Development and Testing of Biomonitoring Tools fo r 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 40 th 

## St. Seattle, WA 98107

** Florida Dept. Environmental Protection 2600 Blair Stone Rd. Tallahassee, Fl 32399-2400

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



### iv 

# TABLE OF CONTENTS

L IST OF 

## T ABLES



## .................................................................................. VII



## L IST OF 

## F IGURES



## .................................................................................. IX



## A CKNOWLEDGMENTS



## ............................................................................. X



## F OREWARD



## .......................................................................................... XI



## A BSTRACT



## .......................................................................................... XII



## C HAPTER 

## 1: I NTRODUCTION



## ................................................................... 1

Backgro und ............................................................................................................. 1

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

Study area

........................................................................................................... 2

Site selection and dat asets................................................................................ 3

Quantifyin g human distur bance........................................................................ 4

Stream macroinver tebrate sam pling

................................................................ 8

M

etric developmen t and testing........................................................................ 9

Index developmen t and testin g....................................................................... 12

Statistical m odel assump tions

........................................................................ 13



## C HAPTER 

## 2: R ESULTS



## ......................................................................... 15

Human disturba nce grad ient ................................................................................ 15

Metric sel ection

...................................................................................................... 15

Taxonomic richness

......................................................................................... 15

Feeding group

................................................................................................... 18

Voltinism

........................................................................................................... 18

Habit

.................................................................................................................. 18

Community st ructur e ....................................................................................... 18

Sensitivity a nd tolera nce ................................................................................. 18Development and Testing of Biomonitoring Tools fo r Macroinvertebrates in Florida Streams March 2007



### v BioRecon me trics............................................................................................. 20

Multimetric index cons truction and evaluati on

................................................... 20

Statistical preci sion of indexes

............................................................................ 23



## C HAPTER 

## 3: D ISCUSSION



## ..................................................................... 39

Human disturba nce grad ient ................................................................................ 39

Biological i ndicators

.............................................................................................. 39

Sources of variance

............................................................................................... 40



## C HAPTER 

## 4: A PPLICATION OF 

## B IOMONITORING 

## T OOLS



## ........................... 42

Regulatory fr amework ........................................................................................... 42

Designate d uses

............................................................................................... 42

Water quality criteria

........................................................................................ 43

Anti-degrada tion pol icy

................................................................................... 43

Tiered aquati c life us es ......................................................................................... 44

Determining the impairment thres hold using the Biological Condition
Gradient model

................................................................................................. 44

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

Biocriteria fo r streams........................................................................................... 51



## C HAPTER 

## 5: C ONCLUSIONS 

## ..................................................................52

R EFERENCES



## ...................................................................................... 53

A PPENDICES



## ....................................................................................... 59

Appendix A. HDG calculati on ............................................................................... 59

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

Appendix C. Se nsitive taxa

................................................................................... 66

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

Appendix E. Regional calibrat i on of metric scores ............................................ 71Development and Testing of Biomonitoring Tools fo r Macroinvertebrates in Florida Streams March 2007



### vi Appendix F. ANOVA output and re gression results for the SCI and
BioRecon........................................................................................... 75

Appendix G. Optimal sample si ze for calc u lating SCI ........................................ 77

Refere nces

........................................................................................................ 81

Appendix H. FDEP Biological c ondi tion gradient workshop summary ............ 82

Backgro und

....................................................................................................... 82

M

ethods............................................................................................................. 82

Sample Se lection

.............................................................................................. 83

Data C ontent

..................................................................................................... 86

Results

.............................................................................................................. 86

Refere nces

........................................................................................................ 87

Appendix I. Change Point Analy sis of Stream Condition Index and Biological Condition Gradient Data ................................................ 91

Backgro und

....................................................................................................... 91

The Detection Proce dure

................................................................................. 91

Analysis of SCI and BCG Data

........................................................................ 93

Summary

........................................................................................................... 95

Refere nces

........................................................................................................ 95

Appendix J. Refe rence si tes

................................................................................. 97Development and Testing of Biomonitoring Tools fo r Macroinvertebrates in Florida Streams March 2007



### vii 

# 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 NH 3 

### (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 NH 3 

### , 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 .........................................................................................72Development and Testing of Biomonitoring Tools fo r Macroinvertebrates in Florida Streams March 2007



### viii 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 ................................................................................ 103Development and Testing of Biomonitoring Tools fo r Macroinvertebrates in Florida Streams March 2007



### ix 

# 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...................................79Development and Testing of Biomonitoring Tools fo r Macroinvertebrates in Florida Streams March 2007



### x 

# ACKNOWLEDGMENTS

Florida Department of Environmental Protecti on (FDEP) biologists provided comments and guidance throughout the development and testi ng 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 coll ected and identified stream macroinvertebrate samples:

Central District (Orlando)

• Dan a Denson

• Phil Kane

• Steve Kent

• Eric Pluchino

Northeast Distri ct (Jacksonville)

• Lee Ban ks

• Patrick O’Connor

Northwest District (Pensacola)

• Fran k Butera

• Glenn Butts

• Laurence Donelan

• Donald Ray

South District (Punta Gorda)

• Rob ert Rutter

• Albert (Ford) Walton Southeast District (Port St. Lucie)

• Mark Thom pson

Southwest District (Tampa)

• Cha rles 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 SpringerDevelopment and Testing of Biomonitoring Tools fo r Macroinvertebrates in Florida Streams March 2007



### xi 

# FOREWARD

The original version of this document wa s completed in 2004. Subsequently, important observations made during the routin e application of thes e 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 biol ogists, 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 app roach, 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 address ed measurement error of the SCI related to laboratory subsampling methods which were subsequent ly modified to improve the precision of the SCI without substantially increasing the cost. This invo lved increasing the number of individuals examined and averaging the results from two independent aliquots from a field sample. Science is an iterative process: incrementa l 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 cond ucted 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.Development and Testing of Biomonitoring Tools fo r Macroinvertebrates in Florida Streams March 2007



### xii 

# ABSTRACT

The Florida Department of Environmental Prot ection 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 evaluat ed the sensitivity and tolerance of over 1,000 stream macroinvertebrat e 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 (per centage filterer individuals); volt inism (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 nor theast). 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 biol ogical condition. These results supported subsequent selection of three assessment categories for interp reting SCI values in a r egulatory 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 subs equently been modified to target two aliquots of 150 individuals, with the resulting SCI values averaged. We also tested biological metrics for a second st ream macroinvertebrate sampling protocol (the BioReconnaissance Index, or BioRecon), which was bas ed 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 fo r the 6 metrics. The stat istical 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 Condi tion 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 distur bance. A secondary, but parallel analysis based on departure from reference condition yielded similar inde x values for the impairment threshold. Based on these complementary results, three categories of aqu atic life use are proposed for Florida streams and described as Category 1 (“except ional” ), Category 2 (“healthy”), and Category 3 (“impaired”).Development and Testing of Biomonitoring Tools fo r Macroinvertebrates in Florida Streams March 2007



### 1 

# CHAPTER 1: INTRODUCTION

Florida, along with a handful of other st ates, 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 Clea n Water Act, states are required to define designated uses for specific waterb odies and develop criteria to protect them (Karr, 1991; Ransel, 1995). Over time, the emphasis has sh ifted from what was primarily the chemical monitoring of pollutants to include the direct meas urement 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 protec tion that they require, as follows:

Class I–Drinking water; Class II–Shellfish propagation or harves ting (primarily for coastal waters); Class III–Recreation and propa gation 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 classificati on for Florida (see Chapter 4 below). We anticipate that FDEP will propose Aquatic Life Use (AL) catego ries, to which these biological monitoring tools for Class I and III streams would also apply. The EPA has developed numerous guidance docume nts 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, 198 1; 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 meas ures, called metrics, that show a consistent response to human disturbance (K arr and Chu, 1999). Metrics are c onverted 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 (Haw kins 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 bas is 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 theDevelopment and Testing of Biomonitoring Tools fo r Macroinvertebrates in Florida Streams March 2007



### 2 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 iden tification 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 pr otocols. For simplicity’s sake, the names of the indexes have been retained for this new analysis, and t he 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 distributi on of macroinvertebrates. Florida can be divided geographically into the northern panhandle, the sout hern peninsula, and a transitional region known as
the northeast. The middle and lower Suwannee Ba sin provides a natural demarcation between the panhandle and peninsula, with the northeast region str addling 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 Area s Inventory [FNAI], 1990). Vegetation communities in the panhandle generally cons ist 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 C onservation 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 th e 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 ups tream 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 t he 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 fo r Macroinvertebrates in Florida Streams March 2007



### 3 The northeast region includes portions of t he 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 headw aters 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). Jack sonville 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 re gions, 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 ex pectations 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 Prot ection (FDEP) has collected thousands of macroinvertebrate samples from stream sites thr oughout the state from 1991–2001. Many sites have been visited repeatedly. Sites and visits were selected fr om 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 variab ility, sites with many repeat visits were needed; to evaluate laboratory subsampling, large samples we re needed. Some datasets contained data from the same sites, and other sites were selected to prov ide 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, i ndependent 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 si tes (32) had repeat visits on the same day. Most of the other repeat visits were during different years, an d 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 SC I 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 macroi nvertebrate 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 fo r Macroinvertebrates in Florida Streams March 2007



### 4 The dataset used to test the SCI and its co mponent 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 25 th and 75 th 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 25 th to the 75 th percentile value. During 1991–2001, laboratory protocols mandated a target number of 100 individuals; howev er, this target was often exceeded due to the laboratory protocols of the time. Several graphs incl ude 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 te sting. Additional analysis (not shown) indicated that the ranges of metric values were similar for an upp er limit of 125 and 175 individuals. In recent years (2004–2006), FDEP has modified laboratory protocol s 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 with in 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, exc ept 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 tr anslates into a greater variety of organisms. Third, water quality mayDevelopment and Testing of Biomonitoring Tools fo r 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. Fi fth, 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 sample s are collected (FDEP, 2004). The index evaluates substrate condition and availability, water veloci ty, habitat smothering (e.g., by sand and silt), channelization, bank stability, and the width and conditi on 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 t he 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, dam s/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 fundam entally alter the nature of the ecosystem

FDEP biologists also routinely collect measurements of turbidity, temperature, dissolved oxygen, conductivity, and nutrient concentration s. Total phosphate (TP), ammonia (NH 3 ), total Kjeldahl nitrogen (TKN), and nitrites/nitrates (NO
x ) were measured to summarize nutrients. We selected NH 3 to summarize water quality because it was the mo st 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 beDevelopment and Testing of Biomonitoring Tools fo r Macroinvertebrates in Florida Streams March 2007



### 6 more closely associated with fertiliz er and farming practices, while NH 3 may be a more general indicator of both urbanization and agriculture. A measure of the type and quantity of energy avail able to organisms living within the stream was not available. However, Brown and colleagues have de veloped an index to estimate the intensity of human land use based on nonrenewable energy flow (B rown 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).


# ( ) ∑

= i i 

## LULDI LDI %*

.

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

NH 3 , the hydrologic index, and the habitat index (Table 4). To define the HDG, we convert ed 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 mi nimal 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 NH 3 , 9 sites had no information. Most of these sites had values for other measures indicating minimal human disturbance. We assumed for these sites that NH 3 was less than 0.1 milligram per liter (mg/L).Development and Testing of Biomonitoring Tools fo r 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-fa mily Residential 9.19
Low-Intensity Central Business District 9.42
High-Intensity Central Business District 10.00Development and Testing of Biomonitoring Tools fo r Macroinvertebrates in Florida Streams March 2007



### 8 Table 4. Correlation table of NH 3 (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 NH 3 , 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).

NH 3 Hydro Habitat LDI_BF Ln (Size) HDG SCI

NH 3 - 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

NH 3 (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-d ay measure of stream site condition. The SCI sampling protocol was meant to be more precise. Both protocols us e 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 typicall y have a sandy substrate that supports very few invertebrates. Therefore, the habitat sampled for ma croinvertebrates includes logs, roots, and undercut banks; partially decayed leaf packs; a quatic 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 fo r Macroinvertebrates in Florida Streams March 2007



### 9 even if it contains more than 100 individuals. T he 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 ta xon is preliminarily identified in the field, and representative specimens of each taxon are pres erved and returned to the laboratory for final identification under a dissecting microscope. From this field-sort protocol, the num ber of taxa present at a site can be measured, but their relative abundance (% composition) cannot.



## Metric development and testing

Several authors have described relevant c andidate metrics for str eam 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 sens itivity for their association with the HDG (Table 6). Within each category, we selected metrics with the highe st 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, t hat relationship would not be biologically significant even if it were statistically significant. All metrics were also eval uated 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 ta xa found within a particular group, such as the Ephemeroptera, Plecoptera, or Tric hoptera, or their combination (EPT).



### Feeding group

Trophic designations followed Merritt and Cummi ns (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, wh ich is called voltinism, may be less than a year, approximately one year, or greater t han 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; Co rbet, 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 fo r 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 visi ts 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 0.61Development and Testing of Biomonitoring Tools fo r 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); t he 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 cy cles, 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), a nd 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 Morish ita 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 ripari an vegetation eliminate t he habitat preferred by clingers.



