## Development and Testing of Biomonitoring Tools

for Macroinvertebrates in Florida Streams

Leska S. Fore Statistical Design

136 NW 40 th St. Seattle, WA 98107
leska@seanet.com



## Final Report
February 2004

Prepared for:

Russel Frydenborg & Ellen McCarron Florida Department of Environmental Protection 2600 Blair Stone Rd. Tallahassee, FL 32399-2400ii TABLE OF CONTENTS

Table of Contents .......................................................................................................... ii

List of tables................................................................................................................. . iv

List of Figures............................................................................................................... i v

Abstract....................................................................................................................... ... 1

Introduction................................................................................................................... 2

Background................................................................................................................. 3

Methods........................................................................................................................ .. 3

Study area.................................................................................................................... 3

Site selection and data sets.......................................................................................... 5

Quantifying human disturbance.................................................................................. 7

Stream macroinvertebrate sampling ......................................................................... 12

Metric development and testing................................................................................ 13

Index development and testing ................................................................................. 18

Statistical model assumptions................................................................................... 19

Results ........................................................................................................................ .. 21

Human disturbance gradient ..................................................................................... 21

Metric selection......................................................................................................... 21

Multimetric index construction and evaluation ........................................................ 25

Statistical precision of indexes.................................................................................. 36

Discussion..................................................................................................................... 49iii Human disturbance gradient ..................................................................................... 49

Biological indicators ................................................................................................. 50

Sources of variance................................................................................................... 51

Conclusions.................................................................................................................. 54

Recommendations....................................................................................................... 55

Acknowledgments ....................................................................................................... 57

References.................................................................................................................... 5 8

Appendix A. List of long-lived taxa

Appendix B. List of sensitive taxa

Appendix C. List of very tolerant taxa

Appendix D. Regression results and ANOVA outputiv LIST OF TABLES

Table 1. Description of data sets......................................................................................... 6 Table 2. Scoring for hydrologic index. ............................................................................... 8

Table 3. Land use and coefficients for LDI. ..................................................................... 10

Table 4. Correlation of NH 3 , hydrologic index, habitat index, LDI, watershed size, HDG, and SCI....................................................................................................................... ...... 11 Table 5. Scoring rules for the human disturbance gradient (HDG).................................. 12

Table 6. Candidate metrics and correlation with HDG..................................................... 14

Table 7. Correlation of BioRecon metrics with HDG and SCI ........................................ 27 Table 8. SCI metric scoring rules ..................................................................................... 28 Table 9. BioRecon metric scoring rules............................................................................ 28 Table 10. Average metric values for ranges of SCI values............................................... 40 Table 11. Description of metric values for ranges of SCI ................................................ 41 Table 12. Seasonal comparison of SCI and metrics ......................................................... 44 Table 13. Categorical descriptions for BioRecon index values........................................ 46 Table 14. Changes in BioRecon and reason for change ................................................... 48

LIST OF FIGURES

Figure 1. EPT and total taxa vs. HDG for two rounds of testing...................................... 22 Figure 2. Six of 10 SCI metrics plotted against HDG ...................................................... 23 Figure 3. Four of 10 SCI metr ics plotted against HDG .................................................... 26 Figure 4. SCI plotted against HDG................................................................................... 30 Figure 5. Old and new BioRecon index plotted against HDG.......................................... 31 Figure 6. SCI vs. HDG by region ..................................................................................... 32 Figure 7. SCI vs. catchment area by HDG........................................................................ 33 Figure 8. SCI vs. HDG by 2-year period .......................................................................... 34 Figure 9. Independent verification of SCI vs. HDG ......................................................... 35 Figure 10. SCI values for repeat visits to the same sites .................................................. 37 Figure 11. SCI variance components................................................................................ 38 Figure 12. Variance components of SCI and metrics ....................................................... 42 Figure 13. Summer vs. winter SCI by region ................................................................... 43 Figure 14. Variance components of BioRecon index and metrics.................................... 45 Figure 15. Range of BioRecon index values by site......................................................... 47ABSTRACT

Florida DEP assesses the chemical, physical and biological cond ition of hundreds of stream sites each year. This study used meas ures of hydrologic condition, riparian and channel habitat condition, water chemistry, a nd intensity of human land use to define a gradient of human disturbance for stream sites. I evaluated the sensi tivity and tolerance of
over 1000 stream macroinvertebrate taxa usi ng the human disturban ce gradient (HDG). I
tested for correlation between the HDG and 36 biological measures of the stream macroinvertebrate assemblage (metrics) a nd selected the 10 most highly correlated metrics within six categories of biologi cal organization. The six categories (and their selected metrics) were taxonomic richness (t otal number of taxa , number of Trichoptera taxa, and number of Ephemeroptera taxa); f eeding group (percentage filterer individuals); voltinism (long-lived taxa richness); habit (cli nger taxa richness); community structure (percentage dominance of the most abundant taxon and percentage Tanytarsini midges); and sensitivity and tolerance (sensitive ta xa richness and percentage very tolerant individuals).

Metrics were combined into an ove rall stream condition index (SCI) by transforming metric values in to unit-less scores and summ ing the scores. The SCI was highly correlated with HDG for an independent data set (Spearman’s r = –0.81, p << 0.001). SCI was independent of watershed size and geographic region (panhandle, peninsula and northeast). Across 10 years of sampling, the index showed a similar response to the HDG. SCI was somewhat highe r for winter vs. summer samples (3.5%). A large portion of the variability of SCI was due to subsampling in the laboratory (49%).
Confidence intervals based on estimates of SCI variance defined 3.7 categories of biological condition that the SC I could detect assuming a single sample. For two site samples, the SCI could detect five categories of biological condition.

Biological metrics were also tested fo r a second stream macroinvertebrate sampling protocol (BioRecon) based on sor ting of invertebrates in the field and taxonomic identification in the laboratory. Of the 10 SCI metrics, six taxa richness metrics were tested using BioRecon data; a ll were highly correlat ed with both SCI and
HDG. From these metrics the BioRecon index wa s calculated as the sum of scores for the
six metrics. The BioRecon index could detect 2.5 categories of bi ological condition for one sample and 3.5 categories for two samples.2 INTRODUCTION

Florida, along with a handful of other stat es, lead the nation in the development and implementation of water protection policies based on biological criteria (McCarron and Frydenborg, 1997; EPA, 2002b). Under the Clean Wa ter Act, states are required to define designated uses for specific water bodies a nd develop criteria to protect them (Karr, 1991; Ransel, 1995). Over time, the emphasis has shifted from primarily chemical monitoring of pollutants to include the di rect measurement of the condition of the biological assemblage (Yoder and Rankin, 1998).

Florida classifies its surface waters acco rding to their designated uses. The five categories are ranked in order of the level of protection that they require: Class I – drinking water; Class II – shellfish propaga tion or harvesting (primarily for coastal waters); Class III – recreation and propaga tion and maintenance of a healthy, wellbalanced population of fish and wildlife; Class IV – agri cultural water; and Class V – navigation and industrial use. This document describes the development and testing of biological monitoring tools for macroinvertebra te stream samples for the assessment of freshwater streams that are applicable to Class I, III, IV, and V waters.

The U.S. Environmental Protection Ag ency (EPA) has developed numerous guidance documents to support biological a ssessment (EPA, 1998; Barbour et al., 1999; Bowman et al., 2000; Jackson et al., 2000; EPA, 2001; EPA, 2002a; EPA, 2003a, b, c; Fore, 2003). These documents typically emphasi ze multimetric indexes to measure the biological condition of surface waters (Karr 1981; Karr et al., 1986; Barbour et al., 1999).
EPA’s recent Draft Report on the Environment 2003 specifically recognizes multimetric indexes as an “approach that addresses the need to measure critical multiple variables” (EPA, 2003a). Multimetric indexes are composed of biological measures, called metrics, that show a consistent response to human disturbance (Karr and Chu, 1999). Metrics are converted to unit-less 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, Eu rope 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 ot her techniques have been developed (Hawkins et al., 2000; EPA, 2003b, c).3 Background

Barbour et al. (1996) developed multimetric inde xes for Florida that have been used since the early 1990’s. They tested metrics by comp aring values at sites with minimal human influence (reference sites) and sites with known disturbance (test sites). The authors identified three unique geographic regions based on the presence of shared taxa and defined metric expectations for each region.

In contrast, metrics for this study were se lected on the basis of their correlation with a gradient of human disturbance (Bry ce et al., 1999). The sens itivity and tolerance
of over 1000 taxa were also evaluated individu ally using the human disturbance gradient (HDG). I used confidence intervals to define the number of cate gories of biological
condition that the indexes c ould reliably detect. These categories support decisions related to listing stream sites as impaired and prioritizing sites for additional evaluation as part of the TMDL (Total Maximum Daily Load) process (Karr a nd Yoder, 2004). These statistically robust categories may also be us ed to monitor degradation of excellent or outstanding waters.

From the original analysis by Barbour et al. (1996), two multimetric indexes were developed for Florida streams based on differe nt sampling and identification protocols. The stream condition index (SCI) was de rived from laborator y processing and identification of samples while the BioRec on (biological reconnaissance) index was based on field sorting and laboratory id entification. This study develops new
bioassessment tools for both sa mpling protocols. For simplicity, the names of the indexes have been retained for this new analysis and the previous indexes are referred to as the
old SCI and the old BioRecon index within this document.

METHODS

Study area

Portions of Florida have been repeatedly inundated by seawater in recent geologic history; as a consequence, elevation and organism r ecruitment from adjacent, noninundated areas play an important role in the present-day geographic distribution of
macroinvertebrates. Florida can be divided into the northern panhandle, the southern peninsula, and a transition region known as the northeast. The middle and lower Suwannee basin provides a natural demarca tion between the panhandle and peninsula, with the northeast regi on straddling the upper Suwa nnee northeast of the Cody escarpment (White, 1970).4 In the panhandle, rivers typically flow from north to south and the elevation of headwater streams typically ex ceed 200 to 250 ft. Recruitment of stream organisms (over geologic time) from upstream, higher elevati on areas (Georgia and Alabama) makes the panhandle richer in freshwater stream taxa than other parts of the state (FNAI, 1990). Vegetation communities in the panhandle generally consist of mixed pine/oak/hickory forests ( Pinus spp., Quercus spp., Carya spp.), longleaf pine forests ( Pinus palustris ), hardwood forests with beech/magnolia climax community ( Fagus grandiflora / Magnolia grandiflora ), and swamp hardwood forests of cypress ( Taxodium spp.) or tupelo ( Nyssa spp.), interspersed by a mosaic of pine plantations, cropland (e.g., corn, soy beans, peanuts), and pasture (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 elevati on of the central ridge is approximately 150 to 200 ft. Rivers west of the ridge flow into the Gulf of Mexico. Of the two large drainages east of the ridge, the Kissimmee River flows sout h into Lake Okeechobee and the St. Johns
River flows north to the Atlantic. Streams in the peninsula tend to have lower water velocity, flowing through wetlands and pine flatwoods (White, 1958). The peninsula has
fewer taxa of freshwater invertebrates due to lack of upstream recruitment sources that did not experience marine inundation. Plec optera, for example, are common in the panhandle but extremely rare in the peninsul a, probably due to a combination of poor recruitment, warmer temperatures, and lowe r water velocities. Pe ninsular vegetation communities on the ridge consist of longleaf pine/turkey oak forests ( Pinus palustris/Quercus laevis ), on flat areas are slash pine ( Pinus eliottii )or loblolly pine ( Pinus teada ) with palmetto/ga llberry understory ( Serenoa repens/Ilex glabra ), and in depressional areas are marsh/wet prairies (m aidencane, 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, tomatoes), and urban areas (Fernald and Purdum, 1992). Dense population centers are located at Tampa and Orlando.

The northeast region includes portions of the Okeefenokee Swamp, parts of the upper Suwannee drainage, the Black Creek drai nage, and the Sea Island flatwoods. From the perspective of stream invertebrates, this region tends to be intermediate between the panhandle and peninsula regions. With the exception of the Black Creek basin with headwaters approximately 200 ft above sea level, other streams in this area tend to be low relief, wetland systems (Snell and Anders on, 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 ( Gordonia5 lasianthus ), pine flatwoods, and marsh (FNAI, 1990). Jacksonville is the only major population center.

Barbour et al. (1996) used macroinvertebrate stream sa mples collected from sites
with minimal human influence to evaluate the influence of geographic locations on species assemblages. They found that the pa nhandle, peninsula, and northeast regions, which are primarily defined according to draina ge patterns, were better predictors of species assemblages than were sub-ec oregions described by Griffith et al

. (1994). Regional differences translate into expecta tions of higher taxono mic diversity in the panhandle relative to the penins ula, with the northeast region representing a transitional

area.

