Development and Testing of Biomonitoring Tools for Macroinvertebrates in Florida Streams March 2007

DEVELOPMENT AND TESTING OF 

BIOMONITORING TOOLS FOR 

MACROINVERTEBRATES IN FLORIDA 

STREAMS (STREAM CONDITION INDEX 

AND BIORECON) 

Final Report 

Prepared for: 

Florida Department of Environmental Protection 

Twin Towers Office Building 

2600 Blair Stone Road 

Tallahassee, FL 32399-2400 

Leska S. Fore*, Russel Frydenborg**, Denise Miller**, 

Tom Frick**, David Whiting**, Julie Espy**, and Loretta Wolfe** 

* Statistical Design, Inc. 

136 NW 40th St. 

Seattle, WA 98107 

** Florida Dept. Environmental Protection 

2600 Blair Stone Rd. 

Tallahassee, Fl 32399-2400 

March 2007 
Development and Testing of Biomonitoring Tools for Macroinvertebrates in Florida Streams March 2007

iii

This project and the preparation of this report were funded in part by 

a Section 319 Nonpoint Source Management Program grant from the 

U.S. Environmental Protection Agency (EPA), through a contract with 

the Watershed Management Program of the Florida Department of 

Environmental Protection. The EPA contributed $64,800 to the cost of 

the project. 

Dedication 

On behalf of the Florida Department of 

Environmental Protection, the Biocriteria 

Committee dedicates this document to 

Dr. James R. Karr. 

We would like to thank Dr. Karr for his 

vision, guidance, and mentoring during 

the development of its monitoring, 

assessment, and water quality 

standards programs. We recognize and 

appreciate his lifelong dedication to the 

creation, improvement, and 

implementation of biological monitoring 

to protect water resources. 
Development and Testing of Biomonitoring Tools for Macroinvertebrates in Florida Streams March 2007

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TABLE OF CONTENTS

LIST OF TABLES .................................................................................. VII

LIST OF FIGURES ..................................................................................IX

ACKNOWLEDGMENTS.............................................................................X

FOREWARD ..........................................................................................XI

ABSTRACT .......................................................................................... XII

CHAPTER 1: INTRODUCTION ...................................................................1

Background ............................................................................................................. 1

Methods ............................................................................................................. 2

Study area ........................................................................................................... 2

Site selection and datasets................................................................................ 3

Quantifying human disturbance........................................................................ 4

Stream macroinvertebrate sampling ................................................................ 8

Metric development and testing........................................................................ 9

Index development and testing....................................................................... 12

Statistical model assumptions........................................................................ 13

CHAPTER 2: RESULTS .........................................................................15

Human disturbance gradient ................................................................................ 15

Metric selection...................................................................................................... 15

Taxonomic richness......................................................................................... 15

Feeding group................................................................................................... 18

Voltinism ........................................................................................................... 18

Habit .................................................................................................................. 18

Community structure ....................................................................................... 18

Sensitivity and tolerance ................................................................................. 18
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BioRecon metrics............................................................................................. 20

Multimetric index construction and evaluation................................................... 20

Statistical precision of indexes ............................................................................ 23

CHAPTER 3: DISCUSSION .....................................................................39

Human disturbance gradient ................................................................................ 39

Biological indicators.............................................................................................. 39

Sources of variance............................................................................................... 40

CHAPTER 4: APPLICATION OF BIOMONITORING TOOLS ...........................42

Regulatory framework ........................................................................................... 42

Designated uses............................................................................................... 42

Water quality criteria ........................................................................................ 43

Anti-degradation policy ................................................................................... 43

Tiered aquatic life uses ......................................................................................... 44

Determining the impairment threshold using the Biological Condition 

Gradient model ................................................................................................. 44

Defining biocriteria using reference site condition....................................... 48

Biocriteria for streams........................................................................................... 51

CHAPTER 5: CONCLUSIONS..................................................................52

REFERENCES ......................................................................................53

APPENDICES .......................................................................................59

Appendix A. HDG calculation ............................................................................... 59 

Appendix B. Long-lived taxa................................................................................. 63

Appendix C. Sensitive taxa ................................................................................... 66

Appendix D. Very tolerant taxa............................................................................. 68

Appendix E. Regional calibration of metric scores ............................................ 71 
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Appendix F. ANOVA output and regression results for the SCI and 

BioRecon........................................................................................... 75

Appendix G. Optimal sample size for calculating SCI ........................................ 77

References ........................................................................................................ 81

Appendix H. FDEP Biological condition gradient workshop summary ............ 82

Background....................................................................................................... 82

Methods............................................................................................................. 82

Sample Selection.............................................................................................. 83

Data Content ..................................................................................................... 86

Results .............................................................................................................. 86

References ........................................................................................................ 87

Appendix I. Change Point Analysis of Stream Condition Index and 

Biological Condition Gradient Data ................................................ 91

Background....................................................................................................... 91

The Detection Procedure ................................................................................. 91

Analysis of SCI and BCG Data ........................................................................ 93

Summary ........................................................................................................... 95

References ........................................................................................................ 95

Appendix J. Reference sites ................................................................................. 97
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LIST OF TABLES 

Table 1. Description of various datasets, number of sites sampled, total number of 

visits to all sites (varied by site), and total number of subsamples taken 

in the laboratory for all visits ..................................................................................4

Table 2. Hydrologic condition of stream site, scoring range for hydrologic index, 

and description of human influences ....................................................................5

Table 3. Description of land use and the coefficient value used to calculate the LDI..........7

Table 4. Correlation table of NH3 (mg/L), hydrologic index, habitat index, LDI for 

the buffer, LDI for the upstream watershed, natural log of watershed 

size, HDG, current SCI, and old SCI for 223 stream sites (except NH3, 

for which n = 214).....................................................................................................8

Table 5. Scoring rules for measures used to calculate the HDG ...........................................8

Table 6. Candidate metric, correlation with the HDG, and whether the metric was 

included in the final SCI, BioRecon, or original SCI ...........................................10

Table 7. Correlation of BioRecon metrics and the old and new BioRecon Indexes 

with the HDG and SCI ............................................................................................21

Table 8. SCI metric name and range of metric values used to assign a score of 0 

(indicating degraded condition) to 10 (indicating minimal disturbance) 

by region.................................................................................................................21

Table 9. BioRecon metric name and range of metric values used to assign a score 

of 0 (indicating degraded condition) to 1 (indicating minimal 

disturbance) by region ..........................................................................................22

Table 10. Results for seasonal comparison of the SCI and its component metrics...........36

Table 11. STORET site code, region, station nickname, observed change in 

BioRecon values through time, and possible reason for change .....................37

Table 12. BCG Workshop Participants ...................................................................................46

Table 13. Statistics and percentile values for the SCI reference sites. ...............................50

Table 14. Proposed aquatic life use categories, corresponding SCI values, and 

descriptions of biological conditions typically found for that category...........51

Table A-1. Laboratory codes for water chemistry measures................................................59 

Table E-1. Percentiles for SCI metric values ..........................................................................71 

Table E-2. Adjusted metric values used in scoring ...............................................................72 

Table E-3. Metric scoring formulae .........................................................................................72 
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Table F-1. General linear model output for the SCI versus HDG by region 

(northeast, panhandle, or peninsula) ...................................................................75

Table F-2. General linear model output for the SCI versus HDG by year class 

(1992–93, 1994–95, 1996–97, 1998–99, and 2000–01) .........................................75

Table F-3. ANOVA estimates of SCI variability for repeat visits to the same site 

within a year ...........................................................................................................76

Table F-4. ANOVA estimates of SCI variability for laboratory subsampling.......................76

Table F-5. ANOVA estimates of BioRecon variability for repeat visits to the same 

site within a year ....................................................................................................76

Table G-1. Percentiles of metric values for the development data set and random 

sampling in 2004 ....................................................................................................78

Table H-1. BCG workshop reference sites .............................................................................84

Table H-2. BCG workshop samples ........................................................................................85

Table H-3. BCG workshop scores summary ..........................................................................88 

Talbe H-4. Experts' categorization of BCG workshop scores ..............................................90 

Table I-1. SCI and BCG scores for the 30 Samples ..............................................................93

Table I-2. Confidence Intervals (CI) for the change points of SCI scores............................95

Table J-1. Reference site locations. ........................................................................................97

Table J-2. Reference sites sample data ................................................................................103
Development and Testing of Biomonitoring Tools for Macroinvertebrates in Florida Streams March 2007

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LIST OF FIGURES 

Figure 1. EPT and total taxa versus the HDG for two rounds of testing..............................16

Figure 2. Six of 10 SCI metrics plotted against the HDG.......................................................17

Figure 3. Four of 10 SCI metrics plotted against the HDG....................................................19

Figure 4. The SCI plotted against the HDG.............................................................................24

Figure 5. The old and new BioRecon Indexes plotted against the HDG..............................25

Figure 6. The SCI versus HDG by region................................................................................26

Figure 7. The SCI versus catchment area by HDG ................................................................27

Figure 8. The SCI versus HDG by two-year period ................................................................28

Figure 9. Independent verification of the SCI versus HDG ...................................................29

Figure 10. SCI values for repeat visits to the same sites......................................................30

Figure 11. SCI variance components ......................................................................................31

Figure 12. Variance components of the SCI and metrics......................................................32

Figure 13. Summer versus winter SCI, by region ..................................................................34

Figure 14. Variance components of the BioRecon Index and metrics.................................35

Figure 15. Range of BioRecon Index values by site..............................................................38 

Figure 16. Linear regression of SCI against BCG scores .....................................................47 

Figure 17. Change point analysis for SCI and BCG scores ..................................................48 

Figure 18. Number of SCI samples by year and program .....................................................50 

Figure A-1 to A-4. Scoring rules for components of HDG.....................................................61 

Figure E-1. Metric scores for percent filterer by bioregion...................................................73 

Figures G-1 to G-3. SCI scores derived from different sample sizes...................................79 
Development and Testing of Biomonitoring Tools for Macroinvertebrates in Florida Streams March 2007

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ACKNOWLEDGMENTS 

Florida Department of Environmental Protection (FDEP) biologists provided comments and 

guidance throughout the development and testing of the stream macroinvertebrate indexes. Ellen 

McCarron provided administrative support and a regulatory perspective. Eric Livingston provided 

administrative and contract support. 

Reviewers from outside FDEP included Michael Barbour (Tetra Tech), Jeroen Gerritsen (Tetra 

Tech), Dr. James Karr (University of Washington), Tony Olsen (U. S. Environmental Protection Agency),

Dr. Manuel Pescador (Florida A&M University), and Teresa Thom (U. S. Fish and Wildlife Service–Eglin 

Air Force Base), who provided many helpful comments. 

Additional comments from Tom Frick, Frank Butera, Sally Levings, Donald Ray, and the FDEP 

Biology section improved many aspects of this document and analysis. Data retrievals were performed by 

Tom Deck, Shannon Gerardi, and Denise Miller. Devan Cobb calculated land cover for stream sites. Julie 

Espy developed datasets and resolved taxonomic questions. Dana Denson, David Evans (Water and Air 

Research), Johnny Richardson, Robert Rutter, and Dr. Robert Wisseman (Aquatic Biology Association) 

provided definitions for long-lived taxa. Michael Heyn provided additional clinger information. Lab 

subsampling was done by Ashleigh Jordan, Nathalie Moise, Cathy Oaks, Brad Richardson, Brian 

Shinner, and Felicia Whiddon. Cover photo courtesy of Jeff Adams (Xerces Society). 

The following biologists throughout Florida collected and identified stream macroinvertebrate 

samples: 

Central District (Orlando) 

• Dana Denson 

• Phil Kane 

• Steve Kent 

• Eric Pluchino 

Northeast District (Jacksonville) 

• Lee Banks 

• Patrick O’Connor 

Northwest District (Pensacola) 

• Frank Butera 

• Glenn Butts 

• Laurence Donelan 

• Donald Ray 

South District (Punta Gorda) 

• Robert Rutter 

• Albert (Ford) Walton 

Southeast District (Port St. Lucie) 

• Mark Thompson 

Southwest District (Tampa) 

• Charles Kovach 

• Peggy Morgan 

• Mary Szafraniec 

Tallahassee Office 

• Tom Deck 

• Julie Espy 

• Ken Espy 

• Tom Frick 

• Russel Frydenborg 

• Shannon Gerardi 

• Michael Heyn 

• Lisa Homann 

• Joy Jackson 

• Liz Miller 

• Johnny Richardson 

• Cheryl Swanson 

• Amy Wheeler 

• Emily Springer 
Development and Testing of Biomonitoring Tools for Macroinvertebrates in Florida Streams March 2007

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FOREWARD 

The original version of this document was completed in 2004. Subsequently, important 

observations made during the routine application of these bioassessment tools, as well as new EPA 

guidance for formulating biological criteria, prompted DEP to revise the document in two key areas. 

First, the thresholds for interpreting the final Stream Condition Index values have been revised. 

This was partially due to responses from DEP biologists, who noted that many reference streams, 

formally evaluated as “excellent” using the pre-2004 SCI criteria, were now considered “fair”, or 

sometimes “poor” using the SCI 2004 thresholds. Additionally, in 2006, EPA promoted using the 

Biological Condition Gradient approach as a nationally standardized and scientifically robust method for 

developing defensible biocriteria. Using this approach, and a second line of evidence concerning the 

variability of the reference condition, new SCI thresholds have been devised. 

An additional revision to the document addressed measurement error of the SCI related to 

laboratory subsampling methods which were subsequently modified to improve the precision of the SCI 

without substantially increasing the cost. This involved increasing the number of individuals examined and 

averaging the results from two independent aliquots from a field sample. 

Science is an iterative process: incremental improvements are made as new evidence and 

experience accumulate. Such was the motivation behind the revisions made in the original document and 

as a result of additional statistical analyses conducted during 2004–2006. This document represents the 

work and expertise of many people, all dedicated to applying the best science possible for the protection 

of Florida’s stream resources. 
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ABSTRACT 

The Florida Department of Environmental Protection assesses the chemical, physical, and 

biological condition of hundreds of stream sites each year. This study used measures of hydrologic 

condition, riparian and channel habitat condition, water chemistry, and intensity of human land use to 

define a gradient of human disturbance for stream sites. We evaluated the sensitivity and tolerance of 

over 1,000 stream macroinvertebrate taxa using the human disturbance gradient (HDG). We tested for 

correlation between the HDG and 36 biological measures of the stream macroinvertebrate assemblage 

(metrics), and selected the 10 most highly correlated metrics within 6 categories of biological 

organization: taxonomic richness (total number of taxa, number of Trichoptera taxa, and number of 

Ephemeroptera taxa); feeding group (percentage filterer individuals); voltinism (long-lived taxa richness); 

habit (clinger taxa richness); community structure (percentage dominance of the most abundant taxon 

and percentage Tanytarsini midges); and sensitivity and tolerance (sensitive taxa richness and 

percentage very tolerant individuals). 

Metrics were combined into an overall Stream Condition Index (SCI) by transforming metric 

values into unitless scores and summing the scores. The SCI was highly correlated with the HDG for an 

independent dataset (Spearman’s r = –0.81, p < 0.001). The SCI was independent of watershed size and 

geographic region (Florida panhandle, peninsula, and northeast). Across 10 years of sampling, the index 

showed a similar response to the HDG. The SCI was somewhat higher for winter versus summer samples 

(3.5%). To summarize the statistical precision of the SCI, we used confidence intervals to calculate that 

the SCI could distinguish 3.7 categories of biological condition. These results supported subsequent 

selection of three assessment categories for interpreting SCI values in a regulatory context. A large 

portion of the variability of the SCI was due to subsampling in the laboratory (49%). To reduce this 

variability, laboratory sorting procedures have subsequently been modified to target two aliquots of 150 

individuals, with the resulting SCI values averaged. 

We also tested biological metrics for a second stream macroinvertebrate sampling protocol (the 

BioReconnaissance Index, or BioRecon), which was based on the sorting of invertebrates in the field and 

taxonomic identification in the laboratory. Of the 10 SCI metrics, 6 taxa richness metrics were tested 

using BioRecon data; all were highly correlated with both the SCI and HDG. From these metrics, the 

BioRecon Index was calculated as the sum of scores for the 6 metrics. The statistical precision of the 

BioRecon index was sufficient to detect 2.5 categories of biological condition which supported the 

subsequent decision to select three assessment categories for regulatory purposes. 

We primarily followed the EPA 2006 Biological Condition Gradient approach for translating index 

values into biocriteria for Florida streams. For this approach, a panel of 22 regional scientists examined 

data representing multiple tiers of aquatic life use for the ultimate purpose of defining biological criteria for 

state water quality standards. In addition, the experts recommended a threshold of biological condition at 

which impairment occurs as a result of human disturbance. A secondary, but parallel analysis based on 

departure from reference condition yielded similar index values for the impairment threshold. Based on 

these complementary results, three categories of aquatic life use are proposed for Florida streams and 

described as Category 1 (“exceptional” ), Category 2 (“healthy”), and Category 3 (“impaired”). 
Development and Testing of Biomonitoring Tools for Macroinvertebrates in Florida Streams March 2007

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CHAPTER 1: INTRODUCTION 

Florida, along with a handful of other states, leads the nation in the development and 

implementation of water protection policies based on biological criteria (McCarron and Frydenborg, 1997; 

U. S. Environmental Protection Agency [EPA], 2002). Under the federal Clean Water Act, states are 

required to define designated uses for specific waterbodies and develop criteria to protect them (Karr, 

1991; Ransel, 1995). Over time, the emphasis has shifted from what was primarily the chemical 

monitoring of pollutants to include the direct measurement of the condition of the biological assemblage 

(Yoder and Rankin, 1998). 

Florida classifies its surface waters according to their designated uses. The five categories are 

ranked in order of the level of protection that they require, as follows: 

Class I–Drinking water; 

Class II–Shellfish propagation or harvesting (primarily for coastal waters); 

Class III–Recreation and propagation and maintenance of a healthy, well-balanced 

 population of fish and wildlife; 

Class IV–Agricultural water; and 

Class V–Navigation and industrial use. 

This document describes the development and testing of biological monitoring tools for 

macroinvertebrate stream samples for the assessment of freshwater streams that are applicable to Class 

I and III waters. FDEP has convened a Designated Uses Policy Advisory Committee (DUPAC) to explore 

an alternative system of designated use classification for Florida (see Chapter 4 below). We anticipate 

that FDEP will propose Aquatic Life Use (AL) categories, to which these biological monitoring tools for 

Class I and III streams would also apply. 

The EPA has developed numerous guidance documents to support biological assessment (EPA, 

1998; Barbour et al., 1999; Bowman et al., 2000; Jackson et al., 2000; EPA, 2001; EPA, 2002; EPA, 

2003a, b, c; Fore, 2003). These documents typically emphasize multimetric indexes to measure the 

biological condition of surface waters (Karr, 1981; Karr et al., 1986; Barbour et al., 1999). The EPA’s 

recent document, Draft Report on the Environment 2003, specifically recognizes multimetric indexes as 

an “approach that addresses the need to measure critical multiple variables” (EPA, 2003a). 

Multimetric indexes are composed of biological measures, called metrics, that show a consistent 

response to human disturbance (Karr and Chu, 1999). Metrics are converted to unitless scores and 

summed to obtain a summary index. Multimetric indexes have been developed for fish, birds, 

invertebrates, and algae in a variety of ecological contexts in North America, Europe, and Asia (Thorne 

and Williams, 1997; Hughes et al., 1998; Hughes and Oberdorff, 1998; Karr and Chu, 1999; Bryce et al., 

2002; Fore and Grafe, 2002; Klemm et al., 2003). Most states use multimetric indexes for biological 

assessment, although other techniques have been developed (Hawkins et al., 2000; EPA, 2003b, c). 

Background 

Barbour et al. (1996) developed multimetric indexes for Florida that have been used since the 

early 1990s. The authors tested metrics by comparing values at sites with minimal human influence 

(reference sites) and sites with known disturbance (test sites). They identified three unique geographic 

regions based on the presence of shared taxa and defined metric expectations for each region. 

In contrast, this study selected metrics on the basis of their correlation with a gradient of human 

disturbance (Bryce et al., 1999). The sensitivity and tolerance of over 1,000 taxa were also evaluated 

individually, using the human disturbance gradient (HDG). We used confidence intervals to estimate the 
Development and Testing of Biomonitoring Tools for Macroinvertebrates in Florida Streams March 2007

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precision associated with the indexes. Working with a panel of regional experts, aquatic life use 

categories were derived to support management decision related to listing stream sites as impaired and 

prioritizing sites for additional evaluation as part of the TMDL (Total Maximum Daily Load) process (Karr 

and Yoder, 2004). 

From the original analysis by Barbour et al. (1996), two multimetric indexes were developed for 

Florida streams based on different sampling and identification protocols. The Stream Condition Index 

(SCI) was derived from laboratory processing and the identification of samples, while the Biological 

Reconnaissance (BioRecon) Index was based on field sorting and laboratory identification. This study 

develops new bioassessment tools for both sampling protocols. For simplicity’s sake, the names of the 

indexes have been retained for this new analysis, and the previous indexes are referred to as the old SCI 

and the old BioRecon Index within this document. 

Methods 

Study area 

Portions of Florida have been repeatedly inundated by seawater in recent geologic history; as a 

consequence, elevation and organism recruitment from adjacent, noninundated areas play an important 

role in the present-day geographic distribution of macroinvertebrates. Florida can be divided 

geographically into the northern panhandle, the southern peninsula, and a transitional region known as 

the northeast. The middle and lower Suwannee Basin provides a natural demarcation between the 

panhandle and peninsula, with the northeast region straddling the upper Suwannee northeast of the Cody 

Escarpment (White, 1970). 

In the Florida panhandle, rivers typically flow from north to south, and the elevation of headwater 

streams typically exceeds 200 to 250 feet. The recruitment of stream organisms (over geologic time) from 

upstream, higher elevation areas (Georgia and Alabama) makes the panhandle richer in freshwater 

stream taxa than other parts of the state (Florida Natural Areas Inventory [FNAI], 1990). Vegetation 

communities in the panhandle generally consist of mixed pine/oak/hickory forests (Pinus spp., Quercus

spp., Carya spp.); longleaf pine forests (Pinus palustris); hardwood forests with a beech/magnolia climax 

community (Fagus grandiflora/Magnolia grandiflora); and swamp hardwood forests of cypress (Taxodium

spp.) or tupelo (Nyssa spp.), interspersed with a mosaic of pine plantations, cropland (e.g., corn, soy 

beans, and peanuts), and pasture (Soil and Water Conservation Society (SWCS), 1989; Fernald and 

Purdum, 1992). The panhandle is less densely populated by humans than the other areas. 

The peninsula has a sandy highland ridge extending down its center almost to Lake Okeechobee. 

The elevation of the central ridge is approximately 150 to 200 feet. Rivers west of the ridge flow into the 

Gulf of Mexico. Of the two large drainages east of the ridge, the Kissimmee River flows south into Lake 

Okeechobee, and the St. Johns River flows north to the Atlantic. Streams in the peninsula tend to have 

lower water velocity, flowing through wetlands and pine flatwoods (White, 1958). The peninsula has fewer 

freshwater invertebrate taxa due to the lack of upstream recruitment sources that did not experience 

marine inundation. Plecoptera, for example, are common in the panhandle but extremely rare in the 

peninsula, probably due to a combination of poor recruitment, warmer temperatures, and lower water 

velocities. Peninsular vegetation communities on the ridge consist of longleaf pine/turkey oak forests 

(Pinus palustris/Quercus laevis); on flat areas are slash pine (Pinus eliottii) or loblolly pine (Pinus teada) 

with a palmetto/gallberry understory (Serenoa repens/Ilex glabra); and in depressional areas are 

marsh/wet prairies (maidencane, pickerel weed) and hardwood wetlands of sweetbay (Magnolia 

virginiana), cypress (Taxodium spp.), and ash (Fraxinus spp.; SWCS, 1989). The dominant land use is 

pasture, cropland (e.g., watermelons, nursery products, and tomatoes), and urban areas (Fernald and 

Purdum, 1992). Dense population centers are located at Tampa and Orlando. 
Development and Testing of Biomonitoring Tools for Macroinvertebrates in Florida Streams March 2007

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The northeast region includes portions of the Okeefenokee Swamp, parts of the upper Suwannee 

drainage, the Black Creek drainage, and the Sea Island flatwoods. From the perspective of stream 

invertebrates, this region tends to be intermediate between the panhandle and peninsula regions. With 

the exception of the Black Creek Basin, with headwaters approximately 200 feet above sea level, other 

streams in this area tend to be low-relief, wetland systems (Snell and Anderson, 1970). Plant 

communities consist of longleaf pine/turkey oak (Pinus palustris/Quercus laevi) on the sandy uplands; 

hardwood wetlands of cypress (Taxodium spp.), tupelo (Nyssa spp.), and loblolly bay (Gordonia 

lasianthus); pine flatwoods; and marsh (FNAI, 1990). Jacksonville is the region’s only major population 

center. 

Barbour et al. (1996) used macroinvertebrate stream samples collected from sites with minimal 

human influence to evaluate the influence of geographic locations on species assemblages. The authors 

found that the panhandle, peninsula, and northeast regions, which are primarily defined according to 

drainage patterns, were better predictors of species assemblages than were subecoregions described by 

Griffith et al. (1994). Regional differences translate into expectations of higher taxonomic diversity in the 

panhandle relative to the peninsula, with the northeast region representing a transitional area. 

Site selection and datasets 

The Florida Department of Environmental Protection (FDEP) has collected thousands of 

macroinvertebrate samples from stream sites throughout the state from 1991–2001. Many sites have 

been visited repeatedly. Sites and visits were selected from this large database using different criteria for 

different testing situations (Table 1). For example, when testing for metric correlation with disturbance, a 

range of site conditions was needed; to evaluate variability, sites with many repeat visits were needed; to 

evaluate laboratory subsampling, large samples were needed. Some datasets contained data from the 

same sites, and other sites were selected to provide an independent test. For some testing situations, 

data from site visits were averaged by sites—for example, when one variable in the analysis had only one 

value for each site, such as the HDG. 

The relationship between human disturbance and biological condition was evaluated in several 

ways. To develop the HDG, FDEP biologists selected sites from their regions that represented the 

broadest possible range of human influence. First, to ensure that the relationships observed between 

human land use and biological metrics were consistent and not specific to a particular set of sites, 2 

independent sets of sites were tested. These data were then combined to obtain 223 sites to test for 

metric correlation with the HDG. An additional, independent set of 23 sites was used to test the 

correlation between the SCI and HDG. 

Additional datasets were compiled to estimate the variability of the biological metrics and indexes 

associated with repeat visits to a site, estimate the variability associated with laboratory subsampling, and 

evaluate the influence of field sorting versus laboratory sorting. To evaluate SCI variability, data from 62 

sites with 2 to 8 visits were selected. About half the sites (32) had repeat visits on the same day. Most of 

the other repeat visits were during different years, and a few were within the same year. Thus, these data 

could not be used to estimate the influence of seasons on variability. Instead, additional data were 

compiled to test for seasonal (winter versus summer) differences in the SCI. To test the influence of 

subsampling during laboratory processing for the SCI protocol, 3 subsamples were selected from 59 

samples collected from 54 sites (5 sites also had 2 duplicate samples each). 

To test metrics and evaluate their variability for the BioRecon protocol, 2 additional datasets were 

used. The first dataset included 116 sites with macroinvertebrate samples collected according to both the 

BioRecon and SCI sampling protocols. We tested BioRecon metrics for correlation with the SCI and HDG. 

Most sites had at least 2 visits, and several had 3 to 6 visits. Of the 116 sites, 53 had values calculated 

for the HDG because they were used initially to develop the HDG. To evaluate the variability of the 

BioRecon metrics and index, 128 sites were selected, with 2 to 7 visits per site. 
Development and Testing of Biomonitoring Tools for Macroinvertebrates in Florida Streams March 2007

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The dataset used to test the SCI and its component metrics against the HDG was large; 

therefore, simple scatter plots of data against the HDG would be difficult to interpret with the many data 

points and overstrikes. Consequently, we used box plots to display the range of values associated with 

different values of the HDG. In all figures, the box defined the 25th and 75th percentiles, the whiskers were 

the nonoutlier range, and the outliers were calculated as values greater (or less) than 1.5 times the length 

of the distance from the 25th to the 75th percentile value. During 1991–2001, laboratory protocols 

mandated a target number of 100 individuals; however, this target was often exceeded due to the 

laboratory protocols of the time. Several graphs include data from samples with 75 to 175 individuals in 

order to more closely match the target sample size. A more restrictive range (e.g., 75–125 individuals) 

would have resulted in too high a loss of sites for testing. Additional analysis (not shown) indicated that 

the ranges of metric values were similar for an upper limit of 125 and 175 individuals. In recent years 

(2004–2006), FDEP has modified laboratory protocols to more often match the target number of 

individuals (see Appendix G). For the BioRecon testing, the number of individuals identified was not 

recorded; therefore, all site visits were used within a particular analysis, unless specifically stated 

otherwise. 

