Development and Testing of Biomonitoring Tools 

for Macroinvertebrates in Florida Streams 

Leska S. Fore 

Statistical Design 

136 NW 40th 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 32399-2400 
ii

TABLE OF CONTENTS 

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

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

List of Figures............................................................................................................... iv

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..................................................................................................................... 49
iii

Human disturbance gradient ..................................................................................... 49 

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

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

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

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

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

References.................................................................................................................... 58

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 output 
iv

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 NH3, 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 metrics 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......................................................... 47 
ABSTRACT 

Florida DEP assesses the chemical, physical and biological condition of hundreds of 

stream sites each year. This study used measures of hydrologic condition, riparian and 

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

gradient of human disturbance for stream sites. I evaluated the sensitivity and tolerance of 

over 1000 stream macroinvertebrate taxa using the human disturbance gradient (HDG). I 

tested for correlation between the HDG and 36 biological measures of the stream 

macroinvertebrate assemblage (metrics) and selected the 10 most highly correlated 

metrics within six categories of biological organization. The six categories (and their 

selected metrics) were taxonomic richness (total number of taxa, number of Trichoptera 

taxa, and number of Ephemeroptera taxa); feeding group (percentage filterer individuals); 

voltinism (long-lived taxa richness); habit (clinger taxa richness); community structure 

(percentage dominance of the most abundant taxon and percentage Tanytarsini midges); 

and sensitivity and tolerance (sensitive taxa richness and percentage very tolerant 

individuals). 

Metrics were combined into an overall stream condition index (SCI) by 

transforming metric values into unit-less scores and summing 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 higher 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 SCI could detect assuming a single sample. For two site 

samples, the SCI could detect five categories of biological condition. 

Biological metrics were also tested for a second stream macroinvertebrate 

sampling protocol (BioRecon) based on sorting 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; all were highly correlated with both SCI and 

HDG. From these metrics the BioRecon index was calculated as the sum of scores for the 

six metrics. The BioRecon index could detect 2.5 categories of biological condition for 

one sample and 3.5 categories for two samples. 
2

INTRODUCTION 

Florida, along with a handful of other states, 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 Water Act, states are required to define 

designated uses for specific water bodies and develop criteria to protect them (Karr, 

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

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

biological assemblage (Yoder and Rankin, 1998). 

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

categories are ranked in order of the level of protection that they require: Class I – 

drinking water; Class II – shellfish propagation or harvesting (primarily for coastal 

waters); Class III – recreation and propagation and maintenance of a healthy, wellbalanced population of fish and wildlife; Class IV – agricultural water; and Class V – 

navigation and industrial use. This document describes the development and testing of 

biological monitoring tools for macroinvertebrate stream samples for the assessment of 

freshwater streams that are applicable to Class I, III, IV, and V waters. 

The U.S. Environmental Protection Agency (EPA) has developed numerous 

guidance documents to support biological assessment (EPA, 1998; Barbour et al., 1999; 

Bowman et al., 2000; Jackson et al., 2000; EPA, 2001; EPA, 2002a; EPA, 2003a, b, c; 

Fore, 2003). These documents typically emphasize multimetric indexes to measure the 

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

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, Europe and Asia (Thorne and Williams, 1997; 

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

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

biological assessment, although other techniques have been developed (Hawkins et al., 

2000; EPA, 2003b, c). 
3

Background 

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

the early 1990’s. They tested metrics by comparing 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 selected on the basis of their correlation 

with a gradient of human disturbance (Bryce et al., 1999). The sensitivity and tolerance 

of over 1000 taxa were also evaluated individually using the human disturbance gradient 

(HDG). I used confidence intervals to define the number of categories of biological 

condition that the indexes could 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 and Yoder, 2004). These 

statistically robust categories may also be used 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 different sampling and identification protocols. 

The stream condition index (SCI) was derived from laboratory processing and 

identification of samples while the BioRecon (biological reconnaissance) index was 

based on field sorting and laboratory identification. This study develops new 

bioassessment tools for both sampling 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 recruitment 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 demarcation between the panhandle and peninsula, 

with the northeast region straddling the upper Suwannee 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 exceed 200 to 250 ft. Recruitment of stream organisms (over 

geologic time) from upstream, higher elevation areas (Georgia and Alabama) makes the 

panhandle richer in freshwater stream taxa than other parts of the state (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 elevation 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 south into Lake Okeechobee and the St. Johns 

River flows north to the Atlantic. Streams in the peninsula tend to have lower water 

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

fewer taxa of freshwater invertebrates due to lack of upstream recruitment sources that 

did not experience marine inundation. Plecoptera, for example, are common in the 

panhandle but extremely rare in the peninsula, probably due to a combination of poor 

recruitment, warmer temperatures, and lower water velocities. Peninsular vegetation 

communities on the ridge consist of longleaf pine/turkey oak forests (Pinus 

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

(Pinus teada) with palmetto/gallberry understory (Serenoa repens/Ilex glabra), and in 

depressional areas are marsh/wet prairies (maidencane, pickerel weed), and hardwood 

wetlands of sweetbay (Magnolia virginiana), cypress (Taxodium spp.), and ash (Fraxinus

spp.; SWCS, 1989). The dominant land use is pasture, cropland (e.g., watermelons, 

nursery products, 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 drainage, and the Sea Island flatwoods. From 

the perspective of stream invertebrates, this region tends to be intermediate between the 

panhandle and peninsula regions. With the exception of the Black Creek basin with 

headwaters approximately 200 ft above sea level, other streams in this area tend to be low 

relief, wetland systems (Snell and Anderson, 1970). Plant communities consist of 

longleaf pine/turkey oak (Pinus palustris/Quercus laevi) on the sandy uplands, hardwood 

wetlands of cypress (Taxodium spp.), tupelo (Nyssa spp.), and loblolly bay (Gordonia 
5

lasianthus), pine flatwoods, and marsh (FNAI, 1990). Jacksonville is the only major 

population center. 

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

with minimal human influence to evaluate the influence of geographic locations on 

species assemblages. They found that the panhandle, peninsula, and northeast regions, 

which are primarily defined according to drainage patterns, were better predictors of 

species assemblages than were sub-ecoregions described by Griffith et al. (1994). 

Regional differences translate into expectations of higher taxonomic diversity in the 

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

area. 

Site selection and data sets 

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

macroinvertebrate samples from stream sites throughout the state. Many sites have been 

visited repeatedly. Sites and visits were selected from this large 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 sites, for example, when one variable in the 

analysis had only one value for each site, such as HDG. 

The relationship between human disturbance and biological condition was 

evaluated in several ways. To develop the human disturbance gradient (HDG), FDEP 

biologists selected sites from their regions that represented the broadest possible range of 

human influence. First, to insure that the 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 evaluate the influence of field sorting vs. 

laboratory sorting. To evaluate SCI variability, 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 during different years, and a few were 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 seasonal (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 variability for the BioRecon protocol, two 

additional data sets were used. The first data set included 116 sites with 

macroinvertebrate samples collected according to both the BioRecon and the SCI 

sampling protocol. I tested BioRecon metrics 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 initially to develop the HDG. To evaluate the 

variability of the BioRecon metrics and index, 128 sites were selected with two to seven 

visits per site. 

Table 1. Description of various data sets, number of sites sampled, total number of visits 

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

visits. Some sites were included in more than 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 & tolerant 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 0 
7

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 associated with different values of HDG. In 

all figures, the box defined the 25th and 75th 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 25th to the 75th 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 minimized 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 recorded. All site visits were used within a 

particular analysis unless specifically 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, changes in water volume or flow timing can 

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

of physical habitat types for food, shelter and reproduction. Greater diversity of substrate 

size, types of vegetation, and bank architecture translate into a greater variety of 

organisms. Third, water quality may be evaluated in terms of turbidity, conductivity, 

nutrient concentrations, or the presence of other chemicals or metals. Fourth, energy 

source describes the seasonal availability, type of organic material, and size and shape of 

material available as food. Fifth, introduction of exotic species or disease 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 scoring system based on their knowledge of water removal, 

patterns of drought, and hydrographs for the sites (Table 2). To measure habitat 

condition, FDEP biologists routinely calculate a habitat condition index whenever stream 

macroinvertebrate samples are collected. The index evaluates substrate condition and 

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

bank stability, and the width and condition of 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 discretion of the biologist based on the extent of disturbance. 

