolved phosphorus in the water column derived from upstream Hawthorn Group sources. 
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Figure 5-5. Hawthorn Group outcrop area (bright green areas along the Florida – Georgia border) in the 

eastern panhandle region of Florida from Scott et al, 2001.

DEP refined the stream bioregions of Florida into nutrient regions based on a review of spatial 

nutrient patterns, primarily phosphorus. Spatial patterns in phosphorus do not entirely 

correspond to the bioregional divisions. DEP initially used the geographic distribution of the 

Peace River Formation and Bone Valley member to delineate a more homogeneous nutrient 

region hereafter known as the Bone Valley region (DEP 2009b). The area was delineated by 

overlaying the geological formation with Hydrologic Unit Codes (HUC) 8 and 12 drainage basin 

GIS coverages. Drainage basins that significantly overlap either formation and drainage basins 

downstream of the formations were included in the region. The Bone Valley region extended 

from the Peace River drainage to the east and south, and Hillsborough River to the north. The 

Hillsborough River, excluding its headwaters in HUC 0310020802002, was included in the Bone 

Valley region. The characteristics of the Hillsborough River headwaters are dominated more by 

the Green Swamp than by the Peace River Formation. The remainder of the Hillsborough River 

is highly influenced by streams draining the Peace River Formation, and these segments are most 

appropriately categorized as part of the Bone Valley region. 

In addition to the Bone Valley region, DEP initially identified a second region with high natural 

phosphorus, located in north central Florida in portions of the Northeast, Panhandle, and 

Peninsula bioregions (Figure 5-6). Ordinary kriging analysis, conducted in the Geostatistical 
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Analysis add-in for ArcGIS, was used to further explore nutrient spatial patterns and help inform 

delineation of more homogenous regions. The kriging algorithm was used to produce contour 

plots of expected stream nutrient levels (Figure 5-6). These contours represent patterns of high 

and low phosphorus concentrations across the state. 

The contours were not used to directly define regional boundaries due to data density limitations 

and the fact that the statistical model does not take into account flow patterns or watershed 

boundaries. Instead, the contours, together with the spatial distribution of benchmark WBID TP 

levels and geologic information, were used to inform decisions regarding where to combine 

watersheds based on similarity in nutrient conditions. Stream TP contour plots were overlaid 

with GIS drainage basins to refine the regionalization in north Florida. Drainage basins (12-digit 

HUCs) overlapping the high natural phosphate were grouped into a new region named the North 

Central region. 
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Figure 5-6. Stream total phosphorus contour plot based on ordinary kriging analysis of benchmark 

stream concentrations across Florida.
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DEP also evaluated the spatial patterns of stream nitrogen concentrations following the same 

general procedure for stream phosphorus concentrations. While TN levels in Florida’s streams 

are not as spatially heterogeneous as are the phosphorus levels, TN levels were generally lower 

in the Panhandle than the rest of the state (Figure 5-7). Areas of relatively high TN corresponded 

to wetland dominated drainages (e.g., Green Swamp, Okefenokee). DEP (2009b) combined the 

Northeast, North Central, Peninsula, and Bone Valley (West Central) regions for purposes of 

nitrogen criteria development because the benchmark streams in these regions exhibited similar 

TN concentration levels, in particular at the upper end of the frequency distributions (Figure 5-

8).

Figure 5-7. Stream total nitrogen contour plot based on ordinary kriging analysis of benchmark 

stream concentrations across Florida.
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Figure 5-8. Boxplot of annual geometric mean total nitrogen concentrations in benchmark Florida streams. 

EPA considered the previous work by DEP on bioregions, nutrient regions, and sub-regions to 

develop a regional stream nutrient classification approach that addressed the natural variations in 

nutrient concentrations (including underlying geology) and reflected the understanding that 

upstream water quality affects downstream water quality. The resulting watershed-based 

classification enabled EPA to address the effects of TN and TP within streams, as well as the 

effects of TN and TP from streams that discharge into downstream lakes or estuaries in the same 

watershed. EPA classified Florida’s streams north of Lake Okeechobee, but including the 

Caloosahatchee drainages to the west of the Lake and St. Lucie and Loxahatchee drainages to the 

east, into Nutrient Watershed Regions (NWR). This was accomplished using WBID descriptions 

and verifying with drainage basin boundaries. The resulting NWRs reflect inherent differences in 

the natural factors that influence nutrient concentrations in streams (e.g., geology, soil 

composition, hydrology). 

Based on these analyses, and considerations based on the Florida stream bioregions, EPA 

initially proposed four NWRs (U.S. EPA 2010a). DEP and EPA identified geographic areas of 

the state having phosphorus-rich soils and geology, such as the northeastern part of Florida (i.e., 

Region

Northeast

North Central

Panhandle

Peninsula

Bone Valley

Total Nitrogen (mg/L)

0.0

0.5

1.0

1.5

2.0

2.5
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the northern Apalachee River watershed and the northern Suwannee River watershed), and the 

area to the east of Tampa Bay. These areas are classified as separate NWRs [i.e., North Central 

and West Central (Bone Valley] because the naturally phosphorus-rich soils in these areas 

significantly influence stream phosphorus concentrations in these watersheds. 

Following its initial proposal and based on comments received from the DEP (DEP 2010a), EPA 

revisited its exploration of underlying geological detail in the Panhandle and its relationship to 

observed patterns in stream chemistry. EPA took into account the portion of the Hawthorn 

Group that lies in the eastern portion of the Panhandle Region (Figure 5-5) and explored 

delineation of the Panhandle Region along watershed boundaries into east and west regions. EPA 

concluded that higher TP concentrations were consistently associated with least-impacted 

streams in the eastern part of the Panhandle and this pattern could be explained by the underlying 

geology. EPA explored how well such a revised regionalization explained observed variability 

in TP concentrations relative to the proposed regionalization. EPA used a linear regression model 

to compare the variance in TP concentration explained by a four region model versus that 

explained by splitting the Panhandle into an east and west region along the Apalachicola River 

basin watershed boundary. Using either Benchmark Population or SCI Population approach, 

splitting the Panhandle Region into east and west regions explained more variability in TP 

concentrations than the original four stream bioregion model. This led EPA to conclude that a 

revision was necessary to divide the proposed Panhandle Region into two new regions—the 

Panhandle East, delineated at the western edge by the Apalachicola River watershed, and at the 

eastern edge by the Suwannee River watershed (or North Central NWR). EPA referred to this 

region as the Panhandle East and has effectively reduced in size the proposed Panhandle Region 

resulting in a Panhandle West NWR (Figure 5-9; EPA 2010b).
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EPA’s final West Central NWR excluded portions of western Manatee, Sarasota and Charlotte 

Counties (i.e., Sarasota Bay and Lemon Bay watersheds). As shown in Figure 5-9, EPA 

included these areas in the Peninsula NWR. However, recent scientific information not 

considered by EPA suggests that the Sarasota Bay and Lemon Bay Watersheds are more 

appropriately included in the West Central Nutrient Watershed Region NWR. The most recent 

and thorough analysis of geology in this area was published by Arthur et al. (2008). The 

information contained within the 2008 geologic report clearly shows that phosphorus rich 

deposits are present in western Manatee, Sarasota and Charlotte Counties in both of the major 

coastal Estuarine Drainage Areas. Inspection of Plates 16-19 (Figure 5-10) clearly demonstrates 

that portions of the Hawthorn Formation as well as phosphatic sand and gravel deposits occur 

near the land surface where they are readily available to naturally enrich surface waters (Figures 

5-11 through 5-14). DEP evaluated this more recent information and determined that it 

Figure 5-9. Map of EPA’s stream classification by NWRs used in final rule.
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supported extending the West Central NWR to include the Sarasota, Don and Roberts, and 

Lemon Bay watershed. 

Figrue 5-10. Cross section locations from Arthur et al. (2008). Image cropped from Plate 1 to show 

area of Sarasota and Charlotte Counties.
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Figure 5-11. Plate 16 from Arthur et al. (2008). 
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Figure 5-12. Plate 17 from Arthur et al. (2008). 
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Figure 5-13. Plate 18 from Arthur et al. (2008). 
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Figure 5-14. Plate 19 from Arthur et al. (2008). 
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DEP also received comment that the areal extent over which water quality is influenced by 

naturally occurring phosphorus deposits in Alachua County is more extensive than that depicted 

by the Nutrient Watershed Regions (NWR) map included in the final EPA rule (Figure 5-9). 

Streams in portions of Alachua County (e.g., Sweetwater Branch, Hogtown Creek, Hatchet 

Creek) cut through the phosphorus rich Hawthorn Group and discharge to the Floridan aquifer 

through active sink holes (Figures 5-10 and 5-11). These stream to sink watersheds do not 

influence other Peninsula watersheds (e.g., St. Johns River), but rather are hydrologically 

connected to the Santa Fe River via ground water and spring discharge. Based on these facts, 

DEP concluded that it was appropriate to move the Watermelon Pond, Hogtown Creek, Ledwith 

Lake, Newberry Drain, and Paynes Praire drainages out of the Peninsula NWR and into the 

North Central. Drainage basin boundaries were defined based on USGS HUC 12 boundaries. 

DEP also concluded that it would not be appropriate to move the Newnans Lake watershed (e.g., 

Hatchet Creek) because the lake may at times be hydrologically connected to the St. Johns River 

watershed via Camps Canal. 

Figure 5-10. Hawthorn Group outcrop area in central Alachua County from Scott et al, 2001.
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Figure 5-11. Hydrologic connections in the Alachua County stream to sink subregion.

DEP modified the final NWRs adopted by EPA (Figure 5-9) based on considerations of more

recent geological information and watershed connections. The changes increased the areas of 

both the North Central and West Central NWRs. There were no stream benchmark sites within 

the modified areas; therefore, the numeric threshold calculations were unaffected by the changes. 

DEP’s final NWRs are as depicted in Figure 5-12. 
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Figure 5-12. Map of DEP’s NWR stream classification used in final rule.
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6 Stressor-Response Analyses for Florida Streams 

6.1 Introduction

As specified in EPA’s guidance and acknowledged in Florida’s Nutrient Criteria Development 

Plan, the most comprehensive and scientifically defensible approach to developing numeric 

nutrient criteria is to relate nutrient concentrations to dependably measured adverse biological 

responses. EPA further suggests that the observed dose-response relationship could be described 

by a model (e.g., trophic state classification, regional predictive model, biocriteria, etc.), which 

in turn would quantitatively link nutrient concentrations to the relative risk of environmental 

harm. DEP supports this approach, since it establishes a correlative relationship between 

nutrients and valued ecological attributes, and is linked to the maintenance of designated uses of 

waterbodies.

In attempting to define the effect of anthropogenic nutrient increases on the biological 

communities in Florida’s streams, DEP conducted extensive statistical evaluations to investigate 

the relationship between nutrients and biological indices such as the Stream Condition Index 

(SCI), and the Stream Diatom Index (SDI) (currently under development) as well as the 

individual metrics that comprise these indices. DEP also evaluated the effects of increased 

nutrient levels on other biological measures such as chlorophyll a, taxonomic composition of 

macroinvertebrate and algal communities, and frequency of occurrence and abundance of algae 

(as measured via the Rapid Periphyton Survey, RPS). 

To investigate the potential relationships between nutrients and biological response measures, 

DEP utilized a variety of statistical techniques, including linear regression, multiple linear 

regression, non-linear regression, LOESS regression, change point analysis, CART, correlation 

analysis, and paired variable plots. A brief discussion of the results from some of the 

assessments performed is provided below, with more detailed information concerning the 

analyses provided in the referenced appendices. 

The results of the analyses generally indicate that many of the biological measures evaluated 

exhibit a significant adverse response to anthropogenic nutrient enrichment. However, the 

statistical relationships between the biological response variables and nutrient levels are weak, 

and DEP could not identify specific thresholds for establishing numeric nutrient criteria from the 

analyses. The direct and indirect adverse effects of nutrient enrichment on biological 

communities have been demonstrated repeatedly under controlled conditions (Stevenson et al.

2007). The analyses did not show strong statistical relationships between nutrients and these 

effects. This may be because the biological responses can be confounded by numerous other 

factors (including low residence time for uptake) and confounding variables under real world 

conditions found in natural streams. This is especially true for Florida streams, which can range 

from:

• crystal clear spring fed streams with low nutrient levels and high conductivity, to 

• highly colored streams fed by wetlands with an abundance of organic nitrogen, to 
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• streams that exhibit naturally high phosphorus levels resulting from geologic phosphate 

deposits lying near the surface, to 

• streams that can be any combination of the above. 

The source water and the geologic conditions also influence other water quality variables such as 

pH and specific conductivity, which can have an overarching effect on the biological 

communities, which in-turn can alter or be more significant than the response to nutrients. In 

addition, most Florida streams are heavily canopied with the resulting light limitation also 

confounding the biological response to nutrients. 

Similar results were reported by Robertson et al. (2008) who found good relationships between 

nutrient enrichment and various adverse biological responses in open non-wadeable Wisconsin 

rivers. The relationships were much weaker in smaller wadeable streams due to the influence of 

other confounding environmental factors, with nutrients alone explaining only a small portion of 

the response (Robertson et al. 2006). Most of the biological measures exhibited a wedge-shaped 

response to increases in nutrient concentrations. At relatively low nutrient concentrations, the 

biotic indices ranged widely, but at relatively high concentrations, the indices generally were 

poor. The wedge-shaped distribution indicates that at low nutrient concentrations, factors other 

than nutrients often limit the health of biotic communities, whereas, at high nutrient 

concentrations, nutrients and factors correlated with high nutrient concentrations are the 

predominant factors. Simply stated, it is difficult to find a healthy population at a nutrient 

enriched site, but common to find a poor population at low nutrient sites due to the influence of 

other factors.

This type of response is not surprising given the fact that the biotic community represents the 

overall ecological integrity of the stream (i.e., physicochemical habitat and biotic integrity) and 

thus provides a broad measure of the cumulative effect of all stressors (Barbour et al. 1999). The 

physicochemical habitats within the streams are in turn controlled by watershed characteristics 

such as geomorphology, geochemistry, hydrology, and land use/land cover and are therefore 

important factors affecting the biotic communities present. In Florida streams, the response to 

nutrients is confounded by broad ranges of biologically important physicochemical parameters 

such as pH, color, and specific conductance that occur naturally, sometimes at small spatial 

scales. As Robertson et al. (2006) concluded, even with these confounding factors, it is 

important to establish numeric nutrient criteria to reduce the risk of adverse effects on the 

biological communities in the water body as well as in downstream receiving waters.

In addition to the inherent variations in the confounding physicochemical factors described 

above, Florida streams also exhibit a very wide range of nutrient levels, especially phosphorus, 

that occur regionally under natural conditions. Phosphorus concentrations can naturally range 

from less than 10 ppb (highly oligotrophic) to many hundred ppb where the waterbody is in 

direct contact with geologic phosphate deposits (see Chapter 5: Regionalization). To account for 

the natural spatial variation in nutrient concentrations, the evaluation of the biological response 

to nutrients was conducted on a regional basis where there were sufficient data. A summary of 

the individual analyses conducted is provided below.
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6.2 Macroinvertebrate Exploratory Analysis

The relationship between the macroinvertebrate community and nutrient levels in streams was 

examined using Florida’s Stream Condition Index (SCI), the individual measures of the 

macroinvertebrate community that comprise the index, and the abundance of other taxonomic 

groupings. Chapter 3 has an explanation of the various biological measures. Multiple linear 

regression techniques were used to evaluate potential relationships between nutrient 

concentrations and the measures of the macroinvertebrate community. 

The results of the analyses indicate that several macroinvertebrate variables, including SCI, % 

sensitive taxa, % clinger taxa, % clingers, % long lived taxa, % very tolerant taxa, and % 

dominant taxa, exhibited a significant adverse response to increased levels of nitrogen. In 

addition, the analyses indicated that the macroinvertebrate community also responded to other 

environmental factors such as water color, conductivity, and pH. It is likely that other 

unmeasured variables such as water velocity, water level, habitat conditions, and antecedent 

conditions also play an important role in determining the macroinvertebrate community 

composition. The adjusted r-squared values for the multiple-linear regression models predicting 

the macroinvertebrate responses ranged from 0.05 to 0.55 in the models that included some form 

of nitrogen and a combination of the other environmental factors. Due to the confounding 

effects of these other factors, the macroinvertebrate response to nutrients was statistically weak 

and could be depicted by a wedge-shaped relationship as described previously. 

Figure 6-1 shows the typical relationship observed between nutrient levels and macroinvertebrate 

response variables. At low nutrient levels, the response is highly variable due to the controlling 

effects of other factors such as pH, conductivity, color, flow, water level, habitat conditions, etc. 

The analyses also demonstrate that the macroinvertebrate community responds negatively to 

increased nutrient levels, however, due to the influence of other environmental factors on the 

response, DEP could not identify specific thresholds for establishing numeric nutrient criteria. 

The macroinvertebrate analyses and results are presented in greater detail in Appendix 6-A.
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Figure 6-1. Example relationships between macroinvertebrate response variables and nutrient levels. TSIN is 

Total Soluble Inorganic Nitrogen (ammonia plus nitrate-nitrite).

 

6.3 Exploratory Analysis of Periphyton Response to Nutrients

Since the algal community can be expected to respond more rapidly and directly to nutrients than 

macroinvertebrates, DEP also evaluated the relationship between the periphyton community and 

nutrient levels in streams. The primary focus of the evaluation was the Florida’s Stream Diatom 

Index (SDI) (see Chapter 3), which is currently under development, and the five individual 

component metrics that comprise the SDI (i.e., % pollution sensitive, % pollution tolerant, % 

taxa requiring high dissolved oxygen (DO), % oligosaprobic taxa, and average of Van Dam 

trophic taxa score), but DEP also evaluated other taxonomic groupings (i.e., phosphorus and 

nitrogen sensitive diatoms). 

A combination of multiple-linear regression techniques and change point analyses were used to 

evaluate potential relationships between nutrient concentrations and the algal response variables. 

The analyses also evaluated the role of other environmental variables such as color, pH, and 

conductivity in determining the algal response. The analyses were conducted on both the raw 

untransformed data as well as data transformed by various techniques. To control the variability 

caused by natural regional differences in nutrient concentrations, the analyses were conducted 
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for each nutrient region independently, starting with the panhandle and peninsula regions, for 

which more data exist.

As with the macroinvertebrate community, the analyses of the periphyton data indicate that 

several algal variables exhibited a significant adverse response to increased nutrient levels. In 

addition, the analyses indicated that periphyton also respond to other environmental factors such 

as water color, conductivity, and pH. However, other unmeasured variables such as water 

velocity, water level, and antecedent conditions likely play an important role in determining the 

composition of the periphyton community. Since the biota are responding to the combined effect 

of all of these factors along with nutrients, the natural variation in these other environmental 

factors confound the observed biological response to nutrients. 

The results of the multiple-linear regression analysis for the panhandle nutrient region are 

summarized in Table 6-1. Due to the confounding interactions between the factors included in 

the models as well as those for which data are not available, all of the adjusted r-squared values 

for the regression equations were below 0.3. The results of the multiple-linear regression 

analyses are provided in more detail in Appendices 6-B and 6-C. 

Since the regression analyses showed that various forms of nutrients were significant factors in 

determining the periphyton response, change point analyses were performed to attempt to 

determine where significant thresholds in the periphyton response to nutrients occurred. Prior to 

performing the change point analyses, the data were adjusted for the other significant variables 

indicated by the regression analyses. An example of the step function and associated change 

point and confidence interval is provided in Figure 6-2, and the results of the change point 

analyses for the periphyton response variables versus TP are summarized in Table 6-2. 

Table 6-1. Summary of multiple-linear regression analyses conducted for the panhandle nutrient region.

PollSens PollTol %Tol low DO % Oligosap VD TSI TP Sens Dia TIN Sens Dia N Metab pH Optima SDI

Data 

Transformation None SQRT arcsin(sqrt(x)) arcsin(sqrt(x)) None None None Recip Recip arcsin(sqrt(x))

1 pH pH pH pH pH CondL pH pH pH pH

2 ColorL ColorL TNL TNL CondL pH TNL

3 TNL TNL TINL TPL

4 TPL TPL TPL TINL

5 ColorL

Adjusted r2

= 0.25 0.10 0.08 0.10 0.27 0.20 0.18 0.11 0.13 0.12

Yellow shading = Pr < 0.05

Response Variable

Panhandle Nutrient Region
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Figure 6-2. Change-Point Model (Step Function) of percent pollution sensitive diatoms vs. total phosphorus. 

Response adjusted for ColorL and CondL.

Table 6-2. Summary of change-point analyses conducted for the panhandle nutrient region.

 

The primary change points indicated in Table 6-2 ranged from 10 ppb to 22 ppb TP and were all 

significant at the 95% level. However, all of the primary change points occurred well below TP 

levels found at the majority of Florida’s minimally disturbed nutrient benchmark sites (see 

Total-P (mg P/L)

Poll sensitve diatoms (Adjusted)

0.0 0.05 0.10 0.15 0.20

20

40

60

80

100

120

0.022

0.083

0.022

PollSens PollTol % high DO % Oligosap VD TSI TP Sens Dia TIN Sens Dia SDI N Metab pH Optima

Data Transformation None log arcsin (sqrt) arcsin (sqrt) sqrt arcsin(sqrt) None

Model SF NL SF SF SF SF SF SF SF SF

1st Change Pt. 

[confidence interval]

22 ppb 

[22,83]

10 ppb 

[8,78]

15 ppb 

[10,93]

18 ppb 

[15,131]

22 ppb 

[22,40]

22 ppb 

[10,60]

22 ppb 

[10,67]

22 ppb 

[22,40]

15 ppb 

[15,93]

22 ppb 

[10,36]

2nd Change Pt. 

[confidence interval]

 83ppb 

[10,131]

78 ppb 

[8,94]

90 ppb 

[18,149] 131 ppb [*] 131 ppb [*] NA 67 ppb [8,129]

82 ppb 

[8,131] 131 ppb [*] NA

2nd Change Pt. 

Confidence Level 75% 80% 80% 80% 80% NA 75% 80% 80% NA

SF = Step Function, NL = Non-Linear, L = Linear, NCP = No Change Point

[*] = Extreme interval outside data range

Response Variable
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Chapter 7) and appear to be highly influenced by factors other than nutrients. Most notably, 

these data were analyzed using two different laboratory method detection limits (MDLs for TP of 

4 μg/L and 20 μg/L), and this inconsistency appeared to have significantly influenced the results. 

The confidence intervals around the change points were also very wide, further limiting their 

usefulness in criteria development. 

For most periphyton variables, a second higher change point was also detected. The secondary 

change points ranged from 67 ppb to 131 ppb TP, however, they were not significant at the 95% 

confidence level. These changes were significant at the 75 to 80% level with extremely wide 

confidence intervals. The change point analyses conducted are presented in greater detail in 

Appendix 6-D.

As with the analyses of the macroinvertebrate data, the results of the periphyton data analyses 

establish that increased nutrient levels have adverse impacts on the periphyton community, 

however, the response to nutrients is confounded by other environmental factors and DEP could 

identify no clear thresholds to form the bases of numeric nutrient criteria. 

6.4 Quantile Regression Analysis of Periphyton Response to Nutrients

As described previously, the biological response to nutrients is confounded by other measured 

environmental parameters as well as a number of unmeasured factors. Often, these confounding 

variables cause the biological measures to exhibit a wedge-shaped response to increased nutrient 

levels. At relatively low nutrient concentrations, the biological measures range widely, but at 

relatively high concentrations, the measure is generally poor with less variation. The wedgeshaped distribution indicates that at low nutrient concentrations, factors other than nutrients often 

limit the health of biotic communities, whereas, at high nutrient concentrations, nutrients and 

factors correlated with high nutrient concentrations are the predominant limiting factors. As

found with the analyses of the macroinvertebrate and periphyton data, linear regression and stepfunction models do not fit this type of response very well. Even though the linear regression and 

change-point analyses indicate that increased nutrient levels have negative biological effects, it is 

important to confirm this finding using statistical techniques better suited to describing complex 

relationships such as those found with nutrient effects on biological communities. Therefore, 

quantile regression was used to confirm that increased nutrient levels result in adverse biological 

responses in Florida streams. 

