EPA Proposed Rule TSD for Florida’s Strea ms and DPVs for Lakes

i 

## T

echnical Support Document for EPA’s Proposed Rule for Numeric Nutrient Criteria to Protect Florida’s Streams and the Downstream Protection of Unimpaired LakesEPA Proposed Rule TSD for Florida’s Strea ms and DPVs for Lakes

ii 

### 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 regu lations;
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 Strea ms and DPVs for Lakes

ii i 

### 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 Wh itlock, 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 Strea ms and DPVs for Lakes

iv 

### Contents

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

— Bio logically 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 Downstre am 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 .....................................................................................................................111EPA Proposed Rule TSD for Florida’s Strea ms and DPVs for Lakes

v 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 1 1. Data Supporting EPA’s Default DPV Derivation Using Regional Regression Models .......................................................................................... A-38EPA Proposed Rule TSD for Florida’s Strea ms and DPVs for Lakes

vi 

### Tables

Table 1

- 1. Distributional statistics (mean, median, 75 th and 90 th 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 o f 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 hab itat 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. ....................................................................................................................110EPA Proposed Rule TSD for Florida’s Strea ms and DPVs for Lakes

vii 

### 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 bounda ries 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 T P, 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. .............................................32EPA Proposed Rule TSD for Florida’s Strea ms and DPVs for Lakes

viii 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 Ben chmark Population before (A) and after (B) screening. ...............34

Figure 1 -20. Plot of TP concentrations among Benchmark reference WBIDs a cross 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 conc entrations 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. .............................65EPA Proposed Rule TSD for Florida’s Strea ms and DPVs for Lakes

ix 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 (upp er 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 b inomial 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 endpoin ts associated with adverse floral and faunal responses in streams from literature review. ..............................................................88EPA Proposed Rule TSD for Florida’s Strea ms and DPVs for Lakes

x Figure 1 -56. Frequency of TN endpoin ts 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. ...................................................................................109EPA Proposed Rule TSD for Florida’s Strea ms and DPVs for Lakes

xi 

### Acronyms

AGM annual geometric mean

BCG Biological Condition Gradient

BPJ B est P rofessional J udgment

CAF Coastal Assessment Framework

CDA C oastal D rainage A rea 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 L andscape D evelopment I ndex

NHD National Hydrography Dataset

NO 2 nitrite

NO 3 nitrate

NOAA National Oceanic and Atmospheric Administration

NPDES National Pollutant Discharge Elimination System

NWR N utrient W atershed R egion

RBZW riparian buffer zone width measure
RPS R apid P eriphyton S urvey SCI S tream C ondition I ndex

SOP standard operating procedure

TKN total Kjeldahl nitrogen

TMDL total maximum daily load

TN total nitrogen

TP total phosphorus

TSD technical support document

WBID water body identification numberEPA Proposed Rule TSD for Florida’s Strea ms and DPVs for Lakes

xii 

### 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 Florid a’s Lakes and Flowing Waters; Final Rule

, EPA

- HQ

- OW -2009-0596, FRL-9228-7 ; herea fter 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 C lean W ater A ct (CWA ) (Section 303(c)) and i ts 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

- balan ced 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 (stream s and downstream protection of unimpaired lakes) for the purposes
of the CWA

.EPA Proposed Rule TSD for Florida’s Strea ms and DPVs for Lakes

xiii 

### Foreword

Excess nitrogen and phosphorus loadings, or nutrient pollution, are a leading cause of water quality degradation in the N ation’s waters. Nutrient pollution may result in harmful algal blooms, reduced spawning grounds and nursery habitats, fish ki lls, and oxygen

- starved hypoxic
or “dead” zones, as well as public health concerns related to impaired drinking water sour ces 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 N ation’s surface waters and are the cornerstone of the water quality -based control program mandated by the C lean W ater A ct (CWA)

. Water quality standards describe the desired condition of a waterbody and consist of the following thr ee princi pal elements: (1) the designated

uses of the state’s waters (e.g., fishing, aqua tic 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 P opulation 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 t otal m aximum d aily 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.

Aq uatic 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 nutrientEPA Proposed Rule TSD for Florida’s Strea ms and DPVs for Lakes

xiv 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). E PA 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 parameter s due to insufficient scientific evidence to support a precise threshold for n umeric nutrient criteria (e.g., chl -a for streams), EPA or the S tate may consider deriving criteria in the future for those parameters.EPA Proposed Rule TSD for Florida’s Strea ms and DPVs for Lakes

1 

### 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 ecos ystems , 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 n itrogen [ 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). S patial and temporal variability in environmental conditions influence the biological responses to excess nutrients. As a result,
adverse effects ca n 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 nutri ents. 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’sEPA Proposed Rule TSD for Florida’s Strea ms 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 genera l 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., Conl ey 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 t he 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 th e 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 nume ric 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 fl owing

waters in compliance with the Court’s order. S pecifically, 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 Strea ms 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
ch anges 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

• Clas s 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 Strea ms and DPVs for Lakes

4 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 i nto 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

stream s using two populations of data to represent nutrient concentrations in Flo rida 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 Strea ms 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 D epartment of Environmental Protection (FD EP ) (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 leas t-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 Populat ion) 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 score s 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 stres sor-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 n utrient 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 wit h 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 c riteria 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 chemist ry 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
ben chmark or reference condition approach will be appropriately supportive of balanced natural populations of flora and fauna inhabiting the different classes. EPA nutrient criteria guidanceEPA Proposed Rule TSD for Florida’s Strea ms and DPVs for Lakes