These changes were associated with human disturbances, not natural events such as 

hurricanes or extreme droughts. 

Condition Score Description 

Excellent 1–2 Flow regime as naturally occurs (slow and fairly continual 

release of water after rains), few impervious surfaces; high 

connectivity with ground water and surface features 

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

berms) 

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

some wetland drainage, some impervious surfaces, some 

ditching 

Moderate 5–6 Flow regime moderately altered; hydrograph moderately 

flashy (scouring after rain events with subsequent 

reductions in flow), groundwater pumping evident; much 

wetland drainage, topographic alterations reduce natural 

water input; more impervious surfaces, dams/control 

structures change normal water delivery schedule 

Poor 7–8 Flow regime highly altered; hydrograph very flashy 

(scouring after rain events with subsequent reductions in 

flow, leading to stagnant or dry conditions, related to large 

amounts of impervious surfaces and/or ditching); water 

withdrawals and impoundments or control structures 

severely alter flows, large amounts of impervious surfaces 

Very poor 9–10 Flow regime entirely human controlled; hydrograph very 

flashy (scouring after rain events with subsequent 

reductions in flow, leading to stagnant or dry conditions, 

related to impervious surfaces and ditching); intensity of 

water withdrawals and impoundments fundamentally alter 

the nature of the ecosystem 
9

FDEP biologists also routinely collect measurements of turbidity, temperature, 

dissolved oxygen, conductivity and nutrient concentrations. Total phosphate (TP), 

ammonia (NH3), total Kjeldahl nitrogen (TKN), and nitrites/nitrates (NOx) were 

measured to summarize nutrients. I selected NH3 to summarize water quality because it 

was the most consistently associated with the other water quality measures and had the 

most complete record of data. TP had similar high correlations and complete data, but 

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

more general indicator of both urbanization and agriculture. 

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

the stream was not available. However, Brown and colleagues have developed an index 

to estimate the intensity of human land use based on nonrenewable energy flow (Brown 

et al., 1998; Brown and Vivas, in review). The landscape development intensity (LDI) 

index was calculated as the percentage area within the catchment of particular types of 

land use multiplied by the coefficient of energy use associated with that land use, 

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

= ∑( ) LDI LDIi* %LUi . 

Where, 

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

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

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

records and published literature, translated reported energy use into standardized units, 

then averaged the values by land use type and standardized to a per unit area. Only nonrenewable energies were used in calculations and included electricity, fuels, fertilizers, 

pesticides, and water (both public water supply and irrigation). 

 Land use was derived from aerial photos manipulated as layers in a 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 region was somewhat arbitrary, LDI was also 

calculated for the entire upstream catchment (LDI_WS) in order to determine which 

spatial scale was a better predictor of site condition. I selected LDI_BF to include in 

HDG because it was slightly more highly correlated with NH3, the hydrologic index, and 

the habitat index (Table 4). 
10

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

Higher values indicate greater intensity of human land use (Brown and Vivas, 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 Pasture (with livestock) 3.74 

Row crops 4.54 

Single Family Residential (Low-density) 6.79 

Recreational / Open Space (High-intensity) 6.92 

High Intensity Agriculture 7.00 

Single Family Residential (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 10.00 
11

To define the human disturbance gradient (HDG), I converted the four measures 

of human disturbance to unit-less scores and 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 measure, hydrologic condition, had an additional 

value of 4 available to indicate extreme levels of disturbance (Table 5). Thus, values for 

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

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

richness plotted against each measure of disturbance. I identified values for each 

disturbance measure for which the number of EPT taxa was consistently lower. For NH3, 

9 sites had no information. Most of these sites had values for other measures indicating 

minimal human disturbance. I assumed for these sites that NH3 was < 0.1 mg/L. 

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

buffer, LDI for the upstream watershed, natural log of watershed size, HDG, the current 

SCI, and the old SCI for 223 stream sites (except NH3 for which n=214). Repeat visits to 

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

(Spearman’s r, p < 0.01). 

NH3 Hydro Habitat LDI_BF Ln(Size) HDG SCI 

NH3 — 0.43 –0.45 0.39 –0.20 0.50 –0.56 

Hydro 0.43 — –0.66 0.61 –0.36 0.84 –0.71 

Habitat –0.45 –0.66 — –0.64 0.41 –0.78 0.66 

LDI_BF 0.39 0.61 –0.64 — –0.37 0.79 –0.60 

LDI_WS 0.34 0.56 –0.55 0.74 –0.28 0.70 –0.59 

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

HDG 0.50 0.84 –0.78 0.79 –0.41 — –0.76 

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

Old SCI –0.43 –0.59 0.56 –0.54 0.29 –0.69 0.86 
12

Stream macroinvertebrate sampling 

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

differ in the number of dip net sweeps collected, the location of sample sorting (field vs. 

laboratory), and the final index used for assessment. The BioRecon sampling protocol 

was designed to be a quick field assessment that could yield a same-day measure of 

stream site condition. The SCI sampling protocol was meant to be more precise. Both 

protocols use a D-frame dip net with a 0.25 m opening and a 600 μm-mesh net. Both 

protocols distribute the sweeps among the best available substrates on an approximately 

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

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

and undercut banks, partially decayed leaf packs, aquatic vegetation, and rocks or large 

cobble. During SCI sampling, if four productive substrates are present (snags, roots, 

vegetation and leaf packs), each substrate would be sampled four times, with sand being 

sampled four times, for a total of 20 sweeps. 

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

Squares are randomly selected and all the material within a square is sorted. 

Macroinvertebrates are identified to the lowest practical taxonomic level. Squares are 

selected until the target sample size of 100 individuals 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 derived 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 

NH3 <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 >3.5 
13

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

Samples are sorted for live macroinvertebrates in the field. The sample is spread in a pan 

and the biologist searches through all the material for every unique taxon present. Each 

taxon is preliminarily identified in the field and representative specimens of each taxon 

are preserved and returned to the laboratory for final identification under a dissecting 

microscope. From this field-sort protocol, the number of taxa present at a site can be 

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

Metric development and testing 

Several authors have described relevant candidate metrics for stream macroinvertebrate 

assemblages (Barbour et al. 1996 and 1999; Kerans and Karr, 1994; and Klemm et al., 

2002). For this study, 36 candidate metrics related to taxonomic richness, 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 biological condition in response to increased human 

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

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

were reliable indicators of disturbance. 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 unavailable for metals, pesticides or other toxics. 

In contrast, I considered an inconsistent response at more highly disturbed sites to be 

unacceptable. 

Taxonomic richness. Richness was calculated as the number of unique taxa 

found within a particular group such as the Ephemeroptera, Plecoptera, 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 metrics were calculated as the 

number of individuals of a particular group divided by the total number of individuals in 

the sample times 100 (percentage). 
14

Table 6. Candidate metric, correlation with the human disturbance gradient (HDG), and 

whether the metric was included in the final 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 yes 
15

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 published sources to identifiy taxa that require more than one 

year to complete their life cycles (Appendix 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 require consistent flows and other favorable 

conditions throughout the year to complete their life cycles. Data on generation length 

were not available for all Florida taxa; therefore, 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 Libellulidae (Odonata); the family Leuctridae and 

selected genera in the Perlidae and Pteronarcidae (Plecoptera); the family Corydalidae 

(Megaloptera); and the genera Ceraclea, Pycnopsyche, Molanna, and Rhyacophila

(Trichoptera). Several Coleoptera taxa (beetles) also require more than a year to complete 

their life cycles, but were not included because the FDEP database does not currently 

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

Non-insects included in the long-lived list were 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 have morphological and behavioral adaptations that allow 
16

them to cling to substrate (Merritt and Cummins, 1996). Channel dredging and removal 

of riparian vegetation eliminates the habitat preferred by clingers. 

