EPA Proposed Rule TSD for Florida’s Streams and DPVs for Lakes

i 

Technical Support Document for EPA’s Proposed Rule 

for Numeric Nutrient Criteria to Protect Florida’s 

Streams and the Downstream Protection of 

Unimpaired Lakes
EPA Proposed Rule TSD for Florida’s Streams and DPVs for Lakes

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Disclaimer

This document is intended to support U.S. EPA’s (hereafter EPA or the Agency) proposed 

numeric water quality criteria for nutrients (EPA-HQ-OW-2009-0596; FRL#9678-6). The 

information provided herein does not substitute for the Clean Water Act or EPA’s regulations; 

nor is this document a regulation itself. Thus, this document cannot and does not impose any

legally binding requirements on EPA, states, authorized tribes, the regulated community, or any 

other party, and might not apply to a particular situation or circumstance.
EPA Proposed Rule TSD for Florida’s Streams and DPVs for Lakes

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Authors

Jacques L. Oliver, Office of Water, U.S. Environmental Protection Agency 

Christopher Prosser,1 Office of Water, U.S. Environmental Protection Agency 

Dana A. Thomas, Office of Water, U.S. Environmental Protection Agency 

Steve Whitlock, Office of Water, U.S. Environmental Protection Agency 

Lester L. Yuan, Office of Water, U.S. Environmental Protection Agency 

Michael J. Paul, Tetra Tech

Jon Butcher, Tetra Tech

Peter Cada, Tetra Tech

Catherine Carter, Tetra Tech

Jonathan B. Harcum, Tetra Tech

John Hochheimer, Tetra Tech

Sam Sarkar, Tetra Tech

 

1 John A. Knauss Sea Grant Fellow, National Oceanic and Atmospheric Administration, National Sea Grant College Program.
EPA Proposed Rule TSD for Florida’s Streams and DPVs for Lakes

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Contents

Chapter 1. Derivation of EPA’s Proposed Numeric Nutrient Criteria for Streams ...........1

1.1 Introduction .......................................................................................................................1

1.2 Development of Nutrient Watershed Regions (NWRs) ....................................................5

1.2.1 Refinement of Regionalization for Nutrient Criteria Development .....................6

1.2.2 Development of the NWRs ...................................................................................8

1.3 Reference Condition Approach for Developing Numeric Nutrient Criteria for 

Streams ............................................................................................................................18

1.3.1 Data Selection and Preparation ...........................................................................18

1.3.2 Selection of Reference Sites––Least Disturbed Sites (Benchmark 

Population) ..........................................................................................................20

1.3.3 Selection of Reference Sites—Biologically Healthy Sites (SCI 

Population) and Impacted Sites ..........................................................................28

1.3.4 Nutrient Concentrations and Distributions of Populations within Each 

NWR ...................................................................................................................29

1.3.5 Selection of Stream Criteria—Magnitude (Benchmark and SCI 

Populations) ........................................................................................................38

1.4 Defining Healthy Aquatic Life in Florida Streams .........................................................44

1.4.1 Development of the SCI......................................................................................45

1.4.2 Establishing Healthy Biological Conditions in Streams—SCI: 

Application of the Reference Condition Approach ............................................46

1.4.3 The BCG Approach ............................................................................................47

1.4.4 Evaluation of BCG Calibration Information.......................................................49

1.4.5 Selecting the Threshold between Healthy and Unhealthy Biological 

Conditions ...........................................................................................................50

1.4.6 Analysis of Biological Data in the Benchmark Population ................................50

1.5 Harmful, Adverse Effects Due to Exceedance of EPA’s Proposed Stream 

Criteria ............................................................................................................................52

1.5.1 Harmful, Adverse Effects Observed by the State of Florida ..............................52

1.5.2 EPA Analyses Demonstrating Harmful, Adverse Effects ..................................59

1.6 Additional Evidence of Harmful Effects in Florida Streams Above EPA’s 

Proposed Criteria ............................................................................................................84

1.6.1 Nutrient Response Thresholds: General .............................................................84

1.7 Duration and Frequency for Streams Numeric Nutrient Criteria....................................89

1.8 Summary .........................................................................................................................90

1.9 References .......................................................................................................................92

Chapter 2. Downstream Protection Values for Unimpaired Lakes .................................100

2.1 Background ...................................................................................................................100

2.2 Downstream Protection of Lakes ..................................................................................102

2.2.1 Approach ...........................................................................................................102

2.2.2 BATHTUB and WASP .....................................................................................104

2.2.3 Default DPV Methods.......................................................................................105

2.3 Summary .......................................................................................................................111

2.3.1 Downstream Protection of Lakes ......................................................................111

2.4 References .....................................................................................................................111
EPA Proposed Rule TSD for Florida’s Streams and DPVs for Lakes

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Appendix 1. Florida Waterbody Identification Numbers (WBIDs) by EPA Nutrient 

Watershed Region (NWR) ............................................................................... A-1

Appendix 2. FDEP’s Process for Assessing Data Usability ................................................ A-1

Appendix 3. Data Supporting EPA’s Reference Approach for Deriving Numeric 

Nutrient Criteria for Florida Streams ............................................................ A-1

Appendix 4. FDEP’s Method for Calculating a Corridor-based Landscape 

Development Intensity Index (LDI) ................................................................ A-2

Appendix 5. FDEP’s Stream Benchmark Summaries ........................................................ A-2

Appendix 6. FDEP’s Development and Testing of Biomonitoring Tools for 

Macroinvertebrates in Florida Streams.......................................................... A-2

Appendix 7. FDEP’s Site Information and Taxa Lists for SCI BCG Workshop 

Samples .............................................................................................................. A-3

Appendix 8. Memo to FDEP Regarding Interval and Equivalence Tests for the SCI ..... A-3

Appendix 9. Mapping Continuous Biological Index Values to BCG Tiers ....................... A-3

Appendix 10. Harmful, Adverse Effects of Nutrients on Stream Macroinvertebrates 

in Florida (Supplemental Analyses) ............................................................... A-4

Appendix 11. Data Supporting EPA’s Default DPV Derivation Using Regional 

Regression Models .......................................................................................... A-38
EPA Proposed Rule TSD for Florida’s Streams and DPVs for Lakes

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Tables

Table 1-1. Distributional statistics (mean, median, 75th and 90th percentiles, and sample 

count) of stream TP and TN by estuarine watershed and aggregated by EPA 

NWR. .............................................................................................................................9

Table 1-2. Results of linear regression model that modeled TP concentrations by different 

NWRs. Regression models used the Benchmark Population and SCI Population. .....12

Table 1-3. Parameters monitored during the Benchmark Stream Survey. ....................................26

Table 1-4. EPA’s proposed numeric nutrient criteria for streams (in bold). .................................42

Table 1-5. Results of interval and equivalence tests conducted on reference sites with two 

SCI results. ...................................................................................................................47

Table 1-6. Summary of multiple-linear regression analyses conducted for the panhandle 

nutrient region (Table 6.1 in FDEP 2009). ..................................................................56

Table 1-7. Summary of change-point analyses conducted for the panhandle nutrient region 

(Table 6-2 from FDEP 2009). ......................................................................................57

Table 1-8. Linear regression coefficients and statistics of nutrient concentration (natural log 

of TP and TN) vs. SCI for sites screened for RBZW, habitat smothering 

(HabSmoth), and water velocity habitat conditions described in the text. ..................77

Table 1-9. Binomial regression coefficients and statistics of nutrient concentration (natural 

log of TP and TN) vs. SCI for sites screened for RBZW, habitat smothering 

(HabSmoth), and water velocity habitat conditions described in the text. ..................77

Table 1-10. The probability SCI scores at a site are less than 30, 40, 46, or 50 at the EPA 

TP and TN criteria. ......................................................................................................82

Table 1-11. Summary of TP and TN endpoints associated with adverse floral and faunal 

response in study locations around North America. ....................................................85

Table 1-12. EPA’s proposed numeric nutrient criteria for Florida streams. ..................................91

Table 2-1. EPA’s numeric nutrient criteria for Florida lakes from the final rule (U.S. EPA 

2010). .........................................................................................................................102

Table 2-2. Lake DPVs computed by upstream-in-lake statistical model. ....................................108

Table 2-3. Sample lake data for calculating a default DPV criterion using the third 

approach. ....................................................................................................................110
EPA Proposed Rule TSD for Florida’s Streams and DPVs for Lakes

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Figures

Figure 1-1. Analytical plan of EPA’s numeric nutrient criteria development process for 

Florida’s streams. .........................................................................................................3

Figure 1-2. Conceptual model showing linkages between nutrient concentrations and 

designated uses in streams. ...........................................................................................4

Figure 1-3. Map of Hawthorn Group in Florida, indicating major phosphatic regions. ..................6

Figure 1-4. Map of surficial geology showing the location of the Peace River Formation 

and Bone Valley Member. ............................................................................................7

Figure 1-5. EPA’s proposed NWRs for classifying Florida streams (U.S. EPA 2010a). ..............10

Figure 1-6. Map of EPA’s proposed NWRs (U.S. EPA 2010a) and the Hawthorn Group 

indicating the presence of phosphate in the eastern Panhandle as well as the 

portions of the West Central (named the Bone Valley at proposal). ..........................11

Figure 1-7. Map of EPA’s stream classification by NWRs used in 2010 final rule (U.S. 

EPA 2010c) and re-proposed here. .............................................................................13

Figure 1-8. Detailed map of EPA’s stream classification by NWRs used in 2010 final rule 

(U.S. EPA 2010c) and re-proposed here. ...................................................................14

Figure 1-9. Detailed map of EPA’s proposed Panhandle NWR boundary (U.S. EPA 2010a) 

and final Panhandle West and East NWRs. Note that the new boundary 

between the Panhandle West and East is defined by the Apalachicola River 

watershed. ...................................................................................................................15

Figure 1-10. Map of the West Central NWR indicating the Hawthorn Group, EPA’s 

proposed NWR boundaries (U.S. EPA 2010a), and EPA’s NWR boundaries 

from its 2010 final rule (U.S. EPA 2010c) and re-proposed here. .............................16

Figure 1-11. Map of the Peninsula and South Florida NWRs from final rule (U.S. EPA 

2010c) and re-proposed here showing the original proposed NWR boundary 

(U.S. EPA 2010a). ......................................................................................................17

Figure 1-12. Quantile plots of TP, Ln(TP), TN, and Ln(TN). .......................................................20

Figure 1-13. Relationship between nutrient loading, nitrogen in panel (a), phosphorus in 

panel (b), and the LDI in the St. Marks watershed, Florida (after Brown and 

Vivas 2003). ...............................................................................................................22

Figure 1-14. Depiction of land use area (light yellow arrow) included in an LDI calculation 

(FDEP 2009). ..............................................................................................................23

Figure 1-15. Plot of TN concentrations among Benchmark reference sites across EPA’s 

NWRs for the Benchmark Population before (A) and after (B) screening. ...............30

Figure 1-16. Plot of TN concentrations among Benchmark reference WBIDs across EPA’s 

NWRs for the Benchmark Population before (A) and after (B) screening. ...............31

Figure 1-17. Plot of TN concentrations among SCI reference sites across EPA’s NWRs for 

the SCI Population before (A) and after (B) screening. .............................................32
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Figure 1-18. Plot of TN concentrations among SCI reference WBIDs across EPA’s NWRs 

for the SCI Population before (A) and after (B) screening. .......................................33

Figure 1-19. Plot of TP concentrations among Benchmark reference sites across EPA’s 

NWRs for the Benchmark Population before (A) and after (B) screening. ...............34

Figure 1-20. Plot of TP concentrations among Benchmark reference WBIDs across EPA’s 

NWRs for the Benchmark Population before (A) and after (B) screening. ...............35

Figure 1-21. Plot of TP concentrations among SCI reference sites across EPA’s NWRs for 

the SCI Population before (A) and after (B) screening. .............................................36

Figure 1-22. Plot of TP concentrations among SCI reference WBIDs across EPA’s NWRs 

for the SCI Population before (A) and after (B) screening. .......................................37

Figure 1-23. Semi-variogram showing the degree to which TP concentrations at closely 

located sites are auto-correlated. ................................................................................39

Figure 1-24. Time series of annual geometric mean TP concentrations at four Panhandle 

Benchmark WBIDs. ...................................................................................................40

Figure 1-25. Box-whisker plots of TP distributions [Ln(TP, mg/L)] across EPA’s NWRs. .........43

Figure 1-26. Box-whisker plots of TN distributions [Ln(TN, mg/L)] across EPA’s NWRs.........44

Figure 1-27. The BCG conceptual model (Davies and Jackson 2006). .........................................48

Figure 1-28. BCG tier assignments modeled with a proportional odds logistic regression. .........50

Figure 1-29. Distribution of SCI across the range of corridor LDI scores (FDEP 2009). .............51

Figure 1-30. Average SCI scores Benchmark Population plotted against corridor LDI 

scores (FDEP 2009). ...................................................................................................51

Figure 1-31. 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 (Figure 6-5 from 

FDEP 2009). ...............................................................................................................54

Figure 1-32. Change-Point Model (Step Function) of percent pollution sensitive diatoms 

vs. total phosphorus. Response adjusted for ColorL and CondL (Figure 6-2 

from FDEP 2009). ......................................................................................................56

Figure 1-33. Example relationships between macroinvertebrate response variables and 

nutrient levels. ............................................................................................................58

Figure 1-34. SCI versus Habitat Score, 2006–2011 data, individual nutrient region 

relationships. ...............................................................................................................60

Figure 1-35. SCI versus Habitat Score, 2006–2011 data, relationship for all regions 

combined. ...................................................................................................................61

Figure 1-36. SCI versus Ln(TP), 2006–2011 data, relationship for all regions combined. ...........62

Figure 1-37. SCI versus Ln(TN), 2006–2011 data, relationship for all regions combined. ..........63

Figure 1-38. Plot of the residuals of TP vs. SCI (SCI2RES1) against RBZW. .............................65
EPA Proposed Rule TSD for Florida’s Streams and DPVs for Lakes

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Figure 1-39. Plot of the residuals of TP vs. SCI (SCI2RES1) against the habitat smothering 

habitat metric (HabSmoth). ........................................................................................66

Figure 1-40. Plot of the residuals of TP vs. SCI (SCI2RES1) against the water velocity 

habitat metric. .............................................................................................................67

Figure 1-41. Plot of TP vs. water column chl-a (ChlCorr), corrected for phaeophytin (ppb). ......68

Figure 1-42. Plot of TN vs. water column chl-a (ChlCorr), corrected for phaeophytin (ppb). .....69

Figure 1-43. Plot of field collected DO (mg/L) vs. water column chl-a (ChlCorr), corrected 

for phaeophytin (ppb). ................................................................................................70

Figure 1-44. Plot of DO (mg/L) vs. SCI score. ..............................................................................71

Figure 1-45. Plot of water column chl-a (ChlCorr), corrected for phaeophytin (ppb) vs. SCI 

score. ...........................................................................................................................72

Figure 1-46. Plot of the natural log of TP concentration (mg/L) vs. SCI score. ............................73

Figure 1-47. Plot of the natural log of TP concentration (mg/L) vs. SCI score represented as 

a binomial where 1 = SCI less than 30 (upper left), SCI less than 40 (upper 

right), SCI less than 46 (lower left), or SCI less than 50 (lower right). .....................74

Figure 1-48. Plot of the natural log of TN concentration (mg/L) vs. SCI score.. ..........................75

Figure 1-49. Plot of the natural log of TN concentration (mg/L) vs. SCI score represented 

as a binomial where 1 = SCI less than 30 (upper left), SCI less than 40 (upper 

right), SCI less than 46 (lower left), or SCI less than 50 (lower right). .....................76

Figure 1-50. SCI versus Ln(TP) (2006–2011 data) for all regions combined (Figure 1-46) 

with Ln(TP) solved for SCI values of 30, 40, 46, and 50 based on linear 

regression models (solid black lines) and binomial regression models (dashed 

blue lines) for sites screened for RBZW, habitat smothering (HabSmoth), and 

water velocity habitat conditions as described in the text. .........................................78

Figure 1-51. SCI versus Ln(TP), 2006–2011 data, represented as box plots of SCI scores by 

TP concentration bins for sites screened for RBZW, habitat smothering 

(HabSmoth), and water velocity habitat conditions as described in the text. ............79

Figure 1-52. SCI versus Ln(TN) (2006–2011 data) for all regions combined (Figure 1-48) 

with Ln(TN) solved for SCI values of 30, 40, 46, and 50 based on linear 

regression models (solid black lines) and binomial regression models (dashed 

blue lines) for sites screened for RBZW, habitat smothering (HabSmoth), and 

water velocity habitat conditions as described in the text. .........................................80

Figure 1-53. SCI versus Ln(TN), 2006–2011 data, represented as box plots of SCI scores 

by TN concentration bins for sites screened for RBZW, habitat smothering 

(HabSmoth), and water velocity habitat conditions as described in the text. ............81

Figure 1-54. Partial dependence plots relating probability of nuisance algae to TN and TP 

concentrations. ............................................................................................................83

Figure 1-55. Frequency of TP endpoints associated with adverse floral and faunal 

responses in streams from literature review. ..............................................................88
EPA Proposed Rule TSD for Florida’s Streams and DPVs for Lakes

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Figure 1-56. Frequency of TN endpoints associated with adverse floral and faunal 

responses in streams from literature review. ..............................................................89

Figure 2-1. Relationships between inflowing (upstream) nutrient concentrations and in-lake 

concentrations of TP and TN. ...................................................................................109
EPA Proposed Rule TSD for Florida’s Streams and DPVs for Lakes

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Acronyms

AGM annual geometric mean

BCG Biological Condition Gradient

BPJ Best Professional Judgment

CAF Coastal Assessment Framework 

CDA Coastal Drainage Area

CFR Code of Federal Regulations

chl-a chlorophyll a

CV coefficient of variation 

CWA Clean Water Act

DPV downstream protection value

EDA estuarine drainage area

FDA fluvial drainage area

GIS geographic information system

GWIS Generalized Water Information System

HA habitat assessment

IWR Impaired Waters Rule

LDI Landscape Development Index

NHD National Hydrography Dataset 

NO2 nitrite

NO3 nitrate

NOAA National Oceanic and Atmospheric Administration

NPDES National Pollutant Discharge Elimination System

NWR Nutrient Watershed Region

RBZW riparian buffer zone width measure

RPS Rapid Periphyton Survey 

SCI Stream Condition Index

SOP standard operating procedure

TKN total Kjeldahl nitrogen 

TMDL total maximum daily load

TN total nitrogen

TP total phosphorus

TSD technical support document

WBID waterbody identification number

 
EPA Proposed Rule TSD for Florida’s Streams and DPVs for Lakes

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Scope and Purpose of this Document

This document contains scientific information EPA used to derive proposed numeric nutrient 

criteria for Florida’s Class I and III inland waters (streams and downstream protection of 

unimpaired lakes) (EPA-HQ-OW-2009-0596; FRL#9678-6). EPA developed these criteria to 

protect the State of Florida’s designated uses for inland waters from the effects of nutrient 

pollution. EPA’s proposed rule follows the U.S. District Court’s order regarding the Agency’s 

final rule for numeric nutrient criteria for Florida’s inland flowing waters signed on November 

14, 2010 and published on December 6, 2010 (Water Quality Standards for the State of Florida’s 

Lakes and Flowing Waters; Final Rule, EPA-HQ-OW-2009-0596, FRL-9228-7; hereafter 2010 

final rule).

For water quality management purposes, the State of Florida has designated the uses of Class I 

and III inland waters pursuant to the Clean Water Act (CWA) (Section 303(c)) and its 

implementing regulations (40 Code of Federal Regulations 131). Class I waters are designated 

for “potable water supplies” while Class III waters are designated for “recreation, propagation 

and maintenance of a healthy, well-balanced population of fish and wildlife” (Rule 62-302.400, 

F.A.C.). Specifically, EPA has derived these numeric criteria for nutrients, applicable to these 

waters, reflecting Rule 62-302.530(47)(b), F.A.C.: 

“In 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.” 

This document consists of chemical, physical, and biological data, meta-data, methods, and 

analyses EPA used to derive the proposed numeric nutrient criteria for Class I and Class III 

inland surface waters (streams and downstream protection of unimpaired lakes) for the purposes 

of the CWA. 
EPA Proposed Rule TSD for Florida’s Streams and DPVs for Lakes

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Foreword

Excess nitrogen and phosphorus loadings, or nutrient pollution, are a leading cause of water 

quality degradation in the Nation’s waters. Nutrient pollution may result in harmful algal 

blooms, reduced spawning grounds and nursery habitats, fish kills, and oxygen-starved hypoxic 

or “dead” zones, as well as public health concerns related to impaired drinking water sources and 

increased exposure to toxic microbes, such as cyanobacteria. Nutrient pollution problems can 

exhibit themselves locally or further downstream leading to degraded water quality in lakes, 

reservoirs, and estuaries. 

One way nutrient pollution can be managed effectively is through the development and adoption 

of numeric nutrient criteria into state water quality standards, and subsequent implementation of 

those water quality standards. State water quality standards are the foundation for protecting the 

quality of the Nation’s surface waters and are the cornerstone of the water quality-based control 

program mandated by the Clean Water Act (CWA). Water quality standards describe the desired 

condition of a waterbody and consist of the following three principal elements: (1) the designated 

uses of the state’s waters (e.g., fishing, aquatic life, drinking water); (2) criteria specifying the 

amounts of various pollutants, in either numeric or narrative form, that may be present in those 

waters that will protect the designated uses; and (3) antidegradation policies providing for 

protection of existing water uses and limitations on degradation of high quality waters (See 

40 Code of Federal Regulations [CFR] 131, Subparts A, B, C). 

As noted in the Scope and Purpose of this Document, the State of Florida has defined the 

designated uses of its lakes and flowing waters as follows: “Potable Water Supply” (Class I) and 

“Recreation, Propagation and Maintenance of a Healthy, Well-Balanced Population of Fish and 

Wildlife” (Class III) (Rule 62-302.400, F.A.C.). The State of Florida’s existing water quality 

criteria for nutrients are expressed as a narrative at Rule 62-302.530(47) (a) and (b), F.A.C. 

62-301.530(47)(b) provides: 

In 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. 

One advantage of numeric nutrient criteria compared to narrative criteria, once adopted and 

implemented, is that states can use the numeric criteria in quantitative state water quality 

assessments and watershed protection management activities. Numeric nutrient criteria support 

National Pollutant Discharge Elimination System (NPDES) permits and total maximum daily 

loads (TMDLs) for nutrients. They can also be used in state- and community-developed 

environmental baselines to measure progress and support partnerships based on activities such as 

nutrient trading, land stewardship, and urban stormwater runoff control strategies. 

Aquatic life water quality criteria consist of three key components: magnitude, duration, and 

frequency. Criteria must contain sufficient constituents to be protective of the designated use and 

must be based on a sound scientific rationale (40 CFR 131.11(a)). When setting water quality 

standards, the water quality standards of downstream waters must be taken into consideration to 

ensure downstream standards are attained and maintained (40 CFR 131.10(b)). In previously 

published guidance, EPA generally recommends that states develop and adopt numeric nutrient 
EPA Proposed Rule TSD for Florida’s Streams and DPVs for Lakes

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criteria for two causal parameters, such as total nitrogen (TN) and total phosphorus (TP), and 

two response parameters, such as chlorophyll a (chl-a) and clarity, into their water quality 

standards to protect against the effects of nutrient pollution. 

For this proposed rule for inland surface waters for the State of Florida, EPA has proposed 

numeric nutrient criteria for streams (TN, TP), and downstream protective values for unimpaired 

lakes (TN, TP). EPA derived criteria for these parameters based on the best available scientific 

data and information at the time. EPA also considered the State’s existing water quality 

standards (e.g., clarity). Where EPA has not established criteria for certain parameters due to 

insufficient scientific evidence to support a precise threshold for numeric nutrient criteria 

(e.g., chl-a for streams), EPA or the State may consider deriving criteria in the future for those 

parameters.
EPA Proposed Rule TSD for Florida’s Streams and DPVs for Lakes

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Chapter 1. Derivation of EPA’s Proposed Numeric Nutrient Criteria 

for Streams

1.1 Introduction

Streams are unique and valuable ecosystems. They provide a wide variety of important and 

valuable services such as serving as habitat for a variety of freshwater species, including 

valuable recreational taxa; supporting a variety of recreational activities; and conveying drinking 

water. Florida is replete with these valuable ecosystems, and the development of numeric 

nutrient criteria will assist in their protection and ability to support a balanced, natural population 

of aquatic flora and fauna.

Nutrients are a natural component of stream ecosystems. In natural concentrations, which can 

and do vary, essential nutrients help maintain stream ecosystem structure and function. However, 

in excess quantities, nutrients can destabilize stream ecosystems, leading to a variety of 

problems, including nuisance plant and algal growth, loss of physical habitat, hypoxia and 

anoxia, species loss, and production of toxins that can present a risk to recreation and drinking 

water (Dodds et al. 2009; Howarth et al. 2002; NRC 2000; Smith 2003). 

The adverse effects of nutrient pollution (excess total nitrogen [TN] and total phosphorus [TP]) 

on streams are well documented (Biggs 2000; Bothwell 1985; Bourassa and Cattaneo 1998; 

Bowling and Baker 1996; Cross et al. 2006; Dodds and Gudder 1992; Elwood et al. 1981; 

Francoeur 2001; Gaiser et al. 2005; Moss et al. 1989; Mulholland and Webster 2010; Notestein 

et al. 2003; Peterson et al. 1985; Rosemond et al. 1993). Spatial and temporal variability in 

environmental conditions influence the biological responses to excess nutrients. As a result, 

adverse effects can be described, but not all of these effects are observed in every stream at all 

times. For example, some streams are shaded, which reduces the near-field algal growth response 

to excess nutrients because light, which is essential for plant or algae growth, is reduced at the 

water surface. In these streams, excess nutrients primarily increase rates of microbial activity and 

heterotrophic respiration in the near field. Other streams have higher velocities, reducing the 

time algae have to take up excess nutrients. However, when the same stream widens and slows 

downstream, or the canopy that provided shade thins, then excess nutrients accelerate plant and 

algal biomass production, an effect reflected in a predominant stream paradigm, the river 

continuum concept (Vannote et al. 1980). Under such dynamic conditions, algae can accumulate 

or be removed rapidly, making it difficult to estimate algal biomass in monitoring, and making 

biomass accumulation in an individual stream difficult to characterize. Due to these and other 

factors, the effects of excess nutrients may be subtle or dramatic, may be easy or difficult to 

capture by measures of plant and algal response (such as chl-a), and may occur in some locations 

along a stream but not others. 

EPA recognizes that nutrient limitation of either TN or TP, or co-limitation by TN and TP, is a 

phenomenon previously observed (Elser et al. 2007; Francoeur 2001; Schindler 1974). Primary 

production in freshwater systems can be limited by available nitrogen or phosphorus, or can be 

co-limited by both nutrients simultaneously. Recent studies have shown that nitrogen limitation 

and co-limitation are more important in freshwater, and phosphorus-limitation is more important 

in saltwater, than previously recognized (e.g., Elser et al. 2007; Smith 2003). For any given 

stream, however, it is difficult to assess a priori which nutrient is the limiting factor. EPA’s 
EPA Proposed Rule TSD for Florida’s Streams and DPVs for Lakes

2 

approach to deriving numeric nutrient criteria is not premised on the limiting nutrient, but rather 

a protective viewpoint that both nutrients influence excess algal growth and that influence may 

vary over space and time. EPA has expressed its recommended approaches in deriving numeric 

nutrient criteria for both TN and TP previously for streams (U.S. EPA 2000) and as a general 

policy matter (Grubbs 2001; Grumbles 2007). The scientific literature concluding that the source 

of many adverse water quality impacts can be traced to degradation of water quality upstream of 

the impacted aquatic system by nutrient pollution (e.g., Conley et al. 2009; Paerl, 2009; 

U.S. EPA 2007) further affirms EPA’s approach to water quality management, through numeric 

nutrient criteria, for both nitrogen and phosphorus.

For EPA’s numeric nutrient criteria for flowing waters (streams), the Federal Court found that 

EPA had not provided sufficient information in its final rule (U.S. EPA 2010c) that would 

explain why or how “harmful” change would occur if the criteria or downstream protection 

values (DPVs) were exceeded. Despite the Court’s indication that EPA’s scientific approach in 

deriving stream criteria (e.g., reference stream, or “sample-set,” approach), including the 

criteria’s duration and frequency, would “survive arbitrary-or-capricious review,”2 the Court 

found that EPA did not explain how the stream criteria would prevent “harmful” change. Thus, 

the Court vacated these aspects of EPA’s final rule and remanded it to the Agency for further 

rulemaking. 

The Court ordered that EPA’s final rule be codified at 40 CFR 131.43 with the exception of the 

stream criteria and the default DPVs for unimpaired lakes.

3

 For the remanded portions of the 

final rule, the Court ordered: “By May 21, 2012 the Administrator must sign for publication a 

proposed rule, or sign for publication a final rule, that sets numeric nutrient criteria for Florida 

streams that are not in the South Florida region.” The Court extended EPA’s deadline to publish 

a proposed rule to November 30, 2012. 

This chapter supports EPA’s proposed rule for numeric nutrient criteria for Florida’s flowing 

waters in compliance with the Court’s order. Specifically, this chapter describes the methodology 

and results of EPA’s additional analysis for deriving numeric nutrient criteria for Florida streams 

in response to the Court’s remand and to protect designated uses in response to the 

environmental threat of nutrient pollution. The additional steps relative to the original criteria 

development process are summarized in EPA’s additional analysis analytical plan shown in 

Figure 1-1. A conceptual model of how nutrients affect designated uses in streams is shown in 

Figure 1-2. 

 

2 See FWF v. Jackson, 4:08-cv-00324-RH-WCS, p. 63 at 3. 3 See FWF v. Jackson, 4:08-cv-00324-RH-WCS, p. 85 at 2.
EPA Proposed Rule TSD for Florida’s Streams and DPVs for Lakes

3 

Definition

A free-flowing, predominantly 

fresh surface water in a 

defined channel, and includes 

rivers, creeks, branches, 

canals, freshwater sloughs, 

and other similar water 

bodies.

Further Classification

Delineate nutrient regions covered by this 

rule on the basis of geology, watershed 

boundaries, and nutrient distributions.

Instream Protection

Distribution Approach

1. Identify reference and impacted stream 

populations for TN and TP.

2. Compute distributions of TN and TP.

3. Select percentile of reference 

distributions as TN, TP criteria.

4. Compare reference and impacted 

distributions.

Stressor-Response Relationship Approach

1. Identify nutrient concentrations 

associated with adverse floral and faunal 

changes along nutrient gradients in 

streams.

Scientific Literature Support

2. Identify nutrient concentrations 

associated with adverse floral and faunal 

changes from literature.

Downstream Protection*

Designated Use Classification for Streams

•Class I & III Waters

•Predominately Freshwater

Stream Classification

Downstream Protection Values (DPVs) for 

streams that flow into lakes may be more 

stringent than instream protection values.