6 recommends classification as an important step in developing protect ive 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 deve lopment. 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 ac ross bioregions indicated that the bioregions were not sufficiently homogenous with Legend

Hawthorn GroupEPA Proposed Rule TSD for Florida’s Strea ms 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 s tream 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 20 th 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 Strea ms and DPVs for Lakes

8 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 inform ation system ( GIS ) site 4 . 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 nutrie nt 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 Admini stration (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 2010EPA Proposed Rule TSD for Florida’s Strea ms and DPVs for Lakes

9 Table 1-1 . Distrib utional statistics (mean, median, 75 th and 90 th 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 75 th
%tile 90 th
%tile Count Mean Median 75 th
%tile 90 th
%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 Strea ms and DPVs for Lakes

10 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 s tream chemistry there ( Figure 1-6 ). As noted above, whenclassifying 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 Centr al 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 . E PA’s proposed NWRs for classifying Florida streams (U.S. EPA 2010a).EPA Proposed Rule TSD for Florida’s Strea ms 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 regio ns explained more variability in TP concentrations than the original four stream bioregion model ( Table 1-2 ). 

## Legend

Hawthorn G roup
EPA NWR 4 Regions ( E PA Propos al )EPA Proposed Rule TSD for Florida’s Strea ms and DPVs for Lakes

12 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 R 2

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 R 2

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 in to 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 bo undaries 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 S arasota 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 Charl otte 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 Strea ms 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 Strea ms 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 TributariesEPA Proposed Rule TSD for Florida’s Strea ms and DPVs for Lakes

15 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 Strea ms 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 re

proposed 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 o ut 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 mad e 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 Strea ms and DPVs for Lakes

17 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)

. 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 geo logical 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 rel atively 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 numeri c 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 a nthropogenic 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 crite ria.EPA Proposed Rule TSD for Florida’s Strea ms and DPVs for Lakes

18 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 th e 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 cri teria is still relevant, so the technical
material related to deriving the proposed criteria using the reference condition approach i s 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 analyse s has been added.

EPA used the reference condition approach for deriving i nstream 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 se rve 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 screeni ng 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 screeni ng 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 RETr i eval)
database and FDEP’s Status and Trends da tabase (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 ). Thi s 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 Strea ms and DPVs for Lakes

19 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 pu rposes). 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 (NO 3 /NO 2 ) concentrations. TN was calculated by summing total Kjeldahl nitrogen (TKN-N) and nitrate/nitrite (N O 3 /NO 2 - 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 value s 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 Strea ms and DPVs for Lakes

20 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

–– L east 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
descr ibed 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 ev aluation 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 theTP (mg/L)Ln(TP,mg/L)



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



### QuantilesEPA Proposed Rule TSD for Florida’s Strea ms and DPVs for Lakes

21 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 wat ers 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 condition s, 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 a verage 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 concentrati ons 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 S core 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 e cological 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 nu trient 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 Strea ms 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: 

# ∑ × = )%( i i Total 

### LU LDC LDI
where,

LDI Total = LDI for the area of influence
%LU i = percent of total area of influence in land use i LDC i = 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 Strea ms and DPVs for Lakes

23 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

an d 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 show n 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.EPA Proposed Rule TSD for Florida’s Strea ms and DPVs for Lakes

24 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 o ther 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 S ection 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 WBID s 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 ( Fe deral 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

wate rshed. 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 Strea ms 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 potentiall y 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 e stuarine 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 fres h 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 consid erations, 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 b enchmark sites as representative of least

- disturbed conditions. Representative sites within t he 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 hy drologic 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 3100 7 [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 Strea ms and DPVs for Lakes

26 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 ) foll owing 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. N ote 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 t o develop a set of reference sites with greater assurance that these sites represented least -disturbed conditions:EPA Proposed Rule TSD for Florida’s Strea ms and DPVs for Lakes

27 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 numerica l 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 co mmunities.

f. Elimination of S ites 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

- field 8 and watershed 9 (or drainage basin) average LDI scores for each of theremaining 137 candidate Benchmark WBIDs. For those WBIDs with average watershed or nearfield LDI values above 3.0— the value that was correlated with a corrido r 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 app roach 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 can didate 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 Strea ms and DPVs for Lakes

28 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 B iologically Unhealthy S ite L ist
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 I mpaired WBIDs

EPA excluded sites located within WBIDs l isted 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

An y 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 th e 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 b iologically 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 c riteria calculation for the SCI Population (see Appendix 3 Data Supporting EPA’s Reference Approach for Deriving Numeric Nutrient Criteria for Florida Streams ).EPA Proposed Rule TSD for Florida’s Strea ms and DPVs for Lakes

29 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 1-15 B through Figure 1-18 B ) 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 1-19 B through Figure 1-22 B) 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 Strea ms and DPVs for Lakes

30 

## 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 cumul ative percent frequencies of TN concentrations at Benchmark sites.EPA Proposed Rule TSD for Florida’s Strea ms and DPVs for Lakes

31 

## 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 Strea ms and DPVs for Lakes

32 

## 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 Strea ms and DPVs for Lakes

33 

## 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 Strea ms and DPVs for Lakes

34 

## 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 Strea ms and DPVs for Lakes

35 

## 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 Strea ms and DPVs for Lakes

36 

## 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 Strea ms and DPVs for Lakes

37 

## 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.EPA Proposed Rule TSD for Florida’s Strea ms and DPVs for Lakes

38 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 scal es 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 bel ow.