### Community structure

Percentage dominance was calculated as the numbe r of individuals in the most abundant taxon, divided by the total number of individuals in the samp le. ”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 to tal sample was not recorded; however, the number of grids in the sample tray that were searched to be st approximate the target number of 100 individuals was recorded. We used the number of grid cells search ed as a measure of the density of invertebrates present, or hyperabundance. Other measures of co mmunity 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 ca lculate the Florida Index (which is more accurately termed a metric), very sensitive taxa are given a scor e 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 NH 3 ) 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 st atistically significant difference (chi-square test, Yates continuity correction). As for any test, the number of sampling points increases the power of theDevelopment and Testing of Biomonitoring Tools fo r Macroinvertebrates in Florida Streams March 2007



### 12 test, such that less extreme difference will be statistica lly 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 thos e 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 st andards for selection more strict ( p << 0.05) for taxa that were more

common. Defining a statistical rule for the selection of to lerant taxa was not possible, because tolerant taxa can also “tolerate” good conditions. Instead, very tole rant 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 cr iteria for inclusion as se nsitive (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 comb ined 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 coul d not be calculated for these data; therefore, only

taxa richness metrics were evaluated. BioRecon metr ics 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 95 th percentile values for each metric and then dividing each metric value by its range and multiplyin g 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 di viding by the metric range. This process was done separately for the northeast, peninsula, and panhandl e regions. We tested for differences in metric expectation associated with different regions by co mparing 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 ev aluate the overlap of the BioRecon and SCI metrics measured at the same sites, we adjusted metric scoring for the Bi oRecon metrics as needed. In order to distinguish between the BioRecon and SCI, BioRecon metrics were scor ed 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 fo r 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 va riance 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 si te 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:

s 2 = 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 biologic al 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 sm aller 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; ther efore, 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 biologica l 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 deriv ed 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 confi dence intervals assumes that the multimetric indexes are normally distributed. Other studies have evaluated the statis tical 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 separat e 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 limitsDevelopment and Testing of Biomonitoring Tools fo r 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 ea ch 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 anal ysis. Rather, the goal for these ANOVAs was to

estimate an error to use in the confidence interval ca lculations and to evaluate the relative contributions of year and error to the metrics and indexes. For estima tion of the error term, the large sample sizes mitigate any potential inaccuracy associated with u nbalanced 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 fo r Macroinvertebrates in Florida Streams March 2007



### 15 

# CHAPTER 2: RESULTS

Human disturbance gradient

The human disturbance gradient (HDG) was a consis tent predictor of biological condition for two independent rounds of testing. When metrics were r egressed against HDG for the two rounds of data, the slopes and intercepts of the regression lines were ne arly identical, indicating that measures included in the HDG yielded a reliable meas ure of human disturbance (Figure 1). Correlations among the four measures of human di sturbance used to defi ne 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 indivi dual 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 l east 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 ea ch of the categories below according to which metric was most highly correlated with the HDG. If two metrics were calculated fr om 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 ta xa richness as separate metrics because they have the potential to respond to different types of di sturbance. 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 t he variety of taxa that a stream site could support.Development and Testing of Biomonitoring Tools fo r 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 taxa

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



### 17 0 10 20 30 40 50 60Total number of taxa Median 25%-75% Non-Outlier Range
Outliers

0 1 2 3 4 5 6 7Number of Eph. taxa 0 20 40 60% Filterer

-1 0 1 2 3 4 5 6Number 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 8Number of Trichoptera taxa

0.8 2.4 4 5.6 7.2 Human disturbance gradient 0 5 10Number of clinger taxaDevelopment and Testing of Biomonitoring Tools fo r Macroinvertebrates in Florida Streams March 2007



### 18 

## Feeding group

The relative abundance of filterer s (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 per centage 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 percent age long-lived individuals. Long-lived taxa included semivoltine insects and noninsects that require mo re 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 ta xa 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 sele cted 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 co rrelated with the HDG, they were calculated from

the same information used to calculate the taxa ric hness 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 metric s (–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 pr eviously 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 fo r Macroinvertebrates in Florida Streams March 2007



### 19 . 20 40 60 80% Dominance

0 5 10 15Number 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 6Development and Testing of Biomonitoring Tools fo r 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 fail ed to meet the criterion of > = 50% of poor sites. L. hoffmeisteri was included as very tolerant becaus e it was very common at all sites. It occurred at 70 out of 85 poor sites, and tended to in crease 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 regard ing the probability of Type I error. The detection of a statistically significant result will occur wi th the approximate frequency of p- value. For a p- value of 0.05 and 168 tests, approximately 8 taxa may be selected by chance alo ne (0.05 * 168 = 8.4). The number of sensitive taxa falsely selected from this analysis is likely much lower because the crit erion 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 Trichopt era 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 nat ural 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 disturba nce (% dominance and % very tolerant individuals);

all others declined with disturbance. BioRecon metric scoring rules differed somewhat fr om 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 fo r Macroinvertebrates in Florida Streams March 2007



### 21 Table 7. Correlation of BioRecon metrics and th e old and new BioRecon Indexes with the HDG and SCI Metrics included in the BioRecon Index are noted (*). A ll correlations shown were statistically significant (Spearman’s r, p < 0.01, one-sided test, n = 116 for SC I, 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 59–0Development and Testing of Biomonitoring Tools fo r 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 lin es for the SCI versus HDG for the northeast and peninsula regions were nearly identical; the line fo r 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 upstr eam 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. T he 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 li nes 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 analys es. Correlation was high (Spearman’s r = –0.81, p < 0.001; Figure 9 ). Two sites with > 300 individuals in the samples had so mewhat higher SCI values than predicted by the HDG, possibly due to the inflation of taxa rich ness measures associated with large samples.Development and Testing of Biomonitoring Tools fo r 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 dat a, 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 di sproportionately large amount of variability to the repeat visits (Figure 10). SCI values were typically much lower fo r these small samples. For this reason, we excluded these 17 samples from the analysis of vari ability, 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 as sociated 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 sm all (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 with in years, about half of which were on the same day.

We used the estimate of error variance from t he ANOVA to estimate the 90% confidence limits around the SCI as +/– 13.4 points, or a 27-point spre ad. 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 co mbinations 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 comp onent 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 rich ness, 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 ta xa richness, and % very tolerant. Overall, the SCI was less variable than most of its component metrics.Development and Testing of Biomonitoring Tools fo r 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 100SCI Median
25%-75% Non-Outlier Range
OutliersDevelopment and Testing of Biomonitoring Tools fo r 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 3Old BioRecon index



### 0135
Human disturbance gradient 0 2 4 6 8 10BioRecon index Median
25%-75% Range
OutliersDevelopment and Testing of Biomonitoring Tools fo r 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 gradientSCI

02468 0 20 40 60 80 100

NE

PS

PHDevelopment and Testing of Biomonitoring Tools fo r 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] > 5440,00060,00081001100150440,00060,00081001100150440,00060,00081001100150Development and Testing of Biomonitoring Tools fo r 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 gradientSCI

0 2 4 6 8 10 0 20 40 60 80 100Development and Testing of Biomonitoring Tools fo r 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 100SCIDevelopment and Testing of Biomonitoring Tools fo r 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 100SCI



### Site

0 20 40 60 80 100SCIDevelopment and Testing of Biomonitoring Tools fo r 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 ComponentsDevelopment and Testing of Biomonitoring Tools fo r 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
SiteSCITotal taxaEph. taxaTri. taxa% FiltererLong-livedClinger% Dom.% Tany.Sensitive% Toler. 

## 0 20 40 60 80 100% Relative varianceDevelopment and Testing of Biomonitoring Tools fo r 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 det ermine which were contributing to the seasonal
differences in the SCI. Five metrics had values in t he winter indicating better biological condition; these were Ephemeroptera taxa richness, % filterers, % T anytarsini, 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 % Tanytars ini. 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 subsam pling on the SCI, we used data that included multiple subsamples from the same site visit. We es timated 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 represent s 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 asso ciated with repeat visits to the sa me 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 inde x 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 coul d not be estimated. The components of variance were similar to those for the SCI, in that the largest com ponent 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 poi nts. Dividing the range of the BioRec on 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 fo r 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 100SCIDevelopment and Testing of Biomonitoring Tools fo r 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. 

## Erro r Year
SiteBioReconTotalEphem.Trichop.Long-livedClingerSensitive 

### 0 20 40 60 80 100% Relative varianceDevelopment and Testing of Biomonitoring Tools fo r 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 (negat ive 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 fo r sites with minimal disturbance (Figure 15). If variance were estimated using only minimally disturbed reference sites, the er ror variance would be smaller. To determine whether high variability was associated with natural variabilit y or changes in human activities, we selected the

sites with the most variable BioRecon values thr ough 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 asso ciated 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 mu ch of the BioRecon variability was associated with real changes in human disturbance, rather than natural variability. Variance results for the BioRecon components metr ics 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 variab ility (year or error) was smaller for three of the
BioRecon metrics than for the same SCI metrics. Lowe r 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 fo r 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 highe st 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 UnknownDevelopment and Testing of Biomonitoring Tools fo r 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 10BioRecon indexDevelopment and Testing of Biomonitoring Tools fo r Macroinvertebrates in Florida Streams March 2007



### 39 

# CHAPTER 3: DISCUSSION

The SCI and BioRecon Index, along with their com ponent metrics, were developed to assess the biological condition of stream sites using samples fr om the stream macroinvertebrate assemblage. These assessment tools will be used primarily to define biologica l 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 metr ics showed a strong and consistent response to an independent measure of human distur bance (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 c an 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 i ndex testing. First, it provided a more stringent
test of metric association with disturbance than a co mparison of reference versus test sites. Percentage Diptera individuals serves as an example of this. Th is 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 condit ion 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. Fina lly, 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 e liminated. The HDG can also be used to describe the intensity of disturbance associ ated 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 t he 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 specif ic 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, tota l 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 hum an 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 group s provides the opportunity for metric signaturesDevelopment and Testing of Biomonitoring Tools fo r Macroinvertebrates in Florida Streams March 2007



### 40 associated with different types of disturbance. For example, E phemeroptera 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 ta xa may indicate increased nutrients (Miltner and Rankin, 1998). Filterers are expected to decline in re sponse 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 adaptati ons that allow them to cling in fast water. Human development near stream sites in Florida o ften 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 repr esents a measure of the ov erall 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 Oh io because of their general sensitivity to human disturbance (DeShon, 1995). Three Tanytarsini midge ta xa 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 me trics easier to justify in a regulatory context. The set of metrics selected for Florida repres ents a convergence with similar studies and programs in other states (Karr, 1998). Many of t he 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; M ebane, 2002). The emergence of a core set of metrics across a variety of geographic contexts furt her 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 y ear (error) and different years. One of the largest contributors to the difference s 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 deri ved 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 subsampli ng 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 source s of variance was completed for this document.)Development and Testing of Biomonitoring Tools fo r Macroinvertebrates in Florida Streams March 2007



### 41 Site by year interaction represents the vari ability associated with some unique site-year combinations. This source of variance represented in dex values from sites that increased (or decreased) from one year to the next in a different or more ex treme 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 a ll 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 invert ebrates 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 asso ciated with greater human disturbance (Karr et al., 1987; Steedman, 1988). For the BioRecon Index, variab ility for several sites was explained by changes in human land use. Road paving to eliminate erosion, th e elimination of toxic runoff, and the discontinuation

of fertilization were all associat ed with improved BioRecon values. For the SCI, winter index values were significant ly 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 d ue 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 wi th 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 fr om 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 fo r the BioRecon may be due to hum an disturbance at the site, different levels of sampling effort applied by field biol ogists, the smaller number of metrics in the index (6 for the BioRecon versus 10 for the SCI), or some comb ination. 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 protoc ol specifies that a minimum of 5 examples of each unique taxon be returned for verification. If multiple examples of each taxon ar e returned, the probability of identifying additional taxa increases as a functi on 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 addi tion, 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.)Development and Testing of Biomonitoring Tools fo r Macroinvertebrates in Florida Streams March 2007



### 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 regulat ory 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 criter ia along with physical and chemical criteria to determine whether stream sites are meeting their desi gnated uses, particularly uses related to aquatic life use support. In this section the regulatory framewor k 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 categorie s 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 determinati on as a secondary line of evidence to support the impairment threshold decision. Additionally, the refe rence site data was used as the primary basis for proposing the biological criteria threshold for site s that may be considered of “exceptional” quality. 

# Regulatory framework

The regulatory context for the management of surfac e waters is derived from the mandates of the Clean Water Act (CWA; EPA, 2005; Davies and Jackson, 20 06). Within this framewor k, 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 crit eria 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 protecti on 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 foundati on for the management and protection of surface waters and pollution control programs. WQS prov ide the basis for determining attainment or nonattainment of water bodies in order to identify im paired 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 identif y and prioritize waters not attaining standards.



## Designated uses

All of the water quality protections availabl e under the CWA follow from the water body’s designated use. All state water bodies have desig nated uses as specified in water quality standards, whether or not the uses are currently being attai ned. When classifying water bodies and determining designated uses, a state must take into considerati on 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 incl udes a general mandate for protecting waters forDevelopment and Testing of Biomonitoring Tools fo r Macroinvertebrates in Florida Streams March 2007



### 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 Cl ass III waters provides an example of this approach: “recreation and propagation and maintenance of a heal thy, 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 expe ctations 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 classifi cation for Florida. The DUPAC is considering the environmental, social, and economic co nsequences 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 id entified 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 pr ograms, 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 support ed. The assumption is that when criteria are met, the designated use is protected. Water quality criter ia 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 pr otect 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 polluta nts or specific biological index values. Biocriteria refer specifically to criteria based on biological as pects 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 th at 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.Development and Testing of Biomonitoring Tools fo r Macroinvertebrates in Florida Streams March 2007



### 44 

# Tiered aquatic life uses

In 2001, the National Resear ch 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 st andards (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 lif e uses. For example, rather than stating that a water body needs to be “fishable,” the designated us e should specifically describe the expected fish assemblage (e.g., cold water fishery, warm wa ter 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 info rmation 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 st atements 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 mo re 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 implementat ion of biocriteria, and the scientific knowledge of aquatic ecology that they represent, into state and tr ibal water quality standards. The driving motivation here is to ensure that listings of impaired waters ar e scientifically defensible and to identify and protect high quality water resources.