Site selection and data sets

The Florida Department of Environmental Pr otection (FDEP) has collected thousands of macroinvertebrate samples from stream site s throughout the state. Many sites have been visited repeatedly. Sites and visi ts were selected from this la rge data base using different criteria for different testing situations (Table 1). For example, when testing for metric correlation with disturbance, a range of site conditions was needed; to evaluate variability, sites with many repeat visits were needed; to evaluate laboratory subsampling, large samples were needed. Some data sets contained data from the same sites, other sites were selected to provide an independent test. For some testing situations, data from site visits were averaged by s ites, for example, when one variable in the analysis had only one value for each site, such as HDG.

The relationship between human dist urbance and biological condition was evaluated in several ways. To develop th e human disturbance gr adient (HDG), FDEP biologists selected sites from their regions th at represented the broa dest possible range of human influence. First, to insure that th e relationships observed between human land use and biological metrics were consistent and not specific to a particular set of sites, two independent sets of sites were tested. These data were then combined to obtain 223 sites
to test for metric correlation with HDG. An additional, independent set of 23 sites was
used to test correlation between SCI and the HDG.

Additional data sets 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 ev aluate the influence of field sorting vs. laboratory sorting. To evaluate SCI variabilit y, data from 62 sites with 2–8 visits were
selected. About half the sites (32) had repeat visits on the same day. Most of the other repeat visits were dur ing different years, and a few we re within the same year. Thus,6 these data could not be used to estimate the influence of seasons on variability. Instead, additional data were compiled to test for seas onal (winter vs. summer) differences in SCI. To test the influence of subsampling during laboratory processing for the SCI protocol, three subsamples were selected from 59 samples collected from 54 sites (5 sites had two duplicate samples each).

To test metrics and evaluate their va riability for the BioRecon protocol, two
additional data sets were used. The fi rst data set included 116 sites with macroinvertebrate samples collected acco rding to both the BioRecon and the SCI sampling protocol. I tested BioRecon metric s for correlation with SCI and HDG. Most
sites had at least two visits and several had 3–6 visits. Of the 116 sites, 53 had values calculated for HDG because they were used initi ally to develop the HDG. To evaluate the variability of the BioRecon metr ics and index, 128 sites were selected with two to seven visits per site.

Table 1. Description of various data sets, numbe r 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 one data set with the exception of the 23 independent sites used to verify correlation between 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 75–175 individuals included) 176 420 0

Test sensitive & tolera nt taxa vs. HDG 226 632 0 Verify SCI with independent data 23 23 0 SCI variability 62 220 0

Seasonal variation of SCI 78 578 0 Laboratory subsampling variability for SCI 54 59 177 (3 each)

Calibrate BioRecon metrics with SCI 116 165 0
Test BioRecon metrics vs. HDG 53 0 BioRecon variability 128 293 07 The data set used to test SCI and its component metrics against the human disturbance gradient was large; therefore, simple scatter plots of data against the HDG would be difficult to interpret with the many data points and overstrikes. Consequently, I used box plots to display the range of values as sociated with different values of HDG. In all figures, the box defined the 25 th and 75 th percentiles, the whiskers were the non-outlier 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. FDEP is currently modifying laboratory protocols to more often match the target of 100 individuals ; therefore, several graphs include data from samples with 75-175 individuals. This range is broader than the current target of 95–110 individuals, but minimi zed the loss of sites from the data set. Additional analysis (not shown) indicated that the ranges of metric values were similar for samples with <125 individuals and <175 individuals. For the BioRecon testing, the
number of individuals identified was not reco rded. All site visits were used within a particular analysis unless sp ecifically stated otherwise.

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). The five factors are 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, ch anges in water volume or flow timing can result in flooding or dry channels. Second, stream macroinvertebra tes depend on a variety of physical habitat types for food, shelter and reproduction. Grea ter diversity of substrate size, types of vegetation, a nd bank architecture translate into a greater variety of organisms. Third, water quality may be ev aluated in terms of turbidity, conductivity, nutrient concentrations, or the presence of other chemicals or metals. Fourth, energy
source describes the seasonal ava ilability, type of organic mate rial, and size and shape of material available as food. Fifth, introduction of exotic species or di sease and harvest of fish and shellfish for sport, commercial, and subsistence 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 scori ng system based on their knowledge of water removal, patterns of drought, and hydrographs for the sites (Table 2). To measure habitat condition, FDEP biologists routinely calculate a habitat condition index whenever stream macroinvertebrate samples are collected. Th e index evaluates substrate condition and availability, water velocity, habitat smothering (e.g., by sand and silt), channelization, bank stability, and the width and cond ition of the riparian vegetation.8 Table 2. Hydrologic condition of stream site , scoring range for hydrologic index, and description of human influences. Assignment of high or low values within each condition class were assigned at the disc retion of the biologist based on the extent of disturbance. These changes were associated with human disturbances, not natu ral events such as hurricanes or extreme droughts.

Condition Score Description

Excellent 1–2 Flow regime as naturall y occurs (slow and fairly continual release of water after rains) , few impervious surfaces; high connectivity with ground wa ter and surface features delivering water (e.g., sandhills , wetlands; no ditches or berms)

Good 3–4 Flow regime minimally cha nged; some water withdrawals; some wetland drainage, some impervious surfaces, some ditching

Moderate 5–6 Flow regime moderately a ltered; hydrograph moderately flashy (scouring after rain events with subsequent reductions in flow), groundwater pumping evident; much wetland drainage, topographic alterations reduce natural water input; more impervi ous surfaces, dams/control structures change normal water delivery schedule

Poor 7–8 Flow regime highly alte red; hydrograph very flashy (scouring after rain events w ith subsequent reductions in flow, leading to stagnant or dry conditions, re lated to large amounts of impervious surfaces and/or ditching); water withdrawals and impoundments or control structures severely alter flows, large amounts of impervious surfaces

Very poor 9–10 Flow regime entirely huma n controlled; hydrograph very flashy (scouring after rain events with subsequent reductions in flow, leading to stagnant or dry conditions, related to impervious surface s and ditching); intensity of water withdrawals and impound ments fundamentally alter the nature of the ecosystem9 FDEP biologists also routinely collect m easurements of turbidity, temperature, dissolved oxygen, conductivity and nutrient concentrations. Total phosphate (TP), ammonia (NH 3 ), total Kjeldahl nitrogen (T KN), and nitrites/nitrates (NO x ) were
measured to summarize nutrients. I selected NH 3 to summarize water quality because it was the most consistently associated with the other water quality measures and had the most complete record of data. TP had sim ilar high correlations and complete data, but may be more closely associated with fe rtilizer and farming practices while NH 3 may be a more general indicator of bot h urbanization and agriculture.

A measure of the type and quantity of en ergy available to organisms living within the stream was not available. However, Br own and colleagues have developed an index to estimate the intensity of human land us e based on nonrenewable energy flow (Brown et al., 1998; Brown and Vivas, in review ). The landscape development intensity (LDI) index was calculated as the percentage area wi thin 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 

### LU LDI LDI % *

. Where,
LDI i = the nonrenewable ener gy land use for land use i , and
%LU i = 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 us e into standardized units, then averaged the values by land use type a nd standardized to a per unit area. Only nonrenewable energies were used in calculations and included el ectricity, fuels, fertilizers, pesticides, and water (both public wa ter supply and irrigation).

Land use was derived from aerial photo s manipulated as layers in a GIS computer program. LDI was calculated two ways. First, a buffer area of 100 m on each side of the stream and 10 km upstream of the sampling point was used (LDI_BF). Because the definition of the buffer regi on was somewhat arbitrary, LDI was also calculated for the entire upstream catchment (LDI_WS) in order to determine which spatial scale was a better pred ictor of site condition. I sele cted LDI_BF to include in HDG because it was slightly more highly correlated with NH 3 , the hydrologic index, and the habitat index (Table 4).10 Table 3. Description of land use and the coe fficient value used to calculate the LDI. Higher values indicate great er intensity of human land use (Brown and Vivas, in review ).

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 Pastur e (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 (Med-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 Multi-family residential 8.66 High intensity commercial 9.18 High Intensity Multi-family residential 9.19 Low Intensity Central Business District 9.42 High Intensity Central Business District 10.0011 To define the human disturbance gradient (HDG), I converted the four measures
of human disturbance to unit-less scores a nd summed the scores to create HDG values for each stream site. Three of the measures had values of 0, 1, or 2 indicating low, moderate or high levels of human influence. One m easure, hydrologic condition, had an additional
value of 4 available to indicate extreme levels of disturbance (Table 5). Thus, values for the HDG ranged from 0 (indicating minimal huma n disturbance) to 9 (indicating extreme disturbance). The scores for th e HDG were derived from a gra phical analysis of EPT taxa richness plotted against each measure of disturbance. I identif ied 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 si tes had values for other measures indicating
minimal human disturbance. I a ssumed for these sites that NH 3 was < 0.1 mg/L.

Table 4. Correlation table of NH 3 (mg/L), hydrologic index, habitat index, LDI for the buffer, LDI for the upstream watershed, natura l log of watershed size, HDG, the current SCI, and the 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

N H 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 0.8612 Stream macroinvertebrate sampling

FDEP uses two protocols to collect inverteb rate samples from streams. The protocols differ in the number of dip ne t sweeps collected, the location of sample sorting (field vs. laboratory), and the final i ndex used for assessment. Th e BioRecon sampling protocol was designed to be a quick field assessment that could yield a same-day measure of stream site condition. The SCI sampling protoc ol was meant to be more precise. Both protocols use a D-frame dip ne t with a 0.25 m opening and a 600 μ m-mesh net. Both protocols distribute the sweep s among the best available s ubstrates on an approximately equal basis. Florida streams typically have a sandy substr ate that supports very few invertebrates. Therefore, the habitat sampled for macroinvert ebrates includes logs, roots and undercut banks, partially decayed leaf p acks, aquatic vegetation, and rocks or large cobble. During SCI sampling, if four productive substrates are present (snags, roots, vegetation and leaf packs), each substrate w ould be sampled four times, with sand being sampled four times, for a total of 20 sweeps.

In the laboratory, the SCI sample is di vided by spreading on to gridded trays. Squares are randomly selected and all the material within a square is sorted.
Macroinvertebrates are identified to the lowe st practical taxonomic level. Squares are selected until the target sample size of 100 i ndividuals is reached. To avoid bias, the final square selected is always finished even if it contains more than 100 individuals. The
number of taxa and the relative abundance of each taxon are derive d from these data.

Table 5. Scoring rules for measures used to calculate the human disturbance gradient (HDG). HDG is the sum of the scores for each site.

Measure 0 1 2 3

NH 3 <0.1 0.1–2 >2

Habitat index >65 50–65 <50

Hydrologic index <6 6–7 8–9 10

LDI <2 2-3.5 >3.513 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 th rough all the material for ever y unique taxon present. Each taxon is preliminarily identified in the field and representative specimens of each taxon are preserved and returned to the laboratory for final id entification under a dissecting microscope. From this field-sort protocol, th e number of taxa pres ent at a site can be measured, but their relative abundance (% composition) cannot.

Metric development and testing

Several authors have describe d relevant candidate metrics for stream macroinvertebrate assemblages (Barbour et al. 1996 and 1999; Ke rans and Karr, 1994; and Klemm et al., 2002). For this study, 36 candidate metrics re lated to taxonomic ric hness, feeding group, voltinism, habit, community structure, and tolerance or sensitivity were evaluated for their association with HDG (Table 6). Within each category, I selected metrics with the highest correlation with HDG (Spearman’s r ). I used one-sided tests to calculate p- values because if a metric indicated better biologi cal condition in response to increased human disturbance, that rela tionship would not be biologically significant even if it were statistically significant. All metrics were also evaluated graphically to ensure that they were reliable indicators of disturbance. I 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 unavail able for metals, pestic ides or other toxics. In contrast, I considered an inconsistent re sponse at more highly disturbed sites to be unacceptable.

Taxonomic richness. Richness was calculated as the number of unique taxa found within a particular group such as the Ephemeroptera, Plecoptera, Trichoptera, or their combination (EPT).

Feeding group. Trophic designations followed Merritt and Cummins (1996); some designations have been modified by FDEP biologists and others to reflect natural histories in Florida (Merritt et al., 1996). All feeding group me trics were calculated as the number of individuals of a particular group divided by the total number of individuals in the sample times 100 (percentage).14 Table 6. Candidate metric, correlation with the human disturbance gradient (HDG), and whether the metric was included in the fi nal SCI, BioRecon, or original SCI index. Correlation was tested for 223 sites with repeat visits to each site averaged before statistical testing. Hyperabundance was tested without averaging, n=577. All correlations shown were significant at p < 0.01 (Spearman’s r, one-sided test).

