U.S. Department of the Interior

U.S. Geological Survey

Scientific Investigations Report 2010–5051

National Water-Quality Assessment Program

Evaluation of Aquatic Biota in Relation to

Environmental Characteristics Measured at 

Multiple Scales in Agricultural Streams of

the Midwest: 1993–2004
Cover photographs

Left and top: Segment and watershed scale view of an agricultural stream: David L. Lorenz, 

USGS—Mounds View, Minn. 

Bottom: Reach scale view of an agricultural stream: Jeffrey D. Stoner, USGS—Eagan, Minn.
Evaluation of Aquatic Biota in Relation to 

Environmental Characteristics Measured at 

Multiple Scales in Agricultural Streams of 

the Midwest: 1993–2004 

By Julie A. Hambrook Berkman, Barbara C. Scudder, Michelle A. Lutz, and Mitchell A. Harris

National Water-Quality Assessment Program

Scientific Investigations Report 2010–5051

U.S. Department of the Interior

U.S. Geological Survey
U.S. Department of the Interior

KEN SALAZAR, Secretary

U.S. Geological Survey

Marcia K. McNutt, Director

U.S. Geological Survey, Reston, Virginia: 2010

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Suggested citation:

Hambrook Berkman, J.A., Scudder, B.C., Lutz, M.A., and Harris, M.A., 2010, Evaluation of aquatic biota in relation to 

environmental characteristics measured at multiple scales in agricultural streams of the Midwest, 1993–2004: U.S. 

Geological Survey Scientific Investigations Report, 2010–5051, 24 p. 

ISBN 978-1-4113-2915-7
iii

Foreword

The U.S. Geological Survey (USGS) is committed to providing the Nation with reliable scientific 

information that helps to enhance and protect the overall quality of life and that facilitates 

effective management of water, biological, energy, and mineral resources (http://www.usgs.

gov/). Information on the Nation’s water resources is critical to ensuring long-term availability 

of water that is safe for drinking and recreation and is suitable for industry, irrigation, and fish 

and wildlife. Population growth and increasing demands for water make the availability of that 

water, now measured in terms of quantity and quality, even more essential to the long-term 

sustainability of our communities and ecosystems.

The USGS implemented the National Water-Quality Assessment (NAWQA) Program in 1991 

to support national, regional, State, and local information needs and decisions related to 

water-quality management and policy (http://water.usgs.gov/nawqa). The NAWQA Program 

is designed to answer: What is the quality of our Nation’s streams and groundwater? How are 

conditions changing over time? How do natural features and human activities affect the quality 

of streams and groundwater, and where are those effects most pronounced? By combining 

information on water chemistry, physical characteristics, stream habitat, and aquatic life, the 

NAWQA Program aims to provide science-based insights for current and emerging water issues 

and priorities. During 1991–2001, the NAWQA Program completed interdisciplinary assessments 

and established a baseline understanding of water-quality conditions in 51 of the Nation’s river 

watersheds and aquifers, referred to as Study Units (http://water.usgs.gov/nawqa/studyu.html). 

National and regional assessments are ongoing in the second decade (2001–2012) of the 

NAWQA Program as 42 of the 51 Study Units are selectively reassessed. These assessments 

extend the findings in the Study Units by determining status and trends at sites that have been 

consistently monitored for more than a decade, and filling critical gaps in characterizing the 

quality of surface water and groundwater. For example, increased emphasis has been placed on 

assessing the quality of source water and finished water associated with many of the Nation’s 

largest community water systems. During the second decade, NAWQA is addressing five 

national priority topics that build an understanding of how natural features and human activities affect water quality, and establish links between sources of contaminants, the transport of 

those contaminants through the hydrologic system, and the potential effects of contaminants 

on humans and aquatic ecosystems. Included are studies on the fate of agricultural chemicals, 

effects of urbanization on stream ecosystems, bioaccumulation of mercury in stream ecosystems, effects of nutrient enrichment on aquatic ecosystems, and transport of contaminants to 

public-supply wells. In addition, national syntheses of information on pesticides, volatile organic 

compounds (VOCs), nutrients, trace elements, and aquatic ecology are continuing. 

The USGS aims to disseminate credible, timely, and relevant science information to address 

practical and effective water-resource management and strategies that protect and restore 

water quality. We hope this NAWQA publication will provide you with insights and information 

to meet your needs, and will foster increased citizen awareness and involvement in the protection and restoration of our Nation’s waters. 
iv

The USGS recognizes that a national assessment by a single program cannot address all waterresource issues of interest. External coordination at all levels is critical for cost-effective management, regulation, and conservation of our Nation’s water resources. The NAWQA Program, 

therefore, depends on advice and information from other agencies—Federal, State, regional, 

interstate, Tribal, and local—as well as nongovernmental organizations, industry, academia, and 

other stakeholder groups. Your assistance and suggestions are greatly appreciated.

Matthew C. Larsen

Associate Director for Water
v

Contents

Foreword ........................................................................................................................................................iii

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

Introduction.....................................................................................................................................................1

Purpose and Scope ..............................................................................................................................2

Study Area..............................................................................................................................................2

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

Data Collection......................................................................................................................................2

Biological Assemblages ............................................................................................................2

Environmental Characteristics .................................................................................................4

Data Analysis.........................................................................................................................................5

Biological Assemblages .............................................................................................................5

Biological Metrics........................................................................................................................5

Environmental Characteristics ..................................................................................................6

Relations between Biological Assemblages, Biological Metrics, and 

Environmental Characteristics ....................................................................................6

Spatial and Temporal Variability of Biological Metrics .........................................................7

Relations between Biota and Multiscale Environmental Characteristics ...........................................7

Biological Assemblages with Environmental Characteristics ......................................................7

At the Reach Scale .....................................................................................................................7

At the Segment Scale ................................................................................................................8

At the Watershed Scale ...........................................................................................................10

Biological Metrics with Environmental Characteristics ..............................................................10

Algal Metrics ..............................................................................................................................10

Invertebrate Metrics .................................................................................................................11

Fish Metrics ................................................................................................................................11

Spatial and Temporal Variability of Metrics ............................................................................................16

Discussion of Findings and Comparison with Other Studies................................................................16

Implications for the Use of Aquatic Biota in Evaluations of Agricultural 

Best-Management Practices.......................................................................................................17

Summary and Conclusions.........................................................................................................................20

Acknowledgments.......................................................................................................................................21

References Cited..........................................................................................................................................21
vi

Figures

 1. Map showing location of 86 agricultural stream sites in the Midwest within 

eight study units of the U.S. Geological Survey National Water-Quality 

Assessment (NAWQA) Program ................................................................................................3

2–3. Graphs showing:

 2 Coefficient of variation between biological metrics from samples 

collected among multiple reaches at agricultural stream sites 

in the Midwest....................................................................................................................18

3. Coefficient of variation between biological metrics from samples 

collected among multiple years at agricultural stream sites 

in the Midwest....................................................................................................................19

Tables

1. Selected environmental characteristics, with metric abbreviations and 

range of data for 86 agricultural stream sites in the Midwest, 

listed by spatial scale...................................................................................................................4

2. Summary of correlations between biological assemblages and 

selected environmental characteristics at each physical scale 

for agricultural stream sites in the Midwest............................................................................8

3. Selected biological metrics, with metric abbreviations and 

range of data for 86 agricultural stream sites in the Midwest, 

listed by biological assemblage .................................................................................................9

4. Correlations between algal metrics and selected environmental 

characteristics for agricultural stream sites in the Midwest..............................................12

5. Correlations between invertebrate metrics and selected environmental 

characteristics for agricultural stream sites in the Midwest..............................................13

6. Correlations between fish metrics and selected environmental 

characteristics for agricultural stream sites in the Midwest..............................................14

7. Summary of ecosystem health indicators for environmental 

characteristics with the strongest relations to biotic assemblages 

for agricultural stream sites in the Midwest..........................................................................15

8. Values for median coefficient of variation between selected 

biological metrics from samples collected among multiple reaches 

and multiple years at each site for agricultural stream sites in the Midwest..................16
vii

Conversion Factors

SI to Inch/Pound

Multiply By To obtain

Length

millimeter (mm) 0.03937 inch (in.)

centimeter (cm) .3937 inch (in.)

meter (m) 3.281 foot (ft) 

kilometer (km) .6214 mile (mi)

Area

square meter (m2) .0002471 acre 

square kilometer (km2) .3861 square mile (mi2

)

Volume

liter (L) .2642 gallon (gal)

cubic meter (m3) 264.2 gallon (gal) 

cubic centimeter (cm3) .06102 cubic inch (in3) 

liter (L) 61.02 cubic inch (in3) 

cubic meter (m3) 35.31 cubic foot (ft3)

Flow rate

meter per second (m/s) 3.281 foot per second (ft/s) 

cubic meter per second (m3/s) 35.31 cubic foot per second (ft3/s)

Temperature in degrees Celsius (°C) may be converted to degrees Fahrenheit (°F) as follows:

°F=(1.8×°C)+32

Temperature in degrees Fahrenheit (°F) may be converted to degrees Celsius (°C) as follows:

°C=(°F-32)/1.8

Vertical coordinate information is referenced to the North American Vertical Datum of 1988 

(NAVD 88).

Horizontal coordinate information is referenced to the North American Datum of 1983 (NAD 83).

Specific conductance is given in microsiemens per centimeter at 25 degrees Celsius (μS/cm at 

25°C).

Concentrations of chemical constituents in water are given either in milligrams per liter (mg/L) 

or micrograms per liter (μg/L).

Evaluation of Aquatic Biota in Relation to Environmental 

Characteristics Measured at Multiple Scales in 

Agricultural Streams of the Midwest: 1993–2004

By Julie A. Hambrook Berkman, Barbara C. Scudder, Michelle A. Lutz, and Mitchell A. Harris

Additionally, environmental characteristics at one scale may 

be highly related to one or more characteristics at other scales 

(Carter and others, 1996). The importance of spatial scaling 

has long been recognized in studies of stream geomorphology 

(Strahler, 1957; Leopold and others, 1964; Hynes, 1975; Frissell and others, 1986; Leopold, 1994). Geology, geomorphology, and climate influence soils and vegetation (Strahler and 

Strahler, 1978) and, in turn, the slope and soils in a watershed 

affect the geomorphology and flow of a stream. Determining the relative influences of environmental characteristics at 

various spatial scales on biological assemblages is difficult; 

however, this study attempts to define these influences using 

multivariate techniques (Wiens, 1989; Resh and Rosenberg, 

1989; Richards and Host, 1994; Carter and others, 1996; Allan 

and others, 1997; Dovciak and Perry, 2002; Goldstein and 

Meador, 2004). 

Previous studies that have examined the interaction of 

multiple aquatic biota (algae, invertebrates, and fish) together 

with physical and (or) chemical characteristics of streams have 

found similarities and differences in the way various groups of 

biota respond to degraded stream conditions (Cuffney and others, 1997; Allen and others, 1999; Lammert and Allan, 1999; 

Fitzpatrick and others, 2001; Fore, 2003). It is important to 

identify which aspects (numbers of taxa, relative abundance of 

taxa, or metrics based on species traits) of the three principal 

biological assemblages best correspond to stream conditions 

and changes in management practices at each spatial scale. 

Efforts have been made to determine environmental preferences of algae (Stevenson and Bahls, 1999; Hill and others, 

2000; Fore and Grafe, 2002; Potapova and Charles, 2007; 

Porter, 2008), invertebrates (DeShon, 1995; Barbour and 

others, 1996, 1999; Fore and others, 1996; Vieria and others, 2006), and fish (Barbour and others, 1999; Goldstein and 

Meador, 2004, 2005) to increase the utility of these biological 

assemblages as monitoring tools for bioassessment of streams. 

Efforts to improve water quality through changes in land-use 

and management practices are more likely to be successful 

if targeted toward environmental characteristics and biological assemblages that are expected to respond most strongly. 

In other words, to evaluate improvements to the health and 

Abstract 

This study evaluated the relations between algal, invertebrate, and fish assemblages and physical environmental 

characteristics of streams at the reach, segment, and watershed 

scale in agricultural settings in the Midwest. The 86 stream 

sites selected for study were in predominantly agricultural 

watersheds sampled as part of the U.S. Geological Survey’s 

National Water-Quality Assessment Program. Species abundance and over 130 biological metrics were used to determine 

which aspects of the assemblages were most sensitive to 

change at the three spatial scales. Digital orthophotographbased riparian land use/land cover was used for analyses of 

riparian conditions at the reach and segment scales. The percentage area of different land-use/land-cover types was also 

determined for each watershed. Out of over 230 environmental 

characteristics examined, those that best explained variation 

in the biotic assemblages at each spatial scale include the following: 1) reach: bank vegetative cover, fine silty substrate, 

and open canopy angle; 2) segment: woody vegetation and 

cropland in the 250-m riparian buffer, and average length of 

undisturbed buffer; and 3) watershed: land use/land cover 

(both total forested and row crop), low-permeability soils, 

slope, drainage area, and latitude. All three biological assemblages, especially fish, correlated more with land use/land 

cover and other physical characteristics at the watershed scale 

than at the reach or segment scales. This study identifies biotic 

measures that can be used to evaluate potential improvements 

resulting from agricultural best-management practices and 

other conservation efforts, as well as evaluate potential impairment from urban development or other disturbances.