## Determining the impairment threshol d 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 deriv ed from any assessment methodology (Davies and Jackson, 2006). This inte rpretive framework must define ecologi cal 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 lif e use designation, along a Biological Condition Gradient (BCG), that illustrates how ecological attri butes 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., ra te measurements such as productivity), but may be inferred via the community composition data (e.g., abunda nce 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 toleranceDevelopment and Testing of Biomonitoring Tools fo r Macroinvertebrates in Florida Streams March 2007



### 45 V. Tolerant taxa

VI. Non-native taxa

VII. Organism condition

VIII. Ecosystem functions

IX. Spatial and temporal ext ent 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 comm unity 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 dev elop 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 macroi nvertebrate 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 collectio n and metric calculation followed standard FDEP protocols. The panel was NOT given the SCI value asso ciated 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 assessmen ts. After discussion, there was general agreement that it was difficult to identify Tier 1 sites (pristi ne, 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 bi ological census of any given site. This lack of data on rare, endemic taxa made it difficult for the gr oup 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 practi cal purposes, to be almost indistinguishable from

“natural.”Development and Testing of Biomonitoring Tools fo r Macroinvertebrates in Florida Streams March 2007



### 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 represent ed. After discussion on specifics associated with the data set, the panelists were given the o pportunity to modify their score. During the national work shops sponsored by EPA, different groups of exper ts independently concurred that the ecological characteristics describe d by Tiers 1–4 corresponded to how they interpret the Clean Water Act (CWA)’s interim goal for protec tion 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 ag reement 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 propos ed the impairment line to be drawn between Tiers 3 and 4. Those panelists expressed a concern that man agement 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 theDevelopment and Testing of Biomonitoring Tools fo r 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 impairmen t, BCG scores were related to SCI values to determine a corresponding numeric criterion for the suppor t of aquatic life use. Two statistical approaches were used: linear regression and change point analys is. 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. St ep 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 st atistical methods yielded very similar results. We averaged the SCI values derived from the two approache s to define the line of impairment to occur at SCI < 35. Using results from the BCG approach and the re commendations of 22 regional experts, SCI values from 0-34 are proposed as Category 3 (provisiona lly interpreted as “impaired”). SCI scores >= 35 represent Categories 2 and 1 (provisionally interp reted as “healthy” and “exceptional”; see below).

123456


### Biological Condition Gradient score (mean) 0 20 40 60 80 100SCI

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 in tervals (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.Development and Testing of Biomonitoring Tools fo r Macroinvertebrates in Florida Streams March 2007



### 48 0 20 40 60 80 100


## SCI 1

2

3

4

5

6Biological Condition Gradient score(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 setti ng the standard for biological condition involves analyzing reference sites. Reference sites may be defined as those with minimal human disturbance, approxim ating the “natural” condition of a region (U.S. EPA, 2006a, b; Hughes, 1995 Stoddard et al. 2006). From a set of reference sites, m easures of site condition, such as a multimetric index of biological condition, can be used to defi ne thresholds for acceptable and unacceptable biological

condition. Lower percentiles, e.g., the 10 th or 25 th 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 su bset of reference sites recognizes that natural stressors can affect the biology of a stream, introducing variability into t he 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 10 th (rather than the 25 th ) percentile.



### Reference site selection

To develop the HDG, FDEP biologists selected site s from each of their regions that represented the broadest possible range of human influence, incl uding 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, programDevelopment and Testing of Biomonitoring Tools fo r 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 start ed their biological monitoring programs later may have identified their reference sites from a probabilistic sampling design. T he 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 othe r sites meeting the criteria for reference sites may
have lower SCI values due to natural vari ability or minimal human disturbance. Reference sites were initially selected as site s with HDG = 0. Recall that HDG = 0 does not mean

no human disturbance, but minimal human distur bance. The criteria for HDG = 0 were:

● NH 3 < 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 furt her 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 fa cility 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 crit erion was raised from 65% to 75% because sites scoring 75% or greater were equivalent to all or most of the habitat assessm ent 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 specif ic conductance criterion (Chapter 62-302.530(61) F.A.C.) Th is study found that for a specif ic conductance of 20 umhos/cm approximately half of the taxa within a SCI samp le would be sensitive taxa and for 600 umhos/cm the proportion of sensitive taxa decreased to almost non e (FDEP, 2005). For the third criterion, sites could not be located below an active facility discharge based on review of GI S 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 moni toring programs. This minimum of 100 individuals is consistent with a target of 100 or ganisms 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 calc ulated 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 10 th percentile SCI value for the reference site distribution was 48.8.

Note that the 10 th percentile could have been derived in more than one way from the reference

site scores. When we calculated these alternate st atistics they provided very similar SCI values. Examples include the 10 th percentile of SCI for all site -visits (44.9, N = 205); the 10 th percentile forDevelopment and Testing of Biomonitoring Tools fo r Macroinvertebrates in Florida Streams March 2007



### 50 reference site mean values (SCI = 50.5, N = 64); the 10 th percentile for the most re cent site-visit (SCI = 50.2, N = 64).Number of sites1994199519961997199819992000200120022003200420052006 0 50 100 150 200 250 300 350

Status & Trends
Basin Studies
Reference sites 5th year sampling
TMDL

Figure 18. Number of stream sites sa mpled 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

90 th percentile 84.0

Maximum 92.6

Matching the approach of other states (EPA, 2006b) , we defined a buffer of SCI values below the

10 th percentile to represent the natural variability associated with field sampling that could be due to water level fluctuation, weather, or microhabitat differenc es. We used the MDD (calculated above in Chapter 2:
Statistical Precision of Indexes ) to estimate the variability in SCI a ssociated with repeat sampling of the same stream site. Specifically, we subt racted half the MDD interval from the 10 th percentile to obtain aDevelopment and Testing of Biomonitoring Tools fo r 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 90 th percentile of SCI values for the 64 reference sites was selected to represent exc eptional sites (SCI score of 84.0) . As for the impairment threshold definition above, we defined a buffer below the 90 th 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 val ue representing exceptional reference condition. As above, we note that the 90 th percentile could have been derived in more than one way from the reference sites. And, again as above, when we calc ulated these alternate statis tics they provided very similar SCI values. Examples include the 90 th percentile of SCI for all site-visits (86.7, N = 205); the 90 th
percentile for reference site mean va lues (SCI = 83.8, N = 64); the 90 th percentile for the most recent sitevisit (SCI = 83.9, N = 64).



# Biocriteria for streams

The above analyses support the est ablishment of three categories of aquatic life use for Florida streams, described as Category 1 (“exceptional”, wi th 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 assess ment for regulatory decisions, two separate, temporally independent site-visits result ing 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 sens itive taxa than found in Category 2; high proportion for Tanytarsini; few individual s 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 spec ies found on average; several different taxa each of Ephemeroptera, Trichopter a, 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 not iceably 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 toler ant taxon; very tolerant individuals represent a large proportion of the individuals collectedDevelopment and Testing of Biomonitoring Tools fo r Macroinvertebrates in Florida Streams March 2007



### 52 

# CHAPTER 5: CONCLUSIONS

The correlation between biological metrics a nd 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 resp onse to human disturbance was repeated across all 3 regions in Florida (panhandle, peninsula, and northea st), 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 freque nt 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 comp lex ways that human activities
influence the environment. The SCI had good statistical precision and could det ect 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 bi ological condition based on a single sample and 3.5 categories for 2 samples. Strong correlation with an independent measure of dist urbance, good statistical precision, immunity to regional differences or wate rshed 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 va lues into biocriteria to be included as part of the water quality standards for the st ate. Two approaches were used to define categories of biological condition. The primary approach involved conveni ng 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 bi ological 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 (“i mpaired”) biological condition for Florida stream site assessments. The results repo rted here provide the scientific basis for managing and protecting Florida’s stream resources.Development and Testing of Biomonitoring Tools fo r Macroinvertebrates in Florida Streams March 2007



### 53 

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### 59 

# 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 (NH 3 , 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, NH 3 , the habitat index, and, in a few instance s, 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 NH 3
Many of the reported NH 3 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 NH 3 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 0Development and Testing of Biomonitoring Tools fo r Macroinvertebrates in Florida Streams March 2007



### 60 

## 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 com parisons 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 per centage measure that ranged from 0–100%.



## Derivation of hydrologic index values

At the April, 2002, Biocriteria Meeting, a traini ng session on the hydrologic index scoring process was conducted. The purpose for assigning the hydr ologic modification scores was to develop one of several components of the HDG. FDEP biologists we re 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 (s ee 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 wi thin 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 surfac es in the watershed that would “spike” the hydrograph.

Since the participants had simultaneously visited ce rtain streams as part of previous Biocriteria Meeting training and quality assurance activities, appro priate hydrologic index sc ores for these example streams were discussed, to promote consis tency 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 scor ing rules, natural break points in the data were used. When no break points were obvious, the ra nge of the data was divided into approximately equal parts.Development and Testing of Biomonitoring Tools fo r Macroinvertebrates in Florida Streams March 2007



### 610.0030.0040.0050.0060.0070.0080.0090.0100.0200.0300.0400.0500.0600.0700.0800.0900.1000.2000.3000.4000.5000.6000.7000.8000.9001.0002.0003.0004.0005.0006.0007.0008.0009.00010.00020.00030.000

NH3 -2 0 2 4 6 8 10 12 14 16 18Number of EPT taxa

Figure A-1. Scoring rules for NH 3 for inclusion in the HDG. NH 3 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 18Number of EPT taxa

Figure A-2. Scoring rules for the habitat index for inclusion in the HDG.Development and Testing of Biomonitoring Tools fo r Macroinvertebrates in Florida Streams March 2007



### 62 0 2 4 6 8 10 12

Hydrologic index -2 0 2 4 6 8 10 12 14 16 18Number 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 35Number of EPT taxa