Metric HDG SCI BioRecon Old SCI

Taxonomic richness
EPT –0.73 yes Trichoptera –0.66 yes yes Ephemeroptera –0.66 yes yes Total taxa –0.52 yes yes yes Diptera –0.44
Chironomidae –0.40 yes Plecoptera –0.37 Oligochaeta 0.32
Non-insect

— 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 yes15 Metric HDG SCI BioRecon Old SCI

% Plecoptera –0.37 % Oligochaeta 0.32 % Non-insect 0.32 % Chironomidae

— % Diptera

— yes Sensitivity/tolerance
Sensitive taxa –0.75 yes yes Florida index (sensitive) –0.71 yes % Very tolerant 0.70 yes Very tolerant taxa 0.61

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 is called voltinism and may be less than a year, approximately one year, or greater than one year. FDEP biologists used publis hed sources to identifiy taxa that require more than one year to complete their life cycles (Appe ndix A; Brigham et al., 1982; Thompson, 1984; Thorp, 1991; Pescador et al., 1995; Corbet , 1999; Pescador et al., 2000; Smith, 2001; Rasmussen and Pescador, 2002). These taxa requ ire consistent flows and other favorable conditions throughout the year to complete th eir life cycles. Data on generation length were not available for all Florida taxa; th erefore, some long-lived taxa may not be
included in the current list.

Included in the list were all taxa in the Cordulegastridae and selected taxa in the families Aeshnidae, Gomphidae and Libellulid ae (Odonata); the family Leuctridae and selected genera in the Perlidae and Pterona rcidae (Plecoptera); the family Corydalidae (Megaloptera); and the genera Ceraclea, Pycnopsyche, Molanna, and Rhyacophila (Trichoptera). Several Coleoptera taxa (beetles) also require mo re than a year to complete their life cycles, but were not included becau se the FDEP database does not currently distinguish between larvae (which cannot travel to find water) and adults (which can). Non-insects included in the long-lived list we re all taxa in the Decapoda; the genera
Fossaria and Pomacea (Gastropoda); and the families Unionidae and Corbiculidae (Pelecypoda).

Habit. Other studies have noted a strong correlation between the taxonomic richness of clingers and stream condition (Fore et al., 2001; Karr and Morishita Rossano, 2001; Mebane, 2002). Clingers ha ve morphological and behavior al adaptations that allow16 them to cling to substrate (Merritt and Cummins, 1996). Channel dredging and removal of riparian vegetation eliminates the habitat prefe rred by clingers.

Community structure. Percentage dominance was cal culated as the number of
individuals in the most abundant taxon divided by the total nu mber of individuals in the sample. ‘Hyperabundance’ represented a measure of the density of stream macroinvertebrates present at a site. Sites with high organic enrichment (e.g., sewage effluent) may have a high dens ity of invertebrates due to th e increased source of food for many species. The actual number of individua ls present in the total sample was not recorded; however, the number of grids in th e sample tray that were searched to best approximate the target number of 100 indi viduals was recorded. I used the number of grid cells searched as a measure of the density of invertebrates present, or ‘hyperabundance.’ Other measures of community structure were based on the number of individuals within specific taxonomic groups divided by the total number of individuals.

Sensitivity and tolerance. Beck (1954) developed a list of sensitive taxa for
Florida that was used to calculate a biotic index which was later modified to be the Florida index. To calculate the Florida index (w hich is more accurately termed a metric), very sensitive taxa are given a score of 2 a nd sensitive taxa a score of 1. The scores are
summed to calculate this metric.

I used the HDG to evaluate the sensitivit y and tolerance of 1195 taxa names. The
taxa names included taxa that were not uni que because the data base included synonyms for the same taxa as well as specimens identi fied to genus and species for the same genus. Because many sites had multiple visits and beca use less disturbed (reference) sites tended to have a greater number of vis its, I summarized data for each taxon by site to avoid bias associated with greater sampling effort at less disturbed sites. I c ounted a taxon as present
at a site if it was found for >= 50% of the site visits. I divided the 226 sites into two groups and, for simplicity, refer to these gr oups as ‘good’ and ‘poor’ for statistical testing. Good sites were defined as those with moderate levels of disturbance for no more than two measures (e.g., LDI or NH 3 ) or high disturbance for no more than one measure (e.g., habitat condition). This translated into HDG <=2 and included 141 sites. The remaining 85 sites were classified as ‘poor .’ Thus, the two groups might be more accurately labeled ‘better’ a nd ‘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 data set.

I used contingency table analysis to determine what percentage of taxon occurrences in the good vs. poor range of HDG values represented a statistically significant difference (chi-square test, Yates c ontinuity correction). As for any test, the17 number of sampling points increases the pow er of the test, such that less extreme difference will be statistically significant. For example, if a taxon occurred at 10 sites, all of the occurrences (100%) would have to be in the good range of the HDG to be statistically significant (p < 0.05). In contrast, if a taxon o ccurred at 38 sites, it would only need to occur at 30 of the good site s (79%) and would st ill be statistically significant. For this reason, I chose not to use a strictly statistical criterion for selecting sensitive taxa. Instead, for more common taxa I only selected those with 87% or more of their occurrences in good sites. Using this criterion of 87%, if a taxon occurred at 30 sites, 27 or more of the sites would have to be in the good range of the HDG. The

p- value associated w ith this would be p < 0.002. Thus, a strictly statistical criterion would
have selected taxa that prefer good conditions rather than taxa that require good conditions. Thus, I used a statis tical rule to define the mi nimum number of occurrences needed for statistical significance but made the standards for selection more strict ( p << 0.05) for taxa that were more common.

Defining a statistical rule for selection of tolerant taxa was not possible because
tolerant taxa can also “toler ate” good conditions. Instead, very tolerant taxa were defined
as those found in at least 10 sites and for which >= 50% of th eir occurrences were in the poor range of the HDG. In some cases, multiple species within a genus failed to meet the criteria for inclusion as sensitive (or very tolerant) because they were not present at enough sites. If several species within a ge nus (or family) were consistently found at good (or poor) sites, I combined the species into the parent genus (or family) and evaluated the genus (or family).

For the BioRecon data, I only tested metrics used in the SCI in order to make the indexes as similar as possible. Relative a bundance metrics could not be calculated for these data; therefore, only taxa richness me trics were evaluated. BioRecon metrics were
tested for correlation with SCI and HDG.18 Index development and testing

Two indexes were defined by selecting metrics that showed the most consistent response to HDG. One index was developed for the SCI protocol (20 dip net samples and laboratory sorting of macroinve rtebrates) and another for th e BioRecon protocol (4 dip net samples and field sorting).

For SCI, metrics were transformed into unit-less scores by determining the 95 th percentile values for each metric and then dividing each metric value by its range and multiplying by 10. Metrics that declined with disturbance were scored from 0–10 and
metrics that increased with disturbance were scored from 10–0 so th at the best biological condition was consistently sc ored as a 10. For skewed me trics, the natural log was calculated before dividing by the metric range. This process was done separately for the northeast, peninsula and panhandl e regions. I tested for differe nces in metric expectation
associated with different re gions by comparing the regre ssion lines for each metric against HDG and adjusted metric scoring as n ecessary to insure similar responses for all metrics in the three regions.

For the BioRecon index, I 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, I adjust ed metric scoring for the BioRecon metrics as needed. In order to distinguish between th e BioRecon and SCI, BioRecon metrics were scored from 0–1 (rather than 0–10). In this wa y, the index values provi de a quick clue as to which index is being reported.

After correlation with disturbance was c onfirmed for both indexes, I evaluated different sources of variability, either due to natural sources (e.g., watershed size or season) or the sampling method (e.g., laboratory subsampling). I used correlation to test for dependence of SCI on watershed size. To te st the influence of season on 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.

To evaluate other sources of variance, su ch as site differences, year differences, and repeat visits, I used an ANOVA model to estimate variance components. For SCI and
BioRecon I estimated variance asso ciated with site, year, site x year interaction, and
error. Error was defined as variance associated w ith repeat visits to the same site within a year. For SCI data, most of the repeat visits within a year occurred on the same day rather19 than at different times during the year. For Bi oRecon, most of the repeat visits were on different days within the same or a di fferent year; very few were same day.

Error estimates from ANOVA (mean squared error) were used to calculate the
90% confidence interval for both indexes a nd the number of cate gories of biological condition that each index can reliably de tect (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 I divided its range (0–100 for SCI and 0–10 for BioRecon) by the confidence interval. Other statistical approaches could be used to calculate the number of categories of biological condition that the indexes can reliably detect. For exam ple, a 90% confidence in terval could also be
constructed based on the difference of SCI values for two sites. This approach would yield a smaller confidence in terval because instead of n= 1 in the equation above, n would equal 2, because two SCI values would be involved in calcula ting the difference. Currently there are no standards for which sta tistical model to use; therefore, I used confidence intervals around SCI for a single si te because they are conceptually simple and somewhat conservative from a statistical poi nt of view. In other words, this approach is less likely to be controversial (J ohnson, 1999; Hoenig and Heisey, 2001).

I also used an ANOVA model to estimate variability associated with laboratory subsampling. For this model, data from 59 sites with three subsamples each were used to
estimate site variance and error variance. Fo r this model, error was defined as the differences associated with subsamples from the same site visit.

Statistical model assumptions

The method described here for calculating confidence intervals assumes that the multimetric indexes are normally distributed. Ot her studies have evaluated the statistical properties of multimetric indexes and found that multimetric indexes for fish and invertebrates are unimodal and symmetric (F ore et al., 1994; Doberstein et al., 2000; Blocksom et al., 2003); thus, an underlying normal distribution is an appropriate assumption for multimetric indexes. A separate and additional consideration is whether to

use z -values from the normal distribution to ca lculate 90% confidence limits or values20 from Student’s t distribution. For small sample sizes, the t- distribution should be used for calculating confidence limits because varian ce may be underestimated; however for large sample sizes (df > 30) the two distributions converge. I used data from 61 sites to estimate variance; therefore, z- values were appropriate.

The two data sets used to estimate vari ance for the SCI and BioRecon index were unbalanced, that is, the number of visits to each site varied for each site. In contrast, the data set used to evaluate laboratory subsamp ling was balanced with three subsamples for
each site visit. ANOVA assumes a balanced desi gn but is fairly robust to violations of this assumption (Milliken and Johnson, 1992). The primary consequence of an unbalanced design is related to significance te sting of model effect s 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 confiden ce interval calculations and to evaluate the relative contributions of year and error to th e metrics and indexes. For estimation of the error term, the large sample sizes mitigate any potential inaccuracy associated with unbalanced cells. Samples sizes greater than about 30 are considered large and this analysis had 61 sites for SCI and 128 for BioRecon.21 RESULTS

Human disturbance gradient

The human disturbance gradient (HDG) was a consistent predictor of biological condition for two independent rounds of testing. When metrics were regressed against HDG for the two rounds of data, the slopes and intercepts of the regression lines were nearly identical indicating that measures included in th e HDG yielded a reliable measure of human disturbance (Figure 1).

Correlations among the four measures of human disturbance used to define the HDG were high, indicating good agreement among the different measures (see Table 4). All the individual measures, however, were more highly correlated with the HDG, suggesting that the HDG was a better overall measure of general human disturbance. The SCI showed a similar pattern and was more highly correlated with the HDG than the
individual measures of human disturbance.

Metric selection

On average, 30 insect taxa were found in each sample for the 629 site visits used to test metrics. Diptera dominated the samples with 23 taxa counted on average; of these taxa, about half were in the family Chironomidae. Of the remaining groups, three oligochaetes, two Trichoptera, one Ephemeroptera and one Plecoptera taxa were found on average.

For many metrics, the change in values from least to most disturbed sites was dramatic. I compared the 20 best sites (HDG = 0 and the lowest values for LDI_WS) to the 18 worst sites (HDG > 6) and found tota l taxa richness went from 33 to 19 on average. Number of Ephemeroptera taxa decl ined from two to zero; Trichoptera taxa from four to zero; clinger taxa from eight to zero; and sensi tive taxa from nine to zero.
The relative abundance of filterers declin ed from 22% to 3% while the relative
abundance of tolerant individuals increased from 5% to 69%.

In general, I selected at least one me tric from each of the categories below according to which metric was most highly correlated with HDG. If two metrics were calculated from the same set of taxa (redunda nt metrics), I only included one metric.