Introduction

Evaluating changes in land-use practices and the effectiveness of best-management practices (BMPs) in reducing 

water-quality effects of agriculture is complex because aquatic 

biota may respond to changes at multiple spatial scales. 
2 Evaluation of Aquatic Biota in Relation to Environmental Characteristics Measured at Multiple Scales

condition of streams, it may be necessary to select environmental measures that show the greatest relation to desired 

biotic measures (for example, to benefit fish) and to manage 

the land use at the scale that would result in this benefit (for 

example, watershed scale). Conversely, if a management practice is to be applied locally near a stream reach and there is an 

interest in monitoring benefits, it would make sense to select a 

biological measure or measures that would likely be the most 

responsive at a local (reach) scale.

Purpose and Scope

The purpose of this study is to evaluate relations between 

biota and physical-environmental characteristics of streams 

at various scales. To do so, the study examined biological 

and environmental data collected at 86 agricultural stream 

sites across areas of the Midwestern United States (hereafter “Midwest”), from 1993 through 2004 as part of the 

U.S. Geological Survey (USGS) National Water-Quality 

Assessment (NAWQA) Program. The study objectives were to 

(1) identify environmental characteristics to which the algal, 

invertebrate, and fish assemblages show the strongest correlations; (2) examine relations between each biotic assemblage 

and the most highly correlated environmental characteristics at 

the reach, segment, and watershed scale; and (3) identify the 

biological measures (metrics) that show the best potential for 

monitoring, assessing, and evaluating changes at the various 

spatial scales to help guide watershed conservation efforts and 

identify potential land-use changes that could cause impairment to streams in the Midwest. On the basis of previous studies that analyzed multiple biotic assemblages, it was hypothesized that fish assemblages would be more strongly correlated 

with watershed-scale characteristics than with segment and 

reach characteristics, whereas algal and invertebrate assemblages would be more strongly influenced by environmental 

characteristics at the reach and segment scales. 

Study Area

The study area included major agricultural watersheds within eight NAWQA study units (fig. 1). The 86 stream 

sites selected for study (described in Lutz and Kennedy, 2010) 

were in watersheds characterized as agricultural (median 

agricultural land cover = 86%), predominantly row crop and 

pasture, with less than 10% urban land use and remaining 

area primarily as forest. Drainage-area sizes ranged from 12 

to 2,922 km2, with a median of 319 km2. Sites were predominantly in glaciated ecoregions of the Central, Eastern, and 

Western Cornbelt Plains; North Central Hardwood Forests; 

and Southeastern Wisconsin Till Plains (Lutz and Kennedy, 

2010). These agricultural watersheds are relatively flat with 

slopes ranging from about 0.1 to 7 m/km and the median 

watershed slope is about 1 m/km. Additional environmental 

characteristics of the study area are given in table 1.

Methods

Physical environmental data and biological samples were 

collected from 86 sites within the agricultural Midwest from 

1993 through 2004 as part of the NAWQA Program. Physical 

data were collected at three spatial scales—reach, segment, 

and watershed—whereas biological data were collected only 

at the reach scale. Data-collection methods were consistent 

with USGS NAWQA protocols. Protocol changes during this 

period were generally minor. Reaches established at sampling 

sites were defined as a length of stream chosen to represent 

the physical, chemical, and biological conditions within a 

stream segment. Reaches ranged in length from 150 to 500 

m or approximately 20 times the width of the stream channel 

(Meador and others, 1993a; Fitzpatrick and others, 1998). A 

stream segment was defined as a length of stream bounded 

by confluences of tributaries or physical or chemical discontinuities, such as major waterfalls or landform features. Segment lengths began at the downstream end of the reach and 

extended upstream to a length (in kilometers) equivalent to the 

base-10 logarithm of the basin area (in square kilometers), or 

about 7.0 to 9.5 km. A watershed was defined as the land area 

that drains to the most downstream end of a reach. A subset 

of the sites was sampled for three or more years and in three 

replicate reaches for algal, invertebrate, and fish-assemblage 

data. These replicate samples were used to evaluate the variance of the biological metrics over space and time to assess 

which were the least variable. For sites where multiple years 

of data were available, the year selected to represent the site 

was chosen to be consistent within a study unit and consistent 

across all study units, where possible. Biological-assemblage 

data (species relative abundance) and metrics computed from 

assemblage data (for example, the percentage of carnivore/

piscivore fish) were used in analyses. 

Data Collection

Biological Assemblages 

Biological samples were collected from May through 

September, generally during periods of stable low flow, 

according to NAWQA methods as detailed below. All biological data are available from the NAWQA Data Warehouse 

(USGS, 2005) at http://water.usgs.gov/nawqa/data. A list of 

station identification numbers, needed for data retrieval, can be 

found in Lutz and Kennedy (2010). Quantitative algal samples 

were collected from a measured area of substrate to enable 

estimation of abundance per area. Areas sampled represented 

either rock or wood habitat in the sampling reach. Separate 

discrete samples were collected from 25 rocks in riffle areas 

(5 locations) or from 5 submerged woody snags and composited to form a single algal sample using standard NAWQA 

methods (Porter and others, 1993; Moulton and others, 2002). 

Semiquantitative benthic-macroinvertebrate samples were 
Figure 1. Location of 86 agricultural stream sites in the Midwest within eight study units of the U.S. Geological Survey National Water-Quality Asse

NEBRASKA

KANSAS

MISSOURI

IOWA

MINNESOTA

SOUTH DAKOTA

NORTHDAKOTA

WISCONSIN

MICHIGAN

ONTARIO

MANITOBA

ILLINOIS

INDIANA 

KENTUCKY

EASTERN

IOWA BASINS

LAKE ERIELAKE ST. CLAIR

DRAINAGES

LOWER ILLINOIS

RIVER BASIN

RED RIVER OF 

THE NORTH BASIN

UPPER ILLINOIS

RIVER BASIN

UPPER MISSISSIPPI

RIVER BASIN

WH

GREA

M

WESTERN LAKE

MICHIGAN

DRAINAGES

100°0'0"W 

40°0'0"N 45°0'0"N

0 150 300 KILOMETERS

0 150 300 MILES

E

NWSNS

Figure 1. Location of 86 agricultural stream sites in the Midwest within eight study units of the U.S. GeologWater-Quality Assessment (NAWQA) Program.
4 Evaluation of Aquatic Biota in Relation to Environmental Characteristics Measured at Multiple Scales

collected from areas where maximum taxa richness was likely 

to occur (rocks in riffle areas or submerged woody snags), 

generally the same habitats and locations as the areas sampled 

for algae, according to NAWQA methods (Cuffney and others, 

1993; Moulton and others, 2002). Fish were collected according to NAWQA methods by field personnel using seines and 

backpack, towed-barge, or boat-mounted electrofishing units 

(Meador and others, 1993b; Moulton and others, 2002). Fish 

were identified, weighed, and measured in the field. When species identification could not be confirmed in the field, several 

unidentified individuals of that species were archived and their 

identification confirmed by taxonomists in the laboratory.

Environmental Characteristics 

 Physical-habitat data used for each site were those that 

were collected closest to the sampling dates for the algal, 

invertebrate, and fish assemblages, generally in the same year 

and season. Physical-habitat data were collected within each 

sampling reach as described in Meador and others (1993a) for 

Table 1. Selected environmental characteristics, with metric abbreviations and range of data (minimum, median or 50th percentile, 

and maximum) for 86 agricultural stream sites in the Midwest, listed by spatial scale.

Metric 

abbreviation Metric description Unit Minimum Median Maximum

Reach characteristics

BankEros Bank erosion percent 8.3 83.3 100

BankVeg Bank vegetative cover percent 13 55.5 90

Fine Fine silty substrate percent 0 12.1 100

Froude Froude number dimensionless 0 .13 .43

OpCanAng Open canopy angle degrees 6.42 64.17 165

OpCanCV Open canopy angle variation percent 2.13 44.2 265.6

Riffle Riffles in reach percent .00 18.4 81.3

Velocity Average stream velocity m/s .01 .26 .87

WetWdth Average wetted channel width meters 2.50 11.4 59.6

W/D Average wetted-channel width-depth ratio ratio 5.86 28.49 163.32

WV50 Woody vegetation in the 50-m buffer percent 0 41.6 98.0

Segment characteristics

BuffFrag Number of buffer fragments per kilometer in stream 

margin

number/km 0 8.4 16.5

BuffLngth Average length of undisturbed buffer per kilometer m/km 0 .2 1.4

Crop250 Percentage of cropland in the 250-m buffer percent 0 40.7 84.9

SegGrad Gradient of stream in segment m/km 0 2.8 15.0

SINUOS Stream sinuosity ratio 1 1.32 3.05

WV150 Woody vegetation in the 150-m buffer percent .44 33.5 93.7

Watershed characteristics

DrainArea Drainage area km2 11.9 319 2922

Forest Percentage of total forest land cover percent .36 6.28 57.1

Latitude Latitude decimal degrees 38.571 43.742 48.197

MxForest Percentage mixed forest land cover percent .01 .46 29.5

Perml Percentage of soils that have low-permeability cm/hr, percent .21 .895 6.11

RowCrop Percentage of row-crop land cover percent 5.71 64.8 94.7

Slope Average watershed slope m/km .07 .94 6.86

WetXArea/DA Average wetted reach cross-sectional area/drainage 

area

m2/km2 0 13.4 134.5
Methods 5

sites sampled during 1993–97 and as modified by Fitzpatrick 

and others (1998) for all remaining sites. All physical-habitat 

data are available from the NAWQA Data Warehouse (USGS, 

2005) at http://water.usgs.gov/nawqa/data. At the reach scale, 

measurements were collected from multiple transects for more 

than 100 reach-level habitat characteristics. Data such as fine 

sediment, bank erosion, bank vegetative cover, velocity, width, 

depth, and Froude number were collected and calculated from 

averaging across the transects. A total of six transects per reach 

were used for sites sampled during 1993–97; 11 transects per 

reach were used for sites sampled after 1997. Segment data 

(more than 80 characteristics) included measures such as 

gradient, sinuosity, and riparian land use/land cover (LULC). 

Watershed data (more than 50 characteristics) included drainage area, LULC, geographic location, and watershed slope. 

Watershed-scale LULC values in this report are percentages of total watershed area. Watershed boundaries were 

delineated in a Geographic Information System (GIS) by using 

1:24,000- to 1:250,000-scale digital topographic and hydrologic maps (Nakagaki and Wolock, 2005) or 30-m resolution 

Elevation Derivatives for National Applications (EDNA) reach 

catchments (U.S. Geological Survey, 2002). LULC information was obtained from 30-m resolution National Land Cover 

Data (NLCD) that were based on satellite imagery from the 

early to mid-1990s (Vogelmann and others, 2001) and modified and enhanced (NLCDe 92) with Geographic Information 

Retrieval and Analysis System (GIRAS) data to give 25 LULC 

categories, as described in Nakagaki and Wolock (2005). 

To develop more detailed descriptions of riparian land 

cover, the LULC features in buffer areas surrounding each 

stream were digitized in a GIS environment using orthophotographs as templates (Lutz and Kennedy, 2010). The digitized 

LULC features within each buffer area were categorized into 

eight categories: barren, cropland, farmstead, grassland, open 

water, urban/built-up land, woody vegetation, and wetland 

(Lutz and Kennedy, 2010). Areas of LULC features were 

computed within 25-m and 50-m buffers on each side of the 

stream along the reach length and within 50-m, 100-m, 150-m, 

and 250-m buffers on each side along the segment length. 

The length of segment buffers began at the downstream end 

of the reach and extended upstream to a length (in kilometers) equivalent to the log10 of the watershed area (in square 

meters) (Lee and others, 2001). Segment lengths were not 

always exactly the same as the length from which segment 

slope and sinuosity were determined, but they represent the 

same scale relative to the biological collection site.

