U.S. Department of the Interior

U.S. Geological Survey

Scientific Investigations Report 2009–5055 

National Water-Quality Assessment Program 

Biological-Community Composition in Small Streams 

and its Relations to Habitat, Nutrients, and Land Use in 

Agriculturally Dominated Landscapes in Indiana and Ohio, 

2004, and Implications for Assessing Nutrient Conditions 

in Midwest Streams 
Cover: Upper left–Cows drinking from stream in the Mad River watershed, August 25, 2003. (Photograph by Stephanie D. Janosy, 

U.S. Geological Survey (USGS).) 

Upper right–Tractor applying fertilizer to a farm field in the Sugar Creek watershed, June 7, 2007. (Photograph by John T. 

Wilson, USGS.)

Bottom left–Field crew electrofishing using barge equipment to collect a fish community sample at Sugar Creek, (USGS STAID 

394340085524601), August 15, 2008. (Photograph by David S. Nail, USGS.)

Bottom right–USGS employee collecting a benthic invertebrate sample at Mud Creek (USGS STAID 393659085340301), 

August 11, 2004. (Photograph by Brian J. Caskey, USGS.)
Biological-Community Composition in 

Small Streams and its Relations to Habitat, 

Nutrients, and Land Use in Agriculturally 

Dominated Landscapes in Indiana and 

Ohio, 2004, and Implications for Assessing 

Nutrient Conditions in Midwest Streams 

By Brian J. Caskey and Jeffrey W. Frey 

National Water-Quality Assessment Program 

Scientific Investigations Report 2009–5055 

U.S. Department of the Interior

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

KEN SALAZAR, Secretary

U.S. Geological Survey

Suzette M. Kimball, Acting Director

U.S. Geological Survey, Reston, Virginia: 2009

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reproduce any copyrighted materials contained within this report.

Suggested citation:

Caskey, B.J., and Frey, J.W., 2009, Biological-community composition in small streams and its relations to habitat, 

nutrients, and land use in agriculturally dominated landscapes in Indiana and Ohio, 2004, and implications for assessing nutrient conditions in Midwest streams: U.S. Geological Survey Scientific Investigations Report 2009–5055, 21 p., 

plus CD-Rom containing four appendixes. 
iii

Foreword

The U.S. Geological Survey (USGS) is committed to providing the Nation with accurate and timely scientific information that helps 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 

suitable for industry, irrigation, and habitat for 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 condition of 

our Nation’s streams and ground water? How are conditions changing over time? How do natural features and 

human activities affect the quality of streams and ground water, 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. 

From 1991-2001, the NAWQA Program completed interdisciplinary assessments and established a baseline 

understanding of water-quality conditions in 51 of the Nation’s river basins and aquifers, referred to as Study 

Units (http://water.usgs.gov/nawqa/studyu.html). 

In the second decade of the Program (2002–2012), a major focus is on regional assessments of water-quality conditions and trends. These regional assessments extend the findings in the Study Units by filling critical 

gaps in characterizing the flow and quality of surface water and ground water, and by determining trends at sites 

that have been consistently monitored for more than a decade. In addition, the regional assessments continue 

to 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. Many of the regional assessments employ 

modeling and other scientific tools, developed on the basis of data collected at individual sites, to help extrapolate and forecast conditions in unmonitored, yet comparable areas within the regions. The models thereby 

enhance the value of our existing data and our understanding of the hydrologic system. In addition, the models 

are useful in evaluating various resource-management scenarios and to predict how our actions, such as by 

adjusting nonpoint and point sources of contamination, converting land use, and altering flow and (or) pumping 

regimes, are likely to affect water conditions within a region.

Other activities planned during the second decade include continuing national syntheses of information on 

pesticides, volatile organic compounds (VOCs), nutrients, and selected trace elements; and continuing national 

topical studies on the fate of agricultural chemicals, effects of urbanization on stream ecosystems, nutrient 

enrichment, bioaccumulation of mercury in aquatic organisms, and transport of contaminants to public-supply 

wells.

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

The USGS recognizes that a national assessment by a single program cannot address all water-resource 

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.

Robert M. Hirsch

Associate Director for Water 
iv

Contents

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

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

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

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

Description of the Study Area ............................................................................................................2

Study Methods ...............................................................................................................................................3

Site Selection.........................................................................................................................................3

Data Collection......................................................................................................................................3

Data Analysis.........................................................................................................................................9

Description of the Sampled Basins ............................................................................................................9

Biological-Community Composition within the Sampled Basins..................................................9

Variables Affecting Biological-Community Composition within the Sampled Basins.............12

Implications for Assessing Nutrient Conditions in Midwest Streams ................................................17

Environmental Gradients ...................................................................................................................17

Biological Indicators ..........................................................................................................................18

Assessing Nutrient Rich Midwest Streams ...................................................................................18

Conclusions...................................................................................................................................................19

Acknowledgments.......................................................................................................................................19

References Cited..........................................................................................................................................19

Appendix 1. Species list and percent relative abundance of Depositional-Targeted 

Habitat (DTH) algal-diatom community at 30 sites in the White River and 

Great and Little Miami River Basins (WHMI) Study Unit of the National 

Water-Quality Assessment (NAWQA) Program, 2004................................. on CD-Rom

Appendix 2. Species list and percent relative abundance of Richest-Targeted Habitat 

(RTH) algal-diatom community at 30 sites in the White River and Great and 

Little Miami River Basins (WHMI) Study Unit of the National Water-Quality 

Assessment (NAWQA) Program, 2004........................................................... on CD-Rom

Appendix 3. Species list and percent relative abundance of Richest-Targeted Habitat 

(RTH) invertebrate community at 30 sites in the White River and Great and 

Little Miami River Basins (WHMI) Study Unit of the National Water-Quality 

Assessment (NAWQA) Program, 2004........................................................... on CD-Rom

Appendix 4. Species list and percent relative abundance of fish community at 30 sites in 

the White River and Great and Little Miami River Basins (WHMI) Study Unit 

of the National Water-Quality Assessment (NAWQA) Program, 2004 ..... on CD-Rom 
v

Figures

1. Map showing location of study sites and Level III Ecoregions in the White River 

and Great and Little Miami River Basins (WHMI) Study Unit of the National 

Water-Quality Assessment (NAWQA) Program, 2004 ............................................................2

2–5. Plots showing:

2. Distribution of concentrations of nitrogen constituents in samples 

collected in August 2004, compared to the U.S. Environmental Protection 

Agency (USEPA) Aggregate Nutrient Ecoregion VI median seasonal values

(summer), in the White River and Great and Little Miami River Basins 

(WHMI) Study Unit of the National Water-Quality Assessment (NAWQA) 

Program...............................................................................................................................11

3. Distribution of concentrations of phosphorus constituents in samples 

collected in August 2004, compared to the U.S. Environmental Protection 

Agency (USEPA) Aggregate Nutrient Ecoregion VI median seasonal values 

(summer), in the White River and Great and Little Miami River Basins 

(WHMI) Study Unit of the National Water-Quality Assessment (NAWQA) 

Program...............................................................................................................................11

4. Canonical correspondence analysis (CCA) ordination plots of the 

Depositional-Targeted Habitat (DTH) algal-diatom community; and the 

Richest-Targeted Habitat (RTH) algal-diatom community, White River and 

Great and Little Miami River Basins (WHMI) Study Unit of the National 

Water-Quality Assessment (NAWQA) Program ...........................................................13

5. Canonical correspondence analysis (CCA) ordination plots of the 

Richest-Targeted Habitat (RTH) invertebrate community; and the fish 

community, White River and Great and Little Miami River Basins (WHMI) 

Study Unit of the National Water-Quality Assessment (NAWQA) Program ............16 

Tables

1. Descriptions of the 30 sites in the White River and Great and Little Miami River 

Basins (WHMI) Study Unit of the National Water-Quality Assessment (NAWQA) 

Program, 2004................................................................................................................................4

2. Habitat variable values of the 30 sites in the White River and Great and 

Little Miami River Basins (WHMI) Study Unit of the National Water-Quality 

Assessment (NAWQA) Program, 2004 ......................................................................................5

3. Nutrient concentration (August), basin, and land-use variable values of the 

30 sites in the White River and Great and Little Miami River Basins (WHMI) 