Taxonomic richness. EPT taxa richness was most highly correlated with HDG followed by Trichoptera and Ephemeroptera taxa richness (see Table 6). Both taxonomic groups disappeared from samples with high leve ls of disturbance (Figure 2). CorrelationFigure 1. Number of EPT taxa and total number of taxa declined as human disturbance increased. Nearly identica l regression lines for two independent sets of sites (rounds 1 and 2) indicate d 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 g radient ( HDG )Number of taxa

0 2 4 6 8 10 0 20 40 60

220 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 dec lined with an increase in human disturbance. Metric values were averag ed 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 taxa

2324 between HDG and Plecoptera taxa richness was somewhat lower, primarily because very few plecopterans were found in samples from the peninsula region. I selected Ephemeroptera and Trichoptera taxa richness as separate metrics be cause they have the potential to respond to different types of disturbance. I di d not select Plecoptera taxa
richness as a metric because it could only be applied in tw o of the three regions. Total taxa richness had the next highest correlati on and was selected as an indicator of the variety of taxa that a st ream site could support.

Feeding group. Relative abundance of filterers (p ercentage filterer individuals) had the highest correlation with HDG and th e most consistent relationship with HDG when graphed. Other feeding groups failed to show the consistent decline with disturbance that percentage filterer di d. I also tested this metric without Cheumatopsyche (a tolerant genus of Trichopter a); but the changes in correlat ion were very small and so all taxa were retained (Spearman’s r = –0.46 with Cheumatopsyche and –0.47 without).

Voltinism. Correct information about genera tion times was not easily obtained for all Florida taxa; therefore, I only tested long-lived taxa richness and percentage longlived individuals. Long-lived taxa include d semi-voltine insects and non-insects that require greater than one year to complete their life cycles. Long-liv ed taxa richness was more highly correlated with HDG than was re lative abundance of long-lived individuals; consequently, long-lived taxa ric hness was included in the SCI.

Habit. Clinger metrics measured either as taxa richness or as percentage of individuals were among the most highly correl ated metrics with HDG. I selected clinger
taxa richness for inclusion in the SCI.

Community structure. Percentage dominance in creased with HDG and was included in the SCI (Figure 3); hyperabunda nce failed to correlate with HDG. Although percentage Ephemeroptera and Trichoptera i ndividuals (relative abundance) were both strongly correlated with HDG, they were calculated from the same information used to calculate taxa richness of those two groups. B ecause the taxa richness metrics were more highly correlated with HDG, relative abundance of Ephemeroptera and Trichoptera were
not included in the index. Correlation for th e relative abundance of Tanytarsini midges was not quite as high as the other compositi on metrics (–0.45), but was included in SCI as the best available measure of the chironomid assemblage.

Sensitivity and Tolerance. Number of taxa selected as sensitive equaled ~12% of the taxa tested; and the number of very tole rant taxa was ~10% of the taxa tested. Many sensitive taxa belonged to the Ephemeropter a or Trichoptera; several chironomids were also included (Appendix B). All the Plecoptera were included though most of the genera25 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 ‘p oor’ section of the HDG. All Plecoptera taxa showed a strong tendency to be found in the best of the good (minimally disturbed) sites. Many of the sensitive taxa selected by this study were previously noted by Beck (1954) and used in the Florida index; but several taxa were new to the list. Many
of the taxa included on Beck’s list were t oo uncommon to test with this data set.

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 C). The very tolerant list was dominated by non-insects and chironomid s 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 which failed to meet the criterion of >=50% of poor sites. L. hoffmeisteri was included as very tolerant because it was very
common at all sites. It occurred at 70 out of 85 poor sites, and tended to increase in
relative abundance with disturbance.

The total number of taxa with 10 or more occurrences was 168. When a large number of tests are conducted questions arise regarding the probability of type I error. Detection of a statistically si gnificant result will occur with the approximate frequency of

p- value. For a p- value of 0.05 and 168 tests, approximate ly eight taxa may be selected by chance alone (0.05 * 168 = 8.4). The number of se nsitive taxa falsely selected from this analysis is likely much lower because the cr iterion for selection was more strict than statistical significance. In other words, the p- values associated w ith 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 H DG and highly correlated with SCI ( r ranged from 0.60–0.73; Table 7). The four SCI metrics calcula ted as percentages co uld not be tested
because relative abundance was not recorded fo r BioRecon data. For this reason, I tested the very tolerant metric in terms of taxa richness rather than as a percentage. Its correlation was poor with HDG and non-signifi cant with SCI and therefore was not
included in the BioRecon index.

Multimetric index construction and evaluation

Ten metrics were selected for inclusion in th e SCI and six were selected for the BioRecon index. Most of the SCI metrics required regi onal scoring (Table 8). In general, Taxa richness tended to be higher in the panhandl e region than in the pe ninsula or northeast. For the SCI, the highest co rrelation between metrics occu rred between Trichoptera and20 40 60 80% Dominance

0 5 10 15Number of sensitive taxa

Figure 3. Four of the ten metrics include d in the SCI plotte d against the human disturbance gradient. Percent domina nce and very tolerant individuals increased with disturbance; sensitive and Tanytarsini mi dge taxa declined. Metric values were averaged for each of the 176 sites. Samples with greater than 175 or less than 75 individuals were excluded before averaging. Number of sites within each box are shown ( N

). 0.8 2.4 4 5.6 7.2 Human disturbance gradient 0 30 60 90% Very tolerant individuals

0.8 2.4 4 5.6 7.2 Human disturbance gradient 0 10 20 30 40% Tanytarsini

N = 80 41 25 8 16 6

2627 clinger taxa (Spearman’s r = 0.77) and clinger an d sensitive taxa ( r = 0.84). To determine whether these metrics were redundant, I evalua ted the taxa included in each metric. Of the clinger taxa, approximately 41% were also sensitive taxa and 33% were also Trichoptera taxa. Thus, I concluded statisti cal 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 dist ribution. For these metrics, I calculated the natural log of metric values be fore dividing by the range of th e metric values. In this way, metric scores were more evenly spread between 0–10. Two SCI metrics increased with disturbance (% dominance and % very tolerant individuals), all others declined with
disturbance.

BioRecon metric scoring rules differed somewhat from SCI metric scoring. Taxa richness tended to be lower in two regions while Ephemeroptera taxa richness was much higher in the panhandle (Table 9). More long-liv ed taxa tended to be found in all three regions for BioRecon samples.

Table 7. Correlation of BioRecon metrics and the old and new BioRecon index with HDG and SCI. Metrics included in the BioR econ (and SCI) index are noted (*). All correlations shown were statis tically significant (Spearman’s r, p <0.01, one-sided test,

n =116 for SCI, n= 53 for HDG). Data from multiple vis its 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 0.6928 Table 8. SCI metric name and range of metr ic values used to assign a score of 0 (indicating degraded condition) to 10 (indicat ing minimal disturbance) by region. Metric values higher or lower than the listed range were assi gned a score of 0 or 10 as appropriate. Metric ranges liste d in reverse order indicate me trics that increased (rather than declined) with disturbance. For two marked metrics ( * ), the natural log was calculated for the range of values before scoring.

SCI metric Northeast Panhandle Peninsula

Total taxa 16–42 16–49 16–41 Ephemeroptera taxa 0–3.5 0–6 0–5 Trichoptera taxa 0–6.5 0–7 0–7
% Filterer 1–42 1–45 1–40 Long-lived taxa 0–3 0–5 0–4 Clinger taxa 0–9 0–15.5 0–8
% Dominance 54–10 43–10 54–10 % Tanytarsini * 0–26 0–26 0–26
Sensitive taxa 0–11 0–19 0–9 % Very tolerant * 78–0 36–0 59–0

Table 9. BioRecon metric name and range of me tric values used to assign a score of 0 (indicating degraded condition) to 1 (indica ting minimal disturbance) by region. Metric values higher or lower than the listed range were assi gned a score of 0 or 10 as appropriate. Bolded text indicates metric ranges that differed from those for 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 0–929 SCI was highly correlated with HDG with minimal overlap between values for extremely disturbed and minimally disturbe d sites (Figure 4). The BioRecon index was also highly correlated with HDG and was mo re responsive across the range of human disturbance than the original BioRecon index (Figure 5).

Regional scoring at the metric level resu lted in an SCI that showed a similar response to human disturbance within each region. The regression lines for SCI vs. HDG for the northeast and peninsula regions were nearly identical; the line for the panhandle region diverged somewhat from the other two regions for more degraded sites (Figure 6; Appendix D). The divergence was associated wi th two sites in the panhandle with SCI values that were higher than expected. I di d not alter the metric scoring rules because several other panhandle sites did obtain the lowest SCI values for high levels of
disturbance.

I calculated the natural log of the area upstream of the sampling site to test for
correlation between SCI and watershed size. Watershed size had an underlying correlation with HDG (see Table 4) because the mo st disturbed sites tended to have small watersheds. To avoid this confounding associat ion, I divided the sites into three groups (low, medium and high disturbance) before testing for correlation between SCI and watershed size. The low disturbance group of sites had HDG values equal to zero. I
defined medium disturbance as HDG values from 2–5 and high disturbance as HDG values >5. SCI was not significantly associated with watershed size for any of the three
levels of disturbance (Figure 7; Spearman’s r = –0.01, –0.005, 0.12, p > 0.5).

The relationship between SCI and HDG was consistent across years as well. When site visits were divided into two-y ear segments for 10 years of sampling (1992- 2001) and SCI regressed against HDG, the five regression lines were nearly identical (Figure 8). This result indicates that the pattern of associ ation between SCI and HDG was independent of year.

To verify that the SCI was correlated w ith HDG, I used an independent set of 23 sites not used in any of the previous analyses. Correlation was high (Spearman’s

r = –0.81, p << 0.001; Figure 9). Two sites with > 300 individuals in the samples had somewhat higher SCI values than predicted by HDG, possibly due to inflation of taxa richness measures associated with large samples.Figure 4. SCI declined as human distur bance increased. Index values were averaged for each of the 176 sites. On ly 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 Outliers

30Figure 5. Original BioRecon index base d on three metrics (total taxa, EPT taxa, and Florida index; upper panel) a nd current BioRecon index based on six metrics (lower panel). Both indexes d eclined 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 Outliers

31Figure 6. SCI declined as human distur bance increased. Regression lines for the peninsula and northeast had nearly identical slopes indicating a similar index response across the gradient. Three vi sits 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 we re included in the regression. N = 58 (NE), 66 (PS), and 52 (PH). 

### Human disturbance gradientSCI

02468 0 20 40 60 80 100

NE
PS

PH

32Figure 7. SCI was not significantly associat ed with watershed size (catchment area) when correlation was tested separate ly for low, moderate, and high levels of human disturbance (HDG). Highly di sturbed sites tended to be small in catchment area and this relationship cr eated a spurious correlation between SCI and catchment size. Shown are averag e 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] > 5440,00060,00081001100150440,00060,00081001100150440,00060,00081001100150

33Figure 8. SCI showed a similar response to human disturbance for different years. Shown are five regression lin es 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 80 100

34Figure 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 100SCI

3536 Statistical precision of indexes

The target number of individuals for labor atory identification for the SCI is 95–115. For the metric testing data set, only 27% of the 629 site visits met this target. Most samples had well over 100 individuals. For these data, 32% of the samples ha d >175 individuals, with some samples ranging up to hundreds of in dividuals. At the other end of the scale, only 36 samples (6%) had less than 90 i ndividuals in the entire sample.

When I compared small sample sizes with standard (or large) sample sizes from the same sites, samples with <75 individua ls contributed a disp roportionately large amount of variability to the re peat visits (Figure 10). SCI values were typically much lower for these small samples. For this reason, I excluded these 17 samples from the analysis of variability which reduced the number of sites from 62 to 61.

Repeat visits to the same sites were used to evaluate sources of variability for SCI
and BioRecon. Sources of varian ce included differences associ ated with sites, years, visits within years, seas on (summer vs. winter), and laboratory subsampling.

From the ANOVA analysis, the variance a ssociated with sites was greatest (304.9, 71%), variance associated with year was relatively small (7.7, 2%), and variance components associated with site x year interaction and erro r were similar (54.6, 12% and 66.6, 15%; Figure 11; see Appendix D). For this m odel, error was defined as repeat visits within years, about half of which were on the same day.

I used the estimate of error varian ce from the ANOVA to estimate the 90%
confidence limits around SCI as +/– 13.4 points, or a 27-point spread. Graphic representation of this range ar ound the observed values for repeat visits captured all visits to the least disturbed sites, th at is, sites with SCI values > 73, but failed to include all the visits to sites with lower SCI values (see Figu re 10). These sites graphically illustrate the
site x year variance, that is, certain site-year combinations that changed in unique ways.
The fact that undisturbed sites did not show this pattern while disturbed sites did, suggests that SCI was measuring real changes in biological condition rather than ‘noise’ associated with natural variability.