Table 1. Description of various datasets, number of sites sampled, total number of visits to all 

sites (varied by site), and total number of subsamples taken in the laboratory for all visits 

Some sites were included in more than 1 dataset, except for the 23 independent sites used to verify the 

correlation between the SCI and HDG. 

Purpose Sites Visits Lab 

Subsamples 

Test HDG consistent for 2 rounds of data 

 Round 1 154 469 0 

 Round 2 69 160 0 

Test metrics vs. HDG 223 629 0 

Score metrics (same data as above, but only samples 

with 75 to 175 individuals included) 176 420 0 

Test sensitive and tolerant taxa versus HDG 226 632 0 

Verify SCI with independent data 23 23 0 

Determine SCI variability 62 220 0 

Determine seasonal variation of SCI 78 578 0 

Evaluate laboratory subsampling variability for SCI 54 59 177 (3 each) 

Calibrate BioRecon metrics with SCI 116 165 0 

Test BioRecon metrics versus HDG 53 0 

Evaluate BioRecon variability 128 293 0 

Quantifying human disturbance 

Karr and colleagues describe five factors to summarize the ways in which humans alter and 

degrade rivers and streams (Karr et al., 1986; Karr et al., 2000): flow regime, physical habitat structure, 

water quality, energy source, and biological interactions. First, the natural flow regime of a stream may be 

altered by dams, water removal, or water return. In extreme cases, changes in water volume or flow 

timing can result in flooding or dry channels. Second, stream macroinvertebrates depend on a variety of 

physical habitat types for food, shelter, and reproduction. A greater diversity of substrate size, types of 

vegetation, and bank architecture translates into a greater variety of organisms. Third, water quality may 
Development and Testing of Biomonitoring Tools for Macroinvertebrates in Florida Streams March 2007

5

be evaluated in terms of turbidity, conductivity, nutrient concentrations, or the presence of other 

chemicals or metals. Fourth, energy source describes the seasonal availability, type of organic material, 

and size and shape of material available as food. Fifth, the introduction of exotic species or disease and 

the harvest of fish and shellfish for sport, commercial, and subsistence uses can alter biological 

interactions such as competition or predation within the natural assemblage. 

Data were available to measure four of the five factors. FDEP biologists developed a hydrologic 

scoring system based on their knowledge of water removal, patterns of drought, and hydrographs for the 

sites (Table 2). To measure habitat condition, FDEP biologists routinely calculate a habitat condition 

index whenever stream macroinvertebrate samples are collected (FDEP, 2004). The index evaluates 

substrate condition and availability, water velocity, habitat smothering (e.g., by sand and silt), 

channelization, bank stability, and the width and condition of riparian vegetation. During the period of data 

collection, the habitat index was modified to include additional measures that raised the total number of 

possible points. To make habitat condition comparable across years, the total habitat score was 

converted to a percentage by dividing the score by the total possible points. 

Table 2. Hydrologic condition of stream site, scoring range for hydrologic index, and description 

of human influences 

The high or low values within each condition class were assigned at the discretion of the biologist based 

on the extent of disturbance. These changes were associated with human disturbances, not natural 

events such as hurricanes or extreme droughts. 

Condition Score Description 

Excellent 1–2 

Flow regime as it naturally occurs (slow and fairly continual release of water 

after rains), few impervious surfaces; high connectivity with ground water and 

surface features delivering water (e.g., sandhills, wetlands; no ditches or 

berms) 

Good 3–4 Flow regime minimally changed; some water withdrawals; some wetland 

drainage, some impervious surfaces, some ditching 

Moderate 5–6 

Flow regime moderately altered; hydrograph moderately flashy (scouring after 

rain events with subsequent reductions in flow), ground water pumping 

evident; much wetland drainage, topographic alterations reduce natural water 

input; more impervious surfaces, dams/control structures change normal 

water delivery schedule 

Poor 7–8 

Flow regime highly altered; hydrograph very flashy (scouring after rain events 

with subsequent reductions in flow, leading to stagnant or dry conditions 

related to large amounts of impervious surfaces and/or ditching); water 

withdrawals and impoundments or control structures severely alter flows; 

large areas of impervious surfaces 

Very poor 9–10 

Flow regime entirely human controlled; hydrograph very flashy (scouring after 

rain events with subsequent reductions in flow, leading to stagnant or dry 

conditions related to impervious surfaces and ditching); intensity of water 

withdrawals and impoundments fundamentally alter the nature of the 

ecosystem 

FDEP biologists also routinely collect measurements of turbidity, temperature, dissolved oxygen, 

conductivity, and nutrient concentrations. Total phosphate (TP), ammonia (NH3), total Kjeldahl nitrogen 

(TKN), and nitrites/nitrates (NOx) were measured to summarize nutrients. We selected NH3 to summarize 

water quality because it was the most consistently associated with the other water quality measures and 

had the most complete record of data. TP had similar high correlations and complete data, but may be 
Development and Testing of Biomonitoring Tools for Macroinvertebrates in Florida Streams March 2007

6

more closely associated with fertilizer and farming practices, while NH3 may be a more general indicator 

of both urbanization and agriculture. 

A measure of the type and quantity of energy available to organisms living within the stream was 

not available. However, Brown and colleagues have developed an index to estimate the intensity of 

human land use based on nonrenewable energy flow (Brown et al., 1998; Brown and Vivas, 2005). The 

Landscape Development Intensity (LDI) Index was calculated as the percentage area within the 

catchment of particular types of land use multiplied by the coefficient of energy use associated with that 

land use, summed over all land use types in the catchment (Table 3). 

= ∑( ) LDI i %* LULDI i . 

Where: 

LDIi = the nonrenewable energy land use for land use i, and 

%LUi = the percentage of land area in the catchment with land use i. 

Brown and colleagues derived the coefficients for each land use type from actual billing records 

and published literature, translated reported energy use into standardized units, and then averaged the 

values by land use type and standardized to a per-unit area. The calculations used only nonrenewable 

energies, which included electricity, fuels, fertilizers, pesticides, and water (both public water supply and 

irrigation). 

Land use was derived from aerial photos manipulated as layers in a geographic information 

system (GIS) computer program. The LDI was calculated in 2 ways. First, a buffer area of 100 meters on 

each side of the stream and 10 kilometers upstream of the sampling point was used (LDI_BF). Because 

the definition of the buffer region was somewhat arbitrary, the LDI was also calculated for the entire 

upstream catchment (LDI_WS) in order to determine which spatial scale was a better predictor of site 

condition. We selected LDI_BF to include in the HDG because it was slightly more highly correlated with 

NH3, the hydrologic index, and the habitat index (Table 4). 

To define the HDG, we converted the 4 measures of human disturbance to unitless scores and 

summed the scores to create HDG values for each stream site (Appendix A). Three of the measures had 

values of 0, 1, or 2, indicating low, moderate, or high levels of human influence. One measure, hydrologic 

condition, had an additional value of 4 available to indicate extreme levels of disturbance (Table 5). Thus, 

values for the HDG ranged from 0 (indicating minimal human disturbance) to 9 (indicating extreme 

disturbance). The scores for the HDG were derived from a graphical analysis of EPT taxa richness plotted 

against each measure of disturbance. We identified values for each disturbance measure for which the 

number of EPT taxa was consistently lower. For NH3, 9 sites had no information. Most of these sites had 

values for other measures indicating minimal human disturbance. We assumed for these sites that NH3

was less than 0.1 milligram per liter (mg/L). 
Development and Testing of Biomonitoring Tools for Macroinvertebrates in Florida Streams March 2007

7

Table 3. Description of land use and the coefficient value used to calculate the LDI 

Higher values indicate greater intensity of human land use (Brown and Vivas, 2005). 

Land Use LDI Value 

Natural Open Water 1.00 

Natural System (no human activity) 1.00 

Pine Plantation 1.58 

Woodland Pasture 2.02 

Pasture 2.77 

Recreational/Open Space (low-intensity) 2.77 

Low-Intensity Pasture (with livestock) 3.41 

Citrus 3.68 

High-Intensity Pasture (with livestock) 3.74 

Row crops 4.54 

Single-Family Residential (low-density) 6.79 

Recreational/Open Space (high-intensity) 6.92 

High-Intensity Agriculture 7.00 

Single-Family Residential (medium-density) 7.47 

Single-Family Residential (high-density) 7.55 

Low-Intensity Highway 7.81 

Low-Intensity Commercial 8.00 

Institutional 8.07 

High-Intensity Highway 8.28 

Industrial 8.32 

Low-Intensity Multifamily Residential 8.66 

High-Intensity Commercial 9.18 

High-Intensity Multi-family Residential 9.19 

Low-Intensity Central Business District 9.42 

High-Intensity Central Business District 10.00 
Development and Testing of Biomonitoring Tools for Macroinvertebrates in Florida Streams March 2007

8

Table 4. Correlation table of NH3 (mg/L), hydrologic index, habitat index, LDI for the buffer, LDI for 

the upstream watershed, natural log of watershed size, HDG, current SCI, and old SCI for 223 

stream sites (except NH3, for which n = 214) 

Repeat visits to individual sites were averaged for each site. All values shown were significant 

(Spearman’s r, p < 0.01). 

NH3 Hydro Habitat LDI_BF Ln (Size) HDG SCI 

NH3 - 0.43 –0.45 0.39 –0.20 0.50 –0.56 

Hydro 0.43 - –0.66 0.61 –0.36 0.84 –0.71 

Habitat –0.45 –0.66 - –0.64 0.41 –0.78 0.66 

LDI_BF 0.39 0.61 –0.64 - –0.37 0.79 –0.60 

LDI_WS 0.34 0.56 –0.55 0.74 –0.28 0.70 –0.59 

Ln (Size) –0.20 –0.36 0.41 –0.37 - –0.41 0.36 

HDG 0.50 0.84 –0.78 0.79 –0.41 - –0.76 

SCI –0.56 –0.71 0.66 –0.60 0.36 –0.76 - 

Old SCI –0.43 –0.59 0.56 –0.54 0.29 –0.69 0.86 

Table 5. Scoring rules for measures used to calculate the HDG 

The HDG is the sum of the scores for each site. 

Measure 0 1 2 3 

NH3 (mg/L) <0.1 0.1–2 >2 

Habitat index (% of total possible points) >65 50–65 <50 

Hydrologic index <6 6–7 8–9 10 

LDI <2 2-3.5 >3.5 

Stream macroinvertebrate sampling 

FDEP uses two protocols to collect invertebrate samples from streams. The protocols differ in the 

number of dip net sweeps collected, the location of sample sorting (field versus laboratory), and the final 

index used for assessment. The BioRecon sampling protocol was designed to be a quick field 

assessment that could yield a same-day measure of stream site condition. The SCI sampling protocol 

was meant to be more precise. Both protocols use a D-frame dip net with a 0.25-meter opening and a 

600-micrometer mesh net. Both protocols distribute the sweeps among the best available substrates on 

an approximately equal basis. Florida streams typically have a sandy substrate that supports very few 

invertebrates. Therefore, the habitat sampled for macroinvertebrates includes logs, roots, and undercut 

banks; partially decayed leaf packs; aquatic vegetation; and rocks or large cobble. During SCI sampling, if 

4 productive substrates are present (snags, roots, vegetation, and leaf packs), each substrate would be 

sampled 4 times, with sand being sampled 4 times, for a total of 20 sweeps. 

In the laboratory, the SCI sample is divided by spreading it onto gridded trays. Squares are 

randomly selected, and all the material within a square is sorted. Macroinvertebrates are identified to the 

lowest practical taxonomic level. For the 2004 to 2006 time period, squares were selected until the target 

sample size of 100 individuals was reached. To avoid bias, the final square selected is always finished, 
Development and Testing of Biomonitoring Tools for Macroinvertebrates in Florida Streams March 2007

9

even if it contains more than 100 individuals. The current method targets two separate aliquots of 150 

individuals. The number of taxa and the relative abundance of each taxon are derived from these data. 

BioRecon data are collected using four dip net sweeps from a stream site. Samples are sorted for 

live macroinvertebrates in the field. The sample is spread in a pan, and the biologist searches through all 

the material for every unique taxon present. Each taxon is preliminarily identified in the field, and 

representative specimens of each taxon are preserved and returned to the laboratory for final 

identification under a dissecting microscope. From this field-sort protocol, the number of taxa present at a 

site can be measured, but their relative abundance (% composition) cannot. 

Metric development and testing 

Several authors have described relevant candidate metrics for stream macroinvertebrate 

assemblages (Barbour et al., 1996 and 1999; Kerans and Karr, 1994; and Klemm et al., 2002). For this 

study, we evaluated 36 candidate metrics related to taxonomic richness, feeding group, voltinism, habit, 

community structure, and tolerance or sensitivity for their association with the HDG (Table 6). Within each 

category, we selected metrics with the highest correlation with the HDG (Spearman’s r). We used onesided tests to calculate p-values because if a metric indicated better biological condition in response to 

increased human disturbance, that relationship would not be biologically significant even if it were 

statistically significant. All metrics were also evaluated graphically to ensure that they were reliable 

indicators of disturbance. We tolerated variability in metric values for minimally disturbed sites (low HDG) 

because not all sources of disturbance were known. For example, water chemistry data were unavailable 

for metals, pesticides, or other toxics. In contrast, we considered an inconsistent response at more highly 

disturbed sites to be unacceptable. 

Taxonomic richness 

Richness was calculated as the number of unique taxa found within a particular group, such as 

the Ephemeroptera, Plecoptera, or Trichoptera, or their combination (EPT). 

Feeding group 

Trophic designations followed Merritt and Cummins (1996); FDEP biologists and others modified 

some designations to reflect natural histories in Florida (Merritt et al., 1996). All feeding group metrics 

were calculated as the number of individuals of a particular group, divided by the total number of 

individuals in the sample times 100 (percentage). 

Voltinism 

Most stream insects spend the majority of their lives as larvae underwater and emerge as adults 

to mate. The length of time between generations, which is called voltinism, may be less than a year, 

approximately one year, or greater than one year. We used published sources to identify taxa that require 

more than one year to complete their life cycles (Appendix B; Brigham et al., 1982; Thompson, 1984; 

Thorp and Covich, 1991; Pescador et al., 1995; Corbet, 1999; Pescador et al., 2000; Smith, 2001; 

Rasmussen and Pescador, 2002). These taxa require consistent flows and other favorable conditions 

throughout the year to complete their life cycles. Data on generation length were not available for all 

Florida taxa; therefore, the current list may not include some long-lived taxa. 
Development and Testing of Biomonitoring Tools for Macroinvertebrates in Florida Streams March 2007

10

Table 6. Candidate metric, correlation with the HDG, and whether the metric was included in the 

final SCI, BioRecon, or original SCI 

Correlation was tested for 223 sites, with repeat visits to each site averaged before statistical testing. 

Hyperabundance was tested without averaging, n = 577. All correlations shown were significant at p < 

0.01 (Spearman’s r, one-sided test). 

Metric HDG SCI BioRecon Old SCI 

 Taxonomic richness 

EPT –0.73 yes 

Trichoptera –0.66 yes yes 

Ephemeroptera –0.66 yes yes 

Total taxa –0.52 yes yes yes 

Diptera –0.44 

Chironomidae –0.40 yes 

Plecoptera –0.37 

Oligochaeta 0.32 

Noninsect - 

 Feeding group 

% Filterer –0.46 yes yes 

% Scavenger –0.38 

% Browser/grazer –0.30 

% Collector/gatherer 0.29 

% Predator –0.21 

% Plant piercer –0.18 

% Parasite - 

% Scraper - 

% Shredder - 

 Voltinism 

Long-lived taxa –0.41 yes yes 

% Long-lived –0.35 

 Habit 

Clinger taxa –0.71 yes yes 

% Clinger –0.61 

 Community structure 

% Dominance 0.47 yes yes 

Hyperabundance - 

% Ephemeroptera –0.58 

% Trichoptera –0.55 

% Tanytarsini (Chironomidae) –0.45 yes 

% Plecoptera –0.37 

% Oligochaeta 0.32 

% Non-insect 0.32 

% Chironomidae - 

% Diptera - yes 

 Sensitivity/tolerance 

Sensitive taxa –0.75 yes yes 

Florida index (sensitive) –0.71 yes 

% Very tolerant 0.70 yes 

Very tolerant taxa 0.61 
Development and Testing of Biomonitoring Tools for Macroinvertebrates in Florida Streams March 2007

11

The list included all taxa in the family Cordulegastridae and selected taxa in the families 

Aeshnidae, Gomphidae, and Libellulidae (Odonata); the family Leuctridae and selected genera in the 

families Perlidae and Pteronarcidae (Plecoptera); the family Corydalidae (Megaloptera); and the genera 

Ceraclea, Pycnopsyche, Molanna, and Rhyacophila (Trichoptera). Several Coleoptera taxa (beetles) also 

require more than a year to complete their life cycles, but were not included because the FDEP database 

does not currently distinguish between larvae (which cannot travel to find water) and adults (which can). 

Noninsects included in the long-lived list were all taxa in the family Decapoda, the genera Fossaria and

Pomacea (Gastropoda), and the families Unionidae and Corbiculidae (Pelecypoda). 

Habit 

Other studies have noted a strong correlation between the taxonomic richness of clingers and 

stream condition (Fore et al., 2001; Karr and Morishita Rossano, 2001; Mebane, 2002). Clingers have 

morphological and behavioral adaptations that allow them to cling to substrate (Merritt and Cummins, 

1996). Channel dredging and the removal of riparian vegetation eliminate the habitat preferred by 

clingers. 

Community structure 

Percentage dominance was calculated as the number of individuals in the most abundant taxon, 

divided by the total number of individuals in the sample. ”Hyperabundance” represented a measure of the 

density of stream macroinvertebrates present at a site. Sites with high organic enrichment (e.g., sewage 

effluent) may have a high density of invertebrates due to the increased source of food for many species. 

The actual number of individuals present in the total sample was not recorded; however, the number of 

grids in the sample tray that were searched to best approximate the target number of 100 individuals was 

recorded. We used the number of grid cells searched as a measure of the density of invertebrates 

present, or hyperabundance. Other measures of community structure were based on the number of 

individuals within specific taxonomic groups divided by the total number of individuals. 

Sensitivity and tolerance 

Beck (1954) developed a list of sensitive taxa for Florida that was used to calculate a biotic index, 

which was later modified to be the Florida Index. To calculate the Florida Index (which is more accurately 

termed a metric), very sensitive taxa are given a score of 2 and sensitive taxa a score of 1. The scores 

are summed to calculate this metric. 

We used the HDG to evaluate the sensitivity and tolerance of 1,195 taxa names. The taxa names 

included taxa that were not unique because the database included synonyms for the same taxa, as well 

as specimens identified to genus and species for the same genus. Because many sites had multiple visits 

and because less-disturbed (reference) sites tended to have a greater number of visits, we summarized 

data for each taxon by site to avoid bias associated with greater sampling effort at less-disturbed sites. 

We counted a taxon as present at a site if it was found for >= 50% of the site visits. We divided the 226 

sites into 2 groups and, for simplicity’s sake, refer to these groups as “good” and “poor” for statistical 

testing. Good sites were defined as those with moderate levels of disturbance for no more than 2 

measures (e.g., the LDI or NH3) or high disturbance for no more than 1 measure (e.g., habitat condition). 

This translated into HDG <= 2 and included 141 sites. The remaining 85 sites were classified as poor. 

Thus, the 2 groups might be more accurately labeled better and worse, rather than good and poor. The 

relatively high number of sites selected to represent good condition reflects the large number of reference 

sites in the dataset. 

We used contingency table analysis to determine what percentage of taxon occurrences in the 

good-versus-poor range of HDG values represented a statistically significant difference (chi-square test, 

Yates continuity correction). As for any test, the number of sampling points increases the power of the 
Development and Testing of Biomonitoring Tools for Macroinvertebrates in Florida Streams March 2007

12

test, such that less extreme difference will be statistically significant. For example, if a taxon occurred at 

10 sites, all of the occurrences (100%) would have to be in the good range of the HDG to be statistically 

significant (p < 0.05). In contrast, if a taxon occurred at 38 sites, it would only need to occur at 30 of the 

good sites (79%) and would still be statistically significant. 

For this reason, we chose not to use a strictly statistical criterion for selecting sensitive taxa. 

Instead, for more common taxa we only selected those with 87% or more of their occurrences in good 

sites. Using this criterion of 87%, if a taxon occurred at 30 sites, 27 or more of the sites would have to be 

in the good range of the HDG. The p-value associated with this would be p < 0.002. A strictly statistical 

criterion would have selected taxa that prefer good conditions, rather than taxa that require good 

conditions. Thus, we used a statistical rule to define the minimum number of occurrences needed for 

statistical significance but made the standards for selection more strict (p << 0.05) for taxa that were more 

common. 

Defining a statistical rule for the selection of tolerant taxa was not possible, because tolerant taxa 

can also “tolerate” good conditions. Instead, very tolerant taxa were defined as those found in at least 10 

sites and for which >= 50% of their occurrences were in the poor range of the HDG. In some cases, 

multiple species within a genus failed to meet the criteria for inclusion as sensitive (or very tolerant) 

because they were not present at enough sites. If several species within a genus (or family) were 

consistently found at good (or poor) sites, we combined the species into the parent genus (or family) and 

evaluated the genus (or family). 

For the BioRecon data, we only tested metrics used in the SCI in order to make the indexes as 

similar as possible. Relative abundance metrics could not be calculated for these data; therefore, only 

taxa richness metrics were evaluated. BioRecon metrics were tested for correlation with the SCI and 

HDG. 

Index development and testing 

We defined 2 indexes by selecting metrics that showed the most consistent response to the HDG. 

One index was developed for the SCI protocol (20 dip net samples and laboratory sorting of 

macroinvertebrates) and another for the BioRecon protocol (4 dip net samples and field sorting). 

For the SCI, we transformed metrics into unitless scores by determining the 95th percentile values 

for each metric and then dividing each metric value by its range and multiplying by 10. Metrics that 

declined with disturbance were scored from 0 to 10, and metrics that increased with disturbance were 

scored from 10 to 0, so that the best biological condition was consistently scored as a 10. For skewed 

metrics, the natural log was calculated before dividing by the metric range. This process was done 

separately for the northeast, peninsula, and panhandle regions. We tested for differences in metric 

expectation associated with different regions by comparing the regression lines for each metric against 

the HDG and adjusted metric scoring as necessary to ensure similar responses for all metrics in the 3 

regions. 

For the BioRecon Index, we scored metrics to match the SCI metric scores as closely as 

possible. Using histograms to evaluate the overlap of the BioRecon and SCI metrics measured at the 

same sites, we adjusted metric scoring for the BioRecon metrics as needed. In order to distinguish 

between the BioRecon and SCI, BioRecon metrics were scored from 0 to 1 (rather than 0 to 10). In this 

way, the index values provide a quick clue as to which index is being reported. 

After correlation with disturbance was confirmed for both indexes, we evaluated different sources 

of variability, either due to natural sources (e.g., watershed size or season) or the sampling method (e.g., 

laboratory subsampling). We used correlation to test for dependence of the SCI on watershed size. To 

test the influence of season on the SCI, 78 sites with summer and winter samples were compared using a 

paired t-test. Summer was defined as May through October and winter as November through April. Within 

each season, data for repeat visits to each site were averaged. 
Development and Testing of Biomonitoring Tools for Macroinvertebrates in Florida Streams March 2007

13

To evaluate other sources of variance, such as site differences, year differences, and repeat 

visits, we used an ANOVA model to estimate variance components. For the SCI and BioRecon, we 

estimated variance associated with site, year, site x year interaction, and error. Error was defined as 

variance associated with repeat visits to the same site within a year. For the SCI data, most of the repeat 

visits within a year occurred on the same day, rather than at different times during the year. For the 

BioRecon, most of the repeat visits were on different days within the same or a different year; very few 

were same day. 

Error estimates from ANOVA (mean squared error) were used to calculate the 90% confidence 

interval for both indexes and the number of categories of biological condition that each index can reliably 

detect (Zar, 1984). Confidence limits were calculated as: 

SCI ⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛

± 645.1*

2

n

s , 

Where: 

s2

 = error variance estimated from ANOVA (mean squared error), and 

n = number of samples taken from the site. 

To calculate the number of categories each index could detect, we divided its range (0 to 100 for 

the SCI and 0 to 10 for the BioRecon) by the confidence interval. Other statistical approaches could be 

used to calculate the number of categories of biological condition that the indexes can reliably detect. For 

example, a 90% confidence interval could also be constructed based on the difference in SCI values for 2 

sites. This approach would yield a smaller confidence interval: instead of n = 1 in the equation above, n 

would equal 2 because 2 SCI values would be involved in calculating the difference. Currently there are 

no standards for which statistical model to use; therefore, we used confidence intervals around the SCI 

for a single site because they are conceptually simple and somewhat conservative from a statistical point 

of view. In other words, this approach is less likely to be controversial (Johnson, 1999; Hoenig and 

Heisey, 2001). The number of categories of biological condition that an index could reliably detect were 

only used to summarize the precision of the indexes. The assessment categories used to define 

biocriteria for water quality standards were derived from an entirely different process (see Chapter 4: 

Application of Biomonitoring Tools below). 

We also used an ANOVA model to estimate variability associated with laboratory subsampling. 

For this model, data from 59 sites with 3 subsamples each were used to estimate site variance and error 

variance. For this model, error was defined as the differences associated with subsamples from the same 

site visit. 

Statistical model assumptions 

The method described here for calculating confidence intervals assumes that the multimetric 

indexes are normally distributed. Other studies have evaluated the statistical properties of multimetric 

indexes and found that multimetric indexes for fish and invertebrates are unimodal and symmetric (Fore 

et al., 1994; Doberstein et al., 2000; Blocksom, 2003); thus, an underlying normal distribution is an 

appropriate assumption for multimetric indexes. A separate and additional consideration is whether to use 

z-values from the normal distribution to calculate 90% confidence limits or values from Student’s t 

distribution. For small sample sizes, the t-distribution should be used for calculating confidence limits 
Development and Testing of Biomonitoring Tools for Macroinvertebrates in Florida Streams March 2007

14

because variance may be underestimated; for large sample sizes (df > 30), however, the two distributions 

converge. We used data from 61 sites to estimate variance; therefore, z-values were appropriate. 

The 2 datasets used to estimate variance for the SCI and BioRecon Index were unbalanced—that 

is, the number of visits to each site varied for each site. In contrast, the dataset used to evaluate 

laboratory subsampling was balanced with 3 subsamples for each site visit. ANOVA assumes a balanced 

design but is fairly robust to violations of this assumption (Milliken and Johnson, 1992). The primary 

consequence of an unbalanced design is related to the significance testing of model effects against the 

error term, which was not the purpose of this analysis. Rather, the goal for these ANOVAs was to 

estimate an error to use in the confidence interval calculations and to evaluate the relative contributions of 

year and error to the metrics and indexes. For estimation of the error term, the large sample sizes 

mitigate any potential inaccuracy associated with unbalanced cells. Sample sizes greater than about 30 

are considered large, and this analysis had 61 sites for the SCI and 128 for the BioRecon. 
Development and Testing of Biomonitoring Tools for Macroinvertebrates in Florida Streams March 2007

15

CHAPTER 2: RESULTS 

Human disturbance gradient 

The human disturbance gradient (HDG) was a consistent predictor of biological condition for two 

independent rounds of testing. When metrics were regressed against HDG for the two rounds of data, the 

slopes and intercepts of the regression lines were nearly identical, indicating that measures included in 

the HDG yielded a reliable measure of human disturbance (Figure 1). 

Correlations among the four measures of human disturbance used to define the HDG were high, 

indicating good agreement among the different measures (see Table 4). All the individual measures, 

however, were more highly correlated with the HDG, suggesting that the HDG was a better overall 

measure of general human disturbance. The SCI showed a similar pattern and was more highly 

correlated with the HDG than the individual measures of human disturbance. 

Metric selection 

On average, 30 insect taxa were found in each sample for the 629 site visits used to test metrics. 