For lakes, identify models (empirical and/or 

mechanistic) to calculate TN and TP in streams, 

which would be protective of downstream lakes.

Figure 1-1. Analytical plan of EPA’s numeric nutrient criteria development process for Florida’s streams.
EPA Proposed Rule TSD for Florida’s Streams and DPVs for Lakes

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Figure 1-2. Conceptual model showing linkages between nutrient concentrations and designated uses in 

streams.

To account for natural variability in nutrient concentrations of Florida streams, EPA organized 

streams into naturally homogenous classes based on geographic variation across the Florida 

landscape (geology), as well as differences in large-scale hydrologic features (watersheds). The 

process of classifying streams to account for and reduce natural variability is an important first 

step in numeric nutrient criteria derivation (U.S. EPA 2000). 

EPA applied a reference condition approach to derive numeric nutrient criteria for Florida 

streams using two populations of data to represent nutrient concentrations in Florida streams. 

The reference condition approach relies on identifying a least-disturbed set of reference sites 

reflecting “relatively undisturbed” conditions reflecting natural integrity (Davies 1997). These 

reference sites provide the appropriate benchmark against which to determine the nutrient 

concentrations present when the designated uses of a waterbody are being met. Setting criteria 

based on conditions observed in these reference sites reflects both the stated goal of the Clean 

Water Act (CWA) and EPA’s National Nutrient Strategy that calls for states to take 

precautionary and preventative steps in managing nutrient pollution to maintain the chemical, 

physical, and biological integrity of the Nation’s waters before adverse biological and/or 

ecological effects are observed (Grubbs 2001, Grumbles 2007). 
EPA Proposed Rule TSD for Florida’s Streams and DPVs for Lakes

5 

EPA identified a population of least-disturbed reference sites (herein referred to as the 

Benchmark Population) using a method based on previous work by Florida Department of 

Environmental Protection (FDEP) (FDEP 2009). That method was presented as an alternative for 

public comment in EPA’s 2010 proposed rule (U.S. EPA 2010a) and was further described in 

EPA’s supplemental notice to the 2010 proposed rule (U.S. EPA 2010b). The method used to 

identify a least-disturbed population of sites consisted of a set of screens based on land cover, 

aerial surveys, site visits, nitrate concentration, CWA section 303(d) impairments, and biological 

condition measures (Stream Condition Index or SCI). The sites were associated with waterbody 

identification numbers (WBIDs), which are spatial units used by FDEP for purposes of water 

quality monitoring and assessment. EPA also identified a reference population of biologically 

healthy sites (herein referred to as the SCI Population) using the approach detailed in U.S. EPA 

2010a and considered further in U.S. EPA 2010b. The SCI Population was determined by 

identifying sites with SCI scores greater than 40 and not in WBIDs impaired for nutrients or 

dissolved oxygen (DO). The SCI is a multi-metric macroinvertebrate index developed by FDEP 

to evaluate biological condition in streams; the threshold value of 40 and higher was determined 

to represent biologically healthy conditions in Florida streams.

Next, EPA explored a series of stressor-response relationships between nutrients and measures of 

floral and faunal condition for the proposed rule that included and complemented similar work 

by FDEP. These analyses are presented as evidence of the linkage between the final numeric 

nutrient criteria and harmful changes in floral and faunal balance. EPA has also supplemented 

them with additional data from FDEP to augment those analyses. Specifically, EPA examined 

the relationships between algal biomass and nutrients, and macroinvertebrate condition and 

nutrients. 

Lastly, EPA reviewed additional scientific literature that associates nutrient concentrations in 

streams with adverse biological responses. EPA conducted this review as an additional line of 

evidence to support its proposed numeric nutrient criteria. 

1.2 Development of Nutrient Watershed Regions (NWRs)

Spatial frameworks are important for structuring research, assessment, monitoring, and 

management of environmental resources. Ecoregions are a common example of a spatial 

framework. Ecoregions may be defined by patterns of homogeneity in a combination of factors 

such as climate, physiography, geology, soils, and vegetation (Griffith et al. 1994). The 

development of regional nutrient criteria by definition balances site-specificity with regional 

applicability. In addition, there is understood natural variability among sites within regions with 

regards to nutrient chemistry due to natural biogeographic factors. Identifying regions to reduce 

natural variability improves the precision of criteria in setting realistic and protective 

expectations for a stream. Classification provides a framework on which to develop and base 

protective nutrient criteria, the goal of which is to account for and reduce the natural variability 

in nutrient chemistry due to geographic factors for any stream within a class. Proper 

classification identifies homogeneous populations of streams with similar nutrient regimes and 

biological communities. Classification helps to ensure that the thresholds selected from a 

benchmark or reference condition approach will be appropriately supportive of balanced natural 

populations of flora and fauna inhabiting the different classes. EPA nutrient criteria guidance 
EPA Proposed Rule TSD for Florida’s Streams and DPVs for Lakes

6 

recommends classification as an important step in developing protective nutrient criteria 

(e.g., U.S. EPA 2000). 

Florida’s geology includes sedimentary deposits of marine origin. Certain marine clays and 

limestone formations (e.g., the Hawthorn Group) that lie near the surface are high in phosphorus 

(Figure 1-3). Some of these phosphatic deposits are mined, and Florida is one of the larger global 

producers of phosphate. The State produces approximately 25% of phosphate used throughout 

the world. The natural phosphatic soils associated with the Hawthorn Group can also contribute 

to increased phosphorus concentrations in streams. For this reason, proper spatial classification is 

essential to capture regional differences in natural nutrient concentrations. 

Source: FDEP (http://www.dep.state.fl.us/gis/, accessed in March 2010).

Figure 1-3. Map of Hawthorn Group in Florida, indicating major phosphatic regions.

1.2.1 Refinement of Regionalization for Nutrient Criteria Development

EPA considered previous bioregion work by FDEP (FDEP 2009) for purposes of classifying 

streams for nutrient criteria development. The four stream bioregions developed by FDEP were 

based on observed biological community similarity within a bioregion, assuming that biological 

responses to nutrients would be more similar within and not between bioregions. Evaluation of 

sites across bioregions indicated that the bioregions were not sufficiently homogenous with 

Legend

Hawthorn Group
EPA Proposed Rule TSD for Florida’s Streams and DPVs for Lakes

7 

regard to nutrient concentration. FDEP also considered geological formations (e.g., the 

Hawthorn Group) and performed geostatistical analysis of ambient nutrient concentrations 

(e.g., kriging) for purposes of classifying streams for numeric nutrient criteria development. A 

sub-region of the Peninsula bioregion—identified at EPA’s proposal as “the Bone Valley” 

(subsequently renamed the West Central NWR for purposes of EPA’s stream classification for 

numeric nutrient criteria development)—and which has high natural phosphate levels, was 

identified by FDEP during initial work to derive reference-based TMDLs for the Northern Lake 

Okeechobee tributaries. The naturally high phosphate areas occur in portions of Hillsborough, 

Polk, Hardee, Manatee, DeSoto, and Sarasota counties, where specific components of the 

Hawthorn Group, which includes the Peace River Formation and the Bone Valley Member 

(originally the Bone Valley Formation of Matson and Clapp 1909, Figure 1-4), occur at the 

surface. The Bone Valley Member is a unique phosphate deposit that provided much of the 

phosphate production in the United States during the 20th Century. Mining of phosphate in the 

outcrop area began in 1888 (Cathcart 1985) and continues to the present. 

Source: FDEP (2009).

Figure 1-4. Map of surficial geology showing the location of the Peace River Formation and Bone Valley 

Member. 
EPA Proposed Rule TSD for Florida’s Streams and DPVs for Lakes

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1.2.2 Development of the NWRs

EPA considered the previous work by FDEP on bioregions, nutrient regions, and sub-regions to 

develop a regional stream nutrient classification approach that addresses the natural variations in 

nutrient concentrations (including underlying geology) and reflects the understanding that 

upstream water quality affects downstream water quality. The resulting watershed-based 

classification enables 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 NWRs. This was accomplished using WBID descriptions and verifying with drainage 

basin boundaries, both available from the FDEP geographic information system (GIS) site4

. The 

resulting NWRs reflect inherent differences in the natural factors that influence nutrient 

concentrations in streams (e.g., geology, soil composition, hydrology). 

For initial watershed regionalization analysis, EPA used nutrient water quality data collected 

across Florida and contained within Florida’s Impaired Waters Rule (IWR) database. Data tables 

of nutrient water quality sampling stations and their associated WBIDs were joined to the 

National Oceanic and Atmospheric Administration (NOAA) Coastal Assessment Framework 

(CAF) delineation of estuarine drainage areas (EDAs), fluvial drainage areas (FDAs), and coastal 

drainage areas (CDAs). WBID centroids were joined to the CAF so that all stations in WBIDs 

were also mapped to an EDA, FDA, or CDA. Stations that were identified in the IWR database 

as “Stream” or “Blackwater” and had corresponding TN and TP data were identified and 

aggregated by EDA, FDA, or CDA. This resulted in approximately 145,000 paired observations 

or sampling events distributed in watershed stream networks across Florida. The distributional 

statistics of all observations were computed for each watershed (Table 1-1).

 

4 http://www.dep.state.fl.us/gis/datadir.htm, accessed March 2010
EPA Proposed Rule TSD for Florida’s Streams and DPVs for Lakes

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Table 1-1. Distributional statistics (mean, median, 75th and 90th percentiles, and sample count) of stream TP 

and TN by estuarine watershed and aggregated by EPA NWR.

Stream TP Concentration (mg/L) Stream TN Concentration (mg/L)

NWR

EDA 

Code Watershed Name Mean Median

75th

%tile

90th

%tile Count Mean Median

75th

%tile

90th

%tile Count

G140x Perdido Bay EDA 0.114 0.026 0.122 0.360 1219 1.55 0.71 1.61 4.35 1219 Panhandle 

West

G130x Pensacola Bay EDA 0.030 0.020 0.035 0.054 2436 0.77 0.56 0.78 1.23 2436 Panhandle 

West

G120x Choctawhatchee Bay 

EDA/FDA

0.040 0.027 0.045 0.066 1087 0.52 0.49 0.65 0.81 1087 Panhandle 

West

G110x St. Andrew Bay EDA 0.019 0.011 0.020 0.020 678 0.50 0.36 0.55 0.75 678 Panhandle 

West

G100x Apalachicola Bay 

EDA/FDA

0.047 0.027 0.047 0.077 1219 0.92 0.75 1.01 1.58 1219 Panhandle 

West

G090x Apalachee Bay EDA 0.105 0.048 0.094 0.156 7523 0.88 0.63 0.94 1.45 7523 Panhandle 

East

G086x Econfina-Steinhatchee 

CDA

0.091 0.069 0.104 0.165 777 0.91 0.87 1.17 1.41 777 Panhandle 

East

G080x Suwannee River 

EDA/FDA

0.302 0.130 0.211 0.449 14469 1.13 1.03 1.29 1.62 14469 North 

Central

G070x Tampa Bay EDA 0.595 0.290 0.620 1.270 14773 1.39 1.13 1.56 2.18 14773 West 

Central/

Peninsula

G060x Sarasota Bay EDA 0.293 0.250 0.370 0.510 356 1.22 1.16 1.42 1.66 356 Peninsula

G050w Charlotte Harbor EDA 0.526 0.350 0.638 1.051 6210 1.58 1.28 1.80 2.55 6210 Peninsula

S190x Indian River/St. Lucie 

EDA

0.238 0.207 0.311 0.440 17182 1.45 1.37 1.66 2.03 17182 Peninsula

S183x Daytona St. Augustine 

CDA

0.117 0.088 0.130 0.190 2190 0.98 0.94 1.22 1.52 2190 Peninsula

S180x St. Johns River 

EDA/FDA

0.182 0.092 0.171 0.392 41428 1.51 1.22 1.67 2.44 41428 Peninsula

S175x Nassau CDA 0.204 0.130 0.194 0.275 455 1.30 1.21 1.51 1.81 455 Peninsula

S170x St. Marys River EDA 0.064 0.042 0.060 0.080 530 1.14 1.09 1.35 1.64 530 Peninsula

G078x Waccasassa CDA 0.074 0.064 0.080 0.100 379 0.90 0.72 0.99 1.44 379 Peninsula

G076x Withlacoochee River 

CDA

0.066 0.042 0.077 0.130 2555 1.09 0.97 1.30 1.73 2555 Peninsula

G074x Crystal-Pithlachascotee 

CDA

0.076 0.031 0.090 0.180 4401 0.77 0.61 1.05 1.49 4401 Peninsula

G050a Caloosahatchee River 

EDA

0.114 0.080 0.133 0.220 5630 1.05 1.00 1.40 1.71 5630 Peninsula

F100x Kissimmee River/Lake 

Okeechobee FDA

0.311 0.175 0.440 0.706 19139 1.70 1.53 1.96 2.50 19139 Peninsula

EDA – Estuarine Drainage Area, CDA – Coastal Drainage Area, FDA – Fluvial Drainage Area.

Source: data obtained through Florida’s IWR database.
EPA Proposed Rule TSD for Florida’s Streams and DPVs for Lakes

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The result of these analyses, and considerations based on FDEP bioregions, resulted in four 

NWRs that EPA proposed (U.S. EPA 2010a) and finalized (U.S. EPA 2010c) in the previous 

rulemaking effort (Figure 1-5). Following the proposal and public comment period, EPA 

reconsidered the westward extent of the Hawthorn Group into the eastern portion of the proposed 

Panhandle region and its influence on stream chemistry there (Figure 1-6). As noted above, when 

classifying Florida’s streams, EPA identified geographic areas of the State having phosphorusrich soils and geology, such as the northeastern part of Florida (i.e., 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 Bone Valley in Figure 

1-5) because the naturally phosphorus-rich soils in these areas significantly influence stream 

phosphorus concentrations in these watersheds. EPA would expect from a general ecological 

standpoint that the associated aquatic life uses, under these naturally-occurring, nutrient-rich 

conditions, would be supported. 

Figure 1-5. EPA’s proposed NWRs for classifying Florida streams (U.S. EPA 2010a).
EPA Proposed Rule TSD for Florida’s Streams and DPVs for Lakes

11 

Figure 1-6. Map of EPA’s proposed NWRs (U.S. EPA 2010a) and the Hawthorn Group indicating the 

presence of phosphate in the eastern Panhandle as well as the portions of the West Central (named the 

Bone Valley at proposal). 

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 and explored delineation of the 

Panhandle region along watershed boundaries into East and West regions. It appeared that higher 

TP concentrations were consistently associated with least-disturbed 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 the Benchmark Population or the SCI Population approach, splitting the panhandle 

into east and west regions explained more variability in TP concentrations than the original four 

stream bioregion model (Table 1-2).

Legend

Hawthorn Group

EPA NWR 4 Regions (EPA Proposal)
EPA Proposed Rule TSD for Florida’s Streams and DPVs for Lakes

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Table 1-2. Results of linear regression model that modeled TP concentrations by different NWRs. 

Regression models used the Benchmark Population and SCI Population.

Reference Population Regionalization R2

Benchmark – TP EPA’s Proposed NWRs 0.52

Benchmark – TP Revised NWRs with a divided Panhandle NWR 0.61

SCI – TP EPA’s Proposed NWRs 0.47

SCI – TP Revised NWRs with a divided Panhandle NWR 0.53

Reference Population Regionalization R2

Benchmark – TN EPA’s Proposed NWRs 0.52

Benchmark – TN Revised NWRs with a divided Panhandle NWR 0.51

SCI – TN EPA’s Proposed NWRs 0.18

SCI – TN Revised NWRs with a divided Panhandle NWR 0.15

The results in Table 1-2 above show that the regional refinement of dividing the Panhandle 

NWR into two regions provides a better representation of the natural range of nutrient conditions 

within the State, taking into account more of the natural variability, as measured by the linear 

regression models. 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), and the Panhandle West. By creating this division, EPA has 

effectively reduced in size the 2010 proposed Panhandle region (Figure 1-7, Figure 1-8). EPA 

has continued to adhere to watershed boundaries in its regionalization to reflect the importance 

of the watershed context in nutrient source, fate, and transport. Smaller portions of the Hawthorn 

Group cut across various smaller Peninsula drainages, but they did not appear to contribute to 

sufficient systematic differences in TP concentrations in the receiving waterbodies to warrant 

further exploration or refinement. 

EPA also re-evaluated its delineation of the West Central (referred to as the Bone Valley in 

EPA’s 2010 proposed rule) and the Peninsula NWR. EPA found that its delineation of the West 

Central NWR was too broad and incorporated watersheds that were not influenced by underlying 

Hawthorn Group geology—especially coastal watersheds along the western boundary (Figure 

1-9). EPA found that the Hawthorn Group boundaries are more constrained in this area than 

believed at its proposal (U.S. EPA 2010a) and that Charlotte Harbor and Sarasota Bay 

watersheds should not be included in the West Central for this reason. Therefore, EPA refined 

the boundaries for both the West Central and Peninsula NWRs. The result for the West Central 

NWR was a modified western boundary that shifts the Charlotte Harbor and Sarasota Bay 

drainages from the West Central (Bone Valley) to the Peninsula region, because these two 

watersheds had minimal presence of Hawthorn Group elements at the land the surface (Figure 

1-10). The Hillsborough River, eastern Tampa Bay drainages, Peace and Myakka River, and 

Sarasota Bay drainages are retained in the West Central NWR. These adjustments to the West 

Central NWR boundary (which also results in modifications to the Peninsula NWR) more 

accurately reflect the watershed boundaries and better reflect natural differences in expected 

stream chemistry. EPA provided these modifications subsequent to its 2010 proposed rule in a 

supplemental notice published on August 3, 2010 (U.S. EPA 2010b) and final rule published on 

December 6, 2010 (U.S. EPA 2010c). 
EPA Proposed Rule TSD for Florida’s Streams and DPVs for Lakes

13 

Figure 1-7. Map of EPA’s stream classification by NWRs used in 2010 final rule (U.S. EPA 2010c) and 

re-proposed here. 
EPA Proposed Rule TSD for Florida’s Streams and DPVs for Lakes

14 

Figure 1-8. Detailed map of EPA’s stream classification by NWRs used in 2010 final rule (U.S. EPA 2010c)

and re-proposed here. Note that watershed boundaries are delineated by NOAA’s CAF of EDAs, FDAs, and 

CDAs.

Legend

Panhandle West

Panhandle East

North Central

Peninsula

West Central

South

Apalachee Bay

Pensacola Bay

St. Johns River 

Watershed

Apalachicola Bay Suwannee River

Watershed 

Kissimmee River/

Lake Okeechobee

Watershed 

Peace River Watershed

Choctawhatchee Bay

Withlacoochee Drainage

St. Andrews Bay

Econfina/Steinhatchee 

Drainage

Loxahatchee/St. Lucie

Watershed 

Caloosahatchee River 

Watershed

Indian River 

Watershed

Waccasassa Drainage

St. Mary's River 

Watershed

Crystal/Pithlachascotee Drainage

Perdido Bay

Hillsborough River Watershed

Nassau Drainage

Myakka River Watershed

Daytona/St. Augustine 

Drainage

Alafia River Watershed

Estero Bay 

Drainage

Sarasota Bay/Lemon Bay

Manatee River Watershed

Little Manatee River Watershed

Tampa Bay Direct Tributaries - West Central

Tampa Bay Direct Tributaries - Peninsula

Charlotte Harbor Direct Tributaries
EPA Proposed Rule TSD for Florida’s Streams and DPVs for Lakes

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Figure 1-9. Detailed map of EPA’s proposed Panhandle NWR boundary (U.S. EPA 2010a) and final 

Panhandle West and East NWRs. Note that the new boundary between the Panhandle West and East is 

defined by the Apalachicola River watershed.
EPA Proposed Rule TSD for Florida’s Streams and DPVs for Lakes

16 

Legend

Hawthorn Group

Panhandle West

Panhandle East

North Central

Peninsula

West Central

South

EPA NWR 4 Regions (Proposed) TN and TP

Figure 1-10. Map of the West Central NWR indicating the Hawthorn Group, EPA’s proposed NWR 

boundaries (U.S. EPA 2010a), and EPA’s NWR boundaries from its 2010 final rule (U.S. EPA 2010c) and reproposed here. 

EPA also made minor modifications to the map of the southern boundary of the Peninsula NWR 

to more accurately reflect the intended watershed boundaries proposed by EPA (U.S. EPA 

2010a). The watershed boundaries are defined by the Caloosahatchee watershed west of Lake 

Okeechobee to the St. Lucie, Loxahatachee watersheds to the east. EPA also moved one WBID 

out of the map of the proposed Peninsula NWR because it was an inter-basin drainage that was 

not specifically part of the Caloosahatchee. EPA moved another WBID out of the map of the 

proposed Peninsula NWR because it was a coastal drainage to West Palm Beach and not to the 

St. Lucie/Loxahatchee region. These WBIDs are in the South Florida region, for which EPA will 

propose numeric nutrient criteria in 2012. EPA made additional minor modifications in the map 

of the southern boundary of the Peninsula NWR to ensure that WBID and drainage boundaries 

are concordant (Figure 1-11). A list of FDEP WBIDs organized by the NWR to which they 

belong is provided in Appendix 1 Florida Waterbody Identification Numbers (WBIDs) by EPA 

Nutrient Watershed Region (NWR). 
EPA Proposed Rule TSD for Florida’s Streams and DPVs for Lakes

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Figure 1-11. Map of the Peninsula and South Florida NWRs from final rule (U.S. EPA 2010c) and reproposed here showing the original proposed NWR boundary (U.S. EPA 2010a). Note that two of largest 

WBIDs that were moved from EPA’s proposed Peninsula NWR to the South Florida NWR are indicated.

EPA chose to use the same regionalization for TN criteria as it used for TP criteria. EPA 

recognizes that the original and revised regionalization relied heavily on underlying geological 

differences among regions associated strongly with differences in phosphorus geology. These 

differences resulted in spatial differences in expected in-stream TP concentrations. At the same 

time, in-stream TN concentrations across the State were relatively more homogeneous across 

these same regions as compared to TP concentrations. EPA considered alternative approaches to 

classifying streams for purposes of TN criteria development. For example, EPA considered 

regionalizing streams and deriving numeric criteria based on a relationship between TN and 

stream color. EPA determined that setting criteria based on color assumes that the relationship 

between color and TN solely reflects the natural dynamics between the two parameters. EPA was 

concerned that anthropogenic effects on either color or nitrogen concentrations associated with 

runoff or effluent confound the relationship between color and TN. The available data were 

insufficient to fully address this issue, and as a result, EPA chose not to pursue a color-based TN 

criterion at this time. It may be feasible to address this issue for site-specific criteria derivation 

where a color-TN relationship can be affirmed as demonstrably independent from anthropogenic 

influence. EPA also considered regionalizing more broadly across two regions, similar to the 

approach considered by FDEP (2009), rather than the four regions described at EPA’s proposal. 

Panhandle TN concentrations are lower than non-Panhandle regions as evidenced by Figure 1-15

through Figure 1-18, and this is reflected in the resulting criteria.
EPA Proposed Rule TSD for Florida’s Streams and DPVs for Lakes

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The statistical modeling indicates that a five region model explains no more or less variability 

than a two region model; this indicates that either approach is defensible. As a result, for 

consistency in application of criteria and in deference to the co-limiting nature of TN and TP, 

EPA has proposed a similar regionalization for TN and TP that is both defensible and protective 

of designated uses.

1.3 Reference Condition Approach for Developing Numeric Nutrient 

Criteria for Streams

While EPA is proposing the same numeric criteria values for streams contained in the 2010 final 

rule (U.S. EPA 2010c), additional evidence is being presented in this proposed rulemaking to 

explain how these values protect against harmful adverse effects to natural populations of aquatic 

flora and fauna. As such, the derivation of the numeric criteria is still relevant, so the technical 

material related to deriving the proposed criteria using the reference condition approach is 

presented again in sections 1.3 and 1.4 for readers but remains unchanged from the 2010 final 

rule Technical Support Document (TSD) (EPA-HQ-OW-2009-0596). Clarifying language 

relevant to supporting the added stressor-response analyses has been added.

EPA used the reference condition approach for deriving instream protective TN and TP criteria. 

This approach, recommended and detailed in EPA guidance (e.g., U.S. EPA 2000), derives 

criteria from the distribution of values in a reference population, defined as “relatively 

undisturbed stream segments that can serve as examples of the natural biological integrity of a 

region” (U.S. EPA 2000, p. 94). For this rulemaking, EPA explored two reference condition 

populations by screening stream sites with nutrients data: (1) the Benchmark Population 

identified by screening for stream sites with nutrient data across the state of Florida based on 

land cover, aerial surveys, site visits, nitrate concentration, CWA section 303(d) impairments, 

and biological condition measures; and (2) the SCI Population, identified by screening stream 

sites with nutrient data based on macroinvertebrate samples on biological condition and CWA 

section 303(d) impairments. The TN and TP criteria for each NWR are based on the Benchmark 

Population for all NWRs except the West Central region, where the criteria are based on the SCI 

Population approach detailed in EPA’s 2010 final rule. 

1.3.1 Data Selection and Preparation

FDEP extracted all available nutrient data from Florida’s STORET (STOrage and RETrieval) 

database and FDEP’s Status and Trends database (Generalized Water Information System, 

GWIS). However, FDEP used only data from the STORET and GWIS databases submitted by 

water quality programs that met data quality standards, as required by FDEP QA rule 62-160 and 

their “Process for Determining Data Usability” (see Appendix 2 FDEP’s Process for Assessing 

Data Usability). This process included screening all data points for potential data quality issues,5

such as improper sample preservation and analysis performed outside hold time. Data deemed to 

meet FDEP data quality requirements were extracted from thousands of sites statewide and are

referred to as the “All Streams Data Set” (see Appendix 3 Data Supporting EPA’s Reference 

Approach for Deriving Numeric Nutrient Criteria for Florida Streams). This initial population 

 

5 EPA reviewed FDEP’s data quality assurance procedures (FDEP 2008) that FDEP used to procure its data and determined that 

these procedures were consistent with EPA quality assurance policies.
EPA Proposed Rule TSD for Florida’s Streams and DPVs for Lakes

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was then screened to identify reference condition sites for use in deriving nutrient criteria using 

the Benchmark and SCI Population approach described in Sections 1.3.2 and 1.3.3 below. 

For the SCI Population analysis, only sites from the All Streams Data Set with macroinvertebrate 

data and multi-metric scores (SCI) were included. These SCI score data for Florida streams were 

obtained from FDEP in two spreadsheets––one with data from 2004 and before, and a second 

with data from 2004 through 2007. These spreadsheets contained SCI scores for specific sites 

across the State and additional data about the sites. They also contained associated water 

chemical and habitat data, including nutrient concentrations from grab samples taken when 

biological sampling occurred (hereafter referred to as grab sample chemistry). These two data 

sets were merged together into a final overall data set containing 2,023 samples from 1,115 sites 

within 614 WBIDs statewide (WBIDs are spatial units used in Florida for water quality 

management purposes). EPA based this analysis on WBIDs defined as of March 2010.6

For both data sets, water chemistry data included TN, TP, and nitrate/nitrite (NO3/NO2) 

concentrations. TN was calculated by summing total Kjeldahl nitrogen (TKN-N) and 

nitrate/nitrite (NO3/NO2-N) concentrations from each sample. Water chemistry data were natural 

log (Ln) transformed prior to analysis to both adhere to assumptions regarding normality 

required of many parametric statistical tests and to reduce the influence of extreme values typical 

of log-normally distributed nutrient concentration data on estimates of central tendency. Figure 

1-12 shows quantile plots of TP, Ln(TP), TN, and Ln(TN), indicating the non-normal 

distribution of raw TP and TN values and the improvement in the approximation of a normal 

distribution provided by natural log transformations. 

Each site for which nutrient data were available was spatially linked to an NWR using a GIS 

when the final NWR classification was determined. Because NWR boundaries adhered to 

WBIDs, both WBIDs and/or sites could be located within an NWR using the GIS. Final nutrient 

criteria were derived using the geometric mean of the annual average nutrient concentrations 

calculated for each site within a WBID. The geometric mean of the annual mean values is herein 

referred to as the “WBID average” concentration.

 

6 EPA accessed FDEP’s WBID GIS coverage in March 2010. This WBID coverage is provided in the 2010 final rule TSD as

EPA-HQ-OW-2009-0596.
EPA Proposed Rule TSD for Florida’s Streams and DPVs for Lakes

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Figure 1-12. Quantile plots of TP, Ln(TP), TN, and Ln(TN). Note that units are in mg/L.

1.3.2 Selection of Reference Sites––Least Disturbed Sites (Benchmark 

Population)

For its 2010 final rule (U.S. EPA 2010c), EPA used a modification of an approach developed by 

FDEP, which identified a population of reference stream sites (Benchmark Population) that was 

least-disturbed by humans and nutrients. FDEP’s approach was described in EPA’s 2010 

proposed rule (U.S. EPA 2010a); the modifications EPA was considering to that approach were 

described in the Agency’s 2010 supplemental notice (U.S. EPA 2010b). FDEP developed a 

multi-step process (described below) to help ensure that the Benchmark Population selected 

represented the least human disturbance and provided designated use support. Similar to the SCI 

Population described in the proposed rule and below, streams documented to be least-disturbed 

by humans—excluding those with evidence of land disturbance, CWA section 303(d) 

impairments, and unhealthy biological conditions where known—can be used as part of 

reference condition approach because the range of nutrient concentrations observed at these sites 

support balanced natural populations of aquatic flora and fauna. 

A critical component of EPA’s modified benchmark approach was the multi-step evaluation 

process used to verify potential benchmark sites to ensure they represented least-disturbed 

reference conditions. This multi-step evaluation is outlined below. Screening to identify the TP (mg/L) Ln(TP,mg/L)

Quantiles

TN (mg/L) Ln(TN,mg/L)

Quantiles
EPA Proposed Rule TSD for Florida’s Streams and DPVs for Lakes

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Benchmark Population of least-disturbed reference sites began with the All Streams data set 

consisting of all available Florida nutrient data of known quality. Additional information about 

the verification process can be found in Appendix 3: 

• The first screen was based on Florida’s Landscape Development Intensity Index, which 

estimates the intensity of human land use. EPA removed potential benchmark sites with a 

corridor landscape development index (LDI) score of greater than 2. The corridor LDI score 

quantifies land use within a 100 m corridor to each side and 10 km above candidate sites, 

using the method described in Brown and Vivas (2005). This step eliminated 76 percent of 

Florida sites from further consideration. 

• Elimination of WBIDs included on the 2008 EPA-approved Florida CWA section 303(d) 

list of impaired waters based on failure to attain Florida’s nutrient or DO water quality 

standards. 

• Elimination of sites in WBIDs with mean nitrate concentrations greater than 0.35 mg/L, a 

concentration associated with excessive algae growth (see Section 3.3 of the 2010 final rule

TSD (Springs Criteria)).