Using the length of the 90% confidence interv al (27 points) to di vide the range of the SCI (0–100) yielded 3.7 categories of biological condition. Calculating the confidence interval for two site visits yielde d a confidence limit of +/– 9.5, or a length of 19 points. This confidence leng th translated into approxi mately five categories of biological condition based on the average SCI value for two site visits. Current FDEP protocol specifies that the two most recent vi sits be used for assessment. I calculatedFigure 10. SCI values for repeat visits to 62 sites are shown. Sites were first sorted according to SCI value. Individua l site names are not shown, but all SCI values for each site are plotted at a si ngle point on the x-axis. Thus, vertical lines of points represent the range of SC I values at each site. Samples with < 75 individuals are indicated by solid points (upper panel). Lines (lower panel) represent the +/- 15 point confid ence interval for the SCI when small samples were eliminated. 0 20 40 60 80 100SCI

Site 0 20 40 60 80 100SCI

37Figure 11. Estimate of variance component s 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 vari ance. The interaction of site and year accounted for 16% of the total variance. Error, 66.6, 15% S x Y, 54.6, 12%

Year, 7.7, 2%

Site, 309.9, 71% 

## SCI Variance Components

3839 average metric values associated with thes e SCI intervals to summarize the biological condition within each category (Table 10). I used these average values to create narrative descriptions for the numeric cate gories of SCI (Table 11).

Variance components calculated for the SCI component metrics showed similar patterns to the SCI. Site differences and site x year variance typi cally contributed the largest percentages to the overall variance (Figure 12). The excepti on was long-lived taxa richness that had a large error component (wit h error defined as variance associated with repeat within year visits – as above). The least variable metrics were Ephemeroptera, clinger and sensitive taxa richness, and % very tolerant. Overall, the SCI was less
variable than most of its component metrics.

For the seasonal comparison, sites were chosen to represent locations with
minimal human disturbance in order to elim inate this potentially confounding source of variability from the analysis. Unfortunately, over half the SCI values for these site visits were <70 indicating moderate disturbance. Thus, the seasonal data set represented a broad range of conditions with the exception of the highly degraded sites (SCI <20)
which were not included. SCI values were on average 3.5 points higher in winter than summer (paired t- test, p = 0.049). The observed difference wa s greater in the northeast (6.1 points) than the panhandl e (1.6 points) or peninsula ( 3.6 points; Figure 13). Using the same data, I evaluated metrics to determ ine which were contribu ting to the seasonal
differences in SCI. Five metrics had values in the winter indi cating better biological condition; these were Ephemer optera taxa richness, % filter ers, % Tanytarsini, sensitive taxa richness, and % very tolerant. Long-liv ed taxa richness had higher values in the summer, opposite of SCI. Although significant, mo st metric differences were rather small compared to the overall range of the metric (Table 12). The paired t- test is a very powerful test and can detect very small differe nces because it compares each site to itself. The exception was % Tanytarsini. With a di fference of 4.6% compared to a possible range of 0–26%, it had the larg est relative change from su mmer to winter. I did not modify metric scoring to corre ct for seasonal differences beca use 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.40 Table 10. Average metric values for ranges of SCI values corre sponding to good, fair, poor and very poor condition. Note that metr ic values have not been corrected for different expectations associ ated with different regions.

Metric Good
[73-100] Fair
[46-73) Poor
[19-46) Very Poor
[0–19)

Total taxa 41 35 29 18 Ephemeroptera taxa 4 3 1 0 Trichoptera taxa 6 4 1 0
% Filterer 27 22 14 5 Long-lived taxa 3 2 1 0 Clinger taxa 10 6 2 1
% Dominance 17 21 30 46 %Tanytarsini 12 10 7 1
Sensitive taxa 10 6 2 0 % Very tolerant 4 9 24 6041 Table 11. Category names, ranges of values for SCI, and example descriptions of biological conditions typically found for that category. Range of SCI values represent 90% confidence intervals for one or two samp les. Square brackets indicate a value is included in the range; round brackets indicate a listed value is not included. Narrative metric descriptions are not used to score metr ics, rather they describe values associated with a range of index values.

SCI category SCI range Description
1 sample

Good [73–100] Similar to natural conditions, up to 10% loss of taxa expected

Fair [46–73) Significantly different from na tural conditions; 20–30% loss of Ephemeroptera, Trichoptera and long-lived taxa; 40% loss of clinger and sensitive taxa; pe rcentage of very tolerant individuals doubles

Poor [19–46) Very different from natural cond itions; 30% loss of total taxa; Ephemeroptera, Trichoptera, l ong-lived, clinger and sensitive taxa uncommon or rare; Filtere r and Tanytarsini individuals decline by half; 25% of indi viduals are very tolerant Very poor [0–19) Extremely degraded; 50% loss of expected taxa; Ephemeroptera, Trichoptera, long- lived, clinger, and sensitive taxa missing or rare; 60% of individuals are very tolerant 2 samples

Excellent [81–100] Proportion and abundance of taxa si milar to natural conditions; minimal loss of taxa

Good [62–81) Similar to natural conditions with up to 10% loss of taxa; 25% loss of Ephemeroptera, Trichopter a, clinger and sensitive taxa

Fair [43–62) 25% loss of total taxa; 50 % loss of Ephemeroptera, Trichoptera, clinger, and sensitiv e taxa; 33% loss of long-lived

taxa

Poor [24–43) High percentage of individuals pr esent belong to very tolerant taxa; only tolerant Ephemeropt era, Trichoptera, and clinger taxa present; one sensitive or long-lived taxon may be present Very poor [0–24) Extremely degraded; 50% loss of expected taxa; Ephemeroptera, Trichoptera, long- lived, clinger, and sensitive taxa missing or rare; 60% of individuals are very tolerantFigure 12. Variance components for SCI a nd its metrics. For the index and several of the metrics, vari ance associated with site differences represented the largest component of variance. Error va riance 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 variance

42Figure 13. SCI values by region (north east, 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 100SCI

4344 To evaluate the influence of laboratory subsampling on SCI, I used data that included multiple subsamples from the same site visit. I estimated variance using ANOVA with stream site-visit (n = 59) as a si ngle factor and the three subsamples from
each visit to estimate the error variance. Error variance for this model was 32.86 which represents about half the e rror variance calculated above when error was defined as differences associated with repeat visits to the same site (32.86/66.6 * 100 = 49%). In other words, of the variance associated with re peat visits to the same site, about half the variability is due to labora tory subsampling. However, because the variance estimates were from different data sets, the co mparison should be cautiously applied.

The BioRecon index was calculated as th e sum of the six scored metrics; therefore, the sum of the scores ranged from 0 to 6. I transf ormed the index to a range of 0-10 by dividing the sum by six. The data set for this ANOVA model was unbalanced with many site-year combinations missing; therefore, the site x year interaction component of variance could not be estimate d. Components of variance were similar to
those for the SCI in that th e largest component of varian ce was associated with site differences and year variance was again small (Figure 14). Error variance for this model was 1.46 with error derived from repeat vis its to the same sites. A 90% confidence interval for the BioRecon index yielded a lim it of +/-2 or a length of 4.0 points. This translated into 2.5 categories of biological condition that Bi oRecon could reliably detect (Table 13). For two visits to the same site, 3.5 categories could be detected.

Table 12. Results for seasonal comparison of SC I and its component metrics. Differences between summer and winter samples (negativ e 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 *Figure 14. Variance components for Bi oRecon and its component taxa richness metrics. Variance associated w ith site differences represented the largest component for metrics and index. Error variance associated with repeat visits within years was similar acros s metrics ranging from 20-30% of the total. Variability associated with a sp ecific year was rela tively small for the index and metrics. 

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

### 0 20 40 60 80 100% Relative variance

4546 Table 13. Categorical descriptions and range of index values for the BioRecon index. Range of values represent 90% confidence in tervals for one or two samples; variance estimated from repeat visits to 128 stream sites. Square brackets indicate a value is included in the range; round brackets i ndicate a value is not included.

BioRecon Index range
1 sample

Pass [6–10]

Fail [0–6) 2 samples

Good [7–10]

Fair [4–7)

Poor [0–4)

These categories for 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 di sturbance (Figure 15). If variance were estimated using only mini mally disturbed reference sites, the error variance would be smaller and the categor ies corresponding to ‘passing’ condition (1 sample) or ‘good’ condition (2 samples) would be more narrow, that is, more restrictive. To determine whether high variability was asso ciated with natural variability or changes in human activities, I selected the sites with the most variable Bi oRecon values through time and asked regional biologi sts to note any change in hum an activities at the site (Table 14). Four of the site s improved where roads had been paved to eliminate sediment run off. Other changes were associated with pesticide spraying, e limination of run off from a waste water treatment plant, road cl osure, and the end of fertilization upstream. From this anecdotal analysis suggest th at much of the BioRecon variability was associated with real changes in human di sturbance rather than natural variability.

Variance results for the BioRecon component s metrics were similar to those for
the SCI metrics with site differences cont ributing the largest component of the total variance (see Figure 14). In general, the me tric variance due to nuisance sources of variability (year or error) was smaller for th ree of the BioRecon metrics than for the same
SCI metrics. Lower error vari ance for total taxa, Trichoptera , and long-lived taxa richness suggests that the BioRecon pr otocol may provide more reliable estimates of these metrics. Direct comparisons of results from different data sets should be cautiously applied, particularly for relative varian ce estimates, because they depend on the magnitude of the site differences.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 pl otted as the endpoints. Note that high and low index values were less va riable for repeat visits through time. 

### Site 0 2 4 6 8 10BioRecon index

4748 Table 14. STORET site code, region, stati on nickname, observed change in BioRecon values through time and possible reason for cha nge. Listed sites represent locations with the highest variability obse rved for BioRecon values.

STORET Region Station Change Reason

20010521 ps JIMREF Decline Unknown
20010525 ps LILHAW@40 Decline Unknown

20030341 ne CECFLD7 Improve Unknown
32030024 ph SFBEARREF Improve Intermittent pesticide spraying

33020064 ph PBRNCSTLR Improve 319 NPS restoration project to pave a road and stop sediment

33020065 ph BRSTWRKB Improve 319 NPS restoration project to pave a road and stop sediment

33020067 ph CNOECPBRNR Improve 319 NPS restoration project to pave a road and stop sediment

33020082 ph SANHOLTST Improve 319 NPS restoration project to pave a road and stop sediment

33030039 ph TRKLNHDCM Improve Partnership with State Forest and TNC closed logging road and eliminated sediment
33030102 ph TURKEYCR Improve Summer aerial pesticide spraying
33040016 ph WILLIAMTST Improve Runoff from WWTP spray field eliminated

33040017 ph DDFLCWC189 Improve Ended fertilization upstr eam of sites in State Forest
24030013 ps HILSTP4REF Improve Unknown
22020062 ph OKLREF Variable Unknown
27010050 ps MOSESUS1 Variable Unknown
27010583 ps LITLTOMOKA Variable Unknown49 DISCUSSION

The SCI and BioRecon indexes along with th eir component metrics were developed to assess the biological conditi on of stream sites using samples from the stream macroinvertebrate assemblage. These assessment tools will be used primarily to define biological criteria for freshwater streams a nd to evaluate the effectiveness of best management practices (Vowell, 2001). Three re sults support the use of the SCI for this purpose. First, the component metrics showed a strong and consistent response to an independent measure of human disturbance ( HDG). SCI was also highly correlated with HDG using an independent data set for veri fication. Second, the SCI was independent of watershed size and geographic region. This re sult means that the SCI can be used across the state to compare stream sites. Third, the influence of seasonal and annual differences on SCI was relatively small. Index values we re about 3.5% higher fo r winter samples and
about 2% of the total variance of SCI was associated with year of sampling. Although low, these sources of variance should conti nue to be evaluated with more complete sampling designs because the influence of both could potentially be eliminated by adjusting index values acco rding to season or year.

Human disturbance gradient

The human disturbance gradient simplified seve ral aspects of metric and index testing. First, it provided a more stringent test of metric association with disturbance than a comparison of reference vs. test sites. Per centage Diptera indivi duals serves as an example of this. This metric was included in the old SCI because it increased at disturbed sites; however, across a gradie nt of disturbance it failed to consistently respond. Second, HDG was independent of biol ogical condition because no measures of biological
condition were included. Therefor e, metric expectations coul d be defined for equivalent levels of disturbance within each region. As a consequence, it was not necessary to correct the multimetric index for regional diffe rences. Third, objective selection criteria
based on association with the HDG were used to define individual taxa as sensitive or very tolerant. Finally, the HDG resolved a s purious correlation between watershed size and SCI. During initial analysis , the SCI appeared to be corr elated with watershed size
because the most disturbed si tes happened to be very small. After controlling for HDG,
the correlation between watershe d size and SCI was eliminated.