Diptera dominated the samples, with 23 taxa counted on average; of these taxa, about half were in the 

family Chironomidae. Of the remaining groups, 3 oligochaete, 2 Trichoptera, 1 Ephemeroptera, and 1 

Plecoptera taxa were found, on average. 

For many metrics, the change in values from least to most disturbed sites was dramatic. We 

compared the 20 best sites (HDG = 0 and the lowest values for LDI_WS) with the 18 worst sites (HDG > 

6) and found that total taxa richness went from 33 to 19, on average. The number of Ephemeroptera taxa 

declined from 2 to 0, Trichoptera taxa from 4 to 0, clinger taxa from 8 to 0, and sensitive taxa from 9 to 0. 

The relative abundance of filterers declined from 22% to 3%, while the relative abundance of tolerant 

individuals increased from 5% to 69%. 

In general, we selected at least one metric from each of the categories below according to which 

metric was most highly correlated with the HDG. If two metrics were calculated from the same set of taxa 

(redundant metrics), we only included one metric. 

Taxonomic richness 

EPT taxa richness was most highly correlated with the HDG, followed by Trichoptera and 

Ephemeroptera taxa richness (see Table 6). Both taxonomic groups disappeared from samples with high 

levels of disturbance (Figure 2). Correlation between the HDG and Plecoptera taxa richness was 

somewhat lower, primarily because very few plecopterans were found in samples from the peninsula 

region. We selected Ephemeroptera and Trichoptera taxa richness as separate metrics because they 

have the potential to respond to different types of disturbance. We did not select Plecoptera taxa richness 

as a metric because it could only be applied in two of the three regions. Total taxa richness had the next 

highest correlation and was selected as an indicator of the variety of taxa that a stream site could support. 
Development and Testing of Biomonitoring Tools for Macroinvertebrates in Florida Streams March 2007

16

Figure 1. Number of EPT taxa and total number of taxa declined as human 

disturbance increased. Nearly identical regression lines for two independent sets 

of sites (rounds 1 and 2) indicated a consistent relationship between HDG and 

biological condition. Number of EPT tax

a

0 2 4 6 8 10

0

5

10

15

20

25

Round 1

Round 2

Human disturbance gradient (HDG)

Number of taxa

0 2 4 6 8 10

0

20

40

60
Development and Testing of Biomonitoring Tools for Macroinvertebrates in Florida Streams March 2007

17

0

10

20

30

40

50

60

Total number of taxa

 Median 

 25%-75%

 Non-Outlier Range

 Outliers

0

1

2

3

4

5

6

7

Number of Eph. taxa

0

20

40

60

% Filterer

-1

0

1

2

3

4

5

6

Number of long-lived taxa

Figure 2. Six of the ten metrics included in the SCI plotted against the human 

disturbance gradient. All metrics declined with an increase in human 

disturbance. Metric values were averaged for each of the 176 sites. Samples 

with greater than 175 or less than 75 individuals were excluded before 

averaging. Number of sites within each box are shown (N). Plotted values are 

less than or equal to values on x-axis.

N = 80 41 25 8 16 6

0.8 2.4 4 5.6 7.2

Human disturbance gradient

0

2

4

6

8

Number of Trichoptera taxa

0.8 2.4 4 5.6 7.2

Human disturbance gradient

0

5

10

Number of clinger taxa
Development and Testing of Biomonitoring Tools for Macroinvertebrates in Florida Streams March 2007

18

Feeding group 

The relative abundance of filterers (percentage filterer individuals) had the highest correlation with 

the HDG and the most consistent relationship with the HDG when graphed. Other feeding groups failed to 

show the consistent decline with disturbance that percentage filterer did. We also tested this metric 

without Cheumatopsyche (a somewhat tolerant genus of Trichoptera), but the changes in correlation were 

very small, and so all taxa were retained (Spearman’s r = –0.46 with Cheumatopsyche and –0.47 

without). 

Voltinism 

Correct information about generation times was not easily obtained for all Florida taxa; therefore, 

we only tested long-lived taxa richness and percentage long-lived individuals. Long-lived taxa included 

semivoltine insects and noninsects that require more than one year to complete their life cycles. Longlived taxa richness was more highly correlated with the HDG than was relative abundance of long-lived 

individuals; consequently, long-lived taxa richness was included in the SCI. 

Habit 

Clinger metrics measured either as taxa richness or as percentage of individuals were among the 

most highly correlated metrics with the HDG. We selected clinger taxa richness for inclusion in the SCI. 

Community structure 

Percentage dominance increased with the HDG and was included in the SCI (Figure 3); 

hyperabundance failed to correlate with the HDG. Although percentage Ephemeroptera and Trichoptera 

individuals (relative abundance) were both strongly correlated with the HDG, they were calculated from 

the same information used to calculate the taxa richness of those two groups. Because the taxa richness 

metrics were more highly correlated with the HDG, the relative abundance of Ephemeroptera and 

Trichoptera was not included in the index. Correlation for the relative abundance of Tanytarsini midges 

was not quite as high as the other composition metrics (–0.45), but was included in the SCI as the best 

available measure of the chironomid assemblage. 

Sensitivity and tolerance 

The number of taxa selected as sensitive equaled ~12% of the taxa tested, and the number of 

very tolerant taxa was ~10% of the taxa tested. Many sensitive taxa belonged to the Ephemeroptera or 

Trichoptera; several chironomids were also included (Appendix C). All the Plecoptera were included, 

though most of the genera and species in this group were too rare to test. Considered as a group, out of 

191 occurrences of Plecoptera taxa, only one was found in the poor section of the HDG. All Plecoptera 

taxa showed a strong tendency to be found in the best of the good (minimally disturbed) sites. Many of 

the sensitive taxa selected by this study were previously noted by Beck (1954) and used in the Florida 

Index, but several taxa were new to the list. Many of the taxa included on Beck’s list were too uncommon 

to test with this dataset. 
Development and Testing of Biomonitoring Tools for Macroinvertebrates in Florida Streams March 2007

19

. 

20

40

60

80

% Dominance

0

5

10

15

Number of sensitive taxa

Figure 3. Four of the ten metrics included in the SCI plotted against the human 

disturbance gradient. Percent dominance and very tolerant individuals increased 

with disturbance; sensitive and Tanytarsini midge taxa declined. Metric values 

were averaged for each of the 176 sites. Samples with greater than 175 or less 

than 75 individuals were excluded before averaging. Number of sites within each 

box are shown (N).

0.8 2.4 4 5.6 7.2

Human disturbance gradient

0

30

60

90

% Very tolerant individuals

0.8 2.4 4 5.6 7.2

Human disturbance gradient

0

10

20

30

40

% Tanytarsini

N = 80 41 25 8 16 6
Development and Testing of Biomonitoring Tools for Macroinvertebrates in Florida Streams March 2007

20

The very tolerant list included one odonate (Argia sedula) and several chironomid taxa (Larsia 

spp., Cricotopus bicinctus, and Polypedilum illinoiense grp.) previously noted as sensitive and included in 

the Florida Index (Appendix D). The very tolerant list was dominated by noninsects and chironomids, 

along with several odonate taxa. One species of tubificid worm, Limnodrilus hoffmeisteri, was included as 

very tolerant, although it was present at 47% of the poor sites that failed to meet the criterion of > = 50% 

of poor sites. L. hoffmeisteri was included as very tolerant because it was very common at all sites. It 

occurred at 70 out of 85 poor sites, and tended to increase in relative abundance with disturbance. Other 

studies have also reported tolerance of L. hoffmeisteri to a variety of disturbance types (Chapman, 2001; 

Chapman et al., 1982) 

The total number of taxa with 10 or more occurrences was 168. When a large number of tests are 

conducted, questions arise regarding the probability of Type I error. The detection of a statistically 

significant result will occur with the approximate frequency of p-value. For a p-value of 0.05 and 168 tests, 

approximately 8 taxa may be selected by chance alone (0.05 * 168 = 8.4). The number of sensitive taxa 

falsely selected from this analysis is likely much lower because the criterion for selection was more strict 

than statistical significance. In other words, the p-values associated with 87% occurrence of more 

common taxa, e.g., taxa present at 20 or more sites, were much smaller. 

BioRecon metrics 

The six taxa richness metrics calculated from BioRecon data were significantly correlated with the 

HDG and highly correlated with the SCI (r ranged from 0.60–0.73; Table 7). The four SCI metrics 

calculated as percentages could not be tested because relative abundance was not recorded for 

BioRecon data. For this reason, we tested the very tolerant metric in terms of taxa richness rather than as 

a percentage. Its correlation was poor with the HDG and nonsignificant with the SCI, and therefore it was 

not included in the BioRecon Index. 

 

Multimetric index construction and evaluation 

Ten metrics were selected for inclusion in the SCI, and six were selected for the BioRecon Index. 

Most of the SCI metrics required regional scoring (Table 8; Appendix E; FDEP, 2007). In general, taxa 

richness tended to be higher in the panhandle region than in the peninsula or northeast. 

For the SCI, the highest correlation between metrics occurred between Trichoptera and clinger 

taxa (Spearman’s r = 0.77) and clinger and sensitive taxa (r = 0.84). To determine whether these metrics 

were redundant, we evaluated the taxa included in each metric. Of the clinger taxa, approximately 41% 

were also sensitive taxa, and 33% were also Trichoptera taxa. Thus, we concluded that the statistical 

correlation was due to an underlying association with human disturbance, rather than redundant 

information. 

Two metrics were somewhat skewed, that is, their distributions had long ”tails” with sparse values 

at one end of their distribution. For these metrics, we calculated the natural log of metric values before 

dividing by the range of the metric values. In this way, metric scores were more evenly spread between 0 

through 10. Two SCI metrics increased with disturbance (% dominance and % very tolerant individuals); 

all others declined with disturbance. 

BioRecon metric scoring rules differed somewhat from SCI metric scoring. Taxa richness tended 

to be lower in two regions, while Ephemeroptera taxa richness was much higher in the panhandle (Table 

9). More long-lived taxa tended to be found in all three regions for BioRecon samples. 
Development and Testing of Biomonitoring Tools for Macroinvertebrates in Florida Streams March 2007

21

Table 7. Correlation of BioRecon metrics and the old and new BioRecon Indexes with the HDG and 

SCI 

Metrics included in the BioRecon Index are noted (*). All correlations shown were statistically significant 

(Spearman’s r, p < 0.01, one-sided test, n = 116 for SCI, n = 53 for HDG). Data from multiple visits were 

averaged for each site. 

Metric/Index HDG SCI 

BioRecon Index -0.58 0.75 

Total taxa* -0.36 0.53 

Ephemeroptera taxa* -0.47 0.62 

Trichoptera taxa* -0.65 0.68 

Long-lived taxa* -0.46 0.65 

Clinger taxa* -0.48 0.67 

Sensitive taxa* -0.63 0.69 

Very tolerant taxa NS NS 

Old BioRecon Index -0.41 0.58 

EPT -0.61 0.71 

Florida Index (metric) -0.53 0.69 

Table 8. SCI metric name and range of metric values used to assign a score of 0 (indicating 

degraded condition) to 10 (indicating minimal disturbance) by region 

Metric values higher or lower than the listed range were assigned a score of 0 or 10 as appropriate. 

Metric ranges listed in reverse order indicate metrics that increased (rather than declined) with 

disturbance. For two marked metrics (*), the natural log was calculated for the range of values before 

scoring. 

SCI Metric Northeast Panhandle Peninsula 

Total taxa 16–42 16–49 16–41 

Ephemeroptera taxa 0–3.5 0–6 0–5 

Trichoptera taxa 0–6.5 0–7 0–7 

% Filterer 1–42 1–45 1–40 

Long-lived taxa 0–3 0–5 0–4 

Clinger taxa 0–9 0–15.5 0–8 

% Dominance 54–10 43–10 54–10 

% Tanytarsini * 0–26 0–26 0–26 

Sensitive taxa 0–11 0–19 0–9 

% Very tolerant * 78–0 36–0 59–0 
Development and Testing of Biomonitoring Tools for Macroinvertebrates in Florida Streams March 2007

22

Table 9. BioRecon metric name and range of metric values used to assign a score of 0 (indicating 

degraded condition) to 1 (indicating minimal disturbance) by region 

Metric values higher or lower than the listed range were assigned a score of 0 or 10 as appropriate. 

Underlined text indicates metric ranges that differed from those for the SCI. 

BioRecon Metric Northeast Panhandle Peninsula 

Total taxa 14–37 16–49 11–36

Ephemeroptera taxa 0–3.5 0–12 0–5 

Trichoptera taxa 0–6.5 0–7 0–7 

Long-lived taxa 0–6 0–10 0–7

Clinger taxa 0–7 0–15.5 0–8 

Sensitive taxa 0–11 0–19 0–9 

The SCI was highly correlated with the HDG, with minimal overlap between values for extremely 

disturbed and minimally disturbed sites (Figure 4). The BioRecon Index was also highly correlated with 

the HDG and was more responsive across the range of human disturbance than the original BioRecon 

Index (Figure 5). 

Regional scoring at the metric level resulted in an SCI that showed a similar response to human 

disturbance within each region. The regression lines for the SCI versus HDG for the northeast and 

peninsula regions were nearly identical; the line for the panhandle region diverged somewhat from the 

other two regions for more degraded sites (Figure 6; Appendix F). The divergence was associated with 

two sites in the panhandle with SCI values that were higher than expected. We did not alter the metric 

scoring rules, because several other panhandle sites did obtain the lowest SCI values for high levels of 

disturbance. 

We calculated the natural log of the area upstream of the sampling site to test for correlation 

between the SCI and watershed size. Watershed size had an underlying correlation with the HDG (see 

Table 4) because the most disturbed sites tended to have small watersheds. To avoid this confounding 

association, we divided the sites into 3 groups (low, medium, and high disturbance) before testing for 

correlation between the SCI and watershed size. The low disturbance group of sites had HDG values 

equal to 0. We defined medium disturbance as HDG values from 2 to 5 and high disturbance as HDG 

values > 5. The SCI was not significantly associated with watershed size for any of the 3 levels of 

disturbance (Figure 7; Spearman’s r = –0.01, –0.005, 0.12, p > 0.5). 

The relationship between the SCI and HDG was consistent across years as well. When site visits 

were divided into 2-year segments for 10 years of sampling (1992–2001) and the SCI regressed against 

the HDG, the 5 regression lines were nearly identical (Figure 8). This result indicates that the pattern of 

association between the SCI and HDG was independent of year. 

To verify that the SCI was correlated with the HDG, we used an independent set of 23 sites not 

used in any of the previous analyses. Correlation was high (Spearman’s r = –0.81, p < 0.001; Figure 9). 

Two sites with > 300 individuals in the samples had somewhat higher SCI values than predicted by the 

HDG, possibly due to the inflation of taxa richness measures associated with large samples. 
Development and Testing of Biomonitoring Tools for Macroinvertebrates in Florida Streams March 2007

23

Statistical precision of indexes 

During the 2004-2006 period, the target number of individuals for laboratory identification for the 

SCI was 95 to 115. For the metric testing dataset, only 27% of the 629 site visits met this target. Most 

samples had well over 100 individuals. For these data, 32% of the samples had > 175 individuals, with 

some samples ranging up to hundreds of individuals. At the other end of the scale, only 36 samples (6%) 

had fewer than 90 individuals in the entire sample. 

When we compared small sample sizes with standard (or large) sample sizes from the same 

sites, samples with < 75 individuals contributed a disproportionately large amount of variability to the 

repeat visits (Figure 10). SCI values were typically much lower for these small samples. For this reason, 

we excluded these 17 samples from the analysis of variability, which reduced the number of sites from 62 

to 61. 

Repeat visits to the same sites were used to evaluate the sources of variability for the SCI and 

BioRecon. The sources of variance included differences associated with sites, years, visits within years, 

season (summer versus winter), and laboratory subsampling. 

From the ANOVA analysis, the variance associated with sites was greatest (304.9, 71%), the 

variance associated with year was relatively small (7.7, 2%), and the variance components associated 

with site x year interaction and error were similar (54.6, 12%, and 66.6, 15%; Figure 11; Appendix F). 

For this model, error was defined as repeat visits within years, about half of which were on the same day. 

We used the estimate of error variance from the ANOVA to estimate the 90% confidence limits 

around the SCI as +/– 13.4 points, or a 27-point spread. Graphic representation of this range around the 

observed values for repeat visits captured all visits to the least disturbed sites (SCI values > 70), but 

failed to include all the visits to sites with lower SCI values (Figure 10). These sites graphically illustrate 

the site x year variance, that is, certain site-year combinations that changed in unique ways. The fact that 

undisturbed sites did not show this pattern, while disturbed sites did, suggests that the SCI was 

measuring real changes in biological condition, rather than “noise” associated with natural variability. 

Using the length of the 90% confidence interval (27 points) to divide the range of the SCI (0 to 100) 

yielded 3.7 categories of biological condition that SCI could reliably detect based on a single site visit. 

Variance components calculated for the SCI component metrics showed similar patterns to the 

SCI. Site differences and site x year variance typically contributed the largest percentages to the overall 

variance (Figure 12). The exception was long-lived taxa richness, which had a large error component 

(with error defined as variance associated with repeat visits within years, as above). The least variable 

metrics were Ephemeroptera, clinger and sensitive taxa richness, and % very tolerant. Overall, the SCI 

was less variable than most of its component metrics. 
Development and Testing of Biomonitoring Tools for Macroinvertebrates in Florida Streams March 2007

24

Figure 4. SCI declined as human disturbance increased. Index values were 

averaged for each of the 176 sites. Only samples with 75-175 individuals are 

shown. Number of sites in each box are shown (N).

N = 79 42 25 7 17 6

0.8 2.4 4.0 5.6 7.2

Human disturbance gradient

0

20

40

60

80

100

SCI

 Median 

 25%-75% 

 Non-Outlier Range

 Outliers
Development and Testing of Biomonitoring Tools for Macroinvertebrates in Florida Streams March 2007

25

Figure 5. Original BioRecon index based on three metrics (total taxa, EPT taxa, 

and Florida index; upper panel) and current BioRecon index based on six 

metrics (lower panel). Both indexes declined as human disturbance increased, 

but the 6-metric index has a broader range of response to disturbance. Values in 

boxes are <= value on x-axis.

N = 27 8 7 7 4 

0

1

2

3

Old BioRecon index

0135

Human disturbance gradient

0

2

4

6

8

10

BioRecon index

 Median 

 25%-75%

 Range

 Outliers
Development and Testing of Biomonitoring Tools for Macroinvertebrates in Florida Streams March 2007

26

Figure 6. SCI declined as human disturbance increased. Regression lines for the 

peninsula and northeast had nearly identical slopes indicating a similar index 

response across the gradient. Three visits to two sites with higher values than 

predicted (circled) increased the slope for panhandle sites somewhat. Index 

values were averaged for each of the 176 sites. Only samples with 75-175 

individuals are shown and were included in the regression. N = 58 (NE), 66 (PS), 

and 52 (PH).

Human disturbance gradient 

SCI

02468

0

20

40

60

80

100

 NE

 PS

 PH
Development and Testing of Biomonitoring Tools for Macroinvertebrates in Florida Streams March 2007

27

Figure 7. SCI was not significantly associated with watershed size (catchment 

area) when correlation was tested separately for low, moderate, and high levels 

of human disturbance (HDG). Highly disturbed sites tended to be small in 

catchment area and this relationship created a spurious correlation between SCI 

and catchment size. Shown are average SCI values for 176 sites. Actual 

catchment area shown below figures (in acres).

Ln(Catchment area)

SCI

5 7 9 11 13 15

0

20

40

60

80

100

5 7 9 11 13 15 5 7 9 11 13 15

HDG: <=1 (1, 5] > 5 150 1100 8100 60,000 440,000 150 1100 8100 60,000 440,000 150 1100 8100

60,000440,000
Development and Testing of Biomonitoring Tools for Macroinvertebrates in Florida Streams March 2007

28

Figure 8. SCI showed a similar response to human disturbance for different 

years. Shown are five regression lines representing data from 1992-3, 1994-5, 

1996-7, 1998-9, and 2000-1. The nearly identical slope lines indicate a 

consistent relationship between SCI and human disturbance for different time 

periods. 

Human disturbance gradient 

SCI

0 2 4 6 8 10

0

20

40

60

80

100
Development and Testing of Biomonitoring Tools for Macroinvertebrates in Florida Streams March 2007

29

Figure 9. Correlation between SCI and the human disturbance gradient was high 

for an independent test of 23 sites not included in any previous analyses 

(Spearman’s r = -0.81, p<<0.01). Four sites were missing data for NH3 (open 

circles) and two sites had >300 individuals (open triangles). 

012345678

Human disturbance gradient

0

20

40

60

80

100

SCI
Development and Testing of Biomonitoring Tools for Macroinvertebrates in Florida Streams March 2007

30

Figure 10. SCI values for repeat visits to 62 sites are shown. Sites were first 

sorted according to SCI value. Individual site names are not shown, but all SCI 

values for each site are plotted at a single point on the x-axis. Thus, vertical lines 

of points represent the range of SCI values at each site. Samples with < 75 

individuals are indicated by solid points (upper panel). Lines (lower panel) 

represent the +/- 15 point confidence interval for the SCI when small samples 

were eliminated.

0

20

40

60

80

100

SCI

Site

0

20

40

60

80

100

SCI
Development and Testing of Biomonitoring Tools for Macroinvertebrates in Florida Streams March 2007

31

Figure 11. Estimate of variance components and their relative percentage for 

SCI. The largest percentage of variance was associated with site differences. 

Replicates collected within a single year (primarily on the same day) accounted 

for approximately 15% of the total observed variance. Year differences 

accounted for 4% of the variance. The interaction of site and year accounted for 

16% of the total variance.

Error, 66.6, 15% S x Y, 54.6, 12%

Year, 7.7, 2%

Site, 309.9, 71%

SCI Variance Components 
Development and Testing of Biomonitoring Tools for Macroinvertebrates in Florida Streams March 2007

32

Figure 12. Variance components for SCI and its metrics. For the index and 

several of the metrics, variance associated with site differences represented the 

largest component of variance. Error variance associated with repeat visits within 

years (primarily on the same day) was highest for long-lived taxa richness. The 

least variable metrics were Ephemeroptera and sensitive taxa richness, and 

percent very tolerant individuals.

 Error

 Site x Yr

 Year

 Site

SCI

Total taxa

Eph. taxa

Tri. taxa

% Filterer

Long-lived

Clinger

% Dom.

% Tany.

Sensitive

% Toler.

0

20

40

60

80

100

% Relative variance
Development and Testing of Biomonitoring Tools for Macroinvertebrates in Florida Streams March 2007

33

For the seasonal comparison, sites were chosen to represent locations with minimal human 

disturbance, in order to eliminate this potentially confounding source of variability from the analysis. 

Unfortunately, over half the SCI values for these site visits were < 70 (the “exceptional” category), 

indicating the seasonal dataset represented a broad range of conditions. Highly degraded sites (SCI < 20) 

were not included in the seasonality analysis. SCI values were on average 3.5 points higher in winter than 

summer (paired t-test, p = 0.049). The observed difference was greater in the northeast (6.1 points) than 

the panhandle (1.6 points) or peninsula (3.6 points; Figure 13). 

Using the same data, we evaluated metrics to determine which were contributing to the seasonal 

differences in the SCI. Five metrics had values in the winter indicating better biological condition; these 

were Ephemeroptera taxa richness, % filterers, % Tanytarsini, sensitive taxa richness, and % very 

tolerant. Long-lived taxa richness had higher values in the summer, the opposite of the SCI. Although 

significant, most metric differences were rather small compared with the overall range of the metric (Table 

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

each site with itself. The exception was % Tanytarsini. With a difference of 4.6% compared with a 

possible range of 0 to 26%, it had the largest relative change from summer to winter. We did not modify 

metric scoring to correct for seasonal differences, because most metric differences were very small and 

the difference between SCI summer and winter values was small (3.5%) relative to the site differences 

through time. 

To evaluate the influence of laboratory subsampling on the SCI, we used data that included 

multiple subsamples from the same site visit. We estimated variance using ANOVA with stream site-visit 

(n = 59) as a single factor and the 3 subsamples from each visit to estimate the error variance. Error 

variance for this model was 32.86, which represents about half the error variance calculated above when 

error was defined as differences associated with repeat visits to the same site (32.86/66.6 * 100 = 49%). 

In other words, of the variance associated with repeat visits to the same site, about half the variability is 

due to laboratory subsampling. However, because the variance estimates were from different datasets, 

the comparison should be cautiously applied. After these variance estimates for this report were 

completed, laboratory subsampling methods were tested and modified to address this variability. (See 

results and discussion in Appendix G.) 

The BioRecon Index was calculated as the sum of the 6 scored metrics; therefore, the sum of the 

scores ranged from 0 to 6. We transformed the index to a range of 0 to 10 by dividing the sum by 6. The 

dataset for this ANOVA model was unbalanced, with many site-year combinations missing; therefore, the 

site x year interaction component of variance could not be estimated. The components of variance were 

similar to those for the SCI, in that the largest component of variance was associated with site differences 

and year variance was again small (Figure 14). The error variance for this model was 1.46, with error 

derived from repeat visits to the same sites. A 90% confidence interval for the BioRecon Index yielded a 

limit of +/- 2, or a length of 4.0 points. Dividing the range of the BioRecon index (0–10) by 4 translated into 

2.5 categories of biological condition that the BioRecon could statistically distinguish. This result 

supported the decision to identify three assessment categories for the BioRecon index in a regulatory 

context (see Chapter 4: Application of Biomonitoring Tools below). 
Development and Testing of Biomonitoring Tools for Macroinvertebrates in Florida Streams March 2007

34

Figure 13. SCI values by region (northeast, peninsula, and panhandle) and 

season. Summer samples (circles in center of box) tended to be lower than 

winter samples (diamond centers) in the northeast and peninsula regions. N = 23 

(NE), 34 (PS) and 31 (PH).

ne ps ph

0

20

40

60

80

100

SCI
Development and Testing of Biomonitoring Tools for Macroinvertebrates in Florida Streams March 2007

35

Figure 14. Variance components for BioRecon and its component taxa richness 

metrics. Variance associated with site differences represented the largest 

component for metrics and index. Error variance associated with repeat visits 

within years was similar across metrics ranging from 20-30% of the total. 

Variability associated with a specific year was relatively small for the index and 

metrics.

 Error

 Year

 Site

BioRecon

Total

Ephem.

Trichop.

Long-lived

Clinger

Sensitive

0

20

40

60

80

100

% Relative variance
Development and Testing of Biomonitoring Tools for Macroinvertebrates in Florida Streams March 2007

36

Table 10. Results for seasonal comparison of the SCI and its component metrics 

Differences between summer and winter samples (negative value indicates higher winter values). 

Significant differences noted (* p < 0.05, paired t-test). 

Index/metric Difference 

SCI –3.5 * 

Total taxa 0.1 

Ephemeroptera taxa –0.4 * 

Trichoptera taxa 0.3 

% Filterer –5.2 * 

Long-lived taxa 0.6 * 

Clinger taxa –0.2 

% Dominance –1.9 

% Tanytarsini –4.6 * 

Sensitive taxa –0.9 * 

% Very tolerant 2.0 * 

These categories for the BioRecon may be somewhat conservative, particularly for sites with 

good biological condition, because the variability estimated from repeat visits was much higher for 

moderately disturbed sites than for sites with minimal disturbance (Figure 15). If variance were estimated 

using only minimally disturbed reference sites, the error variance would be smaller. To determine whether 

high variability was associated with natural variability or changes in human activities, we selected the 

sites with the most variable BioRecon values through time and asked regional biologists to note any 

change in human activities at the site (Table 11). Four of the sites improved where roads had been paved 

to eliminate sediment runoff. Other changes were associated with pesticide spraying, the elimination of 

runoff from a wastewater treatment plant, road closure, and the cessation of fertilization upstream. The 

results from this anecdotal analysis suggest that much of the BioRecon variability was associated with 

real changes in human disturbance, rather than natural variability. 

Variance results for the BioRecon components metrics were similar to those for the SCI metrics, 

with site differences contributing the largest component of the total variance (see Figure 14). In general, 

the metric variance due to nuisance sources of variability (year or error) was smaller for three of the 

BioRecon metrics than for the same SCI metrics. Lower error variance for total taxa, Trichoptera, and 

long-lived taxa richness suggests that the BioRecon protocol may provide more reliable estimates of 

these metrics. Direct comparisons of the results from different datasets should be cautiously applied, 

particularly for relative variance estimates, because they depend on the magnitude of the site differences. 
Development and Testing of Biomonitoring Tools for Macroinvertebrates in Florida Streams March 2007

37

Table 11. STORET site code, region, station nickname, observed change in BioRecon values 

through time, and possible reason for change 

Listed sites represent locations with the highest variability observed for BioRecon values. 