Quantile regression is a useful method for estimating effects associated with a measured subset 

of limiting factors while accounting for the effects of unmeasured factors in an ecologically 

realistic manner (Cade et al. 1999). This regression technique considers changes in a biological 

response variable (e.g., species biomass) as a function of limiting factors (e.g., habitat 

conditions) that are measured and as a function of other limiting factors (e.g., non-habitat factors 

such as weather and disease) that may not be measured. In this example, Cade et al. (1999) note 

that change in species biomass does not exceed limits imposed by the habitat conditions, but can 

be reduced by non-habitat factors. 

The quantile regression approach was applied to the stream periphyton data from the Florida 

panhandle nutrient region by fitting models to the 0.50 and 0.85 quantiles, with the periphyton 
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metrics used in the earlier analysis as dependent variables and TP, TN, TIN, and specific 

conductance as independent variables. The logarithm (Base e) transformation was performed on 

the four predictor variables, and the periphyton metrics were transformed using the 

transformations chosen in the earlier analyses (Table 6-1). The quantile regression approach was 

repeated for periphyton data from the peninsula nutrient region, with models fitted to the 0.50 

and 0.90 quantiles with the same periphyton metrics as dependent variables and TP, TN, TIN, 

and pH as independent variables. In the peninsula analysis, pH was substituted for conductivity 

as an independent variable based on the higher significance of pH found in the earlier analyses. 

Detailed results of the quantile regression analyses are provided in Appendices 6-E and 6-F for 

the Panhandle and Peninsula, respectively. The results for the Panhandle indicated that all of the 

periphyton metrics exhibited significant responses to both total phosphorus and conductivity. In 

addition, the 0.50 quantile for some of the metrics (i.e., TP sensitive diatoms and TIN sensitive 

diatoms) showed significant responses to total nitrogen. An example of the quantile regression 

analysis using SDI as the dependent variable is provided in Figure 6-3. 

In contrast, the results for the peninsula indicate that total nitrogen was the primary nutrient 

influencing the periphyton community in that region. This is not unexpected since the 

phosphorus-rich geology of the peninsula results in much higher natural background phosphorus 

levels when compared with the panhandle. The higher natural phosphorus levels would result in 

less phosphorus limitation in the biological community response in the peninsula. In addition to 

total nitrogen, several periphyton metrics (i.e., Van Dam’s average taxa TSI, average Nmetabolism score, and pH optima score) showed a significant relationship to total inorganic 

nitrogen and some metrics (i.e., % pollution tolerant taxa and % TIN sensitive taxa) were 

significantly correlated to total phosphorus. The analysis also indicated that all of the periphyton 

metrics were influenced by pH. The previous analysis showed that periphyton in the peninsula 

responded more to pH than conductivity, which was the dominant non-nutrient factor in the 

panhandle. As with phosphorus, natural conductivity levels in the peninsula are typically higher 

than those found in the panhandle, which may result in pH exerting a greater influence on the 

peninsula diatoms. However, conductivity and pH are generally highly correlated. 

Figure 6-3. Example of results from quantile regression analyses showing highly significant relationships 

between the Stream Diatom Index and both total phosphorus and conductivity levels.

 

0 100 200 300 400 500

0

20

40

60

80

100

 

0.00 0.05 0.10 0.15 0.20

0

20

40

60

80

100

 

 

 

 

 
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Despite the confounding effects of other physicochemical variables, these results confirm that 

nutrient enrichment, primarily total phosphorus in the panhandle and nitrogen in the peninsula, 

has a significant negative effect on periphyton composition in Florida streams. Although DEP 

could not identify specific thresholds for establishing numeric nutrient criteria, the analyses 

provide further support for the need for nutrient criteria in order to protect against adverse 

biological effects.

6.5 Analysis of Rapid Periphyton Survey Data 

Because excessive abundance of algae has been identified as a nuisance condition that should be 

avoided (see Chapter 4), the role of nutrients in determining the frequency of occurrence and 

abundance of algae in streams was also examined (in addition to the compositional metrics for 

the periphyton community described previously). The abundance of periphyton found in streams 

was evaluated using the Rapid Periphyton Survey (RPS) method, consisting of a series of 99 

observations (9 observations across each of 11 transects along a 100 meter stream reach) of 

periphyton presence, thickness and type, per DEP-SOP-001/01 FS 7130. The RPSs were 

conducted in streams across the state in conjunction with nutrient and other physicochemical 

measurements.

Correlation analysis was conducted to determine which nutrient and physicochemical variables 

exhibited the greatest influence on algal abundance in streams. Due to data limitations, the 

analysis of the algal abundance data was conducted on data collected over the entire state and not 

separated regionally. The results of the analyses are summarized in Table 6-3. The analyses 

indicated that canopy cover, color, pH, total Kjeldahl nitrogen, organic nitrogen, and nitratenitrite were the factors most highly correlated to algal thickness. The strong correlations with 

color and canopy cover were expected since they can cause light limitation and therefore directly 

reduce periphyton growth. The relatively strong correlations between pH, TKN, and organic 

nitrogen and algal biomass were unexpected, but can likely be explained by their strong 

correlations with color (Spearman correlations were 0.79, 0.80, and -0.52, respectively). 
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Table 6-3. Correlation matrix for algal abundance measures from rapid periphyton assessment data vs. 

nutrients and other physicochemical parameters. Darker green shading indicates stronger positive 

relationships and darker red shading indicates stronger negative relationships. Values given are Spearman r 

values.

Sum of 

Algal 

Thickness

Mean Algal 

Thickness

Maximum 

Algal 

Thickness

Sum of 

Algal 

Thickness 

Scores

Mean 

Algal 

Thickness 

Score

Maximum 

Algal 

Thickness 

Score

Sum of 

Algal Type 

Score

Mean 

Algal Type 

Score

Maximum 

Algal Type 

Score

Sum of 

Algal 

Scores

Mean 

Algal 

Score

Maximum 

Algal 

Score

Chlorophyll a 0.14 0.22 0.18 0.12 0.22 0.16 0.08 0.16 0.16 0.1 0.16 0.14

Canopy Cover -0.29 -0.38 -0.38 -0.26 -0.36 -0.37 -0.25 -0.35 -0.36 -0.28 -0.35 -0.35

Maximum 

Canopy Cover

-0.18 -0.26 -0.25 -0.15 -0.24 -0.24 -0.15 -0.23 -0.23 -0.16 -0.22 -0.22

Minimum 

Canopy Cover

-0.32 -0.41 -0.42 -0.29 -0.39 -0.4 -0.27 -0.37 -0.4 -0.31 -0.38 -0.39

Total Nitrogen -0.11 -0.05 -0.09 -0.11 -0.06 -0.12 -0.10 -0.07 -0.08 -0.13 -0.10 -0.13

Total 

Inorganic 

Nitrogen

0.18 0.22 0.18 0.17 0.22 0.17 0.13 0.17 0.16 0.13 0.16 0.14

Organic 

Nitrogen -0.33 -0.29 -0.33 -0.32 -0.30 -0.36 -0.26 -0.25 -0.25 -0.31 -0.29 -0.32

Ammonia -0.19 -0.16 -0.21 -0.18 -0.15 -0.23 -0.20 -0.19 -0.20 -0.25 -0.23 -0.26

Total Kjeldahl 

Nitrogen -0.32 -0.28 -0.32 -0.32 -0.29 -0.35 -0.27 -0.25 -0.25 -0.32 -0.29 -0.33

Nitrate + 

Nitrite

0.23 0.29 0.26 0.22 0.29 0.36 0.19 0.15 0.25 0.20 0.25 0.23

Total 

Phosphorus -0.03 -0.04 -0.04 -0.03 -0.04 -0.03 -0.02 -0.04 -0.03 -0.03 -0.04 -0.05

Dissolved 

Oxygen

0.07 0.01 0.10 0.05 0.00 0.09 0.10 0.06 0.11 0.14 0.09 0.15

Conductivity 0.21 0.25 0.23 0.20 0.24 0.22 0.15 0.18 0.23 0.18 0.22 0.21

pH 0.22 0.24 0.24 0.21 0.24 0.24 0.18 0.20 0.24 0.21 0.24 0.24

Color -0.41 -0.42 -0.42 -0.40 -0.42 -0.42 -0.35 -0.36 -0.38 -0.38 -0.40 -0.41

Temperature 0.07 0.07 0.05 0.09 0.09 0.07 0.08 0.09 0.05 0.05 0.04 0.04

Velocity -0.01 0.00 0.04 -0.01 0.00 0.04 0.00 0.02 0.03 0.02 0.04 0.04
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Figure 6-4. Relationships between periphyton abundance and selected environmental predictors. The smooth 

curves are the locally weighted smoothing lines. The vertical lines are the change points (solid lines) and their 

confidence intervals (dashed lines).

As shown in Figure 6-4, periphyton thickness appears to be significantly correlated with nitratenitrite, canopy cover, and water color, but not with TP. It should be noted that color is a strong 

confounding factor in these analyses since it is correlated to some extent with canopy cover, 

nitrate-nitrite concentration, and TP concentration. The initial analyses of the rapid periphyton 

assessment data are provided in greater detail in Appendix 6-G.

To account for the confounding effect of color, streams were classified based on color, and the 

relationships between algal biomass and nutrients were re-examined. The relationships between 

nutrients and periphyton thickness were much stronger in clear streams (Figure 6-5 a1-a3) than 

in colored streams (Figure 6-5 c1-c3), indicated by the stronger spearman correlations as well as 
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improved LOESS regressions in the clear streams. The main relationships observed were 

between nitrogen parameters and benthic algal thickness. The effect of phosphorus did not 

appear to be significant in these analyses. Where color is increased (increased potential for light 

limitation), nutrients do not exert as important a role in algal biomass accumulations.

Figure 6-5. Relationships between nutrient concentrations and algal thickness in clear (color ≤ 40 pcu, 

Figures a1-3), intermediate (color > 40-100 pcu, Figures b1-3) and highly colored (color > 100 pcu, Figures 

c1-3) streams.
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Analysis was also conducted to evaluate the relationship between nutrients and phytoplankton 

chlorophyll a collected for the same sites. In contrast to algal biomass data, the relationships 

between phytoplankton chlorophyll a and nutrients and were much stronger in colored streams 

(Figure 6-6 b1-c3) than in clear streams (Figure 6-6 a1-a3), especially between total nitrogen and 

chlorophyll a concentrations. The Spearman correlations between chlorophyll a and total 

nitrogen were 0.36 and 0.42, respectively, in intermediate and high color streams, but was -0.16 

in clear streams. With the increased color and stronger light limitation to periphyton, 

phytoplankton appear more able to utilize the available nutrients than periphyton. Chlorophyll a 

appeared to respond somewhat to phosphorus, but all of the responses were statistically weak. 

Figure 6-6. Relationships between nutrient concentrations and chlorophyll a concentrations in clear (<=40 

pcu, Figures a1-3), intermediate (40-100 pcu, Figures b1-3) and highly colored (>100 pcu, Figures c1-3) 

streams.
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Further analysis of unshaded clear streams (i.e., color ≤ 40 and canopy cover ≤ 40%) indicate 

that TN and TIN were the variables having the strongest correlation with algal abundance 

measurements (Figure 6-7 a-c). The analysis also showed that TP concentration alone was not a 

strong predictor of the algal biomass rank (Figure 5-8). However based on the N:P ratios, N and 

P could be co-limiting algal growth in the stream system. To test this hypothesis, both N and P 

concentrations were ranked from 1 to 10 based on their range, mean, and standard deviation, and 

the ranked nutrient variables were combined (TN+ TP and NOx +TP) to test if the addition of TP 

improved the nutrient relationships with algal thickness. As indicated in Figure 6-7 d-e, the 

combined nutrient variables have a stronger correlation to algal thickness than the individual 

nutrient concentrations, potentially indicating N and P co-limitation for some streams. This 

finding also indicates that both nitrogen (TN and NOx) and phosphorus are significant in 

controlling algal thickness in streams. More details concerning these analyses are provided in 

Appendix 6-G.

Figure 6-7. The relationships between periphyton thickness and TN, TP, and NOx concentrations as well as 

combined ranks of nutrients in clear streams (transect canopy ≤ 40% and color ≤ 40 pcu). Each dot 

represents a stream reach. The dashed curves are the loess regression lines 95% confidence limits; and the 

vertical lines are change points and 95% confidence limits.
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6.6 Potential Use of Heterotrophic Bacteria as a Response Variable 

DEP also considered whether nutrient enrichment could be manifested in communities other than 

primary producers and macroinvertebrates in streams. For example, Mallin et al. (2006) note 

that heterotrophic organisms in nutrient enriched streams can increase Biochemical Oxygen 

Demand (BOD), and therefore, have the potential to contribute to hypoxia; however, these 

researchers did not argue that such heterotrophic organisms should be used as a key response 

variable for nutrient criteria development. In fact, the authors’ main conclusion was that nutrient 

criteria should be site-specific, and take into account potential hypoxia caused by increased 

heterotrophic bacteria respiration in certain highly colored waters. Mallin et al. (2006) noted 

that, although the effect of nutrients on photosynthesis and algal abundance is typically 

moderated by the highly colored conditions, nutrient criteria should still apply to such waters. 

Consistent with this concept, which EPA also embraced, DEP applied the numeric nutrient 

standards to blackwater streams. 

The DEP notes that some conclusions by Mallin et al. (2006) were not supported by the data they 

presented. For example, Colley Creek (an undisturbed reference stream), had average TP, TN 

and BOD concentrations of 0.035 mg/L, 0.998 mg/L, and 1.1 mg/L, respectively. Two 

anthropogenically enriched blackwater streams (Black River and Cape Fear River) were 

approximately two to three times higher in TP (0.089 and 0.070 mg/L), but had similar BOD 

values to the reference site (0.9 and 1.0 mg/L). This observation does not appear to support the 

conclusion that all streams with anthropogenic nutrient enrichment are routinely characterized by 

BODs higher than the reference condition. DEP employs an independent DO criterion to ensure 

that DO does not interfere with healthy, well-balanced communities, and will continue to 

determine how nutrients influence DO on a site specific basis. 

6.7 Conclusions

DEP has conducted multiple analyses using a variety of statistical techniques to investigate the 

effects of anthropogenic nutrient increases on the biological communities in Florida’s streams. 

These analyses were performed to define relationships between nutrients and biological response 

variables that could be used to develop numeric nutrient criteria. These analyses evaluated the 

influence of nutrients on DO, biological indices such as the Stream Condition Index, the Stream 

Diatom Index (currently under development), the individual metrics that comprise these indices, 

and other biological measures such as chlorophyll a, taxonomic composition of 

macroinvertebrate and algal communities, and frequency of occurrence and abundance of algae 

(RPS). 

The results of the analyses generally indicate that many of the biological measures evaluated 

exhibit a statistically significant adverse response to nutrient enrichment, however, the 

relationships between the biological response variables and nutrient levels were confounded by 

numerous other factors such as color, pH conductivity, and canopy cover. The confounding 

effects of these other variables result in weak statistical relationships between measures of the 

biological communities and nutrient levels. While DEP believes the effect of nutrients on the 

biological communities is not clear enough to be used as the sole basis for establishing numeric 

nutrient criteria, the observed relationships between nutrients and the various biological 

measures demonstrate the need for nutrient criteria to prevent adverse biological effects in 

Florida streams.
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The statistical significance indicates that numeric nutrient criteria should be established and 

supports the decision to implement an alternative approach to deriving protective criteria, the 

Nutrient Benchmark Distribution Approach that is described in the next chapter. While the 

analysis in this chapter did not produce numeric thresholds that could be used as water quality 

criteria, the relationships that were determined, while relatively weak, do support the values 

derived using the Nutrient Benchmark Approach. Both the analysis of the Rapid Periphyton 

Survey (regarding probability of increased algal thickness) and the analysis of the second change 

point in the stream periphyton response to nutrients indicate that the biological response to 

nutrient enrichment will generally occur at levels higher than the values generated using the 

Benchmark Distribution Approach. 
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7 Florida’s Nutrient Benchmark Site Distributional Approach 

for Rivers and Streams 

7.1 Introduction

The U.S. Environmental Protection Agency’s (EPA) Nutrient Criteria Technical Guidance 

Manual: Rivers and Streams (USEPA 2000) recommended that the most defensible approach 

for criteria development is to establish cause-effect relationships between nutrients and 

biological health endpoints. EPA guidance subsequently states that if these relationships were 

determined to be insufficiently robust for establishing numeric thresholds, the next best 

approach, involving a reference site distribution, should be employed. For this approach, which 

DEP has expanded and called the “Nutrient Benchmark Site Distributional Approach”, EPA 

recommends setting criteria based on an inclusive distribution of values obtained from reference 

sites in a designated ecoregion (based on climate and geology, etc.). 

DEP expanded this approach by identifying streams that were minimally affected by human 

disturbance and nutrients, and also by documenting the existence of full aquatic life full use 

support (using SCI and other floral-based methods). According to published EPA guidance, 

reference reaches may be identified for each class of streams within a state based on best 

professional judgment (BPJ). DEP expanded beyond EPA’s BPJ approach regarding selection of 

reference streams, and developed an extremely rigorous, multi-step process (described below) to 

ensure that the sites eventually selected truly represented minimal human disturbance and full 

designated use support. If streams are documented to be minimally affected by humans and 

characterized by healthy biota, then it logically follows that the range of nutrient concentrations 

within those streams are also protective of the designated use. After deliberating with its TAC, 

DEP selected the 90th percentile of the benchmark distribution for threshold purposes in all 

nutrient regions except the West Central NWR. The 90th percentile is justified primarily because 

of the additional verification steps, including the documentation that the benchmark site 

population had healthy, well-balanced aquatic communities (see discussion below). In the West 

Central NWR, the 75th percentile is used because the streams underlying the approach were 

simply documented as biologically healthy, not minimally disturbed. Advantages of using 

DEP’s Nutrient Benchmark Site Distributional Approach for nutrient criteria development 

include the following:

• Use of the 90th percentile of nutrient concentrations derived from a distribution of 

minimally disturbed streams is inherently protective of aquatic life, including biota 

inhabiting downstream waters; and 

• Documentation of healthy biological communities directly demonstrates that aquatic life 

uses are fully met within the associated range of nutrients.

One disadvantage of using the benchmark approach is that it does not identify the specific 

nutrient levels at which biological impairment occurs. For this reason, it cannot be 

concluded a priori that adverse effects on aquatic life actually occur at concentrations above 

these values. 
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EPA determined that DEP’s reference (Benchmark) approach was a scientifically defensible 

method for developing protective nutrient criteria and ultimately used DEP’s methodology to 

promulgate TP and TN criteria for Florida streams (U.S. EPA 2010b). For the reference-based 

approach, EPA estimated distributional statistics for two principal reference populations: a) a 

Benchmark Population represented by sites evaluated as least-disturbed by humans and b) an 

SCI Population represented by sites with demonstrated biologically healthy conditions. The SCI 

Population approached was used only for the West Central NWR, while the Benchmark 

approach was used for all other regions. 

For the benchmark approach, EPA identified reference sites that met the following criteria: 

1. LDI score <2 for land use within the 100 meter corridor 10 km upstream of the sample 

site; 

2. Not in a waterbody segment (WBID) listed on the EPA-approved Florida CWA section 

303(d) impaired waters list for nutrients and/or dissolved oxygen; 

3. Average nitrate/nitrite concentrations < 0.35 mg/L; 

4. No land uses or nutrient sources, as judged using aerial photographs and FDEP district 

biologist input, that would remove them from consideration as least-impacted sites for 

nutrients; 

5. Not within WBIDs with average SCI scores <40, and; 

6. Watershed or near-field LDI scores <3.

 

For the SCI Population, reference sites were identified that met the following criteria: 

1. Not within WBIDs with average SCI scores <40, and; 

2. Not in WBIDs listed on the EPA-approved Florida CWA section 303(d) impaired waters 

list for nutrients and/or dissolved oxygen. 

EPA’s approaches were based on and consistent with approaches previously proposed by DEP 

(2009a, 2010a) and discussed extensively with the Nutrient TAC. Therefore, DEP determined 

that EPA’s reference site distributions were representative, on a regional basis, of minimally 

disturbed and biologically healthy stream conditions.

7.2Extensive Verification Process for Selection of Benchmark Sites

A critical component of DEP’s benchmark approach is the comprehensive, multi-step 

evaluation process through which potential benchmark sites were thoroughly verified to 

assure that they represented minimally disturbed conditions. This multi-step evaluation 

included: 

• Selection of candidate reference sites by identifying sites with a corridor Landscape 

Development Intensity Index (LDI) score of ≤ 2 (this st ep alone eliminated the 

majority of Florida sites from further consideration (Figure 7-1). Two additional 

benchmark exclusions were ultimately based on a whole watershed LDI analysis 

conducted by Tetra Tech, which used a watershed LDI threshold of 3; 

• Elimination of sites included on the state’s 303(d) list of impaired waters due to 

nutrients or dissolved oxygen related to nutrients; 
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• Elimination of sites with nitrate concentrations greater than the 0.35 mg/L proposed 

nitrate-nitrite criterion, which reduced the possibility of including sites with far-field 

human disturbance from groundwater inputs; 

• Verification of surrounding land-use by examining high resolution aerial photographs 

taken in 2004-2005; 

• Obtaining input from DEP District scientists knowledgeable of the area;

• Performing a statistical outlier analysis of nutrient concentrations to remove 

potentially erroneous data; and 

• Finally, conducting an extensive field evaluation process, including a watershed 

assessment with verification of surrounding land-use and biological evaluation, of a 

large percentage of the remaining waterbodies containing benchmark sites, with the 

emphasis on sites with nutrient concentrations greater than the mid-range of the 

distribution. 

Through this process, candidate reference sites were subjected to a systematic, comprehensive 

evaluation process prior to including them as benchmark sites. Maps, photos, and a summary of 

the data collected at each of the verified benchmark sites can be found in Appendix 7-A. Each of 

the above steps is described in more detail below. 

7.2.1 Landscape Development Intensity Index (LDI) score of ≤ 2 

Candidate benchmark sites were initially selected based on an application of the landscape 

development intensity index (LDI). Brown and Vivas (2003) developed the LDI as an estimate 

of the intensity of human land uses based on nonrenewable energy flow. Application of the LDI 

is based on the ecological principle that the intensity of human dominated land uses in a 

landscape affect ecological processes of natural communities. More intense activities will result 

in greater effects on ecological processes. Natural landscapes with little or no agricultural or 

urban development will likely have intact ecological systems and processes. The LDI was 

developed specifically as an index of human disturbance, and has been shown to provide 

predictive capability regarding nutrient loading (Figure 7-2).
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Figure 7-1. LDI results for 5570 stations by sub-ecoregion for initial candidate reference site evaluation. Sites 

scoring above 2 on the LDI were eliminated from further consideration (except for a single site in the Bone 

Valley Region, which due to the sparseness of reference sites, was accepted at an LDI of 2.2).

Minimally 

Disturbed

LDI ≤ 2

Sub-ecoregion

65f 65g 65h 75a 75b 75c 75d 75e 75f 76a 76b 76c

LDI

1

2

3

4

5

6

7

8

9

10
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Figure 7-2. Relationship between nutrient loading (nitrogen in panel (a), phosphorus in panel (b)) and the 

LDI in the St. Marks Watershed, Florida (from Brown and Vivas 2003).

The LDI is calculated as the area-weighted value of the land uses within an area of influence 

(Figure 7-3). Using the land use coefficients and the percent area occupied by each land use as 

determined by GIS land use coverage developed from high resolution aerial photographs, the 

LDI is calculated as follows:

LDITotal = ∑ (LDCi * %LUi) 

where,

LDITotal = Landscape Development Intensity Index for the area of influence

%LUi = percent of total area of influence in land use i

LDCi = landscape development intensity coefficient for land use i 
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The LDI calculated on the land uses within a 100 meter corridor of a stream was found to be a 

better predictor of ecological health than the LDI calculated on an entire catchment (Fore 2004). 