The HDG can also be used to describe the intensity of disturba nce associated with specific biological losses as measured by SCI and its component metrics. EPA is currently developing guidelines for defi ning categories of human disturbance and50 matching them with their associ ated biological condition; this effort is generally referred to as “Tiered aquatic life uses.” Values for HDG and SCI will be used to define and describe these categories for Florida.

Biological indicators

Multimetric indexes strive to integrate measures from a diverse set of biological categories for two reasons. First, monito ring different aspect s of the biological assemblage improves the likelihood of detecti ng changes associated with different types of disturbance. Second, the potential exists to define metric signatures, that is, associations between specific metrics (or suit es of metrics) that correspond to specific human activities (Norton et al., 2000; Yode r and DeShon, 2002). Metric signatures are particularly relevant to th e TMDL process that allocates responsibility for degraded stream condition among the various human activ ities in the watershed (Karr and Yoder, in press ; EPA, 2000).

Though not specific to any particular gr oup, total taxa richness represents a general measure of the biological complexity found at a site. This metric is one of the most widely applied in biomonitoring program s because of its consistent decline with
human disturbance for stream invertebrate s (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, th ough are often combined with Plecoptera into a single (EPT) metric. Split ting these taxonomic groups apart provides the opportunity for metric signatures associated with different types of disturbance. For example, Ephemeroptera taxa are known to be particularly sensitive to metals and will disappear before other taxa (Clements, et al., 2000; Fore, 2002). On the other hand, an increase in Ephemeroptera taxa may indica te increased nutrient s (Miltner and Rankin, 1998). Filterers are expected to decline in res ponse 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 ev ents of short duration may also eliminate these taxa while other taxa may colonize from unaffected sites.

Clinger taxa have morphological and beha vioral adaptations that allow them to cling in fast water. Human development near st ream sites in Florida often translates into eroding sand that can smother habitat and e liminate these taxa. Other studies have found
these taxa to be sensitive to disturbance a ssociated with mining and urbanization (Fore et al., 2001; Mebane, 2002). Percentage dominan ce of the most abundant taxon increases51 with disturbance as the natu ral taxonomic diversity declin es and very tolerant taxa dominate samples. This metric represented a measure of the overall structure of the assemblage and has been associated with di sturbance in several regions (Klemm et al., 2002). The Tanytarsini midges are used as indi cators by Ohio because of their general sensitivity to human disturbance (DeShon, 1995). Three Tanytarsini midge taxa were also selected for inclusion in the list of sensitive taxa for this study.

Several of the sensitive ta xa agreed with Beck’s ( 1954) designations used to calculate the Florida index; how ever, a few reversals were note d for taxa that satisfied the criteria for very tolerant. The primary diffe rence between the sensitive taxa list and the
Florida index list was the exclusi on of many rare taxa that coul d not be tested. The list of sensitive taxa is now shorter; nonetheless, th e objective criteria used to select these taxa make these metrics easier to justify in a regulatory context.

The set of metrics selected for Florid a represent a convergence with similar studies and programs in other states (Karr, 1998). Many of the Florida metrics also had a strong correlation with disturbance in Colorado, Idaho, Washington, Tennessee, and Japan where they responded to a diverse set of human activities including timber harvest (Fore et al., 1996), recreati on (Karr, 1998), urbanization (Fore et al. 2001; Karr and Morishita Rossano, 2001; Morley and Karr, 200 2), agriculture (Kerans and Karr, 1994), and mining (Mebane, 2001; Fore, 2002; Mebane, 2002). The emergence of a core set of metrics across a variety of ge ographic contexts further suppor ts the use of these metrics as biological indicators.

Sources of variance

I evaluated five sources of nuisance varian ce for the SCI and two for the BioRecon index. For SCI, I estimated variance a ssociated with laboratory subs ampling, repeat visits within a year (error), different seas ons, different years, and site x year interaction. For the BioRecon I estimated variance due to repeat visits within a year (error) and different

years.

One of the largest contributors to th e differences observed for SCI values
collected from the same site within the same year, was the variability associated with laboratory subsampling. Subsampling represen ted approximately 49% of the variance due to repeat visits to a site w ithin the same year. Unfortunately, the two variance estimates were derived from different data sets; thus, this percentage should be interpreted with caution. Nonetheless, subsampli ng in the laboratory seems to be a significant source of variability. Furthermore, the subsampling me thod for this small study was more carefully
controlled than for rou tine sampling in that the target of 100 individuals per sample was52 more frequently attained. Thus, the subsamp ling analysis did not in clude any additional variability associated with variable sa mple sizes; consequently, variance due to subsampling may actually be higher for routine samples.

Site x year interaction represents the variab ility associated with some unique siteyear combinations. This source of variance represented index valu es from sites that increased (or decreased) from one year to the next in a different or more extreme manner than did other sites during those same years. These differences could be due to natural variability, e.g. the effect of a dry year on some sites but not others, or could be due to actual changes in biological condition associ ated with human influence. These two sources could not be distinguished using the current data because all sources of human disturbance were not quantified. For example, agriculture is a dominant land use in Florida. Pesticide treatments represent a potentially significant impact to stream
invertebrates but could not be evaluated for this study. Nonetheless, the higher variability
in both SCI and BioRecon index values observe d for sites with moderate index values compared to sites with high index values, s uggests that both indexes may be measuring real changes associated with human land use rather than nuisance variance. Other studies have found a similar pattern of increased variance associated with greater human disturbance (Karr et al., 1987; Steedman, 1988). For BioRecon, index variability for several sites was explained by changes in human land use. Road paving to eliminate erosion, elimination of toxic run off and discontinuation of fert ilization were all associated with improved BioRecon values.

For SCI, winter index values were significantly higher than summer values collected from the same sites; however, the difference was small (3.5 %). I did not adjust
metric scores to eliminate this difference individual metric differences were somewhat
small and the increase was due to a cumula tive effect across several metrics. The seasonal difference was also relatively sma ll compared to the va riance due to other sources such as repeat visits within a year or laboratory subsampling. The influence of seasonality awaits a better data set that co mpares index values by season for sites with little or no change in human disturbance.

For the BioRecon index, the variance associ ated with repeat visits within years was somewhat higher than the variance observed for the SCI. This is largely because the
site x year interaction term could not be esti mated (due to incomplete data) and this
variance was included in the error term. In addition, the BioRecon data included mostly samples from different days within the year rather than the predominantly same-day samples used for the SCI. As a consequence the variance estimate for the BioRecon index may be somewhat high and the precis ion of the index underestimated.53 The higher variability noted for the BioRecon index may be due to human disturbance changing at the si te, different levels of samp ling effort applied by field biologists, the smaller number of metrics in the index (six vs. 10 for SCI), or some combination. In the field, biologists examine the sample until all unique taxa have been identified. They also bring back exampl es of each taxon for laboratory verification. Biologists may vary in both the amount of tim e they spend searching for unique taxa and
in the number of taxa returned to the lab. Th e protocol specifies th at a minimum of five examples of each unique taxon be returned fo r verification. If multip le examples of each taxon are returned, the probability of identifyi ng additional taxa increas es as a function of
the number of individuals returned. This is be cause many field identific ations can only be made to the genus level whereas in the lab more taxa can be identified to the species
level.54 CONCLUSIONS

Correlation between biological metrics and th e independently derive d human disturbance gradient provided strong evidence in support of the relationship between human influence and biological condition. Th e high correlation between SCI and HDG was verified using an independent set of data. In addition, th e pattern of biological response to human disturbance was repeated across all three regions in Florida, indicating that the multimetric index was independent of geogr aphic context. Finally, the index was consistent across years, with a nearly id entical relationship be tween SCI and HDG for 10 years of data.

To document every source of human dist urbance is prohibitively expensive, consequently the human disturbance gradie nt was inevitably incomplete. I had no information on metals, pesticides, dredging, or other pollutants. Aerial pesticide application is a frequent pr actice on agricultural lands and can be very damaging to stream invertebrates as pesticides are concentr ated 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 cont rast, sites with known disturbance never had high values for SCI. This result supports th e idea that much of the lack of fit between disturbance and biological condi tion is largely due to measur ement error associated with quantifying human disturbance ra ther than variability of bi ological measures (Bryce et al., 1999; Karr and Morishita Rossano, 2001; Fo re, 2003). In short, multimetric indexes accurately synthesize the infl uence of human activities ove r time while measures of human disturbance are at best approximations of the complex ways that human activities
influence the environment.

The SCI had good statistical precision and could detect 3.7 categories of biological condition based on a single sample and five categories based on two samples. The BioRecon was not as precise but had ade quate precision to de tect 2.5 categories of biological condition based on a single samp le and 3.5 categories for two samples.

Strong correlation with an independent measure of disturbance, good statistical precision, immunity to regional differences or watershed size, and minimal sensitivity to seasonal or yearly differences all contribute to the usefulness of these indexes for biological assessment of Florida’s streams.55 RECOMMENDATIONS

• Support categories of biological condition with statistical criteria.

Confidence intervals were used to define th e number of categories of biological condition that SCI and BioRecon can detect for a given level of confidence. Broader categories can be used to define impairment, but smalle r index ranges should not unless a different
statistical model or test is used. The metric values associated with index categories can be translated into narrative descriptions of the numeric standards; for example, “a 40% loss of sensitive taxa is typically associated with a decline in SCI values from good to fair.”

• Modify the SCI subsampling method to ensu re that the number of individuals in each sample is close to the target of 100 .

FDEP is currently modifying laboratory subs ampling procedures to more reliably match the target of 100 individuals pe r sample. Additional subsampli ng of the original material collected from the stream will help elimin ate samples with hundreds of individuals. Precautions are also being implemented to el iminate small sample. Results from this analysis suggest that subsamples with le ss than 75 individuals should probably be disqualified from SCI analysis.

• Standardize the BioRecon sampling effort.

The precision of this index could likely be improved by ensuring that the same level of effort is applied to each sample in the field. In addition, the number of individuals returned to the laboratory for verification should be consistent for each sample.

• Sample a small number of sites over sever al years and during specifically defined seasons to evaluate natural variability associated with season and year.

Variability of SCI due to season and year, though small, represents a source of variability
that could be eliminated. The data for this study were insufficient to fully evaluate the
influence of seasonal differences. Better estim ates could be made with a sampling design that visited each site during every season a nd year. Definitions for season should be made based on climatic factors (e.g., rainfall, temper ature) most relevant to invertebrate life histories. In addition, sites s hould be selected for which the level of human disturbance is constant through time. The best design would al so include laboratory subsampling as part of the design to estimate this component of variance using information from the same
sites.56 • Evaluate whether the increase in precis ion typically associated with larger sample sizes (greater number of individuals) is worth the cost.

Studies in other regions report lower error va riance when a greater number of individuals are identified and/or replicate samples are collected for each site. For SCI, the error variance associated with repeat visits to the same site was 15% of the total and for the
BioRecon index it was 18% of the total. Fore et al. (2001) report error variance of 10% for an approximate 500-count sample. A larger sample (more individuals) would likely yield a more precise assessment, but also re quires additional laborat ory time for analysis. Replicate samples can also be used to increase precision and would also require additional field and laboratory analysis time. A cost/benefit analysis represents a formal process for evaluating the relative advantages of each approach.

• Identify additional long-lived taxa.