STORET Region Station Change Reason 

20010521 Peninsula JIMREF Declined Unknown 

20010525 Peninsula LILHAW@40 Declined Unknown 

20030341 Northeast CECFLD7 Improved Unknown 

32030024 Panhandle SFBEARREF Improved Intermittent pesticide spraying 

33020064 Panhandle PBRNCSTLR Improved 

Section 319 nonpoint source (NPS) 

restoration project to pave a road and stop 

sediment 

33020065 Panhandle BRSTWRKB Improved Section 319 NPS restoration project to pave 

a road and stop sediment 

33020067 Panhandle CNOECPBRNR Improved Section 319 NPS restoration project to pave 

a road and stop sediment 

33020082 Panhandle SANHOLTST Improved Section 319 NPS restoration project to pave 

a road and stop sediment 

33030039 Panhandle TRKLNHDCM Improved 

Partnership with state forest and the Nature 

Conservancy closed logging road and 

eliminated sediment 

33030102 Panhandle TURKEYCR Improved Summer aerial pesticide spraying 

33040016 Panhandle WILLIAMTST Improved Runoff from wastewater treatment plant 

sprayfield eliminated 

33040017 Panhandle DDFLCWC189 Improved Ended fertilization upstream of sites in state 

forest 

24030013 Peninsula HILSTP4REF Improved Unknown 

22020062 Panhandle OKLREF Variable Unknown 

27010050 Peninsula MOSESUS1 Variable Unknown 

27010583 Peninsula LITLTOMOKA Variable Unknown 
Development and Testing of Biomonitoring Tools for Macroinvertebrates in Florida Streams March 2007

38

Figure 15. Range of BioRecon index values for repeat visits to 128 sites. Site 

names are not shown; however, each vertical line represents a single site with 

the minimum and maximum index values plotted as the endpoints. Note that high 

and low index values were less variable for repeat visits through time.

Site

0

2

4

6

8

10

BioRecon index
Development and Testing of Biomonitoring Tools for Macroinvertebrates in Florida Streams March 2007

39

CHAPTER 3: DISCUSSION 

The SCI and BioRecon Index, along with their component metrics, were developed to assess the 

biological condition of stream sites using samples from the stream macroinvertebrate assemblage. These 

assessment tools will be used primarily to define biological criteria for freshwater streams and to evaluate 

the effectiveness of best management practices (BMPs) (Vowell, 2001). Three results support the use of 

the SCI for this purpose. First, the component metrics showed a strong and consistent response to an 

independent measure of human disturbance (HDG). The SCI was also highly correlated with the HDG 

using an independent dataset for verification. Second, the SCI was independent of watershed size and 

geographic region. This result means that the SCI can be used across the state to compare stream sites. 

Third, the influence of seasonal and annual differences on the SCI was relatively small. Index values 

were about 3.5% higher for winter samples, and about 2% of the total variance of the SCI was associated 

with year of sampling. Although low, these sources of variance should continue to be evaluated with more 

complete sampling designs because the influence of both could potentially be eliminated by adjusting 

index values according to season or year. 

Human disturbance gradient 

The HDG simplified several aspects of metric and index testing. First, it provided a more stringent 

test of metric association with disturbance than a comparison of reference versus test sites. Percentage 

Diptera individuals serves as an example of this. This metric was included in the old SCI because it 

increased at disturbed sites; however, across a gradient of disturbance, it failed to consistently respond. 

Second, the HDG was independent of biological condition because no measures of biological condition 

were included. Therefore, metric expectations could be defined for equivalent levels of disturbance within 

each region. As a consequence, it was not necessary to correct the multimetric index for regional 

differences. Third, objective selection criteria based on association with the HDG were used to define 

individual taxa as sensitive or very tolerant. Finally, the HDG resolved a spurious correlation between 

watershed size and the SCI. During initial analysis, the SCI appeared to be correlated with watershed 

size because the most disturbed sites happened to be very small. After controlling for the HDG, the 

correlation between watershed size and the SCI was eliminated. The HDG can also be used to describe 

the intensity of disturbance associated with specific biological losses, as measured by the SCI and its 

component metrics. 

Biological indicators 

Multimetric indexes strive to integrate measures from a diverse set of biological categories for two 

reasons. First, monitoring different aspects of the biological assemblage improves the likelihood of 

detecting changes associated with different types of disturbance. Second, the potential exists to define 

metric signatures, that is, associations between specific metrics (or suites of metrics) that correspond to 

specific human activities (Norton et al., 2000; Yoder and DeShon, 2002). Metric signatures are 

particularly relevant to the TMDL process, which allocates responsibility for degraded stream condition 

among the various human activities in the watershed (Karr and Yoder, 2004; EPA, 2000). 

Though not specific to any particular group, total taxa richness represents a general measure of 

the biological complexity found at a site. This metric is one of the most widely applied in biomonitoring 

programs because of its consistent decline with human disturbance for stream invertebrates (Kerans and 

Karr, 1994; Fore et al., 1996; Karr and Chu, 1999; Klemm et al., 2002), as well as fish (Hughes et al, 

1998), terrestrial invertebrates (Kimberling et al., 2001), and birds (Bryce et al., 2002). Ephemeroptera 

and Trichoptera taxa richness have also been widely applied, though often combined with Plecoptera into 

a single (EPT) metric. Splitting these taxonomic groups provides the opportunity for metric signatures 
Development and Testing of Biomonitoring Tools for Macroinvertebrates in Florida Streams March 2007

40

associated with different types of disturbance. For example, Ephemeroptera taxa are known to be 

particularly sensitive to metals and will disappear before other taxa (Clements et al., 2000; Fore, 2002). 

On the other hand, an increase in Ephemeroptera taxa may indicate increased nutrients (Miltner and 

Rankin, 1998). Filterers are expected to decline in response to disturbance because of the increase in 

sediment and silt that can damage or clog nets. Long-lived taxa are expected to decline as human 

disturbance alters the natural flow regime, because these taxa require water in the channel year-round. 

Pollution events of short duration may also eliminate these taxa, while other taxa may colonize from 

unaffected sites. 

Clinger taxa have morphological and behavioral adaptations that allow them to cling in fast water. 

Human development near stream sites in Florida often translates into eroding sand that can smother 

habitat and eliminate these taxa. Other studies have found these taxa to be sensitive to disturbance 

associated with mining and urbanization (Fore et al., 2001; Mebane, 2002). Percentage dominance of the 

most abundant taxon increases with disturbance as the natural taxonomic diversity declines and very 

tolerant taxa dominate samples. This metric represents a measure of the overall structure of the 

assemblage and has been associated with disturbance in several regions (Klemm et al., 2002). The 

Tanytarsini midges are used as indicators by Ohio because of their general sensitivity to human 

disturbance (DeShon, 1995). Three Tanytarsini midge taxa were also selected for inclusion in the list of 

sensitive taxa for this study. 

Several of the sensitive taxa agreed with Beck’s (1954) designations used to calculate the Florida 

Index; however, a few reversals were noted for taxa that satisfied the criteria for very tolerant. The 

primary difference between the sensitive taxa list and the Florida Index list was the exclusion of many 

rare taxa that could not be tested. The list of sensitive taxa is now shorter; nonetheless, the objective 

criteria used to select these taxa make these metrics easier to justify in a regulatory context. 

The set of metrics selected for Florida represents a convergence with similar studies and 

programs in other states (Karr, 1998). Many of the Florida metrics also had a strong correlation with 

disturbance in Colorado, Idaho, Washington, Tennessee, and Japan, where they responded to a diverse 

set of human activities including timber harvest (Fore et al., 1996), recreation (Karr, 1998), urbanization 

(Fore et al., 2001; Karr and Morishita Rossano, 2001; Morley and Karr, 2002), agriculture (Kerans and 

Karr, 1994), and mining (Mebane, 2001; Fore, 2002; Mebane, 2002). The emergence of a core set of 

metrics across a variety of geographic contexts further supports the use of these metrics as biological 

indicators. 

Sources of variance 

We evaluated five sources of nuisance variance for the SCI and two for the BioRecon Index. For 

the SCI, we estimated variance associated with laboratory subsampling, repeat visits within a year (error), 

different seasons, different years, and site x year interaction. For the BioRecon, we estimated variance 

due to repeat visits within a year (error) and different years. 

One of the largest contributors to the differences observed for the SCI values collected from the 

same site within the same year was the variability associated with laboratory subsampling. Subsampling 

represented approximately 49% of the variance due to repeat visits to a site within the same year. 

Unfortunately, the two variance estimates were derived from different datasets; thus, this percentage 

should be interpreted with caution. Nonetheless, subsampling in the laboratory seems to be a significant 

source of variability. Furthermore, the subsampling method for this small study was more carefully 

controlled than for routine sampling, in that the target of 100 individuals per sample that was in use at the 

time of this study was more frequently attained. Thus, the subsampling analysis did not include any 

additional variability associated with variable sample sizes; consequently, variance due to subsampling 

may actually be higher for routine samples. (See Appendix G for analysis and changes made to the 

subsampling protocol after the evaluation of sources of variance was completed for this document.) 
Development and Testing of Biomonitoring Tools for Macroinvertebrates in Florida Streams March 2007

41

Site by year interaction represents the variability associated with some unique site-year 

combinations. This source of variance represented index values from sites that increased (or decreased) 

from one year to the next in a different or more extreme manner than did other sites during those same 

years. The differences could be due to natural variability, e.g., the effect of a dry year on some sites but 

not others, or actual changes in biological condition associated with human influence. These two sources 

could not be distinguished using the current data, because all sources of human disturbance were not 

quantified. For example, agriculture is a dominant land use in Florida. Pesticide treatments represent a 

potentially significant impact to stream invertebrates but could not be evaluated for this study. 

Nonetheless, the higher variability in both the SCI and BioRecon Index values observed for sites with 

moderate index values compared with sites with high index values suggests that both indexes may be 

measuring real changes associated with human land use, rather than nuisance variance. Other studies 

have found a similar pattern of increased variance associated with greater human disturbance (Karr et al., 

1987; Steedman, 1988). For the BioRecon Index, variability for several sites was explained by changes in 

human land use. Road paving to eliminate erosion, the elimination of toxic runoff, and the discontinuation 

of fertilization were all associated with improved BioRecon values. 

For the SCI, winter index values were significantly higher than summer values collected from the 

same sites; however, the difference was small (3.5%). We did not adjust metric scores to eliminate the 

seasonal difference in the SCI because individual metric differences were relatively small and the 

differences in SCI values was due to a cumulative effect across several metrics. The seasonal difference 

was also relatively small compared with the variance due to other sources, such as repeat visits within a 

year or laboratory subsampling. The influence of seasonality awaits a better dataset that compares index 

values by season for sites with little or no change in human disturbance. 

For the BioRecon Index, the variance associated with repeat visits within years was somewhat 

higher than the variance observed for the SCI. This is largely because the site x year interaction term 

could not be estimated (due to incomplete data), and this variance was included in the error term. In 

addition, the BioRecon data included mostly samples from different days within the year, rather than the 

predominantly same-day samples used for the SCI. As a consequence, the variance estimate for the 

BioRecon Index may be somewhat high and the precision of the index underestimated. 

The higher variability noted for the BioRecon may be due to human disturbance at the site, 

different levels of sampling effort applied by field biologists, the smaller number of metrics in the index (6 

for the BioRecon versus 10 for the SCI), or some combination. In the field, biologists examine the sample 

until all unique taxa have been identified. They also bring back examples of each taxon for laboratory 

verification. Biologists may vary both in the amount of time they spend searching for unique taxa and in 

the number of taxa returned to the lab. The protocol specifies that a minimum of 5 examples of each 

unique taxon be returned for verification. If multiple examples of each taxon are returned, the probability 

of identifying additional taxa increases as a function of the number of individuals returned. This is 

because many field identifications can only be made to the genus level, while in the lab more taxa can be 

identified to the species level. The precision of this index could likely be improved by applying the same 

level of effort to each sample in the field. In addition, the number of individuals returned to the laboratory 

for verification should be made consistent for each sample. (Note: these issues have been addressed in 

the current version of the BioRecon SOP.) 
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42

CHAPTER 4: APPLICATION OF BIOMONITORING 

TOOLS 

This section takes the results from the previous chapters and describes how the biological 

monitoring tools can be integrated into a regulatory framework for managing streams under the Clean 

Water Act. States are required to report to Congress the water bodies that are failing to support their 

designated uses. FDEP intends to use biological criteria along with physical and chemical criteria to 

determine whether stream sites are meeting their designated uses, particularly uses related to aquatic life 

use support. In this section the regulatory framework of the Clean Water Act is summarized; an approach 

for defining more specific designated uses for aquatic life use is described; and biological criteria based 

on the SCI and BioRecon Index are proposed. 

Two approaches were used to define categories of biological condition. The primary approach 

involved the U.S. EPA’s Biological Condition Gradient, which we used as a framework for defining 

biological standards in a regulatory context (EPA, 2005; Davies and Jackson, 2006). A secondary, but 

parallel approach, adopted by many states, defines biological standards based on measurements, such 

as SCI, collected from a set of reference sites (EPA, 2006a, 2006b). We examined the reference site data 

and the variability associated with the SCI determination as a secondary line of evidence to support the 

impairment threshold decision. Additionally, the reference site data was used as the primary basis for 

proposing the biological criteria threshold for sites that may be considered of “exceptional” quality. 

Regulatory framework 

The regulatory context for the management of surface waters is derived from the mandates of the 

Clean Water Act (CWA; EPA, 2005; Davies and Jackson, 2006). Within this framework, states and tribes 

must adopt water quality standards to protect their waters. Water quality standards (WQS) include three 

parts: 1) designated uses, 2) numeric and narrative criteria that protect the uses, and 3) antidegradation 

policies to prevent deterioration of high-quality waters (EPA, 2006a). The CWA authorizes the U.S. EPA 

to determine appropriate minimum levels of protection and provide national oversight to state programs; 

however, considerable flexibility and discretion are left to states and tribes to design their own programs 

and establish levels of protection beyond the national minimums (EPA, 2005). WQS are part of state law 

and define the water quality goals for a water body by designating the use(s) and setting criteria 

necessary to protect the use(s). 

Thus, individual state WQS provide the foundation for the management and protection of surface 

waters and pollution control programs. WQS provide the basis for determining attainment or 

nonattainment of water bodies in order to identify impaired waters and prioritize water bodies for TMDL 

(Total Maximum Daily Load) establishment. Historically, states and tribes have taken different approaches 

to defining their WQS. Different approaches are acceptable to EPA as long as a minimum standard is 

protected. In other words, states are encouraged to be more protective than the national minimum. Once 

WQS are in place, states are authorized to implement monitoring programs that allow them to report on 

the attainment of those standards and to identify and prioritize waters not attaining standards. 

Designated uses 

All of the water quality protections available under the CWA follow from the water body’s 

designated use. All state water bodies have designated uses as specified in water quality standards, 

whether or not the uses are currently being attained. When classifying water bodies and determining 

designated uses, a state must take into consideration public water supplies, protection and propagation of 

fish, shellfish, and wildlife, recreation in and on the water, and agricultural, industrial, and other purposes, 

including navigation. The language of the CWA includes a general mandate for protecting waters for 
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43

fishing and swimming and the protection and propagation of aquatic life. States differ in their approaches 

to defining designated uses under this general mandate for the protection of aquatic life. 

Some states designate a general “aquatic life use” or have narrative biological criteria, which is 

often a general statement such as “aquatic life communities shall be maintained similar to aquatic life as 

naturally occurs”. Florida’s current description for Class III waters provides an example of this approach: 

“recreation and propagation and maintenance of a healthy, well-balanced population of fish and wildlife”. 

For states with a general narrative biological criterion, single thresholds are typically set based on a 

multimetric index of biological integrity, with expectations determined according to regional reference 

conditions (U.S. EPA, 2006a; Stoddard et al., 2006). Water bodies are then assessed to determine 

whether they support or fail to support a single general use for aquatic life. 

 Currently in Florida, FDEP has convened a Designated Uses Policy Advisory Committee 

(DUPAC) to explore an alternative system of designated use classification for Florida. The DUPAC is 

considering the environmental, social, and economic consequences of use designation, and includes the 

interested public, the regulated community, water quality managers, and scientists. The scheme currently 

proposed through the DUPAC involves the separation of human and aquatic life uses and recommends a 

tiered system of aquatic life uses. 

 To be consistent with the anticipated direction of the FDEP DUPAC, this chapter proposes three 

categories for the SCI. Subject to future rule making decisions, those three categories may be 

conceptually interpreted as “exceptional” (Category 1), “healthy” (Category 2), and “impaired” (Category 

3). In this way, exceptional stream resources are identified to support the intent of the anti-degradation 

component of the water quality standards (see below). Additionally, the “impaired” category supports 

decisions needed by other regulatory programs, such as the TMDL program. 

Water quality criteria 

Water quality criteria should be closely tied to a specific designated use and should provide a 

meaningful assessment of whether the use is supported. The assumption is that when criteria are met, 

the designated use is protected. Water quality criteria may be either narrative statements that describe 

the conditions needed for a water body to achieve its designated use, or numeric levels of pollutants or 

chemicals that should not be exceeded so as to protect the designated use. While all States have 

adopted narrative criteria for the protection of aquatic life, only some have adopted numeric criteria such 

as constituent concentration levels for various pollutants or specific biological index values. Biocriteria 

refer specifically to criteria based on biological aspects of the water body, typically measures of fish, 

macroinvertebrates, plants or algae. 

Anti-degradation policy 

This component of the WQS has traditionally received less attention than the other two 

components. An anti-degradation policy is required to ensure 1) water quality is sufficient to support 

existing uses; 2) where water quality is better than that needed to support aquatic life uses, that level of 

quality must be protected unless a public process determines that quality can be lowered to allow 

economic or social development; and 3) the water quality of water bodies with exceptional recreational or 

ecological significance must be maintained and protected (EPA, 1985). Thus, states are required to 

provide a framework for managing water bodies when their water quality changes. It is anticipated the 

DUPAC will establish an anti-degradation policy to continue to maintain the water quality and biological 

health of exceptional water bodies. 
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Tiered aquatic life uses 

In 2001, the National Research Council published a report on Assessing the TMDL Approach to 

Water Quality Management in which the authors recommended tiering designated uses for improving the 

decision-making related to setting water quality standards (NRC, 2001). The NRC found the CWA’s goals 

to be too broad to provide the operational definition of designated uses needed to support aquatic life, 

and recommended greater specificity in defining aquatic life uses. For example, rather than stating that a 

water body needs to be “fishable,” the designated use should specifically describe the expected fish 

assemblage (e.g., cold water fishery, warm water fishery, or salmon, trout, bass, etc.). Tiered aquatic life 

uses (TALUs) are bioassessment-based statements of expected biological condition in specific water 

bodies that allow more precise and measurable definitions of designated aquatic life uses (EPA, 2005). 

Designated uses are written in qualitative, narrative terms; therefore, the challenge is to relate a 

water quality criterion to the designated use. Establishing this relationship is more straightforward when 

the water quality measure, or criterion, is closely and meaningfully related to the designated use. For this 

reason, the NRC recommended the use of biological information to define more appropriate aquatic life 

uses. The TALU approach provides an interpretative framework for developing a technical program that 

will tighten the linkage between narrative use statements and numeric biological criteria. 

Designated aquatic life uses represent a state’s biological goals for their water bodies. Tiered 

aquatic life uses rely on biological information to more precisely define these goals relative to natural 

conditions. Bioassessments can then be used to measure attainment of the goals (EPA, 2005). Thus, the 

TALU approach is designed to support the implementation of biocriteria, and the scientific knowledge of 

aquatic ecology that they represent, into state and tribal water quality standards. The driving motivation 

here is to ensure that listings of impaired waters are scientifically defensible and to identify and protect 

high quality water resources. 

Determining the impairment threshold using the Biological Condition 

Gradient model 

The objective of the Clean Water Act is to “restore and maintain the physical, chemical and 

biological integrity of the Nation’s waters.” To achieve this, a consistent, uniform interpretation of 

biological condition must be applied to data derived from any assessment methodology (Davies and 

Jackson, 2006). This interpretive framework must define ecological condition, allow for variability 

exhibited in the reference condition, and take into account the designated use of a given waterbody. The 

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

Gradient (BCG), that illustrates how ecological attributes change in response to increasing levels of 

human disturbance. The BCG is a conceptual model that assigns the relative health of aquatic 

communities into one of six categories. The BCG is based on fundamental ecological principles and has 

been extensively tested and verified by aquatic biologists throughout the U.S. 

The BCG utilizes ten biological attributes of aquatic systems which respond predictably to 

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

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

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

The attributes are: 

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

II. Sensitive and rare taxa 

III. Sensitive but ubiquitous taxa 

IV. Taxa of intermediate tolerance 
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V. Tolerant taxa 

VI. Non-native taxa 

VII. Organism condition 

VIII. Ecosystem functions 

IX. Spatial and temporal extent of detrimental effects 

X. Ecosystem connectance. 

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

defined during a series of national workshops with experienced aquatic biologists from across the U.S. 

(Davies and Jackson, 2006). The six tiers are described as: 

1. Natural or native condition 

2. Minimal changes in structure of the biotic community and minimal changes in ecosystem function 

3. Evident changes in structure of the biotic community and minimal changes in ecosystem function 

4. Moderate changes in structure of the biotic community with minimal changes in ecosystem 

function 

5. Major changes in structure of the biotic community and moderate changes in ecosystem function 

6. Severe changes in structure of the biotic community and major loss of ecosystem function. 

Davies and Jackson (2006) recommend that regional biological experts adapt the conceptual tiers 

described by the BCG to local conditions using data from a regional monitoring program. The tiers can 

then be used in assessment and biocriteria. To develop a BCG for Florida streams, a panel of 22 

experienced aquatic biologists were convened as part of a workshop. Participants were asked to apply 

and calibrate the general BCG model to stream data collected from Florida streams (Table 12; Appendix 

H). Results from the workshop were used to define biological thresholds for the SCI. 

The panel of experts was provided with macroinvertebrate taxonomic lists and biological metrics 

for 15 reference stream sites and 30 test samples, equally divided into Florida’s three bioregions 

(Panhandle, Northeast, and Peninsula). Data collection and metric calculation followed standard FDEP 

protocols. The panel was NOT given the SCI value associated with any of these sites, or any physical, 

chemical, or habitat descriptions of the sites (other than the Bioregion location). Detailed descriptions of 

the BCG attributes and tiers were provided to the panel prior to the meeting. On the day of the meeting, a 

presentation reiterated these definitions, accompanied by extensive discussion (Frydenborg and Miller, 

2007). 

The panel of 22 Florida scientists assigned the 30 test samples to BCG Tiers 1 through 6, 

generating a total of 660 individual sample assessments. After discussion, there was general agreement 

that it was difficult to identify Tier 1 sites (pristine, natural condition). This was primarily due to inadequate 

information in the data sets to properly characterize BCG Attribute 1, “Historically documented, sensitive, 

long-lived or regionally endemic taxa.” The 20 dip net sweep sampling method, along with the laboratory 

sub-sampling procedure, do not result in a complete biological census of any given site. This lack of data 

on rare, endemic taxa made it difficult for the group to judge a “pristine” condition. Even given this 

constraint, there were approximately twenty instances where panelists assigned a score of Tier 1 to a 

sample, although none of the samples was unanimously assigned to Tier 1 by all 22 experts. However, 

there was complete agreement among the panelists that Tier 2 represented the minimally disturbed 

reference condition, which was considered, for practical purposes, to be almost indistinguishable from 

“natural.” 
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46

Table 12. BCG Workshop Participants 

Name of regional expert, affiliation, and years of experience in benthic macroinvertebrate identification, 

Expert name Affiliation Years 

Jerrell Daigle DSA, Inc. >20 

Dana Denson FDEP Central District 10-15 

Doug Durbin Biological Research Associates >20 

John Epler John Epler Consulting >20 

Julie Espy FDEP Tallahassee 5-10 

David Evans Water and Air Research >20 

Michael Heyn FDEP Tallahassee >20 

Joy Jackson FDEP Tallahassee 10-15 

Deron Lawrence Biological Research Associates 10-15 

Laura Line Water and Air Research 10-15 

Rob Mattson St. Johns River Water Management District >20 

Peggy Morgan FDEP Southwest District >20 

Patrick O'Connor FDEP Northeast District >20 

Manuel Pescador Florida Agricultural and Mechanical University >20 

Marianne Pluchino Reedy Creek Improvement District >20 

Eric Pluchino FDEP Orlando District >20 

Donald Ray FDEP Northwest District >20 

Johnny Richardson Leon County 10-15 

Gitta Schmitt FDEP Southwest District 5-10 

Mary Szafraniec Southwest Florida Water Management District 5-10 

Albert Walton FDEP South District >20 

Gary Warren Florida Fish and Wildlife Commission >20 

During the workshop, the 22 panelists BCG scores were tabulated for each test sample and the 

panel discussed the results. In all cases, the majority of panelists’ scores were assigned to the same tier, 

with a small number of the adjacent tiers represented. After discussion on specifics associated with the 

data set, the panelists were given the opportunity to modify their score. 

During the national workshops sponsored by EPA, different groups of experts independently 

concurred that the ecological characteristics described by Tiers 1–4 corresponded to how they interpret 

the Clean Water Act (CWA)’s interim goal for protection and propagation of aquatic life. They further 

identified Tiers 1 and 2 as indicative of biological integrity (U.S. EPA, 2005). Subsequent to these national 

workshops, several states have adopted a line of impairment near the threshold between Tiers 4 and 5 

(U.S. EPA, 2006b). The Florida panel of experts was similarly asked to offer their opinion as to where 

along the 6 tiered rankings they believed the interim goal of the Clean Water Act (CWA) was met. In other 

words, sites below a selected tier threshold would be considered “impaired.” There was unanimous 

agreement that Tier 2 represented reference conditions, and that Tier 3 clearly met the CWA interim goal 

for biological integrity. There was also unanimous agreement that Tier 5 did not meet the CWA biological 

integrity goal. Approximately half the group thought the impairment threshold occurred between Tiers 4 

and 5. The other half of the Florida experts proposed the impairment line to be drawn between Tiers 3 

and 4. Those panelists expressed a concern that management action may not be taken on sites until they 

were considered “impaired.” In other words, those panelists wanted the impairment line to include a buffer 

to allow more time for management action to prevent sites from becoming impaired. Taking the 
Development and Testing of Biomonitoring Tools for Macroinvertebrates in Florida Streams March 2007

47

recommendations of all 22 experts, we calculated the mid-point to define the line of impairment to be 

below Tier 4. 

 From this definition for the line of impairment, BCG scores were related to SCI values to 

determine a corresponding numeric criterion for the support of aquatic life use. Two statistical approaches 

were used: linear regression and change point analysis. Using linear regression, we found that an SCI 

value of 34 corresponded to a BCG score of 4 (Figure 16). Based on this analysis, the impairment 

threshold could be set at an SCI value < 34. Step function regression, also known as change point 

analysis, identified a significant difference (change or break point) corresponding to an SCI score of 36 

(Figure 17; Appendix I; Niu and Miller, 2007). Based on this analysis, the impairment threshold could be 

set at an SCI value < 36. Thus, the two different statistical methods yielded very similar results. We 

averaged the SCI values derived from the two approaches to define the line of impairment to occur at SCI 

< 35. Using results from the BCG approach and the recommendations of 22 regional experts, SCI values 

from 0-34 are proposed as Category 3 (provisionally interpreted as “impaired”). SCI scores >= 35 

represent Categories 2 and 1 (provisionally interpreted as “healthy” and “exceptional”; see below). 

123456

Biological Condition Gradient score (mean)

0

20

40

60

80

100

SCI

Figure 16. SCI values were highly correlated with the BCG scores assigned by the experts. Shown are the 

regression line (solid black) and its 90% confidence intervals (dashed blue lines). Red dashed lines indicate 

the SCI threshold value of 34.4 based on a BCG score of 4 as the line below which biological impairment 

was defined. 
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48

100 80 60 40 20 0

SCI

1

2

3

4

5

Biological Condition Gradient score 

6

(median)

Meets CWA Interim Goal 

for Biological Integrity

Does Not Meet CWA 

Interim Goal for 

Biological Integrity

Figure 17. Change point model showing the relationship between the median BCG scores for each test 

sample and the corresponding SCI scores (change points = 35.8 and 9.1, alpha < 0.05). 