Sources of disturbance near a stream exert greater influence than do far field human influences 

(Brown and Vivas 2003). Fore (2004) previously demonstrated that LDIs calculated using a 100 

meter corridor were slightly better predictors of biological health (i.e., Stream Condition Index) 

than LDIs calculated on the entire upstream catchment area (watershed). 

The utility of this corridor approach is related to the demonstrated effectiveness of the riparian 

corridor zones in removing pollutants, especially nutrients, from stormwater inputs (both surface 

and subsurface flow). Studies have shown that corridor zone widths of 60 meters are sufficient 

to reduce nutrient loads by up to 95% before reaching the stream (Peterjohn and Corell 1985). 

Additionally, corridor zones in the Coastal Plain areas have been shown to be effective in 

retaining nutrients because of gradual slopes, permeable soils, and the abundance of roots that 

enter the shallow groundwater zones (Lowrance et al. 1997). Since phosphorus is typically 

found bound to sediments, riparian zones retain most of the incoming phosphorus by capturing 

sediments. Other studies have shown that nitrate in shallow groundwater beneath riparian zones 

was removed by 85 to 90% due to plant uptake and denitrification in riparian zones 50-70 meters 

wide (Lowrance 1992; Jordan et al. 1993; Jacobs and Gilliam 1985; Lowrance et al. 1997. 

For purposes of benchmark site selection, LDI values were calculated from land uses within a 

corridor area of 100 meters on each side of the stream and tributaries within a 10 kilometer 

radius upstream of the sampling point as shown in Figure 7-3. While numerous studies have 

concluded that corridor widths of 50 to 70 meters are sufficient to reduce stormwater nutrient 

loads to streams by as much as 95%, additional corridor width provides additional protection to 

the waterbody. Based on these literature findings and the better correlations with biological 

health described above, DEP concluded that using a corridor width of 100 meters would provide 

adequate protection to Florida’s waterbodies and that a LDI calculated based on a 100 meters 

corridor is an appropriate method of selecting candidate benchmark sites with minimal human 

disturbance and healthy biological communities.
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Figure 7-3. Depiction of land use area (light yellow) included in a LDI calculation.

 

An additional analysis by Tetra Tech (Appendix 7-B) compared the nutrient distributions of sites 

characterized by corridor LDIs ≤ 2 with LDIs of the same sites calculated on a larger watershed 

area. This analysis suggested exclusion of two additional sites, one from the Bone Valley region 

(South Prong Alafia River) and one from the North Central region (Camp Branch), and closer 

scrutiny of additional sites in the Bone Valley region. The analysis indicated that higher total 

phosphorus concentrations observed at the South Prong Alafia River and Camp Branch could be 

explained by human disturbance, based on the higher watershed LDI values found at the WBIDs. 

After exclusion of these two sites, DEP recalculated the nutrient distributions, which resulted in 

values slightly different from earlier calculations (Table 7-3).

As discussed in DEP’s Nutrient Plan (2009a), the LDI was specifically designed as a measure of 

human disturbance. LDI values of less than or equal to 2.0 within the 100 meter corridor area 

are indicative of areas with very minimal levels of human disturbance. Numerous studies and 

evaluations have demonstrated, across multiple waterbody types and taxonomic groups, that the 

LDI is an accurate predictor of biological health; that is, healthy well-balanced biological 

systems are much more likely to occur at sites with low LDIs (≤ 2.0) than at higher disturbance 

levels (Fore 2004, Niu 2004, Brown and Reiss 2006, DEP 2009a, Fore et al. 2007). 

Furthermore, it has been demonstrated that a LDI of 2.0 is a consistent and conservative 
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biologically significant break point that can be used to distinguish benchmark conditions from 

potentially disturbed areas. 

A more detailed discussion of the LDI and its use to select minimally impacted benchmark sites 

is provided in Appendix A of Florida’s Numeric Nutrient Criteria Development Plan (DEP 

2009a). Since it has been demonstrated that the LDI is highly correlated with multiple measures 

of biological health, use of the LDI as an initial screening tool to select candidate benchmark 

sites is a conservative and ecologically reliable method. 

It indicated that higher TP concentrations observed at the South Prong Alafia River and Camp 

Branch could be explained by human disturbance, based on the higher watershed LDI scores 

found in the WBIDs. The analysis provided additional evidence that the benchmark reference 

site population, as identified by the corridor LDI approach, did not have extensive land use 

impacts beyond the 100 m/10 km scale examined by FDEP. In a general sense, EPA could not 

conclude from that analysis that a corridor (buffer area) LDI approach alone would yield a leastdisturbed reference population with respect to nutrients. However, additional evaluations and 

analyses, such as those presented herein, could provide the quality assurance to support such a 

conclusion. DEP evaluated the EPA analyses and determined that screening candidate 

Benchmark sites using watershed LDI of <3.0 provided additional rigor to the analysis and a 

scientifically valid demonstration that the Benchmark sites were truly representative of 

minimally disturbed conditions.

7.2.2 Screening against the 303(d) List of Impaired Waters 

Sites located within WBIDs listed on the State’s Verified 303(d) lists as impaired for nutrients or 

dissolved oxygen, where nutrients were identified as the causative parameters, were excluded as 

benchmark sites. Additionally, sites within WBIDs listed on the Verified or Planning 303(d) 

lists for biological impairments, regardless of cause, were excluded from the benchmark 

population. 

It should be noted that since the benchmark sites exhibit low LDIs and minimal human 

disturbance, WBIDs identified as impaired for dissolved oxygen with factors other than nutrients 

as the cause likely represent natural conditions for those sites. Further, moderate dissolved 

oxygen excursions below Florida’s current dissolved oxygen criteria of 5.0 mg/L have not been 

associated with any adverse biological impacts (in fact some benchmark sites with exceptional 

SCI scores had dissolved oxygen less than 5.0 mg/L). Therefore, sites with dissolved oxygen 

levels less than 5.0 mg/L were not excluded from the benchmark data set. 

7.2.3 Screening Against the 0.35 mg/L Proposed Nitrate-nitrite Threshold 

As stated previously, phosphorus adheres tightly to particulates, meaning that soils are 

exceedingly effective at trapping and removing phosphorus in stormwater. For this reason, 

phosphorus generated by human activities beyond a 100 meter corridor is unlikely to reach a 

stream via a groundwater pathway. However, nitrate-nitrite is very mobile in groundwater, and 

may travel in subsurface aquifers for significant distances to be discharged into streams via seeps 

or springs. DEP has determined that anthropogenic activities are responsible for elevated 

groundwater nitrate-nitrite concentrations and is proposing a nitrate-nitrite criterion of 0.35 mg/L 

for spring vents. Since DEP is confident that this response-based proposed criterion is 

protective, candidate benchmark sites that exceed this nitrate concentration (0.35 mg/L) were 
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eliminated from consideration as benchmark sites. This step provides additional assurance that 

the benchmark sites were not influenced by far-field anthropogenic nutrients. 

7.2.4 Verification of Surrounding Land-use by Examining High Resolution 

Aerial Photographs 

The minimally disturbed condition of every candidate site was confirmed via a review of recent 

(2004-2005) high resolution (1-m ground resolution) aerial photographs. This review consisted 

of searching the photos for recent land clearing or development, in particular any disturbance 

that encroached into the 100 meter corridor area used to calculate the LDI. Additionally, sites 

not representative of freshwater streams (e.g., tidally influenced or channelized) were excluded. 

Many sites were excluded based on the review of aerial photographs, including several that 

appeared to be within canals or channelized streams and therefore were not considered 

representative of a minimally disturbed stream condition. 

7.2.5 Obtaining Input from DEP District Biologists 

DEP district scientists familiar with streams in their area were asked to provide feedback on the 

list of candidate benchmark sites. Specifically, they were presented with the following 

information and question: 

For ongoing nutrient criteria development, we are identifying sites with benign 

land uses in their upstream watershed (LDI < 2) to define the benchmark 

condition. Ken Weaver has produced the attached table of low LDI peninsular 

benchmark sites. Can you please look over the list to determine if there are any 

human activities at particular sites, which may not have been captured by the LDI 

that would disqualify the site from being used to define "benchmark" for nutrient 

criteria?

Over twenty study sites were excluded from the benchmark set based on feedback and best 

professional judgment comments provided by District staff. The staff identified additional 

channelized streams, estuarine sites, and potentially disturbed sites. In some cases the staff 

identified potential point source discharges or localized disturbances (e.g., cattle in the stream) 

that may not have been captured in the LDI calculation. In other cases, sites were excluded 

because the reviewer was aware of moderate to high levels of development within the watershed 

that were outside the 100 meter corridor, but in their opinion, could potentially affect the nutrient 

regime. Exclusion of these potentially disturbed sites represents an additional conservative 

component of DEP’s approach designed to ensure that the benchmark set consists solely of 

minimally disturbed locations.

Additional sites were excluded because they were potentially estuarine or tidally influenced 

based on proximity to the coast and a subsequent review of specific conductance data. All 

potentially estuarine sites routinely had specific conductance levels above 1,275 μmhos/cm and 

episodic values above 4,500 μmhos/cm. A conductivity of 4,500 μmhos/cm is approximately 

equivalent to a chloride concentration of 1,500 mg/L, which is used in Florida as the threshold 

between predominantly fresh and marine waters.
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7.2.6 Field Evaluation Process, Including Watershed Assessment and 

Biological Appraisal of Benchmark Sites 

In 2007 and 2008, experienced DEP Tallahassee staff conducted a comprehensive study of a 

large number of the candidate benchmark sites, selected via the above process, as a means of 

providing additional assurance that the sites were truly representative of minimally disturbed 

conditions. The population of candidate benchmark sites selected for additional review consisted 

predominantly of WBIDs with nutrient concentrations higher than the mid-range of the 

distribution. The objective of this final in-field verification step, which included a watershed 

survey and biological assessment, was to build ultimate confidence in the selection of the final 

benchmark sites, focusing especially on those with nutrient concentrations higher than the 

middle of the distribution. 

Sites visited were selected to be representative of most of the WBIDs in the candidate 

benchmark dataset. The site with the most extensive and longest period of nutrient data was 

selected to represent the WBID. 

Site evaluations included a survey of anthropogenic inputs and surrounding land uses. The 

survey included a driving tour of the portions of the watershed accessible to DEP, guided by high 

resolution aerial photographs taken in 2004-2005, and maps of the entire drainage basin. During 

the watershed survey, DEP investigators made a series of observations regarding potential 

human disturbances in the watershed, including potential nonpoint source inputs and hydrologic 

modifications (using the DEP hydrologic scoring system). The hydrologic scoring system was 

originally developed to support the development of Florida’s SCI and is based on knowledge of 

water removal, patterns of drought, and hydrographs for the sites under evaluation, and serves as 

a rough measure of hydrologic disturbance in a system (Fore 2004, Fore et al. 2007). 

Stream Habitat Assessments (HA) were conducted following DEP-SOP-001/01 FT 3100 (DEP, 

2008). The HA evaluates substrate condition and availability, water velocity, habitat smothering 

(e.g., by sand and silt), channelization, bank stability, and the width and condition of riparian 

vegetation. In addition to the 100 meter reach of the stream examined during the HA, 

investigators also physically examined a minimum of 200 meters upstream of the site, including 

potential riparian zone breaches.

At each site, trained and experienced DEP staff also collected and analyzed the biological, 

chemical, and physical parameters listed in Table 7-1 following DEP SOPs: 

(http://www.dep.state.fl.us/labs/sop). Water levels were evaluated by both reviewing 

hydrographs from the given stream or other streams in the general vicinity and by visual 

inspection of the stream habitats. Biological samples (e.g., SCI) were not collected if, based on 

the judgment of the experienced investigator, current or antecedent flow conditions were 

inappropriate, or a majority of the aquatic habitat was exposed to the air rather than being 

inundated. Water chemistry samples were collected at all sites unless there were only 

discontinuous pools of water, in which case no samples were collected. These sites were, 

however, still included in the benchmark data set. Note that sites with an average score of less 

than 40 (the revised impairment threshold) on the SCI were excluded from the benchmark data 

set for calculation of the final nutrient distribution.
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Information acquired during the site and watershed evaluations was used to provide final 

confirmation that the benchmark sites chosen by DEP were in fact representative of minimally 

disturbed conditions for the region. Taken together with the extensive screening criteria, the 

results of the stream surveys provide an extremely high level of confidence that nutrients 

associated with DEP’s benchmark data set fully support healthy, well-balanced aquatic 

communities (see discussion below). 
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Table 7-1. List of parameters monitored during the benchmark stream survey

Biological Parameters

• Stream Condition Index (SCI)

Chemical and Physical Parameters

• Total Phosphorus 

• Rapid Periphyton Assessment • Nitrite + Nitrate

• Qualitative Periphyton Sampling 

(i.e., periphyton taxonomy) 

• Total Kjeldahl Nitrogen

• Ammonia

• Color 

• Habitat Assessment • Turbidity 

• Chlorophyll-a • Total Suspended Solids

• Phaeophytin • Total Organic Carbon 

• Hydrologic Modification Scoring • Specific Conductance (in situ)

• Linear Vegetation Survey • Dissolved Oxygen (in situ)

• Percent Canopy Cover • pH (in situ)

• Water Temperature (in situ)

7.3 Results of Benchmark Screening Process

The initial set of candidate reference sites identified by DEP statewide, with available nutrient 

data of known quality and LDI values less than or equal to 2.0, consisted of 1,171 sites 

distributed among 507 WBIDs. After excluding sites due to the multi-step screening process, a 

total of 493 stations in 129 WBIDs remained. Due to time and resource considerations, not all of 

these WBIDs could be visited; therefore, DEP emphasized field verification of sites with nutrient 

values higher than the mid-range of the distribution. The total number of benchmark sites that 

successfully passed the field verification was 63. Because of hydrologic conditions 

(predominantly low-water conditions), biological sampling was not conducted at all of the fieldverified sites; however, these sites were determined to be minimally disturbed through the 

watershed survey and habitat assessment process. As of the date of this document, SCI samples 

were collected at 51 sites, the Rapid Periphyton Survey was conducted at 54 sites, and 

periphyton community structure data was collected at 60 sites. Summary descriptions and 

evaluations for each of the benchmark sites are included in Table 7-4 at the end of this chapter. 

Further screening of sites was based on outliers identified from the near-field watershed LDI 

analysis by Tetra Tech (Appendix 7-A). DEP (2009c) provided detailed responses to criticisms 

of the reference site approach, including responses for individual reference systems. 

7.4 Analysis of Nutrient Benchmark Sites Biological Data

The SCI scores from the list of field-verified nutrient benchmark sites (based upon all the 

verification steps previously described) were compared to their corresponding LDI, TP, and TN 

values, in order to determine whether the ranges of these parameters were supportive of healthy 

biological communities. The dataset includes 66 sampling events at a total of 49 stations across 
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the state (thirteen stations were sampled twice, and two were sampled three times). All of the 

SCI values were calculated using the SCI_2007 method (Fore et al. 2007). 

As described previously, the SCI is negatively correlated across the whole range of LDI, 

meaning biological health decreases in response to increasing levels of human activities (Figure 

7-4). However, within the low range of LDI (≤ 2) associated with the nutrient benchmark site 

dataset, no correlation was found between LDI score and SCI score (Figure 7-5). (Note that SCI 

scores were averaged for sites with more than one SCI sampling event.) This indicates that LDI 

scores of up to 2.2 (the LDI value at one of the Bone Valley sites), when coupled with DEP’s 

verification process, are associated with healthy biological communities.

Figure 7-4. Relationship between the Landscape Development Intensity Index and Stream Condition Index 

across the entire range of LDI.
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Figure 7-5. In contrast to the figure 7-4, note the lack of correlation between SCI and LDI values of 

minimally disturbed sites (LDI ≤ 2, with one exception where LDI was 2.2).

The graphs of SCI scores versus TN and TP (Figures 7-6 and 7-7) indicate that the benchmark 

sites support healthy well-balanced populations of flora and fauna even at nutrient concentrations 

above the 90th percentile of the benchmark distribution. In fact, exceptional biological 

communities (SCI ≥ 68) were found at sites with TP concentrations as high as approximately 600 

μg/L, and TN concentrations as high as approximately 3 mg/L. Therefore, it can be concluded 

that TP and TN concentrations at least as high as the 90th percentile, which is derived and 

discussed below, are protective of the natural populations of flora and fauna in minimally 

disturbed streams. 

y = 4.4938x + 60.839

R2 = 0.0079

0

10

20

30

40

50

60

70

80

90

100

1 1.2 1.4 1.6 1.8 2 2.2 2.4

Average SCI Score

LDI Score

Benchmark Site LDI vs. Average SCI Score
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Figure 7-6. Benchmark site SCI vs. total nitrogen. Note the lack of correlation between SCI and total 

nitrogen throughout the range of benchmark sites. This indicates that there are no adverse effects by 

establishing nutrient criteria at the upper 90th percentile of the benchmark distribution. Sites scoring less 

than 40 on the SCI (after QA review) were excluded from the benchmark data set for calculation of the final 

nutrient distribution.

Figure 7-7. Benchmark site SCI vs. total phosphorus. Note the lack of correlation between SCI and Total 

phosphorus throughout the range of benchmark sites. This indicates that there are no adverse effects by 

establishing nutrient criteria at the upper 90th percentile of the benchmark distribution. Sites scoring less 

than 40 on the SCI (after QA review) were excluded from the benchmark data set for calculation of the final 

nutrient distribution.
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Periphyton community composition and taxonomy, and the extent and thickness of algae 

coverage (RPS), were also collected at benchmark sites. The periphyton community response to 

environmental factors is complex, and data analysis indicates that factors such as pH, 

conductivity, canopy cover, and color appear to exert significant confounding influences on the 

periphyton response to nutrients (see Chapter 6). The analysis of the periphyton data to date 

suggests that changes in algal community structure and abundance are, in part, significantly 

related to nutrients, however, DEP concluded that the relationships discerned were statistically 

too weak to establish credible numeric nutrient thresholds. Benchmark sites in general exhibited 

low algal coverage and thickness, except for a few sites with limestone outcroppings, where algal 

growth was moderate. Based on the periphyton analyses and observations made during 

benchmark site field studies, evidence supports the selection of the 90th percentile of the 

benchmark site nutrient distribution for establishment of protective criteria.

7.5 Conclusions for Use of the Benchmark Distribution to Establish 

Nutrient Criteria

As stated in the previous chapter, DEP invested significant resources it attempting to derive 

criteria based on dose-response relationships. However, DEP concluded that specific thresholds 

could not be established due to the inherent variability within and between streams and the 

compounding complexity from other factors. Since numeric nutrient standards developed using 

the benchmark distributional approach are fully supportive of the designated use, DEP plans to 

apply these standards to control anthropogenic discharges to streams through source control 

efforts such as the National Pollutant Discharge Elimination System (NPDES) permitting and 

Total Maximum Daily Load (TMDL) programs. 

The nutrient gradient stressor-response analysis (Chapter 6) clearly demonstrated the complexity 

inherent in the relationship between nutrients, the interaction with other environmental factors, 

and biological responses. Although statistically significant relationships were found, the 

variability in biological response explained by nutrients alone was statistically weak, and DEP 

concluded that the analyses provided no basis for establishing specific nutrient thresholds. 

Therefore, following EPA guidance, DEP proposes an upper percentile distribution of the 

benchmark distribution as a viable method to establish criteria. 

DEP considered the advantages and disadvantages of using the 90th, 95th, and 99th percentiles of 

the benchmark distribution in setting criteria. Based upon the statistical model on which the 

distributions were derived, DEP determined that there was less certainty in the inclusiveness of 

the 95th and 99th percentiles given the sparseness of data at the extreme end of the distribution. 

However, DEP had high assurance that the 90th percentile was inclusive of the distribution of 

minimally disturbed sites due to the sufficiency of the data surrounding this range. 

Note that the lack of a demonstration that biological impairment actually occurs at specific 

nutrient levels greater than the 90th percentile of the benchmark sites is a disadvantage of using 

this approach. For this reason, for future assessments, DEP plans to conduct additional 

evaluation at sites with nutrient values higher than the 90th percentile to definitively establish that 

nutrients are a reasonable cause of designated use impairment. 

Identification of impaired waters will be implemented through a multiple step process. At sites 

with nutrient concentrations higher than the 90th percentile, an additional variable that responds 
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to nutrient enrichment would have to be exceeded (i.e., chlorophyll a, biological health criteria, 

Dissolved Oxygen, or “free from” criteria) to verify that biological impairment is occurring and, 

if so, to definitively establish that nutrients are a reasonable cause of designated use impairment. 

In the absence of such confirmatory data, DEP will first place these waters on the Planning List, 

which captures those waterbodies that are potentially impaired and are targeted for follow-up 

monitoring and analysis. If sufficient biological data are not collected during the subsequent 

strategic monitoring, the water will be placed on the Study List to ensure that the data will be 

collected. 

In summary, DEP is proposing to establish numeric nutrient criteria for TP and TN in streams 

using the 90th percentile of the benchmark distribution, except for the West Central nutrient 

region where data is limited, based upon the following reasons: 

• It is consistent with EPA guidance;

• DEP conducted a rigorous verification to demonstrate that the benchmark sites were 

minimally disturbed;

• DEP confirmed that healthy, well balanced biological communities were maintained at 

nutrient levels above the 90th percentile (greatly minimizing Type II error, the mistake of 

classifying an impaired site as acceptable);

• The stressor/response analyses, while demonstrating significant relationships between 

nutrients and biological response, provided no basis for establishing specific nutrient 

thresholds; 

• Use of a 75th percentile would result in an excessive Type I error (25% of benchmark 

sites, and a large number of healthy sites would incorrectly be classified as impaired), and 

subsequent use of resources to “restore” such unimpacted sites would constitute unwise 

public policy, and would contradict State Law (Chapter 403, F.S.); and 

• Although the 95th and 99th percentiles were considered, DEP determined that there was 

insufficient certainty in the inclusiveness of the 95th and 99th percentiles given the 

sparseness of data at the extreme end of the distribution. However, DEP has high 

assurance that the 90th percentile is inclusive of the distribution of minimally disturbed 

sites due to the sufficiency of the data surrounding this range in all nutrient regions 

except for the West Central. In the West Central the 75th percentile was used due to the 

limited amount of data available.

7.6 Calculation of Benchmark Derived Nutrient Criteria

7.6.1 Data Handling 

Nutrient data from the benchmark sites were queried from Florida STORET, DEP’s Status and 

Trends dataset (GWIS database), and the benchmark site verification dataset. Data were 

screened for potential data quality issues (e.g., improper sample preservation, analysis performed 

outside of hold time, etc.). 
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A number of the benchmark waterbodies were sampled numerous times and by different 

agencies. The sampling sites used by the different samplers are often located within several 

hundred meters of each other. Therefore, to avoid biasing the analyses toward the larger water 

bodies with multiple sampling sites within close proximity to each other, the station level data 

were aggregated by WBID. 

With the exception of un-ionized ammonia, elevated nutrient levels are not acutely toxic in the 

aquatic environment; instead, their effects are chronic and cumulative over time. Nutrient 

concentrations are typically variable over time and exhibit a log-normal distribution in the 

aquatic environment. Therefore, instantaneous criteria are not generally considered practical or 

appropriate for nutrients, and are better expressed as an average over a longer period of time. 

Additionally, the geometric mean, rather than an arithmetic mean, is often used to provide a 

more accurate representation of the central tendency of positively skewed data (e.g., log-normal), 

such as nutrient concentrations. The use of the annual geometric mean mutes the short-term 

variability in sampling quality data to provide a more reliable, long-term value for assessing the 

nutrient status in aquatic environments. 