• Elimination of sites that, based on the professional judgment of FDEP scientists, did not 

represent least-disturbed conditions, considering the surrounding landscape conditions based 

on analysis of high-resolution aerial photographs taken in 2004–2005, input from FDEP 

district scientists knowledgeable of the area, and including FDEP watershed assessments, 

field visits, visual assessment of surrounding land use, and biological evaluations. 

• Elimination of sites in WBIDs not meeting an average WBID SCI threshold of 40. 

• Elimination of sites known to have watershed LDI scores > 3. 

Maps, photos, and a summary of the data collected as part of this screening process at a subset of 

verified reference sites with nutrient concentrations greater than the mid-range of the distribution 

can be found in Appendix 5 FDEP’s Stream Benchmark Summaries. Each of the steps outlined 

above is described in greater detail below.

a. Corridor LDI Score of ≤ 2

Candidate benchmark sites were initially selected based on an application of the LDI. Brown and 

Vivas (2003) developed the LDI as an estimate of the intensity of human land use based on 

energy use per land use type. Application of the LDI is based on the ecological principle that the 

intensity of human-dominated land uses in a landscape affects the 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 have more intact natural 

ecological systems and processes. The LDI was developed specifically as an index of human 

disturbance, and it has been shown by Brown and Vivas (2003) to provide predictive capability 

regarding nutrient loading (Figure 1-13). LDI scores range from a high score of 10 (indicates 

high intensity land use) to a low score of 1 (indicates natural lands). 
EPA Proposed Rule TSD for Florida’s Streams and DPVs for Lakes

22 

Note that the arrow is the LDI value at 2.0, which is the benchmark site threshold.

Figure 1-13. Relationship between nutrient loading, nitrogen in panel (a), phosphorus in panel (b), and the 

LDI in the St. Marks watershed, Florida (after Brown and Vivas 2003). 

The LDI is calculated as the area-weighted value of land uses within an area of influence (Figure 

1-14). Using 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 = LDI for the area of influence

%LUi = percent of total area of influence in land use i

LDCi = LDI coefficient for land use i

Sources of disturbance near a stream exert greater influence than do far-field human influences 

(Brown and Vivas 2003). In its benchmarks identification process, FDEP used the corridor LDI, 

calculated based on land uses within a 100 m corridor of a stream and extending 10 km upstream 

from a sample site (i.e., “corridor approach”), because this was found to be good predictor of 

ecological health (Fore 2004).
EPA Proposed Rule TSD for Florida’s Streams and DPVs for Lakes

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Figure 1-14. Depiction of land use area (light yellow arrow) included in an LDI calculation (FDEP 2009). 

The utility of the corridor LDI approach is similar to the demonstrated effectiveness of 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 m are sufficient to 

reduce nutrient loads by up to 95 percent before reaching the stream (e.g., Peterjohn and Corell 

1985). Additionally, Coastal Plain riparian corridors 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 1997). Because phosphorus can be bound to sediments, 

riparian zones retain the incoming phosphorus by capturing sediments. Other studies have shown 

that nitrate in shallow groundwater beneath riparian zones was removed by 85 percent to 

90 percent due to plant uptake and denitrification in riparian zones 50 m to 70 m wide (Jacobs 

and Gilliam 1985; Jordan et al. 1993; Lowrance 1992, 1997). 

For purposes of reference site selection, corridor LDI values were calculated from land uses 

within a corridor area of 100 m on each side of the stream and tributaries within a 10 km radius 

upstream of the sampling point as shown in Figure 1-14. While numerous studies have 

concluded that corridor widths of 50 m to 70 m are sufficient to reduce stormwater nutrient loads 

to streams by as much as 95 percent, additional corridor width can provide greater protection to 

the waterbody. Based on these literature findings and the better correlations with biological 

health described above, FDEP concluded that a corridor width of 100 m would provide 

indication of human-dominated land use and that an LDI calculated based on a 100 m corridor 

was an appropriate method of selecting candidate least-disturbed Benchmark Population 

reference sites.
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As discussed in FDEP’s Nutrient Plan (2009), the LDI was specifically designed as a measure of 

human disturbance. LDI values of less than or equal to 2.0 within the 100 m corridor area are 

indicative of areas with less human impact. Other studies and evaluations have demonstrated, 

across other 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 (Brown and Reiss 2006; 

FDEP 2009; Fore 2004; Fore et al. 2007; Niu 2004). The detailed methodology behind FDEP’s 

corridor LDI approach is provided in Appendix 4 FDEP’s Method for Calculating a Corridorbased Landscape Development Intensity Index (LDI). 

b. Screening against the CWA Section 303(d) List of Impaired Waters 

Sites located within WBIDs listed on the U.S. EPA-approved Florida section 303(d) list as water 

quality limited for nutrients and/or DO were excluded as reference sites. EPA removed these 

sites and WBIDs from further consideration as reference sites. 

c. Screening against the 0.35 mg/L Nitrate+Nitrite Criterion for Springs 

EPA screened data against its nitrate+nitrite criterion of 0.35 mg/L, established for springs in the 

2010 final rule (Federal Register, 75FR 75805, December 6, 2010. Water Quality Standards for 

the State of Florida’s Lakes and Flowing Waters.). This concentration is based on empirical 

stressor-response correlations from laboratory and field data. EPA determined for springs that 

concentrations of nitrate+nitrite of 0.35 mg/L or less are needed to prevent excess algal growth 

(see Chapter 3). Candidate benchmark sites that exceed this nitrate concentration (0.35 mg/L) 

were eliminated from the Benchmark Population of reference sites because the goal was to 

identify sites least-impacted by human disturbance and EPA deemed 0.35 mg/L as consistent 

with pollution. 

d. Elimination of Sites Based on Verification of Surrounding Land Use by Examining HighResolution Aerial Photographs,Input from FDEP District Biologists, and Field Evaluation 

Including Watershed Assessment and Biological Appraisal of Benchmark Sites 

Least-disturbed conditions of sites were confirmed through a review of recent (2004 to 2005) 

high-resolution (1 m ground resolution) aerial photographs by FDEP. That review consisted of 

searching the photos for recent land clearing or development, in particular any disturbance that 

encroached into the 100 m corridor area used to calculate the LDI, but also throughout the 

watershed. Additional sites were excluded based on FDEP’s review of aerial photographs, 

including several sites that appeared to be tidally influenced, within canals, or artificially 

channelized. Therefore, these sites were not considered representative of a least-disturbed stream 

condition. 

FDEP district scientists familiar with streams in their respective areas 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 [of 

FDEP] 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, 
EPA Proposed Rule TSD for Florida’s Streams and DPVs for Lakes

25 

which may not have been captured by the LDI that would disqualify the site from being 

used to define benchmark for nutrient criteria?”

More than 20 study sites were excluded from the candidate benchmark population based on 

feedback and best professional judgment (BPJ) comments provided by FDEP district staff. The 

staff identified additional artificially channelized streams, estuarine sites, and potentially 

disturbed sites. In some cases, FDEP 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, while outside the 100 m corridor, in the 

reviewer’s opinion, could potentially increase nutrient concentrations. 

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 micro Siemens 

per cm (μS/cm) and episodic values above 4,500 μS/cm. A conductivity of 4,500 μS/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 and is also being used to define 

“predominately fresh waters” in the Agency’s rulemaking. 

Results of the BPJ decisions based on aerial photographs and expert biologist opinion resulted in 

a screening score used to eliminate reference sites from the benchmark pool. 

In 2007 and 2008, experienced FDEP staff conducted field verifications of a number of their 

candidate benchmark sites, selected through the above process, as a means of providing additional 

assurance that sites were truly representative of least-disturbed conditions. Due to time and 

resource considerations, not all of their sites could be visited. Therefore, FDEP scientists selected 

candidate benchmark sites for additional review predominantly within WBIDs that had nutrient 

concentrations higher than the mid-range of their original distribution. The objective of this in-field 

verification step, which included a watershed survey and biological assessment, was to reinforce 

confidence in the selection of their benchmark sites as representative of least-disturbed conditions. 

Representative sites within the target WBIDs were visited, and 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 

accessible portions of the watershed, guided by the most recent high-resolution aerial 

photographs taken in 2004–2005 and maps of the drainage basin. During the watershed survey, 

FDEP investigators made a series of observations regarding potential human disturbances in the 

watershed, including potential nonpoint source inputs and hydrologic modifications (using the 

FDEP hydrologic scoring system). The hydrologic scoring system they used in their observations 

was originally developed to support the development of Florida’s SCI. It is based on knowledge of 

water removal, patterns of drought, and hydrographs for the sites under evaluation, and it serves 

as a rough measure of hydrologic disturbance (Fore 2004; Fore et al. 2007). 

Stream Habitat Assessments (HAs) were conducted by FDEP following standard operating 

procedures (SOPs) (DEP-SOP-001/01 FT 31007

 [FDEP 2008]). The HA evaluates substrate 

 

7 http://publicfiles.dep.state.fl.us/dear/sas/sopdoc/2008sops/ft3000.pdf (Accessed May 2012)
EPA Proposed Rule TSD for Florida’s Streams and DPVs for Lakes

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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 m reach of the stream examined during the HA, investigators also physically examined a 

minimum of 200 m upstream of the site, including potential riparian zone breaches. 

At each site, trained and experienced FDEP scientific staff also collected and analyzed 

biological, chemical, and physical parameters (Table 1-3) following FDEP SOPs

(http://www.dep.state.fl.us/labs/sop). Water levels were evaluated both by reviewing 

hydrographs from the given stream or other streams in the general vicinity and by visually 

inspecting 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 under 

water. 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 for consideration in the benchmark data set. Note that sites with an average SCI score 

of less than 40 on the SCI were excluded from the benchmark data set for calculation of the final 

nutrient distribution. 

Table 1-3. Parameters monitored during the Benchmark Stream Survey. 

Biological Parameters Chemical and Physical Parameters

Stream Condition Index (SCI) Total phosphorus

Rapid periphyton assessment Nitrite + Nitrate

Qualitative Periphyton Sampling

(i.e., periphyton taxonomy)

Total Kjeldahl Nitrogen

Ammonia

Color

Habitat assessment Turbidity 

Chl-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)

Source: FDEP (2009)

Information acquired during the site and watershed evaluations was used to provide confirmation 

that benchmark sites were representative of least-disturbed conditions for the region and that 

nutrient concentrations support balanced natural populations of aquatic flora and fauna. The 

results of these surveys were incorporated into the BPJ (BPJ scoring process described above). 

In addition to the screening steps used by FDEP to identify benchmark sites, EPA performed the 

following additional screening steps to develop a set of reference sites with greater assurance that 

these sites represented least-disturbed conditions: 
EPA Proposed Rule TSD for Florida’s Streams and DPVs for Lakes

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e. Elimination of WBIDs Not Meeting SCI Threshold of 40 

EPA calculated average SCI scores for each WBID and eliminated WBIDs with an average 

WBID SCI score of < 40. This screen was not available for all WBIDs because not all WBIDs 

had been sampled for macroinvertebrates. Thus, WBIDs without SCI scores were not eliminated. 

The development of the SCI and the significance of the threshold of 40 are discussed in more 

detail below and in greater detail in Section 1.4. As noted previously, the SCI is a bioassessment 

tool developed by FDEP and calibrated against the Biological Condition Gradient (BCG) as an 

indication of the relative association of numerical SCI scores to ecological and biological 

attributes. Interval and equivalence tests and proportional odds logistic regression were used to 

discern a quantitative value above which Florida streams would be considered to contain healthy 

macroinvertebrate communities.

f. Elimination of Sites with Watershed LDI Scores > 3

An additional analysis by EPA compared the nutrient distributions of sites characterized by the 

corridor LDI approach with LDI scores on the same sites calculated on a larger watershed area. 

EPA calculated near-field8 and watershed9 (or drainage basin) average LDI scores for each of the 

remaining 137 candidate Benchmark WBIDs. For those WBIDs with average watershed or nearfield LDI values above 3.0—the value that was correlated with a corridor LDI value of 2 and 

deemed to be equivalent to woodland pasture and unimproved pasture (Brown and Vivas 

2005)—were also excluded from consideration. LDI scores were calculated using the same 

approach detailed above, but for the entire watershed and/or near-field region. That analysis 

suggested exclusion of two additional sites—one from the West Central NWR (South Prong 

Alafia River) and one from the North Central NWR (Camp Branch)—as well as closer scrutiny 

of additional sites in the West Central NWR. The analysis 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 LDI approach alone would yield a least-disturbed 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. 

The initial set of candidate Benchmark Population reference sites identified statewide, with 

available data for TN or TP of known quality and LDI values ≤ 2.0, consisted of 1,512 sites 

distributed among 614 WBIDs. After excluding sites through the multi-step screening process, a 

total of 200 sites in 70 WBIDs remained. For ease of replication, a list of the screens is provided 

for each step of the process after the initial LDI ≤ 2 screen along with a step-by-step description 

of how EPA applied the screens to arrive at the final set of sites used in the criteria calculation

for the Benchmark Population (see 2010 final rule TSD, EPA-HQ-OW-2009-0596).

 

8 Near Field LDI values were computed from all drainage areas (at the 12-digit HUC level) in Florida within a 10 km straight-line 

radius to the most downstream site within a WBID.

9 Watershed, or drainage basin, LDI values were computed from all drainage areas within Florida upstream of the most 

downstream site within a WBID.
EPA Proposed Rule TSD for Florida’s Streams and DPVs for Lakes

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1.3.3 Selection of Reference Sites—Biologically Healthy Sites (SCI Population)

and Impacted Sites

EPA proposed a reference population site selection process based principally on biological 

condition using the macroinvertebrate based SCI developed by FDEP to evaluate biological 

condition in streams (U.S. EPA 2010a). That approach is utilized in the rulemaking to derive 

criteria in the West Central region. The data used to support this approach are detailed in 

Appendix 3. The process for identifying the biologically healthy SCI Population reference 

condition streams is described below. 

a. Identifying Biologically Unhealthy Site List

Sites were identified that did not meet the SCI threshold of 40 and were, therefore, not 

considered biologically healthy (see Section 1.4 for more on SCI evaluation). For the 2010 final 

rule, the average of SCI scores within a WBID was calculated and WBIDs with an average SCI 

score < 40 were excluded. The basis for the SCI threshold of 40 is provided in Section 1.4. 

Interval and equivalence tests and proportional odds logistic regression were used to estimate an 

SCI value above which Florida streams would be considered to contain healthy 

macroinvertebrate communities (see Section 1.4.2).

b. Identifying Impaired WBIDs

EPA excluded sites located within WBIDs listed on the EPA-approved Florida section 303(d) list 

as water quality limited for nutrients and/or DO. EPA removed these sites and WBID from 

further analysis. A table of WBIDs listed as impaired for nutrients and/or DO was created and 

imported into a referential database. That database contained the nutrient chemical and SCI data 

described above, and individual sites within WBIDs listed for nutrient and/or DO impairments 

were identified and flagged. 

c. Removing Sites 

Any site flagged as biologically unhealthy (SCI < 40) or contained within a WBID listed for 

nutrients and/or DO impairment was removed from consideration as part of the biologically 

healthy site population for deriving the instream protective criterion in the West Central region. 

Once this screening selection was completed, EPA considered the remaining sites to be 

biologically healthy, with no evidence of impairment by nutrients and/or DO. Distributional 

statistics of TN and TP were then calculated for this biologically healthy stream population.

As above, for ease of replication, a list of the screens was provided for each step of the process 

along with a step-by-step description of how EPA applied the screens to arrive at the final set of 

sites used in the criteria calculation for the SCI Population (see Appendix 3 Data Supporting 

EPA’s Reference Approach for Deriving Numeric Nutrient Criteria for Florida Streams).
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1.3.4 Nutrient Concentrations and Distributions of Populations within Each NWR

For illustrative purposes, maps of the distribution of TN and TP concentrations before and after 

screening steps (as described above in Sections 1.3.2) across NWRs for the Benchmark and SCI 

Populations (Figure 1-15 through Figure 1-22) are shown. Data are presented on a reference site 

and reference WBID scale. Cumulative percent frequency curves for each NWR are also 

presented for TN and TP for the Benchmark and SCI Populations. 

Figure 1-15 through Figure 1-22 show the degree to which the screening processes for the 

Benchmark and SCI Populations removed sites and WBIDs resulting in a smaller population of 

reference sites and WBIDs from which stream numeric nutrient criteria were derived.

Cumulative percent frequency curves for TN (Figure 1-15B through Figure 1-18B) indicated that 

TN concentrations were relatively higher across the North Central, Peninsula, and West Central 

NWRs, compared to the Panhandle West and East NWRs, in both the Benchmark and SCI 

Populations, regardless of the scales (site or WBID). Low TN sites and WBIDs were distributed 

across the Panhandle West and East NWRs, with generally lower concentrations in the 

Panhandle West NWR. Cumulative percent frequency curves for TP (Figure 1-19B through 

Figure 1-22B) indicated that TP concentrations were higher in the West Central and North 

Central NWRs, compared to the Panhandle West, Panhandle East, and Peninsula NWRs. Higher 

TP concentrations appear to be localized in the northern half of the North Central NWR. Within 

the Panhandle and Peninsula NWRs, some high concentrations of TP at sites and WBIDs were 

observed, but there did not appear to be a consistent pattern. 
EPA Proposed Rule TSD for Florida’s Streams and DPVs for Lakes

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B 

A 

Figure 1-15. Plot of TN concentrations among Benchmark reference sites across EPA’s NWRs for the 

Benchmark Population before (A) and after (B) screening. Note that concentrations graphed are Ln(TN, 

mg/L). Also shown (in B) are cumulative percent frequencies of TN concentrations at Benchmark sites.
EPA Proposed Rule TSD for Florida’s Streams and DPVs for Lakes

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B 

A 

Figure 1-16. Plot of TN concentrations among Benchmark reference WBIDs across EPA’s NWRs for the 

Benchmark Population before (A) and after (B) screening. Note that concentrations graphed are Ln(TN, 

mg/L). Also shown (in B) are cumulative percent frequencies of TN concentrations at Benchmark WBIDs.
EPA Proposed Rule TSD for Florida’s Streams and DPVs for Lakes

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B 

A 

Figure 1-17. Plot of TN concentrations among SCI reference sites across EPA’s NWRs for the SCI Population 

before (A) and after (B) screening. Note that concentrations graphed are Ln(TN, mg/L). Also shown (in B) 

are cumulative percent frequencies of TN concentrations at SCI sites.
EPA Proposed Rule TSD for Florida’s Streams and DPVs for Lakes

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B 

A 

Figure 1-18. Plot of TN concentrations among SCI reference WBIDs across EPA’s NWRs for the SCI 

Population before (A) and after (B) screening. Note that concentrations graphed are Ln(TN, mg/L). Also 

shown (in B) are cumulative percent frequencies of TN concentrations at SCI WBIDs.
EPA Proposed Rule TSD for Florida’s Streams and DPVs for Lakes

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B 

A 

Figure 1-19. Plot of TP concentrations among Benchmark reference sites across EPA’s NWRs for the 

Benchmark Population before (A) and after (B) screening. Note that concentrations graphed are Ln(TP, 

mg/L). Also shown (in B) are cumulative percent frequencies of TP concentrations at Benchmark sites.
EPA Proposed Rule TSD for Florida’s Streams and DPVs for Lakes

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B 

A 

Figure 1-20. Plot of TP concentrations among Benchmark reference WBIDs across EPA’s NWRs for the 

Benchmark Population before (A) and after (B) screening. Note that concentrations graphed are Ln(TP, 

mg/L). Also shown (in B) are cumulative percent frequencies of TP concentrations at Benchmark WBIDs.
EPA Proposed Rule TSD for Florida’s Streams and DPVs for Lakes

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B 

A 

Figure 1-21. Plot of TP concentrations among SCI reference sites across EPA’s NWRs for the SCI Population 

before (A) and after (B) screening. Note that concentrations graphed are Ln(TP, mg/L). Also shown (in B) 

are cumulative percent frequencies of TP concentrations at SCI sites.
EPA Proposed Rule TSD for Florida’s Streams and DPVs for Lakes

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B 

A 

Figure 1-22. Plot of TP concentrations among SCI reference WBIDs across EPA’s NWRs for the SCI 

Population before (A) and after (B) screening. Note that concentrations graphed are Ln(TP, mg/L). Also 

shown (in B) are cumulative percent frequencies of TP concentrations at SCI WBIDs.
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1.3.5 Selection of Stream Criteria—Magnitude (Benchmark and SCI Populations) 

For the 2010 final rule (U.S. EPA 2010c), EPA calculated TN and TP criteria based on both the 

Benchmark and SCI Populations using a WBID average basis (Table 1-4). EPA considered a 

range of factors in its approach to deriving numeric nutrient criteria for Florida’s streams, 

including the spatial and temporal variability of a WBID or site and time scales over which to 

average water quality data. EPA also considered the distributional statistics for each reference 

population and how they might influence the derivation of scientifically defensible numeric 

nutrient criteria. These factors are discussed below. 

In general, the reference site approach for deriving nutrient criteria is based on the premise that 

nutrient concentrations at reference sites represent the natural variability of concentrations within 

a given study area. In Florida, Benchmark and SCI Populations approaches were used to 

establish the variability of long-term average nutrient concentrations across each NWR. EPA 

conducted analyses to identify a spatial averaging scheme (WBID vs. site) that best represented 

the spatial variability of nutrient concentration within the State, and to identify a temporal 

averaging scheme (long-term average vs. annual averages) that best represented the long-term 

average.

Spatial statistical analysis of geometric mean TP concentrations at different sites across Florida 

indicated that TP concentrations at sites that were close together tended to be more similar than 

sites that were distant from one another. This trend was identified by computing the semivariance associated with different separation distances using the following equation: 

∑=

= −+

)(

1

2 ))()(( )(2

1 )(

hn

i

i i TP hx TP x

hn γ h

where, 

γ(h) = the semi-variance for a separation distance of h 

n(h) = the number of samples separated by h 

xi = the location of site i 

TP() = the geometric mean concentration at the indicated location, xi 

Semi-variances increased with separation distances to a distance of approximately 35 km, which 

indicated that TP concentrations at sites separated by > 35 km in general were not associated 

with one another (Figure 1-23). TP concentrations were more similar between adjacent sites as 

the separation distance between those sites decreased.

Because the average distance between each Benchmark Population site and its nearest neighbor 

was approximately 3.7 km, TP concentrations at many sites in the Benchmark Population were 

correlated with one another. Including all TP concentrations from all of these sites would, in 

essence, over-represent certain areas of Florida in which many sites were sampled in close 

proximity with one another. In contrast, the average distance between each WBID and its nearest 

neighbor was 19 km. Based on these results, EPA concluded that summarizing nutrient 

concentrations by WBID, rather than site-by-site, provided a distribution of nutrient 

concentrations that was more representative of the overall spatial variability of nutrient 
EPA Proposed Rule TSD for Florida’s Streams and DPVs for Lakes

39 

concentrations across the State. The same analysis was conducted for TN with similar results and 

conclusions. 

Figure 1-23. Semi-variogram showing the degree to which TP concentrations at closely located sites are 

auto-correlated. 

Benchmark and SCI populations in Florida were intended to represent the spatial variability of 

long-term average background nutrient concentrations across the state. To this end, a single 

geometric mean value computed for each WBID was identified as the most appropriate temporal 

averaging scheme. The WBID-year averaging approach, in which nutrient concentrations at each 

WBID were summarized by annual geometric mean, and then included as separate values in the 

distribution, has the advantage of increasing the sample size used for estimating criteria. 

However, different annual averages from the same WBID do not necessarily vary in the same 

way as values observed across different WBIDs. The difference between the magnitude of 

temporal variability within a WBID and the magnitude of spatial variability across WBIDs can 

be seen qualitatively by comparing time series of TP concentrations in four Panhandle 

Benchmark WBIDs (Figure 1-24). The magnitude of variability within each WBID was much 

smaller within each site (i.e., the spread of values for any set of points with the same color) 

compared to the magnitude of variability across different sites. 

Formal statistical analysis supports these qualitative insights. A linear mixed model was used to 

quantify the variance associated with across-site variability and within-site inter-annual 

variability. The results of this model indicated that the across-site variance was approximately 

17× greater than within-site inter-annual variance for TP. For TN, the ratio of across-site 

variance to within-site inter-annual variance was approximately 10:1. Thus, including multiple 

annual averages from a given WBID as distinct values in the reference distribution overrepresents that particular WBID. Based on these findings, EPA decided that using a single longterm average from each WBID provided a more representative distribution of nutrient 

concentrations for deriving criteria.
EPA Proposed Rule TSD for Florida’s Streams and DPVs for Lakes

40 

Figure 1-24. Time series of annual geometric mean TP concentrations at four Panhandle Benchmark WBIDs. 

Note that different colored symbols denote different WBIDs.

Once EPA organized TN and TP data by WBID averages, EPA calculated criteria as the 90th

percentile of annual geometric mean concentrations in WBIDs from each NWR (Panhandle East 

and West, North Central, and Peninsula nutrient regions) using the following equation: 

90th Percentile = exp[mean(Ln(nutrient concentration)) + 1.282 SD (Ln(nutrient concentration))]

The criterion for the West Central NWR was calculated as the 75th percentile annual geometric 

mean concentration based on the SCI Population. A lower percentile was selected for the West 

Central due to greater uncertainty in the upper end of the distribution for this region given the 

number of SCI sites and annual average values. In the West Central NWR, benchmark conditions 

were available for only one WBID (see Table 1-5). The equation for the 75th percentile is the 

following: 

75th Percentile = exp [mean(Ln(nutrient concentration)) + 0.674 SD(Ln(nutrient concentration)) 

The selection of percentiles was based both on a judgment of reference site quality and statistical 

considerations. Selection of a central tendency of the reference distribution (i.e., the median or 

geometric mean of a log-normal distribution) would imply that approximately half of the 

reference sites are not attaining their uses. Alternatively, the upper end of the distribution 

(e.g., the 90th percentile) is appropriate if there is confidence that the distribution truly reflects 

minimally impacted reference conditions and can be shown to be supportive of designated uses 

(i.e., balanced natural populations of aquatic flora and fauna). 
EPA Proposed Rule TSD for Florida’s Streams and DPVs for Lakes

41 

EPA concluded that the Benchmark Population and the resulting Benchmark distributions of TN 

and TP were based on sufficient evidence of least-disturbed reference conditions after the 

additional quality assurance screens applied by EPA. Use of many different screens reduced the 

effects of uncertainty inherent in any single screening threshold. Thus, EPA found it reasonable 

to use the 90th percentile of WBID annual averages from this population to establish the final 

rule criteria in four of the five NWRs, which is consistent with EPA’s previously published 

guidance on percentile selection (U.S. EPA 2000). Because of the limited number of Benchmark 

Population sites in the West Central, EPA relied upon the SCI Population to derive TN and TP 

criteria in this region. Fewer screens were used to identify the final SCI Population, and hence, 

identifying these sites as least-disturbed was more uncertain than the Benchmark Population 

approach. Furthermore, as described in detail above, the SCI approach does not rely on a 

quantitative assessment of potential human disturbance through the use of surrounding land 

cover analysis of stream corridor and watershed LDIs. For this reason, EPA reasoned that the 

stream criteria in the West Central region should be based on the 75th percentile values of the 

distribution from the SCI Population, which is also consistent with EPA’s previously published 

guidance (U.S. EPA 2000). 

EPA explored calculating TN criteria for two NWRs as part of its consideration of a two region 

TN model (see above): the Panhandle NWR and combined Northeast, North Central, Peninsula 

and West Central NWR. Nitrogen concentrations did not exhibit the same regional spatial 

patterns as that observed for phosphorus. The primary regional difference was between the 

Panhandle and the remainder of the state. However, there was no loss of variance explained by a 

five NWR model over a two region model. This result combined with the potential for colimitation of nutrients, the similarity of nitrogen concentrations within Panhandle and nonPanhandle regions, and the practical benefits of consistent regionalization, EPA decided to use 

the same NWRs for TN as for TP.

In conclusion, EPA determined that the most appropriate approach for summarizing available 

nutrient concentration data in the Benchmark and SCI Populations was to calculate a single longterm annual average value within each WBID. The distributional statistics of a WBID average 

approach are shown in Table 1-4 and in Figure 1-25 and Figure 1-26 for TP and TN, 

respectively.
EPA Proposed Rule TSD for Florida’s Streams and DPVs for Lakes

42 

Table 1-4. EPA’s proposed numeric nutrient criteria for streams (in bold). 

EPA Benchmark Population – WBID Averages

Parameter Region

Mean

(Ln, mg/L)

Standard

Deviation

(mg/L)

75th

(mg/L)

90th

(mg/L) N

TP (mg/L) Panhandle West -3.992 0.900 0.03 0.06 22

Panhandle East -3.056 1.060 0.10 0.18 10

North Central -2.045 0.669 0.20 0.30 8

Peninsula -2.999 0.666 0.08 0.12 29

West Central -1.508 NC NC NC 1

TN (mg/L) Panhandle West -0.876 0.371 0.53 0.67 22

Panhandle East -0.464 0.388 0.82 1.03 10

North Central 0.101 0.407 1.46 1.87 8

Peninsula -0.119 0.427 1.18 1.54 29

West Central 0.189 NC NC NC 1

EPA SCI Population – WBID Averages

Parameter Region

Mean

(Ln, mg/L)

Standard

Deviation

(mg/L)

75th

(mg/L)

90th

(mg/L) N

TP (mg/L) Panhandle West -4.05 0.87 0.03 0.05 69

Panhandle East -2.68 0.80 0.12 0.19 17

North Central -2.00 1.16 0.30 0.60 11

Peninsula -2.69 0.98 0.13 0.24 50

West Central -1.11 0.59 0.49 0.70 15

TN (mg/L) Panhandle West -0.79 0.85 0.80 1.34 69

Panhandle East -0.55 0.51 0.81 1.10 17

North Central 0.05 0.35 1.33 1.65 11

Peninsula -0.15 0.55 1.25 1.75 50

West Central 0.21 0.43 1.65 2.14 15

Note: the mean, standard deviation, calculated 75th and 90th percentiles, and sample size of TP and TN distributions are 

presented for each NWR. EPA is utilizing the 90th percentile of the Benchmark population (computed as a WBID average), 

with the exception of the West Central for which EPA is utilizing the 75th percentile of the SCI population (computed as a 

WBID average). Mean and standard deviation are in Ln(mg/L) and percentiles are in mg/L. NC: not computed, or descriptive 

statistics that could not be computed due to limited sample size.
EPA Proposed Rule TSD for Florida’s Streams and DPVs for Lakes

43 

Figure 1-25. Box-whisker plots of TP distributions [Ln(TP, mg/L)] across EPA’s NWRs. Note that the median 

is indicated by the solid horizontal line within each box; the 25th and 75th percentiles are indicated by the 

lower and upper horizontal lines of the box, respectively; the 10th and 90th percentiles are indicated by the 

lower and upper whiskers, respectively; and the 5th and 95th percentiles are indicated by the lower and 

upper filled circles, respectively. EPA’s numeric criteria are based on the Benchmark Population using 

WBID averages at the 90th percentile of the distribution for the Panhandle West, Panhandle East, North 

Central, and Peninsula NWRs (light red) and the SCI Population using WBID averages at the 75th percentile 

of the distribution for the West Central NWR (light yellow). Ln(Total Nitrogen, mg/L)

-2.0

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0 North

Central

Panhandle

East Panhandle

West

Peninsula

West

Central

SCI Population (WBID Average, 75th percentile)

Benchmark Population (WBID Average, 90th percentile)
EPA Proposed Rule TSD for Florida’s Streams and DPVs for Lakes

44 

Figure 1-26. Box-whisker plots of TN distributions [Ln(TN, mg/L)] across EPA’s NWRs. Note that the median 

is indicated by the solid horizontal line within each box; the 25th and 75th percentiles are indicated by the 

lower and upper horizontal lines of the box, respectively; the 10th and 90th percentiles are indicated by the 

lower and upper whiskers, respectively; and the 5th and 95th percentiles are indicated by the lower and 

upper filled circles, respectively. EPA’s numeric criteria are based on the Benchmark Population using 

WBID averages at the 90th percentile of the distribution for the Panhandle West, Panhandle East, North 

Central, and Peninsula NWRs (light red) and the SCI Population using WBID averages at the 75th percentile 

of the distribution for the West Central NWR (light yellow).