Defining biocriteria using reference site condition 

Another U.S. EPA-recommended approach for setting the standard for biological condition 

involves analyzing reference sites. Reference sites may be defined as those with minimal human 

disturbance, approximating the “natural” condition of a region (U.S. EPA, 2006a, b; Hughes, 1995 

Stoddard et al. 2006). From a set of reference sites, measures of site condition, such as a multimetric 

index of biological condition, can be used to define thresholds for acceptable and unacceptable biological 

condition. 

Lower percentiles, e.g., the 10th or 25th percentile, of the distribution of biological index values observed in 

reference sites are used by many states to define biological criteria. The selection of the specific 

percentile depends on the relative confidence associated with the initial selection of reference sites. 

Defining the limit of reference condition using a subset of reference sites recognizes that natural 

stressors can affect the biology of a stream, introducing variability into the reference site data distribution. 

In Florida, natural drought will cause dry or stagnant flow conditions, which may be associated with lower 

SCI values. Substrate diversity at some reference sites may be naturally limiting, which also may reduce 

SCI values. For Florida streams, our confidence was high that reference sites truly represented reference 

condition; therefore, we selected the 10th (rather than the 25th) percentile. 

Reference site selection 

To develop the HDG, FDEP biologists selected sites from each of their regions that represented 

the broadest possible range of human influence, including sites that represented the very best or 

minimally disturbed sites in their regions. In the early years of FDEP’s bioassessment program, every 

effort was made to identify the best examples of reference sites in the state. Over time, program 
Development and Testing of Biomonitoring Tools for Macroinvertebrates in Florida Streams March 2007

49

emphasis has shifted to other sampling needs, including status and trend monitoring, TMDL assessment, 

and springs studies (Figure 18). Due to the extensive experience and knowledge of DEP regional 

biologists, most of the minimally disturbed stream sites have already been identified and sampled. In 

contrast, other states that started their biological monitoring programs later may have identified their 

reference sites from a probabilistic sampling design. The criteria for selecting reference sites may be the 

same for both data sets, but the results may differ. Specifically, hand-picked reference sites in Florida 

may represent the very best sites available while other sites meeting the criteria for reference sites may 

have lower SCI values due to natural variability or minimal human disturbance. 

Reference sites were initially selected as sites with HDG = 0. Recall that HDG = 0 does not mean 

no human disturbance, but minimal human disturbance. The criteria for HDG = 0 were: 

● NH3 < 0.1 mg/L 

● Habitat Assessment Index > 65% of total points (scored from 0–100% of total points) 

● Hydrological index < 6 (scored from 0–10), and 

● LDI < 2. 

Sites meeting these initial conditions were further scrutinized to eliminate sites potentially 

influenced by human activities not captured by the HDG. Additional criteria applied to reference sites 

included: 

● Habitat Assessment Index > 75% of total points 

● Site average conductivity < 600 umhos/cm 

● No receiving sites for NPDES-permitted wastewater treatment facility discharges, based on a 

review of GIS coverage information from the Water Facilities Regulation (WAFR) database, 

and 

● The number of individuals identified ranged from 100-175 (inclusive). 

These criteria were established to select the very best reference sites from among sites with a 

HDG score of 0. The habitat assessment index criterion was raised from 65% to 75% because sites 

scoring 75% or greater were equivalent to all or most of the habitat assessment parameters scoring as 

“Optimal” or “Suboptimal” (FDEP, 2004). The < 600 umhos/cm criterion for conductivity was based on 

results uncovered while evaluating a revision of the Class III freshwater specific conductance criterion 

(Chapter 62-302.530(61) F.A.C.) This study found that for a specific conductance of 20 umhos/cm 

approximately half of the taxa within a SCI sample would be sensitive taxa and for 600 umhos/cm the 

proportion of sensitive taxa decreased to almost none (FDEP, 2005). For the third criterion, sites could 

not be located below an active facility discharge based on review of GIS coverages and information from 

district biologists. A lower limit of 100 individuals was set as the minimum sample size for reference sites 

in order to be consistent with other state monitoring programs. This minimum of 100 individuals is 

consistent with a target of 100 organisms used by a majority of other state agencies (54% of 43 state 

programs surveyed; Carter and Resh, 2001). These criteria were established for selection of reference 

sites only; the application of the SCI is not limited to sites or samples conforming to these reference site 

criteria. 

Based on these criteria, 64 reference sites with a total of 205 site-visits were selected (Appendix 

J). For the 64 vetted reference sites, statistics were calculated from the distribution of reference site SCI 

values based on the median SCI value observed for each site. SCI values for reference sites ranged from 

43.4–92.6 representing the natural variability associated with the SCI at sites with minimal human 

disturbance (Table 13). The lower 10th percentile SCI value for the reference site distribution was 48.8. 

Note that the 10th percentile could have been derived in more than one way from the reference 

site scores. When we calculated these alternate statistics they provided very similar SCI values. 

Examples include the 10th percentile of SCI for all site-visits (44.9, N = 205); the 10th percentile for 
Development and Testing of Biomonitoring Tools for Macroinvertebrates in Florida Streams March 2007

50

reference site mean values (SCI = 50.5, N = 64); the 10th percentile for the most recent site-visit (SCI = 

50.2, N = 64). 

Number of sites

1994199519961997199819992000200120022003200420052006

0

50

100

150

200

250

300

350

 Status & Trends 

 Basin Studies

 Reference sites

 5th year sampling

 TMDL

Figure 18. Number of stream sites sampled for SCI by year and program. 

Table 13. Statistics and percentile values for the SCI reference sites. 

Statistics and values for the distribution of SCI values at reference sites (N = 64). 

Statistic Value 

N 64 

Minimum 43.4 

10th percentile 48.8 

25th percentile 56.7 

Median 67.3 

Average 66.3 

Standard Deviation 12.6 

75th percentile 75.2 

90th percentile 84.0 

Maximum 92.6 

Matching the approach of other states (EPA, 2006b), we defined a buffer of SCI values below the 

10th percentile to represent the natural variability associated with field sampling that could be due to water 

level fluctuation, weather, or microhabitat differences. We used the MDD (calculated above in Chapter 2: 

Statistical Precision of Indexes) to estimate the variability in SCI associated with repeat sampling of the 

same stream site. Specifically, we subtracted half the MDD interval from the 10th percentile to obtain a 
Development and Testing of Biomonitoring Tools for Macroinvertebrates in Florida Streams March 2007

51

lower limit of reference value of SCI = 35 (48.8 – 13.4 = 35.4). Thus, an SCI value of 35 represented the 

lowest SCI value we expect to obtain from a reference site. 

Distinguishing between Category 1 and 2 

We used SCI values observed at reference sites to propose a threshold between Category 1 

(“exceptional”) and Category 2 (“healthy”). The upper 90th percentile of SCI values for the 64 reference 

sites was selected to represent exceptional sites (SCI score of 84.0). As for the impairment threshold 

definition above, we defined a buffer below the 90th percentile using half the MDD interval (13.4) to yield 

an SCI value of 71 as the limit of Category 1 (84.0 – 13.4 = 70.6). The MDD interval provides a buffer 

around the point estimate of the lowest SCI value representing exceptional reference condition. 

As above, we note that the 90th percentile could have been derived in more than one way from 

the reference sites. And, again as above, when we calculated these alternate statistics they provided very 

similar SCI values. Examples include the 90th percentile of SCI for all site-visits (86.7, N = 205); the 90th

percentile for reference site mean values (SCI = 83.8, N = 64); the 90th percentile for the most recent sitevisit (SCI = 83.9, N = 64). 

Biocriteria for streams 

The above analyses support the establishment of three categories of aquatic life use for Florida 

streams, described as Category 1 (“exceptional”, with SCI = 71–100), Category 2 (“healthy”, with SCI = 

35–70), and Category 3 (“impaired”, with SCI = 0–34). Table 14 provides a narrative description for the 

numeric ranges of SCI.

The threshold values for the SCI were also used to define biocriteria for the BioRecon Index. Data 

were collected using both protocols at 112 sites. A linear regression equation was used to equate 

threshold values of SCI to corresponding values of the BioRecon Index. This analysis was used to define 

Category 1 (“exceptional”, with BioRecon = 6.5–10), Category 2 (“healthy”, with BioRecon = 3.1–6.4), and 

Category 3 (“impaired”, with BioRecon = 0–3.0) stream condition. 

To have confidence in the final stream assessment for regulatory decisions, two separate, 

temporally independent site-visits resulting in a Category 3 evaluation are required to list a stream site as 

failing to support aquatic life use. 

Table 14. Proposed aquatic life use categories, corresponding SCI values, and descriptions of 

biological conditions typically found for that category 

Narrative metric descriptions were not used to score metrics; rather, they describe values associated with 

a range of SCI values. 

Aquatic life use 

category 

SCI 

Range Description 

Category 1 71–100 

Higher diversity of taxa than for Category 2, particularly for Ephemeroptera and 

Trichoptera; several more clinger and sensitive taxa than found in Category 2; high 

proportion for Tanytarsini; few individuals in the dominant taxon; very tolerant 

individuals make up a very small percentage of the assemblage 

Category 2 35–70 

Diverse assemblage with 30 different species found on average; several different 

taxa each of Ephemeroptera, Trichoptera, and long-lived and, on average, 5 

unique clinger and 6 sensitive taxa routinely found; small increase in dominance 

by a single taxon relative to Category 1; very tolerant taxa represent a small 

percentage of individuals, but noticeably increased from Category 1 

Category 3 0–34 

Notable loss of taxonomic diversity; Ephemeroptera, Trichoptera, long-lived, 

clinger, and sensitive taxa uncommon or rare; half the number of filterers than 

expected; assemblage dominated by a tolerant taxon; very tolerant individuals 

represent a large proportion of the individuals collected 
Development and Testing of Biomonitoring Tools for Macroinvertebrates in Florida Streams March 2007

52

CHAPTER 5: CONCLUSIONS 

The correlation between biological metrics and the independently derived human disturbance 

gradient (HDG) provided strong evidence in support of the relationship between human influence and 

biological condition. The high correlation between the SCI and HDG was verified using an independent 

set of data. In addition, the pattern of biological response to human disturbance was repeated across all 3 

regions in Florida (panhandle, peninsula, and northeast), indicating that the multimetric index was 

independent of geographic context. Finally, the index was consistent across years, with a nearly identical 

relationship between the SCI and HDG for 10 years of data. 

To document every source of human disturbance is prohibitively expensive; consequently, the 

HDG was inevitably incomplete. We had no information on metals, pesticides, dredging, or other 

pollutants. Aerial pesticide application is a frequent practice on agricultural lands and can be very 

damaging to stream invertebrates, as pesticides are concentrated in rain runoff (Hutchens et al., 1998). 

This lack of information may likely explain why low SCI values were observed for sites with no known 

source of disturbance. In sharp contrast, sites with known disturbance never had high values for the SCI. 

This result supports the idea that much of the lack of fit between disturbance and biological condition is 

largely due to measurement error associated with quantifying human disturbance, rather than the 

variability of biological measures (Bryce et al., 1999; Karr and Morishita Rossano, 2001; Fore, 2003). In 

short, multimetric indexes accurately synthesize the influence of human activities over time, while 

measures of human disturbance are at best approximations of the complex ways that human activities 

influence the environment. 

The SCI had good statistical precision and could detect 3.7 categories of biological condition 

based on a single sample and 5 categories based on 2 samples. The BioRecon was not as precise but 

had adequate precision to detect 2.5 categories of biological condition based on a single sample and 3.5 

categories for 2 samples. Strong correlation with an independent measure of disturbance, good statistical 

precision, immunity to regional differences or watershed size, and minimal sensitivity to seasonal or 

yearly differences all contribute to the usefulness of these indexes for the biological assessment of 

Florida’s streams. 

We followed U.S. EPA guidance to translate index values into biocriteria to be included as part of 

the water quality standards for the state. Two approaches were used to define categories of biological 

condition. The primary approach involved convening a panel of regional aquatic scientists to apply a 

nationally endorsed conceptual model of biological condition to Florida stream data, and was used to 

define the threshold of impairment (line between biological condition Categories of 2 and 3). The second 

approach used data from minimally disturbed areas (reference sites) to define expectations for reference 

condition and to define the threshold of exceptional sites (line between biological condition Categories of 

1 and 2). Based on these results, three categories of biological condition are proposed to represent 

Category 1 (“exceptional”), Category 2 (“healthy)”,and Category 3 (“impaired”) biological condition for 

Florida stream site assessments. The results reported here provide the scientific basis for managing and 

protecting Florida’s stream resources. 
Development and Testing of Biomonitoring Tools for Macroinvertebrates in Florida Streams March 2007

53

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APPENDICES 

Appendix A. HDG calculation 

This appendix details the calculations and interim steps involved in deriving an HDG value for 

each site. The HDG combined 4 measures of human disturbance (NH3, habitat index, hydrologic index, 

and LDI) by first converting them to unitless scores (representing low, medium and high disturbance) and 

then summing the scores. When multiple site visits occurred at a single sampling location, the individual 

component measures were averaged for each site and then converted to unitless scores. For a site with 

multiple visits, NH3, the habitat index, and, in a few instances, the hydrologic index, could change. LDI 

could not change because it was calculated only once for each site. When component values were 

missing, they were assumed to be in good condition and scored as 0. 

Coding for NH3 

Many of the reported NH3 values had qualifiers associated with them. In some cases the 

qualifiers were omitted and the results were used as reported and in others the value was simply set to 0 

to simplify coding for the HDG (Table A-1). Another more common approach is to record half of the 

detection limit rather than 0, but for calculating the HDG this interim recoding did not matter because 

either value would be scored as a 0 for the HDG. 

Table A-1. Laboratory codes associated with water chemistry values, their description, and the 

action used to convert them to simple numbers. All the codes are listed but J and Q were not 

found for the NH3 values. 

CODE Description ACTION 

A A-Value reported is the mean of two or more determinations omitted A qualifier and 

used result as reported

I I- The reported value is between the laboratory method detection 

limit and the laboratory practical quantitation limit set result to 0 

J J-Estimated Value omitted J qualifier and 

used result as reported

Q Q-Sample held beyond normal holding time omitted Q qualifier and 

used result as reported

U or BDL U-Material analyzed for but not detected; 

value reported is the minimum detection limit set result to 0 

T T- below detection limit; estimate set result to 0 
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Derivation of habitat index values 

Data for this study were collected from 1991–2001. During that time the habitat assessment 

protocol was modified and additional measurements were added. During these different time periods the 

maximum possible total score changed. To make comparisons of the habitat index across years, we 

divided habitat index values by the total maximum score that was possible for that protocol. In this way 

the habitat index was transformed into a percentage measure that ranged from 0–100%. 

Derivation of hydrologic index values 

At the April, 2002, Biocriteria Meeting, a training session on the hydrologic index scoring process 

was conducted. The purpose for assigning the hydrologic modification scores was to develop one of 

several components of the HDG. FDEP biologists were instructed to use their knowledge of human 

activities within a watershed and upstream of selected sampling sites in order to assign a score from 1 to 

10, following the specific criteria presented (see Table 2, main document). The key issues for 

consideration in the scoring process were: 

• The stream’s hydrograph response to a rain event. 

• The amount of ditching or impoundments within the watershed that would alter 

normal water deliveries to the stream. 

• Topographic alterations in the watershed that would adversely affect water 

deliveries to the stream. 

• Water withdrawals (consumptive use) in the area that may reduce water to the 

stream. 

• The relative amount of impervious surfaces in the watershed that would “spike” 

the hydrograph. 

Since the participants had simultaneously visited certain streams as part of previous Biocriteria 

Meeting training and quality assurance activities, appropriate hydrologic index scores for these example 

streams were discussed, to promote consistency in the application of the process. 

Scoring component measures of the HDG 

In order to calculate the HDG, the 4 component measures were first transformed into unitless 

scores (see Table 5, main document, for scoring rules). The first round of data was used to derive the 

break points for scoring the measures. The graphs used to define the break points are shown below for 

each of the 4 component measures. To define the scoring rules, natural break points in the data were 

used. When no break points were obvious, the range of the data was divided into approximately equal 

parts. 
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0.0030.004 0.005 0.006 0.007 0.008 0.009 0.0100.0200.0300.040 0.050 0.060 0.070 0.080 0.090 0.1000.2000.3000.400 0.500 0.600 0.700 0.800 0.900 1.0002.0003.0004.000 5.000 6.000 7.000 8.000 9.000 10.000

20.00030.000

NH3

-2

0

2

4

6

8

10

12

14

16

18

Number of EPT taxa

Figure A-1. Scoring rules for NH3 for inclusion in the HDG. NH3 plotted against a logarithmic axis. 

10 20 30 40 50 60 70 80 90 100 110

Habitat index (%)

-2

0

2

4

6

8

10

12

14

16

18

Number of EPT taxa

Figure A-2. Scoring rules for the habitat index for inclusion in the HDG. 
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0 2 4 6 8 10 12

Hydrologic index

-2

0

2

4

6

8

10

12

14

16

18

Number of EPT taxa

Figure A-3. Scoring rules for hydrologic index for inclusion in the HDG. 

0123456789

LDI index (buffer)

-5

0

5

10

15

20

25

30

35

Number of EPT taxa

Figure A-4. Scoring rules for the LDI index for inclusion in the HDG. 
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Appendix B. Long-lived taxa 

Taxonomic order, family, genus, or species name of taxa used to calculate long-lived taxa richness. Taxonomic synonym

Florida database are also shown. Long-lived (semivoltine) taxa require more than one year to complete their 

Order Family Genus Species (Long-lived) Syno

Decapoda All families, genera, and species 

Basommatophora Lymnaeidae Fossaria All species 

Mesogastropoda Ampulariidae Pomacea All species 

Veneroida Corbiculiidae Corbicula Corbicula fluminea Corbicula 

Unionoida Unionidae All genera and species 

Odonata Aeshnidae Basiaeschna Basiaeschna janata 

Boyeria All species 

 Cordulegastridae All species  Gomphidae Gomphus All species Gomp

Haegenius Hagenius brevistylus

Progomphus All species 

 Libellulidae Macromia All species 

Somatochlora All species 

Tetragoneuria All species Epit

 Petaluridae Tachopteryx Tachopteryx thoreyi
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Order Family Genus Species (Long-lived) Syno

Plecoptera Pteronarcidae Pteronarcys Pteronarcys dorsata

 Leuctridae Leuctra All species  Peltoperlidae Tallaperla Tallaperla cornelia Perlidae Acroneuria All species 

Agnetina Agnetina annulipes

Eccoptura Eccoptura xanthenes Acroneuria 

Neoperla All species 

Paragnetina All species Bank

Bank

 Perlodidae Clioperla Clioperla clio Isope

Perlinella Perlinella drymo Atoperla 

 Perlinella ephyre/zwicki 

Isoperla Isoperla orata 

Megaloptera Corydalidae All genera and species 

Trichoptera Brachycentridae Brachycentrus Brachycentrus americanus Oligoplectrum 

Micrasema All species 

 Calamoceratidae Heteroplectron Heteroplectron americanum  Hydropsychidae Diplectrona Diplectrona modesta 

Macrostemum Macrostemum carolina Macronem

Macronem

 Lepidostomidae Lepidostoma All species Alepomyia, A

Arcadopsyc

Jenortha, M

Neuropsych

Notiopsych
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Order Family Genus Species (Long-lived) Syno

Oligopsyche, 

Pristosilo, A

 Leptoceridae Ceraclea All species  Limnephilidae Ironoquia Ironoquia punctatissisma 

Pycnopsyche All species 

 Molannidae Molanna All species  Phryganeidae Banksiola Banksiola concatenata Neuronia c

Ptilostomis Ptilostomis postica Neuronia 

Agrypnia Agrypnia vestita 

 Psychomyiidae Lype Lype diversa  Rhyacophilidae Rhyacophila All species  Uenoidae Neophylax All species 
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Appendix C. Sensitive taxa 

Taxonomic order, family, genus or species name; taxonomic synonyms in the current Florida database; whether the taxo

Florida Index (2 = very sensitive; 1 = sensitive); and species name of Florida Index taxa are listed for taxa identified as 

disturbance. 

Order Family Genus Species (sensitive) Synonyms Flor

Acariformes Lebertiidae Lebertia All species 

Isopoda Asellidae Caecidotea Caecidotea Asellus 1 C

Amphipoda Crangonyctidae Crangonyx Crangonyx

Ephemeroptera Baetidae Acerpenna Acerpenna pygmaea Baetis pygmaeus 

 Ephemerellidae All genera and species  Heptageniidae All genera and species 1 

Stena

Sten

 2 Ste

Ste

 Leptophlebiidae All genera and species  Leptohyphidae All genera and species 

Odonata Libellulidae Macromia All species 2 

Plecoptera All families, genera and species 2 

Trichoptera Hydropsychidae Hydropsyche All species 2 H

 Leptoceridae Triaenodes All species  Philopotamidae Chimarra Chimarra 2 
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Order Family Genus Species (sensitive) Synonyms Flor

 Psychomyiidae Lype Lype diversa 

Coleoptera Elmidae Ancyronyx Ancyronyx variegatus 

Gonielmis Gonielmis dietrichi 

Macronychus Macronychus glabratus 

Diptera Chironomidae Microtendipes All species 

Parametriocnemus All species 

Polypedilum Polypedilum aviceps 

Rheocricotopus All species 2 Rhe

Stempellinella All species 

Tanytarsus Tanytarsus sp. d eplerTanytarsus Tanytarsus sp. m epler

Tribelos Tribelos jucundum

 Empididae Hemerodromia Hemerodromia  Simuliidae All genera and species 2 
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Appendix D. Very tolerant taxa 

Taxonomic order, family, genus or species name; taxonomic synonyms in the current Florida database; whether the taxo

Florida Index (2 = very sensitive; 1 = sensitive); and species name of Florida Index taxa are listed for taxa identified as v

disturbance. 

Order Family Genus Species (very tolerant) Synonyms 

Hoplonemertea Tetrastemmatidae Prostoma Prostoma rubrum 

Haplotaxida Naididae Bratislavia Bratislavia unidentata 

Dero All species 

Nais Nais communis complex Nais communis 

 Nais elinguis  Nais pardalis  Nais variabilis 

Pristina Pristina synclites 

 Tubificidae Haber Haber speciosus 

Limnodrilus All species Camptodrilus californicus 

Limnodrilus 

aurantiacus 

Lumbriculida Lumbriculidae Lumbriculus All species 

Rhynchobdellida Glossiphoniidae All genera and species 

Basommatophora Ancylidae Hebetancylus Hebetancylus excentricus 

Laevapex All species 

 Physidae Physella All species  Planorbidae All genera and species 
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Order Family Genus Species (very tolerant) Synonyms 

Mesogastropoda Hydrobiidae Pyrgophorus Pyrgophorus platyrachis Pyrogophorus platyrachis 

 Thiaridae Melanoides All species 

Odonata Libellulidae Pachydiplax Pachydiplax longipennis 

 Coenagrionidae Argia Argia sedula 2 

Enallagma Enallagma cardenium Enallagma coecum

Ischnura All species Anomalagrion 

Heteroptera Mesoveliidae All genera and species Lepidoptera Pyralidae All genera and species 

Coleoptera Haliplidae Peltodytes All species 

Diptera Chironomidae Larsia All species 1 

1 

Chironomus All species Tendipes Chaetolabis 

Cladopelma Cladopelma 

Cricotopus All species 2 

Cryptochironomus All species 

Dicrotendipes Dicrotendipes modestus 

 Dicrotendipes neomodestus 

Glyptotendipes All species 

Goeldichironomus All species 

Kiefferulus Kiefferulus 
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Order Family Genus Species (very tolerant) Synonyms 

Parachironomus All species 

Polypedilum Polypedilum beckae Asheum beckae Pedionomus beckae

 Polypedilum illinoiense grp. Polypedilum illnoense 1 

Polypedilum tritum Chironomus tritum 

Tanypus All species Pelopia 

Tanytarsus Tanytarsus sp. f epler 

 Stratiomyidae Odontomyia Odontomyia Eulalia  Tipulidae Tipula Tipula 

Nippotipula 

Playttipula 

Yamatotipula 
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Appendix E. Regional calibration of SCI metric scores 

After metrics were selected based on their correlation with the human disturbance 

gradient (HDG), they were transformed to unitless scores to compensate for differences in their 

ranges of values. For example, percentage filterer might range from 0 to 60% while long-lived 

taxa range only from 0 to 7. The 5th and 95th percentiles for each metric were used to determine 

the metric values that would be defined as 0 and 10 (Table E-1). To control for differences due to 

natural variability associated with different bioregions, the percentiles were calculated within each 

of the 3 bioregions. 

The next step tested for regional differences in metrics scores by regressing metric 

scores for each bioregion against the HDG and comparing the slopes of their lines. If the 

regression lines for each bioregion failed to match closely, the endpoints for the metrics that 

defined values of 0 and 10 were adjusted away from the 5th and 95th percentiles and tested again 

until the regression lines for the different bioregions yielded a similar relationship with HDG 

(Figure E-1; Table E-2). In some cases, the 5th percentile values were very close and they were 

simply averaged across bioregions for simplicity. 

After determining which metric values define the scores of 0 and 10, the remaining metric 

values between those endpoints must also be scored (Table E-3; FDEP, 2007). As an example, 

for taxa richness in the northeast bioregion the interval between the 2 endpoints is 42 – 16 which 

equals 26. If the lowest endpoint is 16, we would first subtract 16 from the total taxa richness 

observed at a site, divide by the interval length of 26, and multiply the result by 10 to obtain 

scores that range from 0 to 10. Taxa richness values less than 16 would be scored as 0 and 

values greater than 42 would be scored as a 10. 

Table E-1. SCI metric name, 5th and 95th percentile values by bioregion for site-visits with 

75–175 individuals. Underlined values indicate that the metric values used to define a 

score of 0 or 10 were slightly different from the percentile value. 

SCI Metric 

NE 

5th %tile 

PH 

5th %tile

PS 

5th %tile

NE 

95th %tile

PH 

95th %tile 

PS 

95th %tile

Total taxa 17.0 16.0 16.0 42.0 46.0 41.0 

Ephemeroptera taxa 0.0 0.0 0.0 4.0 6.0 5.0 

Trichoptera taxa 0.0 0.0 0.0 7.0 7.0 7.0 

% Filterer 1.4 1.3 0.9 52.0 45.1 40.0 

Long-lived taxa 0.0 0.0 0.0 3.0 5.0 4.0 

Clinger taxa 0.0 0.0 0.0 10.0 14.0 8.0 

% Dominance 10.9 9.6 11.6 47.2 42.6 54.5 

% Tanytarsini 0.0 0.0 0.0 27.9 22.8 26.8

Sensitive taxa 0.0 0.0 0.0 11.0 16.0 9.0 

% Very tolerant 0.0 0.0 0.8 78.2 36.3 58.5 
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Table E-2 (Table 8 from main SCI document). SCI metric name, adjusted metric values 

used to scores metrics (endpoints of the range represent scores of 0 and 10), and 

comment describing how the values differed from the 5th and 95th percentiles. Underlined 

values correspond to the underlined values in Table E-1. 

SCI Metric Northeast Panhandle Peninsula Comment 

Total taxa 16–42 16–49 16–41 Averaged the 5th %tile and used 

same value in all bioregions 

Ephemeroptera taxa 0–3.5 0–6 0–5 Lowered expectations for 95th

%tile for NE 

Trichoptera taxa 0–6.5 0–7 0–7 Lowered expectations for 95th

%tile for NE 

% Filterer 1–42 1–45 1–40 Lowered expectations for 95th

%tile for NE 

Long-lived taxa 0–3 0–5 0–4 

Clinger taxa 0–9 0–15.5 0–8 Lowered expectations for 95th

%tile for NE; raised for PH 

% Dominance 54–10 43–10 54–10 Averaged lower 5th %tile; 

% Tanytarsini 0–26 0–26 0–26 Raised expectations for 95th %tile 

for NE 

Sensitive taxa 0–11 0–19 0–9 Raised expectation for 95th %tile 

for PH 

% Very tolerant 78–0 36–0 59–0 

Table E-3. SCI metric scoring formulae for converting metric values to a metric score 

ranging from 0 to 10. In each equation, ‘X’ stands for the raw metric value; “ln” stands for 

the natural log. 