For the reasons discussed above, annual WBID geometric means were calculated for purposes of 

evaluating the frequency distribution of nitrogen and phosphorus by region for benchmark 

streams. EPA (2010b) also calculated WBID-averages for purposes of estimating the frequency 

distribution of TN and TP by each NWR for the Benchmark and SCI Populations. EPA derived 

numeric nutrient criteria using a reference-based distribution approach for TN and TP (Table 7-

2). Final nutrient criteria were derived from the 90th percentile of TN and TP concentrations in 

the Benchmark Population for Panhandle West, Panhandle East, North Central, and Peninsula 

Regions. The 75th percentile of the SCI population was used for the West Central Region. DEP 

evaluated the EPA (2010b) calculations of TP and TN criteria and determined the EPA’s 

methods were consistent with methods previously proposed by the state (DEP 2009a). 

Furthermore, it was determined that the EPA thresholds were inclusive of the Benchmark and 

SCI population distributions and are therefore inherently protective. Therefore, DEP proposed 

that the state adopt the EPA promulgated stream TP and TN criteria as state water quality 

standards (Table 7-2). 
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Table 7-2. Regional total phosphorus and total nitrogen criteria for Florida streams. DEP established stream 

nutrient criteria as the 90th percentile values, with the exception of TP in the Bone Valley where the 75th 

percentile is more defensible (due to small sample size). These final values are the result of all the various 

verification and exclusion steps mentioned above. 

Nutrient Watershed Region TN (mg/L) TP (mg/L)

Panhandle West 0.67 0.06

Panhandle East 1.03 0.18

North Central 1.87 0.30

West Central 1.65 0.49

Peninsula 1.54 0.12
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Table 7-4. Summary of information collected from field-verified nutrient benchmark sites.
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7.7 Relationship between Stream and River Nutrient Concentrations, 

their Effects on Biological Communities, and Recreational Uses

Public perception of water quality and recreational use is often subjective and based on aesthetics 

(House 1996). A University of Wisconsin study (David 1971) found that people generally 

equate visible constituents, such as odor, cloudy or dark water, as polluted, but not chemicals, 

bacteria, or biological health. Thus, because of the inherent subjectivity and variability, basing 

water quality criteria on recreational use is generally very challenging. 

On the other hand, developing nutrient standards that ensure well balanced natural populations of 

flora and fauna inherently protects recreational uses. Recreational uses could only be impeded 

by nutrients where biological communities are in an imbalanced condition caused by human 

inputs. Furthermore, recreational opportunities are most often impacted by an imbalance in 

floral communities, which are measured and used in the expression of the nutrient standards and 

the standards are set to address an imbalance in natural populations of floral communities. Since 

faunal communities are affected by floral communities, they can also be used to make judgments 

regarding whether a waterbody has a well balanced natural population of flora and fauna. 

Measuring nutrient concentrations is a simple matter, but concentrations of nitrogen and 

phosphorus in a waterbody do not themselves affect recreational uses. Since biological 

responses are the trigger that impacts recreational uses, it is important to develop metrics that can 

measure biological responses and that can measure shifts and imbalances in the natural 

populations of flora and fauna. DEP uses chlorophyll a, the Rapid Periphyton Survey, and the 

Linear Vegetation Survey to measure floral conditions in rivers and streams, and uses the Stream 

Condition Index to measure faunal conditions in rivers and streams. These methods are adopted 

by reference in the Quality Assurance Rule (Chapter 62-160, F.A.C.). The Standard Operating 

Procedures (SOPs) for conducting the RPS, LVS, HA, and SCI methods are available at 

http://www.dep.state.fl.us/water/sas/sop/sops.htm: 

• RPS: DEP SOP FS 7230; 

• LVS : DEP SOP FS 7320; 

• HA: DEP SOP FT 3100; and 

• SCI: DEP SOP SCI 1000.

Chapter 2.7 of the SCI Primer (DEP 2011) has additional information on use of the SCI and 

measures of floral health to assess achievement of nutrient standards.

A vetted, multi-metric index such as the SCI incorporates many elements of macroinvertebrate 

community structure and function that respond to a variety of stressors, including toxic algae. 

For example, several metrics, such as sensitive taxa, Ephemeroptera taxa, Trichoptera taxa, 

percent filter-feeders and number of long-lived taxa are expected to respond negatively to 

increases levels of algae, whether toxic or non-noxious varieties, while other metrics (% very 

tolerant and % dominant taxon) would increase in response to algal issues. Filter-feeding 

organisms feed by straining phytoplankton or other particles from the water column. Sensitive 

taxa, Ephemeroptera taxa, and Trichoptera taxa require low habitat smothering (from algae and 
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sediment) to thrive. It is expected that a threshold level of phytoplankton or periphyton in a 

waterbody would cause reductions in the filter-feeder, Ephemeroptera taxa, Trichoptera taxa, and 

sensitive taxa metrics. Furthermore, many mollusks are not only filter-feeders but are also longlived, organisms that live one or more years and are exposed to long-term as well as episodic 

stressors such as toxic algal blooms. Conversely, the % very tolerant and % dominant taxon 

metrics of the SCI would increase in response to toxic or non-toxic algae expressed in either the 

phytoplankton or periphyton. All of the above metric responses to algae would lower the SCI 

score. 

7.8 Use of Biological Data in Conjunction with Stream and River 

Nutrient Thresholds

The Department conducted many field measurements for macroinvertebrates, diatoms, algae, 

macrophytes, nitrogen, and phosphorus in an attempt to derive criteria based on a cause and 

effect relationship for streams, similar to the nitrate criteria for springs. Unfortunately, there 

were too many confounding factors in streams to find a consistent relationship sufficient to 

derive a generally applicable numeric value for protection. Therefore, DEP (and EPA) needed to 

rely on a reference based approach (also called the benchmark approach) to estimate a level of 

protection, as described above. Using this approach, DEP could estimate a level that was 

reflective of the upper end of an unimpacted and healthy stream exhibiting well balanced natural 

populations of flora and fauna. Streams with nutrient levels below these levels can be presumed 

to meet the narrative nutrient criteria, but since this is not based on a cause and effect 

relationship, there is uncertainty if levels above the threshold will result in imbalance. 

When the reference-based thresholds are used to assess an individual stream, there is uncertainty 

that they provide the precise, necessary protection for that individual stream because of the 

previously discussed confounding factors associated with streams. Therefore, to ensure with 

confidence that nutrient concentrations provide for a well balanced natural population of flora 

and fauna, it is necessary to also measure and evaluate the actual flora and fauna of the stream. 

Since nutrient responses are dependent on many other factors in streams, it is possible that there 

are site specific situations where nutrient concentrations below the reference based thresholds 

may not provide for the needed protection and other site specific situations where nutrient 

concentrations above the reference based thresholds are fully protective.

DEP’s use of biological information, in conjunction with nutrient thresholds in streams, is 

scientifically defensible and was fully supported by the EPA Scientific Advisory Board (SAB 

2010). The SAB emphasized the importance of: 1) establishing linkages among designated uses, 

measured responses, stressors, and measures of stressors; and 2) relating measures of responses 

directly to deleterious effects on designated uses. The SAB stated, “We agree with the statement 

in the Florida Department of Environmental Protection’s letter of September 4, 2009 indicating 

that the most scientifically defensible strategy for managing nutrients within the range of 

uncertainty is to verify a biological response prior to taking a management action. This 

risk/performance-based approach to setting nutrient criteria is evident not only in Florida’s 

program, but also in those developed by California, Maine, and Ohio (Florida Department of 
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Environmental Protection, 2009; Maine Department of Department of Environmental Protection, 

2009; McLaughlin and Sutula, 2007.” Both Florida’s nutrient Technical Advisory Board (TAC) 

and the SAB recommended that risk-based linkages between nutrients in biological response be 

incorporated into numeric standards, and the Department did just that. To demonstrate 

compliance with Florida’s nutrient standards in streams, it must be shown the there are no 

imbalances in aquatic flora, and that either the stream nutrient thresholds are not exceeded or that 

the fauna (SCI) are healthy. Additionally, downstream waters must be protected through site 

specific analyses.

In addition to using the SCI to assess the biological community in the stream, the Department 

has developed a weight of evidence approach that uses results from the Rapid Periphyton Survey 

(RPS), Liner Vegetation Survey (LVS), and chlorophyll samples, to evaluate the floral 

community. This approach is outlined in section 2.7 of the SCI Primer (DEP-SAS-001/11) and 

is briefly described here. To evaluate whether a stream achieves the narrative nutrient criterion, 

the investigator must compile water chemistry data (e.g., Total Nitrogen [TN], Total Phosphorus 

[TP], chlorophyll a, and ancillary parameters such as color, turbidity, DO, pH, conductivity, and 

temperature, etc.) and a minimum of two of each of the following: RPS, LVS (if appropriate), 

Habitat Assessment, and SCI. Although there are currently no “absolute” quantitative endpoints 

for the RPS and LVS, guidance is provided on how to interpret data from these tools to 

determine if the vegetative components of the stream fall within the statewide reference 

distribution. Taken together, these data are used as a weight of evidence to decide whether a 

stream is healthy, with acceptable levels of nutrients.

7.9 Evaluating the Need for Downstream Protection Values

Federal regulations at 40 CFR 131.10(b) state, “In designating uses of a water body and the 

appropriate criteria for those uses, the State shall take into consideration the water quality 

standards of downstream waters and shall ensure that its water quality standards provide for the 

attainment and maintenance of the water quality standards of downstream waters.” 

One may interpret this provision to mean that each and every criteria established by a State must 

ensure the protection of downstream waters, however that interpretation is neither necessary nor 

possible to implement. A valid interpretation is that the State’s collective

Even though having criteria in State standards for both upstream and downstream waters 

provides the necessary protection, the Department also included an explicit standard at Rule 62-

 standards (versus each 

component of its standards) as adopted must ensure the protection of downstream waters. When 

a State’s water quality standards contain protective criteria for both upstream and downstream 

waters, then it is clear that a State’s standards provide for the attainment and maintenance of 

water quality standards of both the upstream and downstream waters. The proposed criteria for 

lakes and estuaries provide de facto standards for the downstream lake and estuary waters that 

provide for their attainment and maintenance, and it is not necessary or reasonable to establish 

stream downstream protection values (DPVs) in upstream waters when the scientific methods 

needed to create the link between upstream and downstream is lacking. Additionally, when 

criteria for the remaining Florida estuaries are promulgated, these criteria will be used to assure 

that upstream loading of nutrients are protective of the estuaries. This is critical because the 

scientific merits of any proposal must be weighed when deciding whether the perception of 

necessity to adopt DPVs outweighs the weaknesses contained in the science. 
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302.531(4), F.A.C., to ensure that loading of nutrients from an upstream waterbody shall be 

limited as necessary to provide for the attainment and maintenance of water quality standards in 

downstream waters. While this is a narrative statement, federal regulations do not require a 

numeric expression. Furthermore, that provision coupled with other provisions of the regulations 

will provide for the downstream protection needed. 
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8 Nutrient Longitudinal Study: Downstream Effects of 

Nutrients in Selected Florida Rivers/Estuaries 

8.1 Introduction

DEP initiated a Nutrient Longitudinal Study during the summer of 2008 to evaluate downstream 

biological responses to naturally high upstream phosphorus levels. Biological responses to 

excess nutrients can be separated in space and time from enrichment sources—i.e., an adverse 

response to nutrients may occur well downstream from the actual enrichment. DEP’s hypothesis 

is that within systems with low levels of human disturbance and intact ecological processes, 

naturally high levels of nutrients can usually be assimilated into the ecosystem without causing 

adverse biological responses to the streams or downstream estuaries (i.e., the systems have 

evolved over time in conjunction with the existing nutrient regime). The goal of this study was 

to determine whether nutrient concentrations representative of the upper portion of the 

benchmark site distribution are protective of the designated use of downstream reaches. 

8.1.1 Project Objectives 

The objectives of the study were as follows: 

(1) Collect physical, chemical, and biological data throughout the length of selected Florida 

river/estuary systems to establish the relationship between nutrient levels and adverse 

biological responses, including the most sensitive (generally downstream) reaches; and 

(2) Analyze the resulting dataset as one line of evidence in DEP’s effort to establish numeric 

nutrient standards, particularly relating to the protection of downstream waters. 

8.1.2 Project Description 

The longitudinal study focused on relating the effects of nutrients on various biological systems, 

from upstream to downstream, including the most sensitive areas, which typically are slowly 

flowing lower reaches or estuaries. Two systems were studied: the Waccasassa River and 

Estuary (Peninsula Nutrient Region) and the Steinhatchee River and Estuary (Panhandle East 

Nutrient Region). Blue Spring in Levy County forms the source of the Waccasassa River, which 

flows south to the Gulf of Mexico. The Steinhatchee River originates in Lafayette County and 

flows south, forming the border between Taylor and Dixie Counties, and empties into the Gulf. 

Both systems were selected to represent conditions of relatively low human disturbance, 

meaning the existing nutrient concentrations represent minimal amounts of anthropogenic 

influence. 

Sampling of both systems was conducted in August 2008 and January 2009. All samples were 

collected according to DEP-SOP-001/01. The DEP Bureau of Laboratories in Tallahassee 

analyzed the water and biological samples. All of the sampling and assessments listed below 

were performed at two upstream freshwater sites in each river. In addition, five estuary sites and 
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an additional freshwater site for each system were sampled for the first two parameters only 

(water chemistry and meter readings). 

• Water Chemistry (total Kjeldahl nitrogen, nitrate-nitrite, ammonia, total phosphorus, 

turbidity, chlorophyll a, color, total organic carbon, total suspended solids) 

• Meter Readings (dissolved oxygen, specific conductance, pH, and temperature) 

• Stream Condition Index (SCI) sampling 

• Habitat Assessment

• Percent canopy cover 

• Rapid Periphyton Survey (RPS) 

• Qualitative Periphyton Sampling

• Linear Stream Vegetation Survey 

Since the objective of the study was to emphasize the effects of nutrients on biota, attempts were 

made to minimize or account for confounding factors during site selection. Habitat suitability 

(substrate diversity and abundance), flow, and length of inundation were examined when 

deciding appropriate sites to sample in each system. A Habitat Assessment and percent canopy 

cover determination were performed at each site where biological sampling was conducted, in 

order to adequately characterize these important variables. 

8.1.3 Sampling Sites 

The following sites were chosen for the nutrient longitudinal study: 

Steinhatchee River and Estuary Sites

• Steinhatchee River at CR 357 (WBID 3573B) 

• Steinhatchee River at Canal Road (WBID 3573A) 

• Steinhatchee River at the waterfall (WBID 3573)

• Steinhatchee Estuary #1, where houses end (WBID 3573C) 

• Steinhatchee Estuary #2, at bridge (WBID 3573C) 

• Steinhatchee Estuary #3, at boat ramp (WBID 3573C) 

• Steinhatchee Estuary #4, at channel marker 38 (WBID 3573C) 

• Steinhatchee Estuary #5, at channel marker 23 (WBID 3573C) 

Waccasassa River and Estuary Sites 

• Waccasassa River at Wildlife Management Area (WMA) (WBID 3699)

• Waccasassa River at OB Road #3 (WBID 3699) 

• Wekiva River at Beck Park (WBID 3731) 
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• Waccasassa Estuary #1, below Wekiva (WBID 3699A) 

• Waccasassa Estuary #2, at USGS station (WBID 3699A) 

• Waccasassa Estuary #3, at “Caution Rocks” sign (WBID 3699B) 

• Waccasassa Estuary #4, at channel marker 33 (WBID 3699B) 

• Waccasassa Estuary #3, at channel marker 24 (WBID 3699B) 

•

Sampling locations were chosen after a reconnaissance trip, and are shown in Figures 8-1 and 8-

2 below. Photographs of the sites can be found in Appendix 8-A.
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Figure 8-1. Map of the Steinhatchee River and estuary sampling locations.
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Figure 8-2. Map of the Waccasassa River and estuary sampling locations.
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8.2 Sampling Results

8.2.1 Water Quality 

8.2.1.1 Steinhatchee River and Estuary 

Tables 8-1 and 8-2 below show the water quality results for the Steinhatchee River and estuary 

from the August 12, 2008 and January 14, 2009 sampling trips, respectively. The sites are listed 

upstream to downstream from left to right. Table 8-3 shows the Peninsula benchmark stream 

50th, 75th, and 90th percentile values for total nitrogen and total phosphorus. These values are 

shown to give a general idea of how the nutrient concentrations in the Steinhatchee compare to 

the distribution of nutrient concentrations at benchmark sites within the same nutrient region. 

Note that the numbers in Tables 8-1 and 8-2 below are instantaneous values, whereas the 

benchmark site nutrient percentile values to which they are compared (Table 8-3) are based on 

annual geometric means (see Chapter 7 of this document for more information on how the 

percentiles were generated.) 

Graphs of the average concentrations per site (mean of the August 2008 and January 2009 

results) of total nitrogen, nitrate-nitrite and total Kjeldahl nitrogen, total phosphorus, and 

chlorophyll a, are shown in Figures 8-3 to 8-6 below. Sites are listed upstream to downstream 

from top to bottom in the graphs. 

Table 8-1. Sampling results for Steinhatchee River and Estuary sites on 8/12/2008.

“U”= Below Method Detection Limit; “I”= Below Practical Quantitation Limit; “A”= Value reported is the mean of two or more 

determinations; “J”= Estimated value; “L” = Actual value is known to be greater than the reported value
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Table 8-2. Sampling results for Steinhatchee River and Estuary sites on 1/14/2009.

“U”= Below Method Detection Limit; “I”= Below Practical Quantitation Limit; “A”= Value reported is the mean of two or more 

determinations; “J”= Estimated value; “L” = Actual value is known to be greater than the reported value

Table 8-3. Distribution of Peninsula Nutrient Benchmark site total nitrogen and phosphorus (annual 

geometric means).

Figure 8-3. Average total nitrogen values at the Steinhatchee River and estuary sites.

Steinhatchee River

Average Total Nitrogen

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

Estuary #5

Estuary #4

Estuary #3

Estuary #2

Estuary #1

Waterfall

Canal Rd

CR 357

mg/L
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Figure 8-4. Average nitrate-nitrite and total Kjeldahl nitrogen values at the Steinhatchee River and estuary 

sites.

Figure 8-5. Average total phosphorus values at the Steinhatchee River and Estuary sites. Percentile lines are 

distribution levels (annual geometric means) for Peninsula Nutrient Benchmark sites, shown for comparison. 

Steinhatchee River

Average Nitrate-Nitrite and Total Kjeldahl Nitrogen

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

Estuary #5

Estuary #4

Estuary #3

Estuary #2

Estuary #1

Waterfall

Canal Rd

CR 357

mg/L

Nitrate-Nitrite

Total Kjeldahl Nitrogen

Steinhatchee River

Average Total Phosphorus

0.00 0.05 0.10 0.15 0.20 0.25

Estuary #5

Estuary #4

Estuary #3

Estuary #2

Estuary #1

Waterfall

Canal Rd

CR 357

mg/L

50th Percentile

75th Percentile

90th Percentile
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Figure 8-6. Average chlorophyll a values at the Steinhatchee River and estuary sites.

Total nitrogen values generally increased from freshwater to estuary sites. As shown in Figure 8-

4, nitrate-nitrite made up only a small portion of the total nitrogen in the Steinhatchee River, and 

most of the TN was TKN. 

The average total phosphorus value of 0.245 mg/L at CR 357 is greater than the 90th percentile of 

the Peninsula benchmark streams (0.116 mg/L), and the average value of 0.101 mg/L at the 

Canal Rd. site is greater than the 75th percentile (0.088 mg/L). Average total phosphorus values 

in the Steinhatchee River decreased with distance downstream, and estuarine concentrations 

were below 0.05 mg/L. 

Chlorophyll a concentrations in the Steinhatchee River were higher in the estuary than in the 

freshwater portions of the river. However all of the average values at the estuary sites were well 

below the threshold for nutrient impairment of estuarine waters in the Impaired Waters Rule (11 

μg/L annual mean for estuaries). Chlorophyll a values for the freshwater sites all were also 

much lower than the threshold for nutrient impairment for streams in the Impaired Waters Rule 

(20 ug/L annual mean for streams). 

8.2.1.2 Waccasassa River and Estuary 

Tables 8-4 and 8-5 below show the water quality results for the Waccasassa River and estuary 

from the August 11, 2008 and January 13, 2009 sampling trips, respectively. Table 8-3 above 

shows the Peninsula benchmark stream 50th, 75th, and 90th percentile values for total nitrogen and 

total phosphorus, as a means of comparison for how the nutrient concentrations in the 

Waccasassa compare to the distribution of benchmark sites within the same nutrient region. As 

0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0

Estuary #5

Estuary #4

Estuary #3

Estuary #2

Estuary #1

Waterfall

Canal Rd

CR 357

μg/L

Steinhatchee River

Average chlorophyll a
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before, note that the numbers in Tables 8-4 and 8-5 below are instantaneous values, whereas the 

percentile values in Table 8-3 are based on annual geometric means. 

Graphs of the average concentrations per site (mean of the August 2008 and January 2009 

results) of total nitrogen, nitrate-nitrite and total Kjeldahl nitrogen, total phosphorus, and 

chlorophyll a are shown in Figures 8-7 to 8-10 below. Sites are listed upstream to downstream 

from top to bottom in the graphs. 

Table 8-4. Sampling results for Waccasassa River and Estuary sites on 8/11/2008.

“U”= Below Method Detection Limit; “I”= Below Practical Quantitation Limit; “A”= Value reported is the mean of two or more 

determinations; “J”= Estimated value; “L” = Actual value is known to be greater than the reported value

Table 8-5. Sampling results for Waccasassa River and Estuary sites on 1/13/2009.

“U”= Below Method Detection Limit; “I”= Below Practical Quantitation Limit; “A”= Value reported is the mean of two or more 

determinations; “J”= Estimated value; “L” = Actual value is known to be greater than the reported value
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Figure 8-7. Average total nitrogen values at the Waccasassa River and estuary sites. Percentile lines are 

distribution levels (annual geometric means) for Peninsula Nutrient Benchmark sites, shown for comparison.

Figure 8-8. Average nitrate-nitrite and total Kjeldahl nitrogen values at the Waccasassa River and estuary 

sites.

Waccasassa River

Average Total Nitrogen

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 1.1 1.2 1.3

Estuary #5

Estuary #4

Estuary #3

Estuary #2

Estuary #1

Beck Park

OB Rd #3

WMA

mg/L

50th Percentile

Waccasassa River

Average Nitrate-Nitrite and Total Kjeldahl Nitrogen

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 1.1

Estuary #5

Estuary #4

Estuary #3

Estuary #2

Estuary #1

Beck Park

OB Rd #3

WMA

mg/L

Nitrate-Nitrite

Total Kjeldahl Nitrogen
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Figure 8-9. Average total phosphorus values at the Waccasassa River and estuary sites. Percentile lines are 

distribution levels (annual geometric means) for Peninsula Nutrient Benchmark sites, shown for comparison.

Figure 8-10. Average chlorophyll a values at the Waccasassa River and estuary sites.

Waccasassa River

Average Total Phosphorus

0.00 0.02 0.04 0.06 0.08 0.10

Estuary #5

Estuary #4

Estuary #3

Estuary #2

Estuary #1

Beck Park

OB Rd #3

WMA

mg/L

50th Percentile

75th Percentile

0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0

Estuary #5

Estuary #4

Estuary #3

Estuary #2

Estuary #1

Beck Park

OB Rd #3

WMA

μg/L

Waccasassa River

Average chlorophyll a
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Average total nitrogen values were higher in the estuary sites than the upstream values. The 

increase in TN in the vicinity of the estuary was probably due to export of organic nitrogen from 

the extensive Spartina and Juncus marshes surrounding the lower Waccasassa system (see 

Figure 8-2). 

Total phosphorus values were also higher in the estuary sites than the freshwater sites. The 

average TP value for the Estuary #2 site (0.098 mg/L) was greater than the 75th percentile for 

benchmark streams (0.088 mg/L), and the OB Rd #3 site and five estuary sites all had values 

greater than the 50th percentile (0.064 mg/L). 