Community structure. Percentage dominance was calculated as the number of 

individuals in the most abundant taxon divided by the total number of individuals in the 

sample. ‘Hyperabundance’ represented a measure of the density of stream 

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

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

many species. The actual number of individuals present in the total sample was not 

recorded; however, the number of grids in the sample tray that were searched to best 

approximate the target number of 100 individuals was recorded. 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 (which is more accurately termed a metric), 

very sensitive taxa are given a score of 2 and sensitive taxa a score of 1. The scores are 

summed to calculate this metric. 

I used the HDG to evaluate the sensitivity and tolerance of 1195 taxa names. The 

taxa names included taxa that were not unique because the data base included synonyms 

for the same taxa as well as specimens identified to genus and species for the same genus. 

Because many sites had multiple visits and because less disturbed (reference) sites tended 

to have a greater number of visits, I summarized data for each taxon by site to avoid bias 

associated with greater sampling effort at less disturbed sites. I counted 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 groups 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 NH3) 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’ and ‘worse’ rather than ‘good’ and ‘poor.’ The relatively high 

number of sites selected to represent ‘good’ condition reflects the large number of 

reference sites in the 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 continuity correction). As for any test, the 
17

number of sampling points increases the power 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 occurred at 38 sites, it would 

only need to occur at 30 of the good sites (79%) and would still be statistically 

significant. For this reason, 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 with 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 statistical rule to define the minimum number of occurrences 

needed for statistical significance but made the standards for selection more strict (p << 

0.05) for taxa that were more common. 

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

tolerant taxa can also “tolerate” good conditions. Instead, very tolerant taxa were defined 

as those found in at least 10 sites and for which >= 50% of their occurrences were in the 

poor range of the HDG. In some cases, multiple species within a genus failed to meet the 

criteria for inclusion as sensitive (or very tolerant) because they were not present at 

enough sites. If several species within a genus (or family) were consistently found at 

good (or poor) sites, 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 abundance metrics could not be calculated for 

these data; therefore, only taxa richness metrics 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 macroinvertebrates) and another for the BioRecon protocol (4 dip 

net samples and field sorting). 

For SCI, metrics were transformed into unit-less scores by determining the 95th

percentile values for each metric and then dividing each metric value by its range and 

multiplying by 10. Metrics that declined with disturbance were scored from 0–10 and 

metrics that increased with disturbance were scored from 10–0 so that the best biological 

condition was consistently scored as a 10. For skewed metrics, the natural log was 

calculated before dividing by the metric range. This process was done separately for the 

northeast, peninsula and panhandle regions. I tested for differences in metric expectation 

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

against HDG and adjusted metric scoring as necessary 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 evaluate the overlap of the BioRecon and SCI 

metrics measured at the same sites, I adjusted metric scoring for the BioRecon metrics as 

needed. In order to distinguish between the BioRecon and SCI, BioRecon metrics were 

scored from 0–1 (rather than 0–10). In this way, the index values provide a quick clue as 

to which index is being reported. 

After correlation with disturbance was confirmed for both indexes, 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 test 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, such as site differences, year differences, 

and repeat visits, I used an ANOVA model to estimate variance components. For SCI and 

BioRecon I estimated variance associated with site, year, site x year interaction, and 

error. Error was defined as variance associated with repeat visits to the same site within a 

year. For SCI data, most of the repeat visits within a year occurred on the same day rather 
19

than at different times during the year. For BioRecon, most of the repeat visits were on 

different days within the same or a different year; very few were same day. 

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

90% confidence interval for both indexes and the number of categories of biological 

condition that each index can reliably detect (Zar, 1984). Confidence limits were 

calculated as: 

SCI ⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛

± *1.645

2

n

s , 

where s2 = error variance estimated from ANOVA (mean squared error), and 

n = number of samples taken from the site. 

To calculate the number of categories each index could detect 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 example, a 90% confidence interval could also be 

constructed based on the difference of SCI values for two sites. This approach would 

yield a smaller confidence interval because instead of n=1 in the equation above, n would 

equal 2, because two SCI values would be involved in calculating the difference. 

Currently there are no standards for which statistical model to use; therefore, I used 

confidence intervals around SCI for a single site because they are conceptually simple 

and somewhat conservative from a statistical point of view. In other words, this approach 

is less likely to be controversial (Johnson, 1999; Hoenig and Heisey, 2001). 

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. For this model, error was defined as the 

differences associated with subsamples from the same site visit. 

Statistical model assumptions 

The method described here for calculating confidence intervals assumes that the 

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

properties of multimetric indexes and found that multimetric indexes for fish and 

invertebrates are unimodal and symmetric (Fore et al., 1994; Doberstein et al., 2000; 

Blocksom 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 calculate 90% confidence limits or values 
20

from Student’s t distribution. For small sample sizes, the t-distribution should be used for 

calculating confidence limits because variance 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 variance for the SCI and BioRecon index were 

unbalanced, that is, the number of visits to each site varied for each site. In contrast, the 

data set used to evaluate laboratory subsampling was balanced with three subsamples for 

each site visit. ANOVA assumes a balanced design but is fairly robust to violations of 

this assumption (Milliken and Johnson, 1992). The primary consequence of an 

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

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

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

relative contributions of year and error to the metrics and indexes. For estimation of the 

error term, the large sample sizes mitigate any potential inaccuracy associated with 

unbalanced cells. 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 the HDG yielded a reliable measure of human 

disturbance (Figure 1). 

Correlations among the four measures of human disturbance used to define the 

HDG were high, indicating good agreement among the different measures (see Table 4). 

All the individual measures, however, were more highly correlated with the HDG, 

suggesting that the HDG was a better overall measure of general human disturbance. The 

SCI showed a similar pattern and was more highly correlated with the HDG than the 

individual measures of human disturbance. 

Metric selection 

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

metrics. Diptera dominated the samples with 23 taxa counted on average; of these taxa, 

about half were in the family Chironomidae. Of the remaining groups, 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 total taxa richness went from 33 to 19 on 

average. Number of Ephemeroptera taxa declined from two to zero; Trichoptera taxa 

from four to zero; clinger taxa from eight to zero; and sensitive taxa from nine to zero. 

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

abundance of tolerant individuals increased from 5% to 69%. 

In general, I selected at least one metric 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 (redundant 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 levels of disturbance (Figure 2). Correlation 
Figure 1. Number of EPT taxa and total number of taxa declined as human 

disturbance increased. Nearly identical regression lines for two independent 

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

and biological condition. Number of EPT tax

a

0 2 4 6 8 10

0

5

10

15

20

25

Round 1

Round 2

Human disturbance gradient (HDG)

Number of taxa

0 2 4 6 8 10

0

20

40

60

22
0

10

20

30

40

50

60

Total number of taxa

 Median 

 25%-75%

 Non-Outlier Range

 Outliers

0

1

2

3

4

5

6

7

Number of Eph. taxa

0

20

40

60

% Filterer

-1

0

1

2

3

4

5

6

Number of long-lived taxa

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

disturbance gradient. All metrics declined with an increase in human 

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

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

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

less than or equal to values on x-axis.