Study Unit of the National Water-Quality Assessment (NAWQA) Program, 2004 .............7

4. Significant environmental variables and data ranges used in the canonical 

correspondence analysis, White River and Great and Little Miami River Basins 

(WHMI) Study Unit of the National Water-Quality Assessment (NAWQA) 

Program, 2004..............................................................................................................................10

5. Summary of the canonical correspondence analysis, White River and Great and 

Little Miami River Basins (WHMI) Study Unit of the National Water-Quality 

Assessment (NAWQA) Program, 2004 ....................................................................................14 
vi

Multiply By To obtain

Length

meter (m) 3.281 foot (ft)

centimeter (cm) 0.3937 inch (in.)

millimeter (mm) 0.03937 inch (in.)

micrometer (μm) 0.001 millimeter (mm)

Area

square kilometer (km2

) 247.1 acre

square kilometer (km2

) 0.3861 square mile (mi2) 

Volume

milliliter (mL) 0.03381 ounce, U.S. liquid (oz)

Mass

kilogram (kg) 2.205 pound, avoirdupois (lb)

Conversion Factors 

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

 °F=(1.8×°C)+32 

Concentrations of chemical constituents are given in milligrams per liter (mg/L). 
Biological-Community Composition in Small Streams 

and its Relations to Habitat, Nutrients, and Land Use in 

Agriculturally Dominated Landscapes in Indiana and Ohio, 

2004, and Implications for Assessing Nutrient Conditions 

in Midwest Streams 

By Brian J. Caskey and Jeffrey W. Frey 

Abstract 

The objective of this study was to relate algal-, invertebrate-, and fish-community composition to habitat, nutrients, 

and land-use variables in small streams in agriculturally dominated landscapes of the Midwest in Indiana and Ohio. Thirty 

sample locations were selected from a single ecoregion; all 

were small wadable streams within agriculturally dominated 

landscapes with similar substrate and canopy. Biological and 

nutrient samples were collected during stable flow conditions 

in August 2004. Canonical correspondence analysis was used 

to determine which variables most influenced each community. Total phosphorus concentrations significantly influenced 

the depositional-targeted habitat algal-diatom community and 

the richest-targeted habitat invertebrate community. Multivariate statistical analysis showed that habitat variables were 

more influential to the richest-targeted habitat algal-diatom 

and fish communities than nutrient concentrations. Although 

the nutrient concentrations measured during this study indicate 

that most streams were not eutrophic, the biological communities were dominated by eutrophic species, suggesting streams 

sampled were eutrophic. Consequently, it was concluded that 

biological relations to nutrients in agriculturally-dominated 

landscapes are complex and habitat variables should be 

included in biological assessments of nutrient conditions in 

agriculturally-dominated landscapes.

Introduction

The U.S. Environmental Protection Agency (USEPA) 

National Water-Quality Inventory identified excess nutrients 

(nitrogen and phosphorus) as the second leading cause of river 

and stream impairment within the United States. The inventory 

also identified agricultural activities as major nonpoint sources 

of nutrient enrichment to surface waters (U.S. Environmental 

Protection Agency, 1996). Because excess concentrations of 

nutrients have ecological and economic consequences, the 

USEPA proposed regional ambient nutrient criteria for streams 

across the United States. The proposed USEPA nutrient criteria have prompted additional studies to assess the ecological 

effects of nutrients on streams (U.S. Environmental Protection 

Agency, 2000a). 

To assess the response of biological communities to 

anthropogenic nutrient concentrations, study sites are often 

selected along a range of environmental conditions commonly 

called an environmental gradient. In regional-gradient studies 

of biological communities, it is important to minimize the 

effects of natural variables that can mask the relations between 

the variables of interest and their effects on the biological community. These natural variables can include geology, soils, land use, and physical habitat (Wang and others, 

1997; Caskey, 2003; Taylor and others, 2006). An underlying 

premise in gradient analysis is that species reside along an 

environmental gradient with the highest species abundances 

at their environmental optima (Gauch, 1982; ter Braak, 1994). 

Traditionally, biological attributes, metrics, and indices were 

commonly used to measure biological communities because 

they have been shown to respond to anthropogenic and other 

variables along a gradient (Wang and others, 1997; Brightbill 

and Bilger, 1998; Carpenter and Waite, 2000; Caskey, 2003; 

Frey and Caskey, 2007). Recent studies have used biologicalcommunity composition and structure to assess nutrient 

enrichment in streams (Bowman and others, 2005; Dodds and 

others, 2002; Munn and others, 2002; Petersen and Femmer, 

2002). Several studies have used multiple biological communities (primarily fish and invertebrate communities) to 

measure stream condition (Cuffney and others, 2000; Miltner 

and Rankin, 1998; Robertson and others, 2006). These studies 

rely less on an index approach and instead use multivariate 

statistics to evaluate multiple biological communities in a 

gradient approach (Cuffney and others, 1997). 
2 Biological-Community Composition in Small Streams in Indiana and Ohio, 2004

Purpose and Scope 

The current study was one of eight regional studies that 

assessed the effects of nutrient enrichment in streams as part 

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

Assessment (NAWQA) Program (Munn and Hamilton, 2003). 

The analysis in this report relates algal-, invertebrate-, and 

fish-community composition to habitat, nutrients, and land-use 

variables along a previously determined nutrient-concentration 

gradient. The study was conducted in 30 agriculturally dominated watersheds within the USEPA Level III Eastern Corn 

Belt Plains (Ecoregion 55) part of the White River and Great 

and Little Miami River Basins (WHMI) NAWQA Study Unit 

in Indiana and Ohio, hereafter termed Midwest. Information 

from this study may help determine which biological-community-composition group or groups are most appropriate in 

nutrient-assessment studies and nutrient-criteria development.

Description of the Study Area

The study area covers 48,400 km2 of central to southern Indiana and central to southwestern Ohio, and is in the 

Ecoregion 55 part of the WHMI NAWQA Study Unit (fig. 1). 

The landscape is dominated by row-crop agriculture, primarily 

corn and soybeans (Debrewer and others, 2000; Schnoebelen 

and others, 1999). The climate is characterized as humid with 

well-defined winters and summers. The mean annual precipitation is 72.4 cm, and the mean annual temperature is 12.2°

C. 

The study area was mostly glaciated, and the soils are nutrientrich and fertile. In addition, soils are poorly drained and often 

require tile drains for row-crop agriculture. If present, the 

riparian zones along the streams, generally consist of narrow 

deciduous forest and woody wetlands adjacent to row-crop 

fields. 

Muncie

Anderson

Indianapolis

Columbus Bloomington

Petersburg

Richmond

84o

85o

40o

INDIANA

OHIO

Springfield

Dayton

Cincinnati

Middletown

Hamilton

86o

87o

39o

KILOMETERS

MILES

0

10

10

20

20

30

30

40 

40 

Based from U.S. Geological Survey digital data, 0

scales ranging from 1:100,000 to 1:250,000

Universal Transverse Mercator projection, Zone 16

North American Datum, 1983

Figure 1. Location of study sites and Level III Ecoregions (U.S. Environmental Protection Agency, 2003) in White River

and Great and Little Miami River Basins (WHMI) Study Unit of the National Water-Quality Assessment (NAWQA) Program,

2004. (Site descriptions are in table 1.)

LEVEL III ECOREGION 55

LEVEL III ECOREGION 71

LEVEL III ECOREGION 72

MAJOR STREAMS

MAJOR CITIES

SITE LOCATIONS WITH SITE NUMBERS

EXPLANATION

22

2

4 1

3

9

8 7 6

5

30

29

28 27

26

25 24 23 22

21

20

19

18 17 15 16 14

13 12

11 10

WHMI

Indiana Ohio

Figure 1. Location of study sites and Level III Ecoregions in the White River and Great and Little Miami River Basins (WHMI) Study 

Unit of the National Water-Quality Assessment (NAWQA) Program, 2004. (Site descriptions are in table 1.) 
Study Methods 3

Study Methods

This study used field and analytical methods from the 

USGS. The following sections describe the site selection and 

data collection; field and laboratory methods used in collecting, processing, and analyzing biological-community, basin 

and riparian characteristics, habitat, and nutrients data; and 

data analysis used in this report.