Figure A-4. Scoring rules for the LDI index for inclusion in the HDG.Development and Testing of Biomonitoring Tools for Macroinvertebrates in Florida StreamsMarch 200763Appendix B. Long-lived taxaTaxonomic order, family, genus, or species name of taxa used tocalculate long-lived taxa richness. Taxonomic synonyms presentin the currentFlorida database are also shown. Long-lived (semivoltine) taxarequire more than one year to complete their life cycles.OrderFamilyGenusSpecies (Long-lived)SynonymsDecapodaAll families, genera, and speciesBasommatophora LymnaeidaeFossariaAll speciesMesogastropoda AmpulariidaePomaceaAll speciesVeneroida CorbiculiidaeCorbiculaCorbicula flumineaCorbicula manilensisUnionoidaUnionidaeAll genera and speciesOdonata AeshnidaeBasiaeschna Basiaeschna janataBoyeriaAll speciesCordulegastridaeAll speciesGomphidaeGomphus All species GomphurusHaegenius Hagenius brevistylusProgomphusAll speciesLibellulidaeMacromiaAll speciesSomatochloraAll speciesTetragoneuriaAll speciesEpithecaPetaluridaeTachopteryx Tachopteryx thoreyiDevelopment and Testing of Biomonitoring Tools for Macroinvertebrates in Florida StreamsMarch 200764OrderFamilyGenusSpecies (Long-lived)SynonymsPlecoptera PteronarcidaePteronarcys Pteronarcys dorsataLeuctridaeLeuctraAll speciesPeltoperlidaeTallaperla TallaperlacorneliaPerlidaeAcroneuriaAll speciesAgnetina Agnetina annulipesEccoptura Eccoptura xanthenesAcroneuria xanthenesNeoperlaAll speciesParagnetinaAll speciesBanksianaBanksiellaPerlodidaeClioperla Clioperla clioIsoperla clioPerlinellaPerlinella drymoAtoperla ephyrePerlinella ephyre/zwickiIsoperla Isoperla orataMegalopteraCorydalidaeAll genera and speciesTrichoptera BrachycentridaeBrachycentrus Brachycentrus americanus Oligoplectrum amercanumMicrasemaAll speciesCalamoceratidaeHeteroplectron Heteroplectron americanumHydropsychidaeDiplectrona Diplectrona modestaMacrostemum Macrostemum carolina Macronema carolinaMacronemum carolinaLepidostomidaeLepidostomaAll speciesAlepomyia, Alepomyiodes,Arcadopsyche, Atomyia,Jenortha, Mormomyia,Neuropsyche, Nosopus,Notiopsyche, Olemira,Development and Testing of Biomonitoring Tools for Macroinvertebrates in Florida StreamsMarch 200765OrderFamilyGenusSpecies (Long-lived)SynonymsOligopsyche, Phanopsyche,Pristosilo, AthripsodesLeptoceridaeCeracleaAll speciesLimnephilidaeIronoquia Ironoquia punctatissismaPycnopsycheAll speciesMolannidaeMolannaAll speciesPhryganeidaeBanksiola Banksiola concatenata Neuronia concatenataPtilostomis Ptilostomis postica Neuronia posticaAgrypnia Agrypnia vestitaPsychomyiidaeLype Lype diversaRhyacophilidaeRhyacophilaAll speciesUenoidaeNeophylaxAll speciesDevelopment and Testing of Biomonitoring Tools for Macroinvertebrates in Florida StreamsMarch 200766Appendix C. Sensitive taxaTaxonomic order, family, genus or species name; taxonomic synonyms in the current Florida database; whether the taxon was included in theFlorida Index (2 = very sensitive; 1 = sensitive); and species name of Florida Index taxa are listed for taxa identified as sensitive to humandisturbance.OrderFamilyGenusSpecies (sensitive)SynonymsFlorida indexAcariformes LebertiidaeLebertiaAll speciesIsopoda AsellidaeCaecidotea CaecidoteaAsellus 1Caecidoteaspp.Amphipoda CrangonyctidaeCrangonyx CrangonyxEphemeroptera BaetidaeAcerpenna Acerpenna pygmaeaBaetis pygmaeusEphemerellidaeAll genera and speciesHeptageniidaeAll genera and species1Stenacron interpunctatumStenonema mexicanumintegrum2Stenonema exiguumStenonema smithaeLeptophlebiidaeAll genera and speciesLeptohyphidaeAll genera and speciesOdonata LibellulidaeMacromiaAll species2Macromiaspp.PlecopteraAll families, genera andspecies2 AllPlecopteraTrichoptera HydropsychidaeHydropsycheAll species2Hydropsychespp.LeptoceridaeTriaenodesAll speciesPhilopotamidaeChimarra Chimarra2Chimarraspp.Development and Testing of Biomonitoring Tools for Macroinvertebrates in Florida StreamsMarch 200767OrderFamilyGenusSpecies (sensitive)SynonymsFlorida indexPsychomyiidaeLype Lype diversaColeoptera ElmidaeAncyronyx Ancyronyx variegatusGonielmis Gonielmis dietrichiMacronychus Macronychus glabratusDiptera ChironomidaeMicrotendipesAll speciesParametriocnemusAll speciesPolypedilum Polypedilum avicepsRheocricotopusAll species 2Rheocricotopus robackiStempellinellaAll speciesTanytarsus Tanytarsus sp. d eplerTanytarsus Tanytarsus sp. m eplerTribelos Tribelos jucundumEmpididaeHemerodromia HemerodromiaSimuliidaeAll genera and species2Simuliumspp.Development and Testing of Biomonitoring Tools for Macroinvertebrates in Florida StreamsMarch 200768Appendix D. Very tolerant taxaTaxonomic order, family, genus or species name; taxonomic synonyms in the current Florida database; whether the taxon was included in theFlorida Index (2 = very sensitive; 1 = sensitive); and species name of Florida Index taxa are listed for taxa identified as very tolerant to humandisturbance.OrderFamilyGenusSpecies (very tolerant)SynonymsFlorida IndexHoplonemertea TetrastemmatidaeProstoma Prostoma rubrumHaplotaxida NaididaeBratislavia BratislaviaunidentataDeroAll speciesNaisNais communiscomplexNais communisNaiselinguisNaispardalisNaisvariabilisPristina Pristina synclitesTubificidaeHaber Haber speciosusLimnodrilusAll speciesCamptodriluscalifornicusLimnodrilusaurantiacusLumbriculida LumbriculidaeLumbriculusAll speciesRhynchobdellida GlossiphoniidaeAll genera and speciesBasommatophora AncylidaeHebetancylus Hebetancylus excentricusLaevapexAll speciesPhysidaePhysellaAll speciesPlanorbidaeAll genera and speciesDevelopment and Testing of Biomonitoring Tools for Macroinvertebrates in Florida StreamsMarch 200769OrderFamilyGenusSpecies (very tolerant)SynonymsFlorida IndexMesogastropoda HydrobiidaePyrgophorus Pyrgophorus platyrachisPyrogophorusplatyrachisThiaridaeMelanoidesAll speciesOdonata LibellulidaePachydiplax Pachydiplax longipennisCoenagrionidaeArgia Argia sedula2Argia sedulaEnallagma Enallagma cardenium Enallagma coecumIschnuraAll species AnomalagrionHeteroptera MesoveliidaeAll genera and speciesLepidoptera PyralidaeAll genera and speciesColeoptera HaliplidaePeltodytesAll speciesDiptera ChironomidaeLarsiaAll species 1Larsia lurida1Larsia decolorataChironomus AllspeciesTendipesChaetolabisCladopelma CladopelmaCricotopusAll species2Cricotopus bicinctusCryptochironomusAll speciesDicrotendipes Dicrotendipes modestusDicrotendipesneomodestusGlyptotendipesAll speciesGoeldichironomusAll speciesKiefferulusKiefferulusDevelopment and Testing of Biomonitoring Tools for Macroinvertebrates in Florida StreamsMarch 200770OrderFamilyGenusSpecies (very tolerant)SynonymsFlorida IndexParachironomusAll speciesPolypedilum Polypedilum beckaeAsheum beckaePedionomus beckaePolypedilumillinoiensegrp.Polypedilumillnoense1Polypedilum illinoiensegrp.Polypedilum tritum Chironomus tritumTanypusAll species PelopiaTanytarsus Tanytarsus sp. f eplerStratiomyidaeOdontomyia OdontomyiaEulaliaTipulidaeTipula TipulaNippotipulaPlayttipulaYamatotipulaDevelopment and Testing of Biomonitoring Tools fo r Macroinvertebrates in Florida Streams March 2007



### 71 

# Appendix E. Regional calibration of SCI metric scores

After metrics were selected based on thei r 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 5 th and 95 th 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, t he 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 matc h closely, the endpoints for the metrics that defined values of 0 and 10 were adjusted away from the 5 th and 95 th 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 5 th percentile values were very close and they were simply averaged across bioregions for simplicity. After determining which metric values define t he 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, 5 th and 95 th 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 diffe rent from the percentile value.

SCI Metric NE

5 th %tile PH

5 th %tile PS

5 th %tile NE

95 th %tile PH

95 th %tile PS

95 th %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 58.5Development and Testing of Biomonitoring Tools fo r Macroinvertebrates in Florida Streams March 2007



### 72 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 5 th and 95 th 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 5 th %tile and used same value in all bioregions

Ephemeroptera taxa 0– 3.5 0–6 0–5 Lowered expectations for 95 th

%tile for NE

Trichoptera taxa 0– 6.5 0–7 0–7 Lowered expectations for 95 th

%tile for NE

% Filterer 1–42 1–45 1–40 Lowered expectations for 95 th

%tile for NE Long-lived taxa 0–3 0–5 0–4

Clinger taxa 0– 9 0– 15.5 0–8 Lowered expectations for 95 th
%tile for NE; raised for PH

% Dominance 54

– 10 43–10 54– 10 Averaged lower 5 th %tile;

% Tanytarsini 0–26 0–26 0–26 Raised expectations for 95 th %tile
for NE

Sensitive taxa 0–11 0– 19 0–9 Raised expectation for 95 th %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 fo r Macroinvertebrates in Florida Streams March 2007



### 73 Human disturbance gradientMetric score for % filterer

-2 0 2 4 6 8 10 0 2 4 6 8 10 Northeast
Panhandle
Peninsula

Human disturbance gradientMetric 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 bi oregion. The upper panel shows metrics scored according
the 5 th and 95 th 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.Development and Testing of Biomonitoring Tools fo r Macroinvertebrates in Florida Streams March 2007



### 74 

## References

Florida Department of Environmental Protecti on (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:

1U http://www.dep.state.fl.us/labs/sop/index.htm

H .Development and Testing of Biomonitoring Tools for Macroinvertebrates in Florida StreamsMarch 200775Appendix F. ANOVA output and regression results for the SCI and BioReconTable F-1. General linear model output for the SCI versus HDG by region (northeast, panhandle, or peninsula)Neither the region nor the interaction of the HDG and region was statistically significant. Test failed to detect a differencein SCI values due toregion.Univariate Tests of Significance for AvgOfSCI6_100 (1NGM_4)SSdfMSFpIntercept 403623.3 1 403623.3 1832.253 0.000000Region361.62180.80.8210.441846HDG2 62750.7 1 62750.7 284.858 0.000000Region*HDG21142.92571.52.5940.077660Error 37449.0 170 220.3Table 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)The interaction term was not significantly different for year class and the HDG, suggesting the lines were parallel (equal slopes). The year classalone, however, was significant, with valuesfor 1994–95 being slightly higher for the SCI.Univariate Tests of Significance for SCI6_100 (1NGM_4)SSdfMSFpIntercept1627060116270605835.6570.000000Year class294847372.6440.032766HDG21344971134497482.3900.000000Year class*HDG279141980.7090.586005Error171749616279Development and Testing of Biomonitoring Tools for Macroinvertebrates in Florida StreamsMarch 200776Table F-3. ANOVA estimates of SCI variability for repeat visits to the same site within a yearError estimate used to calculate confidence interval is underlined.ANOVA for SCI (SCI6_100) (Same_day_SCI)EffectdfMSdfMSFp{1}STORET Random 60 1211.636 39.41542 188.7279 6.420014 0.000000{2}YearRandom6269.43035.76223170.24021.5826480.1807271*2 Random 53 134.481 82.0000066.59522.019385 0.002054Table F-4. ANOVA estimates of SCI variability for laboratory subsamplingError estimate used is underlined.ANOVA Results: SCI subsampling (Metrics)EffectdfMSdfMSFp{1}Sitename Random 58 1393.986 11832.8631242.41795 0.00Table F-5. ANOVA estimates of BioRecon variability for repeat visits to the same site within a yearError estimate used to calculate confidence interval is underlined.ANOVA for BioRecon (BR3_10) (New metrics multiple_2)EffectdfMSdfMSFp{1}STORET Random 126 15.91691 117.3771 1.696009 9.384924 0.000000{2}year Random 8 6.02260 156.00001.4588404.128352 0.000171Development and Testing of Biomonitoring Tools fo r Macroinvertebrates in Florida Streams March 2007



### 77 

# 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 richne ss, 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 modifi ed 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 si te-visits with 75–175 individuals were used in order to more closely approximate the target nu mber 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 developm ent data set to samples with only 100 to 110
individuals would have reduced the number of sa mples 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 na rrow 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 percent iles of the metric values used to define the scoring rules for the SCI and the percentiles of metr ic 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. Approximat ely even numbers of samples were available for the 2004 data in the peninsula and panhandle r egions; 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, 5 th 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 t he largest differences with lower values

observed in 2004 (Table G-1) . Other taxa richness metrics were also lower at the 95 th 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 95 th 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 y ears. If we were to start over with no data onDevelopment and Testing of Biomonitoring Tools fo r 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 re lated 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 t hat 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 Laborato ry biologists has found that averaging the SCI scores from two 150-organism subsamples provides a more reliable assessment than a single 100-organism subsample. T he objective of this analysis was to evaluate how laboratory processing and/or the number of individuals ident ified 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 num bers 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 5 th and 95 th 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 79.8Development and Testing of Biomonitoring Tools fo r Macroinvertebrates in Florida Streams March 2007



### 79 

## Hatchet Creek

100 125 150 160 175 200 300



## Target number of individuals 30 40 50 60 70 80 90 100 110SCI

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 Cr eek. 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 110SCI

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 Cree k. Shown also is the box plot for the average SCI score from 2 150 subsamples.Development and Testing of Biomonitoring Tools fo r Macroinvertebrates in Florida Streams March 2007



### 80 

## Crooked Creek

100 125 150 160 175 200 300



## Target number of individuals 40 50 60 70 80 90 100 110SCI

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 Cree k. 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 make s 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 organi sms, 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 pr eparation SOP that result in the analyst more accurately achieving the target num ber 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 Stream s 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 subsampli ng 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 fo r 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 discriminati on between good, moderate, and poor quality

sites

● Fourth, the use of 150x2 subsamples reduc es the variability in the resulting SCI score by using the average of two 150-organi sm-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 dile mma of sampling streams for macroinvertebrate
richness. Journal of the North Amer ican 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 fo r Macroinvertebrates in Florida Streams March 2007



### 82 

# Appendix H. FDEP Biological condition gradient workshop summary

Russel Frydenborg and Denise Miller (FDEP)



## Background

Florida Department of Environmental Pr otection (FDEP) staff evaluated stream

macroinvertebrate data from the stream condition index (SCI) recalibration dataset with the goal of refining delineations of biological/ecologica l condition based on the SCI 2004 scores. Several approaches were taken for this evaluation, am ong them a Biological Condition Gradient (BCG) Workshop which was conducted by FDEP staff on Oc tober 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 Depa rtment of Environmental Protection, Susan

Jackson of the U.S. Environmental Protecti on 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 SC I 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 pr ovided with background literature on the biocondition gradient concept (Davies and Jacks on, 2006; Stoddard et al., 2006) and descriptions from FDEP of the intended process (Frydenborg an d 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 spec ies; some density changes may occur

• Score of 3: Some replacement of sensit ive-rare species; functions fully maintained

• Score of 4: Some sensitive species maintained but notable replacement by moretolerant taxa; altered distributi ons; functions largely maintainedDevelopment and Testing of Biomonitoring Tools fo r Macroinvertebrates in Florida Streams March 2007



### 83 • 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 di stributed materials from the presentation for reference by the experts (Frydenborg and Miller, 2007: Attachment B). During the workshop, experts we re given the opportunity to discuss their rationale for scoring of a site, and, if they desired, to cha nge their score for a site based on their assessment from the discussions (Delphi method). Scoring s heets 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 be lieved 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 recali bration 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 c ondition site) samples from each of three (3) Florida Bioregions (Panhandle, Peninsula, Nort heast) 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 25 th percentile, median, and 75 th
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 t he 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 recalib ration data set. The specific 10-sample selection methodology for each bioregion was as follows (Table H-2) :

• 4 samples selected based on those hav ing SCI scores closest to the 10 th , 25 th

, median and 75 th 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 repr esented 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 sa mples were randomly drawn based on theDevelopment and Testing of Biomonitoring Tools fo r Macroinvertebrates in Florida Streams March 2007



### 84 range of LDI scores for the samples, afte r 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 entir e non-reference dataset for the bioregion (sorted on HDG score).