1.4 Defining Healthy Aquatic Life in Florida Streams

The ability to determine the biological condition of a waterbody’s aquatic community is critical 

to informing decisions related to implementation of state and federal water quality programs. 

Biological assessment measures and tools are very valuable for determining whether, as 

necessary for this rulemaking, a particular waterbody has a balanced natural population of 

aquatic flora and fauna. They are also important for defining what this means in terms of the 

expected diversity and abundance of aquatic life and structure and function of the aquatic 

community. FDEP uses a percentile of reference sites to define the threshold between balanced 

and harmful biological change. 

This section describes the factors EPA considered in interpreting Florida’s definition of a 

healthy, well-balanced population of natural flora and fauna for purposes of deriving numeric 

nutrient criteria in Florida’s streams. These biological factors were used, in conjunction with Ln(Total Phosphorus, mg/L)

-7

-6

-5

-4

-3

-2

-1

0

1

North

Central

Panhandle

East Panhandle

West

Peninsula

West

Central

Benchmark Population (WBID Average, 90th percentile)

SCI Population (WBID Average, 75th percentile)
EPA Proposed Rule TSD for Florida’s Streams and DPVs for Lakes

45 

other data, to determine the particular nutrient concentrations that might interfere with 

designated use protection and attainment and the prevention of harmful change in the balance of 

aquatic populations. 

To use a biological assessment tool, an understanding of an ecosystem’s biological components and 

sources of variability is important. Aquatic organisms respond to a wide variety of factors, natural 

and anthropogenic. As organisms integrate and respond to these factors over time, a characteristic 

community structure emerges, with a range of natural variability. A portion of this natural 

variability can be explained by random natural events such as floods and drought, which determine 

the relative abundance of inundated substrates available for invertebrate colonization. These natural 

stressors (e.g., flood, drought, natural low substrate diversity, periodic natural low DO) can affect 

all sites—even those with minimal disturbance from humans, to a certain degree.

The response of biological communities to anthropogenic 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 1955).

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

artificial substrate samplers (Hester-Dendy samplers). Hester-Dendy samplers were placed in 

receiving waters for 28 days and data were summarized using the Shannon-Weaver diversity 

index, a biological metric derived from information theory that became a popular index to 

communicate complicated biological results. The index is based on a combination of taxa 

richness at a site and evenness of the distribution of individual abundances. Low diversity scores 

represent conditions where a few pollution-tolerant organisms are very abundant, to the 

exclusion of other less pollution tolerant taxa. The index is specified in Florida’s water quality 

standards as a measure of biological integrity (Rule 62-302.530, F.A.C.). It has been applied by 

comparing site-specific reference sites to test sites.

1.4.1 Development of the SCI

Currently, biological condition is assessed with the SCI. Florida’s SCI was developed in 2004 

and revised in 2007. The SCI is a multi-metric index that assesses stream health using the 

benthic macroinvertebrate community. FDEP 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 SOPs, and additional guidance for sampling and data use is provided through an 

FDEP document titled Sampling and Use of the Stream Condition Index (SCI) for Assessing 

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

after passing a rigorous audit with FDEP, and laboratory taxonomists are regularly tested and 

must maintain > 95 percent species identification accuracy. FDEP evaluates biological condition 

by comparing the SCI score to regional expectations for biological communities defined using 

reference sites. The latter are selected using a combination of watershed/reach-level screens and 

regional professional judgment on least-disturbed or minimally-disturbed stream locations, 
EPA Proposed Rule TSD for Florida’s Streams and DPVs for Lakes

46 

comparable to the process used to select reference sites for nutrient criteria setting. Based on 

reference site community similarity, three stream bioregions, in which there are slightly different 

expectations for the metrics based on natural differences, were established: 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). These bioregions 

are different from NWRs because they reflect natural differences in macroinvertebrate 

community structure rather than expected nutrient dynamics. 

The SCI is composed of 10 metrics, eight of which decrease in response to human disturbance, 

while the remaining 2 (percent very tolerant and percent dominant) increase in response to 

human disturbance. These biological metrics summarize different characteristics of the 

biological assemblage. Metrics that responded to a human disturbance gradient defined using a 

subset of the same elements to define reference sites were selected for inclusion in the overall 

SCI (Fore et al. 2007). Individual metrics were assigned points (0-10) based on the observed 

range in metric values and these were combined into the 0-100 point SCI. The population of SCI 

scores from reference sites was then used to define the balanced condition, and a lower percentile 

of the reference SCI score population defines the boundary between balance and harmful change. 

The state views departure from a reference site defined condition, therefore, as sufficient for 

inferring harmful change. For more information on the development of the SCI, see Appendix 6 

FDEP’s Development and Testing of Biomonitoring Tools for Macroinvertebrates in Florida 

Streams. 

1.4.2 Establishing Healthy Biological Conditions in Streams—SCI: Application of 

the Reference Condition Approach

As mentioned above, FDEP uses a percentile of the reference site distribution SCI scores to 

define what is imbalance or harmful change in biological condition. FDEP examined the lower 

distribution of reference site multi-metric scores to establish aquatic life use support thresholds, 

in combination with the BCG approach (see below), and EPA used the results of this analysis for 

identifying the threshold between healthy and unhealthy biological conditions. 

FDEP conducted statistical interval and equivalence tests with SCI data from 55 streams 

considered to be reference condition streams, including a portion of verified nutrient benchmark 

sites with additional data from the Fore et al. (2007) analysis. These tests were used to determine 

a value for SCI that distinguishes sites that are similar to conditions observed at reference sites, 

(implicitly defined as balanced) from those that are dissimilar (implicitly defined as imbalanced 

or harmfully altered). The interval and equivalence tests provide probabilistic tests of two 

different questions: (1) is the site in question within the range of conditions observed at reference 

sites, and (2) is the site in question outside of the range of conditions observed at reference sites? 

Because of uncertainty associated with defining precisely the range of conditions observed at 

reference sites from a limited number of samples, the answers to these two questions yield two 

different values of SCI (Table 1-5). The FDEP examination of the two most recent visits at 55 

reference streams showed that the 2.5th percentile of reference data was in the range of 35–44 

points. The middle of this range was 40 points, which represents an impairment threshold that 

balanced errors associated with defining whether a site was similar or different from the 

reference condition distribution. One could also choose to use higher percentiles of reference 

biological conditions as thresholds for harmful imbalance, such as the 5th, 10th, or even 25th
EPA Proposed Rule TSD for Florida’s Streams and DPVs for Lakes

47 

percentiles. These percentiles also represent departures from reference, but with a more 

protective or precautionary level of uncertainty related to change in the balance of fauna. Values 

of SCI associated with the 5th and 10th percentiles were estimated to be 46 and 50 (Table 1-5).

SCI values below these represent additional adverse imbalance or harmful change thresholds 

based on the population of reference biological conditions FDEP uses as its standard for 

acceptable balance.

Table 1-5. Results of interval and equivalence tests conducted on reference sites with two SCI results. 

Threshold (Description)

Reference 

Site Mean

Threshold 

(Numeric)

Different 

from 

Reference Undetermined

Similar to 

Reference

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

5th percentile of reference* 65 46 <41 41–49 >49

10th percentile of reference* 65 50 <47 47–53 >53

Note: shown are site mean, threshold, and range for threshold values defined at the 2.5th percentile of reference sites (p < 

0.05; n = 55 reference sites with two SCI values for each site). Reference site values are taken from Fore et al. (2007) and 

comprehensively verified nutrient benchmark sites. *Values reported from original memo on interval and equivalence tests 

referenced in Appendix 8.

When calibrating a threshold between acceptable and unacceptable biological conditions using 

an index, the amount of human disturbance inherent among reference sites is an important issue, 

because in modern human landscapes completely undisturbed or pristine conditions, either 

present or historic, no longer exist. Least-disturbed conditions present an estimate of the 

reference condition (sensu Stoddard et al. 2006). A rigorous reference site selection process 

provides increased confidence that the reference site population is least-disturbed, or minimally 

impacted by human influence. No method completely removes uncertainty in setting 

appropriately protective thresholds Appendix 7 FDEP’s Site Information and Taxa Lists for SCI 

BCG Workshop Samples contains complete taxa data for the samples used in this analysis. 

Appendix 8 Memo to FDEP Regarding Interval and Equivalence Tests for the SCI contains 

additional information on the interval and equivalence tests.

1.4.3 The BCG Approach

The BCG is a conceptual model that assigns the relative health of aquatic communities into one 

of six levels (categories), from natural to severely altered (Davies and Jackson 2006, Figure 

1-27). The BCG uses biological attributes of aquatic systems that predictably respond to 

increasing pollution and human disturbance. Although these attributes are measurable, some are 

not routinely quantified in monitoring programs (e.g., rate measurements such as productivity), 

but may be inferred through community composition data (e.g., abundance of taxa indicative of 

organic enrichment). The following biological attributes are considered in the BCG:

• Historically documented, sensitive, long-lived or regionally endemic taxa

• Sensitive and rare taxa

• Sensitive, but ubiquitous taxa

• Taxa of intermediate tolerance

• Tolerant taxa
EPA Proposed Rule TSD for Florida’s Streams and DPVs for Lakes

48 

• Nonnative taxa 

• Organism condition 

• Ecosystem functions

• Spatial and temporal extent of detrimental effects

• Ecosystem connectance

Figure 1-27. The BCG conceptual model (Davies and Jackson 2006). 

As noted above, the gradient represented by the BCG is divided into six levels (tiers) of 

condition that were defined by consensus (Davies and Jackson 2006) using experienced aquatic 

biologists from across the United States, including Florida. The six tiers are as follows:

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 

pollution tolerant taxa, but reproducing populations of some sensitive taxa are maintained; 
EPA Proposed Rule TSD for Florida’s Streams and DPVs for Lakes

49 

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.

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.

The six levels described above are used to correlate biological index scores with biological 

condition, as part of calibrating an index. FDEP conducted a BCG exercise to calibrate scores for 

the SCI. Twenty-two experts examined taxa lists from 30 stream sites throughout Florida, 10 in 

each ecoregion (Fore et al. 2007) that spanned the range of SCI scores (Appendix 7). Without 

knowledge of SCI scores, the experts reviewed the macroinvertebrate assemblage data from each 

site sample and assigned 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. 

Each expert independently assigned a BCG score to each site and then discussed their scores and 

scoring rationale. Each expert could make an informed change to their scores based on input 

from other participants. 

1.4.4 Evaluation of BCG Calibration Information

A proportional odds logistic regression model (Guisan and Harrell 2000) was used to estimate 

the relationship between SCI scores (a continuous variable) and BCG tiers (a categorical 

variable) (see Appendix 9 Mapping Continuous Biological Index Values to BCG Tiers). This 

model is based on the cumulative probability of a site being assigned to a given BCG 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 1-28, the mean predictions of the proportional odds logistic regression models are 

plotted as solid lines. The 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 percent of experts assigned a sample with the 

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

5 (purple line). In Figure 1-28, 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. Expert 

assignment of BCG scores varied, but there was a central tendency at any given SCI score.

EPA identified a threshold at an SCI score where there was an approximately equally low 

probability of assignment to Tier 5 (i.e., unhealthy) and a low probability of assignment to Tier 2 

(i.e., reference conditions). The resultant threshold balanced the probability of incorrectly 

assessing a degraded site as representing healthy biological conditions with the probability of 
EPA Proposed Rule TSD for Florida’s Streams and DPVs for Lakes

50 

incorrectly assessing a reference site as unhealthy. The resulting score of 42 was similar to the 

threshold of 40 determined by FDEP using the reference site approach. 

Figure 1-28. BCG tier assignments modeled with a proportional odds logistic regression. 

1.4.5 Selecting the Threshold between Healthy and Unhealthy Biological 

Conditions

Weighing these multiple lines, EPA selected an SCI value of 40 as the threshold between healthy 

and unhealthy biological condition. This threshold is supported by the distribution of reference 

site scores and corresponds with a BCG category midway between Tiers 3 and 4 using the BCG 

tool. The proportional odds analysis indicates that a site with biological condition consistent with 

EPA’s BCG Tier 2 (minimal change in biological composition) would not be considered 

unhealthy at a threshold of 40. Even with these two lines of evidence, uncertainty is associated 

with a single threshold that defines sites as healthy or unhealthy. While 40 was selected as the 

threshold, higher thresholds were also evaluated (46 and 50), as discussed above, for the stressorresponse analysis below to compare with the value of 40. 

1.4.6 Analysis of Biological Data in the Benchmark Population

As described previously, the SCI is negatively correlated with LDI, so biological health 

decreases in response to increasing levels of human activities (Figure 1-29). However, within the 

low range of LDI (≤ 2) associated with the nutrient benchmark site data set, no correlation was 

found between LDI score and SCI score (Figure 1-30). (Note that SCI scores were averaged for 

sites with more than one SCI sampling event). However, as the next section will indicate, at 

concentrations above the upper end of the reference condition nutrient distributions, harmful 

changes in faunal assemblages are observed.
EPA Proposed Rule TSD for Florida’s Streams and DPVs for Lakes

51 

Figure 1-29. Distribution of SCI across the range of corridor LDI scores (FDEP 2009). 

Figure 1-30. Average SCI scores Benchmark Population plotted against corridor LDI scores (FDEP 2009). 

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
EPA Proposed Rule TSD for Florida’s Streams and DPVs for Lakes

52 

1.5 Harmful, Adverse Effects Due to Exceedance of EPA’s Proposed 

Stream Criteria

The use of empirical stressor-response relationship models is a method recommended for 

nutrient criteria development, especially in conjunction with other approaches (U.S. EPA 2000, 

2010d). These approaches are built upon scientifically defensible conceptual models of the 

causal relationship between nutrients, and biological responses (U.S. EPA 2010d) and are built 

from databases of nutrients and response variables, which can be floral, faunal, and water 

chemistry. EPA considered stressor-response relationships in the development of nutrient criteria 

for Florida streams (detailed below). As indicated in EPA’s final rule (U.S. EPA 2010c), these 

relationships were judged to be of insufficient precision to be used as the sole basis for setting 

numeric criteria in Florida using the data available. However, further analysis by EPA provided a 

way to link the numeric criteria to the degree of harmful, adverse effects as defined using the 

biological thresholds used by FDEP and described above. 

This section describes the existing and additional stressor-response relationship analyses 

performed using Florida water quality data in support of the numeric nutrient criteria 

development. Floral-nutrient stressor-response model results are discussed first, followed by 

faunal-nutrient response model results. 

1.5.1 Harmful, Adverse Effects Observed by the State of Florida

FDEP conducted a series of stressor-response relationship analyses in support of numeric 

nutrient criteria development. These analyses included relating algal and macroinvertebrate 

responses to nutrients. FDEP found that its stressor-response analyses were statistically 

significant, but not accurate enough to derive numeric nutrient criteria. For these reasons, FDEP 

pursued a reference condition approach to derive numeric nutrient criteria for streams. 

Nevertheless, the analyses were sufficiently conclusive for FDEP to affirm the following:

“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.

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 
EPA Proposed Rule TSD for Florida’s Streams and DPVs for Lakes

53 

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.” (FDEP 2009, p. 96) 

The stressor-response analyses conducted in support of these statements can be found in Chapter 

6 of FDEP (2009) and the associated appendices. The same analyses were included in EPA’s 

2010 proposed rule TSD for inland waters (Appendix B-1 FDEP’s Stressor-Response Analyses 

for Florida Streams, EPA-HQ-OW-2009-0596).

The two stressor-response relationships referenced by FDEP as indicative of adverse biological 

responses occurring at or just above the reference condition-based values are the rapid 

periphyton survey response and the periphyton metric responses. 

The rapid periphyton analysis was described in FDEP (2009) as follows:

“Because excessive abundance of algae has been identified as a nuisance condition that 

should be avoided..., 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....The analyses indicated that canopy cover, color, pH, total Kjeldahl nitrogen, 

organic nitrogen, and nitrate- nitrite 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). 

As shown..., 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 (in 

original document). 

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, (EPA Figure 1-31a1-a3 below)] than in colored streams [Figure 6-5 c1-
EPA Proposed Rule TSD for Florida’s Streams and DPVs for Lakes

54 

c3, (EPA Figure 1-31c1-c3 below)], indicated by the stronger spearman correlations as well 

as 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.” (FDEP 2009, pp. 90-93) 

Figure 1-31. 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 (Figure 6-5 from FDEP 2009). 
EPA Proposed Rule TSD for Florida’s Streams and DPVs for Lakes

55 

The periphyton response refers to analysis of stream diatom data collected by FDEP that was 

converted into periphyton metrics, which are composite measures of diatom condition based on 

diatom species specific pollution/nutrient tolerances. Changes in these diatom metrics are 

indicative of changes in the balance of algal flora in response to pollution. 

The periphyton response analysis was described as follows in FDEP (2009): 

“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 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 (EPA Table 1-6 below). 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 (in original document). 

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 

(EPA Figure 1-32 below), and the results of the change point analyses for the periphyton 

response variables versus TP are summarized in Table 6-2 (EPA Table 1-7 below).” (FDEP 

2009, pp. 85-86) 
EPA Proposed Rule TSD for Florida’s Streams and DPVs for Lakes

56 

Table 1-6. Summary of multiple-linear regression analyses conducted for the panhandle nutrient region 

(Table 6.1 in FDEP 2009). 

Figure 1-32. Change-Point Model (Step Function) of percent pollution sensitive diatoms vs. total 

phosphorus. Response adjusted for ColorL and CondL (Figure 6-2 from FDEP 2009).

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

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
EPA Proposed Rule TSD for Florida’s Streams and DPVs for Lakes

57 

Table 1-7. Summary of change-point analyses conducted for the panhandle nutrient region (Table 6-2 from

FDEP 2009). 

FDEP (2009) concluded from these analyses,

“The primary change points indicated in Table 6-2 (EPA Table 1-7) 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...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 (in original document). 

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.” (FDEP 2009, pp. 87-88) 

FDEP also investigated the relationships in responses of macroinvertebrates to nutrients. FDEP 

concluded the following:

“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...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 % 

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
EPA Proposed Rule TSD for Florida’s Streams and DPVs for Lakes

58 

dominant taxa, exhibited a significant adverse response to increased levels of nitrogen. In 

addition, the analyses indicated that the macroinvertebrate community lso [sic] 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 (EPA Figure 1-33 below) 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 (in original document).” (FDEP 2009, p.84) 

Figure 1-33. Example relationships between macroinvertebrate response variables and nutrient levels. 

TSIN is Total Soluble Inorganic Nitrogen (ammonia plus nitrate-nitrite) (Figure 6-1 from FDEP 2009).
EPA Proposed Rule TSD for Florida’s Streams and DPVs for Lakes

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1.5.2 EPA Analyses Demonstrating Harmful, Adverse Effects

To explore the harmful, adverse effects of nutrients on Florida’s floral and faunal communities, 

EPA analyzed three datasets provided by FDEP, which included simultaneous water chemistry, 

habitat, and macroinvertebrate data collected from 2006–2011 (see Appendix 3). 

EPA analyzed FDEP’s sampling data from 2006–2011 by merging three different files 

representing different time periods and resolving sample site and data incompatibilities (e.g., 

different naming conventions between the two years, etc.). For some water chemistry 

constituents, remark codes were treated as follows:

1. Values with following codes were removed: Y—unpreserved sample, J alone—estimated 

value but no indication how or why, Q—held beyond acceptable holding time, O—

sample lost. 

2. Values with the following codes were retained: U—value is the laboratory detection 

limit, I—the reported value is between the laboratory method detection limit and the 

laboratory practical quantitation limit, A—average of two or more determinations. 

For this analysis, EPA utilized the first replicate of each sample ID. Data for the second replicate

were not used because the files indicated that some metrics may not have been calculated for the 

second replicate, habitat and water chemistry data were not replicated and were denoted as the 

same value as the first replicate value, and EPA was concerned about unequal weighting by 

using replicates for some samples and not others. All water chemistry values were natural log 

transformed (Ln) before analysis. 

The latitude and longitude were available for each station and these were placed into a GIS with 

the NWR coverages provided in the final rule to extract the NWR associated with each station.

This allowed analysis by region as needed. 

Once the final data file (Merged 2006–2011 SCI Data 06292012.xlsx, see Appendix 3) was 

prepared, the data were exported into statistical software for subsequent simple linear regression 

modeling between SCI and nutrient parameters. Sites were labeled by habitat condition class, 

with sites identified as having overall habitat scores > 80, 100, and 120. Initial analysis 

suggested that sites in higher habitat conditions may show stronger relationships between SCI 

and nutrients as a result of removing some of the confounding effects of bad habitat on fauna. 

1.5.2.1 Results—SCI Responses to Habitat Quality and Nutrients

FDEP collected a variety of habitat metrics from sites where they sampled biological condition 

(DEP-SOP-001/01 FT 3100. Stream and River Habitat Assessment).10 Each of 8 metrics 

(substrate diversity, substrate availability, water velocity, habitat smothering, artificial 

channelization, bank stability, riparian buffer zone width, and riparian zone vegetation quality) 

are scored from 0 to 20, and the maximum possible score is 160. 

 

10 http://publicfiles.dep.state.fl.us/dear/sas/sopdoc/2008sops/ft3000.pdf, Accessed May 2012
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60 

EPA analyzed the relationship between habitat and SCI and found that there was a significant 

increase in SCI with Habitat Score (0–160). EPA found that this relationship was consistent 

regardless of the stream NWR(Figure 1-34) and that the general equation indicated a factor 

ceiling, where SCI scores, on average, begin to surpass 40 (i.e., healthy macroinvertebrate 

community) at Habitat Scores > 100 (Figure 1-35).

Given the constraints and confounding effect imposed by habitat condition on biological index 

scores, EPA analyzed the response of SCI scores to nutrients across the entire range of habitat 

conditions as well as among sites only exhibiting higher habitat conditions (>80, >100, and > 

120), which correspond to 50 percent, 63 percent, and 75 percent of maximum possible scores. 

These analyses were conducted across all stream NWRs, since the SCI was scaled to be an 

approximately equivalent measure across the state (Fore et al. 2007), as well as within individual 

stream NWRs (see Appendix 1).

Figure 1-34. SCI versus Habitat Score, 2006–2011 data, individual nutrient region relationships. Fit is an 

exponential regression. Panhandle West: pink closed circle, pink line; Panhandle East: green open triangle, 

green line; Peninsula: black closed square; North Central: red open diamond, red line; West Central: gray 

closed diamond, gray line.

SCI VS. Habitat Score

Habitat Score

SCI2007

0 20 40 60 80 100 120 140 160

0

20

40

60

80

100

120
EPA Proposed Rule TSD for Florida’s Streams and DPVs for Lakes

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Figure 1-35. SCI versus Habitat Score, 2006–2011 data, relationship for all regions combined. Panhandle 

West: pink closed circle, pink line; Panhandle East: green open triangle, green line; Peninsula: black closed 

square; North Central: red open diamond, red line; West Central: gray closed diamond, gray line.

SCI vs. TP: All Habitat Conditions Linear Regression Models 

EPA found that SCI scores decrease significantly with TP concentration across all sites 

regardless of habitat condition (p<0.01, Figure 1-36), although this relationship varied across 

nutrient region. SCI decreased in the Peninsula, Panhandle West, and Panhandle East regions 

(p < 0.01 for all three). Both the North Central and the West Central exhibited increases 

(p < 0.01 and p = 0.27 respectively), however concentrations of TP in these regions were mostly 

observed at the higher end of the distribution. 

SCI VS. Habitat Score

0 20 40 60 80 100 120 140 160

HabScore

0

20

40

60

80

100

120

SCI2007
EPA Proposed Rule TSD for Florida’s Streams and DPVs for Lakes

62 

Figure 1-36. SCI versus Ln(TP), 2006–2011 data, relationship for all regions combined. Linear fit is shown. 

Panhandle West: pink closed circle, pink line; Panhandle East: green open triangle, green line; Peninsula: 

black closed square; North Central: red open diamond, red line; West Central: gray closed diamond, gray 

line.

SCI vs. TN: All Habitat Conditions

As with TP, EPA found that SCI scores decreased significantly as a function of TN concentration 

across all sites regardless of habitat condition (p<0.01, Figure 1-37), although this relationship 

varied across nutrient region. SCI decreased in the Peninsula, Panhandle West, and Panhandle 

East regions (p <0.01 for all three). However, in the North Central and the West Central this 

relationship was flat (p=0.98 and p =0.69, respectively), and, again, these regions principally 

exhibit TN concentrations along the higher end of the nutrient gradient. 
EPA Proposed Rule TSD for Florida’s Streams and DPVs for Lakes

63 

Figure 1-37. SCI versus Ln(TN), 2006–2011 data, relationship for all regions combined. Linear fit is shown. 

Panhandle West: pink closed circle, pink line; Panhandle East: green open triangle, green line; Peninsula: 

black closed square; North Central: red open diamond, red line; West Central: gray closed diamond, gray 

line.

EPA’s analysis indicated that stream macroinvertebrate health, as indicated by FDEP’s SCI, does 

degrade as phosphorus and nitrogen concentrations in the stream increase. This relationship was 

consistent for the SCI across all habitat scores analyzed with respect to TN and TP. EPA 

concluded from this analysis that the consistency of the effects of TN and TP across all habitat 

types is a clear indication of the harmful, adverse effects of nutrients across Florida streams.

Additional analytical details are included as Appendix 10: Harmful, Adverse Effects of Nutrients 

on Stream Macroinvertebrates in Florida (Supplemental Analyses). 
EPA Proposed Rule TSD for Florida’s Streams and DPVs for Lakes

64 

1.5.2.2 Results—SCI Responses to Nutrients in Screened Habitat Regression Models

EPA also examined the biological responses to nutrients under conditions during which nutrients 

would most likely present a stressful condition for stream biota. Such conditions exist when there 

is sufficient light, stable and available substrate for algae to grow (i.e., substrate that is not 

smothered by mobile sediment), and sufficient flow to deliver nutrients to benthic algae. Under 

such conditions, the causal pathway in response to nutrients and adverse effects are more likely 

to be exacerbated, with subsequent harmful effects on higher trophic levels including 

invertebrates. In addition, such habitat conditions would present reduced non-nutrient related 

habitat effects, so the variability in nutrient-biotic response would be reduced and more of the 

variability due to nutrient effects could be identified. In contrast to the analyses conducted using 

all habitat quality information, which is presented in section 1.5.2.1 above, EPA sought to more 

clearly quantify the relationship between nutrients and macroinvertebrates using screened habitat 

regression models. 

The additional data provided by FDEP on SCI from 2006–2011 included habitat condition 

assessments. These data did not include light levels, but they did include a measure of riparian 

buffer zone width, which is an indication of the integrity of the riparian buffer. Thinner buffers 

mean less vegetation and more light striking the stream channel. Similarly, a visual estimate of 

the extent of habitat that was smothered was also available with these data. Lastly, a visual 

habitat score based on water velocity was also available. These specific habitat metrics are 

described in more detail in FDEP’s SOP for habitat assessment (DEP-SOP-001/01 FT 3100. 

Stream and River Habitat Assessment).11

Habitat Screens 

The residuals from the linear regression of TP versus SCI score from the 2006–2011 SCI data 

were used to inform appropriate thresholds to use for the three habitat screening metrics. The 

riparian buffer zone width measure (RBZW) was calculated by summing the values of RBZW 

for the left bank and right bank into one overall score. The residuals of SCI vs. Ln(TP) increased 

with values of RBZW, meaning that SCI scores were better than predicted based on TP when 

riparian zones were widest, and this value was equal to zero at an RBZW value of 16.5 (Figure 

1-38). As a result of this regression, a screening value of 16 was used for this metric. According 

to the SOP for habitat assessment described above, this value is where riparian zones are as thin 

as 12 m. EPA subsequently screened for samples with RBZW scores less than 16 as indicative of 

where canopies are more open and light is less likely to be limiting primary producer responses.

 

11 http://publicfiles.dep.state.fl.us/dear/sas/sopdoc/2008sops/ft3000.pdf, Accessed May 2012
EPA Proposed Rule TSD for Florida’s Streams and DPVs for Lakes

65 

Figure 1-38. Plot of the residuals of TP vs. SCI (SCI2RES1) against RBZW. 

EPA analyzed the habitat smothering metric in the same way. For this habitat metric, SCI scores 

were better than predicted by TP, on average, when habitat smothering scores were greater than 

13.2 (Figure 1-39), so a value of 13 was used as a screening value. This value is where > 25 

percent of the available habitat is covered by sand or silt but there are still an adequate number of 

pools. Based on this result, EPA subsequently screened for samples with habitat smothering 

scores greater than 13 as indicative of where habitat was more likely to be less impacted by 

smothering and/or scouring by silt/sand, stable enough for nutrient responses to be exacerbated 

and where habitat, per se, was less likely to confound the nutrient response. 

SCI Residuals vs. Riparian Buffer Zone Wdth

SCI2RES1 = -17.8443+1.0797*x

-2 0 2 4 6 8 10 12 14 16 18 20 22

RBZW

-60

-40

-20

0

20

40

60

SCI2RES1
EPA Proposed Rule TSD for Florida’s Streams and DPVs for Lakes

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Figure 1-39. Plot of the residuals of TP vs. SCI (SCI2RES1) against the habitat smothering habitat metric 

(HabSmoth). 

Lastly, EPA analyzed the water velocity habitat metric. For this metric, SCI scores were better 

than predicted by TP, on average, when water velocity scores were greater than 14.2 (Figure 

1-40), so a value of 14 was used as a screening value. This value is where maximum observed 

velocities along transects start becoming slower than 0.1 to 0.25 m/s (but no more than 1 m/s). 

Based on this result, EPA subsequently screened for samples from sites with water velocity

scores greater than 14 as indicative of where habitat was more likely to be less impacted by 

reduced/altered flows, likely to increase nutrient supply to the benthos, and, again, likely to 

exacerbate a nutrient response relevant to biota that was less likely to be confounded by habitat 

impacts, since stream invertebrates, generally, require stream flow.