N = 80 41 25 8 16 6

0.8 2.4 4 5.6 7.2

Human disturbance gradient

0

2

4

6

8

Number of Trichoptera taxa

0.8 2.4 4 5.6 7.2

Human disturbance gradient

0

5

10

Number of clinger taxa

23
24

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 because they have the 

potential to respond to different types of disturbance. I did not select Plecoptera taxa 

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

taxa richness had the next highest correlation and was selected as an indicator of the 

variety of taxa that a stream site could support. 

Feeding group. Relative abundance of filterers (percentage filterer individuals) 

had the highest correlation with HDG and the most consistent relationship with HDG 

when graphed. Other feeding groups failed to show the consistent decline with 

disturbance that percentage filterer did. I also tested this metric without Cheumatopsyche 

(a tolerant genus of Trichoptera); but the changes in correlation were very small and so 

all taxa were retained (Spearman’s r = –0.46 with Cheumatopsyche and –0.47 without). 

Voltinism. Correct information about generation times was not easily obtained 

for all Florida taxa; therefore, I only tested long-lived taxa richness and percentage longlived individuals. Long-lived taxa included semi-voltine insects and non-insects that 

require greater than one year to complete their life cycles. Long-lived taxa richness was 

more highly correlated with HDG than was relative abundance of long-lived individuals; 

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

Habit. Clinger metrics measured either as taxa richness or as percentage of 

individuals were among the most highly correlated metrics with HDG. I selected clinger 

taxa richness for inclusion in the SCI. 

Community structure. Percentage dominance increased with HDG and was 

included in the SCI (Figure 3); hyperabundance failed to correlate with HDG. Although 

percentage Ephemeroptera and Trichoptera individuals (relative abundance) were both 

strongly correlated with HDG, they were calculated from the same information used to 

calculate taxa richness of those two groups. Because the taxa richness metrics were more 

highly correlated with HDG, relative abundance of Ephemeroptera and Trichoptera were 

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

was not quite as high as the other composition metrics (–0.45), but was included in 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 tolerant taxa was ~10% of the taxa tested. Many 

sensitive taxa belonged to the Ephemeroptera or Trichoptera; several chironomids were 

also included (Appendix B). All the Plecoptera were included though most of the genera 
25

and species in this group were too rare to test. Considered as a group, out of 191 

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

Plecoptera taxa showed a strong tendency to be found in the best of the good (minimally 

disturbed) sites. Many of the sensitive taxa selected by this study were previously noted 

by Beck (1954) and used in the Florida index; but several taxa were new to the list. Many 

of the taxa included on Beck’s list were too uncommon to test with this 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 chironomids along with several odonate taxa. One 

species of tubificid worm, Limnodrilus hoffmeisteri, was included as very tolerant 

although it was present at 47% of the ‘poor’ sites 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 significant result will occur with the approximate frequency of 

p-value. For a p-value of 0.05 and 168 tests, approximately eight taxa may be selected by 

chance alone (0.05 * 168 = 8.4). The number of sensitive taxa falsely selected from this 

analysis is likely much lower because the criterion for selection was more strict than 

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

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

BioRecon metrics. The six taxa richness metrics calculated from BioRecon data 

were significantly correlated with HDG and highly correlated with SCI (r ranged from 

0.60–0.73; Table 7). The four SCI metrics calculated as percentages could not be tested 

because relative abundance was not recorded for BioRecon data. For this reason, I tested 

the very tolerant metric in terms of taxa richness rather than as a percentage. Its 

correlation was poor with HDG and non-significant with SCI and therefore was not 

included in the BioRecon index. 

Multimetric index construction and evaluation 

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

index. Most of the SCI metrics required regional scoring (Table 8). In general, Taxa 

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

For the SCI, the highest correlation between metrics occurred between Trichoptera and 
20

40

60

80

% Dominance

0

5

10

15

Number of sensitive taxa

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

disturbance gradient. Percent dominance and very tolerant individuals 

increased with disturbance; sensitive and Tanytarsini midge taxa declined. 

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

than 175 or less than 75 individuals were excluded before averaging. Number 

of sites within each box are shown (N).

0.8 2.4 4 5.6 7.2

Human disturbance gradient

0

30

60

90

% Very tolerant individuals

0.8 2.4 4 5.6 7.2

Human disturbance gradient

0

10

20

30

40

% Tanytarsini

N = 80 41 25 8 16 6

26
27

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

whether these metrics were redundant, I evaluated 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 statistical correlation was due to an underlying 

association with human disturbance rather than redundant information. 

Two metrics were somewhat skewed, that is, their distributions had long ‘tails’ 

with sparse values at one end of their distribution. For these metrics, I calculated the 

natural log of metric values before dividing by the range of the metric values. In this way, 

metric scores were more evenly spread between 0–10. Two SCI metrics increased with 

disturbance (% dominance and % very tolerant individuals), all others declined with 

disturbance. 

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

richness tended to be lower in two regions while Ephemeroptera taxa richness was much 

higher in the panhandle (Table 9). More long-lived taxa tended to be found in all three 

regions for BioRecon samples. 

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

HDG and SCI. Metrics included in the BioRecon (and SCI) index are noted (*). All 

correlations shown were statistically significant (Spearman’s r, p <0.01, one-sided test, 

n=116 for SCI, n=53 for HDG). Data from multiple visits were averaged for each site. 

Metric/Index HDG SCI 

BioRecon Index -0.58 0.75 

Total taxa * -0.36 0.53 

Ephemeroptera taxa * -0.47 0.62 

Trichoptera taxa * -0.65 0.68 

Long-lived taxa * -0.46 0.65 

Clinger taxa * -0.48 0.67 

Sensitive taxa * -0.63 0.69 

Very tolerant taxa NS NS 

Old BioRecon Index -0.41 0.58 

EPT -0.61 0.71 

Florida Index (metric) -0.53 0.69 
28

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

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

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

appropriate. Metric ranges listed in reverse order indicate metrics that increased (rather 

than declined) with disturbance. For two marked metrics ( * ), the natural log was 

calculated for the range of values before scoring. 

SCI metric Northeast Panhandle Peninsula 

Total taxa 16–42 16–49 16–41 

Ephemeroptera taxa 0–3.5 0–6 0–5 

Trichoptera taxa 0–6.5 0–7 0–7 

% Filterer 1–42 1–45 1–40 

Long-lived taxa 0–3 0–5 0–4 

Clinger taxa 0–9 0–15.5 0–8 

% Dominance 54–10 43–10 54–10 

% Tanytarsini * 0–26 0–26 0–26 

Sensitive taxa 0–11 0–19 0–9 

% Very tolerant * 78–0 36–0 59–0 

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

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

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

appropriate. 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 0–9 
29

SCI was highly correlated with HDG with minimal overlap between values for 

extremely disturbed and minimally disturbed sites (Figure 4). The BioRecon index was 

also highly correlated with HDG and was more responsive across the range of human 

disturbance than the original BioRecon index (Figure 5). 

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

response to human disturbance within each region. The regression lines for 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 with two sites in the panhandle with SCI 

values that were higher than expected. I did 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 most disturbed sites tended to have small 

watersheds. To avoid this confounding association, 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-year 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 association between SCI and HDG was 

independent of year. 

To verify that the SCI was correlated with 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 disturbance increased. Index values were 

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

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

N = 79 42 25 7 17 6

0.8 2.4 4.0 5.6 7.2

Human disturbance gradient

0

20

40

60

80

100

SCI

 Median 

 25%-75% 

 Non-Outlier Range

 Outliers

30
Figure 5. Original BioRecon index based on three metrics (total taxa, EPT 

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

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

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

in boxes are <= value on x-axis.

N = 27 8 7 7 4 

0

1

2

3

Old BioRecon index

0135

Human disturbance gradient

0

2

4

6

8

10

BioRecon index

 Median 

 25%-75%

 Range

 Outliers

31
Figure 6. SCI declined as human disturbance increased. Regression lines for 

the peninsula and northeast had nearly identical slopes indicating a similar 

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

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

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

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

66 (PS), and 52 (PH).