Site Selection

To minimize natural variables that could mask relations 

between the biological community composition and nutrients, 

potential study sites had to be within small agricultural basins 

(50 to 500 km2) with similar substrates (cobble/gravel riffles) 

that did not have point-source discharges. Sites were selected 

to have similar physical variables when possible, including 

substrates (cobble/gravel riffles) and canopy cover. Also, sites 

were selected to capture a range (gradient) in nutrient concentrations (nitrogen and phosphorus) on the basis of existing data 

and sampling during a reconnaissance survey. The final list 

consisted of 30 sites within Ecoregion 55 that had relatively 

homogenous habitat (table 1).

Data Collection

Biological communities (algal, invertebrate, and fish) 

were assessed at the 30 sites in August 2004 using standard 

NAWQA protocols (Moulton and others, 2002). Two algalcommunity samples were collected from each sampled reach: 

a depositional-targeted habitat (DTH) sample and a richesttargeted habitat (RTH) sample. The DTH algal-community 

samples were a composite of five petri-dish subsamples 

collected in sand/silt substrates. Each DTH subsample was 

collected by pushing the lid of a 47-mm petri dish into the 

sand/silt substrate and then sliding a spatula under the petri 

dish. The material trapped in the petri dish was washed into 

a 500-mL wide-mouth bottle. The RTH algal-community 

samples were collected from riffle habitats within the reach 

that were dominated by gravel/cobble substrates. The sample 

was a composite of five subsamples. The collected subsamples 

represented the average overall density of algal cover within 

the site. The algae from a measured area were scraped off the 

top of the substrate and collected into a 500-mL wide-mouth 

bottle. At the subsample-collection locations for the DTH 

and RTH samples, field personnel measured stream velocity, 

substrate type, and canopy closure. Algal-community samples 

(DTH and RTH) were preserved with sufficient formaldehyde 

to obtain a 5-percent buffered formalin (Moulton and others, 2002) and sent to the Academy of Natural Sciences in 

Philadelphia, Pa., and were identified to the lowest possible 

taxonomic level. 

The invertebrate RTH community samples were a 

composite of five samples collected from riffle habitats 

with gravel/cobble substrates and moderate to high stream 

velocities (Moulton and others, 2002). Each RTH invertebrate 

community subsample was collected using a Slack sampler 

with a 500-μm dolphin bucket, field processed, and preserved 

with 10-percent formalin. The RTH invertebrate community samples were sent to the USGS National Water Quality 

Laboratory in Lakewood, Colo., and identified to the lowest 

possible taxonomic level. 

Fish communities were sampled from a stream-reach 

length that was approximately 20 times the mean wetted-channel width at each of the 30 sites sampled; the lengths ranged 

from 150 to 420 m (table 2). Methods for assessing the fish 

community consisted of two-pass electrofishing techniques 

and seining (Moulton and others, 2002). Fish were identified 

to species, measured, and weighed in the field. 

Physical-habitat assessments of the fish-community 

stream reaches followed protocols developed by the USGS 

NAWQA Program (Fitzpatrick and others, 1998). These reachbased habitat protocols measure physical stream variables 

such as wetted-channel depth and width, velocity, and substrate along 11 equally spaced transects (Brightbill and Munn, 

2008). The mean depth, width, and velocity of the wetted 

channel from the 11 transects are presented in table 2.

Basin and riparian land-use variables were determined 

using Environmental Systems Research Institute’s (ESRI) 

ArcView Geographical Information System (Environmental 

Systems Research Institute, 2005). Basins and land-use variables were determined from digital topographic and hydrologic maps ranging from 1:24,000 to 1:250,000 scale, depending on the size of the basin. The source of the basin data was 

an enhanced version of the USGS National Land-Cover Data 

1992 (U.S. Geological Survey, 1999). The amount of nitrogen 

(N) and phosphorus (P) that was applied within the basin were 

based on combined 2001 estimates of atmospheric data, animal populations, and fertilizer sales within each basin (Ruddy 

and others, 2006; Brightbill and Munn, 2008). Basin land-use 

variables used in the analysis included drainage basin area, 

percent agriculture land-use, percent forest land-use, percent 

urban land-use, and estimates of N and P applied within the 

basin. Additionally, the percent of agriculture land-use in the 

basin was refined to include only row crops and the percent of 

forest within the basin was refined to include only deciduous 

forest (table 3). 

Riparian land-use data were determined in accordance 

with protocols developed by Johnson and Zelt (2005). Riparian land-use variables used in the analysis included percent of 

riparian zone evergreen forest, and percent of riparian zone 

row crops (table 3), although numerous variables were calculated for the study (Brightbill and Munn, 2008). The variables 

for each basin classification were converted from vector to 

raster format at 30-m resolution, then the 30-m resolution for 

each basin classification was used to determine the area in 

which each classification occurred, and these values were then 

converted to percentages. The conversion of basin variables 

allowed for normalization of the data and comparisons among 

basins. Brightbill and Munn (2008) published the complete 

environmental, algae, and benthic invertebrate datasets for 

the Nutrient Enrichment Effect on Stream Ecosystems studies 

from 2003–04.
4 Biological-Community Composition in Small Streams in Indiana and Ohio, 2004

Table 1. Descriptions of the 30 sites in the White River and Great and Little Miami River Basins (WHMI) Study Unit of the National 

Water-Quality Assessment (NAWQA) Program, 2004. (Site descriptions are in figure 1.) 

[USGS, U.S. Geological Survey; STAID, station identification; OH, Ohio; IN, Indiana; FK, Fork; R, River; CR, County Road; N, North; E, East; NR, Near; 

CO, County; RD, Road; FT, feet; US, Upstream; S, South; W, West]

Site 

number USGS STAID USGS station name

Latitude 

(decimal 

degrees)

Longitude 

(decimal 

degrees)