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 semi

variance associated with different separation distances usin g the following equation:



## ∑

= −+ = )(

1 2

))()(( )(2 1 )( hn

i i i x TP hx TP hn h γ

w here

,
γ(h) = the semi

- variance for a separation distance of h n(h) = t he number of samples separated by h

x i = t he location of site i TP() = the geometric mean concentr ation at the indicated location

, x i

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 Flor ida 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 nutrientEPA Proposed Rule TSD for Florida’s Strea ms 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

- si te 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 multipleannual 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 Strea ms 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 90 th percentile of annual geometric mean concentrations in WBID s from each NWR (Panhandle East and West, North Central, and Peninsula nutrient regions) using the following equation:

90 th Percentile = exp[ mean(Ln(nutrient concentration)) + 1.282 SD (Ln(nutrient concentration))]

The criterion for the West Central NWR was calculated as the 75 th 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 75 th percentile is the following:

75 th 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 approx imately half of the reference sites are not attaining their uses. Alternatively, the upper end of the distribution (e.g., the 90 th percentile) is appropriate if there is confidence that the distribution truly reflects minimally impacted reference condition s 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 Strea ms 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 90 th percentile of WBID annual averages from this population to establish the final
rule cr iteria 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 75 th 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 th e Panhandle and the remainder of the state. However, there was no loss of variance explained by afive NWR model over a two region model. This result combined with the potential for colimitation of nutrients, the similarity of nitrogen concentrations with in 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 long

term 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 Strea ms 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) 75 th (mg/L) 90 th (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) 75 th (mg/L) 90 th (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 75 th and 90 th percentiles, and sample size of TP and TN distributions are presented for each NWR. EPA is utilizing the 90 th percentile of the Benchmark population (computed as a WBID average), with the exception of the West Central for which EPA is utilizing the 75 th 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 Strea ms 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 25 th and 75 th percentiles are indicated by the lower and upper horizontal lines of the box, respectively; the 10 th and 90 th percentiles are indicated by the lower and upper whiskers, respectively; and the 5 th and 95 th 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 90 th percentile of the distribution for the Panhandle West, Panhandle East, North Central, and Peninsula NWRs (light red) a nd the SCI Population using WBID averages at the 75 th 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 Strea ms 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 25 th and 75 th percentiles are indicated by the lower and upper horizontal lines of the box, respectively; the 10 th and 90 th percentiles are indicated by the
low er and upper whiskers, respectively; and the 5 th and 95 th 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 90 th percentile of the distribu tion for the Panhandle West, Panhandle East, North Central, and Peninsula NWRs (light red) and the SCI Population using WBID averages at the 75 th percentile of the distribution for the West Central NWR (light yellow).



### 1.4 Defining Healthy Aquatic Life in Flori da 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 withLn(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 Strea ms and DPVs for Lakes

45 other data, to determine the particular nutrient c oncentrations 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

s ources 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., floo d, 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
receiv ed 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 observin g 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, biolo gical 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 Strea ms 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, i n 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, whe re 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 b iological m etrics summarize different characteristics of the biological assemblage. M etrics 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

sc ores from reference sites wa s 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 di stinguishes 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 conditi ons 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 2.5 th 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 harm ful imbalance , such as the 5 th , 10 th , or even 25 thEPA Proposed Rule TSD for Florida’s Strea ms 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 t he 5 th and 10 th percentiles were estimated to be 46 and 50 ( Table 1-5 ). SCI values below these represent additional adverse imbalance or harmfu l change thresholds based on the population of reference biological conditions FDEP uses as its standard for acceptable balance.

Table 1-5

. Results of i nterval and e quivalence t ests c onducted on r eference s ites with t wo SCI r esults

.

Threshold (Description) Reference Site Mean Threshold (Numeric) Different from Reference Undetermined Similar to Reference

2.5 th percentile of reference 65 40 <35 35

– 44 >44

5 th percentile of reference* 65 46 <41 41

– 49 >49

10 th percentile of reference* 65 50 <47 47

– 53 >53

Note: shown are site mean, threshold, and range for threshold values defined at the 2.5 th 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 App endix 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 Sit e 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 eq uivalence 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, Fi gure 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

• S ensitive and rare taxa

• S ensitive

, but ubiquitous taxa

• T axa of intermediate tolerance

• T olerant taxaEPA Proposed Rule TSD for Florida’s Strea ms and DPVs for Lakes

48 • Nonnative taxa

• Organism condition

• E cosystem functions

• S patial and temporal extent of detrimental effects

• E cosystem 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 struc ture 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 Strea ms 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.

Th e 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 site s 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 r epresenting healthy biological conditions with the probability ofEPA Proposed Rule TSD for Florida’s Strea ms 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 t ier a ssignments m odeled with a p roportional o dds logistic r egression

.

1.4.5 Selecting the Threshold between Healthy and Unhealthy Biological Conditions

Weighing these multiple lines, EPA selected an SCI va lue 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 od ds 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 thethreshold, 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 An alysis of Biological Data in the Benchmark Population

As described previously, the SCI is negatively correlated with LDI, so biological health decreases in response to increas ing 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 Strea ms and DPVs for Lakes

51 Figure 1- 29 . Distribution of SCI across the r ange of c orridor LDI s cores ( FDEP 2009)

.