Human disturbance gradient 

SCI

02468

0

20

40

60

80

100

 NE

 PS

 PH

32
Figure 7. SCI was not significantly associated with watershed size (catchment 

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

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

catchment area and this relationship created a spurious correlation between 

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

catchment area shown below figures (in acres).

Ln(Catchment area)

SCI

5 7 9 11 13 15

0

20

40

60

80

100

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

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

60,000440,000

33
Figure 8. SCI showed a similar response to human disturbance for different 

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

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

consistent relationship between SCI and human disturbance for different time 

periods. 

Human disturbance gradient 

SCI

0 2 4 6 8 10

0

20

40

60

80

100

34
Figure 9. Correlation between SCI and the human disturbance gradient was 

high for an independent test of 23 sites not included in any previous analyses 

(Spearman’s r = -0.81, p<<0.01). Four sites were missing data for NH3 (open 

circles) and two sites had >300 individuals (open triangles). 

012345678

Human disturbance gradient

0

20

40

60

80

100

SCI

35
36

Statistical precision of indexes 

The target number of individuals for laboratory 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 had >175 individuals, 

with some samples ranging up to hundreds of individuals. At the other end of the scale, 

only 36 samples (6%) had less than 90 individuals in the entire sample. 

When I compared small sample sizes with standard (or large) sample sizes from 

the same sites, samples with <75 individuals contributed a disproportionately large 

amount of variability to the repeat visits (Figure 10). SCI values were typically much 

lower for these small samples. For this reason, 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 variance included differences associated with sites, years, 

visits within years, season (summer vs. winter), and laboratory subsampling. 

From the ANOVA analysis, the variance associated 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 error were similar (54.6, 12% and 

66.6, 15%; Figure 11; see Appendix D). For this model, error was defined as repeat visits 

within years, about half of which were on the same day. 

I used the estimate of error variance 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 around the observed values for repeat visits captured all visits 

to the least disturbed sites, that is, sites with SCI values >73, but failed to include all the 

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

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

The fact that undisturbed sites did not show this pattern while disturbed sites did, 

suggests that SCI was measuring real changes in biological condition rather than ‘noise’ 

associated with natural variability. 

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

the SCI (0–100) yielded 3.7 categories of biological condition. Calculating the 

confidence interval for two site visits yielded a confidence limit of +/– 9.5, or a length of 

19 points. This confidence length translated into approximately five categories of 

biological condition based on the average SCI value for two site visits. Current FDEP 

protocol specifies that the two most recent visits be used for assessment. I calculated 
Figure 10. SCI values for repeat visits to 62 sites are shown. Sites were first 

sorted according to SCI value. Individual site names are not shown, but all SCI 

values for each site are plotted at a single point on the x-axis. Thus, vertical 

lines of points represent the range of SCI values at each site. Samples with 

< 75 individuals are indicated by solid points (upper panel). Lines (lower 

panel) represent the +/- 15 point confidence interval for the SCI when small 

samples were eliminated.

0

20

40

60

80

100

SCI

Site

0

20

40

60

80

100

SCI

37
Figure 11. Estimate of variance components and their relative percentage for 

SCI. The largest percentage of variance was associated with site differences. 

Replicates collected within a single year (primarily on the same day) 

accounted for approximately 15% of the total observed variance. Year 

differences accounted for 4% of the variance. The interaction of site and year 

accounted for 16% of the total variance. 

Error, 66.6, 15% S x Y, 54.6, 12%

Year, 7.7, 2%

Site, 309.9, 71%

SCI Variance Components 

38
39

average metric values associated with these SCI intervals to summarize the biological 

condition within each category (Table 10). I used these average values to create narrative 

descriptions for the numeric categories 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 typically contributed the 

largest percentages to the overall variance (Figure 12). The exception was long-lived taxa 

richness that had a large error component (with 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 eliminate 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 was greater in the northeast 

(6.1 points) than the panhandle (1.6 points) or peninsula (3.6 points; Figure 13). Using 

the same data, I evaluated metrics to determine which were contributing to the seasonal 

differences in SCI. Five metrics had values in the winter indicating better biological 

condition; these were Ephemeroptera taxa richness, % filterers, % Tanytarsini, sensitive 

taxa richness, and % very tolerant. Long-lived taxa richness had higher values in the 

summer, opposite of SCI. Although significant, most 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 differences because it compares each site to itself. 

The exception was % Tanytarsini. With a difference of 4.6% compared to a possible 

range of 0–26%, it had the largest relative change from summer to winter. I did not 

modify metric scoring to correct for seasonal differences because most metric differences 

were very small and the difference between SCI summer and winter values was small 

(3.5.%) relative to the site differences through time. 
40

Table 10. Average metric values for ranges of SCI values corresponding to good, fair, 

poor and very poor condition. Note that metric values have not been corrected for 

different expectations associated 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 60 
41

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 samples. 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 metrics, 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 natural conditions; 20–30% loss of 

Ephemeroptera, Trichoptera and long-lived taxa; 40% loss of 

clinger and sensitive taxa; percentage of very tolerant 

individuals doubles 

Poor [19–46) Very different from natural conditions; 30% loss of total taxa; 

Ephemeroptera, Trichoptera, long-lived, clinger and sensitive 

taxa uncommon or rare; Filterer and Tanytarsini individuals 

decline by half; 25% of individuals 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 similar 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, Trichoptera, clinger and sensitive taxa 

Fair [43–62) 25% loss of total taxa; 50% loss of Ephemeroptera, 

Trichoptera, clinger, and sensitive taxa; 33% loss of long-lived 

taxa 

Poor [24–43) High percentage of individuals present belong to very tolerant 

taxa; only tolerant Ephemeroptera, 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 tolerant 
Figure 12. Variance components for SCI and its metrics. For the index and 

several of the metrics, variance associated with site differences represented the 

largest component of variance. Error variance associated with repeat visits 

within years (primarily on the same day) was highest for long-lived taxa 

richness. The least variable metrics were Ephemeroptera and sensitive taxa 

richness, and percent very tolerant individuals.

 Error

 Site x Yr

 Year

 Site

SCI

Total taxa

Eph. taxa

Tri. taxa

% Filterer

Long-lived

Clinger

% Dom.

% Tany.

Sensitive

% Toler.

0

20

40

60

80

100

% Relative variance

42
Figure 13. SCI values by region (northeast, peninsula, and panhandle) and 

season. Summer samples (circles in center of box) tended to be lower than 

winter samples (diamond centers) in the northeast and peninsula regions. N = 

23 (NE), 34 (PS) and 31 (PH).

ne ps ph

0

20

40

60

80

100

SCI

43
44

 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 single 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 error variance calculated above when error was defined as 

differences associated with repeat visits to the same site (32.86/66.6 * 100 = 49%). In 

other words, of the variance associated with repeat visits to the same site, about half the 

variability is due to laboratory subsampling. However, because the variance estimates 

were from different data sets, the comparison should be cautiously applied. 

The BioRecon index was calculated as the sum of the six scored metrics; 

therefore, the sum of the scores ranged from 0 to 6. I transformed 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 estimated. Components of variance were similar to 

those for the SCI in that the largest component of variance was associated with site 

differences and year variance was again small (Figure 14). Error variance for this model 

was 1.46 with error derived from repeat visits to the same sites. A 90% confidence 

interval for the BioRecon index yielded a limit of +/-2 or a length of 4.0 points. This 

translated into 2.5 categories of biological condition that BioRecon 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 SCI and its component metrics. Differences 

between summer and winter samples (negative value indicates higher winter values). 

Significant differences noted (* p < 0.05, paired t-test). 