The chlorophyll a values in the Waccasassa River showed a general trend of increasing with 

distance downstream at the estuarine sites. Chlorophyll a concentrations at all three freshwater 

sites were below the method detection limits (MDL, U-qualified data), and thus average values 

all were well below the threshold for nutrient impairment of streams in the Impaired Waters Rule 

(20 μg/L annual mean). For the estuarine sites, average values of validated data also were below 

the threshold for nutrient impairment of estuarine waters in the Impaired Waters Rule (11 μg/L 

annual mean for estuaries). 

Chlorophyll a data for the Waccasassa Estuary #2 site sampled during August 2008 were

excluded from the average for that site, as it was determined to be an anomalously high and 

inexplicably spurious result. The chlorophyll a value at the sites immediately upstream and 

downstream of the Estuary #2 site on the same day were 3.4 μg/L and 5.9 μg/L respectively, and 

there were no observable differences in Secchi depth, color, or turbidity at the Estuary #2 site 

relative to the upstream and downstream sites. 

8.2.2 Historical Trends in Nutrient Concentrations 

8.2.2.1 Steinhatchee River

The graphs below show time series nutrients and chlorophyll a concentrations in the 

Steinhatchee River. Values shown are geometric means of available data from the Impaired 

Waters Rule database for a given Water Body ID (WBID) within a given year. WBIDs are 

shown in the legends from upstream to downstream (top to bottom). WBID 3573Z represents 

Steinhatchee Spring, which is located directly on the river. WBIDs 3573A and 3573 are 

freshwater portions of the river, and WBID 3573C encompasses the estuary. The Peninsula 

benchmark stream percentiles for nutrients (annual geometric means) are shown where relevant. 
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Figure 8-11. Annual WBID geometric mean values of total nitrogen in the Steinhatchee River and estuary 

from 1995-2009. Percentile lines are distribution levels for Peninsula Nutrient Benchmark sites, shown for 

comparison.

Figure 8-12. Annual WBID geometric mean values of total phosphorus in the Steinhatchee River and estuary 

from 1995-2009. Percentile lines are distribution levels for Peninsula Nutrient Benchmark sites, shown for 

comparison.

Steinhatchee River 

Total Nitrogen Over Time

0.00

0.20

0.40

0.60

0.80

1.00

1.20

1.40

1.60

199519961997199819992000200120022003200420052006200720082009

Year

Geometric Mean (mg/L)

3573Z

3573A

3573

50th Percentile 3573C

75th Percentile

Steinhatchee River 

Total Phosphorus Over Time

0.00

0.02

0.04

0.06

0.08

0.10

0.12

1995199619971998

19992000

2001

200220032004

20052006

20072008

2009

Year

Geometric Mean (mg/L)

3573Z

3573A

3573

3573C

75th Percentile

50th Percentile

90th Percentile
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Figure 8-12. Annual WBID geometric mean values of chlorophyll a in the Steinhatchee River from 1999-2009.

The above graphs demonstrate that while total phosphorus levels in the Steinhatchee River have 

typically been above average compared to TP values in Peninsula benchmark streams, there has 

been no adverse effect on chlorophyll a levels within the river. Chlorophyll a concentrations 

were mostly below the MDL, and show no trend over the ten-year time period (the drop in the 

reported concentration in 2009 is due to a lower MDL than for previous years). 

8.2.2.2 Waccasassa River

Time series of nutrients and chlorophyll a concentrations in the Waccasassa River are shown in 

the graphs below. Values shown are yearly geometric means of available data for WBID 3699, 

which encompasses a large freshwater stretch of the river (there were insufficient data for other 

WBIDs). The Peninsula benchmark stream percentiles for nutrients (annual geometric mean) are 

shown where relevant. 

Steinhatchee River 

Chlorophyll-A Over Time

0.0

0.5

1.0

1.5

2.0

2.5

19992000200120022003200420052006200720082009

Year

Geometric Mean (μg/L)

3573
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Figure 8-13. Annual geometric mean values of total nitrogen for WBID 3699 within the Waccasassa River 

from 1995-2009. Percentile lines are distribution levels for Peninsula Nutrient Benchmark sites, shown for 

comparison.

Figure 8-14. Annual geometric mean values of total phosphorus for WBID 3699 within the Waccasassa River 

from 1995-2009. Percentile lines are distribution levels for Peninsula Nutrient Benchmark sites, shown for 

comparison.

Waccasassa River 

Total Nitrogen Over Time

0.00

0.20

0.40

0.60

0.80

1.00

1.20

199519961997199819992000200120022003200420052006200720082009

Year

Geometric Mean (mg/L)

3699

50th Percentile

Waccasassa River 

Total Phosphorus Over Time

0.00

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0.09

199519961997199819992000200120022003200420052006200720082009

Year

Geometric Mean (mg/L)

3699

50th Percentile
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Figure 8-15. Annual geometric mean values of chlorophyll a for WBID 3699 within the Waccasassa River 

from 1999-2009.

The above graphs demonstrate that nutrient levels within the Waccasassa River are associated 

with low chlorophyll a values, with no trend noted in the chlorophyll a data. 

8.2.3 Freshwater Biological Sampling Results 

Tables 8-6 and 8-7 below show the Habitat Assessment and Stream Condition Index (SCI) 

sampling results for the freshwater biological sites from the Steinhatchee River and Waccasassa 

River, respectively. SCI sampling and Habitat Assessments were conducted per DEP-SOP001/0, FS 7320 and FT 3100, respectively. 

Table 8-6. Habitat assessment and Stream Condition index Results for Steinhatchee River sites.

Waccasassa River

Chlorophyll-A Over Time

0.00

0.50

1.00

1.50

2.00

2.50

1999

2000200120022003200420052006200720082009

Year

Geometric Mean (μg/L)

3699
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Table 8-7. Habitat assessment and Stream Condition Index results for Waccasassa River sites.

The average Habitat Assessment scores for each site were in the “Optimal” range (≥ 120 out of a 

possible 160 points). The average SCI scores were all above the impairment threshold of 40, 

except for the Steinhatchee at CR 357. However, the SCI sampling that took place at CR 357 on 

8/12/2008, which yielded a SCI score of 20, was done under low flow conditions. Based on 

USGS hydrograph data from the nearest station downstream of the sampling site, as shown in 

Figure 8-16 below, the discharge rate for the Steinhatchee River had been low for over a month 

before sampling; therefore, it is likely that the CR 357 site had little to no flow and low water 

levels within the weeks prior to sampling, and the SCI score of 20 for the 8/12/2008 sampling 

event was probably due to hydrologic conditions. By the January sampling event, water flow had 

increased, and the SCI score improved. 

Figure 8-16. USGS hydrograph of the Steinhatchee River at Cross City. The mean discharge rate for the 

Steinhatchee River near Cross City was 7.4 ft3

/sec on 8/12/2008 and 23 ft3/sec on 1/14/2009.
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Although the Steinhatchee and Waccasassa sites are all minimally disturbed stream sites, SCI 

results were lower for the Steinhatchee sites, despite high habitat assessment scores. These 

lower SCI scores may be due to the naturally higher specific conductance in the Steinhatchee 

River, most likely due to influences from the limestone substrate. DEP has shown that 

increasing specific conductance is related to lower numbers of sensitive taxa and lower SCI 

scores, even in minimally disturbed systems (DEP 2008b).

Periphyton was collected for taxonomic identification from natural substrates in the freshwater 

sampling sites per the Qualitative Periphyton Sampling method outlined in DEP-SOP-001/01, FS 

7120. The results of this analysis (August and January trips are averaged) are listed below in 

Table 8-8, which shows the percentages of Bacillariophyta (diatoms), Chlorophycota (green 

algae), Cyanophycota (cyanobacteria), Euglenophycota, and Cryptophycophyta identified in the 

samples, as well as the total number of taxa identified. 

Table 8-8. Results of Qualitative Periphyton Sampling for Steinhatchee and Waccasassa River sites.

The periphyton community at each of the sites was dominated by diatoms (Bacillariophyta). The 

percentages of algal taxa and the total number of taxa identified were relatively similar among 

sites. 

In addition to periphyton taxa identification, algal thickness was measured at 99 points within a 

100 m stretch of stream, per the Rapid Periphyton Survey (RPS) method described in DEP-SOP001/01, FS 7130. Results are shown in Figure 8-17 below, along with the descriptions of algal 

thickness ranks. 
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Figure 8-17. Rapid periphyton survey (RPS) results are shown as the percentage of points in each algal 

thickness rank category within a given site. 

At most sites, the majority of periphyton observed was less than 0.5 mm long or non-visible. The 

Steinhatchee at Canal Rd. site had filamentous algae growing on a section of rocky limestone 

substrate within the 100 m assessment area; however, this algal growth did not adversely affect 

the macroinvertebrate community, as evidenced by the average SCI score of 51.5 (see Table 8-

6).

8.2.4 Biological Health in the Steinhatchee and Waccasassa Estuaries 

Seagrass beds are extremely valuable ecological and economic assets to Florida’s coasts. They 

are considered to be a keystone habitat for a diverse array of marine and estuarine species, and 

support important recreational and commercial fisheries. Seagrass beds may be adversely 

affected when high nutrient runoff from rivers causes excess phytoplankton growth in the water 

column, increased epiphyte load on seagrass blades, or macroalgal blooms covering seagrass 

beds, reducing the amount of photosynthetically active radiation reaching seagrasses. 

Maps of seagrass beds near the mouths of the Steinhatchee River (Deadman Bay) and 

Waccasassa River (Waccassassa Bay) were evaluated to determine trends or difference between 

the two systems. Figures 8-18 and 8-19 show the areas of continuous and discontinuous 

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

8/11/08 1/13/08 8/11/08 1/13/08 8/12/08 1/14/08 8/12/08 1/14/08

Waccasassa at 

WMA 

Waccasassa at 

OB Rd #3

Steinhatchee at 

CR 357

Steinhatchee at 

Canal Rd

Percent of sampled points in each category

Longitudinal Sites 

Rapid Periphyton Survey Results

5

4

3

2

1

0

Algal 

Thickness 

Rank
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seagrasses in Deadman and Waccasassa Bays, respectively. This coverage was created by the 

Florida Fish and Wildlife Conservation Commission Fish and Wildlife Research Institute from 

aerial photos dating from 1987 to 2007. Seagrass coverage is more extensive in Deadman Bay 

than Waccasassa Bay. This is most likely due to greater light limitation from the high color in 

Waccasassa Bay, since the average measures of color in the Waccasassa estuary during the 

August and January sampling trips were roughly three times higher than in the Steinhatchee 

estuary. 

Figures 8-20 and 8-21 show the change in seagrasses from 2001 to 2006, north and south of the 

Steinhatchee River, respectively (Carlson et al., In review). Paul Carlson from the Fish and 

Wildlife Research Institute (personal communication) noted that while some physical damage 

took place from the 2004 and 2005 tropical storms, very little seagrass loss has taken place 

around the Steinhatchee River, and the seagrass beds are stable and in good condition. An 

analysis of this type could not be conducted for the Waccasassa Bay because there are 

insufficient data to establish historical trends in seagrass beds for Waccasassa Bay (Mattson et 

al. 2007). There is anecdotal evidence that forestry practices in the watersheds of both rivers 

might have moderately increased color to both estuaries, but there is no evidence to suggest that 

nutrients have had any adverse effects on the estuary (Mattson et al. 2007). 

As shown in the maps below, the seagrass communities in Waccasassa and Deadman Bays 

appear healthy and intact. Therefore, it is reasonable to conclude that the nutrient concentrations 

from the Waccasassa and Steinhatchee Rivers are supporting the health of these downstream 

ecosystems.
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Figure 8-18. Seagrass coverage near the mouth of the Steinhatchee River.
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Figure 8-19. Seagrass coverage near the mouth of the Waccasassa River.
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Figure 8-20 and 8-21. Changes in seagrass coverage north and south of the Steinhatchee River mouth. Areas 

where seagrasses have not diminished between 2001 and 2006 are shown in green. 

Steinhatchee 

River
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As another measure of the biological health of these systems, DEP pursued information on the 

bay fisheries. Commercial fish landings data were not available specifically for the Steinhatchee 

river/estuary (which divides Taylor and Dixie Counties), or the Waccasassa (in Levy County). 

However, the sum of commercial landings from Taylor, Dixie, and Levy Counties in 2007 was 

3,462,694 lbs., which represents approximately 4% of the 2007 statewide commercial landings. 

Additionally, anecdotal information from area fishermen suggests an abundant and stable marine 

fishery at both systems.

8.3 Conclusions

Total phosphorus at the upper Steinhatchee River exceeded the 90th percentile of the nutrient 

benchmark sites and TP in the Waccasassa estuary approached the 90th percentile, yet no adverse 

effects were observed in the sensitive estuarine reaches, where healthy seagrass communities and 

fisheries prevailed. This study found that chlorophyll a concentrations in both estuaries were 

below the 11 ug/L impairment threshold adopted in Chapter 62-303, F.A.C. Compared with their 

respective headwaters, organic nitrogen was higher in both the Waccasassa and Steinhatchee 

estuaries, probably as a result of input from the extensive Spartina and Juncus marshes. This 

study, conducted at two minimally disturbed river/estuary systems, supports the position that 

establishing nutrient criteria at the 90th percentile of the reference site distribution can be 

protective of the biological integrity of sensitive downstream waters.
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9 Basis for the Proposed Lake Chlorophyll a Thresholds in 

Florida’s Numeric Nutrient Criteria Development 

9.1 Introduction

The purpose of this chapter is to summarize the scientific basis for the chlorophyll a thresholds 

used by DEP to establish numeric nutrient standards for lakes. Based on several lines of 

evidence, DEP is proposing a chlorophyll a threshold of 20 μg/L for colored lakes (above 40 

PCU) and clear lakes with hardness above 20 mg/L Ca CO3, and 6 μg/L for clear lakes with 

hardness below 20 mg/L Ca CO3. The Department plans to adopt these thresholds as numeric 

nutrient standards (response variables) and will also use them to develop numeric standards for 

TP and TN (using regression equations that relate nutrient concentrations to annual geometric 

mean chlorophyll a levels) for Florida lakes.

9.2 History of the Trophic State Index

DEP has a long history of using a modification of Carlson’s Trophic State Index (TSI) (Carlson, 

1977) as a measure of lake trophic state and lake water quality for the State’s 305(b) and 303(d) 

assessments. Trophic state reflects the biological response to several factors, including nutrient 

effects on phytoplankton chlorophyll a, which may be modified or mitigated by water retention 

time, grazing, and macrophyte nutrient uptake. Havens (2000) reported that the TSI approach 

provides an effective, low cost method for tracking long-term changes in pelagic structure and 

function and has value in monitoring lake ecology and responses to management actions.

Carlson’s original TSI classified lakes based on chlorophyll a levels and nitrogen and 

phosphorus concentrations, and included three indicators—Secchi depth, chlorophyll a, and total 

phosphorus—to describe a lake’s trophic state. A 10-unit change in the index represents a 

doubling or halving of algal biomass (chlorophyll a). 

Carlson, from Kent State University in Ohio, created the following interpretation scheme for the 

TSI based on nutrient/chlorophyll a responses in northern lakes (Carlson 1977). Note that 

Florida lakes do not have certain attributes of northern lakes, including the presence of coldwater or cool-water fisheries and fully oxygenated hypolimnetic (bottom) areas. 

TSI < 30 Classical Oligotrophy: Clear water, oxygen throughout the year in the hypolimnion, salmonid 

fisheries in deep lakes.

TSI 30 – 40 Deeper lakes still exhibit classical oligotrophy, but some shallower lakes will become anoxic in the 

hypolimnion during the summer. 

TSI 40 - 50 Water moderately clear, but increasing probability of anoxia in hypolimnion during summer. 

TSI 50 – 60 Lower boundary of classical eutrophy: Potential for decreased transparency, anoxic hypolimnia 

during the summer and macrophyte growth, warm-water fisheries only. 

TSI 60 - 70 Dominance of blue-green algae, algal scums probable, extensive macrophyte problems. 

TSI 70 – 80 Heavy algal blooms possible throughout the summer, dense macrophyte beds, but extent limited by 

light penetration. Often would be classified as hypereutrophic. 

TSI > 80 Algal scums, summer fish kills, few macrophytes, dominance of rough fish. 
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Salas and Martino (1991) proposed an alternate TSI categorization based on their work in 

phosphorus limited warm-water tropical lakes, which is more directly applicable to Florida 

conditions. The TSI and chlorophyll a values in Table 9-1 were determined based upon the TSI 

relationship with TP. Note that while Carlson would consider a TSI of 50-60 to represent the 

lower boundary of eutrophy in northern lakes, Salas and Martino considered that same range of 

TSI values to be mesotrophic in warm-water lakes, while eutrophic conditions would not occur 

until a warm water lake exhibited a TSI of 70. 

As stated earlier, the TSI equation describes a theoretical relationship between chlorophyll a, 

total phosphorus, and total nitrogen. Note that, as was the case for Carlson’s TSI, chlorophyll a

doubles with every 10 point increase in the TSI (Table 9-2). 

Table 9-2. Relationship between chlorophyll a, total phosphorus, and total nitrogen, as described by 

Florida’s TSI.

Trophic State 

Index

Chlorophyll a 

(μg/L)

Total Phosphorus 

(mg/L)

Total Nitrogen 

(mg/L)

0 0.3 0.003 0.06 

10 0.6 0.005 0.10 

20 1.3 0.009 0.16 

30 2.5 0.01 0.27 

40 5.0 0.02 0.45 

50 10.0 0.04 0.70 

60 20 0.07 1.2 

70 40 0.12 2.0 

80 80 0.20 3.4 

90 160 0.34 5.6 

100 320 0.58 9.3 

Table 9-1. Warm-water TSI categories (after Salas and Martino 1991).

TSI Category TP (μg/L) Chlorophyll a

40 Oligotrophic 21.3 5 

50-60 Mesotrophic 39.6 10-20 

70 Eutrophic 118.7 40 
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As part of the State’s 305(b) assessment, DEP revised the TSI by a) replacing Secchi depth with 

total nitrogen, and b) adding equations that adjust the nutrient component of the TSI to reflect the 

limiting nutrient. Use of Secchi depth in Florida as a measure of trophic state was unsuccessful 

due to the high frequency of dark-water lakes (< 40 PCU), where tannins originating from the 

breakdown of vascular plant tissues, rather than algae, diminish transparency. 

The resultant TSI is now based on chlorophyll a, total nitrogen, and total phosphorus 

concentrations, as follows: 

TSI = (CHLATSI + NUTRTSI)/2

Where: 

CHLATSI = 16.8 + 14.4 × LN (CHLA), and 

NUTRTSI is based on limiting nutrient considerations, as follows: 

If TN/TP > 30, then lake is phosphorus limited and NUTRTSI = TP2TSI 

TP2TSi = 10 × [2.36 × LN(TP × 1000) – 2.38] 

If TN/TP < 10, then lake is nitrogen limited and NUTRTSI = TN2TSI 

TN2TSI = 10 × [5.96 + 2.15 × LN(TN + 0.0001)] 

If 10 < TN/TP < 30, then co-limited and NUTRTSI = (TPTSI + TNTSI)/2 

TNTSI = 56 + [19.8 × LN(TN)] 

TPTSI = [18.6 × LN(TP × 1000)] –18.4 

These equations were determined based on the analysis of data from 313 Florida lakes, and were 

adjusted so that a chlorophyll a concentration of 20 μg/L was equal to a TSI value of 60. For the 

1998 305(b) report, a TSI threshold of 60 was used to represent “fair” lakes, while lakes above 

70 were assessed as “poor.” 

During development of the Impaired Waters Rule (IWR) in 1999 – 2000, the IWR Technical 

Advisory Committee (TAC) reviewed the TSI used in the 305(b) assessment and recommended 

that it be used to assess lakes for impairment. Based on then current EPA guidance that “fair” 

waters should be included on State 303(d) lists, the TAC recommended that the nutrient 

impairment threshold for most lakes (for those with a color higher than 40 platinum cobalt units) 

should be an annual average TSI of 60. 

While they recommended use of the TSI threshold of 60 for most lakes, they also recognized that 

some lakes are naturally oligotrophic and have significantly lower natural background TSIs. The 

TAC requested that DEP evaluate data from reference lakes from the Department’s 

Bioassessment Sampling Program using principal components analysis (PCA) in an attempt to 

identify different types of lakes based on water quality, with the goal to establish different TSI 

thresholds for each type. 
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While many different parameters were evaluated, the analysis initially focused on a four-part 

chemical classification of Florida lakes consisting of acid-clear, acid-colored, alkaline-clear, and 

alkaline-colored. This classification system had originally been proposed by Shannon and 

Brezonik (1972) and was subsequently confirmed as part of the development of the Lake 

Condition Index for Florida (Gerritsen et al. 2000). However, the analysis conducted for the 

IWR indicated that the most significant differences in the TSI and TSI-related parameters 

(nutrients and chlorophyll) were seen when the lakes were classified by color alone, with lakes 

with a color of less than 40 platinum cobalt units having significantly lower TSIs. This low color 

classification system also covered a previously identified target population of oligotrophic lakes 

that the TAC wanted to address (low color, oligotrophic lakes in the panhandle region of 

Florida). The Department then recommended, and the TAC agreed, to establish a TSI threshold 

of 40 for these lakes, which is equivalent to a chlorophyll a of 5 μg/L. 

9.3 Other Efforts to Establish Chlorophyll a Thresholds 

Appendix 9-A contains a review of literature pertaining to establishment of protective 

chlorophyll a thresholds, predominantly from the USA. The literature suggests six main 

approaches for establishment of protective chlorophyll a thresholds in lakes: 

• Paleolimnologic studies, where pre-human disturbance chlorophyll a values are inferred 

from an analysis of diatom communities in deep sediment cores;

• Expert elicitation, or best professional judgment, for the determination of protective TSI 

or chlorophyll a values;

• Fisheries responses to chlorophyll a or TSI levels, dependent upon type of fisheries 

which are in turn adapted to associated dissolved oxygen conditions (i.e., cold water vs. 

warm water fisheries); 

• Associating lake user visual perceptions (for swimming and aesthetics) with 

simultaneously measured chlorophyll a; 

• Setting the criterion to maintain the existing condition (protection strategy); and

• Using an upper percentile of the distribution of reference lakes. 

9.3.1 Paleolimnological Studies 

Paleolimnologicalstudies in Florida, where pre-human disturbance chlorophyll a values were 

inferred from an analysis of diatom communities in deep sediment cores, indicate that most 

peninsular Florida lakes would be considered to be at the lower boundary of classical eutrophy 

(or above), even prior to human habitation of the state (Whitmore and Brenner 2002; Whitmore 

2003). Paleolimnolgical studies conducted at (colored) Lakes Shipp, Lulu, Haines, May, Conine 

and Bonny in the Florida peninsula suggest that the average chlorophyll a in these lakes would 

naturally range between 14 to 20 μg/L. This is one line of evidence for supporting the 

chlorophyll a threshold of 20 μg/L that is part of the IWR’s TSI threshold. However, 

paleolimnology of (colored) Lakes Wauberg and Hancock suggests that historic chlorophyll a in 

those lakes naturally ranged from 38-48 μg/L and 74-133 μg/L, respectively. Note that although 

Lake Hancock may be somewhat atypical, the paleolimnological results suggest that any 

proposed nutrient criteria will need to allow for site specific alternative criteria (SSAC) in lakes 

with naturally higher (or lower) nutrient levels. 
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9.3.2 Expert Opinion

Several states have used input from scientific advisory committees or a best professional 

judgment (BPJ) approach to establish chlorophyll a or TSI targets. Virginia and Iowa queried a 

panel of experts to establish protective chlorophyll a targets, and the scientists in both states 

independently arrived at a recommended level of 25 μg/L as a yearly average (Gregory 2007; 

Wilton 2008). Virginia’s panel further recommended that chlorophyll a not exceed 50 μg/L as 

an instantaneous measurement. Arizona, using a TSI and a weight of evidence approach, 

established lake summer time (peak season) chlorophyll a targets at 20-30 μg/L (ADEQ 2008). 

Maryland established lake summer time chlorophyll a targets using the rationale that a TSI of 50 

(10 μg/L chlorophyll a) would prevent mesotrophic lakes from becoming eutrophic, and that a 

TSI of 60 (20 μg/L chlorophyll a) would protect against excessive eutrophication (Rule 2004). 