SCI Residuals vs. Habitat Smothering

SCI2RES1 = -11.9457+0.9078*x

0 2 4 6 8 10 12 14 16 18 20 22

HabSmoth

-60

-40

-20

0

20

40

60

SCI2RES1
EPA Proposed Rule TSD for Florida’s Streams and DPVs for Lakes

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Figure 1-40. Plot of the residuals of TP vs. SCI (SCI2RES1) against the water velocity habitat metric. 

Having developed these three screens, they were applied to the 2006–2011 FDEP SCI dataset 

provided to EPA and the sites passing the screens were then analyzed using regression analyses 

for the relationship between nutrients and invertebrate response. 

Nutrients vs. Biological Responses 

The removal of sites where habitat conditions were likely to confound effects of nutrients greatly 

improved the stressor-response relationship models of nutrients and biological response. In the 

causal model, EPA predicted that nutrients increase primary producers, whose excess biomass 

causes a cascade of trophic, habitat, and water chemical responses that adversely impact the 

fauna, including invertebrates. The first predicted response is, therefore, increased algae. When 

the sites were screened for habitat, a significant positive response of water column chl-a (the 

only algal response available in the SCI 2006–2011 dataset) was observed to both TP and TN 

(Figure 1-41 and Figure 1-42, respectively). 

SCI Residuals vs. Water Velocity

SCI2RES1 = -20.3983+1.437*x

-2 0 2 4 6 8 10 12 14 16 18 20 22

WaterVelocity

-60

-40

-20

0

20

40

60

SCI2RES1
EPA Proposed Rule TSD for Florida’s Streams and DPVs for Lakes

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PHW PEN PHE NC WC

Figure 1-41. Plot of TP vs. water column chl-a (ChlCorr), corrected for phaeophytin (ppb). Sites were 

screened for RBZW, habitat smothering (HabSmoth) and water velocity habitat conditions. The red line is a 

linear fit. Also shown are the Florida stream TP criteria values (in red with arrows) for each nutrient region. 

PHW: Panhandle West, PHE: Panhandle East, PEN: Peninsula, NC: North Central, WC: West Central.
EPA Proposed Rule TSD for Florida’s Streams and DPVs for Lakes

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PHW

PHE

NC

Figure 1-42. Plot of TN vs. water column chl-a (ChlCorr), corrected for phaeophytin (ppb). Sites were 

screened for RBZW, habitat smothering (HabSmoth) and water velocity habitat conditions. The red line is a 

linear fit. Also shown are the Florida stream TN criteria values (in red with arrows) for each nutrient region. 

PHW: Panhandle West, PHE: Panhandle East, PEN: Peninsula, NC: North Central, WC: West Central.

The excessive growth of algae in response to nutrient pollution is one of the paths by which 

nutrients impact biota, and a principal pathway by which this occurs is by algae increasing 

organic matter load, which reduces DO. A significant decline in DO was also observed in 

response to increasing algae in the habitat screened data, consistent with predictions (Figure 

1-43). 
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Figure 1-43. Plot of field collected DO (mg/L) vs. water column chl-a (ChlCorr), corrected for phaeophytin 

(ppb). Sites were screened for RBZW, habitat smothering (HabSmoth) and water velocity habitat 

conditions. The red line is a linear fit.

Nutrients can manifest an adverse effect on invertebrates either through chl-a directly (via habitat 

or food quality effects) or through depressed DO from increased respiration of the excess organic 

matter load. Again, using the screened dataset, a significant decline in SCI with decreasing DO

(Figure 1-44) and increasing chl-a was observed (Figure 1-45), consistent with these predictions. 

Dissolved Oxygen vs. Water Column Chlorophyll a

Include condition: RBZW<=16 and HabSmoth>=13 and WaterVelocity>=14

DOField = 7.4358-1.3556*log10(x)

0.50.60.70.80.91.02.03.04.05.06.07.08.09.0

10.020.030.040.0

ChlCorr

2

4

6

8

10

12

14

DOField
EPA Proposed Rule TSD for Florida’s Streams and DPVs for Lakes

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Figure 1-44. Plot of DO (mg/L) vs. SCI score. Sites were screened for RBZW, habitat smothering (HabSmoth) 

and water velocity habitat conditions. The red line is a linear fit and the hatched lines the 75th percentile 

prediction interval.

SCI vs. Dissolved Oxygen

Include condition: RBZW<=16 and HabSmoth>=13 and WaterVelocity>=14

SCI2007 = 25.8377+3.5919*x

2 4 6 8 10 12 14

DOField

0

10

20

30

40

50

60

70

80

90

100

SCI2007
EPA Proposed Rule TSD for Florida’s Streams and DPVs for Lakes

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Figure 1-45. Plot of water column chl-a (ChlCorr), corrected for phaeophytin (ppb) vs. SCI score. Sites were 

screened for RBZW, habitat smothering (HabSmoth) and water velocity habitat conditions. The red line is a 

linear fit.

Having demonstrated that causal links predicted from a nutrient pollution effect are present in 

this dataset, EPA then developed regression models of nutrients against SCI scores directly with 

linear regression and as SCI scores greater than 30, 40, 46, and 50 as binomial responses using 

binomial regression. These modeling approaches are the same as those applied to the unscreened 

data above and were used to explore values of nutrients associated with adverse floral responses 

to nutrients directly.

As with the full dataset, SCI scores decreased significantly with TP concentration (Figure 1-46), 

with the probability of SCI scores less than 30, 40, 46, and 50 increasing with TP concentration 

(Figure 1-47).

SCI vs. Water Column Chlorophyll a

Include condition: RBZW <= 14 and HabSmoth>=10 and WaterVelocity>=13

SCI2007 = 53.0789-20.5649*log10(x)

0.50.60.70.80.91.02.03.04.05.06.07.08.09.0

10.020.030.040.050.0

ChlCorr

10

20

30

40

50

60

70

80

90

100

SCI2007
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Figure 1-46. Plot of the natural log of TP concentration (mg/L) vs. SCI score. Sites were screened for RBZW, 

habitat smothering (HabSmoth) and water velocity habitat conditions. The red line is a linear fit and the 

hatched lines the 75th percentile prediction interval.

SCI vs. TP

Include condition: RBZW<=16 and HabSmoth>=13 and WaterVelocity>=14

SCI2007 = 37.1611-4.9739*x; 0.75 Pred.Int.

-6 -5 -4 -3 -2 -1 0 1 2

LnTP

0

10

20

30

40

50

60

70

80

90

100

SCI2007
EPA Proposed Rule TSD for Florida’s Streams and DPVs for Lakes

74 

Figure 1-47. Plot of the natural log of TP concentration (mg/L) vs. SCI score represented as a binomial 

where 1 = SCI less than 30 (upper left), SCI less than 40 (upper right), SCI less than 46 (lower left), or SCI less 

than 50 (lower right). Sites were screened for RBZW, habitat smothering (HabSmoth) and water velocity 

habitat conditions. The red line is a binomial regression fit.

The same trends were found when TN was used (Figure 1-48 and Figure 1-49).
EPA Proposed Rule TSD for Florida’s Streams and DPVs for Lakes

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Figure 1-48. Plot of the natural log of TN concentration (mg/L) vs. SCI score. Sites were screened for RBZW, 

habitat smothering (HabSmoth) and water velocity habitat conditions. The red line is a linear fit and the 

hatched lines the 75th percentile prediction interval.

SCI vs. TN

Include condition: RBZW<=16 and HabSmoth>=13 and WaterVelocity>=14

SCI2007 = 49.6018-11.8688*x; 0.75 Pred.Int.

-2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5

LnTN

0

10

20

30

40

50

60

70

80

90

100

SCI2007
EPA Proposed Rule TSD for Florida’s Streams and DPVs for Lakes

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Figure 1-49. Plot of the natural log of TN concentration (mg/L) vs. SCI score represented as a binomial 

where 1 = SCI less than 30 (upper left), SCI less than 40 (upper right), SCI less than 46 (lower left), or SCI less 

than 50 (lower right). Sites were screened for RBZW, habitat smothering (HabSmoth) and water velocity 

habitat conditions. The red line is a binomial regression fit.

As with the previous models, EPA used the regression models above to estimate TP and TN 

concentrations associated with SCI thresholds of 30, 40, 46, and 50 for the linear regression 

models (Table 1-8) and with a greater than 50 percent probability of scoring less than 30, 40, 46, 

and 50 for the binomial regression models (Table 1-9).
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Table 1-8. Linear regression coefficients and statistics of nutrient concentration (natural log of TP and TN) 

vs. SCI for sites screened for RBZW, habitat smothering (HabSmoth), and water velocity habitat conditions 

described in the text. 

SCI vs. TP

b p m p

Mean

TP

95th

CI Intercept

95th

CI Slope

SCI > 30 37.16 < 0.001 -4.97 < 0.001 4.22 31.08 43.25 -6.82 -3.13

SCI > 40 37.16 < 0.001 -4.97 < 0.001 0.57 31.08 43.25 -6.82 -3.13

SCI > 46 37.16 < 0.001 -4.97 < 0.001 0.17 31.08 43.25 -6.82 -3.13

SCI > 50 37.16 < 0.001 -4.97 < 0.001 0.08 31.08 43.25 -6.82 -3.13

SCI vs. TN

b p m p

Mean

TN

95th

CI Intercept

95th

CI Slope

SCI > 30 49.60 < 0.001 -11.87 < 0.001 5.22 46.43 52.78 -16.73 -7.01

SCI > 40 49.60 < 0.001 -11.87 < 0.001 2.25 46.43 52.78 -16.73 -7.01

SCI > 46 49.60 < 0.001 -11.87 < 0.001 1.35 46.43 52.78 -16.73 -7.01

SCI > 50 49.60 < 0.001 -11.87 < 0.001 0.97 46.43 52.78 -16.73 -7.01

The intercept (b) and slope (m) are shown along with their significance and 95% confidence intervals (CI). Predicted values 

of total phosphorus and total nitrogen (mg/L) are shown for values of SCI = 40, 46, and 50, coinciding with the 2.5th, 5th, and 

10th percentile of reference site SCI scores, defined as scores where impact to fauna is identified in Florida (Appendix 8) and 

for SCI = 30 for comparison. 

Table 1-9. Binomial regression coefficients and statistics of nutrient concentration (natural log of TP and 

TN) vs. SCI for sites screened for RBZW, habitat smothering (HabSmoth), and water velocity habitat 

conditions described in the text. 

SCI vs. TP

b m χ2 p

Mean TP

(mg/L)

at p = 0.5

95th

CI Intercept

95th

CI Slope

SCI > 30 -0.649 0.431 8.41 0.004 4.50 -0.015 0.002 0.001 0.007

SCI > 40 0.087 0.335 8.14 0.004 0.771 -0.634 0.808 0.095 0.574

SCI > 46 0.459 0.351 9.62 0.002 0.270 -0.263 1.181 0.118 0.583

SCI > 50 0.933 0.385 11.8 < 0.001 0.089 0.180 1.685 0.151 0.619

SCI vs. TN

b m χ2 p

Mean TN

(mg/L)

at p = 0.5

95th

CI Intercept

95th

CI Slope

SCI > 30 -1.714 0.879 4.66 0.031 7.03 -2.221 -1.208 0.036 1.723

SCI > 40 -0.742 0.951 8.57 0.003 2.18 -1.136 -0.348 0.261 1.642

SCI > 46 -0.394 1.128 12.6 < 0.001 1.42 -0.776 -0.013 0.439 1.817

SCI > 50 -0.009 1.040 11.7 < 0.001 1.01 -0.388 0.370 0.391 1.689

The intercept (b) and slope (m) are shown along with their significance and 95% confidence intervals (CI). Predicted values 

of total phosphorus and total nitrogen (mg/L) where the probability of exceeding the SCI thresholds equals 0.5 are shown 

(Mean TP or TN at p=0.5) for SCI values = 40, 46, and 50, coinciding with the 2.5th, 5th, and 10th percentile of reference site 

SCI scores (Appendix 8) and for SCI = 30 for comparison. 
EPA Proposed Rule TSD for Florida’s Streams and DPVs for Lakes

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The predicted values of TN and TP from the different linear and binomial regression models of 

nutrients vs. SCI scores were also compared to the stream criteria values. These predicted values, 

derived from these refined stressor-response models based on the habitat screens, are consistent 

with the results from the previous regression models. Namely that the nutrient concentrations 

derived from the reference-based approach are coincident with TP concentrations where 

biological conditions become worse than those associated with the lower extent of reference site 

biological conditions (SCI = 40, 46, and/or 50), which the State uses to define adverse or harmful 

biological change (Figure 1-50 and Figure 1-51 for data presented as scatterplots and box plots, 

respectively) and with SCI = 30 for reference. The same responses were seen with TN as well 

(Figure 1-52 and Figure 1-53 for data presented as scatterplots and box plots, respectively). 

Figure 1-50. SCI versus Ln(TP) (2006–2011 data) for all regions combined (Figure 1-46) with Ln(TP) solved 

for SCI values of 30, 40, 46, and 50 based on linear regression models (solid black lines) and binomial 

regression models (dashed blue lines) for sites screened for RBZW, habitat smothering (HabSmoth), and 

water velocity habitat conditions as described in the text. Also shown are the Florida stream TP criteria 

values (in red with arrows) for each nutrient region. The linear regression fit is shown (solid red line) along 

with 75th percentile prediction intervals (red dashed lines). PHW: Panhandle West, PHE: Panhandle East, 

PEN: Peninsula, NC: North Central, WC: West Central.
EPA Proposed Rule TSD for Florida’s Streams and DPVs for Lakes

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Figure 1-51. SCI versus Ln(TP), 2006–2011 data, represented as box plots of SCI scores by TP concentration 

bins for sites screened for RBZW, habitat smothering (HabSmoth), and water velocity habitat conditions as 

described in the text. Center boxes are means, box margins are 1 standard error of the mean, and whiskers 

are 95 percent confidence intervals around mean SCI scores for each TP bin. The blue shaded region is the 

2.5th to 10th percentile of SCI reference site scores, which are values of 40 and 50, respectively. Also shown 

are the solutions of TP concentration for SCI scores of 30, 40, 46, and 50 using the linear regression model 

from Figure 1-46 (vertical black lines) and binomial regression models from Figure 1-47 (blue dashed lines) 

of Ln(TP) vs. SCI score for all regions combined and all habitat scores as well as the Florida stream TP 

criteria values for each nutrient region (red arrows).
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Figure 1-52. SCI versus Ln(TN) (2006–2011 data) for all regions combined (Figure 1-48) with Ln(TN) solved 

for SCI values of 30, 40, 46, and 50 based on linear regression models (solid black lines) and binomial 

regression models (dashed blue lines) for sites screened for RBZW, habitat smothering (HabSmoth), and 

water velocity habitat conditions as described in the text. Also shown are the Florida stream TN criteria 

values (in red with arrows) for each nutrient region. The linear regression fit is shown (solid red line) along 

with 75th percentile prediction intervals (red dashed lines). PHW: Panhandle West, PHE: Panhandle East, 

PEN: Peninsula, NC: North Central, WC: West Central.
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Figure 1-53. SCI versus Ln(TN), 2006–2011 data, represented as box plots of SCI scores by TN concentration 

bins for sites screened for RBZW, habitat smothering (HabSmoth), and water velocity habitat conditions as 

described in the text. Center boxes are means, box margins are 1 standard error of the mean, and whiskers 

are 95 percent confidence intervals around mean SCI scores for each TN bin. The blue shaded region is the 

2.5th to 10th percentile of SCI reference site scores, which are values of 40 and 50, respectively. Also shown 

are the solutions of TN concentration for SCI scores of 30, 40, 46, and 50 using the linear regression model 

from Figure 1-48 (vertical black lines) and binomial regression models from Figure 1-49 (blue dashed lines) 

of Ln(TN) vs. SCI score for all regions combined and all habitat scores as well as the Florida stream TN 

criteria values for each nutrient region (red arrows).

Lastly, logit models were used to estimate the probability of a site scoring less than specific SCI 

thresholds (30, 40, 46, and 50) at the EPA nutrient criteria values (Table 1-10). Once again, the 

results of this analysis indicate that above EPA criteria, probability of observing adverse 

biological conditions (SCI score less than 40) increase above 35–53 percent based on TP and 31–

50 percent based on TN. 
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Table 1-10. The probability SCI scores at a site are less than 30, 40, 46, or 50 at the EPA TP and TN criteria.

Probability of observing SCI scores less than specified values was constructed from the complete statewide 

dataset of SCI scores and associated chemical and habitat data from 2006–2011 provided by FDEP to EPA. 

All data were used. Probability was predicted using a logit model of SCI less than the specific threshold (1) 

or greater than the specific threshold (0) versus ln (observed nutrient concentration). Models built for SC = 

40, 46, and 50, coinciding with the 2.5th, 5th, and 10th percentile of reference site SCI scores (Appendix 8) 

and for SCI = 30 for comparison.

EPA TP Criteria (mg/L)

Panhandle West Panhandle East North Central West Central Peninsula

0.060 0.180 0.300 0.490 0.120

Probability SCI

less than:

30 21% 27% 30% 34% 25%

40 35% 44% 49% 53% 41%

46 44% 54% 58% 62% 50%

50 51% 61% 66% 70% 57%

EPA TN Criteria (mg/L)

Panhandle West Panhandle East North Central West Central Peninsula

0.670 1.03 1.87 1.65 1.54

Probability SCI

less than:

30 17% 23% 32% 30% 29%

40 31% 38% 50% 47% 46%

46 39% 48% 60% 57% 56%

50 45% 55% 67% 65% 63%

1.5.2.3 Results—Nuisance Algae Response to Nutrients

Data collected by FDEP from 2007 to 2009 were analyzed to estimate relationships between 

changes in nutrient concentrations and the probability with which algae cover substantial 

proportions of the stream bottom (i.e., the probability of nuisance accumulations of algae). 

Rapid Periphyton Survey (RPS) data were summarized as a true/false response, in which “true” 

indicated nuisance levels of algae were present, defined as >25% of the stream bottom covered 

by algae greater than 6 mm in depth. These RPS observations were then matched with canopy 

cover observed at the time of sampling, and the long-term geometric mean concentrations of TN 

and TP (computed using all available TN and TP data at the site). 

Random forests were used to estimate relationships between the true/false RPS assessment, TN, 

TP, and canopy cover. Random forests is a nonparametric statistical modeling technique that fits 

a large ensemble of different classification and regression trees to bootstrapped versions of the 

original data, using a random selection from a list of possible explanatory variables. Model 

predictions are calculated as the mean of predictions from the entire ensemble (Breiman 2001). 

This technique maintains the inherent advantages of classification and regression trees, including 

allowing interactions among explanatory variables and nonlinear relationships. At the same 

time, though, averaging the predictions from many trees greatly reduces the tendency observed 

in single regression trees for over-fitting the data. Furthermore, the tree fitting process identifies 

the variables that best account for observed variability in the response without a priori

specification. Random forests have been shown to be an effective statistical predictive modeling 

approach in a number of different applications (Cutler et al. 2007, Olson and Hawkins 2012). 
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Partial dependence plots were used to examine the relationship between the probability of 

nuisance algae (as defined above), TN, and TP concentrations, while controlling for the effects 

of canopy cover. Partial dependence plots were constructed by fixing TN and TP at different 

values along their respective ranges, and predicting the mean response across the full ensemble 

of trees, given observed values for all of the other variables. 

Figure 1-54. Partial dependence plots relating probability of nuisance algae to TN and TP concentrations.

The probability of the occurrence of nuisance algae increased substantially at distinct TN and TP 

concentrations. For TN, a sharp increase in the probability of nuisance algae from approximately 

0.55 to 0.8 occurred between TN = 0.3 mg/L and 0.4 mg/L (Figure 1-54). The relationship 

between the probability of nuisance algae occurrence and TP concentration was more variable, 

but a substantial change in the probability was observed between TP = 0.04 mg/L and 0.1 mg/L. 

1.5.2.4 Analysis Summary

EPA received additional invertebrate, habitat, and nutrient data from FDEP and conducted 

additional analyses of invertebrate response. This analysis included looking at relationships 

between invertebrate condition as measured by the SCI with nutrient concentration across 

regions and variable habitat conditions. Similar to the original analysis, the responses were 

variable across regions and habitat conditions, but in general the thresholds associated with 

adverse changes in invertebrate condition based on biological thresholds defined by FDEP and 

EPA were consistent with those derived using the reference condition approach. 

This analysis also included additional refinement of the classification of sites based on habitat 

conditions that, in theory, are the most conducive to conditions that would produce the causal 

paths leading to adverse invertebrate responses to nutrient pollution— sufficient light, stable 

flow, and sufficient substrate conditions. After this classification, while again producing a 

variable response curve, concentrations consistently at or above those derived using the reference 
EPA Proposed Rule TSD for Florida’s Streams and DPVs for Lakes

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condition approach were identified as associated with adverse or harmful change in invertebrate 

condition using the FDEP and EPA SCI targets.

In summary, the SCI vs. nutrient relationships support EPA’s conclusions (U.S. EPA 2010c) 

with respect to the reference condition-based numeric TP and TN criteria for streams. 

Specifically, the numeric nutrient criteria values derived using percentiles of reference sites are 

at concentrations that will protect Florida streams from harmful changes in biological condition. 

As such, the stream criteria are protective of natural populations of aquatic flora and fauna. 

The floral response analyses conducted by FDEP and EPA indicate adverse changes in algal 

measures within the range being proposed as criteria using the reference condition approach, 

with harmful adverse effects occurring consistently above EPA’s stream criteria. FDEP 

concluded from their analyses, that while the responses are confounded by other factors, they do 

establish that nutrients have adverse impacts on the floral assemblage. EPA’s analyses, which 

took into account many of the confounding factors (e.g., habitat, flow, canopy cover), 

corroborated FDEP’s finding and showed clear effects as nutrient concentrations increase in 

streams at concentrations above EPAs stream criteria. 

1.6 Additional Evidence of Harmful Effects in Florida Streams Above 

EPA’s Proposed Criteria

1.6.1 Nutrient Response Thresholds: General

To provide additional support that the concentrations set as the stream TN and TP criteria based 

on the reference condition approach are consistent with those needed to prevent harmful change 

in floral and faunal populations, EPA reviewed the scientific literature related to nutrient impacts 

on streams. In particular, EPA searched for scientific peer reviewed journal articles that used 

existing or experimental nutrient gradients to identify nutrient concentrations consistent with 

direct and indirect measures of biological response consistent with the conceptual model. EPA

conducted a search of the scientific literature based on the following search terms in various 

combinations (values in parentheses are Boolean options): (river or stream), (phosphorus or 

nitrogen or nutrient), (response or stressor-response), eutrophication, alga, algae, algal biomass, 

invertebrate, oxygen, fish, (diatom or diatom assemblage or algal assemblage), threshold, 

breakpoint, Florida, southeast US, sub-tropical, and criteria. A subset of journal articles that 

resulted from the request were reviewed and nutrient concentrations associated with defined or 

described adverse biological endpoints or thresholds related to biological condition or trophic 

state in streams were identified. 

A table of reviewed values associated with adverse floral and faunal responses in streams from 

the literature review is presented in Table 1-11. 
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Table 1-11. Summary of TP and TN endpoints associated with adverse floral and faunal response in study locations around North America. 

Study Location Response TN (μg/L) TP (μg/L) Citation

Multiple Stream oligo-mesotrophic boundary 285-375 23-29Dodds (2006) Stream meso-eutrophic boundary 659-714 48-71

Multiple Chl a < 200 mg/m2 <3000 <400Dodds and Welch (2000) Chl a of 50 mg/m2 (< 100 mg/m2 most of the time) 470 60

Multiple

Stream oligo-mesotrophic boundary (60 mg/m2 chl a 

max) 700 25

Dodds et al. (1998)

Stream meso-eutrophic boundary (200 mg/m2 max) 1500 75

Montana

50 mg/m2 mean Chl a target 450-470 62-65

Dodds et al. (1997)

50 mg/m2 max Chl a target 115-145 16-20

100 mg/m2 mean Chl a target 1423-1600 197-221

100 mg/m2 max Chl a target 252-275 35-38

200 mg/m2 mean Chl a target 3000-7570 415-1020

200 mg/m2 max Chl a target 650 90

Florida Lyngbya wollei 110 ( NO3 –N) Albertin (2009)

Florida Lyngbya wollei 230 (NO3 –N)

250

28 (PO4-P)

33 Stevenson et al. (2007)

Florida Vaucheria sp.

261 (NO3 –N) 

284

22 (PO4-P) 

26 Stevenson et al. (2007)

Oak Ridge National Lab, 

Tennessee Benthic Algal Growth Saturation 50 Hill et al. (2009). 

Texas (Aggregate Nutrient 

Ecoregion IX)

Biological Integrity Decline 20

Consistent nuisance algal growth and loss of algal and King et al. (2009) 

invertebrate species 200

Ohio Decline in Fish and Invertebrate Indexes 310 Miltner and Rankin (1998)

New Jersey Diatom index response 200 50 Ponader et al. (2005)
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Study Location Response TN (μg/L) TP (μg/L) Citation

Wisconsin Non-wadeable 

Streams 

Suspended chl a increases 927 64

Robertson et al. (2008) Secchi depth decreases 1097 91

Invertebrate index decreases 527-1990 35-150

Fish index decreases 634-1965 55-147

Wisconsin Wadeable 

Streams 

Suspended chl a increases 1169 70

Robertson et al. (2006) Secchi depth decreases 3305 106

Benthic Chl a 609-1106 90

Fish index decreases 60

New York Proposed criteria to protect invertebrates 510 23 Smith and Tran (2010)

New York Macroinvertebrate protection 65 Smith et al. (2006)

Mid-Atlantic region Algal attributes 300 10 Stevenson et al. (In Review)

Mid-Atlantic region Diatom nutrient limitation 10-30 Stevenson et al. (2008)

Michigan and 

Kentucky/Indiana Algal biomass 1000 30 Stevenson et al. (2006)

Montana Human nuisance perception Suplee et al. (2009)

Multiple Stream oligo-mesotrophic boundary 700 25U.S. EPA (2000) Stream meso-eutrophic boundary 1500 75

Wisconsin Invertebrate response 90Wang et al. (2007) Fish response 540-610 60-70

Washington State Nuisance coverage Welch et al. (1998)

Various Literature Review for Protectiveness of Uses 350-900 60-280 VWRRC (2006)

Virginia Coldwater fishery 10VWRRC (2005) Warmwater fishery 50

Western USA (regional 

scale) Stream invertebrates 350-500 Yuan (2010)

West Virginia Invertebrate response Zheng et al. (2008)

Virginia Response thresholds as potential criteria 800 50 VWRRC (2007) 

Ozarks, 

Minnesota/Wisconsin, 

Idaho/Nevada

Biotic response 480 Maret et al. (2010)
EPA Proposed Rule TSD for Florida’s Streams and DPVs for Lakes

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Study Location Response TN (μg/L) TP (μg/L) Citation

Minnesota Model of nutrient effects on multiple endpoints in rivers 2700 100 Carleton et al. (2009)

Washington State, 

Nebraska Algal metric responses 590-1790 30-280 Black et al. (2011) 

Maryland Biological Response 1340-1680 250-370 Morgan and Kline (2011)

Ohio Invertebrate and Fish responses 38 Miltner (2010)

Victoria, Canada Protection of streams 115-900 13-48 Newall and Tiller (2001)

Ontario/Quebec Canada

Eutrophic boundary 1500 75

Suspended chl a 945 21 Chambers et al. (2008)

Benthic chl a < 100 mg/m2 1800 46

Ecoregions VII and VIII 

(upper Midwest) Fish and invertebrate integrity 640 60 Weigel and Robertson (2007)

Canada Biological response based criteria 700 25 Chambers et al. (2011)
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The reviewed endpoints were then enumerated by frequency in nutrient concentration bins (<10, 

10–20, etc.). Reported ranges were scored at both the bottom and top of the range. These 

frequencies were then plotted by bin. TP endpoints were most frequent at the lower end of the TP 

concentrations derived as the streams criteria (Figure 1-55). However, values along the entire 

range of stream criteria were represented by at least one value from the reviewed literature on TP 

concentrations associated with adverse biological response. Values from the PHE, NC, and WC 

were on the upper end of literature values, but it was unclear which literature may have come 

from regions with P-rich soils and rock, such as those observed in these regions of Florida. It is 

possible concentrations naturally elevated in these regions would have higher effect 

concentrations if analyzed using methods similar to those from these studies. 

Figure 1-55. Frequency of TP endpoints associated with adverse floral and faunal responses in streams 

from literature review. Values along the axis represent a bin between the labeled value and the next lower 

value (e.g., 150 represents a value in the range 141–150). Also plotted in black with arrows are the stream 

TP criteria for each NWR. PHW: Panhandle West, PHE: Panhandle East, PEN: Peninsula, NC: North Central, 

WC: West Central.

Similarly to TP, TN endpoints were most frequent at the lower end of the TN concentrations 

derived as final criteria, but were more consistent with the most frequent values (500–2000 μg/L 

TN, Figure 1-56). Again, values along the entire range of the stream TN criteria were represented 

by at least one value from the reviewed literature on TN concentrations associated with adverse 

biological response. 
EPA Proposed Rule TSD for Florida’s Streams and DPVs for Lakes

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Figure 1-56. Frequency of TN endpoints associated with adverse floral and faunal responses in streams 

from literature review. Values along the axis represent a bin between the labeled value and the next lower 

value (e.g., 2700 represents a value in the range 2601–2700). PHW: Panhandle West, PHE: Panhandle East, 

PEN: Peninsula, NC: North Central, WC: West Central.

In summary, nutrient response thresholds from the scientific literature EPA was able to review 

and identify indicate a wide range (> one order of magnitude) in effect levels of nutrients on 

biological condition. Moreover, these values span the range of concentrations co-occurring 

within the range of TP and TN concentrations derived from the reference benchmark approach

finalized in EPA’s rule (U.S. EPA 2010c) and re-proposed here. The results further support 

EPA’s contention that harmful changes are likely to occur at or above the numeric criteria.

1.7 Duration and Frequency for Streams Numeric Nutrient Criteria

No changes to the frequency and duration have been made. They remain geometric annual mean 

concentrations not to be exceeded more than once in 3 years. Readers wishing to review the 

technical support for the frequency and duration elements are directed to the 2010 final rule TSD

Section 1.5 (EPA-HQ-OW-2009-0596). 
EPA Proposed Rule TSD for Florida’s Streams and DPVs for Lakes

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1.8 Summary 

EPA developed numeric nutrient criteria for Florida using methods consistent with national 

nutrient criteria guidance (U.S. EPA 2000) that is general in its methodologies and intended to be 

adapted to each state’s unique nutrient criteria derivation status. The latter depends, in part, on 

data quality and availability issues; unique geologic, climatic, and biological conditions; the 

nature of biological responses to nutrients; and so on. Thus, the process described for Florida 

may not necessarily transfer exactly to any other state and would require a consideration of the 

above factors and any other information deemed necessary to derive defensible criteria. The 

general process detailed in the 2000 EPA guidance involves classification, data gathering and 

preparation, data analysis, and criteria derivation.