Index/metric Difference 

SCI –3.5 * 

Total taxa 0.1 

Ephemeroptera taxa –0.4 * 

Trichoptera taxa 0.3 

% Filterer –5.2 * 

Long-lived taxa 0.6 * 

Clinger taxa –0.2 

% Dominance –1.9 

% Tanytarsini –4.6 * 

Sensitive taxa –0.9 * 

% Very tolerant 2.0 * 
Figure 14. Variance components for BioRecon and its component taxa 

richness metrics. Variance associated with site differences represented the 

largest component for metrics and index. Error variance associated with repeat 

visits within years was similar across metrics ranging from 20-30% of the 

total. Variability associated with a specific year was relatively small for the 

index and metrics.

 Error

 Year

 Site

BioRecon

Total

Ephem.

Trichop.

Long-lived

Clinger

Sensitive

0

20

40

60

80

100

% Relative variance

45
46

Table 13. Categorical descriptions and range of index values for the BioRecon index. 

Range of values represent 90% confidence intervals 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 indicate 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 for sites with minimal disturbance (Figure 15). 

If variance were estimated using only minimally disturbed reference sites, the error 

variance would be smaller and the categories 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 associated with natural variability or changes 

in human activities, I selected the sites with the most variable BioRecon values through 

time and asked regional biologists to note any change in human activities at the site 

(Table 14). Four of the sites improved where roads had been paved to eliminate sediment 

run off. Other changes were associated with pesticide spraying, elimination of run off 

from a waste water treatment plant, road closure, and the end of fertilization upstream. 

From this anecdotal analysis suggest that much of the BioRecon variability was 

associated with real changes in human disturbance rather than natural variability. 

Variance results for the BioRecon components metrics were similar to those for 

the SCI metrics with site differences contributing the largest component of the total 

variance (see Figure 14). In general, the metric variance due to nuisance sources of 

variability (year or error) was smaller for three of the BioRecon metrics than for the same 

SCI metrics. Lower error variance for total taxa, Trichoptera, and long-lived taxa richness 

suggests that the BioRecon protocol may provide more reliable estimates of these 

metrics. Direct comparisons of results from different data sets should be cautiously 

applied, particularly for relative variance 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 plotted as the endpoints. Note that 

high and low index values were less variable for repeat visits through time.

Site

0

2

4

6

8

10

BioRecon index

47
48

Table 14. STORET site code, region, station nickname, observed change in BioRecon 

values through time and possible reason for change. Listed sites represent locations with 

the highest variability observed for BioRecon values. 

STORET Region Station Change Reason 

20010521 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 upstream of sites in State 

Forest 

24030013 ps HILSTP4REF Improve Unknown 

22020062 ph OKLREF Variable Unknown 

27010050 ps MOSESUS1 Variable Unknown 

27010583 ps LITLTOMOKA Variable Unknown 
49

DISCUSSION 

The SCI and BioRecon indexes along with their component metrics were developed to 

assess the biological condition of stream sites using samples from the stream 

macroinvertebrate assemblage. These assessment tools will be used primarily to define 

biological criteria for freshwater streams and to evaluate the effectiveness of best 

management practices (Vowell, 2001). Three results support the use of the SCI for this 

purpose. First, the component metrics showed a strong and consistent response to an 

independent measure of human disturbance (HDG). SCI was also highly correlated with 

HDG using an independent data set for verification. Second, the SCI was independent of 

watershed size and geographic region. This result means that the SCI can be used across 

the state to compare stream sites. Third, the influence of seasonal and annual differences 

on SCI was relatively small. Index values were about 3.5% higher for winter samples and 

about 2% of the total variance of SCI was associated with year of sampling. Although 

low, these sources of variance should continue to be evaluated with more complete 

sampling designs because the influence of both could potentially be eliminated by 

adjusting index values according to season or year. 

Human disturbance gradient 

The human disturbance gradient simplified several aspects of metric and index testing. 

First, it provided a more stringent test of metric association with disturbance than a 

comparison of reference vs. test sites. Percentage Diptera individuals serves as an 

example of this. This metric was included in the old SCI because it increased at disturbed 

sites; however, across a gradient of disturbance it failed to consistently respond. Second, 

HDG was independent of biological condition because no measures of biological 

condition were included. Therefore, metric expectations could be defined for equivalent 

levels of disturbance within each region. As a consequence, it was not necessary to 

correct the multimetric index for regional differences. Third, objective selection criteria 

based on association with the HDG were used to define individual taxa as sensitive or 

very tolerant. Finally, the HDG resolved a spurious correlation between watershed size 

and SCI. During initial analysis, the SCI appeared to be correlated with watershed size 

because the most disturbed sites happened to be very small. After controlling for HDG, 

the correlation between watershed size and SCI was eliminated. 

The HDG can also be used to describe the intensity of disturbance associated with 

specific biological losses as measured by SCI and its component metrics. EPA is 

currently developing guidelines for defining categories of human disturbance and 
50

matching them with their associated 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, monitoring different aspects of the biological 

assemblage improves the likelihood of detecting changes associated with different types 

of disturbance. Second, the potential exists to define metric signatures, that is, 

associations between specific metrics (or suites of metrics) that correspond to specific 

human activities (Norton et al., 2000; Yoder and DeShon, 2002). Metric signatures are 

particularly relevant to the TMDL process that allocates responsibility for degraded 

stream condition among the various human activities in the watershed (Karr and Yoder, 

in press; EPA, 2000). 

Though not specific to any particular group, total taxa richness represents a 

general measure of the biological complexity found at a site. This metric is one of the 

most widely applied in biomonitoring programs because of its consistent decline with 

human disturbance for stream invertebrates (Kerans and Karr, 1994; Fore et al., 1996; 

Karr and Chu, 1999; Klemm et al., 2002) as well as fish (Hughes et al, 1998), terrestrial 

invertebrates (Kimberling et al., 2001), and birds (Bryce et al., 2002). Ephemeroptera and 

Trichoptera taxa richness have also been widely applied, though are often combined with 

Plecoptera into a single (EPT) metric. Splitting 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 indicate increased nutrients (Miltner and Rankin, 

1998). Filterers are expected to decline in response to disturbance because of the increase 

in sediment and silt that can damage or clog nets. Long-lived taxa are expected to decline 

as human disturbance alters the natural flow regime because these taxa require water in 

the channel year-round. Pollution events of short duration may also eliminate these taxa 

while other taxa may colonize from unaffected sites. 

Clinger taxa have morphological and behavioral adaptations that allow them to 

cling in fast water. Human development near stream sites in Florida often translates into 

eroding sand that can smother habitat and eliminate these taxa. Other studies have found 

these taxa to be sensitive to disturbance associated with mining and urbanization (Fore et 

al., 2001; Mebane, 2002). Percentage dominance of the most abundant taxon increases 
51

with disturbance as the natural taxonomic diversity declines and very tolerant taxa 

dominate samples. This metric represented a measure of the overall structure of the 

assemblage and has been associated with disturbance in several regions (Klemm et al., 

2002). The Tanytarsini midges are used as indicators by Ohio because of their general 

sensitivity to human disturbance (DeShon, 1995). Three Tanytarsini midge taxa were also 

selected for inclusion in the list of sensitive taxa for this study. 

Several of the sensitive taxa agreed with Beck’s (1954) designations used to 

calculate the Florida index; however, a few reversals were noted for taxa that satisfied the 

criteria for very tolerant. The primary difference between the sensitive taxa list and the 

Florida index list was the exclusion of many rare taxa that could not be tested. The list of 

sensitive taxa is now shorter; nonetheless, the objective criteria used to select these taxa 

make these metrics easier to justify in a regulatory context. 

The set of metrics selected for Florida 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), recreation (Karr, 1998), urbanization (Fore et al. 2001; Karr and 

Morishita Rossano, 2001; Morley and Karr, 2002), agriculture (Kerans and Karr, 1994), 

and mining (Mebane, 2001; Fore, 2002; Mebane, 2002). The emergence of a core set of 

metrics across a variety of geographic contexts further supports the use of these metrics 

as biological indicators. 