Figure 1- 30 . Average SCI s cores Benchmark Population p lotted against c orridor LDI s cores ( 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 2.4Average SCI Score

LDI Score 

### Benchmark Site LDI vs. Average SCI ScoreEPA Proposed Rule TSD for Florida’s Strea ms 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 o f 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
chemi stry . 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 existin g 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 re sponse 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 macroinverteb rate 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 statistical ly 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 eff ects 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 b asis 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 significa nce 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 ofEPA Proposed Rule TSD for Florida’s Strea ms 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 a ppendices. 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 follo ws: “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 composit ional 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 con ducted 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 Appendi x 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 relationsh ips between nutrients and periphyton thickness were much stronger in clear streams [ Figure 6

- 5 a1 -a3, ( EPA Figure 1-31 a1-a3 below )] than in colored streams [ Figure 6

- 5 c1

-EPA Proposed Rule TSD for Florida’s Strea ms and DPVs for Lakes

54 c3, ( EPA Figure 1-31 c1

- 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 Strea ms 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 measu res 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 natu ral 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 o f 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

sum marized 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 Strea ms 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 r 2

= 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 0.022EPA Proposed Rule TSD for Florida’s Strea ms 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 (MD Ls 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 extrem ely 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 increa sed 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 VariableEPA Proposed Rule TSD for Florida’s Strea ms 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 macro invertebrate 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 pr esented 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 Solubl e Inorganic Nitrogen (ammonia plus nitrate

- nitrite) (Figure 6

- 1 from FDEP 2009).EPA Proposed Rule TSD for Florida’s Strea ms and DPVs for Lakes

59 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 dataset s 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 t hree different files representing differe nt time pe riods and resolving sa mple 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—e stimated value but no indication how or why, Q—held beyond acceptable holding time, O—

s ample lost

.

2. Values with the following codes were retained: U —value is the laboratory detection limit, I

— the reported value is between the la boratory 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 i ndicate d 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

t ransformed (L n) 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 fina l 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 sample d 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 2012EPA Proposed Rule TSD for Florida’s Strea ms and DPVs for Lakes

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 s cores. 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 ScoreSCI2007

0 20 40 60 80 100 120 140 160 0 20 40 60 80 100 120EPA Proposed Rule TSD for Florida’s Strea ms and DPVs for Lakes

61 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 120SCI2007EPA Proposed Rule TSD for Florida’s Strea ms and DPVs for Lakes

62 Figure 1- 36 . SCI versus Ln(TP), 2006

– 2011 data, relationship for all regions com bined

. 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 Strea ms 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 Cent ral: gray closed diamond, gray
line.

EPA’s analysis indicate d 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 Strea ms and DPVs for Lakes

64 1.5.2.2 Results

— SCI Responses to Nutrients in Scre ened 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 macroinverteb rates 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. T hinner 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 t he values of RBZW for the left bank and right bank into one overall score. T he residuals of SCI vs. L n(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 1 6 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 2012EPA Proposed Rule TSD for Florida’s Strea ms 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 bet ter 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 adequat e number of pools. Based o n this result

, EPA subsequently screened for samples with habitat smothering

scores g reater than 1 3 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 60SCI2RES1EPA Proposed Rule TSD for Florida’s Strea ms and DPVs for Lakes

66 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

e xacerbate 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 60SCI2RES1EPA Proposed Rule TSD for Florida’s Strea ms and DPVs for Lakes

67 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 2 006–201 1 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 re sponse 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 60SCI2RES1EPA Proposed Rule TSD for Florida’s Strea ms and DPVs for Lakes

68 PHW PHE WC PEN NC

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: Panhan dle East, PEN: Peninsula, NC: North Central, WC: West Central.EPA Proposed Rule TSD for Florida’s Strea ms and DPVs for Lakes

69 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: Pen insula, 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 ).EPA Proposed Rule TSD for Florida’s Strea ms and DPVs for Lakes

70 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)0.50.60.70.80.91.02.03.04.05.06.07.08.09.010.020.030.040.0

ChlCorr 2 4 6 8 10 12 14DOFieldEPA Proposed Rule TSD for Florida’s Strea ms and DPVs for Lakes

71 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 75 th 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 100SCI2007EPA Proposed Rule TSD for Florida’s Strea ms and DPVs for Lakes

72 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 nutrie nts 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)0.50.60.70.80.91.02.03.04.05.06.07.08.09.010.020.030.040.050.0

ChlCorr 10 20 30 40 50 60 70 80 90 100SCI2007EPA Proposed Rule TSD for Florida’s Strea ms and DPVs for Lakes

73 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 75 th 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 100SCI2007EPA Proposed Rule TSD for Florida’s Strea ms 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 3 0 (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 RB ZW, 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 Strea ms and DPVs for Lakes

75 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 75 th 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 100SCI2007EPA Proposed Rule TSD for Florida’s Strea ms and DPVs for Lakes

76 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 S CI 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 ).EPA Proposed Rule TSD for Florida’s Strea ms and DPVs for Lakes

77 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 95 th
CI Intercept 95 th
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 95 th
CI Intercept 95 th
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 2.5 th , 5 th , and 10 th pe rcentile 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 χ 2 p Mean TP (mg/L) at p = 0.5 95 th
CI Intercept 95 th
CI Slope

SC I > 3 0

- 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 χ 2 p Mean TN (mg/L) at p = 0.5 95 th
CI Intercept 95 th
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 2.5 th , 5 th , and 10 th percentile of reference site SCI scores (Appendix 8) and for SCI = 30 for comparison

.EPA Proposed Rule TSD for Florida’s Strea ms and DPVs for Lakes

78 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 chan ge ( 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 75 th 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 Strea ms and DPVs for Lakes