SCI metric Northeast Panhandle Peninsula 

Total taxa 10 * (X–16)/26 10 * (X–16)/33 10 * (X–16)/25 

Ephem. taxa 10 * X /3.5 10 * X /6 10 * X /5 

Trichoptera taxa 10 * X /6.5 10 * X /7 10 * X /7 

% Filterer 10 * (X–1)/41 10 * (X–1)/44 10 * (X–1)/39 

Long-lived taxa 10 * X /3 10 * X /5 10 * X /4 

Clinger taxa 10 * X /9 10 * X /15.5 10 * X /8 

% Dominance 10 – ( 10 * [ ( X–10)/44 ] ) 10 – ( 10 * [ ( X–10)/33 ] ) 10 – ( 10 * [ ( X–10)/44 ] ) 

% Tanytarsini 10 * [ ln( X + 1) /3.3] 10 * [ ln( X + 1) /3.3] 10 * [ ln( X + 1) /3.3] 

Sensitive taxa 10 * X /11 10 * X /19 10 * X /9 

% Very tolerant 10 – (10 * [ ln( X + 1)/4.4 ] ) 10 – (10 * [ ln( X + 1)/3.6 ] ) 10 – (10 * [ ln( X + 1)/4.1 ] ) 
Development and Testing of Biomonitoring Tools for Macroinvertebrates in Florida Streams March 2007

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Human disturbance gradient

Metric score for % filterer

-2 0 2 4 6 8 10

0

2

4

6

8

10 Northeast

Panhandle

Peninsula

Human disturbance gradient

Metric score for % filterer

-2 0 2 4 6 8 10

0

2

4

6

8

10

Figure E-1. Metric scores for percent filterer by bioregion. The upper panel shows metrics scored according 

the 5th and 95th percentiles and the lower panel shows scores after adjustment. The regression line for the 

NE bioregion was lower than the other 2 regions initially and plotted higher and closer to the other 2 lines 

after adjustment. 
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References 

Florida Department of Environmental Protection (FDEP) 2007. Department of Environmental 

Protection Standard Operating Procedures for Laboratory Activities. Table LT7200-1 of LT 

7200 Stream Condition Index (SCI) Determination in DEP-SOP-002/01. LT 7000 

Determination of Biological Indices. Available: 

1Uhttp://www.dep.state.fl.us/labs/sop/index.htmH. 
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Appendix F. ANOVA output and regression results for the SCI and BioR

Table F-1. General linear model output for the SCI versus HDG by region (northeast, panhandle, or pe

Neither the region nor the interaction of the HDG and region was statistically significant. Test failed to detect a difference 

region. 

Univariate Tests of Significance for AvgOfSCI6_100 (1NGM_4) 

SS df MS F p 

Intercept 403623.3 1 403623.3 1832.253 0.000000 

Region 361.6 2 180.8 0.821 0.441846

HDG2 62750.7 1 62750.7 284.858 0.000000 

Region*HDG2 1142.9 2 571.5 2.594 0.077660

Error 37449.0 170 220.3 

Table F-2. General linear model output for the SCI versus HDG by year class (1992–93, 1994–95, 1996–97, 1998

The interaction term was not significantly different for year class and the HDG, suggesting the lines were parallel (equal s

alone, however, was significant, with values for 1994–95 being slightly higher for the SCI. 

Univariate Tests of Significance for SCI6_100 (1NGM_4) 

SS df MS F p

Intercept 1627060 1 1627060 5835.657 0.000000

Year class 2948 4 737 2.644 0.032766

HDG2 134497 1 134497 482.390 0.000000

Year class*HDG2 791 4 198 0.709 0.586005

Error 171749 616 279
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Table F-3. ANOVA estimates of SCI variability for repeat visits to the same site within a year 

Error estimate used to calculate confidence interval is underlined. 

ANOVA for SCI (SCI6_100) (Same_day_SCI) 

Effect df MS df MS F 

{1}STORET Random 60 1211.636 39.41542 188.7279 6.420014 

{2}Year Random 6 269.430 35.76223 170.2402 1.582648 

1*2 Random 53 134.481 82.00000 66.5952 2.019385 

Table F-4. ANOVA estimates of SCI variability for laboratory subsampling 

Error estimate used is underlined. 

ANOVA Results: SCI subsampling (Metrics) 

Effect df MS df MS F 

{1}Sitename Random 58 1393.986 118 32.86312 42.4179

Table F-5. ANOVA estimates of BioRecon variability for repeat visits to the same site within a y

Error estimate used to calculate confidence interval is underlined. 

ANOVA for BioRecon (BR3_10) (New metrics multiple_2) 

Effect df MS df MS F 

{1}STORET Random 126 15.91691 117.3771 1.696009 9.384924 

{2}year Random 8 6.02260 156.0000 1.458840 4.128352 
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Appendix G. Optimal sample size for calculating SCI 

D. Whiting, J. Espy, and L. Wolfe (FDEP) 

Taxa richness increases with the number of individuals identified from a sample. This 

relationship is well documented for many studies (Larsen and Herlihy, 1998), and is further 

supported by recent analyses conducted for FDEP which are described below. Because the SCI 

includes several metrics that quantify taxa richness, the concern here is that SCI values may be 

artificially increased or decreased depending on the number of individuals identified from a 

sample. Therefore, laboratory methods were modified in recent years (2004–2006) to ensure that 

the number of individuals identified is more consistent across samples. 

The SCI data used to develop and test the SCI and BioRecon were collected between 

1991–2001. The laboratory protocols used during that time mandated a target number of 100 

individuals. In practice, however, this target was often exceeded due to the nature of the 

laboratory subsampling methods (many samples had several hundred individuals). During this 

early time period, FDEP did subsample material collected from the field; however, laboratory 

protocols did not allow multiple divisions of the material to achieve the 100-individual target. For 

the data set used to test and develop SCI, only site-visits with 75–175 individuals were used in 

order to more closely approximate the target number of individuals. Thus, metric expectations 

and scoring rules for SCI were based on samples with between 75 and 175 individuals (mean 

122, median 119). Restricting the index development data set to samples with only 100 to 110 

individuals would have reduced the number of samples available for the recalibration effort 

dramatically, thereby reducing confidence in the resulting biomonitoring tool. 

The target number of organisms for the 2004 laboratory protocols for the SCI was 100 

organisms (acceptable range 100-110). Such a narrow acceptable range was made possible by 

modifying the 20-dip net sweep composite sample preparation Standard Operating Procedure. 

This modification resulted in the samples consistently being on target. 

Random site sampling in 2004 from the ambient monitoring program yielded an adequate 

number of samples (n = 201) to compare the effect of the more accurate laboratory subsampling 

methods on metric values. We compared the percentiles of the metric values used to define the 

scoring rules for the SCI and the percentiles of metric values from this more recent data set. For 

the 1992-2001 sampling, 420 samples with between 75 and 175 individuals were used. For the 

2004 sampling, 196 samples were used. Approximately even numbers of samples were available 

for the 2004 data in the peninsula and panhandle regions; not enough sites were sampled in the 

northeast to calculate percentiles. 

This comparison was not ideal because the sampling designs were very different. The 

2004 sites were selected randomly while the earlier sites used in this study were selected for a 

variety of specific reasons (e.g., references sites, 5th years inspections, special studies, etc.). 

Thus, the distribution of percentile values may be shifted due to the laboratory methods or the 

types of sites sampled. If both data sets had been created from random sampling, the 

comparison would more clearly isolate the effect of the change in laboratory methods. 

As expected, total taxa richness showed the largest differences with lower values 

observed in 2004 (Table G-1). Other taxa richness metrics were also lower at the 95th percentile. 

Ephemeroptera and long-lived taxa were lower in peninsula region, Trichoptera had a similar 

distribution, and clinger taxa were lower in the panhandle region. The 95th percentiles for 

sensitive taxa were lower in both regions. 

Developing scientific protocols for use in a regulatory framework is inevitably an iterative 

process. Our understanding of the sources of variability and the influence of precision on 

assessment methods has evolved rapidly in recent years. If we were to start over with no data on 
Development and Testing of Biomonitoring Tools for Macroinvertebrates in Florida Streams March 2007

78

hand and develop a biological monitoring protocol, the number of individuals to identify from each 

sample might be one of the first elements that we would define. The iterative nature of this 

process derives from the fact that knowledge gained regarding one aspect of the analysis 

methods often necessitates that methods related to another aspect of the analysis be 

reconsidered. A large portion of the variability in SCI values was tied to laboratory methods, and 

subsequently, improvements have been made in that area. This change should be understood as 

a calibration of the monitoring protocol. The fundamental relationships between biological metrics 

and human influence are unaffected by this issue. 

A recent analysis by the FDEP Central Laboratory biologists has found that averaging the 

SCI scores from two 150-organism subsamples provides a more reliable assessment than a 

single 100-organism subsample. The objective of this analysis was to evaluate how laboratory 

processing and/or the number of individuals identified affects SCI score variability. For this 

analysis, all the organisms found in one-third (33%) of the material brought to the laboratory for 

each of three samples were identified. Using those data, 250 random draws were conducted for 

each sample for each of six different target numbers of individuals: 100, 125, 150, 175, 200, and 

300 (Figures G-1 to G-3) 

Table G-1. Percentiles of metric values for the development data set and random sampling 

in 2004 

Shown are the 5th and 95th percentile values for SCI metrics from sites sampled during 1992–

2001 (SCI development data set) and 2004 (ambient sampling). 

Panhandle Peninsula 

1992-

2001 

2004 1992-

2001 

2004 1992-

2001 

2004 1992- 

2001 

2004

Percentile 5th 5th 95th 95th 5th 5th 95th 95th 

Total taxa 16.0 13.0 46.0 37.0 16.0 13.0 41.0 35.0 

Ephemeroptera taxa 0.0 0.0 6.0 5.0 0.0 0.0 5.0 3.0 

Trichoptera taxa 0.0 0.0 7.0 6.0 0.0 0.0 7.0 7.0 

% Filterer 1.3 0.9 45.1 45.7 0.9 0.0 40.0 32.0 

Long-lived taxa 0.0 0.0 5.0 5.0 0.0 0.0 4.0 2.0 

Clinger taxa 0.0 0.0 14.0 9.0 0.0 0.0 8.0 7.0 

% Dominance 9.6 11.9 42.6 55.3 11.6 13.2 54.5 68.0 

%Tanytarsini 0.0 0.0 22.8 31.8 0.0 0.0 26.8 20.0 

Sensitive taxa 0.0 0.0 16.0 11.0 0.0 0.0 9.0 7.0 

% Very tolerant 0.0 0.0 36.3 48.1 0.8 1.0 58.5 79.8 
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Hatchet Creek

100 125 150 160 175 200 300

Target number of individuals

30

40

50

60

70

80

90

100

110

SCI 

Avg of 2 

150's

 Median 

 25%-75% 

 Non-Outlier Range 

 Outliers

Figure G-1. Box plots showing the change in SCI score with target number of individuals counted for 250 

random draws of one-third of a sample from Hatchet Creek. Shown also is the box plot for the average SCI 

score from 2 150 subsamples. 

 

Mule Creek

100 125 150 160 175 200 300

Target number of individuals

40

50

60

70

80

90

100

110

SCI 

Avg of 2 

150's

Figure G-2. Box plots showing the change in SCI score with target number of individuals counted for 250 

random draws of one-third of a sample from Mule Creek. Shown also is the box plot for the average SCI 

score from 2 150 subsamples. 
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Crooked Creek

100 125 150 160 175 200 300

Target number of individuals

40

50

60

70

80

90

100

110

SCI 

Avg of 2 

150's

Figure G-3. Box plots showing the change in SCI score with target number of individuals counted for 250 

random draws of one-third of a sample from Crooked Creek. Shown also is the box plot for the average SCI 

score from 2 150 subsamples. 

FDEP Central Lab biologists believe the increase in precision associated with larger and 

multiple subsamples is worth the cost, as it makes the calculated result less variable and provides 

greater confidence in the tool. Prior to the sample preparation SOP being modified to more 

accurately achieve the target number of organisms, the sample processing time at the FDEP 

Central Laboratory averaged 18 hours per sample. The revision to the SOP resulted in the 

processing time being reduced by 56% (approximately 8 hours). While the increase from 100 

organisms to 300 organisms (150x2) results in a significant increase in laboratory workload 

(average processing time based upon preliminary data is about 14 hours), the increase is 

balanced by the earlier changes to the sample preparation SOP that result in the analyst more 

accurately achieving the target number of organisms. The result is the identification of three times 

as many organisms for approximately a 75% increase in laboratory effort. 

A survey of 43 state agencies’ programs found that approximately 54% of those 

programs targeted 100 organisms (Carter and Resh, 2001). The next most commonly used target 

number of organisms was 300 (26%). All other target number of organisms were used by < 7% of 

the programs. Additionally, U.S. EPA has used a target number of organisms of 300 for the 

National Wadeable Streams survey (EPA, 2006). 

The FDEP decision to use 150X2 subsamples was made based upon four key points: 

● First, the 150x2 subsampling will result in 300 organisms being identified, which 

was the second most common subsampling target number in the survey, and 

which is consistent with U.S. EPA minimum subsampling target used for the 

National Wadeable Streams survey 

● Second, the use of 150x2 subsamples rather than one 300 target subsample 

maintains scoring within the range used to calibrate the SCI (75-175) 
Development and Testing of Biomonitoring Tools for Macroinvertebrates in Florida Streams March 2007

81

● Third, targeting 150 organisms in each subsample, rather than 125 organisms 

(approximately the mean number of organisms in the calibration dataset samples 

used to score the SCI metrics), results in a slightly higher score at good quality 

sites, while poor quality sites scores do not appear to increase similarly. This effect 

results in slightly better discrimination between good, moderate, and poor quality 

sites 

● Fourth, the use of 150x2 subsamples reduces the variability in the resulting SCI 

score by using the average of two 150-organism-based. This reduction in variability 

improves consistency with regard to the assessment of category thresholds. 

References 

Carter, J. L. and V. H. Resh. 2001. After site selection and before data analysis: sampling, 

sorting, and laboratory procedures used in stream benthic macroinvertebrate monitoring 

programs by USA state agencies. Journal of the North American Benthological Society 

20:658-682. 

Larsen, D. P. and A. T. Herlihy. 1998. The dilemma of sampling streams for macroinvertebrate 

richness. Journal of the North American Benthological Society 17:359-366. 

U. S. Environmental Protection Agency (EPA). 2006. Wadeable Streams Assessment: A 

Collaborative Survey of the Nation's Streams. EPA 841-B-06-002. Washington, D. C.: 

Office of Water. Available: http://www.epa.gov/owow/streamsurvey/. 
Development and Testing of Biomonitoring Tools for Macroinvertebrates in Florida Streams March 2007

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Appendix H. FDEP Biological condition gradient 

workshop summary 

Russel Frydenborg and Denise Miller (FDEP) 

Background 

Florida Department of Environmental Protection (FDEP) staff evaluated stream 

macroinvertebrate data from the stream condition index (SCI) recalibration dataset with the goal 

of refining delineations of biological/ecological condition based on the SCI 2004 scores. Several 

approaches were taken for this evaluation, among them a Biological Condition Gradient (BCG) 

Workshop which was conducted by FDEP staff on October 24, 2006. This document provides a 

summary of FDEP’s BCG workshop. A more detailed description of the workshop and materials is 

provided in Frydenborg and Miller, 2007. 

In preparing for this workshop, FDEP staff used the framework of prior such workshops 

conducted by Susan Davies of the Maine Department of Environmental Protection, Susan 

Jackson of the U.S. Environmental Protection Agency (Davies and Jackson, 2006), and Jeroen 

Gerritsen of Tetra Tech, Inc. Each of those individuals also contributed thoughtful commentary 

during the planning of this workshop. 

For the FDEP BCG workshop, a group of experts in aquatic biology and stream 

macroinvertebrate identification were requested to review taxonomic data from a subset of 

samples from the SCI 2004 recalibration data set and to provide scores for those samples using 

the BCG framework (scoring based on descriptions of BCG attributes per experts’ review of the 

taxonomic data). Each expert would thus provide their assessment of the biological condition of 

each of 30 samples selected by FDEP to span the range of SCI scores from the SCI 2004 

recalibration data. Experts did not know the SCI scores for the samples. FDEP evaluated the 

experts’ BCG scores together with SCI scores for the samples as one line of evidence in 

determining what delineations or thresholds of SCI scores should be used in regulatory decisionmaking (e.g., what distribution of scores signifies that a site is meeting designated use or not 

meeting designated use). 

FDEP invited participants for this workshop who have expertise in Florida stream 

macroinvertebrate identification, and who hail from local and state regulatory agencies, 

universities and the private sector. 

Methods 

Prior to the workshop, experts were provided with background literature on the 

biocondition gradient concept (Davies and Jackson, 2006; Stoddard et al., 2006) and descriptions 

from FDEP of the intended process (Frydenborg and Miller, 2007: Attachment A). Experts were 

informed that they would be asked to review SCI sample data that would be provided to them at 

the workshop and to assign each sample a score of 1 to 6, signifying their assessment of the 

biological condition represented by the sample (1 best to 6 worst based on the 6 tiers of the 

conceptual BCG summarized from Davies and Jackson, 2006: Figure 2): 

• Score of 1: Native or natural condition 

• Score of 2: Minimal loss of species; some density changes may occur 

• Score of 3: Some replacement of sensitive-rare species; functions fully maintained 

• Score of 4: Some sensitive species maintained but notable replacement by moretolerant taxa; altered distributions; functions largely maintained 
Development and Testing of Biomonitoring Tools for Macroinvertebrates in Florida Streams March 2007

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• Score of 5: Tolerant species show increasing dominance; sensitive species are rare; 

functions altered 

• Score of 6: Severe alteration of structure and function. 

At the workshop, Russel Frydenborg, FDEP’s workshop moderator, gave a presentation 

describing the BCG process and attributes, and distributed materials from the presentation for 

reference by the experts (Frydenborg and Miller, 2007: Attachment B). 

During the workshop, experts were given the opportunity to discuss their rationale for 

scoring of a site, and, if they desired, to change their score for a site based on their assessment 

from the discussions (Delphi method). Scoring sheets were provided to each expert for recording 

both initial and final sample scores. Experts were requested to indicate what relevance they 

ascribed to their BCG scores. More specifically, they were asked to record the minimum BCG 

score(s) (1 being best, 6 worst) that they believed met the interim goals of the Clean Water Act 

(meets designated use), and what maximum score(s) they believed did not meet the interim goals 

of the Clean Water Act (impaired site; does not meet designated use). In other words, sites below 

a selected tier score would be considered “impaired.” 

Sample Selection 

Sample data were drawn from the SCI recalibration data and were limited to those where 

the total number of individual organisms counted in the sample was between 100-175, inclusive. 

Five (5) reference site (minimally disturbed condition site) samples from each of three (3) 

Florida Bioregions (Panhandle, Peninsula, Northeast) were selected to allow the experts an 

assessment of reference expectations separately for each region because prior SCI analysis 

indicated differences in expectations for some metrics between the bioregions (Table H-1). The 

15 reference site samples were selected from sites already identified as reference based on 

criteria defined independently of the biological condition (see Chapter 4: Reference site 

selection). The 5 reference samples from each bioregion were selected to span the range of SCI 

scores for all of the reference site samples from that bioregion, including minimum and maximum 

SCI 2004 scores, and samples with scores closest to the 25th percentile, median, and 75th

percentile values from each bioregion reference range. SCI scores were used for sample 

selection, but were not provided to the experts. Additionally, experts were not provided with 

information about the location or description of the selected sample sites, other than the bioregion 

from which the samples were collected. 

Additionally, 10 samples were selected for each bioregion, and identified to bioregion, for 

analysis of macroinvertebrate data and scoring by the experts. Sites were selected to span a 

range of conditions using existing information on SCI scores and Human Disturbance Gradient 

(HDG), and, as needed, LDI from the SCI recalibration data set. The specific 10-sample selection 

methodology for each bioregion was as follows (Table H-2): 

• 4 samples selected based on those having SCI scores closest to the 10th, 25th, 

median and 75th percentile SCI scores for the non-reference sites for the bioregion; 

• 2 samples randomly selected from non-reference sites for the bioregion having SCI 

scores in the range of 35-50 (sorted on SCI score), to ensure scrutiny of some 

samples in this score range; 

• 3 samples randomly drawn from HDG ranges of 1-2, 3-5, and/or 6-9, based on the 

assessment of the HDG scores already represented from the prior 6 sample selection 

(sorted on HDG scores); 

• For the Northeast bioregion samples only, there were insufficient sites with the target 

HDG ranges (i.e., no HDG scores of 5 through 9, and only one HDG 4 site). 

Therefore, 2 of the intended HDG-based samples were randomly drawn based on the 
Development and Testing of Biomonitoring Tools for Macroinvertebrates in Florida Streams March 2007

84

range of LDI scores for the samples, after an assessment of the LDI scores already 

represented from the prior 7-sample selection. Specifically, there were 6 samples in 

the 1 to < 2 LDI range, and 1 sample in the LDI 6 to < 7 range. Therefore, 2 samples 

were randomly selected from the LDI 2 through 9 range (sorted on LDI scores), 

excepting sites with LDIs >= 6 and < 7; and, 

• 1 sample randomly drawn from the entire non-reference dataset for the bioregion 

(sorted on HDG score). 

Table H-1. BCG workshop reference sites 

Site identification information, bioregion location, date of sampling, SCI value, and LDI value. 

BCGSampleID STORET Station Nickname Bioregion Sample Date SCI LDI 

NERef01 20030341 CECFLD7 Northeast 3/5/1996 70.4 1.90 

NERef02 21010018 FALLNGREF Northeast 7/27/1999 39 1.37 

NERef03 19010099 PIGEONREF Northeast 2/1/2000 58 1.27 

NERef04 21010056 ROBCKCR131 Northeast 3/13/2000 79.2 1.35 

NERef05 20030456 THOMASREFB Northeast 3/14/1996 92.4 1.25 

PanhRef01 33040014 BIGHORSREF Panhandle 8/23/1994 67 1.23 

PanhRef02 32010024 LIMEREF Panhandle 7/14/1998 74.5 1.33 

PanhRef03 33010065 RSTAREARUN Panhandle 2/14/1996 54.7 1.19 

PanhRef04 22030010 STMARKSREF Panhandle 9/18/2001 40.5 1.29 

PanhRef05 31010051 SWTREF Panhandle 7/8/1992 97.8 1.31 

PenRef01 28020237 CYPCKUNK Peninsula 7/21/1999 55.7 1.18 

PenRef02 20030414 GLDHEADREF Peninsula 1/12/2000 100 1.00 

PenRef03 24010002 MANAREF Peninsula 11/21/2000 80.2 1.31 

PenRef04 20020012 OKLRIVREF Peninsula 2/15/2000 68.1 1.01 

PenRef05 20030253 PUTNA44 Peninsula 3/16/1998 24 1.15 
Development and Testing of Biomonitoring Tools for Macroinvertebrates in Florida Streams March 2007

85

Table H-2. BCG workshop samples 

BCG 

SampleID STORET Station Nickname Bioregion Sample Date SCI HDG LDI Selection Rationale 

NE01 19020027 ALIGTRREF Northeast 4/11/2001 47.1 1 1.4 SCI [35-50] random 

NE02 19010049 BLDWIN REF Northeast 2/14/2000 32.9 2 3.22 Random 

NE03 19020053 CALAHANTST Northeast 9/24/2001 24.4 3 1.41 SCI 10th Percentile (22.96) 

NE04 19020053 CALAHANTST Northeast 7/15/1996 40.1 3 1.41 SCI [35-50] random 

NE05 20030549 CECFLD6 Northeast 3/6/1996 73.6 1 2 LDI [2-9] random 

NE06 20030332 JAXHEITTST Northeast 5/5/1992 28.9 4 6.11 HDG [3-5] random 

NE07 21030059 LKBUTTST Northeast 9/15/1992 9.1 3 5.26 LDI [2-9] random 

NE08 19010073 MCCLNYTST Northeast 3/22/1999 68 0 1.55 SCI 75th Percentile (68.27) 

NE09 20030573 MILOG209 Northeast 1/26/1998 30.6 1 1.61 SCI 25th Percentile (30.39) 

NE10 19020059 YULEEREF Northeast 10/28/1996 47 0 1.28 SCI 50th Percentile (45.09) 

Panh01 22020078 ABHOPTST Panhandle 6/4/1996 20.7 3 1 HDG [3-5] random 

Panh02 33040073 CANEYCBELL Panhandle 8/22/2000 30 3 1.71 HDG [3-5] random 

Panh03 31010125 CHATTATST Panhandle 10/13/1998 62.5 0 1.62 SCI 50th Percentile (62.40) 

Panh04 31020071 CHTMDLCRKD Panhandle 7/23/1996 70.5 1 1.72 SCI 75th Percentile (70.50) 

Panh05 31020072 CHTMDLOTTR Panhandle 7/23/1996 43.5 2 1.42 Random 

Panh06 33010112 COFECSTLR Panhandle 2/16/1999 47.7 2 1.05 SCI [35-50] random 

Panh07 31020042 DOZREF Panhandle 9/1/1992 30.2 6 2.83 SCI 25th Percentile (30.10) 

Panh08 31020043 DOZTST Panhandle 9/1/1992 35.8 6 2.75 SCI [35-50] random 

Panh09 22030064 FLAMINREF Panhandle 3/8/1993 4.6 7 8.08 SCI 10th Percentile (4.92) 

Panh10 22050003 TAYLO4 Panhandle 4/10/2000 0.6 3 2.28 HDG [3-5] random 

Pen01 20030585 BUNCNTRL Peninsula 9/8/1998 15.5 6 5.29 HDG [6-9] random 

Pen02 25020550 DESOT34 Peninsula 9/3/1997 37.5 4 2.91 SCI [35-50] random 
Development and Testing of Biomonitoring Tools for Macroinvertebrates in Florida Streams March 2007

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BCG 

SampleID STORET Station Nickname Bioregion Sample Date SCI HDG LDI Selection Rationale 

Pen03 27010580 GROOVRARPT Peninsula 8/22/2000 24.2 1 1.79 SCI 25th Percentile (24.55) 

Pen04 24040117 GULFTST Peninsula 10/8/1998 14.8 7 7.85 SCI 10th Percentile (14.45) 

Pen05 24030044 HILSTP5REF Peninsula 8/18/1993 55 1 2.12 HDG [1-2] random 

Pen06 20010384 LONGBRANCH Peninsula 7/13/1999 38.2 3 2.53 SCI [35-50] random 

Pen07 28010224 NWFRLOXREF Peninsula 9/20/1995 40.8 4 3.87 SCI 50th Percentile (40.81) 

Pen08 25020014 OAKREF Peninsula 8/22/1995 58.3 1 2.1 SCI 75th Percentile (58.70) 

Pen09 20020145 SWEETCK Peninsula 8/10/1998 21.1 2 1.3 Random 

Pen10 20020420 WGCITREF Peninsula 12/1/1992 0 7 8.32 HDG [6-9] random 

Data Content 

Data provided for each sample, whether for reference samples or samples for BCG 

scoring, consisted only of the biological data for each sample and an identification of the sample 

to the Bioregion of its collection. Other data regarding the sample and site (e.g., more specific 

location, collection date, information on physical/chemical characteristics, human disturbance 

metrics such as LDI, hydrologic alteration) were not provided to the experts, but were available 

for analysis with the scoring data from the workshop. 

Biological data that were provided for each sample were retrieved from the FDEP 

Statewide Biological database (SBIO). Those data included the collapsed taxa list with the 

number of individuals of each taxon counted in that sample and a suite of metrics. Details 

regarding the SBIO taxa collapsing procedure are provided in the SBIO database manual (Wolfe 

and Lurding, 1999: p. 84, and Appendix 2). Frydenborg and Miller, 2007 (Attachment C) provides 

a copy of each sample sheet of the taxa list and metrics that was provided to experts, a copy of a 

list of synonyms that was provided to assist in their review of the taxa list, and a copy of the 

sample scoring sheet. 

Results 

Each expert provided a BCG score for each site sample (Table H-3) and an assessment 

of the interpretation that should be applied to each BCG score (e.g., meets designated use, does 

not meet designated use; Table H-4). In several cases, experts reported a “gray” area in the 

relevance they ascribed to their BCG scores. Several experts identified BCG scores they judged 

as clearly meeting designated use, those they judged as clearly not meeting designated use, and 

left a gap between those scores signifying their uncertainty about where such a line could be 
Development and Testing of Biomonitoring Tools for Macroinvertebrates in Florida Streams March 2007

87

drawn. Responses were unanimous that a BCG score of 3 or lower (1-3) meets the CWA interim 

goal for biological integrity and that BCG scores of 5 and 6 do not meet the CWA biological 

integrity goal. Approximately half the panel thought the impairment threshold occurred between 

BCG scores of 4 and 5. The other half of the panel wished the impairment line to be drawn 

between BCG scores of 3 and 4, with many of those panelists expressing the concern that 

management action may not be taken on sites until they were considered “impaired.” 