1 03240500 NORTH FORK MASSIE CREEK AT CEDARVILLE, OH 39.75637 -83.79130

2 03264900 PAINTER CREEK NEAR SUGAR GROVE, OH 40.08353 -84.39552

3 03272200 ELK CREEK AT MILTONVILLE, OH 39.50192 -84.45972

4 03357330 BIG WALNUT CREEK NEAR ROACHDALE, IN 39.81640 -86.75183

5 390948085274301 VERNON FK MUSCATATUCK R AT CR 1220N NEAR ZENAS, IN 39.16440 -85.46230

6 391545085454301 DUCK CREEK AT CR 850E NR NEWBERN, IN 39.26188 -85.76147

7 391726085485101 HAW CREEK AT CR 600N NR NORTONBURG, IN 39.28890 -85.81608

8 391732085414401 CLIFTY CREEK AT CO RD 1150 E NEAR HARTSVILLE, IN 39.29223 -85.69510

9 392402085503001 SLASH CREEK AT CR 850S NR LEWIS CREEK, IN 39.81487 -86.63770

10 392735083544101 TODD FORK CREEK AT HALE RD NR WILMINGTON, OH 39.46008 -83.91093

11 392751085291801 LITTLE FLATROCK RIVER 700 FT US CR 1000S NEAR MILROY, IN 39.46313 -85.48943

12 393619084461200 FOURMILE CREEK AT CAMDEN COLLEGE CORNER RD NEAR 

COVINGTON, OH

39.59335 -84.77042

13 393659085340301 MUD CREEK AT 650W NEAR ARLINGTON, IN 39.61498 -85.56932

14 393723085120201 WILLIAMS CREEK AT SNYDER RD NR CONNERSVILLE, IN 39.62313 -85.20473

15 393828086381301 MILL CREEK AT CR 625W NEAR STILESVILLE, IN 39.64113 -86.63600

16 393837083505401 CAESAR CREEK AT HOOP RD NEAR XENIA, OH 39.64512 -83.84583

17 393930084410901 PAINT CREEK AT CAMDEN SUGAR VALLEY RD NEAR 

CAMDEN, OH

39.65927 -84.68645

18 394211086454801 CLEAR CREEK AT CR 300N NEAR FILLMORE, IN 39.70322 -86.76352

19 394340085524601 SUGAR CREEK AT CO RD 400 S AT NEW PALESTINE, IN 39.73728 -85.88186

20 394510084545801 ELKHORN CREEK AT ESTEB RD NEAR ABINGTON, IN 39.75337 -84.91508

21 394544086305601 WEST FORK WHITE LICK CREEK AT ELLIS PARK AT DANVILLE, IN 39.76272 -86.51507

22 395121083561701 MUD RUN CREEK AT HUNTER RD NEAR ENON, OH 39.85608 -83.93745

23 395327085190801 FLATROCK RIVER AT CR 350E NEAR NEW CASTLE, IN 39.89098 -85.31833

24 395350084353800 TWIN CREEK AT EUPHEMIA-CASTINE RD NEAR WEST 

MANCHESTER, OH

39.89635 -84.59500

25 395623085090401 WEST FORK WHITEWATER RIVER AT HOOVER RD NEAR 

HAGERSTOWN, IN

39.94053 -85.15090

26 395625084010101 HONEY CREEK AT NEW CARLISLE PIKE NEAR NEW CARLISLE, OH 39.94097 -84.01703

27 400421084115601 SPRING CREEK AT PIQUA TROY RD NEAR TROY, OH 40.07348 -84.19552

28 400540084415601 WEST BRANCH GREENVILLE CREEK AT NASHVILLE RD NEAR 

GRANVILLE, OH

40.09632 -84.69997

29 400806085455601 INDIAN CREEK AT CR 200N NEAR HAMILTON, IN 40.13560 -85.76635

30 402901083482601 SOUTH FORK GREAT MIAMI RIVER AT CR 96 NEAR 

BELLE CENTER, OH

40.48482 -83.80682
Table 2. Habitat variable values of the 30 sites in the White River and Great and Little Miami River Basins (WHMI) Study Unit of the National Water-QProgram, 2004. —Continued[m, meter; cm/s, centimeter per second; m2, square meter: Std. dev., standard deviation]

Site 

number

Reach length 

(m)

Mean 

wetted 

channel 

width 

(m)

Mean 

bankfull 

width 

(m)

Mean 

depth 

(m)

Mean 

bankfull 

depth 

(m)

Mean 

instantaneous 

point 

velocity 

(cm/s)

Reach 

surface 

water 

area 

(m2)

Reach 

surfacewater 

gradient 

(unitless)

Mean 

canopy 

closure 

(percent)

Mean 

shape 

index 

(percent)

Boulder 

habitat 

cover 

(percent)

Clay 

dominant 

substrate 

(percent)

Sand 

dominant 

substrate 

(percent)

d

h

(p

1 150 7.0 9.8 0.21 1.03 2.13 1,050 0.0012 51.8 19.8 0 29.1 30.9 2 174 9.2 14.4 .25 1.11 1.22 1,600 .0014 86.8 21.1 7.8 5.6 31.5 3 300 12.0 21.4 .11 .96 3.05 3,600 .0044 66.8 52.4 25.5 1.8 7.3 4 420 19.0 23.6 .53 1.75 11.00 7,980 .0009 86.4 19.7 18.9 0 39.4 5 300 13.4 20.6 .35 1.78 .31 4,020 .0012 57.8 6.7 1.8 36.5 11.5 6 168 7.8 11.1 .20 1.45 0 1,310 .0001 91.2 6.7 0 40.0 45.5 7 150 4.6 12.6 .11 1.36 .61 690 .0016 76.2 6.5 0 20.0 58.2 8 250 14.0 23.5 .12 1.30 1.52 3,500 .0026 89.6 11.5 3.9 0 38.9 9 150 5.2 11.5 .21 1.46 .61 780 .0014 77.8 5.7 0 1.8 83.6 

10 206 8.4 14.7 .24 1.27 2.13 1,730 .0026 79.4 15.2 1.9 3.6 34.5 

11 181 11.3 16.1 .18 1.27 1.22 2,050 .0022 83.7 8.2 3.8 14.5 21.8 

12 300 11.2 21.6 .16 1.07 1.83 3,360 .0098 73.8 22.0 74.5 0 20.0 13 150 5.3 8.0 .14 1.29 1.52 800 .0009 96.0 5.9 1.8 27.3 70.9 14 200 10.2 17.6 .06 1.18 3.05 2,040 .0081 69.2 9.7 10.9 0 12.7 15 251 12.0 16.2 .12 1.65 1.88 3,010 .0007 88.8 10.1 1.8 0 85.5 16 186 7.4 14.1 .38 1.38 .61 1,380 .0017 69.5 7.5 10.9 9.1 32.7 17 150 6.6 11.9 .14 .95 .61 990 .0037 69.3 17.6 5.5 1.8 40.0 18 155 9.5 16.8 .11 1.12 1.52 1,470 .0019 66.3 8.4 5.5 0 61.8 19 218 12.1 18.0 .36 1.63 4.88 2,640 .0009 94.1 14.4 5.7 0 33.3 20 162 9.2 16.1 .09 1.12 2.44 1,490 .0084 97.1 39.3 27.3 0 10.9 21 154 8.2 13.8 .17 1.23 .91 1,260 .0006 78.9 6.2 0 0 80.0 22 165 6.9 9.3 .12 .96 3.66 1,140 .0010 96.8 25.4 0 5.5 5.5 23 156 9.1 10.9 .20 1.19 1.52 1,420 .0007 69.3 6.5 0 3.6 83.6 24 220 10.4 14.0 .29 1.13 .31 2,290 .0003 91.6 14.9 0 30.9 30.9 25 167 8.8 11.8 .18 1.22 2.13 1,470 .0018 83.4 7.5 0 5.5 69.1 26 200 9.6 14.9 .18 1.25 2.44 1,920 .0041 92.0 29.3 18.9 1.8 23.6 27 154 7.6 12.8 .21 .97 .31 1,170 .0032 85.8 40.4 11.3 5.6 1.9 
Table 2. Habitat variable values of the 30 sites in the White River and Great and Little Miami River Basins (WHMI) Study Unit of the National Water-QProgram, 2004. —Continued [m, meter; cm/s, centimeter per second; m2, square meter: Std. dev., standard deviation] Site number Reach length (m) Mean wetted channel width (m) Mean bankfull width (m) Mean depth (m) Mean bankfull depth (m) Mean instanta- neous point velocity (cm/s) Reach surface water area (m2) Reach surface- water gradient (unitless) Mean canopy closure (percent) Mean shape index (percent) Boulder habitat cover (percent) Clay dominant substrate (percent) Sand dominant substrate (percent) 

c

do

h

(p

28 150 7.1 9.0 0.37 1.55 3.05 1,070 0.0003 95.0 12.7 4.0 23.6 58.2 29 150 6.7 8.5 .17 1.17 .31 1,010 .0009 94.1 7.7 1.8 9.1 63.6 30 177 10.4 13.6 .34 1.51 1.83 1,840 .0004 97.6 15.6 12.7 12.7 32.7 

Minimum 150 4.6 8.0 .06 .95 0 690 .0001 51.8 5.7 0 0 1.9 Mean 197 9.3 14.6 .21 1.28 1.95 2,000 .0023 81.9 15.8 8.5 9.7 40.7 Median 171 9.2 14.1 .18 1.24 1.52 1,480 .0014 84.8 12.1 3.9 4.6 33.9 Maximum 420 19.0 23.6 .53 1.78 11.00 7,980 .0098 97.6 52.4 74.5 40.0 85.5 Std. dev. 63 3.0 4.3 .11 .23 2.05 1,450 .0025 12.5 11.6 14.6 12.2 25.3 
Table 3. Nutrient concentration (August), basin, and land-use variable values of the 30 sites in the White River and Great and Little Miami River Basinthe National Water-Quality Assessment (NAWQA) Program, 2004. —Continued[NO2 + NO3, Nitrate plus Nitrite as Nitrogen; mg/L, milligram per liter; TN, Total Nitrogen; OP, Orthophosphate as Phosphorus; TP, Total Phosphorus; km2, square kilometer; N, P, Phosphours; m, meter; Std. dev., standard deviation; Estimated N and P applied to a basin in 2001 (Ruddy and others, 2006)]

Site 

number

NO3+NO2

(mg/L)

TN 

(mg/L)

OP 

(mg/L)

TP 

(mg/L)

Drainage 

basin 

area 

(km2)

Agriculture 

land use 

within 

basin 

(percent)

Forest 

land use 

within 

basin 

(percent)

Urban 

land use 

within 

basin 

(percent)

Estimate 

N applied 

to a basin 

in 2001 

(kg)