79 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 te xt

. 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

2.5 th to 10 th percentile of SCI reference site scores, which ar e 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 dashe d 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).EPA Proposed Rule TSD for Florida’s Strea ms and DPVs for Lakes

80 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 smotheri ng (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 75 th percentil e 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 Strea ms and DPVs for Lakes

81 Figure 1- 53 . SCI versus Ln(TN), 2006

– 2011 data, represented as box p lots 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

2.5 th to 10 th percentile of SCI reference site scores, which are values of 40 and 50, respectively. Also shown
are the solutions of TN concentration for S CI 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 condition s (SCI score less than 40) increase above 35–53 percent based on TP and 31–
50 percent based on TN.EPA Proposed Rule TSD for Florida’s Strea ms and DPVs for Lakes

82 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 2.5 th , 5 th , and 10 th 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 betw een 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. Furt hermore, 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).EPA Proposed Rule TSD for Florida’s Strea ms and DPVs for Lakes

83 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 range s, and predicting the mean response across the full ensemble of trees, given observed values for all of the other variables.

Fi gure 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

concen trations. 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 A nalysis S ummary 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 measure d 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 i nvertebrate 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, w hile again producing a variable response curve, concentrations consistently at or above those derived using the referenceEPA Proposed Rule TSD for Florida’s Strea ms and DPVs for Lakes

84 condition approach were identified as associated with adverse or harmful change in invertebrate condition using the FDEP and EPA SCI t argets.

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 protect ive of natural populations of aq uatic 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 the ir 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 Str eams 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 measure s of biological response consistent with the conceptual model. EPA conducted a search of the scientific literature based o n 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 o r 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 stream s from the literature review is pres ented in Table 1-11

.EPA Proposed Rule TSD for Florida’s Strea ms and DPVs for Lakes

85 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 ( μ g/L) TP ( μ g/L) Citation

Multiple Stream oligo

- mesotrophic boundary 285

- 375 23

- 29 Dodds (2006) Stream meso

- eutrophic boundary 659

- 714 48

- 71

Multiple Chl a < 200 mg/m 2 <3000 <400 Dod ds and Welch (2000) Chl a of 50 mg/m 2 (< 100 mg/m 2 most of the time) 470 60

Multiple Stream oligo

- mesotrophic boundary (60 mg/m 2 chl a max) 700 25 Dodds et al. (1998)

Stream meso

- eutrophic boundary (200 mg/m 2 max) 1500 75

Montana 50 mg/m 2 mean Chl a target 450

- 470 62

- 65

Dodds et al. (1997) 50 mg/m 2 max Chl a target 115

- 145 16

- 20

100 mg/m 2 mean Chl a target 1423

- 1600 197

- 221

100 mg/m 2 max Chl a target 252

- 275 35

- 38

200 mg/m 2 mean Chl a target 3000

- 7570 415

- 1020

200 mg/m 2 max Chl a target 650 90

Florida L yngbya wollei 110 ( NO 3 – N ) Albertin (2009)

Florida Lyngbya wollei 230 ( NO 3 – N )

250 28 ( PO 4 - P )

33 Stevenson et al. (2007)

Florida Vaucheria sp. 261 ( NO 3 –N)

284 22 ( PO 4 -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 King et al. (2009 ) Consistent nuisance algal growth and loss of algal and 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 )EPA Proposed Rule TSD for Florida’s Strea ms and DPVs for Lakes

86 Study Location Response TN ( μ g/L) TP ( μ g/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 25 U.S. EPA (2000) Stream meso

- eutrophic boundary 1500 75

Wisconsin Invertebrate response 90 Wang 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 10 VWRRC (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 Strea ms and DPVs for Lakes

87 Study Location Response TN ( μ g/L) TP ( μ g/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

Chambers et al. (2008) Suspended chl a 945 21

Benthic chl a < 100 mg/m 2 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)EPA Proposed Rule TSD for Florida’s Strea ms and DPVs for Lakes

88 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 natural ly 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 f or 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
de riv e d 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 o ne value fro m the reviewed literature on TN concentrations associated with adverse biological response.EPA Proposed Rule TSD for Florida’s Strea ms and DPVs for Lakes

89 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 mad e. 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
Sec tion 1.5 ( EPA

- HQ

- OW -2009-0596).EPA Proposed Rule TSD for Florida’s Strea ms and DPVs for Lakes

90 

### 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 classifi cation, 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 classific ations. 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 comp ared 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 design ated 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 Dat a 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 d esignated 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 conside red in attainment of the narrative criterion.EPA Proposed Rule TSD for Florida’s Strea ms 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 Ba y 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/Pithlachascote e 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 Waters hed.

* 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 1 0km upstream of the sample site.

2. Not in WBIDs listed on the EPA -approved Florida CWA section 303(d) impaired waters
list for nu trients and/or DO

. 3. Average nitrate/nit rite 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 lea st

- impacted sites for
nutrients.

5. Not within WBIDs w ith average SCI scores < 40.

6. Watershed or near

- field LDI scores < 3.EPA Proposed Rule TSD for Florida’s Strea ms 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 30 3(d) impaired waters
list for nu trients 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 90 th percentile of TN and TP concentrations in the Benchmark Population for Panhandle West, Panhandle East, North Central, and Peninsula regions. The 75 th 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 fro m 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 t o 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 t he CWA

.