In addition, a narrow segment-length buffer area (stream 

margin) was created 15 m from the determined bankfull width 

and intersected with the LULC data layer. The LULC data for 

the 15-m stream margin were summarized as follows: (1) total 

number of individual fragments (changes from one land use 

to another along both sides of the stream margin (minus one)) 

per kilometer, (2) average length of like (similar) fragments, 

(3) percentage of total margin length populated by each fragment type, and (4) the length of the fragments considered 

(a) undisturbed (defined as those with land cover of wetland 

and woody vegetation) or (b) disturbed (defined as those with 

land cover in all other categories, except open water). Because 

the land cover was calculated from the center or midline of 

the stream, the open-water category was considered a surrogate for stream size; therefore, the open-water category 

was removed from the analyses. The digitized LULC features 

surrounding each stream are available online (Lutz and Kennedy, 2010). 

Data Analysis

Biological-assemblage data (species relative abundance) 

and over 130 biological metrics computed from assemblage 

data were used in analyses with environmental characteristics.

Biological Assemblages

Algal, invertebrate, and fish assemblages were identified to the lowest possible taxon. Algae were identified, 

generally to species, and enumerated as described in Charles 

and others (2002). The relative abundance (cells per square 

centimeter as a percentage of total algal cell abundance) and 

relative biovolume (cubic micrometers per square centimeter 

as a percentage of total algal biovolume) of each taxon were 

calculated for each sample. Algal-assemblage data analyses 

were based on 640 algal taxa identified from the 86 sites and 

a reduced dataset of 244 taxa, which included all taxa with 

relative abundance greater than 5 percent at 2 or more sites 

or occurrence at 4 or more sites such that “rare” taxa were 

excluded. In some cases, inclusion of rare taxa can reduce the 

number and strength of correlations in bioassessment and may 

unnecessarily complicate model outputs (Marchant, 2002; Van 

Sickle and others, 2007). Invertebrate samples were identified and enumerated by the USGS National Water-Quality 

Laboratory in Denver, Colorado, following methods described 

by Moulton and others (2000). Invertebrate-assemblage data 

included 458 taxa (all taxa identified) and a reduced dataset of 

246 taxa (all taxa with abundance greater than 5 percent at 3 or 

more sites such that “rare” taxa were excluded). Fish analyses 

included all 116 fish taxa identified from the 86 sites. 

Biological Metrics

Algal metrics were calculated from relative-abundance 

data using autecological information on taxon-specific sensitivity to environmental requirements (Porter, 2008). Taxonomic richness (number of taxa) and biovolume metrics were 

computed using PhycoAide (Sprouffske and others, 2006), 

which summarizes the algal taxonomic data by categories such 

as percentages of various taxonomic groups (for example, 

diatoms, green algae, blue-green algae). Algal metrics were 

selected to characterize trophic ecology, oxygen tolerance, 

salinity sensitivity, organic enrichment (saprobien), motility 
6 Evaluation of Aquatic Biota in Relation to Environmental Characteristics Measured at Multiple Scales

(siltation index), potential nitrogen fixation, and the proportion 

of the assemblage consisting of benthos (attached species) and 

seston (floating species). 

Invertebrate metrics were based on raw and relative 

abundances of invertebrate taxa, and were computed by use 

of the Invertebrate Data Analysis System (IDAS) (Cuffney, 2003). Invertebrate metrics that are commonly used in 

water-quality assessment were computed after use of IDAS 

to resolve ambiguous taxa (when data are reported at one 

or more lower or higher taxonomic levels within the taxonomic hierarchy; Cuffney and others, 2007). Types of metrics 

computed reflected abundance (number of individuals) and 

richness (number of taxa), similarity and diversity, tolerance, 

and functional feeding groups (scrapers, shredders, gathercollectors, filter-collectors, etc.). Functional-feeding-group and 

regional-tolerance metrics included in the calculations were 

derived from Appendix B of the U.S. Environmental Protection Agency (USEPA) Rapid Bioassessment Protocol (Barbour 

and others, 1999). 

Fish metrics were based on abundances of fish taxa and 

computed using Excel. Metrics included species traits such as 

substrate preference, geomorphic preference, trophic ecology 

or feeding preference, locomotion, reproductive strategy, and 

stream-size preference (Goldstein and Meador, 2004). In addition, selected metrics that are commonly included in fish Index 

of Biotic Integrity (IBI) ratings from the Midwest were also 

included, such as pollution tolerance, the number of individuals, the number of taxa, and the abundance of certain families 

(Karr, 1981; Lyons, 1992; Meador and others, 1993b; Barbour 

and others, 1999). 

Environmental Characteristics

At the reach scale, transect-level physical characteristics were summarized into metrics representing minimum, 

maximum, and average values for each reach; summarized 

values at this scale were for instream and streambank metrics 

mentioned earlier. Segment- and watershed-scale metrics other 

than LULC were computed by use of a GIS and digital coverages, according to Fitzpatrick and others (1998). 

Relations between Biological Assemblages, 

Biological Metrics, and 

Environmental Characteristics 

A combination of multivariate techniques and Spearman 

rank correlations was used to relate the biological assemblages 

and environmental characteristics. Species-relative-abundance 

data were square-root transformed; however, environmental 

characteristics were standardized and transformed if necessary 

(generally also square-root or log-transformed). Detrended 

Correspondence Analysis (DCA) was used to examine nonlinear or unimodal patterns in assemblage data for each group of 

biota. DCA was performed in CANOCO version 4.5 software 

(Hill, 1979; ter Braak and Smilauer, 2002). In addition to 

characterizing gradients in taxonomic composition, site scores 

from DCA axes represent latent environmental gradients to 

which each assemblage corresponds (ter Braak, 1987). Site 

scores from the first two DCA axes were used as additional 

biological metrics in Spearman rank correlations and as a 

guide for selecting the subset of environmental characteristics for subsequent multivariate analyses. To further reduce 

the subset of environmental characteristics for multivariate 

analyses, Spearman rank correlations were performed between 

environmental characteristics and biological metrics, where 

the critical rho (rs) value (n = 86) occurs when rs ≥ 0.277 for 

p < 0.01 and when rs ≥ 0.212 for p < 0.05 (two-tailed significance level; Zar, 1974). Biological metrics selected for subsequent analyses were those that had correlations (rs

 > 0.27) with 

environmental characteristics and could serve as representatives for biological metrics with similar (collinear) relations.

Additional relations between biotic assemblages and 

environmental patterns were assessed by means of non-parametric multivariate analyses based on the approach described 

by Clarke and Ainsworth (1993). For each biological assemblage, a Bray Curtis similarity (resemblance) matrix of sites 

was created. For environmental characteristics, a Euclidean 

distance dissimilarity (resemblance) matrix of the sites was 

created with the transformed data at all three spatial scales 

(reach, segment, and watershed). A two-phase regression 

analysis was used to select environmental characteristics that 

best explain patterns in the biological assemblages, by maximizing a rank correlation between the biotic and environmental resemblance matrices and their respective elements. The 

first regression analysis (BVSTEP) performs a stepwise search 

over the environmental characteristics, selects the single 

environmental variable with the strongest correlation and adds 

additional variables, one at a time, to find the combination of 

variables with the strongest overall correlation. This analysis 

was used initially for the large number of trial characteristics 

to select the subset of environmental characteristics that produced the greatest similarity in ranks of the 86 sites. The second regression analysis (BIOENV) calculates all permutations 

of environmental characteristics, and was run on the reduced 

set of environmental characteristics that were identified using 

the first regression analysis. This second procedure identifies 

the best combination of environmental-characteristic predictor characteristics through an iterative process where every 

possible combination is tried, in combinations of one, two, 

three, etc., environmental characteristics. Correlations between 

individual environmental characteristics of a given site and 

site similarity (resemblance) values, based on biologic assemblages, were enumerated using this procedure and those characteristics, whether individual or in combination with one or 

more others, that were most highly correlated with the biotic 

assemblages were selected for further study. Resemblance 

matrices, rank correlation between matrices, and the BIOENV 

and BVSTEP multivariate procedures were performed in 

Primer-E software, Version 6.0 (Clarke and Gorley, 2006).
Relations between Biota and Multiscale Environmental Characteristics 7

To derive a final, reduced subset of 8 to 10 best biological 

metrics from each assemblage, biotic metrics were analyzed 

with the subset of environmental characteristics that were 

identified both as significantly correlated with DCA axes 

scores and as “best explaining” environmental characteristics 

from the BIOENV analysis. Metrics from each group of biota 

(algae, invertebrates, and fish) were used to represent a positive or negative ecosystem health response. Interpretations as 

to whether the biological response was positive or negative 

were based on multiple lines of evidence, such as metrics 

associated with sensitive taxa that have a positive correlation with particular environmental characteristics or metrics 

associated with tolerant taxa that have a negative correlation 

with those same characteristics. Through this process, physical characteristics with positive and negative biotic responses 

were identified.

Spatial and Temporal Variability of 

Biological Metrics

The spatial and temporal (annual) variance between 

reaches and between years of the selected biological metrics 

was examined from approximately 15 percent of the sites by 

calculating and comparing the coefficient of variation (cv) 

from three consecutive reaches and years. The coefficient of 

variation is a unitless measure of relative variability (often 

multiplied by 100) and is computed by dividing the standard 

deviation of the sample by the mean of the sample (Zar, 1974).

Relations between Biota and 

Multiscale Environmental 

Characteristics

In this regional study of agricultural streams in the 

Midwest, physical characteristics from the three spatial scales 

were evaluated, and the relative importance of reach-scale to 

segment- and watershed-scale characteristics was assessed. 

The watershed-scale characteristics correlated with all three 

biotic assemblages but most highly with fish. The stronger relative importance of watershed-scale characteristics was found 

by means of either Spearman rank correlations with DCA-axis 

scores or multivariate analyses with the biotic assemblages. 

The biological assemblages as a whole generally had stronger 

correlations than individual metrics that represented specific 

aspects of the assemblages. 

Biological Assemblages with 

Environmental Characteristics

Summary statistics for those environmental characteristics to which algal, invertebrate, and fish assemblages showed 

the strongest relations are presented in table 1. The results of 

the analyses identified environmental characteristics from each 

of the three scales that together best explained the variation in 

biological assemblages from the 86 sites (table 2). 

Combining characteristics from all spatial scales 

improved the correlations slightly for algal and invertebrate 

assemblages but not for the fish assemblages. Instead, fish 

assemblages were associated (rs > 0.6) with a combination 

of five watershed features: drainage area, soil permeability, 

latitude, mixed forest, and row-crop land cover. The biological 

metrics that were identified as the most responsive to the measured environmental characteristics are summarized in table 3. 

At the Reach Scale 

The characteristics at the reach scale that best explained 

variations in biological assemblages, based on multivariate 

analyses, were bank vegetative cover (BankVeg), percentage 

of fine sediment in the stream substrate (Fine), open canopy 

angle variation (OpCanCV), and percentage of woody vegetation in the 50-m buffer (WV50) (table 2). For both algal and 

fish assemblages, average wetted channel width (WetWdth) 

and Froude number (Froude) were also included. The Froude 

number is a dimensionless metric representing the ratio of the 

inertial forces to the gravitational forces in the stream channel; thus, it relates to the hydrology and the geomorphology of 

the channel.

When the biological-assemblage data, represented by 

DCA axes, were examined, additional reach-scale environmental metrics were identified that had significant correlations 

with one or more biotic assemblages (tables 4, 5, and 6). Additional reach-scale characteristics most strongly correlated with 

algal assemblages included average stream velocity (Velocity), 

WetWdth, and average wetted-channel width-to-depth ratio 

(W/D) (table 4). The other reach-scale characteristics most 

strongly correlated with invertebrate assemblages, as represented by DCA axes, were bank erosion (BankEros), Froude, 

open-canopy angle (OpCanAng), percentage of riffles (Riffle), 

W/D, WetWdth, and Velocity (table 5). The first two invertebrate DCA axes (IDCA_AX1 and IDCA_AX2) appeared to 

represent tolerant taxa, reflected by negative correlations with 

BankVeg, Riffle, Velocity, and WV50 and positive correlations 

with Fine and OpCanAng. Reach characteristics also highly 

correlated with fish assemblages were WetWdth, W/D, Froude, 

Riffle, Velocity, and OpCanAng (table 6). Increasing values of 

Riffle, BankVeg, and WV50 were correlated with increases in 

positive-indicator metrics for all three biological assemblages.

As mentioned above, selected metrics from each group 

of biota (algae, invertebrates, and fish) were predefined to 

represent a positive or negative ecosystem health response. 

Positive-indicator metrics correlating with BankVeg were 

blue-green algal biovolume (BGA_bv), percentage of nitrogenfixing algal cells (pNFIXERS), and percentage of the diatom 

Achnanthidium minutissimum (pAch_min) for the algae; 

caddisfly richness (R_TRICH) and percentage abundance of 
8 Evaluation of Aquatic Biota in Relation to Environmental Characteristics Measured at Multiple Scales

mollusks and crustaceans (pMOLCRU) for the invertebrates; 

and percentages of cobble-substrate fish (pCOBBfish) and 

pollution-sensitive fish (pSENSfish) for the fish. Negativeindicator biotic metrics correlated with BankVeg were percentage of tolerant diatom taxa (pTOLBPC1), percentage of 

Table 2. Summary of correlations between biological assemblages and selected environmental 

characteristics at each physical scale for agricultural stream sites in the Midwest. 