Florida streams were first classified to account for and reduce variability associated with natural 

gradients in nutrient concentrations due to non-anthropogenic factors (e.g., hydrology, geology). 

EPA identified five NWRs that were selected based on geological, soil, hydrological, and 

biological considerations, and were shown to explain the most variance in TP concentrations of 

least-disturbed sites of other candidate classifications. The five NWRs are the Panhandle West, 

Panhandle East, North Central, Peninsula, and West Central. These NWRs were also used for TN 

because the statistical modeling indicated that a five region model explains an equivalent amount 

of variability compared to an alternative regional model for two TN regions. For consistency in 

application of criteria and in deference to the co-limiting nature of TN and TP, EPA decided that 

similar regionalization for TN and TP is both defensible and protective of designated uses.

All nutrient concentration data available within the state of Florida in the STORET and GWIS 

databases were considered, and only those data that met data quality standards as required by 

FDEP QA rule 62-160 and FDEP’s Process for Determining Data Usability were used (FDEP 

2008, see Appendix 2). Similarly, biological data that had been collected and quality assured by 

FDEP were gathered together. These data included data primarily on macroinvertebrates and 

were used to calculate an FDEP measure of biological condition, the SCI. Land cover data were 

gathered for the entire state. EPA also considered the EPA-approved list of CWA section 303(d) 

impaired waters.

EPA derived numeric nutrient criteria using a reference-based distribution approach for TN and 

TP, but not for chl-a. EPA considered deriving chl-a criteria for Florida streams using a 

distribution-based approach. However, the Agency could not identify a specific chl-a threshold 

that would be associated with attaining and maintaining Florida’s designated uses for Class I and 

III streams, and consistent with Florida’s current narrative criteria for nutrients. EPA notes that 

FDEP currently employs a chl-a threshold of impairment (0.020 mg/L) for streams in its water 

quality standards (Impaired Waters Rule, Rule 62-303, F.A.C.). Chl-a values above this 

threshold demonstrate that there is an “imbalance” in flora and fauna such that Florida’s 

narrative nutrient criterion is not attained. Florida has not identified an “attainment threshold,” or 

that level of chl-a below which a stream is considered in attainment of the narrative criterion.
EPA Proposed Rule TSD for Florida’s Streams and DPVs for Lakes

91 

Table 1-12. EPA’s proposed numeric nutrient criteria for Florida streams. 

NWR

Instream Protection Value Criteria

TN (mg/L) * TP (mg/L) *

Panhandle West a 0.67 0.06

Panhandle East b 1.03 0.18

North Central c 1.87 0.30

West Central d 1.65 0.49

Peninsula e 1.54 0.12

Notes: Watersheds pertaining to each NWR were based principally on the NOAA coastal, estuarine, and fluvial drainage 

areas with modifications to the NOAA drainage areas in the West Central and Peninsula Regions that account for unique 

watershed geologies. For more detailed information on regionalization and which WBIDs pertain to each NWR, see Section 

1.2. 

a Panhandle West region includes: Perdido Bay Watershed, Pensacola Bay Watershed, Choctawhatchee Bay Watershed, 

St. Andrew Bay Watershed, Apalachicola Bay Watershed.

b Panhandle East region includes: Apalachee Bay Watershed, and Econfina/Steinhatchee Coastal Drainage Area.

c North Central region includes the Suwannee River Watershed.

d West Central region includes: Peace, Myakka, Hillsborough, Alafia, Manatee, Little Manatee River Watersheds, and small, 

direct Tampa Bay tributary watersheds south of the Hillsborough River Watershed.

e Peninsula region includes: Waccasassa Coastal Drainage Area, Withlacoochee Coastal Drainage Area, 

Crystal/Pithlachascotee Coastal Drainage Area, small, direct Tampa Bay tributary watersheds west of the Hillsborough River 

Watershed, Sarasota Bay Watershed, small, direct Charlotte Harbor tributary watersheds south of the Peace River 

Watershed, Caloosahatchee River Watershed, Estero Bay Watershed, Kissimmee River/Lake Okeechobee Drainage Area, 

Loxahatchee/St. Lucie Watershed, Indian River Watershed, Daytona/St. Augustine Coastal Drainage Area, St. John’s River 

Watershed, Nassau Coastal Drainage Area, and St. Mary’s River Watershed.

* For a given waterbody, the annual geometric mean of TN or TP concentrations shall not exceed the applicable criterion 

concentration more than once in a three-year period.

For the reference-based approach, EPA estimated distributional statistics for two principal 

reference populations—a Benchmark Population represented by sites evaluated as least-disturbed 

by humans and an SCI Population represented by sites with demonstrated biologically healthy 

conditions. 

For the Benchmark approach, reference sites were identified that met the following criteria:

1. LDI score ≤ 2 for land use within the 100m corridor 10km upstream of the sample site.

2. Not in WBIDs listed on the EPA-approved Florida CWA section 303(d) impaired waters 

list for nutrients and/or DO. 

3. Average nitrate/nitrite concentrations < 0.35 mg/L. 

4. No land uses or nutrient sources adjudged 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. 

6. Watershed or near-field LDI scores < 3. 
EPA Proposed Rule TSD for Florida’s Streams and DPVs for Lakes

92 

For the SCI Population, reference sites were identified that met the following criteria:

1. Not within WBIDs with average SCI scores < 40. 

2. Not in WBIDs listed on the EPA-approved Florida CWA section 303(d) impaired waters 

list for nutrients and/or DO. 

WBID averages were calculated for purposes of estimating the frequency distribution of TN and 

TP by each NWR for the Benchmark and SCI Populations. Numeric 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 (Table 1-12). 

The additional analyses provided in this TSD support the linkage between and protectiveness of 

the stream criteria and harmful changes to natural populations of aquatic flora and fauna in 

Florida streams. Specifically, consistent with FDEP’s original contention, while variable, the 

stressor-response analyses Florida and EPA conducted indicate the adverse, harmful changes in 

flora (algal assemblage) and fauna (macroinvertebrate assemblage) occur proximate to the TP 

and TN concentrations derived as stream criteria using the reference condition approach. This is 

not surprising, given that EPA used a population of reference sites that were screened, in part, on 

their ability to support balanced flora and fauna, based on the FDEP SCI score screening tool. It 

is intuitive, therefore, given the vast scientific literature quantifying adverse ecological responses 

associated with nutrient pollution in streams, that nutrient concentrations above those represented 

by the defined reference condition would be consistent with harmful changes in biota. EPA also 

provided extracted nutrient thresholds associated with adverse ecological responses in streams 

from primarily peer-reviewed scientific literature from studies around North America, developed 

for a variety of research applications, and these further reinforce that the EPA’s stream TP and 

TN criteria are consistent with concentrations at which harmful biological responses in streams 

are known to occur. This additional analysis supports that EPA’s stream nutrient criteria are, 

indeed, protective of the designated aquatic life use in Florida and will protect against a harmful 

change in natural populations of aquatic flora and fauna and are consistent with the CWA. 

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quantifying subtle responses. Journal of the North American Benthological Society

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phosphorus enrichment in the Florida Everglades. Journal of Environmental Quality

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Howarth, R.W., A. Sharpley, and D. Walker. 2002. Sources of nutrient pollution to coastal 

waters in the United States: Implications for achieving coastal water quality goals. 

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Jacobs, T.C., and J.W. Gilliam. 1985. Riparian losses of nitrate from agricultural drainage 

waters. Journal of Environmental Quality 14:472–478. 

Jordan, T.E., D.L. Correll, and D.E. Weller. 1993. Nutrient interception by a riparian forest 

receiving inputs from adjacent cropland. Journal of Environmental Quality 22:467–473. 

King, R.S., B.W. Brooks, J.A. Back, J.M. Taylor, and B.A. Fulton. 2009. Linking Observational 

and Experimental Approaches for the Development of Regional Nutrient Criteria for 

Wadeable Streams. Final Report to EPA Region 6, Dallas, TX. 

Lowrance, R. 1992. Groundwater nitrate and denitrification in a coastal plain riparian soil. 

Journal of Environmental Quality 21:401–405. 

Lowrance, R. 1997. Water quality functions of riparian forest buffers in Chesapeake Bay 

watersheds. Environmental Management 21:687–712. 

Maret, T.R., C.P. Konrad, and A.W. Tranmer. 2010. Influence of environmental factors on biotic 

responses to nutrient enrichment in agricultural streams. Journal of the American Water 

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Miltner, R.J. 2010. A method and rationale for deriving nutrient criteria for small rivers and 

streams in Ohio. Environmental Management 45:842–55. 

Miltner, R.J., and E.T. Rankin. 1998. Primary nutrients and the biotic integrity of rivers and 

streams. Freshwater Biology 40:145–158. 

Morgan, R.P. II, and K.M. Kline. 2011. Nutrient concentrations in Maryland non-tidal streams. 

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floodplain lake and river system. I. Hydrology, nutrient sources and phytoplankton 

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Mulholland, P.J., and J.R. Webster. 2010. Nutrient dynamics in streams and the role of J-NABS. 

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the Effects of Nutrient Pollution. Report prepared by the Ocean Study Board and Water 

Science and Technology Board, Commission on Geosciences, Environment and 

Resources, National Resource Council. National Academy Press, Washington, DC. 

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Australia. Environmental Monitoring and Assessment 74:85–103. 

Niu, X. 2004. Change Point Analysis of the Chlorophyll a and LDI Data from the IWRM Status 

Network Cycle 1 Lakes Study. Technical report submitted to the Florida Department of 

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Tallahassee, FL.
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Notestein, S.K., T.K. Frazer, M.V. Hoyer, and D.E. Canfield, Jr. 2003. Nutrient limitation of 

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Chapter 2. Downstream Protection Values for Unimpaired Lakes

2.1 Background

Lakes are a highly valued national resource, but especially in Florida, which has a high 

abundance and unique diversity of lakes. Some lakes are popular for recreational activities such 

as trophy fishing, swimming, and other watersports; other lakes are sought out for wildlife 

viewing and wilderness experience.

Florida has an exceptionally large number of lakes (more than 7,700 lakes greater than 10 acres). 

The lakes range in size from very small ponds to Lake Okeechobee, the seventh-largest 

freshwater lake in surface area in the United States (681 sq. miles). Florida’s lakes comprise 

extreme ranges in water alkalinity, color, and productivity, including crystal-clear ponds and 

lakes in the pine forests; deeply stained (tea-colored) or blackwater lakes; and hard-water lakes 

with moderate to high natural productivity. Although most Florida lakes are shallow, there are 

also a small number of very deep sinkhole lakes. Most of Florida’s lakes are natural, formed by 

solution of limestone into depressions and sinkholes. 

Nutrients, especially nitrogen and phosphorus, are essential to life and are not toxic at natural 

concentrations. Lakes can be classified as oligotrophic (low in nutrients and productivity), 

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

productivity). Oligotrophic lakes generally have low productivity and low biomass, yet often 

have high species diversity. Eutrophic lakes have high productivity and high biomass, but may 

be characterized by very high abundances of few species. Underlying geology, the character and 

size of the watershed, and other natural factors can also determine the trophic status of a 

waterbody.

Anthropogenic activities, such as sewage discharge, soil erosion, and agricultural and urban 

runoff can increase the flow of nutrients into a lake. These activities impact lakes by stimulating 

excess algal growth, including harmful algal blooms, and reducing water clarity (Carlson 1977; 

Downing et al. 2001; Elser et al. 1990, 2007; NAS 1969; Paerl 1988; Schindler et al. 1973; 

Schindler 1974; Smith et al. 1999, 2006; Vollenweider 1968, 1976). This process is termed 

cultural eutrophication, and can occur over a short time (few years). Natural eutrophication, 

where a lake’s trophic state increases due to natural inflow of sediment, nutrients, and organic 

matter from its watershed, is a process that may take thousands of years, if it occurs at all (U.S. 

EPA 2000). Cultural eutrophication is usually reversible, and lakes may recover if the artificial 

nutrient loads are reduced (e.g., Edmondson 1994). 

In Florida, several well-known lakes have been subject to visible and well-documented cultural 

eutrophication (e.g., Lake Okeechobee, Lake Apopka). Nutrient reductions plans and TMDLs 

have been developed for these lakes in response to human nutrient pollution, and some are 

beginning to respond to the nutrient load reductions (e.g., Coveney et al. 2005). EPA’s 

development of water quality criteria for nitrogen and phosphorus is intended to establish 

concentrations that are protective; that is, to prevent the need for TMDLs and restoration of 

culturally eutrophied lakes. 
EPA Proposed Rule TSD for Florida’s Streams and DPVs for Lakes

101 

EPA recognizes that nutrient limitation of either TN or TP, or co-limitation by TN and TP, is a 

phenomenon previously observed in many lakes (Schindler 1974). Primary production in 

freshwater systems can be limited by available nitrogen or phosphorus, or it can be co-limited by

both nutrients simultaneously. Early eutrophication studies clearly demonstrated phosphorus 

limitation and eutrophication by phosphorus pollution in fresh water (e.g., Schindler 1974). 

Recent studies have shown that nitrogen limitation and co-limitation are more important in 

freshwater, and phosphorus-limitation is more important in saltwater, than previously recognized 

(e.g., Brown et al. 2000; Elser et al. 2007, 2009; Guildford and Hecky 2000; Smith 2006). For 

any given lake, however, it is difficult to assess a priori which nutrient is the limiting factor. 

EPA’s approach to deriving numeric nutrient criteria is not premised on the limiting nutrient, but 

rather a protective viewpoint that both nutrients influence excess algal growth and that influence 

may vary over space and time. EPA has expressed its recommended approaches in deriving 

numeric nutrient criteria for both TN and TP previously for lakes (U.S. EPA 2000) and as a 

general policy matter (Grubbs 2001; Grumbles 2007). The scientific literature concluding that 

the source of many adverse water quality impacts can be traced to degradation of water quality 

upstream of the impacted aquatic system by nutrient pollution (e.g., Conley et al. 2009; Paerl

2009; U.S. EPA 2007) further affirms EPA’s approach water quality management, through 

numeric nutrient criteria, for both nitrogen and phosphorus. 

The background, methodology, and results of EPA’s analysis for deriving the proposed numeric 

nutrient criteria for Florida lakes is not reiterated here but readers can consult the 2010 final rule 

TSD (U.S. EPA 2010) for that technical information. The analysis presented here is a 

supplement to the models presented to calculate default DPV concentrations for streams to 

protect downstream lakes by ensuring the attainment and maintenance of the water quality 

standards applicable to the downstream lake. This supplemental analysis is related to the default 

DPVs to the mechanistic modeling approaches (e.g., WASP and BATHTUB) whose application 

was supported by the Court and remains unchanged. The technical material included here relates 

to the default DPV options available to use for unimpaired lakes when data are not available to 

utilize those modeling approaches. These alternative default DPV approaches respond to the 

court’s concern regarding the default DPV option presented in the 2010 final rule for unimpaired 

lakes and the need to demonstrate that the DPV concentrations are necessary to ensure the 

attainment and maintenance of the downstream lake criteria. Three default DPV approaches for 

unimpaired lakes are presented below. 

The first default approach for DPV calculations for unimpaired lakes applies the downstream 

lake criteria as the DPV applicable at the pour point to the lake. The second default approach 

incorporates Florida-specific stream and lake data to empirically link the DPV to the attainment 

and maintenance of downstream lake criteria in each of the three lake classes using empirical 

relationships between upstream and receiving nutrient ratios (Table 2-1). The third approach 

applies the default DPV approach from the 2010 final rule (U.S. EPA 2010) based on ambient 

stream conditions upstream of unimpaired lakes. 

All statistical data analysis was based on Florida’s IWR database, as developed and screened by 

FDEP.
EPA Proposed Rule TSD for Florida’s Streams and DPVs for Lakes

102 

Table 2-1. EPA’s numeric nutrient criteria for Florida lakes from the final rule (U.S. EPA 2010).

Lake Colora

and Alkalinity Chl-a (mg/L)b, * TN (mg/L) TP (mg/L)

Colored Lakesc 0.020 1.27

[1.27–2.23]

0.05

[0.05–0.16]

Clear Lakes,

High Alkalinityd 0.020 1.05[1.05–1.91]

0.03

[0.03–0.09]

Clear Lakes,

Low Alkalinitye 0.006 0.51[0.51–0.93]

0.01

[0.01–0.03]

a Platinum Cobalt Units (PCU) assessed as true color free from turbidity.

b Chl-a is defined as corrected chlorophyll, or the concentration of chl-a remaining after the chlorophyll degradation product, 

phaeophytin a, has been subtracted from the uncorrected chl-a measurement.

c Long-term Color > 40 Platinum Cobalt Units (PCU)

d Long-term Color ≤ 40 PCU and Alkalinity > 20 mg/L CaCO3

e Long-term Color ≤ 40 PCU and Alkalinity ≤ 20 mg/L CaCO3 

* For a given waterbody, the annual geometric mean of chl-a, TN or TP concentrations shall not exceed the applicable 

criterion concentration more than once in a three-year period.

2.2 Downstream Protection of Lakes

An important component of setting appropriately protective water quality criteria is ensuring that 

the criteria also provide for the attainment and maintenance of the water quality standards of 

downstream waters (40 CFR 131.10(b)). Therefore, the lake criteria described above affect the 

derivation of stream criteria. This section describes EPA’s approach in considering the 

downstream water quality criteria EPA derived to protect Florida’s lakes. EPA’s analyses for 

deriving numeric nutrient criteria for streams in Florida, as reflected in the 2010 final rule, 

indicate that the stream criteria values may not be sufficient to fully protect downstream lakes 

(e.g., stream TP for the West Central NWR > lake TP for all three lake classes). EPA’s lake 

criteria can be, in some cases, more stringent than the streams criteria that flow into the lakes. 

The applicable DPV applies to streams at the point of entry into the lake. If the DPV is not 

attained at the point of entry into the lake, then the collective set of streams in the upstream 

watershed stream network does not attain the DPV, which is an applicable water quality criterion 

for the water segments in the upstream watershed. The State or EPA may establish additional 

DPVs at upstream tributary locations that are consistent with attaining the DPV at the point of 

entry into the lake. The State or EPA also have discretion to establish DPVs to account for a 

larger watershed area (i.e., include waters beyond the point of reaching waterbodies that are not 

streams as defined by this rule).

2.2.1 Approach

EPA is providing for a flexible downstream protection approach for lakes that contains several 

alternative approaches to ensure that the applicable criteria for streams that flow into lakes 

provide for the attainment and maintenance of the applicable lake criteria. First, EPA is 

specifying that, where sufficient data and information are available, the U.S. Army Corps of 

Engineers BATHTUB reservoir model (Walker 1999) may be used to compute DPVs necessary 

in the stream to protect a given lake. Second, EPA is allowing the use of scientifically defensible 

models other than BATHTUB, such as the Water Quality Analysis Simulation Program (WASP, 

see Section 2.2.2, that demonstrate protection of water quality standards in downstream lakes. 
EPA Proposed Rule TSD for Florida’s Streams and DPVs for Lakes

103 

EPA believes BATHTUB is appropriate for simplified DPV calculations because BATHTUB 

can represent a number of site-specific variables that may influence nutrient responses. In 

addition, BATHTUB can estimate TN concentrations. The BATHTUB model has been 

previously used for lake water quality management purposes—both nationally and in the State of 

Florida. That model was selected because, while it does not have extensive data requirements, it 

does provide for the capability to be calibrated based on observed site-specific lake data, and it 

provides for reliable estimates that will ensure the protection of downstream lakes. 

EPA also specifically authorizes FDEP or EPA to use a model other than BATHTUB when 

either agency determines that it would be appropriate to use another scientifically defensible 

technical model that demonstrates protection of downstream lakes. While BATHTUB is a peerreviewed and versatile model, there are other models that, when appropriately calibrated and 

applied, can offer additional capability to address more complex situations and address an even 

greater degree of site-specificity.

One example of an alternative model that FDEP or EPA might consider using for particularly 

complex site-specific conditions is the WASP model (Wool et al. 2001). That model allows users 

to conduct detailed simulations of water quality responses to natural and manmade pollutant 

inputs. The WASP model is a dynamic (time-varying) compartment-modeling program for 

aquatic systems, including both the water column and the underlying benthos. The model allows 

the user to simulate systems in 1, 2, or 3 dimensions and with a variety of pollutant types. The 

model can represent time varying processes of advection, dispersion, point and diffuse mass 

loading, and boundary exchange. It also can be linked with hydrodynamic and sediment transport 

models that can provide flows, depths, velocities, temperature, salinity and sediment fluxes. 

Additional technical information may be found online at 

http://www.epa.gov/athens/wwqtsc/html/wasp.html. Like BATHTUB, WASP has also been 

previously used for lake water quality management purposes, both nationally and in Florida. For 

these reason, EPA’s preference is that DPVs are calculated using one of these models when 

sufficient data and information are available.

These first two approaches that utilize BATHTUB or another scientifically defensible model 

were finalized in EPA’s 2010 final rule, upheld under legal challenges, and codified at 40 CFR 

131.43(c)(2)(ii)(B). EPA is not re-proposing these approaches in this proposed rule, but rather 

clarifying its preference that these approaches be preferentially considered provided that 

sufficient data are available.

EPA recognizes that data may not be available for every lake. In the absence of sufficient data to 

calibrate and develop BATHTUB or other models, EPA is providing a set of modified default 

TN and TP DPV criteria methods to derive DPV criteria applicable to streams that flow into the 

lake. EPA considers these default methods to be protective of Florida’s lakes and appropriate 

when data are too limited to use a modeling approach. 

EPA is proposing three default options in instances where there are insufficient data to develop 

BATHTUB or another scientifically defensible model. The first default option provides that, 

where the downstream lake attains its applicable chl-a and TP and/or TN criteria, then the DPV 

for TN and/or TP would be the lake TN and/or TP criteria at the point of entry into the lake. This 

option is comparable to the default approach EPA finalized in its 2010 final rule for the 
EPA Proposed Rule TSD for Florida’s Streams and DPVs for Lakes

104 

downstream protection of impaired lakes and was codified at 40 CFR 131.43(c)(2)(ii)(D). The 

second default option utilizes Florida’s extensive stream and lake data to compute a generalized 

linear regression model, which relates inflowing stream TN and TP concentrations to the TN and 

TP in downstream lakes in Florida. From these generalized models for each lake class, DPVs can 

be calculated by solving each model for the applicable downstream lake criteria. The third 

default option is the approach contained in the 2010 final rule that the court remanded to EPA for 

further explanation, namely that where and when the downstream lake attains its applicable chl-a 

and TP and/or TN criteria, then the DPV for TN and/or TP would be the ambient instream level 

of TN and/or TP at the point of entry into the lake associated with measurements that 

demonstrate attainment of applicable TN and TP criteria in the lake, down to but not more 

stringent than the TN and/or TP criteria for the lake. This proposed default option is a reference 

condition-based DPV approach that is conditioned upon the downstream lake attaining all 

applicable numeric nutrient criteria, TN, TP, and chl-a, including the duration and frequency 

components of the applicable lake criteria.

For all of these approaches, the final applicable criteria for the stream at the point of entry into 

the lake would be the more stringent of either the instream criteria or the DPVs derived using 

one of the three approaches.

2.2.2 BATHTUB and WASP

In 2010, EPA finalized the BATHTUB model, and the use of other scientifically defensible 

models such as WASP, to compute DPVs for lakes. In 2011, the U.S. District Court upheld this 

approach, and the approach was subsequently codified at 40 CFR 131.43(c)(2)(ii)(B). EPA is not 

re-proposing these approaches in this proposed rule, but rather providing general information on 

these models, which EPA prefers to be considered provided that sufficient data are available. 

For more detailed information these models, EPA refers the readers to the 2010 final rule TSD 

and the references cited in it and below.

The BATHTUB model is designed to apply empirical nutrient mass balance and eutrophication 

relationships to both simple and morphometrically complex lakes and reservoirs. The program 

performs steady-state water and nutrient mass balance calculations, uses spatially segmented 

hydraulic networks, and accounts for advective and diffusive transport of nutrients. When 

properly calibrated and applied, BATHTUB predicts annual or seasonally averaged nutrientrelated water quality conditions such as TP, TN, and chl-a concentrations, transparency, and 

hypolimnetic oxygen depletion rates. The model can be applied to a variety of lake sizes, shapes, 

and transport characteristics. A high degree of flexibility is available for specifying model 

segments as well as multiple influent streams. Because water quality conditions are calculated 

from influent loadings using empirically-derived relationships, BATHTUB implicitly accounts 

for internal recycling of nutrients from bottom sediments. Additional technical references are 

available that describe the model, its development, and its applications (Baniukiewicz and 

Gilbert 2004; Gao 2006; Gao and Gilbert 2003; Gao and Gilbert 2006; James et al. 2007; 

Kennedy 1995; Robertson and Rose 2008; Shelley et al. 2005; Walker 1981, 1982, 1999). The 

BATHTUB model has also been studied elsewhere in the scientific literature (James et al. 2002; 

Mankin et al. 2003; Muhammetoglu et al. 2005; Smeltzer and Quinn 1996; Yoko et al. 1997). 
EPA Proposed Rule TSD for Florida’s Streams and DPVs for Lakes

105 

To reflect data limitations or other sources of uncertainty, key inputs to the model can be 

specified in probabilistic terms (mean and coefficient of variation [CV]). Outputs are expressed 

in terms of a mean value and CV for each mass balance term and response variable. Output CVs 

are based upon a first-order error analysis that accounts for input variable uncertainty and 

inherent model error. 

Following development of a satisfactory model, the influent concentrations are varied to achieve 

the in lake criteria for TN, TP, and chl-a (as a function of TN and TP). The most restrictive 

concentration values are then selected as DPVs. The BATHTUB model has been widely applied 

for the development of nutrient TMDLs in Florida and is a tested and accepted approach suitable 

to the estimation of site-specific DPVs.

EPA also examined use of the WASP model (Wool et al. 2001) for establishing DPVs. It is a 

sophisticated, temporally dynamic, mechanistic model that can be used to predict time series of 

nutrients and chl-a within a lake in response to time series inputs. WASP allows the individual 

characteristics of a lake to be represented in the simulation (retention time, light, color, pH, 

nutrient concentrations, and interaction with benthos). The model can also be run in a “steadystate” condition representing a critical period of time (high retention time, summer season), or in 

a time-variable condition where long-term average and exceedance frequencies can be examined. 

The WASP model has also been applied and studied elsewhere in the scientific literature (James 

et al. 1997; James et al. 2005; Kiesling et al. 2001; Pauer et al. 2007; Petrus 2007; Reckhow 

1999; Stansbury and Admiraal 2004; Steinman and Rosen 2000; Wang et al. 2009). 

Application of the WASP model generally requires detailed site-specific information and a 

significant calibration effort. Time series output (e.g., daily, weekly) must be converted to 

growing season averages for comparison to nutrient criteria, and iteration to determine influent 

concentrations that achieve instream criteria can be complex and laborious. A full 

implementation of WASP for site-specific use typically involves multiple segments and multiple 

calibration endpoints, with run times for a 10-year simulation on the order of 30 minutes to an 

hour. 

The WASP model is an appropriate alternative to BATHTUB for setting DPVs, particularly 

where complex site-specific conditions are not adequately addressed by BATHTUB. 

2.2.3 Default DPV Methods

When sufficient data and information are not available, EPA is proposing several default values 

that may be used to calculate DPVs that will ensure the attainment and maintenance of the 

downstream lake criteria. It is possible that these default approaches may result in DPVs that are 

more stringent than necessary. If it is found that the default approaches result in overprotection in 

a site specific application, then EPA recommends more data and information be collected to 

allow the application of the preferred modeling approach as described above.
EPA Proposed Rule TSD for Florida’s Streams and DPVs for Lakes

106 

2.2.3.1 Option 1—Default DPV Derivation Using Lake Criteria

The first default approach simply utilizes the downstream lake criteria as the DPV applicable at 

the pour point to the lake. EPA refers readers to 40 CFR 131.43(c)(1) for the applicable TN and 

TP lake criteria, which would serve as the DPV. It is assumed that, for an unimpaired lake, since 

the lake criteria were derived to protect in-lake uses that a stream meeting these concentrations 

would implicitly protect the lake use, since such concentrations in an upstream source would not 

likely need to be below the lake criteria to protect uses. 

This option is comparable to the default approach EPA finalized in its 2010 final rule for the 

downstream protection of impaired lakes and was codified at 40 CFR 131.43(c)(2)(ii)(D).

2.2.3.2 Option 2—Default DPV Derivation Using Regional Regression Models

The second approach is based on developing lake class specific regression models relating inlake TN and TP concentrations to upstream TN and TP concentrations, which are then solved for 

the lake TN and TP criteria to estimate DPV TN and TP criteria for each lake class.

Statistical relationships between inflowing stream nutrient concentrations and lake nutrient 

concentrations can be used to derive DPVs in lakes in which the magnitude of internal loading of 

nutrients (e.g., from lake sediments) is small relative to the loading supplied by inflowing 

streams. For these lakes, if sufficient data are available to characterize the long-term average

nutrient concentrations in inflowing streams, then one would expect that average nutrient 

concentrations in in-flowing streams would be correlated with average nutrient concentrations in 

the receiving lake.

EPA has tested this approach by calculating annual geometric mean nutrient concentrations in 

inflowing streams and matching these annual mean values with annual geometric mean 

concentrations for that same year in the receiving lake. EPA used lake data from the 2010 final 

rule TSD (Appendix 11, see files called Appendix B1_Reach Year.xls and Appendix 

B1_FDEP_Lake Data with Alkalinity.xlsx), which contained annual geometric mean nutrient 

concentrations and location information for lakes, and stream data from the 2010 final rule TSD 

(see Appendix 11 files called allstr00.step3.xlsx and sites01.final.xls), which were used to 

estimate inflowing stream annual geometric mean nutrient concentrations. Annual geometric 

mean nutrient concentrations were calculated from the raw stream data after screening for

WBIDs that had more than four observations per year for both TN and TP and excluding those 

that did not. Annual geometric means were then calculated for each nutrient variable by site. 

Geometric mean annual upstream nutrient concentrations were calculated as the average of all 

upstream station annual means for each lake. 