### 1.9 References

Albertin, A. 2009. Nutrient Dynamics in Florida Springs and Relationships to Algal Blooms

. Doctoral Dissertation, University of Florida, Gainesville, FL.

Beck, W.M. 1955. Suggested method for reporting biotic data. Sewage and Industrial Wastes 27(10):1193–1197.

Biggs, B.J.F. 2000. Eutrophication of streams and rivers: Dissolved nutrient–chlorophyll relationships for benthic algae. Journal of the North American Benthological Society
19:17–31.

Black, R.W., P.W. Moran, and J.D. Frankforter. 20 11. Responses of algal metrics to nutrients and physical factors and identification of nutrient thresholds in agricultural streams. Environmental Monitoring and Assessment 175:397–417EPA Proposed Rule TSD for Florida’s Strea ms and DPVs for Lakes

93 Bothwell, M.L. 1985. Phosphorus limitation of lotic periphyton growth rates: A n intersite comparison using continuous-flow troughs (Thompson River system, British Columbia). Limnology and Oceanography 30:527–542.

Bourassa, N., and A. Cattaneo. 1998. Control of periphyton biomass in Laurentian streams (Quebec). Journal of the North American Benthological Society 17:420–429.

Bowling, L.C., and P.D. Baker. 1996. Major cyanobacterial bloom in the Barwon- Darling River, Australia, in 1991, and underlying limnological conditions. Marine and Freshwater
Research 47:643–657.

Breiman, L. 2001. Random Forests. Machine Learning 45:5–32.

Brown, M.T., and K.C. Reiss. 2006. Proposed Breakpoint of LDI < 2.0 for Determining Minimally Affected Reference Conditions for Water Bodies . Technical report submitted to the Florida Department of Environmental Protection, and University of Florida, Center for Environmental Policy, Department of Environmental Engineering Sciences, Gainesville, FL.

Brown, M.T., and M.B. Vivas. 2003. A Landscap e Development Intensity Index . Technical report submitted to the Florida Department of Environmental Protection. University of Florida, Center for Environmental Policy, Department of Environmental Engineering Sciences, Gainesville, FL.

Brown, M.T., and M.B . Vivas. 2005. Landscape development intensity Index. Environmental Monitoring and Assessment 101:289–309

Carleton, J.N., R.A. Park, and J.S. Clough. 2009. Ecosystem modeling applied to nutrient criteria development in rivers. Environmental Management 44:485–92.

Cathcart, J.B. 1985. Economic g eology of the l and -p ebble phosphate d istrict of Florida and its
southern e xtension. In Florida Land-Pebble Phosphate District: Geological Society of America Annual Meeting, field trip guidebook , eds. Cathcart, J.B., and Scott, T.M., Orlando, FL. pp. 4–27.

Chambers, P.A., G.A. Benoy, R.B. Brua, and J.M. Culp. 2011. Application of nitrogen and phosphorus criteria for streams in agricultural landscapes. Water Science and Technology 64(11):2185–91.

Chambers, P.A., C. Vis, R.B. Brua, M. Guy, J.M. Culp, and G.A. Benoy. 2008. Eutrophication of agricultural streams: defining nutrient concentrations to protect ecological condition. Water Science and Technology 58(11):2203–10

Conley, D.J., H.W. Paerl, R.W. Howarth, D. F. Boesch, S.P. Seitzinger, K.E. Havens,
C. Lancelot, and G.E. Likens. 2009. Controlling eutrophication: Nitrogen and phosphorus. Science 323:1014–1015.EPA Proposed Rule TSD for Florida’s Strea ms and DPVs for Lakes

94 Cross, W.F., J.B. Wallace, A.D. Rosemond, and S.L. Eggert. 2006. Whole- system nutrient

enr ichment increases secondary production in a detritus- based ecosystem. Ecology
87:1556–1565.

Cutler, D.R., T.C. Edwards, K.H. Beard, A. Cutler, K.T. Hess, J. Gibson, and J.J. Lawler. 2007.
Random f orests for c lassification in ecology. Ecology 88:2783–2792.

Davies, T.T. 1997. Establishing Site Specific Aquatic Life Criteria Equal to Natural Background

. Memorandum to Water Division Directors, Regions 1-10, State and Tribal Water Quality Management Program Directors. U.S. Environmental Protection Agency , Offic e of Water, Office of Science and Technology.

Davies, S.P., and S.K. Jackson. 2006. The biological condition gradient: A descriptive model for interpreting change in aquatic systems. Ecological Applications 16:1251–1266.

Dodds, W.K. 2006. Eutrophication and trophic state in rivers and streams. Limnology and Oceanography 51(1):671–680

Dodds, W.K., and D.A. Gudder. 1992. The ecology of Cladophora. Journal of Phycology
28:415–427.

Dodds, W.K., V.H. Smith, and B. Zander. 1997. Developing nutrient targets to control benthic chlorophyll levels in streams: A case study of the Clark Fork River. Water Research 31(7):1738–1750.

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100 

### 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 producti vity, 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 tr ophic 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 eutro phication, 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 Strea ms and DPVs for Lakes

101 EPA recognizes that nu trient 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 ; Guild ford 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 lak es (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 pre sented 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 no t 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 concen trations 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 downstr eam 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 Strea ms 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 Color a
and Alkalinity Chl

- a (mg/L) b, * TN (mg/L) TP (mg/L)

Colored Lakes c 0.020 1.27 [1.27

– 2.23] 0.05 [0.05

– 0.16]
Clear Lakes, High Alkalinity d 0.020 1.05 [1.05

– 1.91] 0.03 [0.03

– 0.09]
Clear Lakes, Low Alkalinity e 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 CaCO 3

e Long

- term Color ≤ 40 PCU and Alkalinity ≤ 20 mg/L CaCO 3
* 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 standa rds 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 b eyond 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 Strea ms and DPVs for Lakes

103 EPA believes BATHTUB is appropriate for simplified DPV calculations because BATHTUB

can repre sent 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 protecti on 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 downs tream lakes. While BATHTUB is a peer

reviewed 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

- specifi city.