[(#), number of characteristics contributing to the best combinations or highest correlation between biotic assemblages 

and environmental characteristics; bold numbers indicate characteristics that contributed to the BIOENV combined correlation selections; bold abbreviations indicate characteristics that contributed to correlations with all three biological 

assemblages. Metric definitions are given in table 1]

Metric abbreviation Algae Invertebrate Fish 

Reach characteristics—BIOENV 

combined correlation

0.375 (1 7) 0.365 (6) 0.358 (7)

BankEros .021 .137 .055

BankVeg .100 .153 .187

Fine .125 .114 .055

Froude .117 .117 .111

OpCanCV .194 .195 .125

Velocity .071 .127 .057

WetWdth .127 .094 .325

W/D .071 .054 .241

WV50 .081 .045 .070

Segment characteristics—BIOENV 

combined correlation

.259 (3) .235 ( 26) .276 ( 33)

BuffFrag .080 .083 .134

BuffLngth .009 - .056 - .005

Crop250 .191 .138 .143

SegGrad .113 - .047 - .088

SINUOS .088 - .089 - .081

WV150 .177 .150 .130

Watershed characteristics—BIOENV 

combined correlation

.445 (5) .510 (5) .604 (5)

DrainArea .273 .364 .460

Forest .311 .254 .319

Latitude .143 .252 .243

MxForest .244 .264 .409

Perml .246 .213 .307

RowCrop .321 .268 .309

Slope .273 .172 .255

WetXArea/DA .185 .319 .276

All scales of variables combined .534 (9) .523 (7) .604 (5)

1

 Also row crops in 15-m stream margin (0.031).

2 Also woody vegetation in the 250-m buffer (1.92), wetland in the 15-m stream margin (0.050), and urban land in the 

250-m buffer (0.043).

3 Also wetland in the 250-m buffer (0.109). 

eutrophic algal taxa (pEUTROPH), invertebrate taxa richness 

composed of non-midge Diptera and non-insects (R_ODIPNI), 

average USEPA pollution tolerance value for invertebrates 

based on taxa richness (RichTOL), ratio of taxa richness of 

Orthocladiinae taxa midges to all midges (pR_ORTHO_CH), 
Relations between Biota and Multiscale Environmental Characteristics 9

percentage richness of omnivorous invertebrates (pR_OMNI), 

and percentages of creeper locomotion (pCREEPfish) fish, 

percentages of detritivore (pDETRfish) fish, and omnivorous (pOMNIfish) fish, which all had negative correlations 

(tables 4–6). The positive-indicator metrics that correlated 

with Riffle were similar to those for BankVeg. 

At the Segment Scale 

Based on multivariate analyses, the only segment-scale 

characteristic that explained variation in all three biological 

assemblages combined was the number of buffer fragments in 

the margin (BuffFrag), that is, changes from one type of land 

cover in the buffer to another along the segment length. Land 

cover in the buffer area was the other most important feature, 

whether cropland or woody vegetation. However, Spearman 

Table 3. Selected biological metrics, with metric abbreviations and range of data (minimum, median or 50th percentile, and maximum) 

for 86 agricultural stream sites in the Midwest, listed by biological assemblage.

Metric abbreviation Metric description Unit Minimum Median Maximum

Algae

BGA_bv Blue-green algal biovolume μm3/cm2 0.0 1.5 x 107 1.3 x 1010

pAch_min Percentage1

 of diatom Achnanthidium minutissimum percent 0 .6 56.0

pBRACKSL Percentage of brackish-water algal taxa percent 0 4.3 59.4

pEUTROPH Percentage of eutrophic algal taxa percent .2 24.9 77.3

pNFIXERS Percentage nitrogen fixing algal cells percent 0 .0 99.1

pSENBPC3 Percentage of sensitive diatom taxa percent 0 11.4 58.2

pSESTON Percentage of seston percent 0 7.8 86.8

pTOLBPC1 Percentage of tolerant diatom taxa percent 0 2.8 32.3

ShWTTax Shannon Wiener, diversity total taxa dimensionless .1 3.7 5.3

Silt_Ind Diatom Siltation Index (percent of total taxa) percent .7 47.8 92.8

Invertebrates

Margalef Margalef Diversity of total taxa unitless 3.5 9.1 16.7

pFC_abund2 Percentage of filtering collectors percent 1.2 28.0 80.3

pGC_abund3 Percentage of gathering collectors percent .9 27.4 73.5

pMOLCRU Percentage of molluscs and crustaceans percent 0 1.4 53.0

pR_OMNI Percent richness of omnivores percent 0 4.2 18.5

pR_ORTHO_CH Percent richness of Orthocladiinae midges to all midges percent 0 33.3 62.5

R_ODIPNI Richness composed of non-midge Diptera and non-insects number 1.0 7.5 17.0

R_TRICH Richness composed of caddisflies (Trichoptera) number 1.0 5.0 12.0

RichTOL Average USEPA tolerance values for sample based on richness dimensionless 3.6 4.9 6.7

Fish

pCARNfish Percentage of carnivore/piscivore fish percent 0 15.7 94.8

pCOBBfish Percentage of cobble-substrate preference fish percent 0 32.7 100.0

pCREEPfish Percentage of creeper locomotion fish percent 0 13.9 82.7

pDETRfish Percentage of detritivorous fish percent 0 24.9 81.4

pMUDfish Percentage of mud-substrate preference fish percent 0 17.9 76.7

pOMNIfish Percentage of omnivorous fish percent 0 17.1 74.5

pSENSfish Percentage of pollution-sensitive fish percent 0 6.7 64.3

R_TAXAfish Richness of fish taxa number 2 18 35

1

 Percentage refers to the pecent relative abundance of cells or individuals of a specific type compared with the total in a sample.

2

 Better for rocks.

3

 Better for snags.
10 Evaluation of Aquatic Biota in Relation to Environmental Characteristics Measured at Multiple Scales

rho values for all of the segment characteristics together were 

low (rs = 0.235 for algae to 0.276 for fish) and were the weakest of the three physical scales for explaining patterns in the 

biotic assemblages (table 2).

Positive-indicator metrics for all three assemblages were 

correlated with increasing percentages of woody vegetation in 

the 150-m buffer (WV150), whereas negative-indicator metrics 

correlated with the percentage of cropland in the 250-m buffer 

(Crop250) on both sides of the streams (tables 4–7). Increasing percentage of woody vegetation in the stream margin and 

increasing average length of undisturbed buffer (BuffLngth) 

were correlated with positive-indicator metrics for algae and 

fish, whereas the Crop250 was correlated with negative-indicator metrics. 

 For segment-scale characteristics, algal assemblages 

were the most highly correlated to Crop250 and WV150. 

Invertebrate and fish assemblages were also most highly correlated with these two characteristics, as well as BuffLngth. 

Sinuosity (SINUOUS) correlated significantly for invertebrates 

only. Woody vegetation, in this case WV150, was selected to 

be representative of the other woody vegetation buffers that 

were highly correlated with it, for example with the 250-m 

(rs = 0.97) and the 15-m (rs = 0.87) woody vegetation buffers. 

If a significant correlation is found between a biological metric 

and a particular LULC, one might expect similar correlations for that LULC as measured at different buffer widths. 

A negative correlation for algae with SINUOS appeared to be 

due to reduced current velocity, which correlated with a higher 

percentage of seston (phytoplankton) in the benthic samples. 

At the Watershed Scale 

At the watershed scale, variation in each of the three 

biotic assemblages was best explained by changes in soil 

permeability (Perml), latitude, and percentage of land cover/

land use—either total forest (forest), mixed forest (MxForest),

or row crop (RowCrop). Drainage area (DrainArea) had a 

greater correlation with the fish and invertebrate assemblages, 

whereas average watershed slope (Slope) best explained 

variation in algal assemblages (table 2). Average wettedreach cross-sectional area as normalized by drainage area 

(WetXArea/DA) had mixed results among assemblages; for 

example, it showed positive correlations with positive-indicator algal metrics such as percentage of Achnanthidium minutissimum (pAch_min) and negative correlations with percentage 

of seston (pSESTON), a negative-indicator metric (table 4). 

WetXArea/DA was positively correlated with all positive-indicator fish metrics except diversity, and was negatively correlated with all negative-indicator fish metrics (table 6). Both the 

individual characteristics and the multivariate combinations 

had the strongest correlations at the watershed scale for algae, 

invertebrates, and fish. 

When the analysis was run with characteristics combined from all three scales, algal and invertebrate correlations 

improved when fine silty substrate (Fine) and open-canopy 

angle variation (OpCanCV) from the reach scale, and woody 

vegetation in the 150-m buffer (WV150) from the segment 

scale were included. Although the correlations for the fish 

assemblages with environmental characteristics were as 

numerous and strong as the algal and invertebrate assemblages, none of the reach or segment characteristics improved 

the correlation found with watershed characteristics (table 2).

Spearman rank correlation using DCA axis scores to 

represent the biological assemblages were compared with the 

metrics derived from more specific aspects of the biological 

assemblages such as the number of detritivores (tables 4–6). 

In many cases, correlations to environmental characteristics 

were stronger with DCA axes representing the assemblages 

than with the more specific biotic metrics (see table 5). Some 

metrics followed a response pattern similar to one of the DCA 

axes but at varying strengths. One example of this was the 

similarity in response between the metrics for detritivorous 

fish (DETRfish) and fish DCA axis 1 (FDCA_AX1). DETRfish

correlated with land cover at all three physical scales, showing 

a negative correlation at the reach scale to woody vegetation 

in the 50-m buffer (WV50) (rs = -0.28) and positive correlations at the segment scale with cropland in the 250-m buffer 

(Crop250) (rs = 0.50) and at the watershed scale with percentage of row-crop land cover (RowCrop) (rs = 0.56). 

In general, biotic assemblages were positively correlated 

to watershed characteristics, with the exception of negative correlations with increasing amounts of RowCrop and 

DrainArea (table 7). Although diversity and taxa richness 

increased for algae and fish, respectively, with DrainArea, the 

resulting community was more tolerant (tables 4, 6, and 7). 

For invertebrates, only the relative abundance of filtering collectors (pFC_abund) increased with drainage area. 

The combination of the best physical characteristics from 

each spatial scale for each biotic assemblage indicated that in 

addition to the watershed metrics that correlated most strongly, 

other important metrics were Fine for algae, OpCanCV for 

algae and invertebrates, BankVeg for invertebrates and fish, 

and WV150 for algae (table 7). These reach- and segmentscale characteristics correlated strongly enough to be included 

as characteristics in the combined model (table 2).

Biological Metrics with 

Environmental Characteristics

Biological metrics that describe specific aspects of a 

biological assemblage are useful for assessing how a particular taxonomic or functional group might vary with changes 

in some environmental characteristic. However, metrics like 

DCA axis scores that more broadly described assemblages had 

generally stronger correlations with environmental characteristics than did the more specific biological metrics. This finding 

agrees with other studies that recommend the use of multimetric approaches for bioassessment (for example, see Karr, 1981; 

Lücke and Johnson, 2009). 
Relations between Biota and Multiscale Environmental Characteristics 11

Algal Metrics

The selection of algal metrics that correlated most 

strongly with the environmental characteristics was based on 

both positive and negative character of the biological metrics (table 4). Indicators of impairment, such as percentage 

of tolerant diatom taxa (pTOLBPC1), the Diatom Siltation 

Index (Silt_Ind), and percentage of seston (pSESTON) in the 

benthic samples were selected to represent negative-indicator 

metrics (Bahls, 1993; Porter, 2008). In contrast, metrics such 

as percentage of the diatom Achnanthidium minutissimum

(pAch_min) and biovolume of blue-green algae (BGA_bv) 

consistently correlated opposite to designated negative-indicator metrics and thus were considered to be positive-indicator 

metrics. Percentage of sensitive diatom taxa (pSENBPC3) and 

BGA_bv were negatively correlated with fine silty substrate 

(Fine) at the reach scale; however, pSENBPC3 and pAch_min

were positively correlated with average length of undisturbed 

buffer (BuffLngth) at the segment scale (table 4). Positive-indicator metrics such as pAch_min and percentage of nitrogenfixing cells (pNFIXERS) correlated negatively with drainagearea size (DrainArea) and increasing percentages of row crop 

in the watershed (RowCrop), and correlated positively with 

forest land cover (both Forest and MxForest). The negativeindicator metric pSESTON increased with the percentage of 

cropland in the buffer (Crop250) and row-crop land cover. The 

Shannon-Wiener diversity index for algae (ShWTTax) correlated with many environmental characteristics, but the direction of the response was similar to negative-indicator metrics 

(table 4). Overall algal taxa richness (metric not shown) did 

not have significant correlations with environmental characteristics but was correlated with the abundance of diatoms (not 

shown) and ShWTTax. As a result, we selected ShWTTax as the 

representative algal diversity metric that was most responsive 

to environmental characteristics. 