Sources of variance 

I evaluated five sources of nuisance variance for the SCI and two for the BioRecon index. 

For SCI, I estimated variance associated with laboratory subsampling, repeat visits within 

a year (error), different seasons, 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 the differences observed for SCI values 

collected from the same site within the same year, was the variability associated with 

laboratory subsampling. Subsampling represented approximately 49% of the variance due 

to repeat visits to a site within the same year. Unfortunately, the two variance estimates 

were derived from different data sets; thus, this percentage should be interpreted with 

caution. Nonetheless, subsampling in the laboratory seems to be a significant source of 

variability. Furthermore, the subsampling method for this small study was more carefully 

controlled than for routine sampling in that the target of 100 individuals per sample was 
52

more frequently attained. Thus, the subsampling analysis did not include any additional 

variability associated with variable sample sizes; consequently, variance due to 

subsampling may actually be higher for routine samples. 

Site x year interaction represents the variability associated with some unique siteyear combinations. This source of variance represented index values from sites that 

increased (or decreased) from one year to the next in a different or more extreme manner 

than did other sites during those same years. 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 associated with human influence. These two 

sources could not be distinguished using the current data because all sources of human 

disturbance were not quantified. For example, agriculture is a dominant land use in 

Florida. Pesticide treatments represent a potentially significant impact to stream 

invertebrates but could not be evaluated for this study. Nonetheless, the higher variability 

in both SCI and BioRecon index values observed for sites with moderate index values 

compared to sites with high index values, suggests that both indexes may be measuring 

real changes associated with human land use rather than nuisance variance. Other studies 

have found a similar pattern of increased variance associated with greater human 

disturbance (Karr et al., 1987; Steedman, 1988). For 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 fertilization 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 cumulative effect across several metrics. The 

seasonal difference was also relatively small compared to the variance due to other 

sources such as repeat visits within a year or laboratory subsampling. The influence of 

seasonality awaits a better data set that compares index values by season for sites with 

little or no change in human disturbance. 

For the BioRecon index, the variance associated with repeat visits within years 

was somewhat higher than the variance observed for the SCI. This is largely because the 

site x year interaction term could not be estimated (due to incomplete data) and this 

variance was included in the error term. In addition, the BioRecon data included mostly 

samples from different days within the year rather than the predominantly same-day 

samples used for the SCI. As a consequence the variance estimate for the BioRecon index 

may be somewhat high and the precision of the index underestimated. 
53

The higher variability noted for the BioRecon index may be due to human 

disturbance changing at the site, different levels of sampling 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 examples of each taxon for laboratory verification. 

Biologists may vary in both the amount of time they spend searching for unique taxa and 

in the number of taxa returned to the lab. The protocol specifies that a minimum of five 

examples of each unique taxon be returned for verification. If multiple examples of each 

taxon are returned, the probability of identifying additional taxa increases as a function of 

the number of individuals returned. This is because many field identifications can only be 

made to the genus level whereas in the lab more taxa can be identified to the species 

level. 
54

CONCLUSIONS 

Correlation between biological metrics and the independently derived human disturbance 

gradient provided strong evidence in support of the relationship between human influence 

and biological condition. The high correlation between SCI and HDG was verified using 

an independent set of data. In addition, the pattern of biological response to human 

disturbance was repeated across all three regions in Florida, indicating that the 

multimetric index was independent of geographic context. Finally, the index was 

consistent across years, with a nearly identical relationship between SCI and HDG for 10 

years of data. 

To document every source of human disturbance is prohibitively expensive, 

consequently the human disturbance gradient was inevitably incomplete. I had no 

information on metals, pesticides, dredging, or other pollutants. Aerial pesticide 

application is a frequent practice on agricultural lands and can be very damaging to 

stream invertebrates as pesticides are concentrated in rain runoff (Hutchens, et al., 1998). 

This lack of information may likely explain why low SCI values were observed for sites 

with no known source of disturbance. In contrast, sites with known disturbance never had 

high values for SCI. This result supports the idea that much of the lack of fit between 

disturbance and biological condition is largely due to measurement error associated with 

quantifying human disturbance rather than variability of biological measures (Bryce et 

al., 1999; Karr and Morishita Rossano, 2001; Fore, 2003). In short, multimetric indexes 

accurately synthesize the influence of human activities over time while measures of 

human disturbance are at best approximations of the complex ways that human activities 

influence the environment. 

The SCI had good statistical precision and could detect 3.7 categories of 

biological condition based on a single sample and five categories based on two samples. 

The BioRecon was not as precise but had adequate precision to detect 2.5 categories of 

biological condition based on a single sample and 3.5 categories for 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 the 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 smaller 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 ensure that the number of individuals in 

each sample is close to the target of 100 . 

FDEP is currently modifying laboratory subsampling procedures to more reliably match 

the target of 100 individuals per sample. Additional subsampling of the original material 

collected from the stream will help eliminate samples with hundreds of individuals. 

Precautions are also being implemented to eliminate small sample. Results from this 

analysis suggest that subsamples with less 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 several 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 estimates could be made with a sampling design 

that visited each site during every season and year. Definitions for season should be made 

based on climatic factors (e.g., rainfall, temperature) most relevant to invertebrate life 

histories. In addition, sites should be selected for which the level of human disturbance is 

constant through time. The best design would also 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 precision typically associated with larger 

sample sizes (greater number of individuals) is worth the cost. 

Studies in other regions report lower error variance 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 requires additional laboratory 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 variable 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 macroinvertebrate indexes. R. Frydenborg provided data sets, 

developed the hydrologic scoring index, resolved taxonomic questions, and provided 

biogeographic interpretation of metrics. E. McCarron provided administrative support 

and a regulatory perspective. E. Livingston provided administrative and contract support. 

Reviewers from outside FDEP included M. Barbour, 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. Miller, D. Ray, and the FDEP Biology section 

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

T. Deck, S. Gerardi, and D. Miller. D. Cobb 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 provided additional clinger information. Lab 

subsampling was done by A. Jordan, N. Moise, C. Oaks, B. Richardson, B. Shinner, and 

F. Whiddon. Biologists throughout 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. Springer, C. Swanson, M. Szafraniec, M. Thompson, 

F. Walton, and A. Wheeler. This project and the preparation of this report were funded in 

part by a Section 319 Nonpoint Source Management Program grant from the U. S. 

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

Management Program of the Florida Department 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

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Appendix A. Long-lived taxa 

Taxonomic order, family, genus or species name of taxa used to calculate long-lived taxa richness. Taxonomic sy

the current Florida data base are also shown. Long-lived (semi-voltine) taxa require more than one year to compl

Order Family Genus Species (Long-lived) Synonym

Decapoda All families, genera and species Basommatophora Lymnaeidae Fossaria All species Mesogastropoda Ampulariidae Pomacea All species Veneroida Corbiculiidae Corbicula Corbicula fluminea Corbicula manilensUnionoida Unionidae All genera and species Odonata Aeshnidae Basiaeschna Basiaeschna janata 

Boyeria All species 

 Cordulegastridae All species  Gomphidae Gomphus All species Gomphurus 

Haegenius Hagenius brevistylusProgomphus All species 

 Libellulidae Macromia All species 

Somatochlora All species Tetragoneuria All species Epitheca 

 Petaluridae Tachopteryx Tachopteryx thoreyiPlecoptera Pteronarcidae Pteronarcys Pteronarcys dorsata Leuctridae Leuctra All species  Peltoperlidae Tallaperla Tallaperla cornelia Perlidae Acroneuria All species 

Agnetina Agnetina annulipes

Eccoptura Eccoptura xanthenes Acroneuria xanthenNeoperla All species Paragnetina All species Banksiana 

Banksiella 

 Perlodidae Clioperla Clioperla clio Isoperla clio 
Order Family Genus Species (Long-lived) Synonym