Estimate 

P applied 

to a basin 

in 2001 

(kg)

Riparian 

zone 

(75 to 105 m) 

evergreen 

forest 

within basin 

(percent)

Riparian 

zone 

(75 to 105 m

row crops 

within basi

(percent)

1 1.25 1.52 0.015 0.034 74.3 97.3 2.4 0.1 901,000 153,000 0 84.7 

2 .35 1.04 .054 .099 117.0 96.4 2.2 1.1 1,850,000 401,000 0 81.5 3 .37 .58 .005 .017 120.0 80.9 16.9 1.8 987,000 170,000 1.3 27.2 4 .44 .89 .019 .064 340.0 95.3 3.7 .7 2,570,000 446,000 0 66.9 5 .52 1.05 .034 .079 95.6 83.8 15.7 .1 891,000 173,000 .7 19.2 6 .14 1.47 .055 .200 51.4 93.9 4.4 .5 480,000 85,000 .1 43.1 7 .58 1.00 .098 .148 58.5 93.5 3.9 2.0 516,000 88,000 0 37.1 8 1.69 2.11 .060 .098 228.0 95.0 4.2 .3 2,650,000 524,000 .1 34.5 9 2.38 2.46 .012 .027 54.6 95.1 2.0 2.7 467,000 82,000 0 91.1 

10 1.11 1.35 .012 .029 86.0 91.4 7.7 .6 1,110,000 186,000 .2 59.5 

11 2.02 2.35 .059 .086 120.0 96.5 2.5 .6 1,280,000 240,000 0 45.8 

12 2.08 2.41 .015 .032 99.7 91.5 8.3 .1 1,180,000 210,000 .1 58.4 13 3.94 3.98 .052 .007 43.2 98.4 1.2 .1 468,000 88,000 0 69.1 14 .88 1.14 .011 .030 73.9 84.6 14.7 .3 568,000 103,000 1.1 23.7 15 .65 .89 .015 .028 136.0 92.8 6.2 .8 928,000 156,000 0 51.6 16 1.52 2.09 .011 .064 106.0 92.0 4.7 2.5 1,160,000 198,000 .2 56.0 17 .80 1.19 .514 .557 36.6 80.6 10.2 7.4 370,000 65,000 .1 32.7 18 1.02 1.28 .080 .119 75.1 86.8 10.6 .5 496,000 86,000 0 34.9 19 .19 .49 .036 .063 246.0 92.2 5.0 1.9 2,070,000 364,000 0 43.6 20 1.63 1.84 .016 .026 58.1 85.1 12.7 .3 505,000 88,000 .4 44.6 21 1.38 1.63 .018 .030 74.5 94.3 3.6 1.6 516,000 87,000 0 59.1 22 5.07 5.28 .015 .030 49.4 86.2 9.1 4.5 525,000 87,000 .1 47.8 23 3.45 3.70 .019 .039 61.6 95.2 3.9 .3 505,000 83,000 0 72.3 24 .31 .75 .055 .083 72.8 96.9 2.5 .4 1,120,000 238,000 0 81.0 25 1.64 1.83 .006 .013 64.3 86.9 12.5 .2 457,000 79,000 0 60.8 26 2.92 3.06 .012 .052 92.3 89.3 8.8 1.5 1,030,000 172,000 0 50.4 
Table 3. Nutrient concentration (August), basin, and land-use variable values of the 30 sites in the White River and Great and Little Miami River Basinthe National Water-Quality Assessment (NAWQA) Program, 2004. —Continued[NO + NO , Nitrate plus Nitrite as Nitrogen; mg/L, milligram per liter; TN, Total Nitrogen; OP, Orthophosphate as Phosphorus; TP, Total Phosphorus; km2, square kilometer; N, 

2 3

P, Phosphours; m, meter; Std. dev., standard deviation; Estimated N and P applied to a basin in 2001 (Ruddy and others, 2006)]

Riparian Riparian Agriculture Forest Urban Estimate Estimate zone 

Drainage zone land use land use land use N applied P applied (75 to 105 m) 

Site NO +NO TN OP TP basin (75 to 105 m

3 2 within within within to a basin to a basin evergreen 

number (mg/L) (mg/L) (mg/L) (mg/L) area row crops basin basin basin in 2001 in 2001 forest 

(km2) within basi

(percent) (percent) (percent) (kg) (kg) within basin 

(percent) (percent)

27 1.64 1.93 0.008 0.019 64.5 94.0 5.7 0.2 678,000 115,000 0.1 57.4 28 1.08 1.44 .005 .024 59.4 92.4 7.1 .2 931,000 204,000 0 83.3 29 .06 .34 .009 .044 46.3 95.4 2.2 1.5 430,000 71,000 0 49.1 30 .52 .81 .017 .040 122.0 87.0 12.1 .5 1,030,000 173,000 0 64.5 

Minimum .06 .34 .005 .007 36.6 80.6 1.2 .1 370,000 65,000 0 19.2 Mean 1.39 1.73 .045 .073 97.6 91.4 6.9 1.2 956,000 174,000 .2 54.4 Median 1.10 1.46 .017 .040 74.4 92.6 5.4 .6 896,000 155,000 0 53.8 Maximum 5.07 5.28 .514 .557 340.0 98.4 16.9 7.4 2,650,000 524,000 1.3 91.1 Std. dev. 1.19 1.11 .092 .101 66.4 5.0 4.5 1.5 609,000 118,000 .3 19.0 

1 Variable is a refinement of agriculture land-use variable. 2 Variable is a refinement of forest land-use variable.
Description of the Sampled Basins 9

Water chemistry was sampled for nutrients in August 

2004 in accordance with protocols developed by the USGS 

NAWQA Program (Shelton, 1994). Table 3 lists water samples 

collected and analyzed for nitrate + nitrite (NO3+NO2) as 

nitrogen, total nitrogen (TN), orthophosphate (OP) as phosphorus, and total phosphorus (TP), during low-flow periods 

in August 2004. Nutrient concentrations were analyzed at the 

USGS National Water Quality Laboratory in Lakewood, Colo., 

using colorimetric methods; ammonia (NH3) plus organic 

nitrogen, and TP by microkjeldahl digestion (Patton and Truitt, 

1992), NH3 by salicylate hypochlorite (Fishman, 1993); NO2 

by diazotization (Patton and Truitt, 1992); NO3+NO2 by cadmium reduction (Patton and Truitt, 1992); and OP by phosphomolybdate (Patton and Truitt, 1992). 

Data Analysis

The distribution of nutrient concentrations (NO3+NO2, 

TN, OP, and TP) for the August sample period was compared 

to the USEPA Aggregate Nutrient Ecoregion VI median 

25th percentile for the summer values, because these values 

were more representative of our sampling period. The USEPA 

developed the proposed 25th percentile annual nutrient criteria 

that incorporated the median values for each stream by season. 

The median of the four seasonal values was used to calculate 

the proposed annual criteria using the 25th percentile for each 

nutrient constituent by ecoregion. 

Canonical correspondence analysis (CCA) was used to 

relate the biological communities to selected environmental 

variables. Prior to analysis, the algal community data were 

processed to remove all non-diatom data; next, both the algaldiatom and invertebrate-community data were processed to 

remove ambiguous taxa. Finally, the DTH algal diatom-, RTH 

algal diatom-, RTH invertebrate-, and fish-community data 

were imported into CANOCO v. 4.54 (ter Braak and Smilauer, 

2002) for CCA unimodel analysis. In each analysis, the raw 

abundance data were square-root transformed, and rare species 

(taxa) were downweighted. 

Data on more than 400 environmental variables, including 4 nutrients, were collected in this study; consequently, 

statistical procedures were required to reduce the number of 

environmental variables for each biological dataset. A principal component analysis and regression analysis was run on 

each category (habitat, nutrient, and basin) of environmental 

data to show which variables within a category data type were 

related to one another and to identify possible collinearity, 

covariablity, and outliers within the datasets. This procedure 

reduced the original list of more than 400 variables to 30 variables, including the 4 nutrients. The selected variables were 

normalized because the majority of the variables were not 

normally distributed. The manual forward selection procedure 

in CANOCO 4.54 was used to further decrease the number 

of variables. This procedure used a Monte Carlo Permutation 

Test (p ≤ 0.05) with unrestricted permutations to determine 

which of the selected variables were important to describe 

most of the variation within each biological community. 