Table H-1. BCG workshop reference sites Site identification information, bioregion locati on, date of sampling, SCI value, and LDI value.

BCGSampleID STORET Station Nickname Bioregion Sample Date SCI LDI

NERef01 20030341 CECFLD7 No rtheast 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 No rtheast 3/14/1996 92.4 1.25

PanhRef01 33040014 BIGHORSREF Panhandle 8/23/1994 67 1.23

PanhRef02 32010024 LIMEREF P anhandle 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 P eninsula 7/21/1999 55.7 1.18

PenRef02 20030414 GLDHEADREF Peninsula 1/12/2000 100 1.00

PenRef03 24010002 MANAREF Peni nsula 11/21/2000 80.2 1.31

PenRef04 20020012 OKLRIVREF Peninsula 2/15/2000 68.1 1.01

PenRef05 20030253 PUTNA44 P eninsula 3/16/1998 24 1.15Development and Testing of Biomonitoring Tools fo r 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 Northeas t 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 Panhandl e 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 Panh andle 3/8/1993 4.6 7 8.08 SC I 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] randomDevelopment and Testing of Biomonitoring Tools fo r Macroinvertebrates in Florida Streams March 2007



### 86 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 9/20/19 95 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 Peninsul a 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 rega rding the sample and site (e.g., more specific location, collection date, information on physic al/chemical characterist ics, 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). Fryde nborg 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. Se veral 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 beDevelopment and Testing of Biomonitoring Tools fo r 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 score s 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 biologic al condition gradient: a descriptive model for interpreting change in aquatic systems. Ec ological Applications 16(4):1251-1266.

Frydenborg, R. and D. Miller. 2007. Biological Co ndition 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.

1U ftp://ftp.dep.state.fl.us/pub/l abs/biology/biodb/dbmanual.pdf

H .Development and Testing of Biomonitoring Tools for Macroinvertebrates in Florida StreamsMarch 200788Table H-3. BCG workshop scores summaryExpert CodeE01E02E03E04E05E06E07E08E09E10E11E12E13E14E15E16E17E18E19E20E21E22MedianBCG ScoreSCIScoreBCGSampleIDFinal BCG ScoreNE013434333334334343333234347.1NE02 5 5 5 5 5 5 5 5 5 5 4 4 5 5 5 5 5 4 4 4 5 5 5 32.9NE034555555555555554555535524.4NE04 3 4 4 3 4 4 3 4 3 3 3 3 3 3 3 3 3 3 3 3 4 4 3 40.1NE052122212222222122212112273.6NE06 4 5 4 5 3 5 5 5 3 5 5 5 4 5 5 4 4 4 4 5 4 4 5 28.9NE07665666666666666666565669.1NE08 3 3 3 2 3 4 5 3 3 3 4 3 2 3 2 3 3 3 3 3 3 3 3 68NE094454534455444454444444430.6NE10 3 3 4 2 3 4 4 3 3 3 2 3 3 3 4 3 3 3 3 3 3 3 3 47Panh015445555444354454454344420.7Panh02 5 4 4 4 5 5 5 5 4 5 4 4 5 5 5 4 5 5 5 4 4 5 5 30Panh032222212222222122122231262.5Panh04 2 2 2 2 2 2 2 2 2 2 2 3 2 1 2 1 1 2 2 2 2 2 2 70.5Panh054432443433434333334333343.5Panh06 3 2 2 2 2 2 2 3 2 2 2 2 3 2 2 2 2 2 2 2 3 2 2 47.7Development and Testing of Biomonitoring Tools for Macroinvertebrates in Florida StreamsMarch 200789Expert CodeE01E02E03E04E05E06E07E08E09E10E11E12E13E14E15E16E17E18E19E20E21E22MedianBCG ScoreSCIScorePanh075555555555455555555445530.2Panh08 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 6 5 4 5 5 5 35.8Panh09666656566666666666566664.6Panh10 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 0.6Pen015565565655555656655566515.5Pen02 3 3 3 4 3 3 3 4 4 4 3 3 4 4 3 4 4 3 4 3 3 3 3 37.5Pen035444544455445545455454424.2Pen04 5 5 5 5 5 5 5 5 5 5 4 5 5 6 5 6 5 5 5 5 5 6 5 14.8Pen053222222322222222333222255Pen06 4 4 4 3 5 4 4 4 5 4 4 4 4 5 4 4 4 4 4 4 4 4 4 38.2Pen073322333333333343333333340.8Pen08 2 2 1 1 2 2 2 1 1 2 1 2 1 2 2 2 2 2 2 2 2 2 2 58.3Pen095555545655555555555565521.1Pen10 6 6 6 6 6 5 6 6 6 6 6 5 6 6 6 6 6 6 6 6 6 6 6 0Development and Testing of Biomonitoring Tools fo r Macroinvertebrates in Florida Streams March 2007



### 90 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 Univer sity) and Denise Miller (FDEP)



## Background

For the purpose of scrutinizing Stream Condi tion 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 provi ded 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 wa s 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 condi tion gradient (BCG) from Davies and Jackson, 2006, listed below:

• Score of 1: Native or natural condition

• Score of 2: Minimal loss of specie s; some density changes may occur

• Score of 3: Some replacement of sensitiv e-rare species; functions fully maintained

• Score of 4: Some sensitive species ma intained but notable replacement by moretolerant taxa; altered distributi ons; functions largely maintained

• Score of 5: Tolerant species show in creasing 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 panelis ts) 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 pr ocedure 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 { 

### ( ,) i i 

### X Y

, 

### 1, 2, ... , i n

= } 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 {



### , i 

### X 

### 1, 2, ... , i n

= } are distinct. If several 

### ' i 

### Y s are corresponding to a

single X value, the median of the 

### ' i 

### Y s is taken to be the response value for the X

value.

• { 

### ( ,) i i 

### X Y

, 

### 1, 2, ... , i n

= } are sorted according to the values of X from least to

greatest.

For each integer 

### 1 l > , 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



### () () () () i i i 

### Y l lSl l β β ε =+ +

, 

### 1, 2, ... , i n

= , (1)

where for a fixed l , the 

### ( )' i 

### l s ε are assumed to be independent and identically distributed

normal random variables with mean zero and variance 2 

### ( ) l σ

.

Step 2 . Calculate the values { 1 1 

### ( ) ( ) / ( ( )) L l l se l β β = ) )

, 

### 2, 3, ... , ( 1) l n

=

− } where

1 

### ( ( )) se l β ) is the estimated standard error of 1 

### ( ) l β )

.

Step 3 . Let 1 

### ( ) max{ (2), (3), ... , ( 1)} Ll L L Ln = − 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 

### 0.10 

### α 

### = (or 

### 0.05 α = ), 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 l 

### X

with a level-shift 1 

### ( ) l 

### β )

.

Step 4 . Let *

11 1 

### () () ii i 

### Y Y lSl β = − . Repeat Steps 1-3 on the new response variable

*

i 

### Y for detecting a possible second change poin t. 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 ll l 

### X X X . Fit the model

0 11 2 2 

### ( ) ( ) ... ( ) i ii k i k i 

### Y Sl Sl Sl β ββ βε = + + ++ +

, 

### 1, 2, ... , i n

= . (2)

Then the estimated coefficients 1 2 

### { , , ... , } k 

### β β β ) ) ) 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 ba sed 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(l 1 

### ) = 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(l 1 

### ) = 3.87 and is also significant at the 5% level (95% confidence). Th e 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-BC G 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. “Regula r” 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 me thod 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, ... , } i i i pi 

### Z 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 in tervals 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 ca ses 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 

### ˆ θ to form a confidence interval * * 

### ˆ ˆ [, ] Lower Upper 

### θ θ for the change point θ

,

where ** * (1) (2) ( ) 

### ˆˆ ˆ { , , ... , } M 

### θθ θ are the ordered bootstrap estimates of the change point; * 

### ˆ

Lower 

### θ

and * 

### ˆ
Upper 

### θ 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 SC I and Median BCG Scores. We used a modified bootstrapping method to construct confidence interv als for the change points. Specifically, we

### 95 first randomly drew 90% of the 30 pairs (or dr opped 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 T able 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 c ondition 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 (b etter) 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 det ails of the BCG workshop and the change-point analysis are provided in Frydenborg and Mille r, 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 Biologi cal Condition Gradient: A Descriptive Model for Interpreting Change in Aquatic Systems.” Ec ological Applications. 16(4): 1251-1266. Ecological Society of America.