References

Davies, S.P. and S.K. Jackson. 2006. The biological condition gradient: a descriptive model for 

interpreting change in aquatic systems. Ecological Applications 16(4):1251-1266. 

Frydenborg, R. and D. Miller. 2007. Biological Condition Gradient Workshop, October 24, 2006. 

Florida Department of Environmental Protection. 

Stoddard, J.L., D.P. Larsen, C.P. Hawkins, R.K. Johnson, and R.H. Norris. 2006. Setting 

expectations for the ecological condition of streams: the concept of reference condition. 

Ecological Applications 16(4):1267-1276. 

Wolfe, L.E. and K.M. Lurding. 1999. Statewide Biological Database. Florida Department of 

Environmental Protection. 1Uftp://ftp.dep.state.fl.us/pub/labs/biology/biodb/dbmanual.pdfH. 

 
Development and Testing of Biomonitoring Tools for Macroinvertebrates in Florida Streams March 2007

Table H-3. BCG workshop scores summary 

Expert Code E01 E02 E03 E04 E05 E06 E07 E08 E09 E10 E11 E12 E13 E14 E15 E16 E17 E18 E19 E20 E

BCGSampleID Final BCG Score NE01 3 4 3 4 3 3 3 3 3 4 3 3 4 3 4 3 3 3 3 2 NE02 5 5 5 5 5 5 5 5 5 5 4 4 5 5 5 5 5 4 4 4 NE03 4 5 5 5 5 5 5 5 5 5 5 5 5 5 5 4 5 5 5 5 NE04 3 4 4 3 4 4 3 4 3 3 3 3 3 3 3 3 3 3 3 3 NE05 2 1 2 2 2 1 2 2 2 2 2 2 2 1 2 2 2 1 2 1 NE06 4 5 4 5 3 5 5 5 3 5 5 5 4 5 5 4 4 4 4 5 NE07 6 6 5 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 5 6 NE08 3 3 3 2 3 4 5 3 3 3 4 3 2 3 2 3 3 3 3 3 NE09 4 4 5 4 5 3 4 4 5 5 4 4 4 4 5 4 4 4 4 4 NE10 3 3 4 2 3 4 4 3 3 3 2 3 3 3 4 3 3 3 3 3 Panh01 5 4 4 5 5 5 5 4 4 4 3 5 4 4 5 4 4 5 4 3 Panh02 5 4 4 4 5 5 5 5 4 5 4 4 5 5 5 4 5 5 5 4 Panh03 2 2 2 2 2 1 2 2 2 2 2 2 2 1 2 2 1 2 2 2 Panh04 2 2 2 2 2 2 2 2 2 2 2 3 2 1 2 1 1 2 2 2 Panh05 4 4 3 2 4 4 3 4 3 3 4 3 4 3 3 3 3 3 4 3 Panh06 3 2 2 2 2 2 2 3 2 2 2 2 3 2 2 2 2 2 2 2 
Development and Testing of Biomonitoring Tools for Macroinvertebrates in Florida Streams March 2007

Expert Code E01 E02 E03 E04 E05 E06 E07 E08 E09 E10 E11 E12 E13 E14 E15 E16 E17 E18 E19 E20 E

Panh07 5 5 5 5 5 5 5 5 5 5 4 5 5 5 5 5 5 5 5 4 Panh08 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 6 5 4 Panh09 6 6 6 6 5 6 5 6 6 6 6 6 6 6 6 6 6 6 5 6 Panh10 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 Pen01 5 5 6 5 5 6 5 6 5 5 5 5 5 6 5 6 6 5 5 5 Pen02 3 3 3 4 3 3 3 4 4 4 3 3 4 4 3 4 4 3 4 3 Pen03 5 4 4 4 5 4 4 4 5 5 4 4 5 5 4 5 4 5 5 4 Pen04 5 5 5 5 5 5 5 5 5 5 4 5 5 6 5 6 5 5 5 5 Pen05 3 2 2 2 2 2 2 3 2 2 2 2 2 2 2 2 3 3 3 2 Pen06 4 4 4 3 5 4 4 4 5 4 4 4 4 5 4 4 4 4 4 4 Pen07 3 3 2 2 3 3 3 3 3 3 3 3 3 3 4 3 3 3 3 3 Pen08 2 2 1 1 2 2 2 1 1 2 1 2 1 2 2 2 2 2 2 2 Pen09 5 5 5 5 5 4 5 6 5 5 5 5 5 5 5 5 5 5 5 5 Pen10 6 6 6 6 6 5 6 6 6 6 6 5 6 6 6 6 6 6 6 6 
Development and Testing of Biomonitoring Tools for Macroinvertebrates in Florida Streams March 2007

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Table H-4: Experts’ categorization of BCG workshop scores 

Expert Code: Not Impaired/ 

Meets Goal Gray Area Does Not Meet Goal/Impaired Comments 

E01 3 4 

E02 3 4 5 

E03 3 

Not impaired 3-

4, closer to 4, 

but not below 4 

5 

E04 4 5 

E05 4 5 

E06 3 4 

E07 3 4 

E08 3 4 

E09 3 4 

E10 3 4 

E11 3 4 

E12 4 5 

E13 3 4 

E14 4 5 

Impairment line 4-5, but concerned 

that the category 5 sites will not be 

improved and category 4 sites will 

slip into category 5 and then may be 

clumped with the other 5's as 

"irretrievable" 

E15 3 4 

E16 3 line between 4 

and 5 5 

E17 3 

Impairment 

Line 4-5, 4 

probably meets 

5 

E18 4 5 

E19 4 5 

E20 4 5 

E21 4 5 

E22 4 5 
91

Appendix I. Change Point Analysis of Stream Condition 

Index and Biological Condition Gradient Data 

Xufeng Niu (Florida State University) and Denise Miller (FDEP) 

Background 

For the purpose of scrutinizing Stream Condition Index (SCI) macroinvertebrate data with 

the goal of refining delineations of biological/ecological condition, Florida Department of 

Environmental Protection (FDEP) staff conducted a biological condition gradient (BCG) workshop 

in October 2006 (Frydenborg and Miller, 2007). Participants in the BCG workshop (the panel) 

consisted of twenty-two (22) experienced aquatic biologists in Florida who agreed to assist FDEP 

as part of this delineation effort. 

During the BCG workshop, the panel was provided with species lists and metrics for 15 

reference streams and 30 test samples, equally divided into Florida’s three bioregions 

(Panhandle, Northeast, and Peninsula). The panel was not given the SCI scores associated with 

any of the sites, or any physical, chemical, or habitat descriptions of the sites (other than the 

Bioregion location). Decisions were based on biological community composition data collected via 

the 20-dip net sweep method associated with SCI sampling. The scores (1 through 6) were based 

on the six tiers of the conceptual biological condition gradient (BCG) from Davies and Jackson, 

2006, listed below: 

• Score of 1: Native or natural condition 

• Score of 2: Minimal loss of species; some density changes may occur 

• Score of 3: Some replacement of sensitive-rare species; functions fully maintained 

• Score of 4: Some sensitive species maintained but notable replacement by moretolerant taxa; altered distributions; functions largely maintained 

• Score of 5: Tolerant species show increasing dominance; sensitive species are 

rare; functions altered 

• Score of 6: Severe alteration of structure and function 

The analyses conducted in this report were based on the results from the BCG workshop 

(BCG scores for 30 samples by each of 22 panelists) and the SCI scores for those same samples 

to address FDEP’s goal of refining delineations of biological/ecological condition for SCI scores. 

The Detection Procedure 

Niu et al. (2000) introduced an iterative procedure for detecting and modeling level-shift 

change points. The procedure is similar to that suggested by Chang (1982) and further developed 

by Chang et al. (1988) for detecting outliers and level shifts in time series analysis. Statistical 

details of this procedure can also be found in Pankratz (1991, Chapter 8). 

For simplicity, let us consider a response variable Y, after an appropriate transformation. 

Suppose that observations {( ,) Xi i Y , i n = 1, 2, ... , } are available where n is the sample size 

and X is an independent variable. Moreover, we assume that the observations are arranged in 

the following manner: 
92

• The values { , Xi i n = 1, 2, ... , } are distinct. If several ' Y s i are corresponding to a 

single X value, the median of the ' Y s i is taken to be the response value for the X

value. 

• {( ,) Xi i Y , i n = 1, 2, ... , } are sorted according to the values of X from least to 

greatest. 

For each integer l > 1, define the step variable () 0 i S l = for i l < and () 1 i S l = for 

i l ≥ . 

Step 1. Fit the linear regression model: 

0 1 () () () () Y l lSl l i i =+ + β β ε i , i n = 1, 2, ... , , (1) 

where for a fixed l, the ( )' i ε l s are assumed to be independent and identically distributed 

normal random variables with mean zero and variance 2 σ ( )l . 

Step 2. Calculate the values { 1 1 L l l se l ( ) ( ) / ( ( )) = β β

) ) , l n = 2, 3, ... , ( 1) − } where 

1 se l ( ( )) β

) is the estimated standard error of 1 β ( )l ) . 

Step 3. Let 1 Ll L L Ln ( ) max{ (2), (3), ... , ( 1)} = − and compare 1 L l( ) with the critical 

value C=3.0 (or C=3.5). The critical value C=3.0 (or C=3.5) corresponds roughly to α = 0.10

(orα = 0.05), or the 10% (or the 5%) significance level, based on the simulation results of Chang 

et al. (1988). If 1 L l( ) is significant, we conclude that the response Y has a change point at 1 Xl

with a level-shift 1 β ( )l ) . 

Step 4. Let *

11 1 () () Y Y lSl ii i = − β . Repeat Steps 1-3 on the new response variable 

* Yi for detecting a possible second change point. Continue the process until no further change 

point can be identified. 

Step 5. Suppose that k change points are detected in the response variable Y and the 

corresponding X values are 1 2 { , , ... , }k Xll l X X . Fit the model 

0 11 2 2 ( ) ( ) ... ( ) Y Sl Sl Sl i ii k = + + ++ + β ββ βε i k i , i n = 1, 2, ... , . (2) 

Then the estimated coefficients 1 2 { , , ... , } β β β k

) ) ) will be the k estimated level-shift 

values. 
93

Analysis of SCI and BCG Data 

Change Point Analysis 

Table I-1 contains a list of the SCI values for the 30 samples from the BCG workshop 

along with their median and mean BCG scores based on the individual scores given by the 

experts. Change-point analysis of the SCI values was conducted in this report using the median 

BCG scores as the dependent variable. Median BCG scores rather than mean BCG scores were 

used to avoid potential outlier problems. 

Two change points were detected in the SCI values. The first change point was at 

SCI=35.8, which has the statistic L(l1) = 9.79 and is significant at the 5% level (95% confidence). 

The second change point occurred at SCI=9.1 which has the statistic L(l1) = 3.87 and is also 

significant at the 5% level (95% confidence). The R-square of the regression is 0.86, indicating 

that the step-function regression model fits the median BCG scores very well. 

Table I-1. SCI and BCG scores for the 30 Samples 

STORET Station Sample Date SCI Score BCG Median 

Score BCG Mean Score 

20030549 3/6/1996 73.6 2 1.7 

31020071 7/23/1996 70.5 2 1.9 

19010073 3/22/1999 68 3 3 

31010125 10/13/1998 62.5 2 1.9 

25020014 8/22/1995 58.3 2 1.7 

24030044 8/18/1993 55 2 2.2 

33010112 2/16/1999 47.7 2 2.2 

19020027 4/11/2001 47.1 3 3.2 

19020059 10/28/1996 47 3 3.1 

31020072 7/23/1996 43.5 3 3.3 

28010224 9/20/1995 40.8 3 3 

19020053 9/24/2001 40.1 3 3.3 

20010384 7/13/1999 38.2 4 4.1 

25020550 9/3/1997 37.5 3 3.4 

31020043 9/1/1992 35.8 5 5 

19010049 2/14/2000 32.9 5 4.8 

20030573 1/26/1998 30.6 4 4.2 

31020042 9/1/1992 30.2 5 4.9 

33040073 8/22/2000 30 5 4.6 

20030332 5/5/1992 28.9 4.5 4.4 

19020053 9/24/2001 24.4 5 4.8 

27010580 8/22/2000 24.2 4 4.5 

20020145 8/10/1998 21.1 5 5 

22020078 6/4/1996 20.7 4 4.3 
94

STORET Station Sample Date SCI Score BCG Median 

Score BCG Mean Score 

20030585 9/8/1998 15.5 5 5.4 

24040117 10/8/1998 14.8 5 5.1 

21030059 9/15/1992 9.1 6 5.9 

22030064 3/8/1993 4.6 6 5.9 

22050003 4/10/2000 0.6 6 6 

20020420 12/1/1992 0 6 5.9 

Change Points: 1). SCI=35.8 with the test statistic of 9.79 and confidence level > 95%; 

 2). SCI=9.1 with the test statistic of 3.87 and confidence level > 95%. 

Highlighted numbers are the SCI-BCG Scores at the change points. 

Confidence Intervals for the Change Points 

In this section, we constructed confidence intervals for the detected change points based 

on a modified bootstrapping method. “Regular” bootstrapping is a statistical method for estimating 

the sampling distribution of an estimator by resampling with replacement from the original sample 

(Efron, 1979; Efron and Tibshirani, 1993). This method is widely used in parameter estimation, 

regression, time series, and other statistical problems. 

In regression analysis, there are two basic approaches to perform bootstrapping. The first 

approach is bootstrapping cases. Assume that we want to fit a regression model with response 

variable Y and predictors { 1, ... , ... p x x }, and we have a sample of n cases 

1 { ( , , ... , ... ), 1, ... , } Zi i i pi = = Yx x i n . In bootstrapping, we select n cases randomly from the 

original observations with replacement. For each bootstrapping sample, we fit the regression 

model and save the coefficients. Confidence intervals for the regression coefficients can be 

constructed based on M (a given large number) bootstrapping samples. The second approach in 

bootstrapping regression analysis is resampling the residuals. In this way a regression model is 

fitted to the original data first. The fitted values and residuals are calculated based on the fitted 

model. The residuals are randomly resampled and added back to the fitted values. After 

generating a new set of bootstrapping data, the regression model is refitted. 

Efron and Tibshirani (1993, Chapter 9, p. 113) pointed out that bootstrapping cases is 

less sensitive to model assumptions such as normality and constant variance than bootstrapping 

residuals. In this study, the bootstrapping cases method was used to construct confidence 

intervals for change points. 

There are several approaches to constructing bootstrap confidence intervals for change 

points. The bootstrap percentile interval approach was used in this study. This approach uses the 

empirical quantiles of ˆθ to form a confidence interval ˆ ˆ * * [, ] θ θ Lower Upper for the change point θ, 

where ** *

(1) (2) ( )

ˆˆ ˆ { , , ... , } θθ θ M are the ordered bootstrap estimates of the change point; ˆ* θ Lower

and ˆ* θ Upper are the α/2 percentile and the (1 − α/2) percentile of the bootstrap estimates of the 

change point, respectively. 

For this analysis, we had 30 pairs of SCI and Median BCG Scores. We used a modified 

bootstrapping method to construct confidence intervals for the change points. Specifically, we 
95

first randomly drew 90% of the 30 pairs (or dropped 10%) from the original data and conducted 

change-point analysis. This procedure is also called the drop-d Jackknife method in statistics 

(Efron and Tibshirani, 1993, Page 149). Three thousand (3,000) samples were drawn from the 

original data, and the results are presented in Table I-2. For the first change point at SCI=35.8, 

the 90% and 95% confidence intervals based on the percentiles of the 3,000 estimated change 

points were identical. 

Table I-2. Confidence Intervals (CI) for the change points of SCI scores 

based on M=3,000 drop-10% samples. 

 First Change Points Second Change Points 

SCI Based on Original Data 35.8 9.1 

SCI Based on Average of drop-10% Estimates 35.8 8.8 

SCI Based on Median of drop-10% Estimates 35.8 9.1 

Standard Error of the Change Point 1.6 2.7 

SCI Percentile Interval, 95% CI [32.9, 38.2] [4.6, 13.4] 

SCI Percentile Interval, 90% CI [32.9, 38.2] [4.6, 12.0] 

Summary 

This change-point analysis was conducted for the purpose of addressing FDEP’s goal of 

refining delineations of biological/ecological condition for Stream Condition Index (SCI) scores. 

The BCG (biological condition gradient) scores that were used to evaluate changes in SCI scores 

were provided by a panel of 22 experienced aquatic biologists in Florida. The results of this 

change-point analysis indicated a first change point at SCI=35.8, with the 95% confidence interval 

[32.9, 38.2]. In other words, SCI scores higher (better) than 35.8 were determined to be different 

from lower scores based on the biological condition assigned to taxonomic data for the samples 

by experienced aquatic biologists. Additional details of the BCG workshop and the change-point 

analysis are provided in Frydenborg and Miller, 2007 and Niu and Miller, 2007, respectively. 

References 

Chang, I. 1982. “Outliers in Time Series," Unpublished Ph.D. Dissertation, Department of 

Statistics, University of Wisconsin, Madison. 

Chang, I., G.C. Tiao, and C. Chen. 1988. “Estimation of Time Series Parameters in the Presence 

of Outliers," Technometrics, 30: 193-204. 

Davies, S.P. and S.K. Jackson. 2006. “The Biological Condition Gradient: A Descriptive Model for 

Interpreting Change in Aquatic Systems.” Ecological Applications. 16(4): 1251-1266. 

Ecological Society of America. 

Efron, B. 1979. “Bootstrap Methods: Another Look at the Jackknife.” Annals of Statistics 7:1-26. 

Efron, B. and R. J. Tibshirani. 1993. An Introduction to the Bootstrap. New York: Chapman and 

Hall. 
96

Frydenborg, R. and D. Miller. 2007. “Biological Condition Gradient Workshop, October 24, 2006”. 

Florida Department of Environmental Protection. 

Niu, X., P. Lin, and D. Meeter. 2000. “Detecting Change Points in the Species Composition and 

Water Quality Data of WCA2A”. Department of Statistics, Florida State University, 

Tallahassee. Technical Report Submitted to the Florida Department of Environmental 

Protection. 

Niu, X. and D. Miller. 2007. “Change Point Analysis and Model Comparison of Stream Condition 

Index and Biological Condition Gradient Data”. Technical Report Submitted to the Florida 

Department of Environmental Protection. 

Pankratz, A. 1991. Forecasting with Dynamic Regression Models. John Wiley & Sons Inc. New 

York. 

Schwartz, G. 1978. Estimating the Dimension of a Model. The Annals of Statistics, 6: 461-464. 
Appendix J. Reference sites 

Table J-1. Reference site locations. 

STORET Site Description Bioregion Waterbody Name Facility Information from GIS Coverages 

20030245 Ates Creek @ CR 315 Northeast BLACK CREEK No point sources or stormwater inputs upstream 20030437 

BLACK CR., NORTH PRONG, 1/4 ABOVE BIG BRANCH Northeast BLACK CREEK 

One large stormwater constructin permit upstream, possibly 

connected through wetlands issued in 6/2005. 

20030419 Black Creek, South Prong Northeast BLACK CREEK Camp blanding is upstream of this site 20030723 

N. FORK BLACK CRK ABOVE CARTER SPENCER RD. Northeast BLACK CREEK 

Dupont Highland Mine facility discharges, but this site is above this. 

20030247 

South Fork Black Creek @ SR 16 Northeast BLACK CREEK No point sources or stormwater inputs upstream 

20030570 

Bradley Crk @ 739B-Peter's crk basin Northeast BRADLEY CREEK 

Subdivision (Silver Creek) stormwater permits (2003 &2005), discharge may flow towards this site. 

20030487 

Bull Creek @ NE Crossing of CR 215 Northeast BULL CREEK No point sources or stormwater inputs upstream 

19010042 

Calkins Ck, off Hamp Register Rd. Northeast CALKINS CREEK No point sources or stormwater inputs upstream 

21010043 

Deep Creek upstream of bridge @Old River Rd Northeast DEEP CREEK No point sources or stormwater inputs upstream 

21010018 

Falling Creek @ CR 131, above falls Suwannee R. Northeast FALLING CREEK 

KOA Domestic WWTF ~3000 m upstream of site, not NPDES permitted, design capacity = 0.0150. Not NPDES permitted, ansmall design capacity likely means that this is a percolation ponDistrict Biologist is not aware of a KOA discharge in this area. 

19010064 

John Rowe Br, test site for NE FL State Hosp FYI Northeast JOHN ROWE BRANCH 

No point sources or stormwater inputs upstream; The hospital doesn't show up as permitted site on the GIS coverage 
STORET Site Description Bioregion Waterbody Name Facility Information from GIS Coverages 

20030571 

Peter's Crk above Rosemary hill Rd- Basin Northeast PETERS CREEK 

Clay Co. Dept. of Env. Services has 2 stormwater permits right the site and 2 stormwater outfall sites 

19010099 

Pigeon Creek at US 1 St. Marys-Nassau River Northeast PIGEON CREEK No point sources or stormwater inputs upstream 

21010056 

PCS Phosphate FYI5, control site Northeast ROBINSON CREEK No point sources or stormwater inputs upstream 

21010032 

Rocky Ck @ Woodpecker Rd Northeast ROCKY CREEK No point sources or stormwater inputs upstream 

20030265 

Sal Taylor Creek below Rowell Creek Northeast SAL TAYLOR CREEK 

One large stormwater constructin permit above these sites issuin 12/2004. 

21030115 

Santa Fe Rv- Control A BMP Northeast SANTA FE RIVER A few stormwater permits pretty far upstream 

20030456 

Thomas Ck, Forestry BMP Study Ref Site B Northeast THOMAS CREEK No point sources or stormwater inputs upstream 

19010072 McClenny WWTP upstream Northeast TURKEY CREEK 

A few stormwater permits around, look to be small and may not actually discharge to this stream 

20030341 

Yellow Water Creek above Sal Taylor Creek Northeast YELLOW WATER CREEK

One large stormwater constructin permit above these sites issuin 12/2004. 

20030342 

Yellow Water Creek below Sal Taylor Creek Northeast YELLOW WATER CREEK

One large stormwater constructin permit above these sites issuin 12/2004. 

22020070 

BIG CYPRESS BRANCHTIMBER ENERGY FYI CNTRL SITE PanhandleBIG CYPRESS BRANCH No point sources or stormwater inputs upstream 

33040014 

BIG HORSE CREEK S.R.2 OKA.CO. PanhandleBIG HORSE CREEK No point sources or stormwater inputs upstream 

21030064 

Cedar Head Run, above Ichetucknee River PanhandleCEDAR HEAD RUN 

Ichetucknee Family Campground WWTF upstream of site, not NPDES permitted (?), design capacity=0.007. Subsequent additional information indicates no discharge. Stream begins in State Park. 

31010050 

CROOKED CREEK HWY.270 GADSDEN CO. PanhandleCROOKED CREEK No point sources or stormwater inputs upstream 
STORET Site Description Bioregion Waterbody Name Facility Information from GIS Coverages 

32030023 

ECONFINA CREEK AT SCOTT RD PanhandleECONFINA CREEK No point sources or stormwater inputs upstream 

22050077 

Econfina River Forestry BMP Study Ref Site B PanhandleECONFINA RIVER No point sources or stormwater inputs upstream 

33040064 

Jacks Branch, Forestry BMP Study Ref Site B PanhandleJACK'S BRANCH No point sources or stormwater inputs upstream 

32010024 

Limestone Ck @ Beckridge Rd PanhandleLIMESTONE CREEK No point sources or stormwater inputs upstream 

32020063 

LITTLE CROOKED CREEK S.R.79 BAY CO. PanhandleLTL CROOKED CREEK No point sources or stormwater inputs upstream 

22030062 

MCBRIDE SLOUGH HWY.267 WAKULLA CO. PanhandleMCBRIDE SLOUGH No point sources or stormwater inputs upstream 

33010054 

MCDAVID CREEK AT SR 99 PanhandleMCDAVID CREEK No point sources or stormwater inputs upstream 

22020049 

MULE CREEK S.R.12 LIBERTY CO. PanhandleMULE CREEK No point sources or stormwater inputs upstream 

22020062 

Oklawaha Ck, SR 267, S of Quincy PanhandleOKLAWAHA CREEK 

Sand mine operations (permitted IWF's). Subsequent additionainformation indicates no discharge to sampled site. 

31020073 

Pitts Mill Creek above conf with Cypress Ck PanhandlePITTS MILL CREEK No point sources or stormwater inputs upstream 

22020010 

QUINCY CR above SR12, ref for Quincy STP FYI PanhandleQUINCY CREEK 

Engelhard IWF upstream and numerous other small IWF's just that may/may not flow towards this site. Subsequent additional information indicates no discharge to site except maybe during hurricanes. 

22030010 

St. Marks R approx 4 mi N of Hwy 98 (@ "rapids") PanhandleST. MARKS RIVER No point sources or stormwater inputs upstream 

31010051 

SWEETWATER CREEK HWY.270 LIBERTY CO. PanhandleSWEETWATER CREEK No point sources or stormwater inputs upstream 
STORET Site Description Bioregion Waterbody Name Facility Information from GIS Coverages 

31020040 

TEN MILE CREEK S.R.73 CALHOUN CO. PanhandleTEN MILE CREEK 

One small WWTP and one Industrial Wastewater permitted facility upstream; Stormwater construction at sampling point. Wfacilities at least 1.5 to 2 km upstream. Additional subsequent information from District Biologist is that site is appropriate for reference. 

33010065 

Rest Area Run Creek below I 10 PanhandleUNNAMED CREEK 

Surrounded by stormwater construction, potential inputs from Perdido landfill upstream. Subsequent additional information froDistrict Biologist is that this site was reference at the time relevato the SCI sampling for the calibration dataset. 

22040038 

Unnamed Creek above Monticello WWTP PanhandleUNNAMED CREEK 

A few stormwater permits around, look to be small and may not actually discharge to this stream 

32020066 

Wright Ck, ref for Noma STP FYI PanhandleWRIGHTS CREEK Site is upstream of Noma WWTP 

20010455 

BLACKWATER CREEK AT HWY 44A Peninsula BLACKWATER CREEK No point sources or stormwater inputs upstream 

28020239 Cypress brch above SR 78 Peninsula CYPRESS BRANCH No point sources or stormwater inputs upstream 28020237 

Cypress Creek 2 mi above SR78 Peninsula CYPRESS CREEK 

Small stormwater construction permit for Wayne Homes; permistarted in 9/2004 

26010972 

DAVENPORT CR AT SR 545 BRIDGE Peninsula DAVENPORT CREEK Gas pipeline relocation stormwater permit started in 2/2005 

26010593 

FISHEATING CREEK ABOVE US 27 Peninsula FISHEATING CREEK 

Lykes Campground WWTF nearby, not NPDES permitted (?); Depending on flow direction could impact site. Subsequent additional information indicates that site is upstream from campground. 

20030414 

GOLD HEAD BR. IN STATE PARK Peninsula GOLD HEAD BRANCH No point sources or stormwater inputs upstream 

24030142 

Hillsborough R upstrm of Southside Br, Zephyrhil Peninsula HILLSBOROUGH RIVER 

Zephyrhills WWTF upstream of site, but can't determine where outfall is. Lots of monitoring wells around. Subsequent additioninformation indicates probably a percolation pond. No discharge to the site - the site was just visited. 
STORET Site Description Bioregion Waterbody Name Facility Information from GIS Coverages 

24030048 Hillsborough River at SR 39 Peninsula HILLSBOROUGH RIVER 

Zephyrhills WWTF upstream of site, but can't determine where outfall is. Lots of monitoring wells around. Subsequent additioninformation indicates no discharge to the site - the site was just visited. 

25020111 

Horse Creek @ SR 72 Bridge Peninsula HORSE CREEK No point sources or stormwater inputs upstream 

20010454 

JUNIPER CREEK AT HIGHWAY 19 Peninsula JUNIPER CREEK No point sources or stormwater inputs upstream 

20020004 

Little Orange Creek @ Hwy 21 Peninsula LITTLE ORANGE CREEK 

Ga Pacific-Hawthorne Plywood STW discharges upstream, but may flow into Little Orange Lake. Subsequent additional information indicates no discharge to the stream. 