Invertebrate Metrics

The invertebrate metrics selected were those that correlated most highly with measured environmental characteristics (tables 3 and 5); in general, the final invertebrate metrics 

selected were only weakly to moderately correlated with each 

other except for MOLCRUp and ODIPNIR (rs = 0.71), and for 

the percentage abundances of filtering collectors (pFC_abund) 

and gathering collectors (pGC_abund) (rs = -0.71). Correlations between environmental characteristics and invertebrate 

assemblages, as represented by DCA axes, were generally 

slightly higher if rare taxa were excluded. Therefore, multivariate analyses used the invertebrate dataset with rare taxa 

excluded. Correlations at a higher rs were more numerous 

for invertebrates collected from rocks and snags combined 

(n=86) and rocks alone (n=45) than from snags alone (n=41). 

For this reason, and to allow examination of patterns among 

86 sites across the three groups of biota, subsequent discussions focus on results for all invertebrate taxa and for rocks 

and snags combined (noting important differences in results 

by substrate). 

Positive-indicator metrics for invertebrates represented 

qualities of healthy assemblages based on studies in the 

literature (Lenat, 1988; Kerans and Karr, 1994; Barbour and 

others, 1996). In contrast, negative-indicator metrics are generally associated with degraded assemblages. In our study, the 

strongest positive-indicator metrics for invertebrates were taxa 

richness of caddisflies (R_TRICH), the percentage abundance 

of mollusks and crustaceans (pMOLCRU), pFC_abund and 

pGC_abund (of these two, the former was better for rocks 

and the latter was better for snags), and Margalef diversity 

(Margalef, 1958) (table 5). With degrading water quality, 

invertebrate assemblages may change from those dominated 

by aquatic insect larvae, such as members of the pollutionsensitive orders Ephemeroptera-Plecoptera-Trichoptera 

(EPT; mayflies-stoneflies-caddisflies), to those dominated by 

non-insects. Percent richness of EPT taxa (pR_EPT) was less 

useful as a positive-indicator metric in this study due to the 

lack of correlations for snag samples. Significant correlations 

for pR_EPT were found for rock samples, such as with woody 

vegetation and cropland in the buffer (rs = 0.33 for WV100 to 

rs = 0.43 for WV250; rs = -0.33 for percentage of cropland in 

the segment 15-m margin to rs = -0.37 for Crop250). Metrics based on EPT taxa can be affected by relatively tolerant 

mayfly or caddisfly taxa, as seemed to be the case in this study. 

Tolerant mayfly taxa such as Baetis flavistriga, B. intercalaris, 

and Caenis spp. and tolerant caddisfly taxa such as Cheumatopsyche spp., Hydropsyche spp., and Hydroptila spp. were 

dominant at many sites and may have led to the decreased 

sensitivity of the EPT metrics. 

The best negative-indicator metrics for invertebrates were 

taxa richness composed of non-midge Diptera and non-insects 

(R_ODIPNI), USEPA pollution tolerance value based on taxa 

richness (RichTOL), ratio of taxa richness of Orthocladiinae 

taxa midges to all midges (pR_ORTHO_CH), and percentage taxa richness of omnivorous invertebrates (pR_OMNI). 

RichTOL values decreased with increasing woody vegetation 

in the buffer (WV150) but increased from narrow to wider 

cropland buffer (Crop250). As mentioned earlier, the first two 

DCA axes appeared to represent primarily tolerant taxa, as 

evidenced by correlations with negative-indicator metrics and 

physical characteristics such as cropland. Several other metrics 

showed relatively high correlations to selected environmental 

characteristics; however, they were redundant with the abovementioned metrics and did not correlate highly with as many 

physical characteristics.

Fish Metrics

Fish metrics found to be the most strongly correlated with 

physical environmental characteristics in this study (tables 2 

and 6) included the positive-indicator metrics percentage of 

cobble-substrate fish (pCOBBfish), pollution-sensitive fish 

(pSENSfish), and carnivore or piscivore fish (pCARNfish); 
Table 4. Correlations between algal metrics and selected environmental characteristics for agricultural stream sites in the Midwest. [bold, p< 0.01, based on the Spearman correlation coefficient rs (Zar, 1974); n, number of sites in comparison. Metric definitions are given in tables 1 and 3] Ecosytem health indicators Positive Negative Metric abbreviation ADCA_AX1 ADCA_AX2 pAch_min pSENBPC3 BGA_bv pNFIXERS ShWTTax Diversity pTOLBPC1 Silt_Ind pSESTON pBR

Reach characteristics BankEros -0.15 -0.21 0.33 0.01 0.26 0.08 -0.13 -0.07 -0.11 -0.26 -

BankVeg - .08 - .38 .33 - .14 .41 .39 - .38 - .41 - .28 - .28 - Fine - .12 - .14 - .11 - .28 - .24 .06 - .06 .00 .10 .15 - Froude - .11 - .45 .35 - .02 .21 .22 - .41 - .34 - .32 - .38 - OpCanAng .21 .15 - .19 - .04 - .15 - .18 .19 .13 .21 .25 OpCanCV - .19 - .09 .16 - .08 .18 .23 - .15 - .11 - .07 - .18 - Riffle .01 - .20 .36 .11 .30 .21 - .28 - .36 - .23 - .31 - Velocity - .11 - .44 .30 .07 .18 .15 - .35 - .24 - .30 - .39 - WetWdth .15 - .37 - .12 .27 .01 - .38 .31 .23 .20 - .32 W/D .34 .45 - .02 .16 .11 - .35 .30 .17 .22 .31 WV50 - .18 - .25 .33 .10 .26 .02 - .38 - .41 - .32 - .23 - 

Segment characteristics BuffFrag - .04 - .04 - .12 - .25 - .05 .07 - .08 - .09 .14 .02 - 

BuffLngth - .18 - .10 .35 .23 .11 .11 - .10 - .09 - .36 - .23 - Crop250 .32 .33 - .42 - .17 - .21 - .32 .34 .37 .55 .47 SegGrad .34 - .09 .15 - .12 - .03 .08 .01 - .10 - .01 .14 - SINUOS .22 .25 - .33 - .16 - .19 .03 .16 .13 .28 .34 WV150 - .29 - .34 .32 .11 .30 .27 - .41 - .33 - .43 - .37 - 

Watershed characteristics DrainArea .24 .41 - .34 .10 - .28 - .39 .28 .21 .25 .34 

Forest - .32 - .35 .65 .07 .31 .35 - .41 - .39 - .57 - .42 - Latitude .14 - .30 - .08 - .09 - .19 .23 - .16 - .14 - .26 - .22 - MxForest .02 - .37 .47 .02 .11 .30 - .25 - .25 - .36 - .36 - Perml - .05 - .43 .52 .10 .26 .28 - .21 - .40 - .54 - .46 - RowCrop .34 .33 - .55 - .04 - .26 - .42 .43 .41 .57 .41 Slope - .40 - .40 .49 - .07 .26 .38 - .40 - .39 - .42 - .37 - WetXArea/DA - .31 - .37 .40 .08 .37 .27 - .27 - .19 - .23 - .32 - 
Table 5. Correlations between invertebrate metrics and selected environmental characteristics for agricultural stream sites in the Midwes[bold, p< 0.01, based on the Spearman correlation coefficient rs (Zar, 1974); n, number of sites in comparison. Metric definitions are given in tables 1 and 3]

Ecosytem health indicators Positive Negative

Metric 

abbreviation IDCA_AX1 IDCA_AX2 R_TRICH pMOLCRU pFC_abund pGC_abund Margalef Diversity R_ODIPNI RichTOL pR_ORTHO_CH 

Reach characteristics

BankEros -0.35 -0.20 0.33 0.25 -0.15 0.24 0.11 0.26 -0.10 0.18 BankVeg - .42 - .10 .37 .33 - .26 .23 .16 .32 - .38 .34 Fine .29 .22 .02 - .10 - .02 - .11 - .29 - .10 .08 - .11 Froude - .41 - .23 .36 .13 - .03 .10 - .04 .22 - .42 .42 OpCanAng .28 .34 - .09 - .17 .29 - .04 - .29 - .24 .06 - .04 OpCanCV - .28 - .31 .08 .23 - .27 .00 .22 .18 .09 .05 Riffle - .56 - .27 .43 .45 - .22 .20 .14 .49 - .18 .16 Velocity - .38 - .24 .30 .07 .05 .10 - .06 .18 - .41 .40 WetWdth .39 .14 - .19 - .42 .30 - .24 .07 - .34 - .08 - .34 W/D .40 .37 - .20 - .36 .18 - .18 .11 - .27 - .09 - .29 WV50 - .29 - .38 .15 .17 - .12 .09 .32 .34 - .23 .00 

Segment characteristics

BuffFrag .03 .02 - .05 - .03 .09 - .05 - .13 .05 .09 - .06 BuffLngth - .27 - .12 .16 .16 - .17 .11 .23 .11 - .16 .09 Crop250 .56 .36 - .27 - .41 .22 - .19 - .29 - .37 .25 - .15 SegGrad - .44 - .17 .17 .21 - .13 .16 - .01 .21 - .37 .30 SINUOS .31 .35 - .22 - .14 .05 .00 - .15 - .14 .07 - .13 WV150 - .43 - .38 .22 .21 - .10 .07 .36 .27 - .33 .04 

Watershed characteristics

DrainArea .67 .23 - .32 - .62 .40 - .26 - .09 - .48 .02 - .40 Forest - .60 - .27 .22 .42 - .18 .16 .21 .28 - .21 .17 Latitude - .15 - .17 .08 .20 - .14 .27 - .12 .13 - .04 .29 MxForest - .50 - .16 .29 .33 - .28 .46 .09 .23 - .29 .48 Perml - .50 - .36 .36 .34 - .34 .39 .06 .31 - .37 .35 RowCrop .67 .37 - .24 - .52 .14 - .12 - .20 - .36 .09 - .22 Slope - .51 - .36 .33 .29 - .18 .12 .19 .27 - .26 .19 WetXArea/DA - .71 - .35 .34 .54 - .28 .19 .21 .45 - .10 .32 
Table 6. Correlations between fish metrics and selected environmental characteristics for agricultural stream sites in the M[bold, p< 0.01, based on the Spearman correlation coefficient rs (Zar, 1974); n, number of sites in comparison. Metric definitions are given in table

Ecosytem health indicators Positive Negative Metric abbreviation FDCA_AX1 FDCA_AX2 pCOBBfish pSENSfish pCARNfish R_TAXAfish (Diversity) pDETRfish pOMNIfish pMU

Reach characteristics BankEros -0.25 -0.00 0.14 0.17 0.42 -0.36 -0.34 -0.27 -

BankVeg - .45 .29 .42 .32 .25 - .22 - .36 - .33 - Fine .06 - .42 - .27 - .32 .02 - .04 .23 .25 Froude - .47 .33 .44 .36 .25 - .28 - .37 - .38 - OpCanAng .10 - .31 - .30 - .26 - .12 .10 .40 .37 OpCanCV - .19 .20 .26 .11 .16 - .24 - .41 - .33 - Riffle - .31 .44 .55 .41 .31 - .30 - .44 - .27 - Velocity - .41 .33 .45 .36 .26 - .25 - .33 - .32 - WetWdth .75 .06 - .21 .07 - .39 .62 .44 .36 W/D .60 .13 - .21 .04 - .43 .58 .40 .34 WV50 - .22 .36 .40 .38 .15 - .10 - .28 - .23 - 

Segment characteristics BuffFrag - .12 - .25 - .20 - .07 .03 - .04 .23 - .01 

BuffLngth .02 .33 .33 .22 .06 - .01 - .29 - .27 - Crop250 .26 - .48 - .50 - .45 - .25 .28 .50 .56 SegGrad - .34 - .09 .13 - .12 .17 .02 .36 .06 SINUOS .08 - .23 - .25 - .16 - .30 .16 .32 .35 WV150 - .11 .59 .59 .56 .14 - .03 - .38 - .37 - 

Watershed characteristics DrainArea .61 - .21 - .37 - .24 - .38 .52 .50 .44 

Forest - .28 .40 .49 .46 .46 - .33 - .51 - .46 - Latitude - .77 - .19 .13 - .17 .31 - .48 - .27 - .21 MxForest - .61 .16 .35 .23 .45 - .47 - .46 - .35 - Perml - .54 .32 .47 .46 .41 - .35 - .45 - .44 - RowCrop .38 - .34 - .48 - .44 - .47 .15 .56 .48 Slope - .22 .27 .33 .40 .32 - .29 - .44 - .34 - WetXArea/DA - .25 .49 .49 .56 .32 - .31 - .49 - .45 - 
Relations between Biota and Multiscale Environmental Characteristics 15

Table 7. Summary of ecosystem health indicators for 

environmental characteristics with the strongest relations to 

biotic assemblages for agricultural stream sites in the Midwest.