The p-value is the probability of obtaining the computed test 

statistic, or one even less likely, when the null hypothesis is 

true (Helsel and Hirsch, 1992). Table 4 lists the significant 

variables for each biological community and their respective 

data distributions.

Description of the Sampled Basins

The land use of the drainage basin and habitat within the 

sample locations were relatively homogenous. Streamflow for 

many streams in the study area was controlled by tile drains 

and surface runoff during much of the year and by ground 

water during low-flow periods. Drainage basin areas ranged in 

size from 36.6 to 340 km2, with a mean of 97.6 km2 (table 3), 

and the mean land use within the drainage basin areas was 

dominated by agriculture (91.4 percent), followed by forest 

(6.9 percent), and urban area (1.2 percent). 

Streams sampled within Ecoregion 55 have some of the 

highest nutrient loadings in the United States (Mueller and 

Spahr, 2006). Although streams within the ecoregion are nutrient enriched, there was a nutrient gradient for August samples 

(table 3). In the 30 basins that were studied, nutrient concentrations ranged as follows: NO3

+NO2, from 0.060 to 5.07 mg/L 

as N; TN, from 0.34 to 5.28 mg/L; OP, from 0.005 to 

0.514 mg/L as P; and TP, from 0.007 to 0.557 mg/L (table 3). 

Ruddy and others (2006) noted the estimated N applied to 

a basin in 2001 ranged from 370,000 to 2,650,000 kg, and 

estimated P applied to a basin in 2001 ranged from 65,000 to 

524,000 kg (table 3). When nutrient concentrations were compared to USEPA Aggregate Nutrient Ecoregion VI median seasonal (summer) concentration values, 67 percent of NO3

+NO2, 

23 percent of TN (fig. 2), 27 percent of OP, and 20 percent of 

TP (fig. 3) samples collected equaled or exceeded the published USEPA values for proposed 25th percentile nutrient 

criteria (U.S. Environmental Protection Agency 2000b), suggesting eutrophication at some of the sampling locations. 

Biological-Community Composition within the 

Sampled Basins

The DTH algal-diatom community consisted of 36,397 

individuals, representing 211 taxa and ranged from 51 to 

87 diatom taxa per site (appendix 1). Three genera (Amphora, 

Navicula, and Nitzschia) accounted for 65.9 percent of the 

total number of individuals collected. The most abundant taxa 

collected were Amphora pediculus (17.2 percent relative abundance), and Navicula minima (6.6 percent relative abundance). 

The RTH algal-diatom community consisted of 18,859 

individuals, representing 157 taxa and ranged from 24 to 

72 diatom taxa per site (appendix 2). Three genera (Amphora, 

Navicula, and Nitzschia) accounted for 66.2 percent of the 

total number of individuals collected. The most abundant taxa 

collected were Amphora pediculus (23.2 percent relative abundance), and Navicula minima (9.2 percent relative abundance). 
10 Biological-Community Composition in Small Streams in Indiana and Ohio, 2004

Table 4. Significant environmental variables and data ranges used in the canonical correspondence analysis, White River and Great 

and Little Miami River Basins (WHMI) Study Unit of the National Water-Quality Assessment (NAWQA) Program, 2004. 

[Min, minimum; Max, maximum; m, meter; m2, square meter; mg/L, milligram per liter; kg, kilogram]

Category Variable code Variable name Mean (Min - Max)

Habitat BOPct Boulder habitat cover (percent) 4 8.5 (0 - 74.5)

Pct Clay Clay substrate (percent) 4 9.7 (0 - 40)

CanClosr Mean canopy closure (percent) 2 81.9 (51.8 - 97.6)

BFDepthA Mean bankfull depth (m) 3, 4 1.28 (.95 - 1.78)

BFWidthA Mean bankfull width (m) 4 14.6 (8.0 - 23.6)

ShapeAvg Mean shape index (percent) 1 15.8 (5.7 - 52.4)

Pool/Rif Ratio percent pool to percent riffle 1, 2 2.23 (0 - 30.7)

RchArea Reach surface-water area (m2) 3 2,000 (690 - 7,980)

Grad Reach surface-water gradient (unitless) 4 .0023 (.0001 - .0098)

Pct Riff Riffle habitat present (percent) 4 18.2 (2.3 - 67.1)

Pct Sand Sand substrate (percent) 3 40.7 (1.9 - 85.5)

P vcGrav Very coarse gravel (percent) 2 8.4 (0 - 32.7)

Nutrient 

concentration

TP Total phosphorus-August (mg/L) 1, 3 .073 (.007 - .557)

Land-use Basinp-d Deciduous forest within basin (percent) 1, 4 6.7 (1.3 - 15.4)

PA_01 Estimated Phosphorus applied to a basin 

in 2001 (kg) 4

174,000 (65,000 - 524,000)

Rip-eve Riparian zone (75 to 105 m) evergreen 

forest within basin (percent) 2

.2 (0 - 1.3)

Rip-rowc Riparian zone (75 to 105 m) row crops 

within basin (percent) 2

54.4 (19.2 - 91.1)

1

 Variable used in Depositional Targeted Habitat (DTH) algal diatom community data analysis. 

2

 Variable used in Richest Targeted Habitat (RTH) algal diatom community data analysis. 

3

 Variable used in Richest Targeted Habitat (RTH) invertebrate community data analysis. 

4

 Variable used in fish community data analysis. 
Description of the Sampled Basins 11

Figure 2. Distribution of concentrations of nitrogen constituents in samples collected in August 2004, compared to the 

U.S. Environmental Protection Agency (USEPA) Aggregate Nutrient Ecoregion VI median seasonal values (summer) (U.S. Environmental 

Protection Agency, 2002a), in the White River and Great and Little Miami River Basins (WHMI) Study Unit of the National Water-Quality 

Assessment (NAWQA) Program. 

Figure 3. Distribution of concentrations of phosphorus constituents in samples collected in August 2004, compared to the 

U.S. Environmental Protection Agency (USEPA) Aggregate Nutrient Ecoregion VI median seasonal values (summer) (U.S. Environmental 

Protection Agency, 2002a), in the White River and Great and Little Miami River Basins (WHMI) Study Unit of the National Water-Quality 

Assessment (NAWQA) Program. Concentration, in milligrams per liter (mg/L), as Nitrogen

x

x

x

x

x

Nitrate plus nitrite Total nitrogen

0

1

2

3

4

5

6 30 30

30 number of 

 values

EXPLANATION

 schematic boxplot

x

x

upper adjacent

75th percentile

median

25th percentile

lower adjacent

lower outside

USEPAAggregate upper outside

Nutrient Ecoregion VI

median seasonal value

(summer)

0.61 mg/L

2.15 mg/L

Figure 2. Distribution of concentrations of nitrogen constituents in samples collected in August 

2004, compared to the USEPA Aggregate Nutrient Ecoregion VI median seasonal values (summer)

(U.S. Environmental Protection Agency, 2000a), in the White River and Great and Little Miami River 

Basins (WHMI) Study Unit of the National Water-Quality Assessment (NAWQA) Program.

Concentration in milligrams per liter (mg/L), as Phosphorus

x

Orthophosphophate Total phosphorus

0.005

0.010

0.050

0.100

0.500

1.000 30 30

USEPA Aggregate

Nutrient Ecoregion VI

median seasonal value

(summer)

0.09 mg/L

0.054 mg/L

Figure 3. Distribution of concentrations of phosphorus constituents in samples collected in August 

2004, compared to the USEPA Aggregate Nutrient Ecoregion VI median seasonal values (summer) 

(U.S. Environmental Protection Agency, 2000a), in the White River and Great and Little Miami River 

Basins (WHMI) Study Unit of the National Water-Quality Assessment (NAWQA) Program.

30 number of 

 values

EXPLANATION

 schematic boxplot

x

x

upper adjacent

75th percentile

median

25th percentile

lower adjacent

lower outside

upper outside
12 Biological-Community Composition in Small Streams in Indiana and Ohio, 2004

The invertebrate community consisted of 165,336 individuals, representing 76 taxa and ranged from 20 to 32 taxa 

per site (appendix 3). Communities were dominated by the 

orders Ephemeroptera and Diptera, which accounted for 

46.2 percent of the total number of individuals collected. The 

most abundant taxa collected were Cheumatopsyche species 

(15.8 percent relative abundance). The second most dominant 

taxa collected was Ceratopsyche bronta (12.8 percent relative 

abundance).