Long-lived taxa richness was the most vari able metric in SCI, probably due to low
numbers of taxa. If additional taxa could be added to this list, its statistical precision would likely improve.57 ACKNOWLEDGMENTS

Florida DEP biologists provided comments and guidance throughout the development and testing of the stream m acroinvertebrate indexes. R. Frydenborg provided data sets, developed the hydrologic sc oring index, resolved taxonom ic questions, and provided biogeographic interpretation of metrics. E. McCarron prov ided administrative support and a regulatory perspective. E. Livingston provided administrative a nd contract support.
Reviewers from outside FDEP included M. Ba rbour, J. Gerritsen, J. Karr, T. Olsen, M. Pescador, and T. Thom who provided many helpful comments. Additional comments from F. Butera, J. Espy, S. Levings, D. M iller, D. Ray, and the FDEP Biology section improved many aspects of this document and an alysis. Data retrieva ls were performed by T. Deck, S. Gerardi, and D. Miller. D. C obb calculated land cover for stream sites. J. Espy developed data sets and resolved taxonomic questions. Discussions with J. Espy, S. Levings and D. Whiting improved the method for selecting sensitive taxa. D. Denson, D. Evans, J. Richardson, B. Rutter, and R. Wisseman (Aquatic Biology Assoc.) provided definitions for long-lived taxa. M. Heyn pr ovided additional clinger information. Lab subsampling was done by A. Jordan, N. Moise, C. Oaks, B. Richardson, B. Shinner, and F. Whiddon. Biologists th roughout Florida collected and identified stream macroinvertebrate samples, including L. Banks , F. Butera, G. Butts, T. Deck, D. Denson, L. Donalan, J. Espy, K. Espy, T. Frick, R. Frydenborg, S. Gerardi, M. Heyn, L. Homann, J. Jackson, P. Kane, S. Kent, C. Kovach, L. Miller, P. Morgan, P. O’Conner, E. Pluchino, D. Ray, J. Richardson, B. Rutter, E. Springe r, C. Swanson, M. Szafraniec, M. Thompson, F. Walton, and A. Wheeler. This project and th e preparation of this report were funded in part by a Section 319 Nonpoint Source Manage ment Program grant from the U. S. Environmental Protection Agency (U. S. EP A) through a contract with the Watershed Management Program of the Florida Departme nt of Environmental Protection. The total cost of the project was $64,800 of which $64,800, or 100%, was provided by the U. S.
EPA.58 REFERENCES

Barbour M. T., Gerritsen J., G. E. Griffith, R. Frydenborg, E. McCarron, J. S. White, and M. L. Bastian. 1996. A framework for biological criteria for Florida streams using benthic macroinvertebrates. Journal of the North American Benthological Society 15:185-211. Barbour M. T., Gerritsen J., Snyder B. D. and Stribling J. B. 1999. Rapid bioassessment protocols for use in streams and wadeable rivers: Peri phyton, benthic macroinvertebrates, and fish. Second edition. EPA 841-B-99-002. U. S. E nvironmental Protection Agency, Office of Water, Washington, DC. Beck, W. M. 1954. Studies in stream pollution biology. I. A simplified ecological classification of organisms. Quarterly Journal of the Fl orida Academy of Sciences 17(4):212-227. Blocksom, K. A. 2003. A performance comparison of metric scoring methods for a multimetric index for Mid-Atlantic Highlands st reams. Environmental Management, in press

. Bowman, M. L., J. Gerritsen, G. R. Gibson, Jr., and B. D. Snyder. 2000. Estuarine and coastal marine waters: Bioassessment and biocrite ria technical guidance. EPA-822-B-00-024. Office of Water, U. S. Environmental Protection Agency, Washington, DC. Brigham, A. R., W. Brigham, and A. Gnilka. (Eds.) 1982. Aquatic Insects of North and South Carolina. Midwest Aquatic Enterprises, Mahomet, IL. 4.1-4.100. Brown, M. T., N. Parker, and A. Foley. 1998. Spatial Modeling of Landscape Development Intensity and Water Quality in the St. Mark s River Watershed. DEP Contract #GW138. Center for Wetlands, University of Florida, Gainesville. Brown, M. T. and B. Vivas. In review. A landscape development inte nsity index. Environmental Monitoring and Assessment. Bryce, S. A., R. M. Hughes, and P. R. Kaufmann. 2002. Development of a bird integrity index: using bird assemblages as indicators of ripa rian condition. Environmental Management 30:294-310. Bryce, S. A., D. P. Larsen, R. M. Hughes, and P. R. Kaufmann. 1999. Assessing relative risks to aquatic ecosystems: A mid-Appalachian case study, Journal of the American Water Resources Association 35(1): 23-36. Clements, W. H., D. M. Carlisle, J. M. Lazo rchak, and P. C. Johnson. 2000. Heavy metals structure benthic communities in Colorado m ountain streams. Ecological Applications 10:626-638. Corbet, P. 1999. Dragonflies: Behavior and Ecolog y of Odonata. Cornell University Press. Ithaca, New York. DeShon, J. E. 1995. Development and application of the invertebrate community index (ICI). In Biological Assessment and Criteria: Tools for Water Resource Planning and Decision Making (Eds. W. S. Davis and T. P. Simon), pp. 217-244. CRC Press, Boca Raton, FL. Doberstein, C. P., J. R. Karr, L. L. Conquest. 2000. The effect of fixed-count subsampling on macroinvertebrate biomonitoring in small streams. Freshwater Biology 44:355-371. Environmental Protection Agency (EPA). 1998. Lake and Reservoir Bioassessment and Biocriteria: Technical Guidance Document

. EPA 841-B-98-007. U. S. EPA, Washington, DC. www.epa.gov/owow/monitoring/tech/lakes.html Environmental Protection Agency (EPA). 2000. Stressor Identification Guidance Document. EPA-822-B-00-025. U. S. EPA, Office of Wate r, Office of Research and Development, Washington, DC.59Environmental Protection Agency (EPA). 2001. National Coastal Condition Report. EPA-620/R01/005. Office of Research and Development/O ffice of Water, U. S. EPA, Washington, DC. www.epa.gov/owow/oceans/NCCR/index. Environmental Protection Agency (EPA). 200 2a. Methods for Evaluating Wetland Condition: Developing Metrics and Indexes of Biological Integrity. EPA-822-R-02-016.Office of Water, U. S. EPA, Washington DC. Environmental Protection Agency (EPA). 2002b. Summary of Biological Assessment Programs and Biocriteria Development for States, Trib es, Territories, and Interstate Commissions: Streams and Wadeable Rivers. EPA-822-R- 02-048. U.S. Environmental Protection Agency, Office of Environmental Informati on and Office of Water, Washington, DC.Environmental Protection Agency (EPA). 2003a. Draft Report on the Environment 2003. EPA260-R-02-006. Office of Environmental Information and Office of Research and Development, U. S. EPA, Washington, DC. www.epa.gov/indicators. Environmental Protection Agency (EPA). 2003b. Biological Indicators of Watershed Health. www.epa.gov/bioindicators. Environmental Protection Agency (EPA). 2003c. Guidance for 2004 Assessment, Listing and Reporting Requirements Pursuant to Sections 303(d) and 305(b) of the Clean Water Act. Memorandum. Office of Water, U. S. EPA, Washington DC. www.epa.gov/owow/tmdl/tmdl0103. Fore L. S. 2002. Biological assessment of mi ning disturbance on stream invertebrates in mineralized areas of Colorado. In: Biologi cal Response Signatures: Indicator Patterns Using Aquatic Communities (Ed T. P. Simon), pp. 445-480. CRC Press LLC, Boca Raton, FL. Fore, L. S. 2003. Developing Biological Indicator s: Lessons Learned from Mid-Atlantic Streams. EPA 903/R-003/003. U.S. Environmental Protections Agency, Office of Environmental Information and Mid-Atlantic Integrated A ssessment Program, Region 3, Ft. Meade, MD. www.epa.gov/bioindicators/ Fore, L. S. and C. Grafe. 2002. Using diatoms to assess the biological condition of large rivers in Idaho (USA). Freshwater Biology 47:2015-2037. Fore L. S., Karr J. R. and Conquest L. L. 1994. Statistical properties of an index of biotic integrity used to evaluate water resources. Canadian Journal of Fisheries and Aquatic Sciences 51:212-231. Fore, L. S., J. R. Karr, and R. W. Wisseman. 1996. Assessing invertebrate response to human activities: evaluating alternative approaches. Journal of the North American Benthological Society 15:212-231. Fore L. S., Paulsen K., K. O’Laughlin. 2001. Assessing the performance of volunteers in monitoring streams. Freshwater Biology 46:109-123 Fernald, E. A. and E. D. Purdum (Eds.) 1992. A tlas of Florida. University Press of Florida. 280
pp. Florida Natural Areas Inventory (FNAI). 1990. Guide to the Natural Communities of Florida. Florida Natural Areas Inventory and Florida Department of Natural Resources. 111 pp. Griffith, G. E., J. M. Omernik, C. M. Rohm, and S. M. Pierson. 1994. Florida regionalization project. U. S. EPA. Environmental Rese arch Laboratory, Corvallis, OR. 83 pp. Hawkins, C. P., R. H. Norris, J. N. Hogue, and J. W. Feminella. 2000. Development and evaluation of predictive models for measur ing the biological integrity of streams. Ecological Applications:10:1456–1477. Hoenig, J. M. and D. M. Heisey. 2001. The a buse of power: the pervasive fallacy of power calculations for data analysis. Th e American Statistician 55:19-24.60 Hughes, R. M., P. R. Kaufmann, A. T. Herlihy, T. M. Kincaid, L. Reynolds, and D. P. Larsen. 1998. A process for developing and evaluating indices of fish assemblage integrity. Canadian Journal of Fisheries a nd Aquatic Scienc es 55:1618-1631. Hughes, R. M. and T. Oberdorff. 1998. Application of IBI concepts and metrics to waters outside the United States. In: Assessing the Sustaina bility and Biological Integrity of Water Resources Using Fish Communities (Ed T. P. Simon) pp. 79-93. CRC Press, Boca Raton, FL. Hutchens, J. J. Jr., K. Chung, and J. B. Wallace. 1998. Temporal variability of stream macroinvertebrate abundance and biomass followi ng pesticide disturbance. Journal of the North American Benthological Society 17:518-534. Jackson, L. E., J. C. Kurtz, and W. S. Fisher (Eds.). 2000. Evaluation Guidelines for Ecological Indicators. EPA/620/R-99/005. U.S. Environm ental Protection Agency, Office of Research and Development, Research Triangle Park, NC. 107 pp. Johnson, D. H. 1999. The insignificance of statistical significance testing. Journal of Wildlife Management 63:763-772. Karr, J. R. 1981. Assessment of biotic integrity using fish communities. Fisheries 6(6):21-27. Karr J. R. 1991. Biological integrity: a long-neglected aspect of water resource management. Ecological Applications 1:66-84. Karr, J. R. 1998. Rivers as sentinels: using the bi ology of rivers to guide landscape management. In: River Ecology and Management: Lessons fro m the Pacific Coastal Ecosysten (Eds. R. J. Naiman & R. E. Bilby), pp. 502-528. Springer, NY. Karr J. R., Allan J. D. and Benke A. C. 2000. Ri ver conservation in the United States and Canada. In: Global Perspectives on River Conservation: Sc ience, Policy, Practice (Eds P. J. Boon, B. R. Davies and G. E. Petts), pp. 3-39. J. Wiley, Chichester, UK. Karr J. R. and Chu E. W. 1999. Restoring Life in Running Waters: Better Biological Monitoring. Island Press, Washington, DC. Karr J. R., Fausch K. D., Angermeier P. L., Yant P. R. and Schlosser I. J. 1986. Assessment of biological integrity in running water: a me thod and its rationale. Illinois Natural History Survey Special Publication Number 5, Champaign, IL. Karr., J. R. and E. Morishita Rossano. 2001. Appl ying public health lessons to protect river health. Ecology and Civil Engineering 4:3-18. Karr, J. R., P. R. Yant, and K. D. Fausch. 1987. Spatial and temporal variability of the index of biotic integrity in three Midwestern streams. Transactions of the American Fisheries Society 116:1-11. Karr, J. R. and C. O. Yoder. 2004

. Biological assessment and criteria improve TMDL decision making. Journal of Environmental Engineering, in press. Kerans, B. L., and J. R. Karr. 1994. A benthic i ndex of biotic integrity (B-IBI) for rivers of the Tennessee River Valley. Ecological Applications 4:768-785. Kimberling, D. N., J. R. Karr, and L. S. Fore. 2001