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, W ASP 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 finaliz ed 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. I n 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 theEPA Proposed Rule TSD for Florida’s Strea ms 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 am bient 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. Whenproperly 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 int ernal 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 Strea ms 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 ea ch 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). T he model can also be run in a “steady

state” 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 Strea ms 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 la ke 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 ModelsThe 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 annu al 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 layer 12 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 2012EPA Proposed Rule TSD for Florida’s Strea ms and DPVs for Lakes

107 waterbodies for these locations were intersected with NHD Plus medium resolution flowlines 13 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 la ke 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 lake s 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 nutrie nt 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 la ke 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:

TP Up

, ij = b 1, i + b 2, i TP Lake

, ij

where:

TP Lake

, ij = the annual geometric lake concentration for lake i and year j

TP Up

, 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 2012EPA Proposed Rule TSD for Florida’s Strea ms and DPVs for Lakes

108 The coefficients that define the linear relationship are allowed to vary among lakes as follows:

b 1, i = β 1 + B 1, i

b 2, i = β 2 + B 2, i

Where:

β 1 and β 2 = the average coefficient values across all lakes

B 1, i and B 2, 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 Strea ms and DPVs for Lakes

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

- lake concentrations
of TP and TN . Op en 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 dischar ging 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 a nd 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 moreEPA Proposed Rule TSD for Florida’s Strea ms 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 standard s . To the extent that it is determined that the def ault 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 colore d 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 upstr eam 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 90 th 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 Strea ms 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 support ed 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 an alyses 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 a llowing 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 uni mpaired 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.



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Wool, T.A., R.B. Ambrose, J.L. Martin, and E.A. Comer. 2001. Water Quality Analysis Simulation Program (WASP), Version 6.0, Draft: User’s Manual . U.S. Environmental Protection Agency, Region 4, Atlanta, GA.

Yoko, S., R. Axler, M. McDonald, and D. Wilcox. 1997. Recovery of a mine pit lak e from aquacultural phosphorus enrichment: model predictions and mechanisms. Ecological Engineering 8(3 ):195–218.

## Technical Support Document for EPA’s Proposed Rule for Numeric Nutrient Criteria to Protect Florida’s Streams and the Downstream Protection of Unimpaired Lakes

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

A-1 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 D ocket, 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 (EPA

HQ

- 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 E PA

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

A-2 

### Appendix 4. FDEP’s Method for Calculating a Corridor

- based Landsca pe 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 Cente r 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 Ap pendix is cross-referenced with “Appendix A4. FDEP’s Method for Calculating aCorridor-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
Fr iday, 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 ofBiomonitoring 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

A-3 

### Appendix 7. FDEP’s Site Informat ion 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) D ocket 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 Equival ence 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 throu gh 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.EPA Proposed Rule TSD for Florida’s Streams and DPVs for Lakes

A-4 

### Appendix 10. Harmful, Adverse Effects of Nutrients on Stream Macroinvertebrates in Florida (Supplemental Analyses)

SCI vs. TP: Ha bitat 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

tre nds 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 dia mond, 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 120SCI2007 LnTP:SCI2007: y = 39.8669 - 4.013*x; r = -0.2789, p = 0.0000EPA Proposed Rule TSD for Florida’s Streams and DPVs for Lakes

A-5 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.EPA Proposed Rule TSD for Florida’s Streams and DPVs for Lakes

A-6 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 120SCI2007 LnTP:SCI2007: y = 42.1206 - 3.6309*x; r = -0.2501, p = 0.0000EPA Proposed Rule TSD for Florida’s Streams and DPVs for Lakes

A-7 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: r ed open diamond, red line; West Central: gray closed diamond, gray line.EPA Proposed Rule TSD for Florida’s Streams and DPVs for Lakes

A-8 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; P eninsula: 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 120SCI2007 LnTP:SCI2007: y = 42.7736 - 4.0597*x; r = -0.2690, p = 0.0000EPA Proposed Rule TSD for Florida’s Streams and DPVs for Lakes

A-9 Figure A10 –6 . SC I 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.EPA Proposed Rule TSD for Florida’s Streams and DPVs for Lakes

A-10 TP vs. SCI Linear Regression Summary
Regression equations from each relationship of Ln(TP) vs. SCI were developed using least squares regression for each hab itat score criterion ( Table A10 –1 ). These equations were then solved for SCI values of 40, 46, and 50, which correspond to the 2.5 th , 5 th , and 10 th percentile of SCI scores, respectively, from minimally impaired streams in Florida according to FDEP (Sectio n 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 b y 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 9 5 th
CI Intercept 9 5 th
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 Beyond Data Range Habitat Score > 100
Habitat Score > 120

SCI>40 vs. Ln(Total Phosphorus)

b p m p Mean TP at SCI = 40 95 th CI Intercept 95 th 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 95 th CI Intercept 95 th 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 9 5 th
CI Intercept 9 5 th
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 2.5 th , 5 th , and 10 th 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