The fish community consisted of 21,103 individuals, 

representing 62 taxa and ranged from 9 to 32 taxa per site 

(appendix 4). Four species—central stoneroller (Campostoma 

anomalum), bluntnose minnow (Pimephales notatus), creek 

chubsucker (Erimyzon oblongus), and rainbow darter (Etheostoma caeruleum)—accounted for 52.2 percent of the number 

of individuals collected. The most abundant species collected 

were central stoneroller (25.7 percent relative abundance) and 

bluntnose minnow (11.1 percent relative abundance). 

Variables Affecting Biological-Community 

Composition within the Sampled Basins

A CCA analysis was conducted to determine which variable most influenced each of the four biological communities. 

Eigenvalues (λ ) for each CCA axis determine the relative 

importance of each axis to explain the data set; the sum of 

all eigenvalues and the cumulative percent variation (CPV) 

measure the percentage of data dispersion on a particular axis 

(Jongman and others, 1995; McCune and Grace, 2002). Higher 

eigenvalues and/or CPV equate to greater diversity within a 

dataset (meaning that data among the sites are dissimilar and 

distributed along a wide range on an axis). Lower eigenvalues and/or CPV indicate that the majority of the biological 

communities at each site are similar along the first CCA axis, 

as is the case with the data from this study. Sites with similar 

biological communities are placed in the center of the CCA 

plot(s) or near one another, whereas sites with dissimilar biological communities are placed on extreme ends of the CCA 

plot(s). Because many of the sites were placed close together 

on the CCA plots, the interpretation of the analysis focused on 

differences between sites observed at the extreme ends of the 

axes where differences between community compositions are 

greatest. Table 4 lists the significant variables used in the CCA 

along with their respective data ranges.

DTH algal-diatom community.—Four variables (one 

nutrient, two habitat, and one land-use) explained the variability among the sites in the DTH algal-diatom community 

(fig. 4A, table 5). A nutrient variable (TP, intersite correlation 

= 0.5509) and a habitat variable (ratio percent pool-to-percent 

riffle, intersite correlation = 0.7364) were dominant on the first 

and second CCA axes, respectively (table 5). The first CCA 

axis accounted for 6.9 percent of the variation in the distribution of the DTH algal diatom community, and the second 

axis explained 6.2 percent. Low eigenvalues (λ1 = 0.069 and 

λ2 = 0.064) and CPV (6.9 and 13.1) for the first and second 

CCA axes, respectively, suggest that overall the DTH algal 

diatom communities were similar among the 30 sites (table 5). 

Although TP explained the most variability, this relation might also be associated with where the DTH samples 

were collected (areas with clay sediments). The DTH sample 

locations are mostly composed of fine clay particles that tend 

to have high phosphorus concentrations, so the significant 

relations may be a function of sampling method. Differences 

between the sites on the extreme ends (sites 8 and 17) along 

the first CCA axis show the influences of TP on the physical 

variables and the species composition of the DTH algal-diatom community (fig. 4A). Site 8 had higher nitrogen constituent (NO3

+NO2 and TN) concentrations, estimated N and P 

applied to a basin in 2001, agriculture land-use within the 

basin, mean wetted channel width, mean instantaneous point 

velocity, and drainage basin area (table 2 and table 3). Site 17 

had higher phosphorus constituents (TP and OP) concentrations, forest land-use within the basin, and mean wetted channel depth (table 2 and table 3). The diversity at the sites ranged 

from 65 taxa (site 17) to 76 taxa (site 8) (appendix 1), which 

suggests that as TP increased the number of taxa decreased . 

Both sites 8 and 17 were dominated by the same 

genera (Amphora and Navicula), but the order of dominant 

species differed slightly. Site 8 was dominated by Amphora 

pediculus (17.7 percent relative abundance), Navicula minima

(17.1 percent relative abundance), and Navicula cryptotenella

(7.5 percent relative abundance); in contrast, site 17 was 

dominated by Nitzschia inconspicua (26.9 percent relative 

abundance), Amphora pediculus (12.2 percent relative 

abundance), and Navicula minima (7.0 percent relative 

abundance). Of the 65 taxa at site 17, 22 taxa were not found 

at site 8; these 22 taxa accounted for 9.3 percent relative 

abundance of individuals collected at site 17. Of the 76 taxa at 

site 8, 33 taxa were not documented at site 17; these 33 taxa 

accounted for 11.5 percent relative abundance of individuals 

collected at site 8. The five most dominant taxa for each site 

(8 and 17) were present at both locations (appendix 1). 

RTH algal-diatom community. —Five variables (three 

habitat and two land-use) explained the variability among the 

sites in the RTH algal communities (fig 4B, table 4). A habitat 

variable (ratio percent pool-to-percent riffle, intersite correlation = 0.7561) and a basin variable (riparian-zone evergreen 

forest within the basin, intersite correlation = 0.4737) were 

dominant on first and second CCA axes, respectively (table 5). 

The first CCA axis accounted for 8.7 percent of the variation 

in the distribution of the RTH algal diatom community, and 

the second CCA axis explained 6.8 percent. Low eigenvalues 

(λ1 = 0.103 and λ2 = 0.081) and CPV (8.7 and 15.5) for the 

first and second CCA axes suggest the RTH algal-diatom community were similar among the 30 sites (table 5). 
Description of the Sampled Basins 13

A

B

CCA Axis 1

CCA Axis 2

-0.6 1.0

-0.6 1.0

TP

Pool/Rif

ShapeAvg

Basinp-d

1

2

3

4

5

6

7

9 8

10

11

12

13 15 14

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

-1.0 2.0

-0.6 0.8

PvcGrav Pool/Rif

CanClosr

Rip-eveRip-rowc

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15 16

17 18

19

20

21

22 23

24

25

26

27

28

29

30

Figure 4. Canonical correspondence analysis (CCA) ordination plot of the (A) Depositional-Targeted Habitat

(DTH) algal-diatom community; and the (B) Richest-Targeted Habitat (RTH) algal-diatom community, White 

River and Great and Little Miami River Basins (WHMI) Study Unit of the National Water-Quality Assessment 

(NAWQA) Program (site numbers as in table 1; variable codes as in table 4). CCA Axis 2

CCA Axis 1

DTH Algal-Diatom

Community

RTH Algal-Diatom

Community

Figure 4. Canonical correspondence analysis (CCA) ordination plots of the (A) DepositionalTargeted Habitat (DTH) algal-diatom community; and the (B) Richest-Targeted Habitat (RTH) 

algal-diatom community, White River and Great and Little Miami River Basins (WHMI) Study Unit 

of the National Water-Quality Assessment (NAWQA) Program (site numbers as listed in table 1; 

variable codes as listed in table 4). 
Table 5. Summary of the canonical correspondence analysis (n=30), White River and Great and Little Miami River Basins (WHMI) Study Unit of the NAssessment (NAWQA) Program, 2004. [DTH, Depositional Targeted Habitat; RTH, Richest Targeted Habitat; m, meter; m2, square meter; mg/L, milligram per liter; kg, kilogram; BOLD text, variable most influences 

Variable name DTH algal diatom community RTH algal diatom community RTH invertebrate community 

Axis 1 Axis 2 Axis 1 Axis 2 Axis 1 Axis 2 

Habitat Boulder habitat cover (percent) 4 

Clay substrate (percent) 4 Mean canopy closure (percent) 2 0.4081 0.2073 Mean bankfull depth (m) 3, 4 0.1585 0.8821 Mean bankfull width (m) 4 Mean shape index (percent) 1 -0.0091 -0.6432 Ratio percent pool to percent riffle 1, 2 -.3388 .7364 .7561 .0513 Reach surface-water area (m2) 3 -.0725 .7027 Reach surface-water gradient (unitless) 4 Riffle habitat present (percent) 4 Sand substrate (percent) 3 -.5394 -.3741 Very coarse gravel (percent) 2 -.3793 .0387 Nutrient concentration Total phosphorus-August (mg/L) 1, 3 .5509 .4751 .5735 .3286 Land-use Deciduous forest within basin (percent) 1, 4 .5403 -.2153 Estimated Phosphorus applied to a basin in 2001 (kg) 4 Riparian zone (75 to 105 m) evergreen forest within basin (percent) 2 -.4634 .4737Riparian zone (75 to 105 m) row crops within basin 