West Virginia used a BPJ approach to establish a chlorophyll a threshold of 33 μg/L. 

Additionally, Iowa has established annual average TMDL targets in specific lakes for the 

protection of aquatic life use (Lost Lake), which were subsequently approved by EPA, using a 

chlorophyll a threshold of 33 μg/L (EPA 2008). 

The various BPJ thresholds that would protect against excessive eutrophication, expressed as 

annual or summertime averages, yields a range from 20 to 33 μg/L of chlorophyll a. This range 

of values suggests that Florida’s Impaired Waters Rule TAC recommendation of 20 μg/L in 

colored lakes is as protective as those established by many other states.

9.3.3 Biological Responses 

The responses of valued ecological attributes, such as benthic macroinvertebrates or fish, to 

various chlorophyll a levels, would provide the most direct method for establishing targets that 

would protect aquatic life. Florida investigated establishment of a Lake Condition Index, using 

benthic macroinvertebrates as a response variable in lakes. Unfortunately, although initial results 

were promising, Florida eventually concluded that color was more responsible for explaining 

benthic response than were human disturbance measures, such as the Landscape Development 

Intensity Index (Fore 2007). 

Other states have used a fisheries response variable. For example, the state of Virginia 

conducted an analysis to determine the effect of chlorophyll a levels on the health of fisheries, 

and concluded that summer average chlorophyll a concentrations of 25 μg/L in coolwater lakes 

and 35-60 μg/L in warmwater lakes were protective of fish health (Gregory 2007). Minnesota, 

using multiple lines of evidence, including regional patterns, reference lakes, fish response, lake 

user perception, paleolimnolgy, and nuisance algal bloom frequency, established summer mean 

chlorophyll a targets of 3 to 5 μg/L for designated coldwater trout fisheries, 9-22 μg/L for deep 

lakes, and 20-30 μg/L for shallow (< 4.5 m) lakes (Heiskary and Wilson 2008). Colorado 

proposed that summer average chlorophyll a be maintained below 25 μg/L to assure high quality 

fisheries (Saunders 2009). Note that only warm water fisheries occur in Florida. 

DEP investigated fish community composition data collected from Florida lakes by the Florida 

Fish and Wildlife Conservation Commission for comparison to chlorophyll a data. Results of 

the analyses did not yield a notable response signal in the data and thus didnot further inform the 

determination of chlorophyll a targets. 
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9.3.4 User Perceptions 

In Texas, a study of lake user perceptions indicated that in reservoirs without inorganic turbidity 

(> 1 m Secchi) chlorophyll a levels below approximately 20-25 μg/L still support full immersion 

recreational uses, as well as aesthetics (Glass 2006). For this study, lake users were asked to fill 

out a questionnaire concerning their visual perceptions (for swimming and aesthetics) while 

chlorophyll a was simultaneously measured. A similar study conducted in Florida demonstrated 

that there were differences in user perceptions depending upon lake region (Hoyer et al. 2004). 

In the Florida study, when lake users responded to a question concerning suitability of the lake 

for recreation and aesthetic enjoyment by saying, “beautiful, could not be nicer,” chlorophyll a

ranged from approximately 30 μg/L in the Central Valley Lake region (generally high color or 

high alkalinity) to approximately 3 μg/L in the Trail Ridge Region (generally uncolored, low 

alkalinity lakes) (Hoyer et al. 2004). These studies did not associate chlorophyll a values with 

public health concerns, only the public perception of whether swimming was desirable or not. 

Based on user perceptions, clear and colored/high alkalinity lakes in Florida may need different 

chlorophyll a targets.

9.3.5 Maintaining Existing Conditions 

The State of Alabama’s approach to establishing lake or reservoir chlorophyll a targets may be 

described as a method designed to “maintain the existing condition” (Macindoe 2006). 

Alabama’s chlorophyll a targets for specific lakes or reservoirs range from 5 μg/L to 27 μg/L. 

The Florida IWR TAC’s recommendation of a TSI of 40 for Florida clear reference lakes was 

based upon the concept of maintaining the current condition of panhandle region sandhill lakes. 

A TSI of 40 equates to a chlorophyll a value of 5 μg/L. 

9.3.6 Reference Approach

Finally, a reference site approach, coupled with other techniques, including contour plot 

interpolation, was suggested as a method to establish chlorophyll a thresholds in Florida (Paul 

and Gerrittsen 2003). Based on the 75th percentile of reference sites, determined via BPJ with 

contour plot interpolation, Tetra Tech proposed chlorophyll a targets for Florida clear lakes (< 40 

PCU) ranging from 2 μg/L to 8 μg/L, and colored lake targets ranging from 9 μg/L to 18 μg/L. 

Since that study, Florida has proposed using a 90th percentile of reference conditions for 

establishment of nutrient standards for streams when other responses to nutrient dose were not 

available and the reference sites were thoroughly verified. The lakes Tetra Tech included for 

their reference site approach were based primarily upon BPJ, therefore they suggested a lower 

percentile.

9.4 Investigating Relationships between Cyanobacteria Abundance 

and Chlorophyll a

It is well established that cyanobacteria can become very abundant and completely dominate the 

phytoplankton community in lakes when conditions are right. Some cyanobacteria blooms can 

be toxic and present a health risk to people recreating in and on the water. The World Health 

Organization (WHO) established recommendations for recreational exposure during 

cyanobacterial blooms (WHO 1999). Their recommendations reflect their findings that a 

chlorophyll a level of 10 μg/L in which cyanobacteria are dominant presents a relatively low 

probability of mild irritative or allergenic effects, while a chlorophyll a level of 50 μg/L in which 
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cyanobacteria are dominant presents a moderate risk of adverse health effects. The WHO’s 

guidance for recreation in waters (WHO 2003, section 8.1) states that 46 species of 

cyanobacteria have been shown to cause toxic effects in vertebrates, and that any species or 

genera of cyanobacteria cannot be ruled out as potentially toxic. They caution that “it is prudent 

to presume a toxic potential in any cyanobacterial population.” 

Due to the potential human health risks associated with cyanobacteria blooms, DEP considered 

the possibility of a chlorophyll a threshold that might be associated with a high probability of 

cyanobacteria blooms. DEP examined the relationship between chlorophyll a and the percent 

cyanobacteria in 1,364 phytoplankton samples from small and large lakes randomly sampled 

between 2000 and 2006 in Florida’s probabilistic sampling network. Figure 9-1 shows 

chlorophyll a values regressed against the percent cyanobacteria for each sample. Based on the 

graph, there does not appear to be any increased probability of cyanobacteria dominance as 

chlorophyll a increases. Samples dominated by one of the 13 harmful algal bloom (HAB) taxa 

listed by the WHO (WHO 2003, section 8.1) did not show an increasing trend of cyanobacteria 

dominance with chlorophyll a either (also, see discussion in Chapter 11 below). 

Researchers from the University of Florida conducted a survey of microcystin concentrations at 

187 lakes in Florida throughout 2006 (Bigham et al. 2009). Chlorophyll a values in 862 samples 

from these 187 lakes ranged from 0.3 μg/L to 280 μg/L, and microcystin concentrations ranged 

from <0.1 μg/L to 32 μg/L. Table 3 of Bigham et al. (2009) shows that a lake with a chlorophyll 

a concentration of 20 μg/L is associated with an approximately 5% probability of microcystin 

detection above the WHO drinking water guideline of 1 μg/L. Microcystin concentrations did 

not exceed the WHO recreational guideline of 20 μg/L until chlorophyll a exceeded 130 μg/L. 

Based on this dataset, a chlorophyll a limit of 20 μg/L would have been protective of drinking 

water in 95% if those systems and recreational uses in all the systems. 
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Figure 9-1. The relationship between chlorophyll a concentrations (note the log scale) and the percent 

cyanobacteria in 1,364 lake samples collected for Florida’s statewide probabilistic monitoring program.

The proposed numeric nutrient standards in Chapter 62-302.531, F.A.C., implement the existing 

narrative criterion for nutrients, which states “[i]n no case shall nutrient concentrations of a body 

of water be altered so as to cause an imbalance in natural populations of aquatic flora or fauna,” 

Rule 62-302.530(47)(b). The maintenance of a balanced natural population of aquatic flora and 

fauna is inherently protective of potable water supply uses from the harms caused by excessive 

nutrients. Protection of well balanced natural populations of aquatic flora and fauna is protective 

of potable water supply uses that would be expected to occur under those biological conditions. 

The sensitive biological indicators considered for these waters included aquatic vegetation,

phytoplankton, phytoplankton biomass as chlorophyll a, invertebrates, and fish. Concentrations 

of total nitrogen and phosphorus do not, in and of themselves, inhibit potable water supply (note 

that the State already has a nitrate criterion of 10 mg/L for drinking water to protect human 

health). Rather, it is imbalances in aquatic flora (excessive algal blooms) caused by nutrient 

enrichment that may inhibit potable water supply use. 

The Department concluded that maintaining the expected healthy biological status of the 

waterbodies would simultaneously maintain other applicable designated uses. Maintenance of 

well balanced natural population of flora would in-turn continue to support potable water supply 

STATUS Phytoplankton Data

April 2000 - July 2006

5 50 500

Chl-a

-20

0

20

40

60

80

100

120

CyanophycotaPct

 Dominated by HAB 

 Not Dominated by HAB
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uses expected under un-impacted biological conditions. For example, maintenance of an annual 

geometric mean of 20 μg/L is associated with a 4% probability of having individual cholorophyll 

a concentrations greater than 50 μg/L. 

While there was insufficient information to base numeric interpretations of the narrative criterion 

on impacts to potable water supply, both water quality standards (Chapter 62-302, F.A.C.) and 

the Impaired Waters Rule (Chapter 62-303, F.A.C.) contain several provisions that protect waters 

against problematic algal blooms. Waters with algal blooms or mats in sufficient quantities to 

pose a nuisance or hinder reproduction of threatened or endangered species are listed as impaired 

(Rules 62-303.350[1], 62-303.351[3], 62-303.352[2], 62-303.353[3], 62-303.354[2], 62-303.450, 

F.A.C.). 

A waterbody can be listed for the occurrence of harmful algal blooms even if the annual 

geometric mean is less than the applicable chlorophyll a criterion for the waterbody. The IWR 

also includes a provision (Rule 62-303(1)(b), F.A.C.) that lists a water on the planning list if a 

public water system demonstrates that either a) treatment costs to meet applicable drinking water 

criteria have increased by at least 25% to treat blue-green algae or other nuisance algae in the 

source water, or b) the system has changed to an alternative supply because of additional costs 

that would be required to treat their surface water source. 

9.5 Conclusions

Carlson and Simpson (1996) noted that trophic state is not synonymous with the concept of 

water quality. While trophic state is an absolute scale that describes the biological condition of a 

waterbody, water quality is used to describe the condition of a waterbody in relation to human 

needs or values, relative to the use of the water and the expectations of the user. Water quality 

standards are created to protect the designated uses of waterbodies. In the case of Florida lakes, 

the designated uses are for the protection of healthy, well balanced populations of fish and 

wildlife, and for recreation. Criteria must provide protection for these sometimes competing 

interests. For example, an oligotrophic to mesotrophic lake may have water quality deemed 

desirable for swimming, however this same lake may not be considered to be optimal for bass 

fishing. For this reason, DEP has taken a weight of evidence approach for establishing 

protective chlorophyll a thresholds. 

Multiple lines of evidence were used to evaluate the rigor of protection inherent in the Impaired 

Waters Rule (IWR) Technical Advisory Committee’s TSI- based chlorophyll a

recommendations, which were adopted into the IWR in 2002 (Chapter 62-303, FAC). Table 9-3 

contains a summary of the various approaches.

Table 9-3. Lines of evidence used in determining support of the 2002 Florida Impaired Waters Rule 

Technical Advisory Committee’s chlorophyll a target recommendations.

Line of Evidence Chlorophyll a target State

Paleolimnological studies 14 to 20 μg/L (higher for 

some lakes) Florida 
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Expert opinion 20-33 μg/L Virginia, Iowa, West Virginia, 

Maryland

Fisheries responses 

(warmwater)

Fisheries responses (coldwater 

trout and coolwater) 

35-60 μg/L 

3-5 μg/L and 25 μg/L, 

respectively

Virginia

Minnesota, Colorado 

Lake user perceptions

20-25, up to 30 μg/L in 

colored lakes; as low as 3 

μg/L in Florida Trail Ridge 

clear lakes

Texas and Florida 

Existing levels approach 5-27 μg/L Alabama

Reference lake approach

2-8 μg/L in clear lakes, 9-18 

μg/L in colored and high 

alkalinity clear lakes

Florida, using 75th percentile

Multiple lines of evidence, including paleolimnnolgy, fisheries success, and user perception, 

converge to support the Florida IWR TAC’s original recommendation that 20 μg/L of 

chlorophyll a in colored lakes is protective of designated uses. It has been hypothesized that 

phytoplankton populations may switch to communities dominated by cyanobacteria at 

chlorophyll a levels above 20 μg/L, however, this pattern was not observed in an analysis of 

1,364 Florida lakes. Cyanobacteria are usually an unfavorable food source to zooplankton and 

many other aquatic animals, and some may even produce toxins, which could be harmful to fish 

and other animals. For this reason, the World Health Organization considers it to be a moderate 

risk for swimming when waters are dominated by cyanobacteria and accompanied by an 

instantaneous chlorophyll a of 50 μg/L (symptoms such as skin irritation and conjunctivitis may 

be more prevalent). Based upon the above multiple lines of evidence, DEP concluded that an 

annual average chlorophyll a of 20 μg/L in colored and high alkalinity clear lakes is protective of 

the designated uses of recreation and aquatic life support. 

IWR TAC recommended that an annual geometric mean chlorophyll a of 5 μg/L be maintained 

in clear lakes, which was based on a “maintain existing condition approach” and which was 

primarily targeted at a specific geographic region of Florida (the panhandle). Although some 

Alabama lakes do have a target that low (again, based on maintenance of existing condition), the 

range of acceptable chlorophyll a in Alabama ranged from 5-27 μg/L. Coldwater trout fisheries 

(which do not exist in Florida) require chlorophyll a in the 3-5 μg/L range. A reference lake 

approach proposed by Tetra Tech suggests that chlorophyll a values of up to 8 μg/L in clear 

lakes represent the 75th percentile of reference lakes. Moreover, the TSI categorization of Salas 

and Martino (1991), based on warm water lakes, would consider a chlorophyll a of 10 μg/L (TSI 

of 50) to be mesotrophic. Thus, a multiple lines of evidence approach suggests that a chlorophyll 

a concentration <10 μg/L would be a protective threshold for Florida’s clear lakes. DEP 

solicited input from the Nutrient TAC in June, 2009, and the Nutrient TAC also suggested that 

maintaining chlorophyll a below 10 μg/L in low alkalinity (<20 mg CaCO3/L) clear lakes would 
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be protective of the designated use, since a value of <10 μg/L would still be categorized as 

oligotrophic. Therefore, DEP initially proposed the low alkalinity clear lake chlorophyll a

threshold at 9 μg/L, and engaged EPA. 

EPA (2010) used three lines of evidence to inform the final derivation of its chlorophyll a 

threshold concentration for clear, low alkalinity lakes. First, EPA judged that the appropriate 

trophic state of clear, low alkalinity lakes is oligotrophic, and therefore, the work of Salas and 

Martino suggested a chlorophyll a criterion of 10 μg/L. Least-disturbed lakes provide a second 

line of evidence, suggesting criteria ranging from 5 – 8 μg/L, depending on whether one selects 

the 75th or 90th percentile of the distribution. Finally, examination of the distribution of 

chlorophyll a concentrations in all sampled lakes of this class indicated that 25th and 75th 

percentiles of existing chlorophyll a concentrations were 2 and 7 μg/L. EPA was uncertain 

regarding the degree to which the least-disturbed lakes represented minimally-disturbed 

conditions. With minimally disturbed lakes, the 90th percentile would be appropriate (as applied 

with the streams nutrient criteria), but uncertainty in the selection of these lakes suggested a 

criterion somewhat less than the 90th percentile value. Conversely, the similarity between the 

existing lakes and the least-disturbed distributions suggests that a higher proportion of all 

sampled lakes in this class are least-disturbed than is typically observed. Hence, a relatively high 

percentile of the existing lakes distribution was appropriate. Finally, the boundary between 

mesotropic and oligotrophic conditions was higher than values derived from either leastdisturbed or existing lakes, but uncertainties with regard to the applicability of these numbers to 

Florida led EPA to weigh this line of evidence less strongly than the other two. Therefore, EPA 

adjusted down from the 90th percentile value of least-disturbed lakes because of uncertainty 

regarding the condition of these lakes, and adjusted substantially up from the 25th percentile of 

all sampled lakes because chlorophyll a concentrations in lakes. These considerations led EPA to 

finalize a criterion value of 6 μg/L. DEP reviewed the information and analyses presented by 

EPA and concluded that 6 μg/L was an appropriate and protective threshold for clear, low 

alkalinity lakes.

The TAC suggested that different nutrient and chlorophyll a expectations should be established 

for high alkalinity (>20 mg CaCO3/L or specific conductance >100 μmhos/cm) clear lakes 

because of the naturally higher, aquifer-derived phosphorus levels this subset of clear lakes. The 

TAC suggested that nutrient thresholds in clear, high conductivity lakes be based on preventing 

the annual average chlorophyll a from exceeding 20 μg/L. EPA (2010) determined that because 

natural geological sources increase TP concentrations in these clear, high alkalinity lakes that the 

appropriate trophic state of this class of lakes is mesotrophic. EPA agreed with the state 

threshold of threshold of 20 μg/L for these lakes.

The literature also noticeably supported the concept of allowing site specific alternative criteria 

(SSACs) for lakes where either higher or lower levels could be justified, based upon scientific 

information, and DEP plans to allow development of SSACs for nutrients. This is consistent 

with provisions that allow development of site-specific thresholds that better represent the levels 

at which nutrient impairment occurs and the use of higher chlorophyll and nutrient targets if 

paleolimnological data indicate a lake was naturally above mesotrophic conditions. 
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10 Stressor-Response Analyses of Florida Lakes 

10.1 Introduction

As previously stated for streams, the most comprehensive and scientifically defensible approach 

to developing numeric nutrient criteria is to establish cause and effect relationships between 

nutrients (stressors) and valued ecological attributes. The approach is further strengthened when 

the valued ecological attribute response can be linked to designated use support. Various lines of 

evidence discussed in Chapter 9 provided justification for use of chlorophyll a as an indicator of 

designated use support, primarily as measure of excessive algal growth, which can result in 

imbalances of natural populations of flora or fauna. Additionally, the Lake Vegetation Index 

(LVI) is a direct assessment of the floral community and can therefore be used to demonstrate

use support. 

DEP evaluated responses in both chlorophyll a and the Lake Vegetation Index to total 

phosphorus and total nitrogen concentrations. Lakes were initially categorized based on color 

categories previously adopted in Florida’s Impaired Waters Rule. Lakes with period of record 

color less than or equal to 40 platinum cobalt units (PCU) were categorized as clear, and lakes 

with color greater than 40 PCU were categorized as colored. Based upon recommendations from 

the Nutrient TAC, DEP also evaluated whether there were any differences in the relationships 

between nutrients and chlorophyll a in clear lakes with specific conductance values above and 

below 100 μmhos/cm. The specific conductance threshold was designed to capture lakes that 

receive input from calcareous aquifer sources, which naturally contain higher levels of 

phosphorus than do lakes that receive most of their water from (low conductivity) rainfall. 

10.2 Macrophyte Analyses

The relationship between lake macrophytes and nutrients was examined using Florida’s Lake 

Vegetation Index (LVI). Initial analysis evaluated the response of the LVI to nutrients using 

instantaneous nutrient measurements collected the same day as the LVI. These initial analyses 

showed weak, yet statistically significant relationships in clear lakes (Table 10-1), but only the 

relationship between LVI and TP was significant in colored lakes. The weak initial relationships 

are not surprising because macrophytes integrate lake nutrient conditions over time and primarily 

obtain nutrients through uptake from sediments rather than the water column. Therefore, DEP 

evaluated the LVI response to long-term nutrient conditions, specifically average conditions one 

year prior to the LVI sampling event. 

Table 10-1. Spearman rank correlation coefficients between instantaneous nutrient concentrations and 

Florida’s Lake Vegetation Index.

All 

Lakes

Clear Lakes Colored 

Lakes

TP -0.396 -0.397 -0.364

TN -0.324 -0.314 -0.211
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Lake Vegetation Index samples from 91 clear and 53 colored lakes were paired with nutrient 

data from STORET, DEP’s Ambient Program’s database (GWIS), and Florida’s biological 

database (SBIO). Nutrient data collected during the one year period prior to LVI sample 

collection were averaged using a geometric mean concentration. Only lakes with a minimum of 

three water chemistry samples during the period were analyzed further for LVI analyses (note 

that all analyses with chlorophyll a had a minimum of four samples per year). Statistically 

significant relationships were found between the LVI and one-year geometric mean TP and TN 

concentrations in both colored and clear lakes (Figures 10-1 and 10-2). The analyses indicate 

that lake vegetation exhibits a significant adverse response to nutrients. However, the adjusted 

R2 values were still low, primarily because the macrophyte community also responds to other 

environmental factors such as sediment conditions, physical disturbance, introduction of exotic 

taxa, and the presence or absence of herbivores. Since the biology responds to the combined 

effect of all these factors along with nutrients, the natural variation in these other environmental 

factors blur the observed biological response to nutrients. With the exception of the clear lakes 

TP and LVI relationship, the LVI response to nutrients was insufficiently robust to be used as the 

basis to establish numeric nutrient criteria. The TP relationship could potentially be used as a 

line of evidence to support numeric nutrient criteria to protect populations of flora in Florida’s 

clear lakes. 
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Figure 10-1. Relationships between LVI and geometric mean (A) TP and (B) TN in clear (color ≤ 40 PCU) 

Florida lakes. The nutrient concentrations were calculated as the geometric mean of samples collected within 

the lakes during the 365 day period prior to LVI collection. Solid black line is the least squares regression 

and the blue dashed lines are the 95% C.I. of the regression. Note that x-axis is expressed on a log-scale.

One Year Geometric Mean TP (mg/L)

0.001 0.01 0.1 1

LVI

0

10

20

30

40

50

60

70

80

90

100

One Year Geometric Mean TN (mg/L)

0.1 1 10

LVI

10

20

30

40

50

60

70

80

90

100

y = -12.6 Ln(x) + 0.3

R2 = 0.441

p<0.0001

y = -15.74 Ln(x) + 43.16

R2

 = 0.287

p<0.001

A)

B)
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Figure 10-2. Relationships between LVI and geometric mean (A) TP and (B) TN in colored (color > 40 PCU) 

Florida lakes. The nutrient concentrations were calculated as the geometric mean of samples collected within 

the lakes during the 365 day period prior to LVI collection. Solid black line is the least squares regression 

and the blue dashed lines are the 95% C.I. of the regression. Note that x-axis is expressed on a log-scale.

One Year Geometric Mean TP (mg/L)

0.01 0.1 1

LVI

0

10

20

30

40

50

60

70

80

90

100

One Year Geometric Mean TN (mg/L)

0.1 1 10

LVI

10

20

30

40

50

60

70

80

90

100

y = -7.819 Ln(x) + 35.02

R2

 = 0.198

p=0.001

y = -11.5 Ln(x) + 60.02

R2

 = 0.124

p= 0.011

A)

B)
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10.3 Chlorophyll a Analyses

Water chemistry and chlorophyll a data in Florida lakes were queried from the Florida STORET 

and the GWIS database. The initial dataset consisted of 33,622 samples from 4,417 sites 

distributed within 1,599 lakes. All data were spatially linked to USGS lake reach codes based on 

station coordinates. Water chemistry and chlorophyll a data were averaged by parameter, lake 

reach code and date. Data from 324 lakes were used to access chlorophyll a response to 

nutrients based on over 9,600 paired results. 