Efron, B. 1979. “Bootstr ap 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 Co ndition 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 Analys is 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: 461-464.97Appendix J. Reference sitesTable J-1. Reference site locations.LatitudeLongitudeSTORETSite DescriptionBioregionWaterbody NameFacility Information from GIS CoveragesDegMinSecDegMinSec20030245 Ates Creek @ CR 315 Northeast BLACK CREEKNo point sources or stormwater inputs upstream29 54 38 81 53 520030437BLACK CR., NORTHPRONG, 1/4 ABOVE BIGBRANCHNortheast BLACK CREEKOne large stormwater constructin permit upstream, possiblyconnected through wetlands issued in 6/2005.30 6 48.13 81 54 23.3420030419 Black Creek, South Prong Northeast BLACK CREEKCamp blanding is upstream of this site29 56 3.15 81 55 9.6320030723N. FORK BLACK CRKABOVE CARTERSPENCER RD.Northeast BLACK CREEKDupont Highland Mine facility discharges, but this site is abovethis. 30 49.281 58 43.920030247South Fork Black Creek @SR 16 Northeast BLACK CREEK No point sourcesor stormwater inputs upstream 29 58 45 81 51 1020030570Bradley Crk @ 739B-Peter'scrk basinNortheast BRADLEY CREEKSubdivision (Silver Creek) stormwater permits (2003 &2005),discharge may flow towards this site.30 2 48.8 81 47 5.520030487Bull Creek @ NE Crossingof CR 215 Northeast BULL CREEK No point sources orstormwater inputs upstream 30 2 3.2 81 54 6.819010042Calkins Ck, off HampRegister Rd.Northeast CALKINS CREEK No point sources orstormwater inputs upstream 30 19 50.95 82 14 30.4721010043Deep Creek upstream ofbridge @Old River Rd Northeast DEEP CREEKNo point sources or stormwater inputs upstream30 22 35.7 82 38 16.121010018Falling Creek @ CR 131,above falls Suwannee R.Northeast FALLING CREEKKOA Domestic WWTF ~3000 m upstream of site, not NPDESpermitted, design capacity = 0.0150. Not NPDES permitted, andsmall design capacity likely means that this is a percolation pond.District Biologist is not aware of a KOA discharge in this area.30 15 39.47 82 40 5.1919010064John Rowe Br, test site forNE FL State Hosp FYI Northeast JOHN ROWE BRANCHNo point sources or stormwater inputs upstream; The hospitaldoesn't show up as permitted site on the GIS coverage 30 13 47.4 82 8 16.498LatitudeLongitudeSTORETSite DescriptionBioregionWaterbody NameFacility Information from GIS CoveragesDegMinSecDegMinSec20030571Peter's Crk aboveRosemary hill Rd- Basin Northeast PETERS CREEKClay Co. Dept. of Env. Services has 2 stormwater permits right atthe site and 2 stormwateroutfall sites30 0 25.9 81 44 55.419010099Pigeon Creek at US 1 St.Marys-Nassau RiverNortheast PIGEON CREEK No point sources or stormwater inputs upstream30 45 40.73 81 58 3.0721010056PCS Phosphate FYI5,control site Northeast ROBINSON CREEK No point sources or stormwater inputs upstream 30 19 14 82 39 44.121010032Rocky Ck @ WoodpeckerRdNortheast ROCKY CREEK No point sources or stormwater inputs upstream30 32 40.27 82 44 1.9120030265Sal Taylor Creek belowRowell Creek Northeast SAL TAYLOR CREEKOne large stormwater constructin permit above these sites issuedin 12/2004. 30 11 59 81 54 5021030115Santa Fe Rv- Control ABMPNortheast SANTA FE RIVER A few stormwaterpermits pretty far upstream29 50 46.9 82 12 13.820030456Thomas Ck, Forestry BMPStudy Ref Site B Northeast THOMAS CREEK No point sources or stormwater inputs upstream 29 54 57.6 81 51 44.919010072 McClenny WWTP upstream Northeast TURKEY CREEKA few stormwater permits around, look to be small and may notactually discharge to this stream30 16 6.4 82 7 34.920030341Yellow Water Creek aboveSal Taylor Creek Northeast YELLOW WATER CREEKOne large stormwater constructin permit above these sites issuedin 12/2004. 30 12 3.2 81 55 10.3520030342Yellow Water Creek belowSal Taylor CreekNortheast YELLOW WATER CREEKOne large stormwater constructin permit above these sites issuedin 12/2004.30 11 44.45 81 55 5.822020070BIG CYPRESS BRANCHTIMBER ENERGY FYICNTRL SITEPanhandleBIG CYPRESS BRANCH No point sources or stormwater inputs upstream30 19 0 84 48 033040014BIG HORSE CREEK S.R.2OKA.CO.PanhandleBIG HORSE CREEK No point sources orstormwater inputs upstream 30 55 19.3 86 35 42.621030064Cedar Head Run, aboveIchetucknee RiverPanhandleCEDAR HEAD RUNIchetucknee Family Campground WWTF upstream of site, notNPDES permitted (?), designcapacity=0.007. Subsequentadditional information indicates no discharge. Stream begins inState Park. 29 58 50.2 82 45 30.9231010050CROOKED CREEKHWY.270 GADSDEN CO.PanhandleCROOKED CREEKNo point sources or stormwaterinputs upstream 30 34 59.79 84 52 51.4999LatitudeLongitudeSTORETSite DescriptionBioregionWaterbody NameFacility Information from GIS CoveragesDegMinSecDegMinSec32030023ECONFINA CREEK ATSCOTT RD PanhandleECONFINA CREEK Nopoint sources or stormwater inputs upstream30 33 19.3 85 26 622050077Econfina River ForestryBMP Study Ref Site BPanhandleECONFINA RIVER No point sources orstormwater inputs upstream 30 9 52.8 83 50 14.633040064Jacks Branch, Forestry BMPStudy Ref Site B PanhandleJACK'S BRANCH No point sources or stormwater inputs upstream 30 52 10.3 86 29 21.732010024Limestone Ck @ BeckridgeRdPanhandleLIMESTONE CREEK No point sources orstormwater inputs upstream 30 59 21.58 86 5 9.8132020063LITTLE CROOKED CREEKS.R.79 BAY CO. PanhandleLTL CROOKED CREEK No point sources or stormwater inputs upstream30 24 48.4 85 52 6.522030062MCBRIDE SLOUGHHWY.267 WAKULLA CO.PanhandleMCBRIDE SLOUGH No point sourcesor stormwater inputs upstream 30 14 10 84 16 2033010054MCDAVID CREEK AT SR99 PanhandleMCDAVID CREEKNo point sources or stormwater inputs upstream 30 44 21.6 87 26 54.822020049MULE CREEK S.R.12LIBERTY CO.PanhandleMULE CREEK No point sources or stormwater inputs upstream30 30 43.55 84 49 44.122020062Oklawaha Ck, SR 267, S ofQuincy PanhandleOKLAWAHA CREEKSand mine operations (permitted IWF's). Subsequent additionalinformation indicates no discharge to sampled site. 30 27 2.18 84 38 36.8931020073Pitts Mill Creek above confwith Cypress CkPanhandlePITTS MILL CREEK No point sources or stormwater inputs upstream30 16 43.9 85 14 23.622020010QUINCY CR above SR12,ref for Quincy STP FYIPanhandleQUINCY CREEKEngelhard IWF upstream and numerous other small IWF's just Sthat may/may not flow towards this site. Subsequent additionalinformation indicates no discharge to site except maybe duringhurricanes. 30 35 34.6 84 33 50.322030010St. Marks R approx 4 mi Nof Hwy 98 (@ "rapids") PanhandleST. MARKS RIVER No point sources or stormwater inputs upstream 30 14 24.8 84 8 43.431010051SWEETWATER CREEKHWY.270 LIBERTY CO.PanhandleSWEETWATER CREEK No point sources orstormwater inputs upstream 30 31 26.68 84 58 25.15100LatitudeLongitudeSTORETSite DescriptionBioregionWaterbody NameFacility Information from GIS CoveragesDegMinSecDegMinSec31020040TEN MILE CREEK S.R.73CALHOUN CO. PanhandleTEN MILE CREEKOne small WWTP and one Industrial Wastewater permittedfacility upstream; Stormwater construction at sampling point. WWfacilities at least 1.5 to 2 km upstream. Additional subsequentinformation from District Biologist is that site is appropriate forreference. 30 30 1.27 85 12 1.9133010065Rest Area Run Creek belowI 10PanhandleUNNAMED CREEKSurrounded by stormwater construction, potential inputs fromPerdido landfill upstream. Subsequent additional information fromDistrict Biologist is that this sitewas reference at the time relevantto the SCI sampling for the calibration dataset.30 34 5.4 87 24 10.622040038Unnamed Creek aboveMonticello WWTP PanhandleUNNAMED CREEKA few stormwater permits around, look to be small and may notactually discharge to thisstream 30 30 59.5 83 51 25.332020066Wright Ck, ref for NomaSTP FYIPanhandleWRIGHTS CREEK Site is upstream of Noma WWTP 30 58 47.7 85 35 54.420010455BLACKWATER CREEK ATHWY 44A Peninsula BLACKWATER CREEK Nopoint sources or stormwaterinputs upstream 28 52 38.96 81 29 19.9128020239 Cypress brch above SR 78 Peninsula CYPRESS BRANCH Nopoint sources or stormwater inputs upstream26 48 49 81 20 728020237Cypress Creek 2 mi aboveSR78Peninsula CYPRESS CREEKSmall stormwater constructionpermit for Wayne Homes; permitstarted in 9/200426 45 5 81 37 1426010972DAVENPORT CR AT SR545 BRIDGE Peninsula DAVENPORT CREEK Gas pipeline relocationstormwater permit startedin 2/2005 28 16 15.2 81 35 28.0626010593FISHEATING CREEKABOVE US 27 Peninsula FISHEATING CREEKLykes Campground WWTF nearby, not NPDES permitted (?);Depending on flow direction could impact site. Subsequentadditional information indicates that site is upstream fromcampground.26 56 12 81 19 3420030414GOLD HEAD BR. IN STATEPARK Peninsula GOLD HEAD BRANCH No point sourcesor stormwater inputsupstream 29 49 56.03 81 56 46.6424030142Hillsborough R upstrm ofSouthside Br, ZephyrhilPeninsula HILLSBOROUGH RIVERZephyrhills WWTF upstream ofsite, but can't determine whereoutfall is. Lots of monitoring wells around. Subsequent additionalinformation indicates probably a percolation pond. No dischargeto the site - the site was just visited. 28 11 8.4 82 10 30.9101LatitudeLongitudeSTORETSite DescriptionBioregionWaterbody NameFacility Information from GIS CoveragesDegMinSecDegMinSec24030048 Hillsborough River at SR39 Peninsula HILLSBOROUGH RIVERZephyrhills WWTF upstream ofsite, but can't determine whereoutfall is. Lots of monitoring wells around. Subsequent additionalinformation indicates no discharge to the site - the site was justvisited. 28 11 38.7 82 10 2.825020111Horse Creek @ SR 72BridgePeninsula HORSE CREEK No point sources or stormwater inputs upstream27 11 59.91 81 59 15.2820010454JUNIPER CREEK ATHIGHWAY 19 Peninsula JUNIPER CREEK No point sourcesor stormwater inputsupstream 29 12 49.41 81 39 17.520020004Little Orange Creek @ Hwy21Peninsula LITTLE ORANGE CREEKGa Pacific-Hawthorne Plywood STW discharges upstream, butmay flow into Little Orange Lake. Subsequent additionalinformation indicates no discharge to the stream. 