A-11 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 oc curs 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 stre am 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.
Panh andle 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

A-12 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 95 th 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 10 th percentile of reference sites) above TP concentrations of 0.018 mg/L (Ln(TP)= -4) and these continue to decline towards the 2.5 th pe rcentile 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

2.5 th to 10 th percentile of SCI reference site scores, which are values of 40 and 50, respectively. Also shown (black vertica l 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).EPA Proposed Rule TSD for Florida’s Streams and DPVs for Lakes

A-13 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 develo ped 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 scor es

> 120 (lower right)

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

A-14 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 (l ower right)

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

A-15 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)

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

A-16 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)

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

A-17 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 χ 2 p Mean TP at p=0.5 95 th
CI Intercept 95 th
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
Beyond the Data Range Habitat Score > 100

Habitat Score > 120

SCI>40 vs. Ln(Total Phosphorus)

b m χ 2 p Mean TP at p=0.5 95 th
CI Intercept 95 th
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 χ 2 p Mean TP at p=0.5 95 th
CI Intercept 95 th
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 χ 2 p Mean TP at p=0.5 95 th
CI Intercept 95 th
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 χ 2 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 2.5 th , 5 th , and 10 th p ercentile of reference site SCI scores (Appendix 8) and SCI=30 for comparison.EPA Proposed Rule TSD for Florida’s Streams and DPVs for Lakes

A-18 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 o f 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 we re 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).EPA Proposed Rule TSD for Florida’s Streams and DPVs for Lakes

A-19 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 75 th percentile prediction intervals (red dashed lines). PHW: Panhandle West, PHE: Panhandle East, PEN: Peni nsula, 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 l ine.EPA Proposed Rule TSD for Florida’s Streams and DPVs for Lakes

A-20 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 thres holds 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 percentile s, 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 blu e shaded region is the 2.5 th to
10 th 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 A 10 –7 (vertical black lines) and binomial regression models from Figure A 10 –9 through Figure A 10

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

A-21 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

an d 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 c losed 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

A-22 Figure A10

– 16 . SCI versus Ln(TN), 2006

– 2011 data, relations hips 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.EPA Proposed Rule TSD for Florida’s Streams and DPVs for Lakes

A-23 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.EPA Proposed Rule TSD for Florida’s Streams and DPVs for Lakes

A-24 Figure A10

– 18 . S CI 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.EPA Proposed Rule TSD for Florida’s Streams and DPVs for Lakes

A-25 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 d iamond, gray line.EPA Proposed Rule TSD for Florida’s Streams and DPVs for Lakes

A-26 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.EPA Proposed Rule TSD for Florida’s Streams and DPVs for Lakes

A-27 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
2.5 th , 5 th , and 10 th 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 stre am 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 95 th
CI Intercept 9 5 th
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 95 th
CI Intercept 95 th
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 9 5 th
CI Intercept 9 5 th
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 9 5 th
CI Intercept 9 5 th
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 2.5 th , 5 th , and 10 th percentile of reference site SCI scores, defined as scores where impact to fauna is identified in Florida (Appendix 8) and SCI=30 for comparison.EPA Proposed Rule TSD for Florida’s Streams and DPVs for Lakes

A-28 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 e arlier 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.EPA Proposed Rule TSD for Florida’s Streams and DPVs for Lakes

A-29 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 95 th 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 10 th percentile of reference sites) above TN concentrations of 0.60 mg/L (Ln(TN)=-0.5) and these continue to decline towards the 2.5 th percentile SCI score (SCI = 40) which is included in the 95 th percentile confidence interval of mean SCI scores when TN is greater than 1.65 m g/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

2.5 th to 10 th 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).EPA Proposed Rule TSD for Florida’s Streams and DPVs for Lakes

A-30 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 regre ssed 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 Proposed Rule TSD for Florida’s Streams and DPVs for Lakes

A-31 Figure A10

– 24 . Binomial SCI response based on an SCI threshold of 40 regressed agains t 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 Proposed Rule TSD for Florida’s Streams and DPVs for Lakes

A-32 Figure A10

– 25 . Binomial SCI response based on an SCI threshold of 46 regressed against Ln(TN) fo r all habitat scores (upper left), habitat scores > 80 (upper right), habitats scores > 100 (lower left), and habitat

scores > 120 (lower right)

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

A-33 Figure A10

– 26 . Binomial SCI response based on an SCI threshold of 50 regressed against Ln(TN) for all
habit at 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 th e 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.EPA Proposed Rule TSD for Florida’s Streams and DPVs for Lakes

A-34 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 χ 2 p Mean TN at p=0.5 95 th
CI Intercept 9 5 th
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 χ 2 p Mean TN at p=0.5 9 5 th
CI Intercept 9 5 th
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 χ 2 p Mean TN at p=0.5 9 5 th

CI Intercept 9 5 th
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 χ 2 p Mean TN at p=0.5 9 5 th
CI Intercept 9 5 th
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 χ 2 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 2.5 th , 5 th , and 10 th 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 fi nal 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 T N criteria

. EPA TN Criteria (m g/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 regres sion 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

A-36 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 75 th 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 A 10 –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

A-37 Figure A10

– 28 . SCI versus Ln(TN), 2006

– 2011 data, represented as box plots of SCI scores by TN

concentrat ion 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

2.5 th to 10 th percentile of SCI reference site scores, wh ich 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 t hrough 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.EPA Proposed Rule TSD for Florida’s Streams and DPVs for Lakes

A-38 

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