10.3.1 Clear Lakes

As was the case for the LVI data, the Department categorized the data into clear and colored 

lakes. Clear lakes were further sub-divided based upon low and high conductivity (using 100 

μmhos/cm as the demarcation point). The sub-categorization of clear lakes was based on 

recommendations from the Nutrient TAC to evaluate differences in natural nutrient expectations 

within clear lakes. The TAC recommended that the clear lakes needed to be sub-categorized 

based on morphoedaphic factors to capture the differences between lakes receiving groundwater 

input from calcareous aquifer sources (higher alkalinity), which contain natural higher levels of 

phosphorus, from lakes that receive most of their water from (low alkalinity) rainfall. They 

recommended that an alkalinity threshold of 20 mg CaCO3/L or specific conductance of 100 

μmhos/cm (in the absence of alkalinity data) would provide a scientifically defensible and 

implementable basis for sub-categorizing Florida’s clear lakes. 

Where necessary, data were log-transformed. Paired nutrient and chlorophyll a data were 

available for 195 clear lakes. Regional differences among the lakes were evaluated, but clear 

lakes showed similar chlorophyll a responses regardless of location, with some differences in the 

range of nutrient concentrations (Figures 10-3 and 10-4). Chlorophyll a concentrations exhibited 

statistically significant positive responses to both total phosphorus and nitrogen on an annual 

average basis (Figure 10-5). These relationships explain a large portion of the annual average 

variability observed in chlorophyll a concentrations (R2

=0.68-0.77). Figure 10-6 shows that 

there are no significant seasonal chlorophyll a differences in the data set used to develop the 

nutrient criteria, indicating a yearly average is appropriate. Therefore, the regression 

relationships shown in Figures 10-3 and 10-4 can be used to develop scientifically defensible 

nutrient criteria designed to prevent excess algal growth based on protective chlorophyll a

thresholds of 6 and 20 μg/L for clear lakes with alkalinity concentrations above and below 20 mg 

CaCO3/L, respectively (Table 10-3). 
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Figure 10-3. Relationship between Ln transformed chlorophyll a and total phosphorus in clear lakes by 

nutrient region. Note: that the lakes exhibit a similar chlorophyll response to TP independent of region.

Figure 10-4. Relationship between Ln transformed chlorophyll a and total nitrogen in clear lakes by nutrient 

region. Note: that the lakes exhibit a similar chlorophyll response to TN independent of region.

Ln Total Phosphorus (mg/L)

-7 -6 -5 -4 -3 -2 -1 0

Ln Chlorophyll-a (μg/L)

-2

-1

0

1

2

3

4

5

6

Peninsula

Panhandle

Bone Valley

North Central

Northeast

Ln Total Nitrogen (mg/L)

-3.0 -2.5 -2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0

Ln Chlorophyll-a (μg/L) -3

-2

-1

0

1

2

3

4

5

6

Peninsula

Panhandle

Bone Valley

North Central

Northest
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Figure 10-5. Regression analyses annual geometric mean chlorophyll a concentrations and annual geometric 

mean (A) TP and (B) TN concentrations in clear Florida lakes. Note that x-axis and y-axis are both expressed 

on a log-scale.

Annual Geometric Mean TP (mg/L)

0.001 0.01 0.1 1

Annual Geometric Mean Chl-a (μg/L)

0.1

1

10

100

1000

Annual Geometric Mean TN (mg/L)

0.1 1 10

Annual Geometric MeanChl-a (μg/L)

0.1

1

10

100

1000

Ln (y) = 1.136 Ln(x) + 6.370

R2 = 0.677

p<0.001

Ln (y) = 1.729 Ln (x) + 2.482

R2 = 0.769

p<0.001

Regression Line

50% Prediction Interval

Regression Line

50% Prediction Interval
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Figure 10-6. Box plots of chlorophyll a by season in clear (left panel) and colored (right panel) lakes.

10.3.2 Colored Lakes

Paired nutrient and chlorophyll a data were available for 129 colored lakes. Initial analyses 

revealed statistically significant (p <0.001) yet weak relationships between chlorophyll a and 

both TP (R2= 0.38) and TN (R2=0.47). Other factors influencing the chlorophyll a response 

were then investigated in an attempt to improve the relationship with nutrients. Despite initial 

lake sub-categorization by color, a significant inverse relationship (Spearman R=-0.25) remained 

between color and chlorophyll a, with the influence of color most pronounced in lakes with color 

in excess of 150 to 200 PCU. Chlorophyll a level in these highly colored lakes were typically 

reduced when compared to the levels in less colored lakes, despite similar nutrient 

concentrations; that is, color in excess of approximately 150 PCU depresses the nutrient response 

(light limitation). 

 A multiple regression model (adjusted R2= 0.507) was constructed between chlorophyll a 

(dependent variable) and TP and TN (independent variables) to investigate the influence of lake 

color on chlorophyll a response in colored lakes (Table 10-2). Model residual error was plotted 

against both color expressed as a long-term geometric mean (period of record) and annual 

geometric mean color. This evaluation demonstrated that the nutrient regression model tended to 

underestimate (positive residuals) chlorophyll a concentrations at lakes with color less than 

approximately 150 PCU and overestimated (negative residuals) chlorophyll a levels at lake with 

color over approximately 150 PCU (Figure 10-7). 

Classification and Regression Tree (CART) analysis was used to discriminate a breakpoint in the 

model residual error. A significant breakpoint that explained 36.4% percent of the relative error 

in the residuals was found at a long-term lake color of 143 PCU. Additional breakpoints were 

found at annual geometric mean colors of 54 and 360 PCU. These subsequent breakpoints 

provided only marginal improvements in the amount of explained variance (Figure 10-8). Based 

on the CART analysis, the colored lakes were further sub-categorized to long-term ranges of 

>40-140 PCU (moderately colored; n=100 lakes) and >140 PCU (highly colored; n=29 lakes), 

for purposes of investigating nutrient responses, to account for the substantial remaining 

influence of color on the chlorophyll a. 

 

Jan.-April, Oct.-Dec. May-Sept.

Chlorophyll 

a (μg/L)

0.01

0.1

1

10

100

1000

Jan.-April, Oct.-Dec. May-Sept.

Chlorophyll 

a (μg/L)

0.1

1

10

100

1000

10000
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Regional differences among the moderately colored lakes (color between 40 and 140 PCU) were 

evaluated, but it was concluded that these colored lakes showed similar chlorophyll a responses 

regardless of location, although there were differences in the range of nutrient concentrations 

(Figures 10-9 and 10-10). Chlorophyll a exhibited statistically significant positive responses to 

both total phosphorus and nitrogen on an annual average basis in the moderately colored lakes 

(Figure 10-11). These relationships are sufficiently robust to develop scientifically defensible 

and protective criteria. 

The relationships between TP and TN and chlorophyll a in the highly colored lakes (greater than 

140 PCU) were significant but weak (Figure 10-12). These relationships demonstrate that 

nutrients influence chlorophyll a response (excess algal growth) in highly colored lakes and thus 

provide support for the need to develop numeric nutrient criteria to protect the designated use. 

However, the relationships are not sufficiently robust to directly derive numeric nutrient criteria 

given the high level of uncertainty and unexplained variance. In the absence of a strong and 

robust nutrient-chlorophyll a relationship in the highly colored (>140 PCU) lakes, fully 

protective criteria for these systems can be developed based on the response relationships from 

the moderately colored lakes (40-140 PCU), although these criteria will be somewhat 

overprotective given that high color will reduce algal response and biomass. 

Table. 10-2. Summary of the linear multiple regression between Ln transformed chlorophyll a and Ln 

transformed TP and TN in colored Florida lakes. The regression multiple R2 and adjusted R2 were 0.510 

and 0.507, respectively. 

Effect Coefficient Std 

Error

Std Coef. Tolerance t P(2 

Tail)

CONSTANT 2.703 0.272 0 . 9.922 0.0000

LTP 0.347 0.085 0.213 0.457 4.091 0.0000

LTN 1.546 0.149 0.542 0.457 10.395 0.0000

Source Sum-ofSquaresdf MeanSquareF-ratio P

Regression 449.764 2 224.882 205.223 0.0000

Residual 432.839 395 1.096
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Figure 10-7. Relationship between the residual error in the TP and TN chlorophyll model and (A) period of 

record geometric mean lake color and (B) annual geometric mean lake color. Note that both relationships 

exhibit a significant negative slope.

Split Variable PRE* Improvement

1 POR_COLOR 0.333 0.333

2 Annual_COLOR 0.371 0.039

3 Annual_COLOR 0.409 0.037

*Proportion reduction in error. 

Figure 10-8. Classification and Regression Tree (CART) analysis, using a least-squares fitting method, of the 

residual error from the TP and TN model for chlorophyll a response in colored Florida lakes. The analysis 

demonstrates that colored lakes can be split into two large groups, using the first CART split, where the 

chlorophyll a response to TP and TN differs due to the confounding effect of color.

Period of Record Color (PCU)

0 100 200 300 400 500 600

Model Residual

-5

-4

-3

-2

-1

0

1

2

3

y = -7.1x + 0.90

R2 = 0.384

Annual Geometric Mean Color (PCU)

0 200 400 600 800 1000

-5

-4

-3

-2

-1

0

1

2

3

y = -3.74x + 0.60

R2 = 0.246

A) B)

RESIDUAL

Mean=-0.003

SD=1.058

N=362

Mean=-1.042

SD=0.982

N=99

Mean=0.388

SD=0.787

N=263

POR_COLOR<143.660

Mean=-2.819

SD=0.801

N=8

Mean=-0.886

SD=0.834

N=91

ANNUAL_COLOR<556.976

Mean=0.103

SD=0.785

N=127

Mean=0.654

SD=0.693

N=136

ANNUAL_COLOR<76.207
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Ln Total Phosphorus (mg/L)

-6 -5 -4 -3 -2 -1 0

Ln Chlorophyll-a (μg/L)

-2

-1

0

1

2

3

4

5

6

Peninsula

Panhandle

Bone Valley

North Central

Northeast

Figure 10-9. Relationship between Ln transformed chlorophyll a and TP in moderately colored lakes by 

nutrient region. Note that the lakes exhibit a similar chlorophyll response to TP independent of region. 

Figure 10-10. Relationship between Ln transformed chlorophyll a and TN in moderately colored lakes by 

nutrient region. Note that the lakes exhibit a similar chlorophyll response to TN independent of region.

Ln Total Nitrogen (mg/L)

-2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5 3.0

Ln Chlorophyll-a (μg/L) -3

-2

-1

0

1

2

3

4

5

6

Peninsula

Panhandle

Bone Valley

North Central

Northest
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Figure 10-11. Regression analyses between annual geometric mean chlorophyll a concentrations and annual 

geometric mean (A) TP and (B) TN concentrations in moderately colored (>40-140 PCU) Florida lakes. Note 

that the x-axis and y-axis are both expressed on a log-scale.

Annual Geometric Mean TP (mg/L)

0.01 0.1 1

Annual Geometric Mean Chl-a (μg/L)

1

10

100

Annual Geometric Mean TN (mg/L)

0.1 1 10

Annual Geometric Mean Chl-a (μg/L)

0.1

1

10

100

1000

Ln (y) = 1.128 Ln(x) + 5.729

R2 = 0.581

p<0.001

Ln (y) = 1.965 Ln(x) + 1.995

R2 = 0.645

p<0.001

Regression Line

50% Prediction Interval

A)

B)
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Figure 10-12. Regression analyses between annual geometric mean chlorophyll a concentrations and annual 

geometric mean (A) TP and (B) TN concentrations in highly colored (>140 PCU) Florida lakes. Note: x-axis 

and y-axis are both expressed on a log-scale.

Annual Geometric Mean TP (mg/L)

0.01 0.1 1

Annual Geometric Mean Chl-a (μg/L)

0.01

0.1

1

10

100

Annual Geometric Mean TN (mg/L)

0 1 2 3

Annual Geometric Mean Chl-a (μg/L)

0.1

1

10

100

Ln (y) = 0.534 Ln(x) + 2.610

R2

 = 0.122

p<0.001

Ln (y)= 0.827Ln (x) + 0.985

R2

 = 0.062

p=0.009
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10.4 Criteria Derivation

Regression models describe the relationship between two variables where the magnitude of one 

variable (dependent) is assumed to be a function of one or more independent variables; that is, a 

degree of the variance in the dependent variable is explained by the independent variable(s). 

The regression line and equation define the average relationship (i.e., half the data points fall 

above the line and half below it). Essentially, there is a 50% probability that a given level of N 

or P will elicit the chlorophyll a response corresponding to the regression equations shown in 

Figures 10-5 and 10-11. The DEP concluded that a simple application of an average response 

was not adequately protective and that a more complex application was needed to account for 

uncertainty in the dose-response relationship. 

In the case of nutrient criteria, response uncertainty can be managed by considering nutrient 

concentrations in a range between a level that is unlikely (e.g., 25% probability) to elicit a given 

threshold of response and a level that is likely to elicit a response (e.g., 75% probability). 

Regression prediction intervals provide a range above and below the regression line that 

incorporate the unexplained variability of the independent variable, as well as the uncertainty in 

the model parameters (slope and intercept). Within this range of nutrient concentrations 

(between the upper and lower prediction interval), there is less certainty that a response 

(exceedance of the chlorophyll target) will or will not occur. This represents a range of 

conditions in which nutrients may be managed while considering the potential for Type I 

(incorrectly identifying a water as impaired) and Type II (failing to identify an impaired water) 

errors. 

Nutrient concentrations less than or equal to the lower end (upper prediction interval) are 

unlikely to elicit the response threshold and therefore can be used as the basis for protective 

criteria, with a low probability of Type II statistical error but a high potential for Type I error. 

Conversely, a high likelihood of an undesirable response occurs when the nutrient concentration 

exceeds the upper end of the range (lower prediction interval). The probabilities of statistical 

errors at the upper end of the nutrient range are inverted compared with those at the lower end; 

that is, there is a low probability of Type I error and a higher probability for Type II error. 

Because algal response is influenced by factors other than nutrients (grazing, macrophyte 

nutrient uptake, and water retention time), the most scientifically defensible strategy for 

managing nutrients within the range of uncertainty is to verify a biological response prior to 

taking management action. If data demonstrate that a given lake is biologically healthy and does 

not experience excess algal growth (e.g., < 20 μg chlorophyll a/L in a colored lake or high 

alkalinity clear lake) despite having nutrient concentrations within the range of uncertainty, then 

no nutrient reductions are needed. However, if the lake exhibits excess algal growth or 

biological impairment within this band of uncertainty, corrective action is warranted. In the 

absence of chlorophyll a data, decisions should be made with an abundance of caution and 

assume an impaired condition if nutrients exceed the lower threshold. If chlorophyll a data 

subsequently indicate that the designated use is indeed maintained at nutrient levels within the 

upper and lower prediction interval, then those existing levels should be deemed acceptable. 

Given this “performance based approach” and using annual geometric mean chlorophyll a values 

of 20 μg/L for colored lakes and higher alkalinity clear lakes, and 6 μg/L for clear, low alkalinity

Florida lakes, respectively, criteria ranges associated with protection of designated uses can be
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defined based on the 50% prediction intervals. Following this approach, EPA (2010b) defined 

the nutrient concentration yielding 25% probability of exceeding a chlorophyll a target as the 

baseline criteria [Figures 2-28 through 2-31 in EPA (2010b)]. Nutrient concentrations less than 

or equal to upper threshold (upper prediction interval) are unlikely to exceed the response 

threshold and therefore can be used as the basis for protective criteria. The resultant lower and 

upper thresholds for clear/low alkanity lakes, clear/high alkalinity lakes, and colored lakes, are 

provided in Table 10-3. 

Table 10-3. Total phosphorus and total nitrogen criteria ranges for clear (<40 PCU) and colored Florida 

lakes (>40 PCU). The lower and upper thresholds were based on the intersection of chlorophyll a response 

concentrations with the 50% predictions intervals shown in Figures 2-28 through 2-31 in EPA (2010b). 

Lake Type Response 

(Chl-a 

μg/L) 

Stressor Lower Threshold Upper Threshold

Clear and 

Low 

Conductivity 

(≤ 40 PCU 

and ≤ 20 

mg/L CaCO3)

6 TP (mg/L) 0.01 0.03

6 TN (mg/L) 0.51 0.93

Clear but 

High 

Conductivity 

(≤40 PCU but 

> 20 mg/L 

CaCO3)

20 TP (mg/L) 0.03 0.09

20 TN (mg/L) 1.05 1.91

Colored

20 TP (mg/L) 0.05 0.161

20 TN (mg/L) 1.27 2.23

1

For lakes with color > 40 PCU in the West Central Nutrient Watershed Region, the maximum TP limit shall be the 

0.49 mg/L TP streams threshold for the region (see discussion below for explanation). 

As discussed in Chapter 5, there is a strong scientific basis for concluding that a large fraction of 

the Bone Valley (West Central Peninsula NWR) limnetic phosphorus is derived from natural 

sources. Residuals analysis showed that statewide TP regression models tended to overestimate 

chlorophyll a response in the West Central NWR Lakes (Figure 10-14, DEP 2010b). DEP 

evaluated the relationship between chlorophyll and TP and TN in West Central NWR lakes using 

least squares regression. Strong and statistically significant relationships were found between 

chlorophyll a and both TP (R2

=0.45; p<0.001) and TN (R2=0.68; p<0.001) in clear lakes. The 

relationship between TN and chlorophyll a was also robust and significant (R2=0.66; p<0.001). 

However, the relationship between TP and chlorophyll a in colored West Central lakes was 

extremely weak (R2

=0.028; p=0.315) suggesting that other factors (e.g., N-limitation, residence 

time) greatly confound the influence of TP on algal response (Figure 10-15). 

EPA used the intersections between 20 μg/L and the upper and lower 50% prediction intervals to 

set the upper and lower TP ranges for colored Florida Lakes. However, when only colored West 

Central lakes are evaluated, these intersection points are outside of the calibration dataset. In 
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fact, the upper limit (lower prediction interval) would not intersect 20 μg/L until well above 1.0 

mg/L of TP (Figure 10-15). The lack of a strong predictive relationship demonstrates that little 

would be gained, in terms of within lake designated use protection, by controlling TP in colored 

West Central NWR. However, waters discharged from lakes must be protective of downstream 

flowing waters; therefore, DEP decided to cap the upper TP threshold for West Central colored 

lakes at the regional stream TP threshold of 0.49 mg/L 

Figure 10-14. Residual error (observe-predicted) by Florida Lake Region for chlorophyll a using the EPA 

proposed TP based regression equations. Results for both clear and colored lakes were combined. Negative 

Residuals indicate an over prediction of lake chlorophyll response, while positive residuals indicate an under 

prediction of response.
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Figure 10-15. Regression analyses between annual geometric mean chlorophyll a concentrations and annual 

geometric mean TP and concentrations in colored West Central NWR lakes.

10.5 Establishing Lake Color Levels

Because color (PCU) is important in determining the applicable nutrient criteria, DEP performed 

an analysis to establish the most appropriate averaging period for classifying a lake as clear or 

colored. For this analysis, DEP obtained color data sets from several example lakes. Color data 

from multiple stations and years in a lake were then calculated as annual geometric means for 

each year. A rolling average was then calculated from the annual geometric means using varying 

time periods to evaluate the time period over which to average in order to minimize the variance 

in the resultant data set. Results indicated that a five year rolling average was generally 

sufficient to ensure minimization of the variance (see Figure 10-16 for example data set).

TP (mg/L)

0.001 0.01 0.1 1 10

Chlorophyll 

a (μg/L)

0.1

1

10

100

1000

Regression

50% Prediction Interval

Ln Chl-a=Ln(TP)*0.594+3.942

R2=0.0281

p=0.351
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Figure 10-16. Variance in Lake Okeechobee color data from 8 pelagic stations, averaged over varying time 

periods.

10.6 Algal Blooms and Seasonality

Because it is possible that algae may respond to nutrients over a short period of time (i.e., days or 

weeks), DEP conducted analyses to demonstrate that the annual geometric mean would be 

protective and found that the annual geometric mean is influenced by short term blooms (see 

Figure 11-1). Furthermore, the short-term relationship between nutrients and algal response is 

substantially more variable (noisy) than a longer term response because there is a lag between 

nutrient dose and algal response. Algae do not instantaneously respond to nutrient enrichment, 

but rather to the nutrient conditions at some past time. Algae require a period of time to uptake 

nutrients and incorporate these into additional biomass, assuming other factors (e.g., light, 

grazing pressure, other nutrients, carbon) do not limit growth. Averaging over a year inherently 

incorporates the lag between dose and response that would otherwise require extremely frequent 

monitoring to characterize. 

DEP investigated seasonality in algal response as well as chlorophyll a levels and found that 

there was no consistent seasonal pattern (Figures 10-17 and 10-18). Furthermore, the 

relationships between chlorophyll and TP and TN were not significantly different between the 

wet and dry seasons, meaning there is no particular critical sampling period that would capture 

“bloom events” on a statewide basis. Monitoring requirements need to be established on 

waterbody specific basis. In fact, an effective monitoring plan should include event based 
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monitoring, particularly during blooms. However, adding this as requirement to the rule goes 

beyond its scope and would overly complicate the rule. 

Since the data do not indicate that there is a seasonal component to algal blooms, the four-sample 

per year requirement was selected to be consistent with the derivation of the criteria, provide a 

minimal level of certainty, and allow assessment of as many waterbodies as possible based on 

data collect by numerous entities for a variety of purposes (“found data”). If there are bloom 

events of concern in a waterbody, DEP is confident that they will be flagged by the criteria. 

Regular monitoring (e.g., quarterly, monthly) is typically supplemented with additional “eventbased” monitoring, particularly in cases of algal blooms; that is, additional monitoring is 

conducted to characterize the severity and duration of blooms. These additional data would be 

combined with more routine data to conduct assessments. The event-based monitoring may 

actually result in a “high bias” in the data set, making resulting assessments inherently 

conservative.

Figure 10-17. Boxplot of chlorophyll a concentrations in colored and high alkalinity clear 

Florida lakes by month. Note that high concentrations (blooms) can and do occur during any 

month of the year with approximately equivalent frequencies. 

 

Month

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Chlorophyll 

a

0.1

1

10

100

1000

10000
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Figure 10-18. Boxplot of chlorophyll a concentrations in low alkalinity clear Florida lakes by 

month. Note that high concentrations (blooms) can and do occur during any month of the year 

with approximately equivalent frequencies. 

 

Month

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Chlorophyll 

a

0.1

1

10

100

1000
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11 Magnitude, Frequency, and Duration of Numeric Nutrient 

Standards

The numeric nutrient standards described in this document are expressed as annual geometric 

means that shall not be exceeded more than once in a three year period. Great consideration was 

given to whether or not this was the appropriate expression, and this section provides discussion 

of these considerations. The annual geometric mean expression of the criteria duration is rooted 

precisely in how the criteria were calculated. To switch to a different duration would not be 

consistent with the underlying science behind the criteria. The exceedance frequency (no more 

than once in a three year period) was based on EPA’s Technical Support Document for Water 

Quality-Based Toxics Control, March 1991 (EPA number: 505290001), which when applied to 

non-toxic substances, such as nutrients, is inherently protective. 

11.1 Yearly Compliance

Appropriate duration and frequency components of criteria should be based on how the data used 

to derive the criteria were analyzed and what the implications are for protecting designated uses 

given the effects of exposure at the specified criterion concentration for different periods and 

recurrence patterns. For lakes, the stressor-response relationship was based on an annual 

geometric mean of individual years at individual lakes. The appropriate expression of the 

duration component is therefore annual. 

DEP considered studies by Bachmann et al. (2003) that examined bloom frequency. Their 

analyses predicted that an annual mean of 20 μg/L chlorophyll a could result in blooms greater 

than 40 μg/L or 50 μg/L chlorophyll a approximately 10 and 5 percent or the time, respectively. 