To do this, EPA had to link upstream stations to receiving lakes. The original lake data file 

contained data for 304 unique lakes (304 unique lake Reach Codes). The locations of these 304 

lakes were entered into a GIS. A National Hydrography Dataset (NHD Plus) waterbody data 

layer12 was added, and lakes that intersected with an NHD Plus waterbody were identified. There 

were 290 lakes that intersected; 14 were discarded. Of the 290 remaining lake locations, the 

 

12 http://www.horizon-systems.com/nhdplus/HSC-wth03.php, Accessed April 2012
EPA Proposed Rule TSD for Florida’s Streams and DPVs for Lakes

107 

waterbodies for these locations were intersected with NHD Plus medium resolution flowlines13

in GIS to assess whether the waterbody was isolated or connected to a drainage network. The 

NHD waterbodies were buffered with a 10 foot buffer and NHD flowlines that were within the 

buffered waterbodies were selected. Of the total, 84 lakes were found to be connected to an 

upstream flowline network within the buffer. Five of the 84 lakes were found to either have an 

upstream drainage network that was too complex [reach codes: 03080101034736, 

03080101011546, 03090201000436] or were duplicate lake points within one NHD waterbody 

polygon [reach codes: 03080101036078, 03080101036092]. In the case where duplicate points 

in a single lake were discarded, the farthest downstream lake point was retained. These five lakes 

were removed from the dataset, leaving 79 lakes. The 14 lakes that were discarded during the 

first step were reassessed to determine whether they were located on a lake from the Florida lake 

area geospatial dataset.14 Three lake points were found to coincide with this lake coverage and 

have upstream stream data points. These three lakes [reach codes: 03100206020194, 

03120001004378, 03080103033077] were added to the 79 lakes, totaling 82 lakes for further 

assessment. Stream data points were created in GIS from latitude and longitude coordinate data 

provided in the stream files referenced above from the final rule. Each of the 82 remaining lakes 

was individually assessed to select all upstream stream data points for each lake. Exactly 47 

lakes were found to have upstream stream data. Through this data screening effort, 35 lakes were 

discarded and 47 lakes remained that had available paired receiving lake and upstream annual 

geometric mean nutrient data (Appendix 11 files called 

FloridaLakes_LakeData_and_UpstreamData_Final.xlsx). Finally, lakes for which the majority 

(>50%) of the paired annual geometric mean inflowing stream concentrations were less than the 

corresponding annual geometric mean lake concentrations were excluded from the dataset 

because nutrient concentrations in these lakes were likely affected by internal loading (OECD 

1982). Two final datasets (one for TP and one for TN) passing all the above screens were 

produced and used for the regression modeling described below. All the data files described 

above are provided in Appendix 11 Data Supporting EPA’s Default DPV Derivation Using 

Regional Regression Models with accompanying descriptions. 

After screening the data, relationships between inflowing stream and lake concentrations were 

estimated using the following Bayesian hierarchical model:

TPUp,ij = b1,i + b2,iTPLake,ij

where:

TPLake,ij = the annual geometric lake concentration for lake i and year j

TPUp,ij = the annual geometric mean concentration in the matched inflowing stream for 

lake i and year j

 

13 http://www.horizon-systems.com/nhdplus/HSC-wth03.php, Accessed April 2012 14 http://www.dep.state.fl.us/gis/datadir.htm, Accessed April 2012
EPA Proposed Rule TSD for Florida’s Streams and DPVs for Lakes

108 

The coefficients that define the linear relationship are allowed to vary among lakes as follows:

b1,i = β1 + B1,i

b2,i = β2 + B2,i

Where:

β1 and β2 = the average coefficient values across all lakes

B1,i and B2,i = differences from the average coefficients specific to lake i

These lake-specific differences are assumed to be normally distributed with a mean value of 

zero. The model was fit using Markov Chain Monte Carlo simulations (Lunn et al. 2000). A 

model of identical form was estimated for TN.

Models for both TN and TP showed a statistically significant mean association between 

inflowing stream and lake concentrations (Figure 2-1). This mean association can then be used to 

predict the average nutrient concentration in in-flowing streams that was associated with 

criterion concentrations in the lakes. That is, the mean association is used to derive DPVs which 

on average, will maintain lake TN and TP concentrations at the appropriate criterion values. 

Hence, these DPVs ensure the attainment and maintenance of the downstream lake criteria 

(Table 2-2).

Table 2-2. Lake DPVs computed by upstream-in-lake statistical model. 

Lake Type TN (mg/L) TP (mg/L)

Clear, low alkalinity 0.87*

(0.64 – 1.17)

0.06*

(0.03 – 0.11)

Clear, high alkalinity 1.40

(1.22 – 1..64)

0.09

(0.06 – 0.12)

Colored 1.59

(1.40 – 1.84)

0.11

(0.08 – 0.14)

Values indicated with asterisks for clear, low alkalinity lakes were outside the range of observed 

data. Numbers in parentheses indicate 95% confidence intervals on estimated mean values.
EPA Proposed Rule TSD for Florida’s Streams and DPVs for Lakes

109 

Figure 2-1. Relationships between inflowing (upstream) nutrient concentrations and in-lake concentrations 

of TP and TN. Open circles show annual geometric mean nutrient concentrations matched by year and 

lake. Dotted line shows the 1:1 relationship. Dashed vertical lines show criterion values for different types 

of lakes. Red line shows the estimated mean relationship across all lakes between upstream nutrient 

concentration and in-lake nutrient concentration. Black line segments show the relationships for individual 

lakes.

2.2.3.3 Option 3—Default DPV Derivation Using the Reference Condition Approach

The third proposed option is a reference condition-based DPV approach that is premised on the 

downstream lake attaining all applicable numeric nutrient criteria, TN, TP, and chlorophyll-a, 

including the duration and frequency components of the applicable lake criteria. To compute a 

DPV using this option, the period of record during which the downstream lake was attaining all 

applicable criteria must be determined. At a minimum, and pursuant to 40 CFR 131.43(c)(1), the 

lake must not exceed any applicable numeric nutrient criteria. These criteria are expressed as 

annual geometric means, with exceedances of the annual geometric means to be no more than 

once in a three-year period. If this condition is met, then a DPV for that lake can be computed 

using TN and TP data from the stream discharging into the lake coincident in time with the 

period of record when the lake was attaining all applicable numeric nutrient criteria. Due to the 

hydrologic link between streams and the downstream lakes to which they flow, it follows that 

nitrogen and phosphorus concentrations in the stream would be sufficient to meet the lake 

criteria provided that the lake was meeting all applicable numeric nutrient criteria. In general, 

this approach is less refined compared to the modeling approach EPA promulgated at 40 CFR 

131.43(c)(1)(ii)(B) because it does not incorporate the water quality parameters and data that 

would be necessary to derive a site-specific DPV, for either TN or TP, using a water quality 

model such as BATHTUB. Nonetheless, EPA believes that the data and information that would 

support this third option, in the absence of additional data that would support modeling, is still 

sufficient to ensure the protection of the downstream lake because of the hydrologic linkage 

between the stream and downstream lake. A DPV calculated under this option may be more 
EPA Proposed Rule TSD for Florida’s Streams and DPVs for Lakes

110 

stringent than a DPV calculated using a water quality model. This default option is intended to 

ensure that water quality standards are not only restored when found to be impaired, but are 

maintained when found to be attained, consistent with the CWA. Higher levels of TN and/or TP 

may be allowed in watersheds where it is demonstrated that such higher levels will fully protect 

the lake’s water quality standards. To the extent that it is determined that the default DPV for a 

given lake tributary is over protective, applying a water quality model as set out in EPA’s 

preferred approach will result in a more refined definition of the DPV for that tributary. 

Example:

For the third default approach, an example colored lake in the Peninsula stream nutrient region is 

shown that meets the chl-a and nutrient criteria (Table 2-3). Associated with it are 5 years of 

upstream TN and TP data, which meet the instream criteria. The default DPV in this case would 

become the ambient stream condition, which is defined as that value based on the current 

ambient stream condition, which would not violate the 1 in 3 year frequency component. 

Table 2-3. Sample lake data for calculating a default DPV criterion using the third approach. 

Lake 

Class

Stream 

Region Year Chl a

Lake 

AGM TN

Lake 

AGM TP

Inflowing 

Stream 

AGM TN

Inflowing 

Stream 

AGM TP

colored Peninsula 1997 1.8 1.17 0.03 1.27 0.10

colored Peninsula 1998 1.9 1.01 0.04 1.45 0.09

colored Peninsula 1999 2.2 1.20 0.06 1.49 0.11

colored Peninsula 2000 4.2 1.34 0.02 1.28 0.08

colored Peninsula 2001 4.8 1.16 0.03 1.45 0.07

colored Peninsula 2002 2.1 1.20 0.04 1.40 0.06

colored Peninsula 2003 7.3 1.07 0.05 1.25 0.10

For a lake meeting the final lake criteria for chl-a, TN and TP and the instream criteria for TN and TP, the DPV would be that 

value, derived from the ambient stream criteria, which would not violate the 1 in 3 year frequency component or the instream 

criterion.

Using the 90th percentile of instream annual geometric mean (AGM) for TN and TP, for 

example, yields a TN AGM of 1.50 mg/L and TP of 0.11 mg/L. Both of these concentrations 

would not be violated more than 1 in 3 years and both are still less than the instream criteria. 

These would become the DPV values under the third approach. 
EPA Proposed Rule TSD for Florida’s Streams and DPVs for Lakes

111 

2.3 Summary 

2.3.1 Downstream Protection of Lakes

An important component of setting appropriately protective water quality criteria is ensuring that 

the criteria also provide for the attainment and maintenance of the water quality standards of 

downstream waters (40 CFR 131.10(b)). This position is supported by the scientific literature, 

which has concluded that the source of many adverse water quality impacts can be traced to 

degradation of water quality upstream of the impacted aquatic system (e.g., Conley et al. 2009; 

Paerl 2009; U.S. EPA 2007). EPA’s analyses for deriving numeric nutrient criteria for streams in 

Florida indicate that the stream criteria values may not be sufficient to fully protect downstream 

lakes. Thus, EPA’s lake criteria are, in some cases, more stringent than the stream criteria that 

flow into the lakes. 

EPA is providing for a flexible downstream protection approach for lakes that contains several 

alternative approaches to ensure that the applicable criteria for streams that flow into lakes 

provides for the attainment and maintenance of the applicable downstream lake criteria. First, 

EPA is specifying that the U.S. Army Corps of Engineers BATHTUB reservoir model (Walker 

1999) may be used to compute DPVs necessary in the stream to protect a given lake. EPA is 

providing flexibility by allowing the use of scientifically defensible models other than 

BATHTUB that demonstrate protection of WQS in downstream lakes. This approach was 

finalized in EPA’s 2010 final rule and was codified at 40 CFR 131.43(c)(2)(ii)(B). EPA is not 

re-proposing its DPV approach here, but rather clarifying its order of preference when computing 

DPVs.

Alternatively, in the absence of sufficient data to derive DPVs using BATHTUB or other 

models, EPA is proposing three default options. These options are not intended to supersede or 

limit the two approaches EPA provided in the final rule, codified at 40 CFR 131.43(c)(2)(ii), 

which were upheld by the Court. Rather, the default options are intended to provide flexibility in 

deriving a DPV in the situation where there is not sufficient information to develop a DPV using 

a water quality model. Thus, EPA views the proposed DPV options as supplemental to EPA’s 

other established approaches for deriving DPVs. All three options for default DPVs are designed 

to ensure that the unimpaired lake criteria would be attained and maintained when the inflowing 

stream’s TN and TP concentrations meet the DPV at the pour point. For all of these options, the 

applicable criteria for the stream that flows into the lake would be the more stringent of either the 

instream criteria as described in Chapter 1 or the DPVs derived using either the preferred or one 

of the default options. 

2.4 References

Baniukiewicz, A., and D. Gilbert. 2004. Nutrient TMDL for Lake Hunter. Florida Department of 

Environmental Protection, Southwest District. <http://www.dep.state.fl.us/water/tmdl> 

Brown, C.D., M.V. Hoyer, R.W. Bachmann, and D.E. Canfield, Jr. 2000. Nutrient–chlorophyll 

relationships: An evaluation of empirical nutrient–chlorophyll models using Florida and 

north-temperate lake data. Canadian Journal of Fisheries and Aquatic Sciences 57:1574–

1583. 
EPA Proposed Rule TSD for Florida’s Streams and DPVs for Lakes

112 

Carlson, R.E. 1977. A trophic state index for lakes. Limnology and Oceanography 22:361–369. 

Conley, D.J., H.W. Paerl, R.W. Howarth, D. F. Boesch, S.P. Seitzinger, K.E. Havens, 

C. Lancelot, G.E. Likens. 2009. Controlling eutrophication: Nitrogen and phosphorus. 

Science 323:1014–1015. 

Coveney, M.F., E.F. Lowe, L.E. Battoe, E.R. Marzolf, and R. Conrow. 2005. Response of a 

eutrophic, shallow subtropical lake to reduced nutrient loading. Freshwater Biology 

50:1718–1730. 

Downing, J.A., S.B. Watson, and E. McCauley. 2001. Predicting cyanobacteria dominance in 

lakes. Canadian Journal of Fisheries and Aquatic Sciences 58:1905–1908. 

Edmondson, W.T. 1994. Sixty years of Lake Washington: a curriculum vitae. Lake and 

Reservoir Management 10(2):75–84. 

Elser, J.J., E.R. Marzolf, and C.R. Goldman. 1990. Phosphorus and nitrogen limitation of 

phytoplankton growth in the freshwaters of North America: A review and critique of 

experimental enrichments. Canadian Journal of Fisheries and Aquatic Science 47:1468–

1477. 

Elser, J.J., M.E.S. Bracken, E.E. Cleland, D.S. Gruner, W.S. Harpole, H. Hillebrand, J.T. Ngai, 

E.W. Seabloom, J.B. Shurin, and J.E. Smith. 2007. Global analysis of nitrogen and 

phosphorus limitation of primary producers in freshwater, marine and terrestrial 

ecosystems. Ecology Letters 10:1135–1142. 

Elser, J.J, T. Andersen, J.S. Baron, A.-K. Bergström, M. Jansson, M. Kyle, K.R. Nydick, 

L. Steger, and D.O. Hessen. 2009. Shifts in lake N:P stoichiometry and nutrient limitation 

driven by atmospheric nitrogen deposition. Science 326:835–837. 

Gao, X. 2006. Nutrient and Unionized Ammonia TMDLs for Lake Jesup, WBIDs 2981 and 

2981A. Florida Department of Environmental Protection, Central District, Middle St. 

Johns Basin. <http://www.dep.state.fl.us/water/tmdl> 

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Technical Support Document for EPA’s Proposed Rule 

for Numeric Nutrient Criteria to Protect Florida’s 

Streams and the Downstream Protection of 

Unimpaired Lakes

Appendices
EPA Proposed Rule TSD for Florida’s Streams and DPVs for Lakes

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A

Appendix 1. Florida Waterbody Identification Numbers (WBIDs) by 

EPA Nutrient Watershed Region (NWR)

The Appendix is being provided in electronic form on CD. A copy of the CD can be obtained by 

contacting EPA’s docket office. The docket identification number is EPA-HQ-OW-2009-0596. 

The Office of Water (OW) Docket Center is open from 8:30 a.m. to 4:30 p.m., Monday through 

Friday, excluding legal holidays. The OW Docket Center telephone number is 202-566-1744 and 

the Docket address is OW Docket, EPA West, Room 3334, 1301 Constitution Ave., NW, 

Washington, DC 20004. 

The Appendix is cross-referenced with “Appendix A1. Florida Waterbody Identification 

Numbers (WBIDs) by EPA Nutrient Watershed Region (NWR)” from EPA’s final rule (EPAHQ-OW-2009-0596) published on December 6, 2010. 

Appendix 2. FDEP’s Process for Assessing Data Usability

The Appendix is being provided in electronic form on CD. A copy of the CD can be obtained by 

contacting EPA’s docket office. The docket identification number is EPA-HQ-OW-2009-0596. 

The Office of Water (OW) Docket Center is open from 8:30 a.m. to 4:30 p.m., Monday through 

Friday, excluding legal holidays. The OW Docket Center telephone number is 202-566-1744 and 

the Docket address is OW Docket, EPA West, Room 3334, 1301 Constitution Ave., NW, 

Washington, DC 20004. 

The Appendix is cross-referenced with “Appendix A2. FDEP’s Process for Assessing Data 

Usability” from EPA’s final rule (EPA-HQ-OW-2009-0596) published on December 6, 2010. 

Appendix 3. Data Supporting EPA’s Reference Approach for 

Deriving Numeric Nutrient Criteria for Florida Streams

Appendix 3 contains the data EPA used to derive numeric nutrient criteria for Florida streams 

and to conduct the analyses demonstrating harmful, adverse effects. It is being provided in 

electronic form on CD. A copy of the CD can be obtained by contacting EPA’s docket office. 

The docket identification number is EPA-HQ-OW-2009-0596. 

The Office of Water (OW) Docket Center is open from 8:30 a.m. to 4:30 p.m., Monday through 

Friday, excluding legal holidays. The OW Docket Center telephone number is 202-566-1744 and 

the Docket address is OW Docket, EPA West, Room 3334, 1301 Constitution Ave., NW, 

Washington, DC 20004. 
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Appendix 4. FDEP’s Method for Calculating a Corridor-based 

Landscape Development Intensity Index (LDI)

The Appendix is being provided in electronic form on CD. A copy of the CD can be obtained by 

contacting EPA’s docket office. The docket identification number is EPA-HQ-OW-2009-0596. 

The Office of Water (OW) Docket Center is open from 8:30 a.m. to 4:30 p.m., Monday through 

Friday, excluding legal holidays. The OW Docket Center telephone number is 202-566-1744 and 

the Docket address is OW Docket, EPA West, Room 3334, 1301 Constitution Ave., NW, 

Washington, DC 20004. 

The Appendix is cross-referenced with “Appendix A4. FDEP’s Method for Calculating a 

Corridor-based Landscape Development Intensity Index (LDI)” from EPA’s final rule (EPAHQ-OW-2009-0596) published on December 6, 2010. 

Appendix 5. FDEP’s Stream Benchmark Summaries

The Appendix is being provided in electronic form on CD. A copy of the CD can be obtained by 

contacting EPA’s docket office. The docket identification number is EPA-HQ-OW-2009-0596. 

The Office of Water (OW) Docket Center is open from 8:30 a.m. to 4:30 p.m., Monday through 

Friday, excluding legal holidays. The OW Docket Center telephone number is 202-566-1744 and 

the Docket address is OW Docket, EPA West, Room 3334, 1301 Constitution Ave., NW, 

Washington, DC 20004. 

The Appendix is cross-referenced with “Appendix A5. FDEP’s Stream Benchmark Summaries” 

from EPA’s final rule (EPA-HQ-OW-2009-0596) published on December 6, 2010. 

Appendix 6. FDEP’s Development and Testing of Biomonitoring 

Tools for Macroinvertebrates in Florida Streams

The Appendix is being provided in electronic form on CD. A copy of the CD can be obtained by 

contacting EPA’s docket office. The docket identification number is EPA-HQ-OW-2009-0596. 

The Office of Water (OW) Docket Center is open from 8:30 a.m. to 4:30 p.m., Monday through 

Friday, excluding legal holidays. The OW Docket Center telephone number is 202-566-1744 and 

the Docket address is OW Docket, EPA West, Room 3334, 1301 Constitution Ave., NW, 

Washington, DC 20004. 

The Appendix is cross-referenced with “Appendix A6. FDEP’s Development and Testing of 

Biomonitoring Tools for Macroinvertebrates in Florida Streams” from EPA’s final rule (EPAHQ-OW-2009-0596) published on December 6, 2010. 
EPA Proposed Rule TSD for Florida’s Streams and DPVs for Lakes

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Appendix 7. FDEP’s Site Information and Taxa Lists for SCI BCG 

Workshop Samples

The Appendix is being provided in electronic form on CD. A copy of the CD can be obtained by 

contacting EPA’s docket office. The docket identification number is EPA-HQ-OW-2009-0596. 

The Office of Water (OW) Docket Center is open from 8:30 a.m. to 4:30 p.m., Monday through 

Friday, excluding legal holidays. The OW Docket Center telephone number is 202-566-1744 and 

the Docket address is OW Docket, EPA West, Room 3334, 1301 Constitution Ave., NW, 

Washington, DC 20004. 

The Appendix is cross-referenced with “Appendix A7. FDEP’s Site Information and Taxa Lists 

for SCI BCG Workshop Samples” from EPA’s final rule (EPA-HQ-OW-2009-0596) published 

on December 6, 2010. 

Appendix 8. Memo to FDEP Regarding Interval and Equivalence 

Tests for the SCI

The Appendix is being provided in electronic form on CD. A copy of the CD can be obtained by 

contacting EPA’s docket office. The docket identification number is EPA-HQ-OW-2009-0596. 

The Office of Water (OW) Docket Center is open from 8:30 a.m. to 4:30 p.m., Monday through 

Friday, excluding legal holidays. The OW Docket Center telephone number is 202-566-1744 and 

the Docket address is OW Docket, EPA West, Room 3334, 1301 Constitution Ave., NW, 

Washington, DC 20004. 

The Appendix is cross-referenced with “Appendix A8. Memo to FDEP Regarding Interval and 

Equivalence Tests for the SCI” from EPA’s final rule (EPA-HQ-OW-2009-0596) published on 

December 6, 2010. 

Appendix 9. Mapping Continuous Biological Index Values to BCG 

Tiers

The Appendix is being provided in electronic form on CD. A copy of the CD can be obtained by 

contacting EPA’s docket office. The docket identification number is EPA-HQ-OW-2009-0596. 

The Office of Water (OW) Docket Center is open from 8:30 a.m. to 4:30 p.m., Monday through 

Friday, excluding legal holidays. The OW Docket Center telephone number is 202-566-1744 and 

the Docket address is OW Docket, EPA West, Room 3334, 1301 Constitution Ave., NW, 

Washington, DC 20004. 

The Appendix is cross-referenced with “Appendix A9. Mapping Continuous Biological Index 

Values to BCG Tiers” from EPA’s final rule (EPA-HQ-OW-2009-0596) published on December 

6, 2010. 
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Appendix 10. Harmful, Adverse Effects of Nutrients on Stream 

Macroinvertebrates in Florida (Supplemental Analyses) 

SCI vs.TP: Habitat Score > 80 Linear Regression Models

When only sites exhibiting habitat scores > 80 were used, the general relationships did not 

change appreciably. Across all regions, there was still a decline in SCI score with TP (p< 0.01), 

and the same trends within specific regions were observed as before (Figure A10–1)—declines 

in regions exhibiting the widest range in nutrients [Panhandle West (p<0.01), Panhandle East 

(p=0.18), and Peninsula (p<0.01)], and no trend in the West Central (p=0.64) and increasing 

trends in the North Central (p<0.01), both of the latter regions, again, where observations of TP 

concentrations were on the high end of the distribution (Figure A10–2).

Figure A10–1. SCI versus Ln (TP), 2006–2011 data, relationship for all regions combined for sites with 

habitat score > 80. Linear fit is shown. Panhandle West: pink closed circle, pink line; Panhandle East: green 

open triangle, green line; Peninsula: black closed square; North Central: red open diamond, red line; West 

Central: gray closed diamond, gray line.

SCI vs. Ln(TP) 

Habitat Score > 80

-6 -5 -4 -3 -2 -1 0 1 2 3

LnTP

0

20

40

60

80

100

120

SCI2007

 LnTP:SCI2007: y = 39.8669 - 4.013*x;

 r = -0.2789, p = 0.0000
EPA Proposed Rule TSD for Florida’s Streams and DPVs for Lakes

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Figure A10–2. SCI versus Ln(TP), 2006–2011 data, relationships for individual nutrient regions and sites 

with habitat score > 80. Linear fits are shown. Panhandle West: pink closed circle, pink line; Panhandle 

East: green open triangle, green line; Peninsula: black closed square; North Central: red open diamond, red 

line; West Central: gray closed diamond, gray line.
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SCI vs. TP: Habitat Score > 100 Linear Regression Models 

When only sites exhibiting habitat scores > 100 were used, the general relationship, again, did 

not change appreciably. Across all regions, there was still a decline in SCI score with TP 

(p<0.01), and the same trends within specific regions were observed as before Figure A10–3)—

declines in regions exhibiting the widest range in nutrients (Panhandle West (p<0.01) although 

now weaker in the Panhandle East (p=0.26) and Peninsula (p=0.17), no trend in the West Central 

(p=0.98), and increasing trends in the North Central (p<0.01) where observations of TP 

concentrations were again on the high end of the distribution (Figure A10–4).

Figure A10–3. SCI versus Ln(TP), 2006–2011 data, relationship for all regions combined for sites with 

habitat score > 100. Linear fit is shown. Panhandle West: pink closed circle, pink line; Panhandle East: 

green open triangle, green line; Peninsula: black closed square; North Central: red open diamond, red line; 

West Central: gray closed diamond, gray line.

SCI vs. Ln(TP) 

Habitat Score > 100

-6 -5 -4 -3 -2 -1 0 1 2 3

LnTP

0

20

40

60

80

100

120

SCI2007

 LnTP:SCI2007: y = 42.1206 - 3.6309*x;

 r = -0.2501, p = 0.0000
EPA Proposed Rule TSD for Florida’s Streams and DPVs for Lakes

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Figure A10–4. SCI versus Ln(TP), 2006–2011 data, relationships for individual nutrient regions and sites 

with habitat score > 100. Linear fits are shown. Panhandle West: pink closed circle, pink line; Panhandle 

East: green open triangle, green line; Peninsula: black closed square; North Central: red open diamond, red 

line; West Central: gray closed diamond, gray line.
EPA Proposed Rule TSD for Florida’s Streams and DPVs for Lakes

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SCI vs. TP: Habitat Score > 120 Linear Regression Models

Finally, when only sites exhibiting habitat scores > 120 were used, the general relationship, 

again, did not change appreciably. Across all regions, encompassing the full range of both 

nutrient gradient and biological response, there was still a decline in SCI score with Ln(TP) 

(p<0.01), and mostly similar trends within specific regions as before (Figure A10–5)—declines 

in regions exhibiting the widest range in nutrients including the Panhandle West (p<0.01) and a 

now weaker declining relationship in the Panhandle East (p=0.18), weakly increasing in the 

North Central (p=0.15) and now essentially flat in both the Peninsula (p=0.81) and West Central 

(p=0.61)(Figure A10–6).

Figure A10–5. SCI versus Ln(TP), 2006–2011 data, relationship for all regions combined for sites with 

habitat score > 120. Linear fit is shown. Panhandle West: pink closed circle, pink line; Panhandle East: 

green open triangle, green line; Peninsula: black closed square; North Central: red open diamond, red line; 

West Central: gray closed diamond, gray line.

SCI vs. Ln(TP) 

Habitat Score > 120

-6 -5 -4 -3 -2 -1 0 1 2 3

LnTP

0

20

40

60

80

100

120

SCI2007

 LnTP:SCI2007: y = 42.7736 - 4.0597*x;

 r = -0.2690, p = 0.0000
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Figure A10–6. SCI versus Ln(TP), 2006–2011 data, relationships for individual nutrient regions and sites 

with habitat score > 120. Linear fits are shown. Panhandle West: pink closed circle, pink line; Panhandle 

East: green open triangle, green line; Peninsula: black closed square; North Central: red open diamond, red 

line; West Central: gray closed diamond, gray line.
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TP vs. SCI Linear Regression Summary 

Regression equations from each relationship of Ln(TP) vs. SCI were developed using least 

squares regression for each habitat score criterion (Table A10–1). These equations were then 

solved for SCI values of 40, 46, and 50, which correspond to the 2.5th, 5th, and 10th percentile of 

SCI scores, respectively, from minimally impaired streams in Florida according to FDEP 

(Section 1.4 and see Appendix 8) as well as SCI = 30 for comparison, and include the SCI 

thresholds used by EPA (40). As such, these values represent the lower end of scores expected 

for streams that are relatively unimpacted and overlap with those reported from streams with 

known impacts (e.g., see Appendix B-13 in EPA 2010 proposed rule TSD, EPA-HQ-OW-2009-

0596). Values for TP in this range are, therefore, at the threshold of where harmful changes in 

the faunal balance of Florida streams exist, consistent with the definition proposed by FDEP as 

their thresholds for unacceptable faunal imbalance. Predicted values for SCI=30 for any but the 

all habitat scores subset were beyond the range of the data.

Table A10–1. Regression coefficients and statistics of Ln(TP) vs. SCI for sites with any habitat score or 

habitat scores > 80, 100, or 120, respectively. 

SCI>30 vs. Ln(Total Phosphorus)

b p m p

Mean TP at

SCI = 30

95th

CI Intercept

95th

CI Slope

All Habitat Scores 36.48 < 0.001 -4.50 < 0.001 4.21 34.20 38.76 -5.23 -3.78

Habitat Score > 80

Habitat Score > 100 Beyond Data Range

Habitat Score > 120

SCI>40 vs. Ln(Total Phosphorus)

b p m p

Mean TP at

SCI = 40

95th

CI Intercept

95th

CI Slope

All Habitat Scores 36.48 < 0.001 -4.50 < 0.001 0.46 34.20 38.76 -5.23 -3.78

Habitat Score > 80 39.87 < 0.001 -4.01 < 0.001 0.97 37.45 42.28 -4.77 -3.26

Habitat Score > 100 42.12 < 0.001 -3.63 < 0.001 1.79 39.46 44.78 -4.45 -2.81

Habitat Score > 120 42.77 < 0.001 -4.06 < 0.001 1.98 39.21 46.33 -5.14 -2.98

SCI>46 vs. Ln(Total Phosphorus)

b p m p

Mean TP at

SCI = 46

95th

CI Intercept

95th

CI Slope

All Habitat Scores 36.48 < 0.001 -4.50 < 0.001 0.12 34.20 38.76 -5.23 -3.78

Habitat Score > 80 39.87 < 0.001 -4.01 < 0.001 0.22 37.45 42.28 -4.77 -3.26

Habitat Score > 100 42.12 < 0.001 -3.63 < 0.001 0.34 39.46 44.78 -4.45 -2.81

Habitat Score > 120 42.77 < 0.001 -4.06 < 0.001 0.45 39.21 46.33 -5.14 -2.98

SCI>50 vs. Ln(Total Phosphorus)

b p m p

Mean TP at

SCI = 50

95th

CI Intercept

95th

CI Slope

All Habitat Scores 36.48 < 0.001 -4.50 < 0.001 0.05 34.20 38.76 -5.23 -3.78

Habitat Score > 80 39.87 < 0.001 -4.01 < 0.001 0.08 37.45 42.28 -4.77 -3.26

Habitat Score > 100 42.12 < 0.001 -3.63 < 0.001 0.11 39.46 44.78 -4.45 -2.81

Habitat Score > 120 42.77 < 0.001 -4.06 < 0.001 0.17 39.21 46.33 -5.14 -2.98

The intercept (b) and slope (m) are shown along with their significance and 95% confidence intervals (CI). Predicted values 

of total phosphorus (mg/L) are for values of SCI = 40, 46, and 50, coinciding with the 2.5th, 5th, and 10th percentile of 

reference site SCI scores respectively (Appendix 8) and for SCI=30 for comparison.
EPA Proposed Rule TSD for Florida’s Streams and DPVs for Lakes

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The range in TP concentrations represented by the stream criteria (red arrows in Figure A10–7 

and Figure A10–8) are consistent with that nutrient range observed to coincide with harmful 

changes in biological condition, as defined by the SCI and its thresholds in Florida. On average, 

the likelihood of Florida streams exhibiting SCI scores above 40 decreases with TP 

concentration, and this occurs in the range of the final criteria concentrations and certainly above 

these concentrations. This is consistent with FDEP’s interpretation of earlier stressor-response 

relationship results (FDEP 2009). This provides additional evidence that the TP stream criteria 

concentrations protect against harmful changes in biological condition. 