[Bold, variables selected when all scales combined; “-” negative indicator for 

the biological assemblage; “+”, positive indicator for the biological assemblage; “+/-”, mixed indicators for the biological assemblage; blank spaces, no 

definitive indicator for the biological assemblage; metric definitions are given 

in table 1]

Metric abbreviation Algae Invertebrates Fish

Reach characteristics

BankEros + +/- +

BankVeg + + +

Fine - - -

Froude + + +

OpCanCV - + -

Riffle + + +

Velocity + + +

WetWdth +/- + +/-

W/D - +/-

WV50 + + +

Segment characteristics

BuffFrag - -

BuffLngth + +

Crop250 - - -

SINUOS -

WV150 + + +

Watershed characteristics

DrainArea - +/- -

Forest + + +

Latitude +/-

MxForest + + +

Perml + +/- +

RowCrop - - -

Slope + + +

WetXArea/DA + +/- +
16 Evaluation of Aquatic Biota in Relation to Environmental Characteristics Measured at Multiple Scales

negative-indicator metrics were the percentages of omnivore 

fish (pOMNIfish), detritivore fish (pDETRfish), and mud-substrate preference fish (pMUDfish). Although pOMNIfish and 

pDETRfish were intercorrelated (rs = 0.83), these metrics represent distinct aspects of the fish assemblage and pOMNIfish

is a common metric used by watershed managers to assess 

impact. Omnivore fish have broad diets that include plant and 

animal material as well as detritus. The pDETRfish also had a 

positive correlation (rs = 0.55) with the algal metric pSESTON,

which is considered a negative algal water-quality indicator 

for streams. Although the fish assemblages were more strongly 

correlated with watershed characteristics, as shown by the 

multivariate results, there were many correlations between 

fish metrics and reach-level characteristics. As with invertebrate assemblages, fish assemblages and metrics were highly 

correlated with land cover: negatively with the percentage of 

cropland and positively with woody vegetation in the riparian buffer at reach and segment scales (15- to 250-m buffer 

widths) but especially at the segment-scale 250-m buffer. The 

pSENSfish and pCOBBfish metrics, as well as the positiveindicator metrics for algae (BGA_bv, pSENBPC1), were positively correlated with increased riffle area (Riffle) and negatively correlated with percentage of fine silt substrate (Fine).

Spatial and Temporal Variability 

of Metrics

Biological metrics were less variable between reaches 

and between years for metrics based on taxonomic richness 

than those based on percentage abundance, as shown by 

coefficients of variation for these metrics (table 8). Richness metrics (R_TAXAalg, R_Diatom, RichTOL, R_ODIPNI, 

R_TAXAinv, and R_TAXAfish) and diversity indices (ShWTTax, 

Margalef) had relatively low variance (generally <20%), 

whereas Bahl’s siltation index (Silt_Index) and pollution 

tolerance index (TOLBPC1) for algae varied from relatively 

low to high variance. The percentage abundances for the fish 

metrics were less variable than those for the algae metrics 

(pAch_min) and invertebrate metrics (pFC_abund), which 

were more consistent between reaches but varied between 

years (figs. 2 and 3). Wang and others (2006) found that 

habitat and fish assemblage measures can vary greatly among 

years, especially at sites with low habitat diversity.

Discussion of Findings and Comparison 

with Other Studies

Few regional studies have examined patterns of multiple biological-assemblage associations with environmental 

characteristics representing multiple scales. Allen and others (1999) assessed environmental gradients and patterns of 

taxonomic composition among multiple biotic assemblages 

in lakes in New England, and Fore (2003) assessed biological indications of stream condition in the mid-Atlantic area. 

General findings from these assessments were concordant with 

our study and supported the observation by Carter and others 

(1996) regarding the confounding influence of watershed-scale 

features on benthic assemblages, which is due to dependence 

of smaller-scale characteristics and processes on larger-scale 

characteristics and processes. Although physical factors at 

one scale may influence those at another—and categorizations into reach-segment-watershed are somewhat arbitrary in 

some cases—it is useful to attempt to understand the relative 

importance of these scales of environmental characteristics 

to different groups of biota. In Allen and others (1999) and 

in Fore (2003), the fish communities also correlated most 

with landscape and watershed-scale characteristics compared 

to reach-scale characteristics. Fore (2003) suggested that 

Table 8. Values for median coefficient of variation between selected biological metrics from samples collected among multiple 

reaches and multiple years at each site for agricultural stream sites in the Midwest.

[Coefficient values are in percent; n, number of sites. Metric definitions are given in table 3]

Low variability High variability

Algae n ShWTTax R_TAXAalg R_Diatom Silt_Ind pAch_min pSESTON BGA_bv pTOLBPC1

Multiple reach 11 7.7 9.7 10.4 17.0 19.6 62.3 68.4 69.0

Multiple year 11 10.8 9.9 10.9 32.2 72.0 75.6 72.0 75.1

Invertebrates RichTOL R_TAXAinv Margalef R_ODIPNI pFC_abund pR_OMNI pR_ORTHO_CH pGC_abund

Multiple reach 14 3.2 11.4 17.1 24.4 20.2 29.3 30.3 32.2

Multiple year 14 12.3 16.4 11.1 15.6 66.6 26.1 22.6 25.8

Fish R_TAXAfish pCOBBfish pCARNfish pOMNIfish pCREEPfish pDETRfish pMUDfish pSENSfish

Multiple reach 16 11.0 16.0 26.0 27.5 34.0 34.0 40.9 44.8

Multiple year 16 12.2 28.4 27.4 40.0 23.1 29.8 36.0 49.4
Implications for the Use of Aquatic Biota in Evaluations of Agricultural Best-Management Practices 17

the different sensitivity to watershed characteristics across 

assemblage groups might reflect the relative size of the organisms, as discussed by Allen and others (1999) from their study 

of New England lakes. In that study, large-bodied organisms 

responded more strongly to watershed-scale characteristics 

than to local-scale characteristics. These results correspond to 

those in this study where it was observed that fish assemblages 

correlated strongly to the watershed-scale attributes including 

latitude, drainage area, and mixed-forest land cover. Assemblages of smaller-bodied organisms (diatoms and zooplankton) 

generally have been found to have stronger correlations with 

more local-scale characteristics than to broader watershedscale characteristics (Allen and others, 1999; Fore, 2003; Allan 

and others, 1997). 

Allan and others (1997) suggested that the broader issue 

of landscape influence across multiple scales needs further 

study to distinguish the relative importance of local and 

regional influences. They concluded that the extent of agricultural land at the subcatchment (watershed) scale was the best 

single predictor of local stream conditions as evaluated by fish 

IBI values for the River Raisin, Michigan. In contrast, Stauffer 

and others (2000) found that local-scale characteristics such as 

riparian cover were more important to fish assemblages. In the 

current study, although the percentage of cropland and woody 

vegetation at various widths at the segment-scale were the 

best predictors for all three biotic assemblages at the segment 

scale, the segment-scale characteristics generally were not 

the best predictors of stream conditions across all study sites 

when compared to watershed-scale characteristics. Fitzpatrick 

and others (2001) found that fish metrics responded most to 

watershed-scale characteristics, and fish IBI scores were most 

affected by land use in the watershed and buffer widths less 

than 200 m in the entire stream network. As Allan and others 

(1997) and Stewart and others (2001) suggest, these contrasting results could be due to the geographic extent of individual 

studies and the complex influences of landscape characteristics 

at various scales. 

Fore (2003) found no relations between diatoms and 

watershed size; although correlations were found between 

diatoms and other watershed characteristics, these relations 

were the weakest among all three assemblages analyzed. The 

current study included analysis of non-diatom metrics such as 

the biovolume of blue-green algae and the percentage of seston (planktonic species pSESTON) found in benthic samples. 

In typical watersheds, the amount of seston increases from the 

headwater streams to wider, downstream sites, where more 

light and nutrients are typically available and residence times 

are longer (Vannote and others, 1980). Percentage of seston 

in the current study increased with drainage area, correlating 

with the percentage of cropland in the 250-m buffer (Crop250) 

and with the mean length of margin areas that are in crop land 

cover. Frothingham and others (2002) recognized the occurrences of open canopies in headwater prairie streams and considered the functions of these streams as a reversal of the river 

continuum in east-central Illinois, where natural land cover 

has been eradicated by agriculture and croplands now extend 

to the margins of streams throughout the headwaters. In these 

settings, riparian forest remains only along medium and large 

rivers. The removal of streamside vegetation that accompanies 

channelization reduces shading, thereby increasing diurnal 

temperature variations, eliminating cover for fish, and decreasing organic inputs. Some fish species have been extirpated by 

channelization (Frothingham and others 2002) and although 

diversity has increased in channelized streams over time, habitat still limits fish assemblages in these streams.

Implications for the Use of Aquatic 

Biota in Evaluations of Agricultural 

Best-Management Practices

The findings of this study have implications for the 

design of monitoring programs to evaluate ecological benefits 

to streams that may result from the implementation of bestmanagement practices (BMPs) to agricultural lands. In general 

terms, if management practices are implemented at only a 

local scale, then algal or invertebrate metrics might be the best 

choice to assess site-specific changes. Because the current 

study indicates that fish may respond to watershed-scale factors, fish metrics might be the best choice to evaluate BMPs 

across an entire watershed. Typically, BMPs are implemented 

progressively in isolated areas of a watershed rather than 

simultaneously over the entire watershed. Because fish are 

mobile and may travel throughout a watershed, they might be 

appropriate targets for long-term watershed monitoring; special attention could be placed on changes to physical stream 

characteristics that result in fewer detritivores and mud-substrate fish and more pollution-sensitive fish, carnivorous fish, 

and cobble-substrate fish in suitable reaches. Additional, more 

specific implications are discussed below.

Implementation of BMPs can produce changes to geomorphological and ecological dynamics of streams. The selection of biological metrics to use as measures of progress could 

take into consideration the intermediate disturbance hypothesis 

described by Connell (1978), which relates species richness to 

dynamic variability in disturbance. This hypothesis suggests 

that intermediate disturbance may result in high taxa richness when it interrupts the process of competitive elimination 

among species. In the current study, in terms of multireach 

and multiyear variation, biological metrics that represent taxa 

richness were the least variable at sites for all three groups of 

biota. In contrast, the biological metrics that represent a percentage of the assemblage were much more variable because 

they included not only an individual characteristic but also 

its relation to the rest of the assemblage. The annual stage of 

development of the assemblage (life-history characteristics), 

interannual differences in population dynamics, and sampling error are also important. It has been suggested that taxa 

richness and the Shannon-Wiener Diversity Index generally 

characterize biotic integrity (Stevenson and Bahls, 1999). In 
18 Evaluation of Aquatic Biota in Relation to Environmental Characteristics Measured at Multiple Scales

Figure 2. Coefficient of variation between biological metrics from samples collected among multiple 

reaches at agricultural stream sites in the Midwest. [Metric definitions are given in table 3.]

ShWTTax

R_TAXAalg

R_Diatom

Silt_Index

pAch_min

pSESTON

BGA_bv

pTOLBPC1

0.0

0.4

0.8

1.2

1.6

C

OEFFICIEN

T

OF VA

RIA

TIO

N

Algae

RichTOL

R_TAXAinv

Margalef

R_ODIPNI

pFC_abund

pR_OMNI

pR_ORTHO_CH

pGC_abund

0.0

0.4

0.8

1.2

1.6

C

OEFFICIEN

T

OF VA

RIA

TIO

N

Invertebrates

R_TAXAfish

pCOBBfish

pCARNfish

pOMNIfish

pCREEPfish

pDETRfish

pMUDfish

pSENSfish

0.0

0.4

0.8

1.2

1.6

C

OEFFICIEN

T

OF VA

RIA

TIO

N

Fish

Figure 2. Coe
cient of variation between biological metrics from samples collected among multiple reaches 

at agricultural stream sites in the Midwest. [Metric denitions are given in table 3.]