Perlinella Perlinella drymo 

Atoperla ephyre 

Perlinella ephyre/zwicki 

Isoperla Isoperla orata 

Megaloptera Corydalidae All genera and species Trichoptera Brachycentridae Brachycentrus Brachycentrus americanus Oligoplectrum ame

Micrasema All species 

 Calamoceratidae Heteroplectron Heteroplectron americanum  Hydropsychidae Diplectrona Diplectrona modesta 

Macrostemum Macrostemum carolina Macronema carolin

Macronemum caro

 Lepidostomidae Lepidostoma All species 

Alepomyia, AlepomArcadopsyche, Ato

Jenortha, MormomNeuropsyche, NosNotiopsyche, OlemOligopsyche, PhanPristosilo, Athripso

 Leptoceridae Ceraclea All species  Limnephilidae Ironoquia Ironoquia punctatissisma 

Pycnopsyche All species 

 Molannidae Molanna All species  Phryganeidae Banksiola Banksiola concatenata Neuronia concaten

Ptilostomis Ptilostomis postica Neuronia postica 

Agrypnia 

Agrypnia vestita 

 Psychomyiidae Lype Lype diversa  Rhyacophilidae Rhyacophila All species  Uenoidae Neophylax All species 
Appendix B. Sensitive taxa 

Taxonomic order, family, genus or species name; taxonomic synonyms in the current Florida data base; whether included in the Florida index (2=very sensitive; 1=sensitive); and species name of Florida index taxa are listed fo

sensitive to human disturbance. 

Order Family Genus Species (sensitive) Synonyms Florida index 

Acariformes Lebertiidae Lebertia All species Isopoda Asellidae Caecidotea Caecidotea Asellus 1 Caecidotea Amphipoda Crangonyctidae Crangonyx CrangonyxEphemeroptera Baetidae Acerpenna Acerpenna pygmaea Baetis pygmaeus Ephemerellidae All genera and species 

Heptageniidae All genera and species 1 Stenacron in

Stenonema integrum 

2 Stenonema 

Stenonema 

 Leptophlebiidae All genera and species  Leptohyphidae All genera and species Odonata Libellulidae Macromia All species 2 Macromia spPlecoptera All families, genera and 

species 

2 All Plecopte

Trichoptera Hydropsychidae Hydropsyche All species 2 Hydropsych Leptoceridae Triaenodes All species  Philopotamidae Chimarra Chimarra 2 Chimarra sp Psychomyiidae Lype Lype diversa Coleoptera Elmidae Ancyronyx Ancyronyx variegatus 

Gonielmis Gonielmis dietrichi Macronychus Macronychus glabratus
Order Family Genus Species (sensitive) Synonyms Florida index 

Diptera Chironomidae Microtendipes All species 

Parametriocnemus All species Polypedilum Polypedilum aviceps Rheocricotopus All species 2 RheocricotoStempellinella All species Tanytarsus Tanytarsus sp. d eplerTanytarsus Tanytarsus sp. m eplerTribelos Tribelos jucundum

 Empididae Hemerodromia Hemerodromia  Simuliidae All genera and species 2 Simulium sp
Appendix C. Very tolerant taxa 

Taxonomic order, family, genus or species name; taxonomic synonyms in the current Florida data base; whether included in the Florida index (2=very sensitive; 1=sensitive); and species name of Florida index taxa are listed fo

very tolerant to human disturbance. 

Order Family Genus Species (very tolerant) Synonyms Flo

Hoplonemertea Tetrastemmatidae Prostoma Prostoma rubrum Haplotaxida Naididae Bratislavia Bratislavia unidentata 

Dero All species Nais Nais communis complex Nais communis  Nais elinguis  Nais pardalis  Nais variabilis Pristina Pristina synclites 

 Tubificidae Haber Haber speciosus 

Limnodrilus All species Camptodrilus 

californicus Limnodrilus aurantiacus 

Lumbriculida Lumbriculidae Lumbriculus All species Rhynchobdellida Glossiphoniidae All genera and species Basommatophora Ancylidae Hebetancylus Hebetancylus excentricus 

Laevapex All species 

 Physidae Physella All species  Planorbidae All genera and species Mesogastropoda Hydrobiidae Pyrgophorus Pyrgophorus platyrachis Pyrogophorus 

platyrachis 

 Thiaridae Melanoides All species Odonata Libellulidae Pachydiplax Pachydiplax longipennis  Coenagrionidae Argia Argia sedula 2 Arg
Order Family Genus Species (very tolerant) Synonyms Flo

Enallagma Enallagma cardenium Enallagma coecumIschnura All species 

Anomalagrion 

Heteroptera Mesoveliidae All genera and species Lepidoptera Pyralidae All genera and species Coleoptera Haliplidae Peltodytes All species Diptera Chironomidae Larsia All species 1 Lar

 1 Lar

Chironomus All species Tendipes 

Chaetolabis 

Cladopelma Cladopelma Cricotopus All species 2 CricCryptochironomusAll species Dicrotendipes Dicrotendipes modestus  Dicrotendipes neomodestus Glyptotendipes All species Goeldichironomus All species Kiefferulus Kiefferulus Parachironomus All species Polypedilum Polypedilum beckae 

Asheum beckae

Pedionomus beckae

Polypedilum illinoiense grp. Polypedilum 

illnoense 

1 Poly

illin

 Polypedilum tritum Chironomus tritum Tanypus All species Pelopia Tanytarsus Tanytarsus sp. f epler 

 Stratiomyidae Odontomyia Odontomyia Eulalia  Tipulidae Tipula Tipula Nippotipula 

Playttipula Yamatotipula 
Appendix 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 interactiowas statistically significant. Test failed to detect a difference in SCI values due to region. Univariate Tests of Significance for AvgOfSCI6_100 (1NGM_4)

SS df MS F p

Intercept 403623.3 1 403623.3 1832.253 0.000000

Region 361.6 2 180.8 0.821 0.441846

HDG2 62750.7 1 62750.7 284.858 0.000000

Region*HDG2 1142.9 2 571.5 2.594 0.077660

Error 37449.0 170 220.3

Table 2. General linear model output for SCI vs. HDG by year class (1992-3, 1994-5, 1996-7, 1998-9, and 2000-

term was not significantly different for year class and HDG suggesting the lines were parallel (equal slopes). The however, was significant with values for 1994-5 being slightly higher for SCI. 

Univariate Tests of Significance for SCI6_100 (1NGM_4) 

SS df MS F p

Intercept 1627060 1 1627060 5835.657 0.000000

Year class 2948 4 737 2.644 0.032766

HDG2 134497 1 134497 482.390 0.000000

Year class*HDG2 791 4 198 0.709 0.586005

Error 171749 616 279
Table 3. ANOVA estimates of SCI variability for repeat visits to the same site within year. Error estimate used to 

interval underlined. 

ANOVA for SCI (SCI6_100) (Same_day_SCI)

Effect df MS df MS F 

{1}STORET Random 60 1211.636 39.41542 188.7279 6.420014 

{2}Year Random 6 269.430 35.76223 170.2402 1.582648 

1*2 Random 53 134.481 82.00000 66.5952 2.019385 

Table 4. ANOVA estimates of SCI variability for laboratory subsampling. Error estimate used are underlined ANOVA Results: SCI subsampling (Metrics)

Effect df MS df MS F 

{1}Sitename Random 58 1393.986 118 32.86312 42.417

Table 5. ANOVA estimates of BioRecon variability for repeat visits to the same site within year. Error estimate uconfidence interval underlined. ANOVA for BioRecon (BR3_10) (New metrics multiple_2)

Effect df MS df MS F 

{1}STORET Random 126 15.91691 117.3771 1.696009 9.384924 

{2}year Random 8 6.02260 156.0000 1.458840 4.128352