29 32 28.42 81 57 16.6726011019LIVINGSTON CREEK ATRUCKS DAIRY RD. Peninsula LIVINGSTON CREEK No point sources or stormwaterinputs upstream27 42 29.61 81 26 49.0228010223N FK LOXAHATCHEERIVER REF BIO STATIONPeninsula LOXAHATCHEE RIVER No point sources orstormwater inputs upstream27 0 4.76 80 6 58.4824010002Manatee R 20 m belowSR64 bridge (TP1) Peninsula MANATEE RIVER Wingate CreekMine flows SE into Johnson Crk 27 28 24.14 82 12 39.4820020012OKLAWAHA RIVER AT SR316Peninsula OKLAWAHA RIVER No point sources orstormwater inputs upstream 29 22 24.34 81 54 6.3520020404ORANGE CREEK 50 YDS.UP FROM HWY. 21Peninsula ORANGE CREEKWater bottling facility (IWF) right at the sampling lat/longs.Subsequent additional information from DEP Bureau of Labs staffis that sampling trips in this area indicate that the IWF facility islocated downstream of this site. 29 30 33.44 81 56 47.820030253RICECR #3@BR SR#3/250yds above Bardine Peninsula RICE CREEK No point sources or stormwater inputs upstream 29 41 34 81 43 2720020317SILVER RUN ATCONFLUENCE WITHBOAT RAMP CANAL Peninsula SILVER RUNOne small WWTP (Sharpes Ferry Mobile Home Park) permittedfacility upstream, design capacity = 0.0190. Subsequentadditional information indicates the site is upstream of anydischarge.29 12 35.7 81 59 3628020221TELEGRAPH CREEK ATBABCOCK RANCH Peninsula TELEGRAPH CREEKOne large stormwater construction nearby (permitted 3/2005), butflows into Big Island Canal whichis also the receiving water forTelegraph Creek26 44 54.4 81 40 13.2102LatitudeLongitudeSTORETSite DescriptionBioregionWaterbody NameFacility Information from GIS CoveragesDegMinSecDegMinSec26011020TIGER CREEK ATWALKING WATERS RD. Peninsula TIGER CREEK No point sources or stormwater inputs upstream 27 48 43.53 81 26 38.6323010464Withlacoochee R @ countypark off Auton Rd (TP3)Peninsula WITHLACOOCHEE RIVERSmall stormwater construction permit for bike trail; permit startedin 4/200528 21 0 82 7 28.223010446 WITHLACOOCHEE RIVER Peninsula WITHLACOOCHEE RIVERNo point sources or stormwater inputs upstream; site belowWITHREF 28 18 13.9 82 4 0.1103Table J-2. Reference sites sample dataSTORETSite DescriptionBioregionWaterbody NameSCIHDGLDITotal # individualscountedSample Date20030245 Ates Creek @ CR 315Northeast BLACK CREEK 75.8 0 1.2 153 4/17/199720030437 BLACK CR., NORTH PRONG, 1/4 ABOVE BIG BRANCH Northeast BLACK CREEK56.1 0 1.2 103 8/1/199520030437 BLACK CR., NORTH PRONG, 1/4 ABOVE BIG BRANCH Northeast BLACK CREEK57.7 0 1.2 108 8/8/200020030437 BLACK CR., NORTH PRONG, 1/4 ABOVE BIG BRANCH Northeast BLACK CREEK66.9 0 1.2 117 8/8/200020030437 BLACK CR., NORTH PRONG, 1/4 ABOVE BIG BRANCH Northeast BLACK CREEK71.5 0 1.2 122 1/30/199620030437 BLACK CR., NORTH PRONG, 1/4 ABOVE BIG BRANCH Northeast BLACK CREEK67.3 0 1.2 123 7/20/199420030419 Black Creek, South ProngNortheast BLACK CREEK 68.4 0 1.4 103 1/24/199520030419 Black Creek, South ProngNortheast BLACK CREEK 64.3 0 1.4 106 7/20/199420030419 Black Creek, South ProngNortheast BLACK CREEK 79.3 0 1.4 106 3/20/199720030419 Black Creek, South ProngNortheast BLACK CREEK 66.1 0 1.4 119 7/11/200020030419 Black Creek, South ProngNortheast BLACK CREEK 81.3 0 1.4 150 7/7/199820030723 N. FORK BLACK CRK ABOVE CARTER SPENCERRD. Northeast BLACK CREEK43.4 0 1.2 108 7/12/199920030247 South Fork Black Creek @ SR 16Northeast BLACK CREEK90.2 0 1.1 123 4/17/199720030570 Bradley Crk @ 739B-Peter's crk basinNortheast BRADLEY CREEK 85.1 0 1.3 144 1/28/199820030487 Bull Creek @ NE Crossing of CR 215Northeast BULL CREEK63.9 0 1.2 164 4/17/199719010042 Calkins Ck, off Hamp Register Rd.Northeast CALKINS CREEK75.7 0 1.2 102 8/3/199419010042 Calkins Ck, off Hamp Register Rd.Northeast CALKINS CREEK80.4 0 1.2 108 11/1/199419010042 Calkins Ck, off Hamp Register Rd.Northeast CALKINS CREEK50.0 0 1.2 114 7/10/200121010043 Deep Creek upstream of bridge @Old RiverRdNortheast DEEP CREEK66.9 0 1.4 101 11/10/199721010018 Falling Creek @ CR 131, above falls SuwanneeR. Northeast FALLINGCREEK 51.5 0 1.4 112 7/12/199421010018 Falling Creek @ CR 131, above falls SuwanneeR. Northeast FALLINGCREEK 77.9 0 1.4 126 7/17/199721010018 Falling Creek @ CR 131, above falls SuwanneeR. Northeast FALLINGCREEK 39.0 0 1.4 132 7/27/199921010018 Falling Creek @ CR 131, above falls SuwanneeR. Northeast FALLINGCREEK 42.7 0 1.4 160 2/8/199421010018 Falling Creek @ CR 131, above falls SuwanneeR. Northeast FALLINGCREEK 87.8 0 1.4 161 1/4/199519010064 John Rowe Br, test site for NE FL State HospFYI Northeast JOHN ROWEBRANCH 62.7 0 1.8 142 11/1/199420030571 Peter's Crk above Rosemary hill Rd- Basin NortheastPETERSCREEK 89.001.2 110 1/26/1998104STORETSite DescriptionBioregionWaterbody NameSCIHDGLDITotal # individualscountedSample Date19010099 Pigeon Creek at US 1 St. Marys-Nassau River NortheastPIGEONCREEK 78.801.3 105 3/7/199419010099 Pigeon Creek at US 1 St. Marys-Nassau River NortheastPIGEONCREEK 76.801.3 108 7/25/199419010099 Pigeon Creek at US 1 St. Marys-Nassau River NortheastPIGEONCREEK 78.901.3 109 3/6/199519010099 Pigeon Creek at US 1 St. Marys-Nassau River NortheastPIGEONCREEK 58.001.3 111 2/1/200019010099 Pigeon Creek at US 1 St. Marys-Nassau River NortheastPIGEONCREEK 83.501.3 156 3/3/199319010099 Pigeon Creek at US 1 St. Marys-Nassau River NortheastPIGEONCREEK 54.801.3 157 7/9/199621010056 PCS Phosphate FYI5, control siteNortheast ROBINSON CREEK79.2 0 1.4 113 3/13/200021010032 Rocky Ck @ Woodpecker RdNortheast ROCKY CREEK 56.7 0 1.2 152 8/24/199420030265 Sal Taylor Creek below Rowell CreekNortheast SAL TAYLOR CREEK 68.6 0 1.8 138 3/5/199620030265 Sal Taylor Creek below Rowell CreekNortheast SAL TAYLOR CREEK 35.1 0 1.8 146 10/2/199521030115 Santa Fe Rv- Control A BMPNortheast SANTA FE RIVER 75.7 0 1.5 148 11/6/199820030456 Thomas Ck, Forestry BMP Study Ref SiteBNortheast THOMAS CREEK75.7 0 1.2 118 7/2/199720030456 Thomas Ck, Forestry BMP Study Ref SiteBNortheast THOMAS CREEK82.6 0 1.2 128 2/12/199820030456 Thomas Ck, Forestry BMP Study Ref SiteBNortheast THOMAS CREEK92.4 0 1.2 129 3/14/199620030456 Thomas Ck, Forestry BMP Study Ref SiteBNortheast THOMAS CREEK86.7 0 1.2 159 2/21/199719010072 McClenny WWTP upstreamNortheast TURKEY CREEK 78.2 0 1.6 160 3/22/199920030341 Yellow Water Creek above Sal Taylor CreekNortheast YELLOW WATERCREEK 70.4 0 1.9 107 3/5/199620030341 Yellow Water Creek above Sal Taylor CreekNortheast YELLOW WATERCREEK 66.4 0 1.9 147 10/2/199520030342 Yellow Water Creek below Sal Taylor CreekNortheastYELLOWWATERCREEK 43.5 0 1.8 103 10/2/199520030342 Yellow Water Creek below Sal Taylor CreekNortheastYELLOWWATERCREEK 74.8 0 1.8 166 3/5/199622020070 BIG CYPRESS BRANCH-TIMBER ENERGY FYI CNTRL SITEPanhandle BIG CYPRESS BRANCH 53.6 0 1.3 125 8/9/199933040014 BIG HORSE CREEK S.R.2 OKA.CO.Panhandle BIG HORSE CREEK 67.4 0 1.2 114 7/15/199633040014 BIG HORSE CREEK S.R.2 OKA.CO.Panhandle BIG HORSE CREEK 41.7 0 1.2 119 2/17/199333040014 BIG HORSE CREEK S.R.2 OKA.CO.Panhandle BIG HORSE CREEK 78.4 0 1.2 126 1/30/199533040014 BIG HORSE CREEK S.R.2 OKA.CO.Panhandle BIG HORSE CREEK 75.3 0 1.2 148 2/8/200133040014 BIG HORSE CREEK S.R.2 OKA.CO.Panhandle BIG HORSE CREEK 67.0 0 1.2 154 8/23/199433040014 BIG HORSE CREEK S.R.2 OKA.CO.Panhandle BIG HORSE CREEK 96.3 0 1.2 173 7/15/199821030064 Cedar Head Run, above Ichetucknee RiverPanhandle CEDAR HEADRUN 42.2 0 1.0 100 8/1/199421030064 Cedar Head Run, above Ichetucknee RiverPanhandle CEDAR HEADRUN 40.6 0 1.0 105 2/1/1995105STORETSite DescriptionBioregionWaterbody NameSCIHDGLDITotal # individualscountedSample Date21030064 Cedar Head Run, above Ichetucknee RiverPanhandle CEDAR HEADRUN 46.2 0 1.0 108 2/6/199621030064 Cedar Head Run, above Ichetucknee RiverPanhandle CEDAR HEADRUN 69.4 0 1.0 111 3/15/200031010050 CROOKED CREEK HWY.270 GADSDEN CO.Panhandle CROOKED CREEK 71.1 0 1.1 108 2/13/199531010050 CROOKED CREEK HWY.270 GADSDEN CO.Panhandle CROOKED CREEK 58.5 0 1.1 132 3/25/199331010050 CROOKED CREEK HWY.270 GADSDEN CO.Panhandle CROOKED CREEK 84.8 0 1.1 151 7/9/199632030023 ECONFINA CREEK AT SCOTT RDPanhandle ECONFINA CREEK 68.2 0 1.2 102 2/7/199532030023 ECONFINA CREEK AT SCOTT RDPanhandle ECONFINA CREEK 70.4 0 1.2 106 8/25/199232030023 ECONFINA CREEK AT SCOTT RDPanhandle ECONFINA CREEK 62.3 0 1.2 124 2/6/200132030023 ECONFINA CREEK AT SCOTT RDPanhandle ECONFINA CREEK 81.3 0 1.2 151 6/28/199332030023 ECONFINA CREEK AT SCOTT RDPanhandle ECONFINA CREEK 87.9 0 1.2 162 2/5/199722050077 Econfina River Forestry BMP Study Ref SiteB PanhandleECONFINARIVER 59.8 0 1.1 135 5/13/199833040064 Jacks Branch, Forestry BMP Study Ref SiteB Panhandle JACK'S BRANCH 63.4 0 1.7 111 2/22/199633040064 Jacks Branch, Forestry BMP Study Ref SiteB Panhandle JACK'S BRANCH 63.6 0 1.7 140 2/25/199733040064 Jacks Branch, Forestry BMP Study Ref SiteB Panhandle JACK'S BRANCH 66.9 0 1.7 173 7/1/199832010024 Limestone Ck @ Beckridge RdPanhandle LIMESTONE CREEK 74.5 0 1.3 171 7/14/199832020063 LITTLE CROOKED CREEK S.R.79 BAY CO.Panhandle LTL CROOKED CREEK 78.6 0 1.2 100 1/21/199932020063 LITTLE CROOKED CREEK S.R.79 BAY CO.Panhandle LTL CROOKED CREEK 67.4 0 1.2 113 2/9/199532020063 LITTLE CROOKED CREEK S.R.79 BAY CO.Panhandle LTL CROOKED CREEK 62.3 0 1.2 116 8/30/199432020063 LITTLE CROOKED CREEK S.R.79 BAY CO.Panhandle LTL CROOKED CREEK 71.5 0 1.2 126 6/29/199332020063 LITTLE CROOKED CREEK S.R.79 BAY CO.Panhandle LTL CROOKED CREEK 72.6 0 1.2 129 2/23/199432020063 LITTLE CROOKED CREEK S.R.79 BAY CO.Panhandle LTL CROOKED CREEK 80.1 0 1.2 153 7/16/199632020063 LITTLE CROOKED CREEK S.R.79 BAY CO.Panhandle LTL CROOKED CREEK 52.7 0 1.2 156 3/1/199322030062 MCBRIDE SLOUGH HWY.267 WAKULLA CO.Panhandle MCBRIDE SLOUGH 41.6 0 1.0 112 7/8/199222030062 MCBRIDE SLOUGH HWY.267 WAKULLA CO.Panhandle MCBRIDE SLOUGH 60.2 0 1.0 138 3/21/199422030062 MCBRIDE SLOUGH HWY.267 WAKULLA CO.Panhandle MCBRIDE SLOUGH 57.3 0 1.0 147 2/11/199733010054 MCDAVID CREEK AT SR 99Panhandle MCDAVID CREEK 73.20 1.3 101 1/24/199533010054 MCDAVID CREEK AT SR 99Panhandle MCDAVID CREEK 75.60 1.3 115 2/22/200133010054 MCDAVID CREEK AT SR 99Panhandle MCDAVID CREEK 68.70 1.3 118 2/18/199333010054 MCDAVID CREEK AT SR 99Panhandle MCDAVID CREEK 48.50 1.3 126 8/10/1994106STORETSite DescriptionBioregionWaterbody NameSCIHDGLDITotal # individualscountedSample Date33010054 MCDAVID CREEK AT SR 99Panhandle MCDAVID CREEK 70.20 1.3 127 8/12/199733010054 MCDAVID CREEK AT SR 99Panhandle MCDAVID CREEK 76.90 1.3 141 8/17/199233010054 MCDAVID CREEK AT SR 99Panhandle MCDAVID CREEK 88.70 1.3 173 2/14/199422020049 MULE CREEK S.R.12 LIBERTY CO.Panhandle MULE CREEK49.9 0 1.2 104 3/25/199322020049 MULE CREEK S.R.12 LIBERTY CO.Panhandle MULE CREEK65.7 0 1.2 106 2/13/199522020049 MULE CREEK S.R.12 LIBERTY CO.Panhandle MULE CREEK57.7 0 1.2 112 2/23/199922020049 MULE CREEK S.R.12 LIBERTY CO.Panhandle MULE CREEK58.6 0 1.2 114 7/8/199222020049 MULE CREEK S.R.12 LIBERTY CO.Panhandle MULE CREEK67.8 0 1.2 125 7/9/199622020062 Oklawaha Ck, SR 267, S of QuincyPanhandle OKLAWAHA CREEK52.0 0 1.1 100 9/9/199822020062 Oklawaha Ck, SR 267, S of QuincyPanhandle OKLAWAHA CREEK41.8 0 1.1 101 8/1/199422020062 Oklawaha Ck, SR 267, S of QuincyPanhandle OKLAWAHA CREEK45.2 0 1.1 105 