EXPLANATION 

VERTICAL LINES —Lines from

rectangle extend to the last data

point that is less than 1.5 times 

the semiquartile range

75th PERCENTILE

MEDIAN

25th PERCENTILE

SEMIQUARTILE 

RANGE

OUTSIDE VALUE—Values are more 

than 1.5 times the semiquartile 

range from the top or bottom of the

rectangle

METRIC
Discussion of Findings and Comparison with Other Studies 19

Figure 3. Coefficient of variation between biological metrics from samples collected among multiple 

years at agricultural stream sites in the Midwest. [Metric definitions are given in table 3.]

R_TAXAalg

ShWTTax

R_Diatom

Silt_Index

pAch_min

BGA_bv

pTOLBPC1

pSESTON

0.0

0.4

0.8

1.2

1.6

C

OEFFICIEN

T

OF VA

RIA

TIO

N

Figure 3. Coecient of variation between biological metrics from samples collected among multiple 

years at agricultural stream sites in the Midwest. [Metric denitions are given in table 3] 

EXPLANATION 

VERTICAL LINES —Lines from

rectangle extend to the last data

point that is less than 1.5 times

 the semiquartile range

75th PERCENTILE

MEDIAN

25th PERCENTILE

SEMIQUARTILE 

RANGE

OUTSIDE VALUE—Values are more 

than 1.5 times the semiquartile 

range from the top or bottom of the

rectangle

Algae

RichTOL

Margalef

R_ODIPNI

R_TAXAinv

pR_ORTHO_CH

pR_OMNI

pGC_abund

pFC_abund

0.0

0.4

0.8

1.2

1.6

C

OEFFICIEN

T

OF VA

RIA

TIO

N

Invertebrates

R_TAXAfish

pCREEPfish

pCARNfish

pCOBBfish

pDETRfish

pMUDfish

pOMNIfish

pSENSfish

0.0

0.4

0.8

1.2

1.6

C

OEFFICIEN

T

OF VA

RIA

TIO

N

Fish

METRIC
20 Evaluation of Aquatic Biota in Relation to Environmental Characteristics Measured at Multiple Scales

contrast, metrics in the current study—such as average USEPA 

tolerance value for a sample based on richness (RichTOL) and 

the percentage of sensitive diatom taxa (pSENBPC3)—are 

more specifically related to ecological conditions and causes 

of impairment. These results indicate that using richness as a 

measure of integrity may be problematic because richness can 

be relatively insensitive; for example, some species (possibly 

sensitive) could be displaced by other species (possibly tolerant), so richness would not necessarily show a net gain or loss 

and therefore would appear stable, and CVs would be low. 

It may be especially important to consider the measures of 

ecological condition as well as taxa richness in the assessment 

of the prairie streams of the Midwest, where higher fish-taxa 

richness has been associated with channelized streams (Frothingham and others, 2002). 

Biological metrics selected for this study have potential 

for monitoring, assessing, and evaluating changes at multiple 

spatial scales and therefore may help guide watershed-conservation efforts in the Midwest. Physical characteristics that 

are frequently modified to improve ecological condition in 

streams through BMPs were analyzed to identify correlations 

with one or more biotic metrics. The results suggest that fish 

and algal metrics (including percentage of cobble-substrate 

fish, percentage of pollution-sensitive fish, biovolume of bluegreen algae, and percentage of pollution-sensitive diatom taxa) 

could serve as metrics for assessing rehabilitation of riparian 

buffer and reduction of sediment runoff. Invertebrate data 

collected from riffle samples were correlated more strongly 

with physical characteristics of streams than invertebrate data 

collected from snag samples. This indicates the importance of 

considering both the metric and the substrate in selecting monitoring tools. Positive-indicator metrics for all three groups of 

biota correlated positively with percent bank vegetative cover. 

This indicates that multiple biotic assemblages could be used 

in future studies targeted at assessing improvements in the 

ecological condition of agricultural streams after implementation of best management practices that included restoration of 

streambank vegetation. 

Goldstein and Meador (2005) suggest using fish-trait 

category frequencies to examine relations between potential 

sources of degradation and specific “functional” responses. 

This approach was followed in the current study by identifying, where possible, the functional traits of various algal, 

invertebrate, and fish taxa. For example, percentage of seston 

(pSESTON) algae is a function of available light and nutrients, 

as well as hydrologic characteristics; percentage richness of 

invertebrate omnivores (pR_OMNI) is a response measure for 

the amount and diversity of available food, usually increasing 

with algal biomass; and percentage detritivorous fish (pDETRfish) may serve as a measure of available benthic organic 

matter that can accumulate in poorly managed systems. These 

results suggest that metrics that provide information about 

the structure and function of aquatic assemblages may be of 

greater importance than purely chemical measures to monitoring recovery of stream integrity and therefore in assessing 

BMP effectiveness. In contrast, some biological metrics have 

been developed as indicators of water chemistry (Potopova 

and Charles, 2007) and may have limited application for BMP 

assessment. In addition, the use of metrics based on species traits may also be less geographically constrained than 

analyses that rely on taxonomic designations (Goldstein and 

Meador, 2005). Trait data have recently been published for fish 

(Goldstein and Meador, 2004), invertebrates (Vieira and others, 2006), and algae (Porter, 2008) in North America. 

Based on results from this study in the agricultural 

Midwest, where low-gradient streams are common, it may 

be possible to use functional traits to identify a spectrum of 

degradation due to siltation. In other words, a continuum 

may be identified that includes streams that are still functioning as lotic systems at one end of the spectrum to those that 

are functioning more like silted ponds (lentic systems) at the 

opposite end of the spectrum. There were several aspects of 

algal and fish functional groups that showed similar response 

across the study area. Conditions that favored sestonic algae 

and silt-tolerant diatoms were correlated with omnivorous 

and mud-substrate fish, whereas increases in a silt-intolerant 

diatom (Achnanthidium minutissimum) and benthic blue-green 

algae occurred along with carnivorous, cobble-substrate, and 

pollution-sensitive fish. 

The ability of researchers to accurately relate biological response to BMPs will be enhanced as the areal extent of 

BMPs becomes documented and available in GIS database 

coverages. When these coverages are available, it should be 

possible to collect and analyze data in terms of the scale at 

which BMPs are applied throughout watersheds. Shields and 

others (2006) state that further work is needed to develop a 

conceptual model or index based on monitoring data associated with specific agricultural BMPs at multiple scales; such 

a model or index would allow us to build a scientific foundation to be established for assessing improvements to rivers in 

the Midwest.

Summary and Conclusions

This report evaluated relations between biota and physical environmental characteristics of streams measured at three 

spatial scales—reach, segment, and watershed. Data on algae, 

invertebrates, fish, and environmental characteristics were 

collected by the USGS with nationally-consistent protocols at 

86 stream sites from 1993 through 2004. Sites were located 

within agricultural river basins of the Midwest in eight USGS 

NAWQA study units. Data analyses (1) identified environmental characteristics to which algal, invertebrate, and fish 

assemblages showed the strongest correlations; (2) examined 

relations between each biotic assemblage and the most highly 

correlated physical environmental characteristics at the reach, 

segment, and watershed scale; and (3) identified biological 

measures (metrics) that showed the most potential for monitoring, assessing, and evaluating changes at the various spatial 
References Cited 21

scales to assess the effectiveness of BMPs in agricultural areas 

in the Midwest. 

Characteristics that best explained variation in the biotic 

assemblages were bank vegetative cover, fine silty substrate, 

and open-canopy angle at the reach scale; woody vegetation 

and cropland in the 250-m riparian buffer and average length 

of undisturbed buffer at the segment scale; and land cover 

(both total forested and row crop), low-permeability soils, 

slope, drainage area, and latitude at the watershed scale. As 

hypothesized, variations among fish assemblages correlated 

more with watershed-scale characteristics than with segment 

and reach characteristics, whereas algal and invertebrate 

assemblages correlated more with physical environmental 

characteristics at reach and segment scales.

The strong relations between stream biota and watershed 

characteristics found in this study reinforce the importance 

of managing whole watersheds to maintain healthy streams 

and underscore difficulties with measuring responses implemented at smaller scales. These response patterns in agricultural watersheds across the Midwest have relevance to state 

and federal farm-management-related agencies. The relative 

importance of environmental characteristics should be taken 

into consideration to measure the effects of BMPs designed 

to improve water quality in agricultural watersheds. Because 

fish appear to be more strongly influenced by watershed-scale 

features, algal and invertebrate metrics may prove to be better 

indicators for BMPs applied locally (at the reach scale).

Acknowledgments

We are grateful to the many USGS NAWQA scientists 

who collected the original algal, invertebrate, fish, and habitat 

data, including Brian J. Caskey, Jeffrey W. Frey, Robert M. 

Goldstein, Michael J. Lydy, Stephanie Janosy, Stephen J. 

Kalkhoff, Kathy E. Lee, Jason C. McVay, Stephen J. Rheaume, 

Faith A. Fitzpatrick, Jana S. Stewart, and Daniel J. Sullivan. We also wish to thank the following USGS employees: 

James L. Carter, Faith A. Fitzpatrick, and Jana S. Stewart for 

guidance during data analysis; James L. Kennedy, Michaela 

R. Johnson, and Naomi Nakagaki for assistance with riparian buffer characterization; and Amanda H. Bell for technical 

assistance. We also thank USGS employees James L. Carter, 

Robert M. Goldstein, C. Michael Eberle, Daniel J. Sullivan, 

Daren M. Carlisle, Amanda H. Bell, and Marie C. Peppler for 

comments provided on an earlier version of this manuscript.

References Cited

Allan, J.D., Erickson D.L., and Fay, J., 1997, The influence 

of catchment land use on stream integrity across multiple 

spatial scales: Freshwater Biology, v. 37, p. 149–161.

Allen, A.P., Whittier, T.R., Larsen, D.P., Kaufmann, P.R., 

O’Connor, R.J., Hughes, R.M., Stemberger, R.S., Dixit, 

S.S., Brinkhurst, R.O., Herlihy, A.T., and Paulsen, S.G., 

1999, Concordance of taxonomic composition patterns 

across multiple lake assemblages—Effects of scale, body 

size, and land use: Canadian Journal of Fisheries and 

Aquatic Sciences, v. 56, no. 11, p. 2029–2040.

Bahls, L.L., 1993, Periphyton bioassessment methods for 

Montana streams: Helena, Mont., Montana Water Quality 

Bureau, Department of Health and Environmental Sciences, 

129 p.

Barbour, M.T., Gerritsen, J., Griffith, G.E., Frydenborg, R., 

McCarron, E., White, J.S., and Bastian, M.L., 1996, A 

framework for biological criteria for Florida streams using 

benthic macroinvertebrates: Journal of the North American 

Benthological Society, v. 15, no. 2, p. 185–211.

Barbour, M.T., Gerritsen, J., Snyder, B.D., and Stribling, J.B., 

1999, Rapid bioassessment protocols for use in wadeable 

streams and rivers—Periphyton, benthic macroinvertebrates 

and fish (2d ed.): Washington, D.C., U.S. Environmental 

Protection Agency, Office of Water, EPA 841–B–99–002, 

variously paged. 

Carter, J.L., Fend, S.V., and Kennelly, S.S., 1996, The relationships among three habitat scales and stream benthic 

invertebrate community structure: Freshwater Biology, 

v. 35, p. 109–124.

Charles, D.F., Knowles, C., and Davis, R.S., eds., 2002, Protocols for the analysis of algal samples collected as part of 

the U.S. Geological Survey National Water-Quality Assessment Program: Academy of Natural Sciences Report 02–06, 

121 p.

Clarke, K.R., and Ainsworth, M., 1993, A method of linking 

multivariate community structure to environmental variables: Marine Ecology Progress Series 92, p. 205–219.

Clarke, K.R., and Gorley, R.N., 2006, Primer V6 for Windows—User manual/tutorial: Plymouth, U.K., Primer-E 

Ltd., variously paged. 

Connell, J.H., 1978, Diversity in tropical rain forests and coral 

reefs: Science, v. 199, no. 4335, p. 1302–1310.

Cuffney, T.F., 2003, User manual for the National Water-Quality Assessment Program Invertebrate Data Analysis System 

(IDAS) software, version 3.0: U.S. Geological Survey 

Open-File Report 03–172, 103 p.

Cuffney, T.F., Bilger, M.D., and Haigler, A.M., 2007, Ambiguous taxa: effects on the characterization and interpretation 

of invertebrate assemblages: Journal of the North American 

Benthological Society v. 26, no. 2, p, 286–307.
22 Evaluation of Aquatic Biota in Relation to Environmental Characteristics Measured at Multiple Scales

Cuffney, T.F., Gurtz, M.E., and Meador, M.R., 1993, Methods 

for collecting benthic invertebrate samples as part of the 

National Water-Quality Assessment Program: U.S. Geological Survey Open-File Report 93–406, 66 p.