. Measuring human disturbance using terrestrial invertebrates in the shrub-steppe of eastern Washington (USA). Ecological Indicators 1:63-81. Klemm, D. J., K. A. Blocksom, F. A. Fulk, A. T. Herlihy, R. M. Hughes, P. R. Kaufmann, D. V. Peck, J. L. Stoddard, W. T. Thoeny, M. B. Griffith, and W. S. Davis. 2003. Development and evaluation of a macroinvertebrate biotic integrity index (MB II) for regionally assessing Mid-Atlantic Highlands streams. Envi ronmental Management 31(5):656-669. Klemm, D. J., K. A. Blocksom, W. T. Thoeny, F. A. Fulk, A. T. Herlihy, P. R. Kaufmann, S. M. Cormier. 2002. Using macroinvertebrates as indicators of ecological conditions for streams61 in the Mid-Atlantic Highlands region. Environmental Monitoring and Assessment 78:169- 212. McCarron, E. and R. Frydenborg. 1997. The Flor ida bioassessment program: an agent for change. Human and Ecological Risk Assessment 3:967-977. Mebane, C. A. 2001. Testing bioassessment metr ics: macroinvertebrate, sculpin, and salmonid responses to stream habitat sediment and metals. Environmental Monitoring and Assessment 67:293-322. Mebane, C. A. 2002. Effects of metals on freshwa ter macroinvertebrates: a review and case study of the correspondence of multimetric index, toxici ty testing, and copper concentrations in sediment and water. In: Biological Response Si gnatures: Indicator Patterns Using Aquatic Communities (Ed T.P. Simon), pp. 287-312. CRC Press LLC, Boca Raton, FL. Merritt R. W. and K. W. Cummins. (Eds.). 1996. An Introduction to the Aquatic Insects of North America, 3rd ed. Kendall/Hunt Publishing Company, Dubuque, IA. Merritt, R. W., J. R. Wallace, M. J. Higgins, M. K. Alexander, M. B. Berg, W. T. Morgan, K. W. Cummins, and B. Vandeneeden. 1996. Procedures for the functional analysis of invertebrate communities of the Kissimmee ri ver floodplain ecosystem. Florida Scientist 59:216-274. Milliken, G. A. and D. E. Johnson. 1992. An alysis of Messy Data. Volume 1: Designed Experiments. Chapman and Hall. New York, NY. Miltner, R. J. and E. T. Rankin. 1998. Primary nut rients and the biotic integrity of rivers and streams. Freshwater Biology 40:145-158. Morley, S. A. and J. R. Karr. 2002. Assessing the biological health of urban streams: tools for restoration and conservation. C onservation Biology 16:1498-1509. Norton, S. B., S. M. Cormier, M. Smith, R. Ch ristian Jones. 2000. Can biological assessments discriminate among types of stress? A case study from the Eastern Corn Belt Plains ecoregion. Environmental Toxicology and Chemistry 19:1113-1119. Pescador, M., Rasmussen, A., Harris, S. 1995 . Identification Manual for the Caddisfly (Trichoptera) Larvae of Florida. Florida Department of Environmental Protection. Pescador, M., Rasmussen, A., Richard, B. 2000. A Guide to the Stoneflies (Plecoptera) of Florida. Florida Department of Environmental Protection. Ransel K. P. 1995. The sleeping giant awakes: PUD No. 1 of Jefferson County v. Washington Department of Ecology. Environmental Law 25:255-283. Rasmussen, A. and M. Pescador. 2002. A Guide to the Megaloptera and Aquatic Neuroptera of Florida. Florida Department of Environmental Protection. Soil and Water Conservation Society (SWCS). 1989. Twenty-six ecological communities of Florida. Florida Soil and Water Conservation Society. 146 pp. Smith, D. 2001. Pennak’s Freshwater Invertebrat es of the United States. John Wiley and Sons. New York, NY. Snell, L. J. and W. Anderson. 1970. Water res ources of Northeast Florida. Florida Bureau of Geology. Invest. No. 54. 77 pp. Steedman, R. J. 1988. Modification and assessment of an index of biotic integrity to quantify stream quality in southern Ontario. Canadian Journal of Fisheries and Aquatic Sciences 45:492-501. Thompson, F. 1984. Freshwater Snails of Florida, A Manual for Identification. University Presses of FL, Gainesville, FL.62 Thorne R. St. J. and Williams W. P. 1997. The response of benthic macroinvertebrates to pollution in developing countries: a multimetric system of bioassessment. Freshwater Biology 37: 671-686. Thorp, J., and A. Covich (Eds.). 1991. Ecolog y and Classification of North American Freshwater Invertebrates. Vowell, J. L. 2001. Using stream bioassessment to monitor best management practice effectiveness. Forest Ecology and Management 143:237-244. White, W. A. 1958. Some geomorphic features of Ce ntral Peninsular Florida. Florida Bureau of Geology. Geological Bulletin 41. 92 pp. White, W. A. 1970. The geomorphology of the Florida peninsula. Florida Bureau of Geology. Geological Bulletin 51. 164 pp. Yoder, C. O. and J. E. DeShon. 2002. Using biological response signatures within a framework of multiple indicators to assess and diagnose causes and sources of impairments to aquatic assemblages in selected Ohio rivers and st reams. In: Biological Response Signatures: Indicator Patterns Using Aquatic Commun ities (Ed T.P. Simon), pp. 23-82. CRC Press LLC, Boca Raton, FL. Yoder, C. O. and E. T. Rankin. 1998. The role of biological indicators in a state water quality management process. Environmental Monitoring and Assessment 51:61-88. Zar J.H. 1984. Biostatistical Analysis, 2nd ed . Prentice-Hall, Inc., Englewood Cliffs, NJ.Appendix A. Long-lived taxaTaxonomic order, family, genus or species name of taxa usedto calculate long-lived taxa richness. Taxonomic synonyms present inthe current Florida data base are also shown. Long-lived (semi-voltine) taxa require morethan one year to complete their lifecycles.Order Family Genus Species (Long-lived) SynonymsDecapoda All families, genera and speciesBasommatophora LymnaeidaeFossariaAll speciesMesogastropoda AmpulariidaePomaceaAll speciesVeneroida CorbiculiidaeCorbicula Corbicula fluminea Corbicula manilensisUnionoida Unionidae All genera and speciesOdonata AeshnidaeBasiaeschna Basiaeschna janataBoyeriaAll speciesCordulegastridaeAll speciesGomphidaeGomphusAll speciesGomphurusHaegenius Hagenius brevistylusProgomphusAll speciesLibellulidaeMacromiaAll speciesSomatochloraAll speciesTetragoneuriaAll speciesEpithecaPetaluridaeTachopteryx Tachopteryx thoreyiPlecoptera PteronarcidaePteronarcys Pteronarcys dorsataLeuctridaeLeuctraAll speciesPeltoperlidaeTallaperla Tallaperla corneliaPerlidaeAcroneuriaAll speciesAgnetinaAgnetina annulipesEccoptura Eccoptura xanthenesAcroneuria xanthenesNeoperlaAll speciesParagnetinaAll speciesBanksianaBanksiellaPerlodidaeClioperla Clioperla clioIsoperla clioOrder Family Genus Species (Long-lived) SynonymsPerlinella Perlinella drymoAtoperla ephyrePerlinella ephyre/zwickiIsoperla Isoperla orataMegaloptera Corydalidae All 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,Oligopsyche, Phanopsyche,Pristosilo, AthripsodesLeptoceridaeCeracleaAll speciesLimnephilidaeIronoquia Ironoquia punctatissismaPycnopsycheAll speciesMolannidaeMolannaAll speciesPhryganeidaeBanksiola Banksiola concatenata Neuronia concatenataPtilostomis Ptilostomis postica Neuronia posticaAgrypniaAgrypnia vestitaPsychomyiidaeLype Lype diversaRhyacophilidaeRhyacophilaAll speciesUenoidaeNeophylaxAll speciesAppendix B. Sensitive taxaTaxonomic order, family, genus or species name; taxonomic synonyms in the current Florida database; whether the taxon wasincluded in the Florida index (2=very sensitive; 1=sensitive); and species name of Florida index taxa are listed for taxa identified assensitive to human disturbance.Order Family Genus Species (sensitive) Synonyms Florida indexAcariformes LebertiidaeLebertiaAll speciesIsopoda AsellidaeCaecidotea CaecidoteaAsellus1Caecidoteaspp.Amphipoda CrangonyctidaeCrangonyx CrangonyxEphemeroptera BaetidaeAcerpennaAcerpenna pygmaeaBaetis pygmaeusEphemerellidaeAll genera and speciesHeptageniidaeAll genera and species1Stenacron interpunctatumStenonema mexicanumintegrum2Stenonema exiguumStenonema smithaeLeptophlebiidaeAll genera and speciesLeptohyphidaeAll genera and speciesOdonata LibellulidaeMacromiaAll species 2Macromiaspp.PlecopteraAll families, genera andspecies2 All PlecopteraTrichoptera HydropsychidaeHydropsycheAll species 2Hydropsychespp.LeptoceridaeTriaenodesAll speciesPhilopotamidaeChimarra Chimarra2Chimarraspp.PsychomyiidaeLype Lype diversaColeoptera ElmidaeAncyronyxAncyronyx variegatusGonielmis Gonielmis dietrichiMacronychus Macronychus glabratusOrder Family Genus Species (sensitive) Synonyms Florida indexDiptera ChironomidaeMicrotendipesAll speciesParametriocnemusAll speciesPolypedilum Polypedilum avicepsRheocricotopusAll species 2Rheocricotopus robackiStempellinellaAll speciesTanytarsus Tanytarsus sp. d eplerTanytarsus Tanytarsus sp. m eplerTribelos Tribelos jucundumEmpididaeHemerodromia HemerodromiaSimuliidaeAll genera and species 2Simuliumspp.Appendix C. Very tolerant taxaTaxonomic order, family, genus or species name; taxonomic synonyms in the current Florida database; whether the taxon wasincluded in the Florida index (2=very sensitive; 1=sensitive); and species name of Florida index taxa are listed for taxa identified asvery tolerant to human disturbance.Order Family Genus Species (very tolerant) Synonyms Florida indexHoplonemertea TetrastemmatidaeProstoma Prostoma rubrumHaplotaxida NaididaeBratislavia Bratislavia unidentataDeroAll speciesNaisNais communiscomplexNais communisNais elinguisNais pardalisNais variabilisPristina Pristina synclitesTubificidaeHaber Haber speciosusLimnodrilusAll speciesCamptodriluscalifornicusLimnodrilusaurantiacusLumbriculida LumbriculidaeLumbriculusAll speciesRhynchobdellida GlossiphoniidaeAll genera and speciesBasommatophora AncylidaeHebetancylus Hebetancylus excentricusLaevapexAll speciesPhysidaePhysellaAll speciesPlanorbidaeAll genera and speciesMesogastropoda HydrobiidaePyrgophorus Pyrgophorus platyrachisPyrogophorusplatyrachisThiaridaeMelanoidesAll speciesOdonata LibellulidaePachydiplax Pachydiplax longipennisCoenagrionidaeArgiaArgia sedula2Argia sedulaOrder Family Genus Species (very tolerant) Synonyms Florida indexEnallagma Enallagma cardenium Enallagma coecumIschnuraAll speciesAnomalagrionHeteroptera MesoveliidaeAll genera and speciesLepidoptera PyralidaeAll genera and speciesColeoptera HaliplidaePeltodytesAll speciesDiptera ChironomidaeLarsiaAll species 1Larsia lurida1Larsia decolorataChironomusAll speciesTendipesChaetolabisCladopelma CladopelmaCricotopusAll species 2Cricotopus bicinctusCryptochironomusAll speciesDicrotendipes Dicrotendipes modestusDicrotendipes neomodestusGlyptotendipesAll speciesGoeldichironomusAll speciesKiefferulus KiefferulusParachironomusAll speciesPolypedilum Polypedilum beckaeAsheum beckaePedionomus beckaePolypedilum illinoiense grp.Polypedilumillnoense1Polypedilumillinoiense grp.Polypedilum tritum Chironomus tritumTanypusAll speciesPelopiaTanytarsus Tanytarsus sp. f eplerStratiomyidaeOdontomyia Odontomyia EulaliaTipulidaeTipula TipulaNippotipulaPlayttipulaYamatotipulaAppendix D. ANOVA output and regression results for SCI and BioRecon.Table 1. General linear model output for SCI vs. HDG by region (ne,ph, or ps). Neither region nor the interaction of HDG and regionwas statistically significant. Testfailed to detect a differencein SCI values due to region.Univariate Tests of Significance for AvgOfSCI6_100 (1NGM_4)SSdfMSFpIntercept403623.31403623.31832.2530.000000Region361.62180.80.8210.441846HDG262750.7162750.7284.8580.000000Region*HDG21142.92571.52.5940.077660Error37449.0170220.3Table 2. General linear model output for SCI vs. HDG by yearclass (1992-3, 1994-5, 1996-7, 1998-9,and 2000-1). The interactionterm was not significantly different for year class and HDG suggesting thelines were parallel (equalslopes). The year class alone,however, was significant with values for 1994-5 being slightly higher for SCI.Univariate Tests of Significance for SCI6_100 (1NGM_4)SSdfMSFpIntercept1627060116270605835.6570.000000Year class294847372.6440.032766HDG21344971134497482.3900.000000Year class*HDG279141980.7090.586005Error171749616279Table 3. ANOVA estimates of SCI variability forrepeat visits to the same site withinyear. Error estimate used to calculate confidenceinterval underlined.ANOVA for SCI (SCI6_100) (Same_day_SCI)EffectdfMSdfMSFp{1}STORETRandom601211.63639.41542188.72796.4200140.000000{2}YearRandom6269.43035.76223170.24021.5826480.1807271*2Random53134.48182.0000066.59522.0193850.002054Table 4. ANOVA estimates of SCI variability for laboratory subsampling. Error estimate used are underlinedANOVA Results: SCI subsampling (Metrics)EffectdfMSdfMSFp{1}SitenameRandom581393.98611832.8631242.417950.00Table 5. ANOVA estimates of BioRecon variabilityfor repeat visits to the same site within year. Error estimate used to calculateconfidence interval underlined.ANOVA for BioRecon (BR3_10) (New metrics multiple_2)EffectdfMSdfMSFp{1}STORETRandom12615.91691117.37711.6960099.3849240.000000{2}yearRandom86.02260156.00001.4588404.1283520.000171