8/12/199722020062 Oklawaha Ck, SR 267, S of QuincyPanhandle OKLAWAHA CREEK48.3 0 1.1 108 2/13/199522020062 Oklawaha Ck, SR 267, S of QuincyPanhandle OKLAWAHA CREEK82.9 0 1.1 147 2/13/199631020073 Pitts Mill Creek above conf with Cypress CkPanhandle PITTS MILL CREEK 68.2 0 1.5 115 7/23/199622020010 QUINCY CR above SR12, ref for Quincy STPFYI Panhandle QUINCYCREEK 45.5 0 1.5 105 11/6/199522030010 St. Marks R approx 4 mi N of Hwy 98 (@ "rapids") Panhandle ST. MARKS RIVER 40.5 0 1.3 100 9/18/200122030010 St. Marks R approx 4 mi N of Hwy 98 (@ "rapids") Panhandle ST. MARKS RIVER 56.0 0 1.3 105 3/18/199722030010 St. Marks R approx 4 mi N of Hwy 98 (@ "rapids") Panhandle ST. MARKS RIVER 46.8 0 1.3 108 8/22/200031010051 SWEETWATER CREEK HWY.270 LIBERTY CO.Panhandle SWEETWATER CREEK 69.9 0 1.3 104 2/13/199631010051 SWEETWATER CREEK HWY.270 LIBERTY CO.Panhandle SWEETWATER CREEK 75.2 0 1.3 110 3/21/199431010051 SWEETWATER CREEK HWY.270 LIBERTY CO.Panhandle SWEETWATER CREEK 97.8 0 1.3 110 7/8/199231010051 SWEETWATER CREEK HWY.270 LIBERTY CO.Panhandle SWEETWATER CREEK 59.7 0 1.3 129 8/1/199431010051 SWEETWATER CREEK HWY.270 LIBERTY CO.Panhandle SWEETWATER CREEK 66.7 0 1.3 132 3/25/199331010051 SWEETWATER CREEK HWY.270 LIBERTY CO.Panhandle SWEETWATER CREEK 70.0 0 1.3 151 2/13/199531010051 SWEETWATER CREEK HWY.270 LIBERTY CO.Panhandle SWEETWATER CREEK 84.1 0 1.3 162 7/20/199331020040 TEN MILE CREEK S.R.73 CALHOUN CO.Panhandle TEN MILE CREEK50.5 0 1.2 107 8/26/199431020040 TEN MILE CREEK S.R.73 CALHOUN CO.Panhandle TEN MILE CREEK72.9 0 1.2 113 8/20/199231020040 TEN MILE CREEK S.R.73 CALHOUN CO.Panhandle TEN MILE CREEK82.0 0 1.2 124 2/7/199531020040 TEN MILE CREEK S.R.73 CALHOUN CO.Panhandle TEN MILE CREEK55.3 0 1.2 125 2/23/1993107STORETSite DescriptionBioregionWaterbody NameSCIHDGLDITotal # individualscountedSample Date31020040 TEN MILE CREEK S.R.73 CALHOUN CO.Panhandle TEN MILE CREEK76.1 0 1.2 126 2/21/199431020040 TEN MILE CREEK S.R.73 CALHOUN CO.Panhandle TEN MILE CREEK62.0 0 1.2 129 6/24/199331020040 TEN MILE CREEK S.R.73 CALHOUN CO.Panhandle TEN MILE CREEK67.7 0 1.2 130 2/4/199731020040 TEN MILE CREEK S.R.73 CALHOUN CO.Panhandle TEN MILE CREEK49.4 0 1.2 141 2/10/199833010065 Rest Area Run Creek below I 10Panhandle UNNAMED CREEK 82.2 0 1.2 113 7/12/199533010065 Rest Area Run Creek below I 10Panhandle UNNAMED CREEK 54.7 0 1.2 147 2/14/199622040038 Unnamed Creek above Monticello WWTPPanhandle UNNAMED CREEK 54.7 0 1.7 153 12/12/199532020066 Wright Ck, ref for Noma STP FYIPanhandle WRIGHTS CREEK 73.9 0 1.7 123 4/29/199320010455 BLACKWATER CREEK AT HWY 44APeninsula BLACKWATER CREEK 59.0 0 1.0 109 3/29/199420010455 BLACKWATER CREEK AT HWY 44APeninsula BLACKWATER CREEK 56.5 0 1.0 111 7/19/199420010455 BLACKWATER CREEK AT HWY 44APeninsula BLACKWATER CREEK 80.9 0 1.0 126 8/26/199620010455 BLACKWATER CREEK AT HWY 44APeninsula BLACKWATER CREEK 54.0 0 1.0 127 9/28/199320010455 BLACKWATER CREEK AT HWY 44APeninsula BLACKWATER CREEK 51.9 0 1.0 128 8/11/199220010455 BLACKWATER CREEK AT HWY 44APeninsula BLACKWATER CREEK 59.7 0 1.0 133 2/8/199320010455 BLACKWATER CREEK AT HWY 44APeninsula BLACKWATER CREEK 33.5 0 1.0 143 2/7/199528020239 Cypress brch above SR 78Peninsula CYPRESS BRANCH 57.6 0 1.3 135 8/20/199728020239 Cypress brch above SR 78Peninsula CYPRESS BRANCH 70.3 0 1.3 142 8/20/199728020237 Cypress Creek 2 mi above SR78Peninsula CYPRESS CREEK 55.7 0 1.2 132 7/21/199928020237 Cypress Creek 2 mi above SR78Peninsula CYPRESS CREEK 70.4 0 1.2 135 8/5/199726010972 DAVENPORT CR AT SR 545 BRIDGEPeninsula DAVENPORT CREEK63.0 0 2.0 104 8/16/199426010972 DAVENPORT CR AT SR 545 BRIDGEPeninsula DAVENPORT CREEK66.3 0 2.0 110 9/30/199726010972 DAVENPORT CR AT SR 545 BRIDGEPeninsula DAVENPORT CREEK73.6 0 2.0 113 2/14/199526010972 DAVENPORT CR AT SR 545 BRIDGEPeninsula DAVENPORT CREEK79.4 0 2.0 167 1/10/200026010593 FISHEATING CREEK ABOVE US 27Peninsula FISHEATING CREEK 60.8 0 1.1 113 3/9/199320030414 GOLD HEAD BR. IN STATE PARKPeninsula GOLD HEAD BRANCH 90.0 0 1.0 100 8/21/199620030414 GOLD HEAD BR. IN STATE PARKPeninsula GOLD HEAD BRANCH 72.2 0 1.0 102 1/24/199520030414 GOLD HEAD BR. IN STATE PARKPeninsula GOLD HEAD BRANCH 80.8 0 1.0 109 7/20/199420030414 GOLD HEAD BR. IN STATE PARKPeninsula GOLD HEAD BRANCH 100.0 0 1.0 111 1/12/200020030414 GOLD HEAD BR. IN STATE PARKPeninsula GOLD HEAD BRANCH 89.2 0 1.0 121 3/10/1998108STORETSite DescriptionBioregionWaterbody NameSCIHDGLDITotal # individualscountedSample Date20030414 GOLD HEAD BR. IN STATE PARKPeninsula GOLD HEAD BRANCH 77.4 0 1.0 123 2/15/199424030142 Hillsborough R upstrm of Southside Br, ZephyrhilPeninsula HILLSBOROUGH RIVER 87.10 1.6 145 8/21/199624030048 Hillsborough River at SR 39Peninsula HILLSBOROUGH RIVER60.8 0 1.5 109 8/21/199625020111 Horse Creek @ SR 72 BridgePeninsula HORSE CREEK 70.4 0 1.8 110 2/17/199925020111 Horse Creek @ SR 72 BridgePeninsula HORSE CREEK 66.60 1.8 147 10/24/199420010454 JUNIPER CREEK AT HIGHWAY 19Peninsula JUNIPER CREEK 55.8 0 1.1 110 1/31/199520010454 JUNIPER CREEK AT HIGHWAY 19Peninsula JUNIPER CREEK 29.6 0 1.1 115 8/3/199320010454 JUNIPER CREEK AT HIGHWAY 19Peninsula JUNIPER CREEK 75.5 0 1.1 146 3/4/199620020004 Little Orange Creek @ Hwy 21Peninsula LITTLE ORANGE CREEK 73.2 0 1.2 133 2/9/199820020004 Little Orange Creek @ Hwy 21Peninsula LITTLE ORANGE CREEK 82.5 0 1.2 134 2/12/199620020004 Little Orange Creek @ Hwy 21Peninsula LITTLE ORANGE CREEK 86.8 0 1.2 138 8/30/199420020004 Little Orange Creek @ Hwy 21Peninsula LITTLE ORANGE CREEK 82.2 0 1.2 139 2/15/199420020004 Little Orange Creek @ Hwy 21Peninsula LITTLE ORANGE CREEK 90.3 0 1.2 150 9/26/199520020004 Little Orange Creek @ Hwy 21Peninsula LITTLE ORANGE CREEK 75.3 0 1.2 161 8/15/200026011019 LIVINGSTON CREEK AT RUCKS DAIRY RD.Peninsula LIVINGSTON CREEK 71.2 0 1.5 118 9/5/200126011019 LIVINGSTON CREEK AT RUCKS DAIRY RD.Peninsula LIVINGSTON CREEK 61.5 0 1.5 156 2/18/199728010223 N FK LOXAHATCHEE RIVER REF BIO STATIONPeninsula LOXAHATCHEE RIVER34.3 0 1.0 106 9/21/199828010223 N FK LOXAHATCHEE RIVER REF BIO STATIONPeninsula LOXAHATCHEE RIVER49.2 0 1.0 115 9/22/199528010223 N FK LOXAHATCHEE RIVER REF BIO STATIONPeninsula LOXAHATCHEE RIVER59.9 0 1.0 124 10/7/199928010223 N FK LOXAHATCHEE RIVER REF BIO STATIONPeninsula LOXAHATCHEE RIVER33.9 0 1.0 134 8/20/199228010223 N FK LOXAHATCHEE RIVER REF BIO STATIONPeninsula LOXAHATCHEE RIVER54.8 0 1.0 146 8/25/199428010223 N FK LOXAHATCHEE RIVER REF BIO STATIONPeninsula LOXAHATCHEE RIVER33.8 0 1.0 158 3/22/199928010223 N FK LOXAHATCHEE RIVER REF BIO STATIONPeninsula LOXAHATCHEE RIVER44.7 0 1.0 161 2/20/199624010002 Manatee R 20 m below SR64 bridge (TP1)Peninsula MANATEE RIVER60.3 0 1.3 109 3/17/199324010002 Manatee R 20 m below SR64 bridge (TP1)Peninsula MANATEE RIVER76.3 0 1.3 126 2/2/199424010002 Manatee R 20 m below SR64 bridge (TP1)Peninsula MANATEE RIVER77.2 0 1.3 129 7/17/199624010002 Manatee R 20 m below SR64 bridge (TP1)Peninsula MANATEE RIVER96.7 0 1.3 145 2/2/199424010002 Manatee R 20 m below SR64 bridge (TP1)Peninsula MANATEE RIVER80.2 0 1.3 172 11/21/200020020012 OKLAWAHA RIVER AT SR 316Peninsula OKLAWAHA RIVER 77.9 0 1.0 101 8/29/1995109STORETSite DescriptionBioregionWaterbody NameSCIHDGLDITotal # individualscountedSample Date20020012 OKLAWAHA RIVER AT SR 316Peninsula OKLAWAHA RIVER 65.2 0 1.0 117 3/20/199520020012 OKLAWAHA RIVER AT SR 316Peninsula OKLAWAHA RIVER 67.2 0 1.0 117 8/10/199320020012 OKLAWAHA RIVER AT SR 316Peninsula OKLAWAHA RIVER 68.1 0 1.0 137 2/15/200020020012 OKLAWAHA RIVER AT SR 316Peninsula OKLAWAHA RIVER 80.6 0 1.0 151 8/11/199820020012 OKLAWAHA RIVER AT SR 316Peninsula OKLAWAHA RIVER 49.6 0 1.0 153 2/8/199320020404 ORANGE CREEK 50 YDS. UP FROM HWY. 21Peninsula ORANGE CREEK 93.1 0 1.4 105 8/17/199320020404 ORANGE CREEK 50 YDS. UP FROM HWY. 21Peninsula ORANGE CREEK 94.5 0 1.4 110 9/22/199220020404 ORANGE CREEK 50 YDS. UP FROM HWY. 21Peninsula ORANGE CREEK 92.1 0 1.4 111 8/30/199420020404 ORANGE CREEK 50 YDS. UP FROM HWY. 21Peninsula ORANGE CREEK 83.1 0 1.4 118 3/16/199320020404 ORANGE CREEK 50 YDS. UP FROM HWY. 21Peninsula ORANGE CREEK 91.8 0 1.4 135 8/15/200020020404 ORANGE CREEK 50 YDS. UP FROM HWY. 21Peninsula ORANGE CREEK 94.3 0 1.4 139 2/15/199420020404 ORANGE CREEK 50 YDS. UP FROM HWY. 21Peninsula ORANGE CREEK 72.0 0 1.4 144 8/15/200020020404 ORANGE CREEK 50 YDS. UP FROM HWY. 21Peninsula ORANGE CREEK 96.3 0 1.4 168 2/28/199520030253 RICECR #3@BR SR #3/250yds above Bardine Peninsula RICE CREEK67.1 0 1.1 125 5/11/199920030253 RICECR #3@BR SR #3/250yds above Bardine Peninsula RICE CREEK24.0 0 1.1 136 3/16/199820020317 SILVER RUN AT CONFLUENCE WITH BOAT RAMPCANAL Peninsula SILVERRUN 34.801.3 133 9/1/199220020317 SILVER RUN AT CONFLUENCE WITH BOAT RAMPCANAL Peninsula SILVERRUN 42.301.3 136 8/10/199320020317 SILVER RUN AT CONFLUENCE WITH BOAT RAMPCANAL Peninsula SILVERRUN 53.501.3 142 9/4/200120020317 SILVER RUN AT CONFLUENCE WITH BOAT RAMPCANAL Peninsula SILVERRUN 57.601.3 142 2/8/199420020317 SILVER RUN AT CONFLUENCE WITH BOAT RAMPCANAL Peninsula SILVERRUN 54.201.3 150 3/20/199520020317 SILVER RUN AT CONFLUENCE WITH BOAT RAMPCANAL Peninsula SILVERRUN 76.001.3 166 9/16/199628020221 TELEGRAPH CREEK AT BABCOCK RANCHPeninsula TELEGRAPH CREEK 84.4 0 1.0 107 9/16/199628020221 TELEGRAPH CREEK AT BABCOCK RANCHPeninsula TELEGRAPH CREEK 80.8 0 1.0 146 7/27/199426011020 TIGER CREEK AT WALKING WATERS RD.Peninsula TIGER CREEK74.4 0 1.0 109 7/14/199226011020 TIGER CREEK AT WALKING WATERS RD.Peninsula TIGER CREEK76.5 0 1.0 111 2/18/199726011020 TIGER CREEK AT WALKING WATERS RD.Peninsula TIGER CREEK75.7 0 1.0 142 2/21/199526011020 TIGER CREEK AT WALKING WATERS RD.Peninsula TIGER CREEK73.4 0 1.0 161 7/20/199923010464 Withlacoochee R @ county park off Auton Rd (TP3) Peninsula WITHLACOOCHEERIVER 43.4 0 1.2 100 9/28/199423010464 Withlacoochee R @ county park off Auton Rd (TP3) Peninsula WITHLACOOCHEERIVER 46.9 0 1.2 100 9/2/1993110STORETSite DescriptionBioregionWaterbody NameSCIHDGLDITotal # individualscountedSample Date23010464 Withlacoochee R @ county park off Auton Rd (TP3) Peninsula WITHLACOOCHEERIVER 67.8 0 1.2 120 3/9/199923010464 Withlacoochee R @ county park off Auton Rd (TP3) Peninsula WITHLACOOCHEERIVER 50.6 0 1.2 122 7/26/199923010464 Withlacoochee R @ county park off Auton Rd (TP3) Peninsula WITHLACOOCHEERIVER 49.6 0 1.2 125 3/11/199323010464 Withlacoochee R @ county park off Auton Rd (TP3) Peninsula WITHLACOOCHEERIVER 66.1 0 1.2 136 2/13/199623010446 WITHLACOOCHEE RIVERPeninsula WITHLACOOCHEE RIVER 50.0 0 1.6 147 7/28/1999