Figure A10–7. SCI versus Ln(TP), 2006–2011 data, relationship for all regions combined with Ln(TP) solved 

for SCI values of 30, 40, 46, and 50 based on regression models for all habitat combined (vertical black 

lines) as well as the Florida stream TP criteria values for each nutrient region (red arrows). Linear fit is 

shown. PHW: Panhandle West, PHE: Panhandle East, PEN: Peninsula, NC: North Central, WC: West Central. 

Panhandle West: pink closed circle, pink line; Panhandle East: green open triangle, green line; Peninsula: 

black closed square; North Central: red open diamond, red line; West Central: gray closed diamond, gray 

line.
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These trends are especially noticeable when focused on the central tendency in SCI response to 

TP concentration (Figure A10–8). The box and whiskers created for bins of TP concentration 

indicate that mean SCI scores (and the 95th percentile confidence intervals around them) decline 

with increasing TP, and these scores begin to overlap with the range of SCI on the lower end of 

reference site scores (SCI = 50 is the 10th percentile of reference sites) above TP concentrations 

of 0.018 mg/L (Ln(TP)= -4) and these continue to decline towards the 2.5th percentile SCI score 

(SCI = 40) which is included in the distribution of SCI scores when TP is greater than 

0.135 mg/L (Ln(TP) = -2) as indicated by the overlap of the 95 percent confidence interval with 

the score of 40 in that TP bin. SCI scores less than 30 are associated with concentrations. On 

average, above 4.2 mg/L (Ln(TP) = 1.44). 

Figure A10–8. SCI versus Ln(TP), 2006–2011 data, represented as box plots of SCI scores by TP 

concentration bins. Center boxes are means, box margins are 1 standard error of the mean, and whiskers 

are 95 percent confidence intervals around mean SCI scores for each TP bin. The blue shaded region is the 

2.5th to 10th percentile of SCI reference site scores, which are values of 40 and 50, respectively. Also shown 

(black vertical lines) are the solutions of TP concentration for SCI scores of 30, 40, 46, and 50 using the 

linear regression model of Ln(TP) vs. SCI score for all regions combined and all habitat scores (Figure A10–

7) (vertical black lines) as well as the Florida stream TP criteria values for each nutrient region (red arrows).
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SCI vs. TP: All Habitat Conditions Logistic Regression Models 

The same SCI data can also be represented as binomial responses (e.g., a sample below an SCI 

threshold is given a value of 1 and a sample above, a value of 0), and the resulting binomial 

relationship between SCI and TP can be analyzed using a logit model, which predicts the 

probability of observing SCI less than the target (30, 40, 46, or 50) as the TP concentration 

increases. EPA developed such models to further examine the relationship between TP and SCI 

scores. Results indicate that the probability of observing SCI thresholds less than 30, 40, 46, or 

50 increases significantly with TP concentration across a range of habitat scores (Figure A10–9 

through Figure A10–12).

Figure A10–9. Binomial SCI response based on an SCI threshold of 30 regressed against Ln(TP) for all habitat 

scores (upper left), habitat scores > 80 (upper right), habitats scores > 100 (lower left), and habitat scores 

> 120 (lower right). 
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Figure A10–10. Binomial SCI response based on an SCI threshold of 40 regressed against Ln(TP) for all 

habitat scores (upper left), habitat scores > 80 (upper right), habitats scores > 100 (lower left), and habitat 

scores > 120 (lower right). 
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Figure A10–11. Binomial SCI response based on an SCI threshold of 46 regressed against Ln(TP) for all 

habitat scores (upper left), habitat scores > 80 (upper right), habitats scores > 100 (lower left), and habitat 

scores > 120 (lower right). 
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Figure A10–12. Binomial SCI response based on an SCI threshold of 50 regressed against Ln(TP) for all 

habitat scores (upper left), habitat scores > 80 (upper right), habitats scores > 100 (lower left), and habitat 

scores > 120 (lower right). 
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EPA then used the binomial regression equations from these models (Table A10–2) and estimated 

TP concentrations where the probability of scoring less than the SCI thresholds (30, 40, 46, or 50) 

was 0.5 or greater. These concentrations increased as sites with better habitat scores were used, but 

decreased as increasing SCI reference thresholds were modeled. The model predictions for SCI=30 

for any but the all habitat scores subset were beyond the range of the data.

Table A10–2. Binomial regression coefficients and statistics of Ln(TP) vs. SCI for sites with any habitat score 

or habitat scores > 80, 100, or 120, respectively. 

Chapter 3. SCI>30 vs. Ln(Total Phosphorus)

b m χ2 p

Mean TP 

at p=0.5

95th

CI Intercept

95th

CI Slope

All Habitat Scores -0.452 0.316 51.39 < 0.001 4.19 -0.703 -0.201 0.228 0.404

Habitat Score > 80

Habitat Score > 100 Beyond the Data Range

Habitat Score > 120

SCI>40 vs. Ln(Total Phosphorus)

b m χ2 p

Mean TP 

at p=0.5

95th

CI Intercept

95th

CI Slope

All Habitats 0.358 0.346 78.7 < 0.001 0.355 0.121 0.594 0.266 0.425

Habitat Score > 80 0.062 0.308 52.9 < 0.001 0.818 -0.196 0.321 0.222 0.394

Habitat Score > 100 -0.188 0.255 52.9 < 0.001 2.090 -0.473 0.097 0.163 0.348

Habitat Score > 120 -0.15 0.315 25.2 < 0.001 1.610 -0.534 0.234 0.189 0.440

SCI>46 vs. Ln(Total Phosphorus)

b m χ2 p

Mean TP 

at p=0.5

95th

CI Intercept

95th

CI Slope

All Habitat Scores 0.726 0.34 80.3 < 0.001 0.118 0.487 0.965 0.263 0.418

Habitat Score > 80 0.481 0.314 59.4 < 0.001 0.216 0.226 0.736 0.231 0.396

Habitat Score > 100 0.259 0.274 38.2 < 0.001 0.389 -0.02 0.538 0.184 0.362

Habitat Score > 120 0.25 0.327 30.67 < 0.001 0.466 -0.124 0.623 0.208 0.447

SCI>50 vs. Ln(Total Phosphorus)

b m χ2 p

Mean TP 

at p=0.5

95th

CI Intercept

95th

CI Slope

All Habitat Scores 1.101 0.381 98.7 < 0.001 0.056 0.853 1.35 0.302 0.46

Habitat Score > 80 0.859 0.346 72.6 < 0.001 0.084 0.596 1.121 0.263 0.429

Habitat Score > 100 0.662 0.315 51.6 < 0.001 0.122 0.379 0.946 0.226 0.404

Habitat Score > 120 0.666 0.378 42.2 < 0.001 0.172 0.288 1.044 0.258 0.497

The intercept (b) and slope (m) are shown along with their significance (using a χ2 test) and 95% confidence intervals (CI). 

Predicted values for total phosphorus (mg/L) where the probability of exceeding the SCI thresholds equals 0.5 are shown 

(Mean TP at p=0.5) for SCI values = 40, 46, and 50, coinciding with the 2.5th, 5th, and 10th percentile of reference site SCI 

scores (Appendix 8) and SCI=30 for comparison.
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In a similar way, the same equations can be used to estimate the percent of sites scoring below a 

specific SCI threshold for any given TP concentration. This was done for each of the final 

regional TP criteria using the binomial equations above using the all habitats models. Anywhere 

from 35 percent to 53 percent of sites score less than SCI of 40 at the final TP criteria, on 

average as predicted with these binomial models (Table A10–3). This further reflects the 

observations that harmful biological impacts are expected around the range of final TP criteria 

concentrations across regions in Florida. 

Table A10–3. Percent of sites expected to score below SCI thresholds of 40, 46, and 50 based on the 

binomial regression models in Table A10–2 and using the final TP criteria. 

EPA TP Criteria (mg/L)

Panhandle 

West

Panhandle 

East

North 

Central

West 

Central Peninsula

0.06 0.18 0.3 0.49 0.12

Probability

SCI<30 21% 27% 30% 34% 25%

SCI<40 35% 44% 49% 53% 41%

SCI<46 44% 54% 58% 62% 50%

SCI<50 51% 61% 66% 70% 57%

For comparison, predicted TP values were estimated from the binomial regression models for all 

four SCI thresholds when the probability of scoring less than the thresholds was equal to 0.5 or 

greater, and using all habitat conditions was plotted along with thresholds from the linear 

regression models (Figure A10–13 and A10–14). TP concentrations associated with SCI 

thresholds from the binomial regression models were similar to those from the linear regression 

model except for the SCI = 40 threshold, which resulted in lower TP for the binomial regression 

model (0.36 mg/L) versus the linear regression model (0.46 mg/L). 
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Figure A10–13. SCI versus Ln(TP) (2006–2011 data) for all regions combined (Figure A10–7) with Ln(TP) 

solved for SCI values of 30, 40, 46, and 50 based on linear regression models (solid black lines) and binomial 

regression models (dashed blue lines) for all habitat scores combined. Also shown are the Florida stream 

TP criteria values (in red with arrows) for each nutrient region. The linear regression fit is shown (solid red 

line) along with 75th percentile prediction intervals (red dashed lines). PHW: Panhandle West, PHE: 

Panhandle East, PEN: Peninsula, NC: North Central, WC: West Central. Panhandle West: pink closed circle, 

pink line; Panhandle East: green open triangle, green line; Peninsula: black closed square; North Central: 

red open diamond, red line; West Central: gray closed diamond, gray line.
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Once again, the same data presented in Figure A10–13 as individual samples are provided as box 

plots of SCI scores by TP concentration bins as in Figure A10–8, but this time with the binomial 

thresholds also plotted (Figure A10–14). The binomial thresholds are similar to the linear 

regression thresholds and further demonstrate the conclusion that biological condition worsens 

with increasing nutrient concentrations and that adverse biological responses, as defined by 

FDEP’s reference site SCI percentiles, occur at TP concentrations consistent with the EPA 

nutrient criteria.

Figure A10–14. Center boxes are means, box margins are 1 standard error of the mean, and whiskers are 95 

percent confidence intervals around mean SCI scores for each TP bin. The blue shaded region is the 2.5th to 

10th percentile of SCI reference site scores, which are values of 40 and 50, respectively. Also shown are the 

solutions of TP concentration for SCI scores of 30, 40, 46, and 50 using the linear regression model from 

Figure A10–7 (vertical black lines) and binomial regression models from Figure A10–9 through Figure A10–

12 (blue dashed lines) of Ln(TP) vs. SCI score for all regions combined and all habitat scores as well as the 

Florida stream TP criteria values for each nutrient region (red arrows).
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SCI vs. TN: Habitat Score > 80

When only sites exhibiting habitat scores > 80 were used, the general relationships did not 

change appreciably. Across all regions, there was still a decline in SCI score with Ln(TN) 

(p<0.01, Figure A10–15) and the same trends within specific regions were observed as before 

(Figure A10–16)—declines in regions exhibiting the widest range in TN concentration 

[Panhandles West (p<0.01) and East (p=0.03) and Peninsula (p<0.01)], and in the North Central 

and the West Central this relationship was still flat (p=0.77 and p =0.86, respectively). 

Figure A10–15. SCI versus Ln(TN), 2006–2011 data, relationship for all regions combined for sites with 

habitat score > 80. Linear fit is shown. Panhandle West: pink closed circle, pink line; Panhandle East: green 

open triangle, green line; Peninsula: black closed square; North Central: red open diamond, red line; West 

Central: gray closed diamond, gray line.
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Figure A10–16. SCI versus Ln(TN), 2006–2011 data, relationships for individual nutrient regions and sites 

with habitat score > 80. Linear fits are shown. Panhandle West: pink closed circle, pink line; Panhandle 

East: green open triangle, green line; Peninsula: black closed square; North Central: red open diamond, red 

line; West Central: gray closed diamond, gray line.
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SCI vs. TN: Habitat Score > 100 

When only sites exhibiting habitat scores > 100 were used, the general relationship, again, did 

not change appreciably. Across all regions, there was still a decline in SCI score with Ln(TN) 

(p<0.01) and the same trends within specific regions were observed as before (Figure A10–17)—

declines in regions exhibiting the widest range in TN concentration [Panhandles West (p<0.01) 

and East (p=0.03) and Peninsula (p<0.01)], and in the North Central and the West Central, this 

relationship was still flat (p=0.69 and p =0.71, respectively) (Figure A10–18).

Figure A10–17. SCI versus Ln(TN), 2006–2011 data, relationship for all regions combined for sites with 

habitat score > 100. Linear fit is shown. Panhandle West: pink closed circle, pink line; Panhandle East: 

green open triangle, green line; Peninsula: black closed square; North Central: red open diamond, red line; 

West Central: gray closed diamond, gray line.
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Figure A10–18. SCI versus Ln(TN), 2006–2011 data, relationships for individual nutrient regions and sites 

with habitat score > 100. Linear fits are shown. Panhandle West: pink closed circle, pink line; Panhandle 

East: green open triangle, green line; Peninsula: black closed square; North Central: red open diamond, red 

line; West Central: gray closed diamond, gray line.
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SCI vs. TN: Habitat Score > 120

Finally, when only sites exhibiting habitat scores > 120 were used, the general relationship, 

again, did not change appreciably. Across all regions, there was still a decline in SCI score with 

Ln(TN) (p<0.01) and similar trends within specific regions as before (Figure A10–19)—declines 

in regions exhibiting the widest range in TN concentration [Panhandles West (p<0.01) and East 

(p<0.01) and Peninsula (p<0.01)], and in the North Central and the West Central, this 

relationship was still flat (p=0.39 and p =0.33, respectively) (Figure A10–20).

Figure A10–19. SCI versus Ln(TN), 2006–2011 data, relationship for all regions combined for sites with 

habitat score > 120. Linear fit is shown. Panhandle West: pink closed circle, pink line; Panhandle East: 

green open triangle, green line; Peninsula: black closed square; North Central: red open diamond, red line; 

West Central: gray closed diamond, gray line.
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Figure A10–20. SCI versus Ln(TN), 2006–2011 data, relationships for individual nutrient regions and sites 

with habitat score > 120. Linear fits are shown. Panhandle West: pink closed circle, pink line; Panhandle 

East: green open triangle, green line; Peninsula: black closed square; North Central: red open diamond, red 

line; West Central: gray closed diamond, gray line.
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TN vs. SCI Regression Summary 

Regression equations from each relationship of Ln(TN) vs. SCI were developed using least squares 

regression for each habitat score criterion (Table A10–4). These equations were then solved for 

SCI value of 40, 46, and 50 which correspond to the SCI threshold used by EPA as well as the 

2.5th, 5th, and 10th percentile of SCI scores from minimally impaired streams in Florida according 

to FDEP (Appendix 8). As such, these values represent the lower end of scores expected for 

streams that are relatively unimpacted, the upper end of where harmful changes in stream faunal 

assemblages begin, and these values overlap with those reported from streams with known impacts 

(Appendix B-13 in EPA 2010 proposed rule TSD, EPA-HQ-OW-2009-0596). Predictions at 

SCI=30 were also added for comparison. Values for TN in this range should be considered those at 

the threshold of where known harmful changes in the faunal balance of Florida streams exist, 

consistent with the very definition proposed by FDEP as their own SCI thresholds. Predicted 

nutrient values for SCI=30 for Habitat Scores >100 and >120 were beyond the range of the data.

Table A10–4. Regression coefficients and statistics of Ln(TN) vs. SCI for sites with any habitat score or 

habitat scores > 80, 100, or 120, respectively. 

SCI>30 vs. Ln(Total Nitrogen)

b p m p

Mean TN at

SCI = 30

95th

CI Intercept

95th

CI Slope

All Habitat Scores 47.52 < 0.001 -10.27 < 0.001 5.51 46.46 48.57 -11.70 -8.84

Habitat Score > 80 49.62 < 0.001 -9.10 < 0.001 8.64 48.50 50.75 -10.59 -7.60

Habitat Score > 100 Beyond Data Range Habitat Score > 120

SCI>40 vs. Ln(Total Nitrogen)

b p m p

Mean TN at

SCI = 40

95th

CI Intercept

95th

CI Slope

All Habitat Scores 47.52 < 0.001 -10.27 < 0.001 2.08 46.46 48.57 -11.70 -8.84

Habitat Score > 80 49.62 < 0.001 -9.10 < 0.001 2.88 48.50 50.75 -10.59 -7.60

Habitat Score > 100 50.86 < 0.001 -8.54 < 0.001 3.57 49.65 52.07 -10.13 -6.95

Habitat Score > 120 52.72 < 0.001 -8.83 < 0.001 4.23 51.14 54.30 -10.78 -6.87

SCI>44 vs. Ln(Total Nitrogen)

b p m p

Mean TN at

SCI = 46

95th

CI Intercept

95th

CI Slope

All Habitat Scores 47.52 < 0.001 -10.27 < 0.001 1.16 46.46 48.57 -11.70 -8.84

Habitat Score > 80 49.62 < 0.001 -9.10 < 0.001 1.49 48.50 50.75 -10.59 -7.60

Habitat Score > 100 50.86 < 0.001 -8.54 < 0.001 1.77 49.65 52.07 -10.13 -6.95

Habitat Score > 120 52.72 < 0.001 -8.83 < 0.001 2.14 51.14 54.30 -10.78 -6.87

SCI>50 vs. Ln(Total Nitrogen)

b p m p

Mean TN at

SCI = 50

95th

CI Intercept

95th

CI Slope

All Habitat Scores 47.52 < 0.001 -10.27 < 0.001 0.79 46.46 48.57 -11.70 -8.84

Habitat Score > 80 49.62 < 0.001 -9.10 < 0.001 0.96 48.50 50.75 -10.59 -7.60

Habitat Score > 100 50.86 < 0.001 -8.54 < 0.001 1.11 49.65 52.07 -10.13 -6.95

Habitat Score > 120 52.72 < 0.001 -8.83 < 0.001 1.36 51.14 54.30 -10.78 -6.87

The intercept (b) and slope (m) are shown along with their significance and 95% confidence intervals (CI). Predicted values 

of total nitrogen (mg/L) are shown for values of SCI = 40, 46, and 50, coinciding with the 2.5th, 5th, and 10th percentile of 

reference site SCI scores, defined as scores where impact to fauna is identified in Florida (Appendix 8) and SCI=30 for 

comparison.
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From Figure A10–21, it can be seen that the range in TN concentrations represented by the final 

criteria (red arrows) are consistent with that nutrient range observed to coincide with harmful 

changes in biological condition, as defined by the SCI and its thresholds in Florida. On average, 

the likelihood of Florida streams to exhibit SCI scores above 40 decreases with TN 

concentrations in the range of the final criteria concentrations and certainly above these 

concentrations. This is consistent with FDEP’s interpretation of earlier stressor-response 

relationship results (FDEP 2009). This provides additional evidence that the final TN

concentrations protect against harmful changes in biological condition. 

Figure A10–21. SCI versus Ln(TN), 2006–2011 data, relationship for all regions combined with Ln(TN) solved 

for SCI values of 30, 40, 46, and 50 based on regression models for all habitat combined as well as the 

Florida stream TN criteria values for each nutrient region. These SCI values represent the thresholds where 

impacts to fauna have been defined. Linear fit is shown. PHW: Panhandle West, PHE: Panhandle East, PEN: 

Peninsula, NC: North Central, WC: West Central, Panhandle West: pink closed circle; Panhandle East: green 

open triangle; Peninsula: black closed square; North Central: red open diamond; West Central: gray closed 

diamond.
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The data in Figure A10–21 can be viewed as box and whisker plots of SCI scores by TN 

concentration bins (Figure A10–22), as was done for TP. The box and whiskers created for bins 

of TN concentration indicate that mean SCI scores (and the 95th percentile confidence intervals 

around them) decline with increasing TN and these scores begin to overlap with the range of SCI 

on the lower end of reference site scores (SCI = 50 is the 10th percentile of reference sites) above 

TN concentrations of 0.60 mg/L (Ln(TN)=-0.5) and these continue to decline towards the 2.5th

percentile SCI score (SCI = 40) which is included in the 95th percentile confidence interval of 

mean SCI scores when TN is greater than 1.65 mg/L (Ln(TN) = 0.5) as indicated by the overlap 

of the 95 percent confidence interval with the score of 40 in the TN bin above that concentration. 

Figure A10–22. SCI versus Ln(TN), 2006–2011 data, represented as box plots of SCI scores by TN 

concentration bins. Center boxes are means, box margins are 1 standard error of the mean, and whiskers 

are 95 percent confidence intervals around mean SCI scores for each TN bin. The blue shaded region is the 

2.5th to 10th percentile of SCI reference site scores, which are values of 40 and 50, respectively. Also shown 

(black vertical lines) are the solutions of TN concentration for SCI scores of 30, 40, 46, and 50 using the 

linear regression model of Ln(TN) vs. SCI score for all regions combined and all habitat scores (Figure A10–

21) (vertical black lines) as well as the Florida stream TN criteria values for each nutrient region (red 

arrows).
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SCI vs. TN: All Habitat Conditions Logistic Regression Models 

As for TP, the same SCI data can also be represented as binomial responses (e.g., a sample 

below an SCI threshold is given a value of 1 and a sample above, a value of 0) and the resulting 

binomial relationship between SCI and TN can be analyzed using a logit model, which predicts 

the probability of observing SCI less than the target (30, 40, 46, or 50) as the TN concentration 

increases. EPA developed such models to further examine the relationship between TN and SCI 

scores. Results indicate that the probability of scoring less than SCI thresholds of 30, 40, 46, or 

50 increases significantly with TN concentration across a range of habitat scores (Figure A10–23 

through Figure A10–26).

Figure A10–23. Binomial SCI response based on an SCI threshold of 30 regressed against Ln(TN) for all 

habitat scores (upper left), habitat scores > 80 (upper right), habitats scores > 100 (lower left), and habitat 

scores > 120 (lower right). 
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Figure A10–24. Binomial SCI response based on an SCI threshold of 40 regressed against Ln(TN) for all 

habitat scores (upper left), habitat scores > 80 (upper right), habitats scores > 100 (lower left), and habitat 

scores > 120 (lower right). 
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Figure A10–25. Binomial SCI response based on an SCI threshold of 46 regressed against Ln(TN) for all 

habitat scores (upper left), habitat scores > 80 (upper right), habitats scores > 100 (lower left), and habitat 

scores > 120 (lower right). 
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Figure A10–26. Binomial SCI response based on an SCI threshold of 50 regressed against Ln(TN) for all 

habitat scores (upper left), habitat scores > 80 (upper right), habitats scores > 100 (lower left), and habitat 

scores > 120 (lower right). 

EPA then used the binomial regression equations from these models (Table A10–5) and 

estimated TN concentrations where the probability of scoring less than the SCI thresholds (30, 

40, 46, or 50) was 0.5 or greater. These concentrations increased as sites with better habitat 

scores were used, but decreased as increasing SCI reference thresholds were modeled. Model 

predictions for SCI=30 at Habitat Scores > 100 or 120 were beyond the data range. 
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Table A10–5. Binomial regression coefficients and statistics of Ln(TN) vs. SCI for sites with any habitat score 

or habitat scores > 80, 100, or 120, respectively. 

SCI>30 vs. Ln(Total Nitrogen)

b m χ2 p

Mean TN 

at p=0.5

95th

CI Intercept

95th

CI Slope

All Habitat Scores -1.237 0.788 70.98 < 0.001 4.81 -1.367 -1.108 0.595 0.982

Habitat Score > 80 -1.473 0.626 35.54 < 0.001 10.5 -1.621 -1.325 0.413 0.839

Habitat Score > 100

Beyond Data Range Habitat Score > 120

SCI>40 vs. Ln(Total Nitrogen)

b m χ2 p

Mean TN 

at p=0.5

95th

CI Intercept

95th

CI Slope

All Habitat Scores -0.505 0.789 94.6 < 0.001 1.90 -0.617 0.393 0.62 0.957

Habitat Score > 80 -0.697 0.679 60 < 0.001 2.79 -0.821 -0.573 0.5 0.859

Habitat Score > 100 -0.811 0.603 40.35 < 0.001 3.84 -0.945 -0.676 0.41 0.795

Habitat Score > 120 -0.941 0.602 25.3 < 0.001 4.77 -1.118 -0.764 0.358 0.847

SCI>46 vs. Ln(Total Nitrogen)

b m χ2 p

Mean TN 

at p=0.5

95th

CI Intercept

95th

CI Slope

All Habitat Scores -0.115 0.825 110.7 < 0.001 1.15 -0.225 -0.005 0.662 0.989

Habitat Score > 80 -0.289 0.743 78.76 < 0.001 1.48 -0.408 -0.17 0.57 0.917

Habitat Score > 100 -0.409 0.697 60.08 < 0.001 1.80 -0.537 -0.28 0.512 0.881

Habitat Score > 120 -0.576 0.721 40.69 < 0.001 2.22 -0.743 -0.408 0.487 0.955

SCI>50 vs. Ln(Total Nitrogen)

b m χ2 p

Mean TN 

at p=0.5

95th

CI Intercept

95th

CI Slope

All Habitat Scores 0.161 0.875 125.1 < 0.001 0.83 0.05 0.273 0.711 1.039

Habitat Score > 80 0.01 0.785 90.47 < 0.001 0.99 -0.106 0.126 0.614 0.956

Habitat Score > 100 -0.109 0.721 67.67 < 0.001 1.16 -0.236 0.018 0.541 0.901

Habitat Score > 120 -0.285 0.769 49.58 < 0.001 1.45 -0.45 -0.121 0.542 0.995

The intercept (b) and slope (m) are shown along with their significance (using a χ2 test) and 95% confidence intervals (CI). 

Predicted values of total nitrogen (mg/L) where the probability of exceeding the SCI thresholds equals 0.5 are shown (Mean 

TN at p=0.5) for SCI values = 40, 46, and 50, coinciding with the 2.5th, 5th, and 10th percentile of reference site SCI scores 

(Appendix 8) and SCI=30 for comparison.

As for TP, the equations in Table A10–5 can be used to estimate the percent of sites scoring 

below a specific SCI threshold for any given TN concentration. This was done for each of the 

final regional TN criteria using the binomial equations above using the all habitats models. 

Anywhere from 31 percent to 50 percent of sites score less than SCI of 40 at the final TN 

criteria, on average as predicted with these binomial models (Table A10–6). This further reflects 

the observations that harmful biological impacts are expected around the range of final TN 

criteria concentrations across regions in Florida. 
EPA Proposed Rule TSD for Florida’s Streams and DPVs for Lakes

A-35 

Table A10–6. Percent of sites expected to score below SCI thresholds of 30, 40, 46, and 50 based on the 

binomial regression models in Table A10–5 and using the final TN criteria. 

EPA TN Criteria (mg/L)

Panhandle 

West

Panhandle 

East

North 

Central

West 

Central Peninsula

0.67 1.03 1.87 1.65 1.54

Probability

SCI<30 17% 23% 32% 30% 29%

SCI<40 31% 38% 50% 47% 46%

SCI<46 39% 48% 60% 57% 56%

SCI<50 45% 55% 67% 65% 63%

For comparison, predicted TN values estimated from the binomial regression models for all four 

SCI thresholds when the probability of exceeding the thresholds was equal to 0.5 or greater using 

all habitat conditions was plotted along with thresholds from the linear regression models (Figure 

A10–27. TN concentrations associated with SCI thresholds from the binomial regression models 

were similar to those from the linear regression model except for the SCI = 40 threshold, which 

resulted in lower TN for the binomial regression model (1.90 mg/L) versus the linear regression 

model (2.08 mg/L). 
EPA Proposed Rule TSD for Florida’s Streams and DPVs for Lakes

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Figure A10–27. SCI versus Ln(TN) (2006–2011 data) for all regions combined (Figure A10–21) with Ln(TN) 

solved for SCI values of 30, 40, 46, and 50 based on linear regression models (solid black lines) and binomial 

regression models (dashed blue lines) for all habitat scores combined. Also shown are the Florida stream 

TN criteria values (in red with arrows) for each nutrient region. The linear regression fit is shown (solid red 

line) along with 75th percentile prediction intervals (red dashed lines). PHW: Panhandle West, PHE: 

Panhandle East, PEN: Peninsula, NC: North Central, WC: West Central, Panhandle West: pink closed circle; 

Panhandle East: green open triangle; Peninsula: black closed square; North Central: red open diamond; 

West Central: gray closed diamond.

Once again, the same data presented in Figure A10–27 as individual samples are provided as box 

plots of SCI scores by TN concentration bins as in Figure A10–22, but this time with the 

binomial thresholds also plotted (Figure A10–28). The binomial thresholds are similar to the 

linear regression thresholds and further demonstrate the conclusion that biological condition 

worsens with increasing nutrient concentrations and that adverse biological responses, as defined 

by FDEP’s reference site SCI percentiles, occur at TN concentrations consistent with the EPA 

nutrient criteria.
EPA Proposed Rule TSD for Florida’s Streams and DPVs for Lakes

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Figure A10–28. SCI versus Ln(TN), 2006–2011 data, represented as box plots of SCI scores by TN 

concentration bins. Center boxes are means, box margins are 1 standard error of the mean, and whiskers 

are 95 percent confidence intervals around mean SCI scores for each TP bin. The blue shaded region is the 

2.5th to 10th percentile of SCI reference site scores, which are values of 40 and 50, respectively. Also shown 

are the solutions of TN concentration for SCI scores of 30, 40, 46, and 50 using the linear regression model 

from Figure A10–21 (vertical black lines) and binomial regression models from Figure A10–23 through 

Figure A10–26 (blue dashed lines) of Ln(TN) vs. SCI score for all regions combined and all habitat scores as 

well as the Florida stream TN criteria values for each nutrient region (red arrows).

References

FDEP (Florida Department of Environmental Protection). 2009. Technical Support Document: 

Development of Numeric Nutrient Criteria for Florida Lakes and Streams. Florida 

Department of Environmental Protection, Tallahassee, FL. June 2009. 

<http://www.dep.state.fl.us/water/wqssp/nutrients/docs/tsd_nutrient_crit.docx>. Accessed 

September 6, 2010. 
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Appendix 11. Data Supporting EPA’s Default DPV Derivation Using 

Regional Regression Models

Appendix 11 contains the data EPA used to derive numeric nutrient criteria for Florida streams 

and to conduct the analyses demonstrating harmful, adverse effects. It is being provided in 

electronic form on CD. A copy of the CD can be obtained by contacting EPA’s docket office. 

The docket identification number is EPA-HQ-OW-2009-0596. 

The Office of Water (OW) Docket Center is open from 8:30 a.m. to 4:30 p.m., Monday through 

Friday, excluding legal holidays. The OW Docket Center telephone number is 202-566-1744 and 

the Docket address is OW Docket, EPA West, Room 3334, 1301 Constitution Ave., NW, 

Washington, DC 20004.