DEP replicated the methods of Bachmann et al. (2003) for colored and clear high alkalinity lakes 

using the IWR Run 43 database. The Department’s analysis predicts that an annual geometric 

mean chlorophyll a concentration of 20 μg/L could be associated with instantaneous chlorophyll 

a concentrations of greater than 40 μg/L or 50 μg/L approximately 8 and 4 percent of the time, 

respectively (Figure 11-1). 

A bloom of 50 μg/L, under conditions when cynobacteria are dominant, may represent a level 

that could affect full contact recreational uses. The World Health Organization (1999) concluded 

that a chlorophyll a level of 50 μg/L, only when cyanobacteria are dominant, presents a 

moderate risk of adverse health effects. However, high chlorophyll concentrations do not 

necessarily indicate the dominance of cyanobacteria or harmful algal species. Due to the 

potential human health risks associated with cyanobacteria blooms, DEP considered the 

possibility of a chlorophyll a threshold that might be associated with a high probability of 

cyanobacteria blooms. DEP examined the relationship between chlorophyll a and the percent 

cyanobacteria in 1,364 phytoplankton samples from small and large lakes randomly sampled 

between 2000 and 2006 in Florida’s probabilistic sampling network. Figure 9-1 shows 

chlorophyll a values plotted against the percent cyanobacteria for each sample. Based on the 

graph, there does not appear to be any increased probability of cyanobacteria dominance as 

chlorophyll a increases. Samples dominated by one of the 13 harmful algal bloom (HAB) taxa 
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listed by the WHO (WHO 2003, section 8.1) did not show an increasing trend of cyanobacteria 

dominance with chlorophyll a either. 

Figure 11-1. Percent of annual samples when chlorophyll a exceeds 20, 40, and 50 μg/L as a 

function of annual geometric mean chlorophyll a. 

Bigham et al. (2003) surveyed microcystin in 187 Florida lakes and related lake chlorophyll 

concentrations to the probability of exceeding WHO microcystin recreational guidance values 

(20 μg/L microcystin). They reported that there was no risk (0% probability) of exceeding 20 

μg/L microcystin below chlorophyll concentrations of 130 μg/L. The risk of exceeding the 

recreational threshold was reported as 7% for chlorophyll concentrations ranging from 130-280 

μg/L. It should be noted that the authors did note that results of the study were reported with 

caution due to some limitations of the study. However, the Bigham et al. (2003) study suggests 

that there is a margin of safety between levels of chlorophyll potentially associated with adverse 

recreation effects (i.e., >100 μg/L) and DEP’s chlorophyll a standard. 

Although the standards allow the annual geometric chlorophyll a, as well as the TP and TN 

concentrations, to be above the thresholds once in a three-year period, the reality is that the longterm average conditions must be below the threshold in order for a waterbody to consistently 

achieve (e.g., at least 90% of the time) the criteria. This is because there is variability around the 

 

Annual Geometric Mean

0 20 40 60 80 100 120 140 160 180 200

Proportion of Annual Samples

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

Proportion > 20 μg/L

Proportion > 40 μg/L

Proportion > 50 μg/L
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long-term average, both due to natural factors and sampling and testing. This results in measured 

annual geometric means both above and below the true long-term geometric mean condition. 

Based on the binomial distribution and assumption of inter-annual independence (i.e., no or 

minimal autocorrelation between years), it can be expected with 90% confidence that the 80th

percentile geometric mean concentration will be exceeded no more than once in a three-year 

period. In other words, it is expected that the 80th percentile would be exceeded more than once 

(i.e., two or three times) in a three-year period only 10% of the time on a long-term basis, which 

represents an acceptable type I error rate. If the long-term 80th percentile represents a level 

associated with an expected one in three year exceedance (10% of the time), then it logically 

follows that the true long-term average (geometric mean) must be substantially lower than the 

threshold. 

DEP evaluated inter-annual variability in lake chlorophyll a levels in lakes and found that interannual standard deviation (natural log-transformed) typically ranges from 0.305 to 0.533. Given 

this level of variability, the long-term geometric chlorophyll a concentration in a lake would 

need to be between 12.8 and 15.5 μg/L to be consistently found in compliance with the 

chlorophyll a standard of 20 μg/L. Furthermore, it is highly unlikely that a lake with long-term 

geometric concentrations in this range would experience chlorophyll levels near concentrations 

that could potentially adverse affect recreational uses (e.g., 40-50, 100 μg/L). In fact, it is highly 

improbable for the annual geometric mean to exceed 25 to 31 μg/L (expected 95th percentile 

range) in a lake in only one year, thereby achieving the nutrient criteria. Thus, not only are the 

chlorophyll a thresholds highly protective of recreational uses, but lakes must maintain 

conditions well below these thresholds in order to consistently achieve the criteria. 

It is highly unlikely for an impacted lake (i.e., one with chlorophyll levels greater than 20 μg/L) 

to repeatedly exceed the threshold only once in a three year period, because the probability of 

exceeding the threshold increases as the long-term, or the three-year average increases. As 

previously discussed, a long-term geometric mean chlorophyll a concentration between 12.8 and 

15.5 μg/L is required to consistently attain the criteria in at least 90% of the 3-year periods. As 

concentrations increase from these levels, the probability of exceeding 20 μg/L in greater than 

one out of three years increases such that there is a 50% probability at a long-term geometric 

mean concentration of 20 μg/L. 

The expected exceedance rate at a given average condition is dependent on the variability 

(standard deviation) around that average. Figure 11-2, illustrates how the probability of 

exceeding the one-in-year assessment increases dramatically as average chlorophyll 

concentration increases above 20 μg/L. It is highly improbable for a lake that has shifted above 

mesotrophic conditions (e.g., from a chlorophyll of 20 μg/L up to 30 μg/L) not to exceed the 

threshold more than once in a three year period. 
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Figure 11-2. Probability of exceeding an annual geometric mean chlorophyll a concentration of 

20 μg/L more than once in three-year period (i.e., two or three years) as a function of long-term 

geometric mean concentration for two estimates of lake inter-annual variability. The two 

estimates of lake inter-annual variability bracket the range typically observed in Florida lakes. 

The proceeding discussion regarding the probability of exceedance assumes that the annual 

geometric mean concentrations are random (i.e., a function of mean and standard deviation) with 

no auto-correlation between years. In reality, nutrient concentrations and algal responses tend to 

be auto-correlated, meaning that high years tend to follow high years, low years tend to follow 

low years, and increasing and decreasing trends tend to continue in subsequent years. Figure 11-

3 shows example time series plots for three Florida lakes selected for their long period of 

continuous monitoring. These plots illustrate that years above 20 μg/L tend to be followed by 

one or more years above 20 μg/L. 

This pattern is partially explained by the fact that nutrient runoff into lakes is highly influenced 

by climate, which is highly cyclical and itself auto-correlated. Additionally, the pool of nutrients 

within a lake, including anthropogenic enrichment, will continue cycling within a lake for a 

period of time typically greater than a year. This pool of nutrients is known as the lake’s internal 

load. The period over which nutrients continue cycling is related to the lakes residence time 

(how long it takes for there to be a completely flushing or to experience a complete change of 

water) as well as biological processes that sequester nutrients into plant biomass and ultimately 

sediments. However, sequestered nutrients can and do re-enter the water column, through the 

decomposition of plants as well as sediment release and resuspension, where they can again 

affect algal biomass during sequent years. Thus, a lake that experiences nutrient enrichment 

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 10 20 30 40 50 60

Exceedance Probability

Long-term Geometric Mean Chl-a (μg/L)

Std. Dev.=0.305

Std. Dev.=0.533
Nutrient Criteria Technical Support Document Chapter 11: Magnitude, Frequency, Duration of Criteria 

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during one year is highly likely to continue exhibiting the effects (i.e., exceeding thresholds) 

during subsequent years. 

The probability of observable impacts will increase if enrichment is continued in the subsequent 

years, which is highly likely for human sources. In fact, increasing trends of nutrients and 

chlorophyll will most likely be observed if the enrichment continues into subsequent years 

because the overall pool of nutrients within the lake would continue to increase since the internal 

and external loads exceed flushing and internal biological assimilative processes. Thus, an 

impacted waterbody would be expected to continue exceeding chlorophyll and nutrient 

thresholds. 

DEP’s one-in-three-year test was designed to test whether the frequency of exceedance is 

consistent with random variability around a healthy well balanced condition. A healthy lake 

would be expected to follow this type of random variability and experience infrequent 

exceedances due solely to natural conditions, although extreme climatic conditions could 

potential perturb lake conditions (i.e., increase annual load and/or release sequestered nutrients) 

and cause it to exceed the one-in-three year frequency. In contrast, an enriched or impacted lake 

will have an elevated long-term mean and contain an elevated internal nutrient load, which will 

act to increase the probability of exceeding the threshold more than once in a three-year period. 
Nutrient Criteria Technical Support Document Chapter 11: Magnitude, Frequency, Duration of Criteria 

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Figure 11-3. Time series plots of annual geometric mean chlorophyll a concentrations in (A) 

Lake Yale (Lake County), (B) Lake Hatchineha (Osceola County), and (C) Lake Okeechobee. 

The correlation coefficients for a one-year lag are shown in each of the respective plots.

0

5

10

15

20

25

30

35

40

45

1989 1994 1999 2004 2009

Annual Geometric mean Chl-a (μg/L)

Year

0

5

10

15

20

25

30

1989 1994 1999 2004 2009

Annual Geometric mean Chl-a (μg/L)

Year

0

5

10

15

20

25

30

1989 1994 1999 2004 2009

Annual Geometric mean Chl-a (μg/L)

Year

(A)

R=0.457

(B)

R=0.604

(C)

R=0.674
Nutrient Criteria Technical Support Document Chapter 11: Magnitude, Frequency, Duration of Criteria 

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11.2 Annual Geometric Mean

DEP established a minimum sample size of four samples to calculate an annual geometric mean. 

This requirement represents the minimum sample size below which DEP believes there is too 

much uncertainty to confidently assess numeric nutrient standard attainment. DEP selected the 

4-sample minimum based on prior work to develop TP and TN TMDLs for tributaries to Lake 

Okeechobee. The DEP found that increasing the required sample size from 4 up to 6 or 10 per 

year did not significantly alter the numeric threshold, but did substantially reduce the number of 

waterbodies included in the evaluation (Weaver 2006 and 2008). The four sample minimum 

represents a balance point between having a minimum level of confidence in the estimated 

annual geometric mean and the ability to assess as many waters as possible. Setting a higher 

requirement will limit the number of waterbodies that can be assessed under the Impaired Waters 

Rule. Furthermore, DEP does not have the authority to dictate sample design for ambient 

purposes, including sample frequency, to outside entities. These outside entities collect water 

quality data for a variety of purposes and design the monitoring to achieve those purposes within 

budgetary constraints. Sample frequency should be set based on each waterbodies unique 

conditions (variability) and the project’s objectives (e.g., desired power). 

Although the Everglades P-criterion sets a precedent for the use of the geometric mean to 

establish and assess attainment of numeric nutrient criteria, it does not logically follow that it 

establishes a precedent for sampling frequency. The characteristics of the Everglades marsh are 

different from those of lakes and streams. The Everglades undergoes annual wet and dry cycles 

during which portions of the marsh go completely dry. During drought years, the dry periods 

can last for a substantial portion of the year. The highly organic peat sediments of the 

Everglades will oxidize, releasing phosphorus, during these dry-down episodes. This liberated 

phosphorus is mobilized into the water column when the marshes re-wet, resulting in a natural 

spike in phosphorus. The six samples per year requirement was established to ensure that 

compliance with the criterion would not be overly biased by these natural wet-dry cycles. If a 

portion of the marsh was dry for a large portion of the year, then the annual geometric mean for 

stations within that portion would be overly influenced by natural conditions rather than 

anthropogenic enrichment. Thus, there is a likelihood of a false positive result, unless the post 

dry-down data are balanced with data from wetter periods. 

The Everglades is also atypical in that it is a highly studied and monitored waterbody. The South 

Florida Water Management District (SFWMD) committed to maintaining a dedicated monitoring 

network for purposes of assessing attainment of the Everglades P-criterion. This network is 

monitored on a monthly basis, largely due to precedents established under the Everglades 

Settlement Agreement. Under the Everglades Settlement Agreement, large portions of the 

Everglades marsh were already being monitoring at a monthly frequency. Thus, the monthly 

frequency was extended to the entire P-criterion assessment network for consistency and 

logistical reasons. The monthly sampling within the Everglades was highly dependent on the 

commitment of the SFWMD to conduct the monitoring on a continued basis. The SFWMD, as 

well as other affected parties, desired a high statistical power (ability to detect a small difference) 

in the Everglades; therefore, a high sample frequency was specified. As previously stated, DEP 

does not have the authority to dictate ambient sampling design on outside groups. Furthermore, 

DEP does not have the resources to conduct monthly monitoring across the entire state. 
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Robinson et al. (2004) investigated the effect of sampling frequency on data distributions, and 

stated that “the statistical distribution of concentrations of all water quality parameters measured 

over the duration of weekly sampling could have been closely described had the sample 

collection frequency been bi-weekly, tri-weekly, monthly, bi-monthly, or even quarterly rather 

than weekly.” The only differences found by Robinson et al. (2004) was in the characterization 

of nitrate loads. The bi-monthly and quarterly sampling produced nitrate load estimates that 

were significantly different from the weekly sampling strategy. Likewise, Robertson and 

Roerish (1999) investigated the effect of different sampling strategies on load estimates. 

The DEP agrees that accurate load estimates require substantially more frequent sampling than 

quarterly. However, it does not logically follow that estimation of frequency distributions (e.g., 

annual geometric mean) requires a more frequent sampling schedule, as demonstrated by 

Robinson et al. (2004). Florida’s numeric nutrient standards were specifically designed to assess 

the frequency and duration of a magnitude. The geometric mean, which is a statistical measure 

of location (central tendency), in DEP’s criteria is directly linked to the desired (protective) 

frequency distribution. 

Stansfield (2001) evaluated the effect of sampling frequency on trend determination based on 

individual samples and not annual averages or geometric means. The trend tests specified in 

Chapter 62-303, F.A.C., are being used to detect potential increasing trends in annual geometric 

mean concentrations. Additionally, Stansfield (2001) was interested in detecting seasonal trends, 

which logically requires more frequent sampling than quarterly. Stansfield actually concluded 

that 1) “in the case of quarterly data, the standard error may occasionally be large enough to 

discount a trend that is evident using monthly data” and, 2) “trends detected using quarterly data 

were slightly different compared with trends detected using monthly data.” Stansfield did not 

conclude that quarterly data cannot or should not be used to assess trends. 

Sokal and Rolff (2012) mention uncertainty (increased standard error) associated with various 

factors. The authors discuss standard error on pages 135-137, and provide two examples, 

comparing the standard errors of arithmetic means based on sample sizes of n=5 and n=35. They 

demonstrate that standard error of the mean based on small sample sizes is greater than when the 

mean is estimated based on a larger sample size. This outcome is not surprising since the 

standard error is calculated as the standard deviation divided by the square root of sample size 

(n). As such, means based on larger samples will always be associated with smaller standard 

errors assuming nearly equivalent standard deviations. 

This condition is not unique to the arithmetic mean or geometric mean; it is true of any statistical 

estimate. Sokal and Rolff (2012) do not state a preference for any given measure of central 

tendency nor do they state that the median is a better measure of central tendency than the 

geometric mean. They do state that in instances involving asymmetric distributions the “median 

is considered a more representative measure of location than is the arithmetic mean”. Regarding 

other means (e.g., geometric, harmonic), they state that: 

Some beginners in statistics have difficulty accepting the fact that 

measures of location or central tendency other than the arithmetic mean 

are permissible or even desirable. They feel that the arithmetic mean is 
Nutrient Criteria Technical Support Document Chapter 11: Magnitude, Frequency, Duration of Criteria 

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the “logical” average and than any other mean would distort the data. 

The attitude raises the question of the proper scale of measurement for 

representing data; this scale is not always the linear scale familiar to 

everyone but is sometimes, by preference, a logarithmic or reciprocal 

scale.

Clearly, the authors support the use of the geometric mean in cases where the distribution 

approximates a lognormal distribution. 

The geometric is the mean of the logarithms, transformed back to the original data. For 

positively skewed data, the geometric mean is typically very close to the median. In fact, when 

the logarithms of the data are symmetric, the geometric mean is an unbiased estimate of the true 

median (Helsel and Hirsch 2002). For distributions that are positively skewed and vary over 

orders of magnitude (such as nutrients or bacteria counts), the geometric mean is a more accurate 

indicator of the central tendency than the arithmetic mean (Sanders et al. 2003). 

The use of a geometric mean, coupled with a defined period, has precedent both within Florida 

and nationally. The Everglades phosphorus criterion is expressed as both annual and long-term 

geometric means. Geometric means are used in EPA-approved NNC in Hawaii and Oklahoma. 

EPA (2011) demonstrated that Florida Stream TP and TN data do not follow a normal 

distribution, but are instead skewed to the right and more closely follow a log-normal 

distribution (Figure 11-4). They further demonstrated that lake TP, TN and chlorophyll a were 

log-normal distributed; that is, the natural log transformed data approximated a normal 

distribution. Thus, the use of geometric mean is statistically and scientifically defensible
Nutrient Criteria Technical Support Document Chapter 11: Magnitude, Frequency, Duration of Criteria 

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Figure 11-4. Quantile plots of TP, Ln(TP), TN, and Ln(TN) for Florida stream data, from EPA 

(2011). Units are mg/L. 

The use of geometric means was previously upheld by the Division of Administrative Hearings 

[Miccosukee Tribe of Indians vs. Department of Environmental Protection and Environmental 

Regulation and United States Sugar Corporation and South Florida Water Management District 

(Case No. 03-2872RP); Friends of the Everglades vs. Department of Environmental Protection 

and Environmental Regulation and United States Sugar Corporation and South Florida Water 

Management District (Case No. 03-2873RP); Sugar Cane Growers Cooperative of Florida vs. 

Department of Environmental Protection and South Florida Water Management District (Case 

No. 03-2884RP). The hearing included testimony by Dr. Parkhurst on his 1998 journal article as 

well several other statistical experts. David M. Maloney, Administrative Law Judge ultimately 

found that: 

1. While a geometric mean discounts high values, an arithmetic mean, on the other hand, 

may be too influenced by high values if the aim is to find central tendency. A high value, 

especially if data points are few, will raise the arithmetic mean substantially. In 

particular, in the case of data that exhibits a log-normal distribution, the arithmetic mean 

might be significantly removed from point of central tendency if there were some data 

point that was unusually high in relation to the remainder of the data; 

2. It was statistically appropriate, therefore, that a geometric mean be used in establishment 

of the phosphorus criterion; and, 

3. Application of the geometric mean to a data set demonstrating a log-normal distribution 

results in a more accurate estimate of the true central tendency of the population of 

measures and therefore a more accurate estimate of the concentration of water column 

total phosphorus in the areas sampled over most of the time. 

Parkhurst (1998) raised concerns about the use of the geometric mean, in particular that it is 

biased and does not represent mass balance properly. However, both criticisms are based on 

faulty premises. The first faulty premise is that geometric mean should be a good estimator of 

arithmetic mean. Parkhurst evaluated how well the arithmetic mean, geometric mean, and two 

different bias-correct geometric means predicted the true arithmetic mean. He found that for 

“normal or left-skewed distribution, there is little bias with any of the estimates at any of the 

sample sizes”. In other words, the geometric mean provides a reasonable estimate of central 

tendency if data are normally distributed. However, when he compared the geometric mean 

estimates for log-normal distributions to the theoretical population arithmetic mean he found that 

the geometric means were biased high. This finding only supports the idea that a geometric is 

not the same as an arithmetic mean, particularly for right-skewed data. 

It would have been more appropriate to evaluate the bias of all four means relative to the true 

central tendency. In fact, inspection of Figure 11-5 from Parkhurst (1998) clearly shows that the 

arithmetic mean is biased high relative to the central tendency of right-skewed data; that is, it 

over estimates the true center of the distribution. Furthermore, the more skewed that data are the 

more biased the arithmetic mean is likely to become. This is a particularly relevant point 

because DEP’s criteria were derived as geometric means. The assessment of attainment should 

be conducted in a manner consistent with derivation of the criteria. Use of arithmetic means to 
Nutrient Criteria Technical Support Document Chapter 11: Magnitude, Frequency, Duration of Criteria 

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assess compliance with the geometric mean based criteria would result in a high bias and 

unacceptable false positive rate. 

Figure 11-5. Probability density plots used in simulations by Parkhurst (1998). Figure taken 

from Parkhurst (1998). The vertical dashed lines represent the theoretical arithmetic mean or 

each distribution, which was set to 100. In plot (a) the theoretical arithmetic mean is very close 

to the peak of the curve, indicating that the arithmetic mean is representative of the central 
Nutrient Criteria Technical Support Document Chapter 11: Magnitude, Frequency, Duration of Criteria 

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tendency. In the case of the normal distribution the arithmetic mean corresponds exactly to the 

central tendency. However, in plot (b) the arithmetic mean line is well to the right peak of the 

curves, indicating that the arithmetic mean is actually a biased measure of central tendency. 

While it is true that the geometric mean does not represent mass balance, this is not relevant for 

concentration-based criteria. The criteria were neither derived to represent a mass balance nor 

will assessment of compliance attempt to characterize mass. Other measures, such as annual 

load or flow weighted means, would have been utilized if mass balance had been the objective of 

the criteria. The criteria are actually designed to maintain a criterion magnitude and allow for 

variability around that magnitude within the specified frequency and duration. 

Ultimately, the criteria expression must be consistent with the science used to derive the criteria. 

The duration and magnitude (annual geometric mean condition) are expressed precisely as they 

were justifiably calculated, and expressing them any other way would not be supported by the 

underlying method. Furthermore, DEP evaluated which minimum sample size was relevant to 

the calculations and found that increasing the minimum number of samples needed beyond 4 

samples did not meaningfully change the results. Therefore, since a minimum sample size of 4 

was used to derive the criteria, deviating from that expectation in the setting the criteria would 

deviate from the underlying science, and not be justified. 

11.3 Consideration of Extreme Events

Assessments conducted by the Department routinely include the data collected in a wide variety 

of water levels, flow conditions, and seasons. Rule 62-303.450(6), F.A.C., includes a provision 

that requires DEP to evaluate whether extreme conditions or changes in a monitoring network 

are “solely” responsible for placing a waterbody on the verified list for TMDL development. For 

example, hurricanes can mobilize organic materials from swamps and floodplains, leading to 

temporary reductions in dissolved oxygen, increases in Total Suspended Solids (TSS), color, and 

nutrients associated with TSS and color (Tomasko et al. 2006). Extreme droughts may lead to 

the situation where normally perennial streams are reduced to disconnected pools, and the 

nutrient regime in such pools would be highly influenced from sediment interactions, and 

therefore, be potentially unrepresentative of anthropogenic effects. This was observed in a 

reference stream in Alachua County (Hatchett Creek), a minimally disturbed stream that 

exhibited an increase in chlorophyll a during a period of drought, when only disconnected pools 

remained. 

Unless a large data set is involved (e.g., monthly samples for 20 years), these extreme hydrologic 

events may not be representative of typical ambient conditions and water quality conditions 

during these events may overly skew the data distribution. While these events are representative 

of the full range of natural variability, they will very likely be overly influential when evaluating 

shorter periods of record . If the data set is sufficiently large (e.g., monthly for 10 to 20 years), 

these events are less likely to be overly influential and could be included because they are 

representative of the full range of natural variability. The effect of extreme event data on the 

overall distribution (i.e., on the 90th and 95th percentiles) should be evaluated and overly 

influential data should be excluded if it can be demonstrated and documented that these data 

were associated with unusual hydrologic conditions. 
Nutrient Criteria Technical Support Document Chapter 11: Magnitude, Frequency, Duration of Criteria 

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Finally, when criteria are established based on the data distribution from a particular set of open 

water sampling stations, that distribution will likely change if stations are relocated from open 

water areas to swamp or marsh influenced areas. DEP will require that all excluded data be 

identified and clear documentation as to the basis for exclusion must be provided in the 

supporting documentation.
Nutrient Criteria Technical Support Document Chapter 12: References 

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