Cuffney, T.F., Meador, M.R., Porter, S.D., and Gurtz, M.E., 

1997, Distribution of fish, benthic invertebrate, and algal 

communities in relation to physical and chemical conditions, Yakima River Watershed, Washington, 1990: 

U.S. Geological Survey Water-Resources Investigations 

Report 96–4280, 94 p. 

DeShon, J.E., 1995. Development and application of the 

Invertebrate Community Index (ICI), in Davis, W.S., and 

Simon, T.P., eds., Biological assessment and criteria—Tools 

for water resource planning and decision making: Boca 

Raton, Fla., Lewis Publishers, p. 217–243.

Dovciak, A.L., and Perry, J.A., 2002, In search of effective scales for stream management—Does agroecoregion, 

watershed, or their intersection best explain the variance 

in stream macroinvertebrate communities?: Environmental 

Management, v. 30, no. 3, p. 365–377.

Fitzpatrick, F.A., Waite, I.R., D’Arconte, P.J., Meador, M.R., 

Maupin, M.A., and Gurtz, M.E., 1998, Revised methods for 

characterizing stream habitat in the National Water-Quality 

Assessment Program: U.S. Geological Survey WaterResources Investigations Report 98–4052, 67 p.

Fitzpatrick, F.A., Scudder, B.C., Lenz, B.N., and Sullivan, 

D.J., 2001. Effects of multi-scale environmental characteristics on agricultural stream biota in eastern Wisconsin: 

Journal of the American Water Resources Association, v. 37 

no. 6, p. 1489–1507.

Fore, L.S., 2003, Developing biological indicators—Lessons 

learned from the Mid-Atlantic streams: U.S. Environmental 

Protection Agency, EPA/903/R–03/003, 41 p.

Fore, L.S., and Grafe, C., 2002, Using diatoms to assess the 

biological condition of large rivers in Idaho (U.S.A.): Freshwater Biology, v. 47, p. 2015–2037.

Fore, L.S., Karr, J.R., and Wisseman, R.W., 1996, Assessing invertebrate response to human activities—Evaluating 

alternative approaches: Journal of the North American 

Benthological Society, v. 15, p. 212–231.

Frissell, C.A., Liss, W.J., Warren, C.E., and Hurley, M.D., 

1986, A hierarchical framework for stream habitat classification—Viewing streams in a watershed context: Environmental Management, v. 10, no. 2, p. 199–214.

Frothingham, K.M., Rhodes, B.L., and Herricks, E.E., 2002, A 

multiscale conceptual framework for integrated ecogeomorphological research to support stream naturalization in the 

agricultural Midwest: Environmental Management, v. 29, 

no. 1, p. 16–33.

Goldstein, R.M., and Meador, M.R., 2004, Comparisons 

of fish species traits from small streams to large rivers: 

Transactions of the American Fisheries Society, v. 133, 

p. 971–983.

Goldstein, R.M., and Meador, M.R., 2005, Multilevel assessment of fish species traits to evaluate habitat degradation in 

streams of the upper Midwest: North American Journal of 

Fisheries Management, v. 25, p. 180–194.

Hill, B.H., Herlihy, A.T., Kaufmann, P.R., Stevenson, R.J., 

McCormick, F.H., and Johnson, C.B., 2000, Use of periphyton assemblage data as an index of biotic integrity: Journal 

of the North American Benthological Society, v. 19, no. 1, 

p. 50–67. 

Hill, M.O., 1979, DECORANA—A FORTRAN program for 

detrended correspondence analysis and reciprocal averaging: Ithaca, N.Y., Cornell University, Section of Ecology 

and Systematics. 

Hynes, H.B.N., 1975, The stream and its valley: Internationale 

Vereinigung für Theoretische und Angewandte Limnologie, 

v. 19, p. 1–15.

Karr, J.R., 1981, Assessment of biotic integrity using fish communities: Fisheries, v. 6, no. 6, p. 21–27.

Kerans, B.L., and Karr, J.R., 1994, A Benthic Index of Biotic 

Integrity (B-IBI) for rivers of the Tennessee Valley: Ecological Applications, v. 4, no. 4, p. 768–785. 

Lammert, Mary, and Allan, J.D., 1999, Assessing biotic integrity of streams—Effects of scale in measuring the influence 

of land use/cover and habitat structure on fish and macroinvertebrates: Environmental Management, v. 23, no. 2, 

p. 257–270.

Lee, K.E., Goldstein, R.M., and Hanson, P.E., 2001, Relation 

between fish communities and riparian zone conditions at 

two spatial scales: Journal of the American Water Resources 

Association, v. 37, no. 6, p. 1465–1473.

Lenat, D.R., 1988, Water quality assessment of streams using 

a qualitative collection method for benthic macroinvertebrates: Journal of the North American Benthological Society, v. 7, no. 3, 222–233.

Leopold, L.B., Wolman, M.G., and Miller, J.P., 1964, Fluvial 

processes in geomorphology: New York, Dover Publications, 522 p.

Leopold, L.B., 1994, A view of the river: Cambridge, Mass., 

Harvard University Press, 298 p.

Lücke, J.D., and Johnson, R.K., 2009, Detection of ecological change in stream macroinvertebrate assemblages using 

single metric, multimetric, or multivariate approaches: 

Ecological Indicators, v. 9, p. 659–669.
References Cited 23

Lutz, M.A., and Kennedy, J.L., Jr., 2010, Digital orthophotograph-based riparian land cover for 86 stream segments 

in the eight study units in Major River Watershed 3 of the 

National Water-Quality Assessment Program: U.S. Geological Survey Data Series 536, available at http://pub.usgs.gov/

ds/536/.

Lyons, John, 1992, Using the Index of Biotic Integrity (IBI) 

to measure environmental quality in warm water streams of 

Wisconsin: St. Paul, Minn., U.S. Department of Agriculture, 

Forest Service, North Central Forest Experiment Station, 

General Technical Publication NC–149, 51 p.

Marchant, Richard, 2002, Do rare species have any place 

in multivariate analysis for bioassessment?: Journal of 

the North American Benthological Society, v. 21, no. 2, 

p. 311–313.

Margalef, Ramon, 1958, Information theory in ecology: General Systems, v. 3, p. 36–71.

Meador, M.R., Hupp, C.R., Cuffney, T.F., and Gurtz, M.E., 

1993a, Methods for characterizing stream habitat as part 

of the National Water-Quality Assessment Program: 

U.S. Geological Survey Open-File Report 93–408, 48 p.

Meador, M.R., Cuffney, T.F., and Gurtz, M.E., 1993b, Methods for sampling fish communities as part of the National 

Water-Quality Assessment Program: U.S. Geological Survey Open-File Report 93–104, 40 p.

Moulton, S.R., II, Carter, J.L., Grotheer, S.A., Cuffney, 

T.F., and Short, T.M. 2000, Methods of analysis by the 

U.S. Geological Survey National Water Quality Laboratory—Processing, taxonomy, and quality control of benthic 

macroinvertebrate samples: U.S. Geological Survey OpenFile Report 00–212, 49 p.

Moulton, S.R., II, Kennen, J.G., Goldstein, R.M., and Hambrook, J.A., 2002, Revised protocols for sampling algal, 

invertebrate, and fish communities as part of the National 

Water-Quality Assessment Program: U.S. Geological Survey Open-File Report 02–150, 75 p. 

Nakagaki, Naomi, and Wolock, D.M., 2005, Estimation of 

agricultural pesticide use in drainage watersheds using land 

cover maps and county pesticide data: U.S. Geological 

Survey Open-File Report 2005–1188, 46 p.

Porter, S.D., 2008, Algal attributes—An autecological classification of algal taxa collected by the National Water-Quality 

Assessment Program: U.S. Geological Survey Data Series 

329 [online only], accessed March 7, 2008, at http://pubs.

usgs.gov/ds/ds329/

Porter, S.D., Cuffney, T.F., Gurtz, M.E., and Meador, M.R., 

1993, Methods for collecting algal samples as part of the 

National Water-Quality Assessment Program: U.S. Geological Survey Open-File Report 93–409, 39 p.

Potapova, Marina, and Charles, D.F., 2007, Diatom metrics 

for monitoring eutrophication in rivers of the United States: 

Ecological Indicators, v. 7, p. 48–70.

Resh, V.H., and Rosenberg, D.M., 1989, Spatial-temporal variability and the study of aquatic insects: Canadian Entomologist, v. 121, p. 941–963.

Richards, Carl, and Host, George, 1994, Examining land use 

influences on stream habitats and macroinvertebrates—

A GIS approach: Water Resources Bulletin, v. 30, no. 4, 

p. 729–738.

Shields, F.D., Jr., Langendoen, E.J., and Doyle, M.W., 2006, 

Adapting existing models to examine effects of agricultural 

conservation programs on stream habitat quality: Journal 

of the American Water Resources Association, v. 42, no. 1, 

p. 25–33.

Sprouffske, K., Mellott, D., Charles, D., and Potapova, M., 

2006, Phyco-AIDE—Algal Indicators and Data Exploration, version 2.0: Philadelphia, Pa., Academy of Natural 

Sciences, Patrick Center for Environmental Research, Phycology Section (computer program).

Stauffer, J.C., Goldstein, R.M., and Newman, R.M., 2000, 

Relationship of wooded riparian zones and runoff potential 

to fish community composition in agricultural streams: 

Canadian Journal of Fisheries and Aquatic Sciences, v. 57, 

no. 2, p. 307–316.

Stevenson, R.J., and Bahls, L.L., 1999, Periphyton protocols, 

in Barbour, M.T., Gerritsen, J., Snyder, B.D., and Stribling, 

J.B., Rapid bioassessment protocols for use in streams and 

wadeable rivers—Periphyton, benthic macroinvertebrates, 

and fish (2d ed.): U.S. Environmental Protection Agency, 

EPA 841–B–99–002, p. 6–1 to 6–22.

Stewart, J.S.; Wang, Lizhu; Lyons, John; Horwatich, J.A.; and 

Bannerman, Roger, 2001, Influences of watershed, ripariancorridor, and reach-scale characteristics on aquatic biota 

in agricultural watersheds: Journal of the American Water 

Resources Association, v. 37, no. 6, p. 1475–1487.

Strahler, A.N., 1957, Quantitative analysis of watershed 

geomorphology: Transactions of the American Geophysical 

Union, v. 38, p. 913–920.

Strahler, A.N., and Strahler, A.H., 1978, Modern physical 

geography: New York, John Wiley and Sons, 502 p.

ter Braak, C.J.F., 1987, Ordination, in Jongman, R.H.G., ter 

Braak, C.J.F., and van Tongeren, O.F.R., eds., Data analysis 

in landscape and community ecology: Pudoc, Wageningen, 

the Netherlands, p. 91–173.

ter Braak, C.J.F., and Similauer, P., 2002, CANOCO reference 

manual and user’s guide to Canoco for Windows: Ithaca, 

N.Y., Software for Canonical Community. 
24 Evaluation of Aquatic Biota in Relation to Environmental Characteristics Measured at Multiple Scales

U.S Geological Survey, 2002, Elevation Derivatives for 

National Applications (EDNA) [digital data], available at 

http://edna.usgs.gov.

U.S. Geological Survey, 2005, National Water-Quality Assessment Data Warehouse, biological community data: accessed 

April 28, 2005 (invertebrate data), and August 9, 2005 (fish 

data), at http://infotrek.er.usgs.gov/traverse/f?p=NAWQA:H

OME:2608300149918183.

Vannote, R.L., Minshall, G.W., Cummins, K.W., Sedell, J.R., 

and Cushing, C.E., 1980, The river continuum concept: 

Canadian Journal of Fisheries and Aquatic Sciences, v. 37, 

no. 1, p. 130–137. 

Van Sickle, John, Larsen, D.P., and Hawkins, C.P., 2007, 

Exclusion of rare taxa affects performance of the O/E index 

in bioassessment: Journal of the North American Benthological Society, v. 26, no. 2, p. 319–331.

Vieira, N.K.M., Poff, N.L., Carlisle, D.M., Moulton, S.R., II, 

Koski, M.L., and Kondratieff, B.C., 2006, A database of 

lotic invertebrate traits for North America: U.S. Geological 

Survey Data Series 187, accessed March 7, 2008, at http://

pubs.water.usgs.gov/ds187.

Wang, Lizhu, Lyons, John, and Kanehl, Paul, 2006, Habitat 

and fish responses to multiple agricultural Best Management 

Practices in a warm water stream: Journal of the American 

Water Resources Association, v. 42, no. 4, p. 1047–1062.

Wiens J.A., 1989, Spatial scaling in ecology: Functional Ecology, v. 3, no. 3, p. 385–397. 

Zar J.H., 1974, Biostatistical analysis: Englewood Cliffs, N.J., 

Prentice-Hall, p. 34.

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