26011019 

LIVINGSTON CREEK AT RUCKS DAIRY RD. Peninsula LIVINGSTON CREEK No point sources or stormwater inputs upstream 

28010223 

N FK LOXAHATCHEE RIVER REF BIO STATION Peninsula LOXAHATCHEE RIVER No point sources or stormwater inputs upstream 

24010002 

Manatee R 20 m below SR64 bridge (TP1) Peninsula MANATEE RIVER Wingate Creek Mine flows SE into Johnson Crk 

20020012 

OKLAWAHA RIVER AT SR 316 Peninsula OKLAWAHA RIVER No point sources or stormwater inputs upstream 

20020404 

ORANGE CREEK 50 YDS. UP FROM HWY. 21 Peninsula ORANGE CREEK 

Water bottling facility (IWF) right at the sampling lat/longs. Subsequent additional information from DEP Bureau of Labs stis that sampling trips in this area indicate that the IWF facility is located downstream of this site. 

20030253 

RICECR #3@BR SR #3/250yds above Bardine Peninsula RICE CREEK No point sources or stormwater inputs upstream 

20020317 

SILVER RUN AT CONFLUENCE WITH BOAT RAMP CANAL Peninsula SILVER RUN 

One small WWTP (Sharpes Ferry Mobile Home Park) permitted facility upstream, design capacity = 0.0190. Subsequent additional information indicates the site is upstream of any discharge. 

28020221 

TELEGRAPH CREEK AT BABCOCK RANCH Peninsula TELEGRAPH CREEK 

One large stormwater construction nearby (permitted 3/2005), bflows into Big Island Canal which is also the receiving water for Telegraph Creek 
STORET Site Description Bioregion Waterbody Name Facility Information from GIS Coverages 

26011020 

TIGER CREEK AT WALKING WATERS RD. Peninsula TIGER CREEK No point sources or stormwater inputs upstream 

23010464 

Withlacoochee R @ county park off Auton Rd (TP3) Peninsula WITHLACOOCHEE RIVER

Small stormwater construction permit for bike trail; permit startein 4/2005 

23010446 WITHLACOOCHEE RIVER Peninsula WITHLACOOCHEE RIVER

No point sources or stormwater inputs upstream; site below WITHREF 
Table J-2. Reference sites sample data 

STORET Site Description Bioregion Waterbody Name SCI HDG LDI 

To

20030245 Ates Creek @ CR 315 Northeast BLACK CREEK 75.8 0 1.2 20030437 BLACK CR., NORTH PRONG, 1/4 ABOVE BIG BRANCH Northeast BLACK CREEK 56.1 0 1.2 20030437 BLACK CR., NORTH PRONG, 1/4 ABOVE BIG BRANCH Northeast BLACK CREEK 57.7 0 1.2 20030437 BLACK CR., NORTH PRONG, 1/4 ABOVE BIG BRANCH Northeast BLACK CREEK 66.9 0 1.2 20030437 BLACK CR., NORTH PRONG, 1/4 ABOVE BIG BRANCH Northeast BLACK CREEK 71.5 0 1.2 20030437 BLACK CR., NORTH PRONG, 1/4 ABOVE BIG BRANCH Northeast BLACK CREEK 67.3 0 1.2 20030419 Black Creek, South Prong Northeast BLACK CREEK 68.4 0 1.4 20030419 Black Creek, South Prong Northeast BLACK CREEK 64.3 0 1.4 20030419 Black Creek, South Prong Northeast BLACK CREEK 79.3 0 1.4 20030419 Black Creek, South Prong Northeast BLACK CREEK 66.1 0 1.4 20030419 Black Creek, South Prong Northeast BLACK CREEK 81.3 0 1.4 20030723 N. FORK BLACK CRK ABOVE CARTER SPENCER RD. Northeast BLACK CREEK 43.4 0 1.2 20030247 South Fork Black Creek @ SR 16 Northeast BLACK CREEK 90.2 0 1.1 20030570 Bradley Crk @ 739B-Peter's crk basin Northeast BRADLEY CREEK 85.1 0 1.3 20030487 Bull Creek @ NE Crossing of CR 215 Northeast BULL CREEK 63.9 0 1.2 19010042 Calkins Ck, off Hamp Register Rd. Northeast CALKINS CREEK 75.7 0 1.2 19010042 Calkins Ck, off Hamp Register Rd. Northeast CALKINS CREEK 80.4 0 1.2 19010042 Calkins Ck, off Hamp Register Rd. Northeast CALKINS CREEK 50.0 0 1.2 21010043 Deep Creek upstream of bridge @Old River Rd Northeast DEEP CREEK 66.9 0 1.4 21010018 Falling Creek @ CR 131, above falls Suwannee R. Northeast FALLING CREEK 51.5 0 1.4 21010018 Falling Creek @ CR 131, above falls Suwannee R. Northeast FALLING CREEK 77.9 0 1.4 21010018 Falling Creek @ CR 131, above falls Suwannee R. Northeast FALLING CREEK 39.0 0 1.4 21010018 Falling Creek @ CR 131, above falls Suwannee R. Northeast FALLING CREEK 42.7 0 1.4 21010018 Falling Creek @ CR 131, above falls Suwannee R. Northeast FALLING CREEK 87.8 0 1.4 19010064 John Rowe Br, test site for NE FL State Hosp FYI Northeast JOHN ROWE BRANCH 62.7 0 1.8 20030571 Peter's Crk above Rosemary hill Rd- Basin Northeast PETERS CREEK 89.0 0 1.2 
STORET Site Description Bioregion Waterbody Name SCI HDG LDI 

To

19010099 Pigeon Creek at US 1 St. Marys-Nassau River Northeast PIGEON CREEK 78.8 0 1.3 19010099 Pigeon Creek at US 1 St. Marys-Nassau River Northeast PIGEON CREEK 76.8 0 1.3 19010099 Pigeon Creek at US 1 St. Marys-Nassau River Northeast PIGEON CREEK 78.9 0 1.3 19010099 Pigeon Creek at US 1 St. Marys-Nassau River Northeast PIGEON CREEK 58.0 0 1.3 19010099 Pigeon Creek at US 1 St. Marys-Nassau River Northeast PIGEON CREEK 83.5 0 1.3 19010099 Pigeon Creek at US 1 St. Marys-Nassau River Northeast PIGEON CREEK 54.8 0 1.3 21010056 PCS Phosphate FYI5, control site Northeast ROBINSON CREEK 79.2 0 1.4 21010032 Rocky Ck @ Woodpecker Rd Northeast ROCKY CREEK 56.7 0 1.2 20030265 Sal Taylor Creek below Rowell Creek Northeast SAL TAYLOR CREEK 68.6 0 1.8 20030265 Sal Taylor Creek below Rowell Creek Northeast SAL TAYLOR CREEK 35.1 0 1.8 21030115 Santa Fe Rv- Control A BMP Northeast SANTA FE RIVER 75.7 0 1.5 20030456 Thomas Ck, Forestry BMP Study Ref Site B Northeast THOMAS CREEK 75.7 0 1.2 20030456 Thomas Ck, Forestry BMP Study Ref Site B Northeast THOMAS CREEK 82.6 0 1.2 20030456 Thomas Ck, Forestry BMP Study Ref Site B Northeast THOMAS CREEK 92.4 0 1.2 20030456 Thomas Ck, Forestry BMP Study Ref Site B Northeast THOMAS CREEK 86.7 0 1.2 19010072 McClenny WWTP upstream Northeast TURKEY CREEK 78.2 0 1.6 20030341 Yellow Water Creek above Sal Taylor Creek Northeast YELLOW WATER CREEK 70.4 0 1.9 20030341 Yellow Water Creek above Sal Taylor Creek Northeast YELLOW WATER CREEK 66.4 0 1.9 20030342 Yellow Water Creek below Sal Taylor Creek Northeast YELLOW WATER CREEK 43.5 0 1.8 20030342 Yellow Water Creek below Sal Taylor Creek Northeast YELLOW WATER CREEK 74.8 0 1.8 22020070 BIG CYPRESS BRANCH-TIMBER ENERGY FYI CNTRL SITEPanhandle BIG CYPRESS BRANCH 53.6 0 1.3 33040014 BIG HORSE CREEK S.R.2 OKA.CO. Panhandle BIG HORSE CREEK 67.4 0 1.2 33040014 BIG HORSE CREEK S.R.2 OKA.CO. Panhandle BIG HORSE CREEK 41.7 0 1.2 33040014 BIG HORSE CREEK S.R.2 OKA.CO. Panhandle BIG HORSE CREEK 78.4 0 1.2 33040014 BIG HORSE CREEK S.R.2 OKA.CO. Panhandle BIG HORSE CREEK 75.3 0 1.2 33040014 BIG HORSE CREEK S.R.2 OKA.CO. Panhandle BIG HORSE CREEK 67.0 0 1.2 33040014 BIG HORSE CREEK S.R.2 OKA.CO. Panhandle BIG HORSE CREEK 96.3 0 1.2 21030064 Cedar Head Run, above Ichetucknee River Panhandle CEDAR HEAD RUN 42.2 0 1.0 21030064 Cedar Head Run, above Ichetucknee River Panhandle CEDAR HEAD RUN 40.6 0 1.0 
STORET Site Description Bioregion Waterbody Name SCI HDG LDI 

To

21030064 Cedar Head Run, above Ichetucknee River Panhandle CEDAR HEAD RUN 46.2 0 1.0 21030064 Cedar Head Run, above Ichetucknee River Panhandle CEDAR HEAD RUN 69.4 0 1.0 31010050 CROOKED CREEK HWY.270 GADSDEN CO. Panhandle CROOKED CREEK 71.1 0 1.1 31010050 CROOKED CREEK HWY.270 GADSDEN CO. Panhandle CROOKED CREEK 58.5 0 1.1 31010050 CROOKED CREEK HWY.270 GADSDEN CO. Panhandle CROOKED CREEK 84.8 0 1.1 32030023 ECONFINA CREEK AT SCOTT RD Panhandle ECONFINA CREEK 68.2 0 1.2 32030023 ECONFINA CREEK AT SCOTT RD Panhandle ECONFINA CREEK 70.4 0 1.2 32030023 ECONFINA CREEK AT SCOTT RD Panhandle ECONFINA CREEK 62.3 0 1.2 32030023 ECONFINA CREEK AT SCOTT RD Panhandle ECONFINA CREEK 81.3 0 1.2 32030023 ECONFINA CREEK AT SCOTT RD Panhandle ECONFINA CREEK 87.9 0 1.2 22050077 Econfina River Forestry BMP Study Ref Site B Panhandle ECONFINA RIVER 59.8 0 1.1 33040064 Jacks Branch, Forestry BMP Study Ref Site B Panhandle JACK'S BRANCH 63.4 0 1.7 33040064 Jacks Branch, Forestry BMP Study Ref Site B Panhandle JACK'S BRANCH 63.6 0 1.7 33040064 Jacks Branch, Forestry BMP Study Ref Site B Panhandle JACK'S BRANCH 66.9 0 1.7 32010024 Limestone Ck @ Beckridge Rd Panhandle LIMESTONE CREEK 74.5 0 1.3 32020063 LITTLE CROOKED CREEK S.R.79 BAY CO. Panhandle LTL CROOKED CREEK 78.6 0 1.2 32020063 LITTLE CROOKED CREEK S.R.79 BAY CO. Panhandle LTL CROOKED CREEK 67.4 0 1.2 32020063 LITTLE CROOKED CREEK S.R.79 BAY CO. Panhandle LTL CROOKED CREEK 62.3 0 1.2 32020063 LITTLE CROOKED CREEK S.R.79 BAY CO. Panhandle LTL CROOKED CREEK 71.5 0 1.2 32020063 LITTLE CROOKED CREEK S.R.79 BAY CO. Panhandle LTL CROOKED CREEK 72.6 0 1.2 32020063 LITTLE CROOKED CREEK S.R.79 BAY CO. Panhandle LTL CROOKED CREEK 80.1 0 1.2 32020063 LITTLE CROOKED CREEK S.R.79 BAY CO. Panhandle LTL CROOKED CREEK 52.7 0 1.2 22030062 MCBRIDE SLOUGH HWY.267 WAKULLA CO. Panhandle MCBRIDE SLOUGH 41.6 0 1.0 22030062 MCBRIDE SLOUGH HWY.267 WAKULLA CO. Panhandle MCBRIDE SLOUGH 60.2 0 1.0 22030062 MCBRIDE SLOUGH HWY.267 WAKULLA CO. Panhandle MCBRIDE SLOUGH 57.3 0 1.0 33010054 MCDAVID CREEK AT SR 99 Panhandle MCDAVID CREEK 73.2 0 1.3 33010054 MCDAVID CREEK AT SR 99 Panhandle MCDAVID CREEK 75.6 0 1.3 33010054 MCDAVID CREEK AT SR 99 Panhandle MCDAVID CREEK 68.7 0 1.3 33010054 MCDAVID CREEK AT SR 99 Panhandle MCDAVID CREEK 48.5 0 1.3 
STORET Site Description Bioregion Waterbody Name SCI HDG LDI 

To

33010054 MCDAVID CREEK AT SR 99 Panhandle MCDAVID CREEK 70.2 0 1.3 33010054 MCDAVID CREEK AT SR 99 Panhandle MCDAVID CREEK 76.9 0 1.3 33010054 MCDAVID CREEK AT SR 99 Panhandle MCDAVID CREEK 88.7 0 1.3 22020049 MULE CREEK S.R.12 LIBERTY CO. Panhandle MULE CREEK 49.9 0 1.2 22020049 MULE CREEK S.R.12 LIBERTY CO. Panhandle MULE CREEK 65.7 0 1.2 22020049 MULE CREEK S.R.12 LIBERTY CO. Panhandle MULE CREEK 57.7 0 1.2 22020049 MULE CREEK S.R.12 LIBERTY CO. Panhandle MULE CREEK 58.6 0 1.2 22020049 MULE CREEK S.R.12 LIBERTY CO. Panhandle MULE CREEK 67.8 0 1.2 22020062 Oklawaha Ck, SR 267, S of Quincy Panhandle OKLAWAHA CREEK 52.0 0 1.1 22020062 Oklawaha Ck, SR 267, S of Quincy Panhandle OKLAWAHA CREEK 41.8 0 1.1 22020062 Oklawaha Ck, SR 267, S of Quincy Panhandle OKLAWAHA CREEK 45.2 0 1.1 22020062 Oklawaha Ck, SR 267, S of Quincy Panhandle OKLAWAHA CREEK 48.3 0 1.1 22020062 Oklawaha Ck, SR 267, S of Quincy Panhandle OKLAWAHA CREEK 82.9 0 1.1 31020073 Pitts Mill Creek above conf with Cypress Ck Panhandle PITTS MILL CREEK 68.2 0 1.5 22020010 QUINCY CR above SR12, ref for Quincy STP FYI Panhandle QUINCY CREEK 45.5 0 1.5 22030010 St. Marks R approx 4 mi N of Hwy 98 (@ "rapids") Panhandle ST. MARKS RIVER 40.5 0 1.3 22030010 St. Marks R approx 4 mi N of Hwy 98 (@ "rapids") Panhandle ST. MARKS RIVER 56.0 0 1.3 22030010 St. Marks R approx 4 mi N of Hwy 98 (@ "rapids") Panhandle ST. MARKS RIVER 46.8 0 1.3 31010051 SWEETWATER CREEK HWY.270 LIBERTY CO. Panhandle SWEETWATER CREEK 69.9 0 1.3 31010051 SWEETWATER CREEK HWY.270 LIBERTY CO. Panhandle SWEETWATER CREEK 75.2 0 1.3 31010051 SWEETWATER CREEK HWY.270 LIBERTY CO. Panhandle SWEETWATER CREEK 97.8 0 1.3 31010051 SWEETWATER CREEK HWY.270 LIBERTY CO. Panhandle SWEETWATER CREEK 59.7 0 1.3 31010051 SWEETWATER CREEK HWY.270 LIBERTY CO. Panhandle SWEETWATER CREEK 66.7 0 1.3 31010051 SWEETWATER CREEK HWY.270 LIBERTY CO. Panhandle SWEETWATER CREEK 70.0 0 1.3 31010051 SWEETWATER CREEK HWY.270 LIBERTY CO. Panhandle SWEETWATER CREEK 84.1 0 1.3 31020040 TEN MILE CREEK S.R.73 CALHOUN CO. Panhandle TEN MILE CREEK 50.5 0 1.2 31020040 TEN MILE CREEK S.R.73 CALHOUN CO. Panhandle TEN MILE CREEK 72.9 0 1.2 31020040 TEN MILE CREEK S.R.73 CALHOUN CO. Panhandle TEN MILE CREEK 82.0 0 1.2 31020040 TEN MILE CREEK S.R.73 CALHOUN CO. Panhandle TEN MILE CREEK 55.3 0 1.2 
STORET Site Description Bioregion Waterbody Name SCI HDG LDI 

To

31020040 TEN MILE CREEK S.R.73 CALHOUN CO. Panhandle TEN MILE CREEK 76.1 0 1.2 31020040 TEN MILE CREEK S.R.73 CALHOUN CO. Panhandle TEN MILE CREEK 62.0 0 1.2 31020040 TEN MILE CREEK S.R.73 CALHOUN CO. Panhandle TEN MILE CREEK 67.7 0 1.2 31020040 TEN MILE CREEK S.R.73 CALHOUN CO. Panhandle TEN MILE CREEK 49.4 0 1.2 33010065 Rest Area Run Creek below I 10 Panhandle UNNAMED CREEK 82.2 0 1.2 33010065 Rest Area Run Creek below I 10 Panhandle UNNAMED CREEK 54.7 0 1.2 22040038 Unnamed Creek above Monticello WWTP Panhandle UNNAMED CREEK 54.7 0 1.7 32020066 Wright Ck, ref for Noma STP FYI Panhandle WRIGHTS CREEK 73.9 0 1.7 20010455 BLACKWATER CREEK AT HWY 44A Peninsula BLACKWATER CREEK 59.0 0 1.0 20010455 BLACKWATER CREEK AT HWY 44A Peninsula BLACKWATER CREEK 56.5 0 1.0 20010455 BLACKWATER CREEK AT HWY 44A Peninsula BLACKWATER CREEK 80.9 0 1.0 20010455 BLACKWATER CREEK AT HWY 44A Peninsula BLACKWATER CREEK 54.0 0 1.0 20010455 BLACKWATER CREEK AT HWY 44A Peninsula BLACKWATER CREEK 51.9 0 1.0 20010455 BLACKWATER CREEK AT HWY 44A Peninsula BLACKWATER CREEK 59.7 0 1.0 20010455 BLACKWATER CREEK AT HWY 44A Peninsula BLACKWATER CREEK 33.5 0 1.0 28020239 Cypress brch above SR 78 Peninsula CYPRESS BRANCH 57.6 0 1.3 28020239 Cypress brch above SR 78 Peninsula CYPRESS BRANCH 70.3 0 1.3 28020237 Cypress Creek 2 mi above SR78 Peninsula CYPRESS CREEK 55.7 0 1.2 28020237 Cypress Creek 2 mi above SR78 Peninsula CYPRESS CREEK 70.4 0 1.2 26010972 DAVENPORT CR AT SR 545 BRIDGE Peninsula DAVENPORT CREEK 63.0 0 2.0 26010972 DAVENPORT CR AT SR 545 BRIDGE Peninsula DAVENPORT CREEK 66.3 0 2.0 26010972 DAVENPORT CR AT SR 545 BRIDGE Peninsula DAVENPORT CREEK 73.6 0 2.0 26010972 DAVENPORT CR AT SR 545 BRIDGE Peninsula DAVENPORT CREEK 79.4 0 2.0 26010593 FISHEATING CREEK ABOVE US 27 Peninsula FISHEATING CREEK 60.8 0 1.1 20030414 GOLD HEAD BR. IN STATE PARK Peninsula GOLD HEAD BRANCH 90.0 0 1.0 20030414 GOLD HEAD BR. IN STATE PARK Peninsula GOLD HEAD BRANCH 72.2 0 1.0 20030414 GOLD HEAD BR. IN STATE PARK Peninsula GOLD HEAD BRANCH 80.8 0 1.0 20030414 GOLD HEAD BR. IN STATE PARK Peninsula GOLD HEAD BRANCH 100.0 0 1.0 20030414 GOLD HEAD BR. IN STATE PARK Peninsula GOLD HEAD BRANCH 89.2 0 1.0 
STORET Site Description Bioregion Waterbody Name SCI HDG LDI 

To

20030414 GOLD HEAD BR. IN STATE PARK Peninsula GOLD HEAD BRANCH 77.4 0 1.0 24030142 Hillsborough R upstrm of Southside Br, Zephyrhil Peninsula HILLSBOROUGH RIVER 87.1 0 1.6 24030048 Hillsborough River at SR 39 Peninsula HILLSBOROUGH RIVER 60.8 0 1.5 25020111 Horse Creek @ SR 72 Bridge Peninsula HORSE CREEK 70.4 0 1.8 25020111 Horse Creek @ SR 72 Bridge Peninsula HORSE CREEK 66.6 0 1.8 20010454 JUNIPER CREEK AT HIGHWAY 19 Peninsula JUNIPER CREEK 55.8 0 1.1 20010454 JUNIPER CREEK AT HIGHWAY 19 Peninsula JUNIPER CREEK 29.6 0 1.1 20010454 JUNIPER CREEK AT HIGHWAY 19 Peninsula JUNIPER CREEK 75.5 0 1.1 20020004 Little Orange Creek @ Hwy 21 Peninsula LITTLE ORANGE CREEK 73.2 0 1.2 20020004 Little Orange Creek @ Hwy 21 Peninsula LITTLE ORANGE CREEK 82.5 0 1.2 20020004 Little Orange Creek @ Hwy 21 Peninsula LITTLE ORANGE CREEK 86.8 0 1.2 20020004 Little Orange Creek @ Hwy 21 Peninsula LITTLE ORANGE CREEK 82.2 0 1.2 20020004 Little Orange Creek @ Hwy 21 Peninsula LITTLE ORANGE CREEK 90.3 0 1.2 20020004 Little Orange Creek @ Hwy 21 Peninsula LITTLE ORANGE CREEK 75.3 0 1.2 26011019 LIVINGSTON CREEK AT RUCKS DAIRY RD. Peninsula LIVINGSTON CREEK 71.2 0 1.5 26011019 LIVINGSTON CREEK AT RUCKS DAIRY RD. Peninsula LIVINGSTON CREEK 61.5 0 1.5 28010223 N FK LOXAHATCHEE RIVER REF BIO STATION Peninsula LOXAHATCHEE RIVER 34.3 0 1.0 28010223 N FK LOXAHATCHEE RIVER REF BIO STATION Peninsula LOXAHATCHEE RIVER 49.2 0 1.0 28010223 N FK LOXAHATCHEE RIVER REF BIO STATION Peninsula LOXAHATCHEE RIVER 59.9 0 1.0 28010223 N FK LOXAHATCHEE RIVER REF BIO STATION Peninsula LOXAHATCHEE RIVER 33.9 0 1.0 28010223 N FK LOXAHATCHEE RIVER REF BIO STATION Peninsula LOXAHATCHEE RIVER 54.8 0 1.0 28010223 N FK LOXAHATCHEE RIVER REF BIO STATION Peninsula LOXAHATCHEE RIVER 33.8 0 1.0 28010223 N FK LOXAHATCHEE RIVER REF BIO STATION Peninsula LOXAHATCHEE RIVER 44.7 0 1.0 24010002 Manatee R 20 m below SR64 bridge (TP1) Peninsula MANATEE RIVER 60.3 0 1.3 24010002 Manatee R 20 m below SR64 bridge (TP1) Peninsula MANATEE RIVER 76.3 0 1.3 24010002 Manatee R 20 m below SR64 bridge (TP1) Peninsula MANATEE RIVER 77.2 0 1.3 24010002 Manatee R 20 m below SR64 bridge (TP1) Peninsula MANATEE RIVER 96.7 0 1.3 24010002 Manatee R 20 m below SR64 bridge (TP1) Peninsula MANATEE RIVER 80.2 0 1.3 20020012 OKLAWAHA RIVER AT SR 316 Peninsula OKLAWAHA RIVER 77.9 0 1.0 
STORET Site Description Bioregion Waterbody Name SCI HDG LDI 

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20020012 OKLAWAHA RIVER AT SR 316 Peninsula OKLAWAHA RIVER 65.2 0 1.0 20020012 OKLAWAHA RIVER AT SR 316 Peninsula OKLAWAHA RIVER 67.2 0 1.0 20020012 OKLAWAHA RIVER AT SR 316 Peninsula OKLAWAHA RIVER 68.1 0 1.0 20020012 OKLAWAHA RIVER AT SR 316 Peninsula OKLAWAHA RIVER 80.6 0 1.0 20020012 OKLAWAHA RIVER AT SR 316 Peninsula OKLAWAHA RIVER 49.6 0 1.0 20020404 ORANGE CREEK 50 YDS. UP FROM HWY. 21 Peninsula ORANGE CREEK 93.1 0 1.4 20020404 ORANGE CREEK 50 YDS. UP FROM HWY. 21 Peninsula ORANGE CREEK 94.5 0 1.4 20020404 ORANGE CREEK 50 YDS. UP FROM HWY. 21 Peninsula ORANGE CREEK 92.1 0 1.4 20020404 ORANGE CREEK 50 YDS. UP FROM HWY. 21 Peninsula ORANGE CREEK 83.1 0 1.4 20020404 ORANGE CREEK 50 YDS. UP FROM HWY. 21 Peninsula ORANGE CREEK 91.8 0 1.4 20020404 ORANGE CREEK 50 YDS. UP FROM HWY. 21 Peninsula ORANGE CREEK 94.3 0 1.4 20020404 ORANGE CREEK 50 YDS. UP FROM HWY. 21 Peninsula ORANGE CREEK 72.0 0 1.4 20020404 ORANGE CREEK 50 YDS. UP FROM HWY. 21 Peninsula ORANGE CREEK 96.3 0 1.4 20030253 RICECR #3@BR SR #3/250yds above Bardine Peninsula RICE CREEK 67.1 0 1.1 20030253 RICECR #3@BR SR #3/250yds above Bardine Peninsula RICE CREEK 24.0 0 1.1 20020317 SILVER RUN AT CONFLUENCE WITH BOAT RAMP CANAL Peninsula SILVER RUN 34.8 0 1.3 20020317 SILVER RUN AT CONFLUENCE WITH BOAT RAMP CANAL Peninsula SILVER RUN 42.3 0 1.3 20020317 SILVER RUN AT CONFLUENCE WITH BOAT RAMP CANAL Peninsula SILVER RUN 53.5 0 1.3 20020317 SILVER RUN AT CONFLUENCE WITH BOAT RAMP CANAL Peninsula SILVER RUN 57.6 0 1.3 20020317 SILVER RUN AT CONFLUENCE WITH BOAT RAMP CANAL Peninsula SILVER RUN 54.2 0 1.3 20020317 SILVER RUN AT CONFLUENCE WITH BOAT RAMP CANAL Peninsula SILVER RUN 76.0 0 1.3 28020221 TELEGRAPH CREEK AT BABCOCK RANCH Peninsula TELEGRAPH CREEK 84.4 0 1.0 28020221 TELEGRAPH CREEK AT BABCOCK RANCH Peninsula TELEGRAPH CREEK 80.8 0 1.0 26011020 TIGER CREEK AT WALKING WATERS RD. Peninsula TIGER CREEK 74.4 0 1.0 26011020 TIGER CREEK AT WALKING WATERS RD. Peninsula TIGER CREEK 76.5 0 1.0 26011020 TIGER CREEK AT WALKING WATERS RD. Peninsula TIGER CREEK 75.7 0 1.0 26011020 TIGER CREEK AT WALKING WATERS RD. Peninsula TIGER CREEK 73.4 0 1.0 23010464 Withlacoochee R @ county park off Auton Rd (TP3) Peninsula WITHLACOOCHEE RIVER 43.4 0 1.2 23010464 Withlacoochee R @ county park off Auton Rd (TP3) Peninsula WITHLACOOCHEE RIVER 46.9 0 1.2 
STORET Site Description Bioregion Waterbody Name SCI HDG LDI 

To

23010464 Withlacoochee R @ county park off Auton Rd (TP3) Peninsula WITHLACOOCHEE RIVER 67.8 0 1.2 23010464 Withlacoochee R @ county park off Auton Rd (TP3) Peninsula WITHLACOOCHEE RIVER 50.6 0 1.2 23010464 Withlacoochee R @ county park off Auton Rd (TP3) Peninsula WITHLACOOCHEE RIVER 49.6 0 1.2 23010464 Withlacoochee R @ county park off Auton Rd (TP3) Peninsula WITHLACOOCHEE RIVER 66.1 0 1.2 23010446 WITHLACOOCHEE RIVER Peninsula WITHLACOOCHEE RIVER 50.0 0 1.6