(percent) 2

.2201 .1444

-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------Eigenvalue .069 .064 .103 .081 .127 .099 Species-environment correlations .886 .931 .945 .816 .906 .911 Cumulative percentage variance (CPV) of

species dataspecies-environment relation

6.9

30.1

13.157.6

8.7

31.3

15.555.9

7.7

36.1

13.664.4

Total inertia──Sum of unconstrained eigenvalues 1.012 1.179 1.653 Sum of canonical eigenvalues .231 .328 .351 

1 Variable used in Depositional Targeted Habitat (DTH) algal diatom community data analysis. 2 Variable used in Richest Targeted Habitat (RTH) algal diatom community data analysis. 3 Variable used in Richest Targeted Habitat (RTH) invertebrate community data analysis. 4 Variable used in fish community data analysis. 
Description of the Sampled Basins 15

Differences between the sites on the extreme ends (sites 6 

and 23) along the first CCA axis show the influences of ratio 

percent pool-to-percent riffle on the composition of the RTH 

algal-diatom community (fig. 4B). Site 6 had a higher ratio 

percent pool-to-percent riffle, phosphorus constituents (OP and 

TP), mean canopy closure, and forest land-use within the basin 

(table 2 and table 3). Site 23 had a larger drainage basin area 

and higher nitrogen constituents (NO3+NO2 and TN) concentration, mean instantaneous point velocity, reach surface-water 

gradient, and agriculture land-use within the basin (table 2 and 

table 3). The diversity at the sites ranged from 60 taxa (site 6) 

to 43 taxa (site 23), which suggests that as the ratio percent 

pool-to-percent riffle increased the number of taxa increased 

(appendix 2).

Both sites were dominated by the same genus (Amphora

and Navicula). Site 6 was dominated by Aulacoseira muzzanensis (24.6 percent relative abundance) Navicula minima 

(10.4 percent relative abundance), and Amphora pediculus

(9.8 percent); site 23 was dominated by Amphora pediculus

(18.0 percent relative abundance), Navicula minima

(10.8 percent relative abundance), and Navicula cryptotenella

(10.2 percent relative abundance). During the sampling period, 

site 6 had low water levels and stream velocities because of 

beaver dams; this resulted in lower reach surface-water gradient. Consequently, these natural variables led to a stream reach 

with high pool-to-riffle ratios. Of the 60 taxa at site 6, 32 were 

not at site 23; these 32 taxa accounted for 48.2 percent relative 

abundance of the individuals collected at site 6, including 

the most dominant species (Aulacoseira muzzanensis) found 

at site 6 (appendix 2). Of the 43 taxa at site 23, 15 were not 

collected at site 6; these 15 taxa accounted for 18.2 percent 

relative abundance of individuals collected at site 23, including the third most dominant species (Navicula cryptotenella) 

found at site 23 (appendix 2). 

RTH invertebrate community. —Four variables (three 

habitat and one nutrient ) explained the variability among the 

sites in the RTH invertebrate community (fig. 5A, table 5). A 

basin-calculated nutrient variable (TP, intersite correlation = 

0.5735) and a habitat variable (mean bankfull depth, intersite 

correlation = 0.8821) were dominant on the first and second 

CCA axes, respectively (table 5). The first CCA axis accounted 

for 7.7 percent of the variation in the distribution of the invertebrate species, and the second axis explained 5.9 percent. The 

low eigenvalues (λ1 = 0.127 and λ2 = 0.099) and CPV (7.7 

and 13.6) for the first and second CCA axes, respectively, suggest overall that RTH invertebrate communities were similar 

(table 5). 

Total phosphorus accounted for the majority of the variation along the first CCA axes, (table 5). The effects of the 

TP along the first CCA axis can be observed by examining 

the extreme ends (sites 8 and 30) of the TP vector along the 

first CCA axis (fig. 5A). Site 8 had higher estimated N and P 

applied to a basin in 2001, nutrient constituents (NO3+NO2, 

TN, OP, and TP) concentrations, drainage basin area, agriculture land-use within the basin, reach length, and reach surfacewater gradient (table 2 and table 3). Site 30 had higher mean 

depths, mean instantaneous point velocities, and forested landuse within the basin (table 2 and table 3). The diversity at the 

sites ranged from 20 taxa (site 8) to 29 taxa (site 30), which 

suggests that as TP increased the number of taxa decreased. 

The two most dominant taxa at site 8 were not found 

at site 30; the third most dominant taxa found at site 8 was 

the most dominant taxa at site 30. The most dominant taxa 

at site 8 were Stempellinella species (21.8 percent relative 

abundance), Psephenus herricki (19.8 percent relative 

abundance), and Ceratopsyche bronta (14.7 percent relative 

abundance). The three most dominant taxa found at site 30 

were also found at site 8; including Ceratopsyche bronta

(21.9 percent relative abundance), Baetis intercalaris

(11.0 percent relative abundance), and Cheumatopsyche

species (8.9 percent relative abundance). Of the 20 taxa found 

at site 8, 12 were not found at site 30; these 12 taxa accounted 

for almost 69.0 percent relative abundance of individuals 

collected at site 8 and includes the two most dominant taxa 

(Stempellinella species and Psephenus herricki). Of the 

29 taxa found at site 30, 21 were not found at site 8; these 

21 taxa accounted for 49.5 percent relative abundance of 

individuals collected at site 30 (appendix 3). 

Fish community. —Eight variables (six habitat and two 

land-use) explained the variability among the sites in the fish 

community (fig. 5B, table 5). Two habitat variables, mean 

bankfull depth (intersite correlation = -0.7674) and mean 

bankfull width (correlation = 0.7157), were dominant on the 

first and second CCA axes, respectively (table 4). The first 

CCA axis accounted for 15.2 percent of the variation in the 

distribution of the fish species, and the second CCA axis 

accounted for 7.7 percent. The eigenvalues (λ1 = 0.201 and 

λ2 = 0.102) and the CPV (15.2 and 22.9) for the first and 

second CCA axes, respectively, were the highest among the 

four community datasets, which suggests that the fish communities differ across the first CCA axis more than the other three 

biological communities.

Differences between the sites on the extreme ends (sites 4 

and 17) along the first CCA axis show the influences of mean 

bankfull depth on the fish community (fig. 5B). Site 4 had 

higher mean bankfull depth and width, agricultural land-use 

within basin, mean wetted channel width, mean instantaneous 

point velocity, and drainage basin area (table 2 and table 3). 

Site 17 had higher nutrient constituent (NO3+NO2, OP, TN, 

and TP) concentrations, urban and forested land–use within 

basin, and reach surface-water gradient (table 2 and table 3). 

The diversity at the sites ranged from 32 (site 4) to 13 (site 17) 

taxa, which suggests that as the mean bankfull depth increased 

the number of taxa increased.
16 Biological-Community Composition in Small Streams in Indiana and Ohio, 2004

Figure 5. Canonical correspondence analysis (CCA) ordination plots of the (A) Richest-Targeted 

Habitat (RTH) invertebrate community; and the (B) fish community, White River and Great and 

Little Miami River Basins (WHMI) Study Unit of the National Water-Quality Assessment (NAWQA) 

Program (site numbers as listed in table 1; variable codes as listed in table 4). 

A

B

CCA Axis 1

CCA Axis 2

-0.8 1.0 -0.6 0.8

TP 

BFDepthA

Pct Sand

RchArea

1

2

3 4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

-1.0 1.0 -0.6 1.0

PA_01

Grad

Pct Riff

BFWidthA

BFDepthA

Pct Clay

BOPct

Basinp-d

1

2

3

4

5

6

7

8

9

10 11

12

13

14

15 16

17

18

19 20

21

22

23

24

25

26

27

28

29

30

CCA Axis 2

CCA Axis 1

RTH Invertebrate

Community

Fish Community

Figure 5. Canonical correspondence analysis (CCA) ordination plot of the (A) Richest-Targeted Habitat (RTH)

invertebrate community; and the (B) fish community, White River and Great and Little Miami River Basins 

(WHMI) Study Unit of the National Water-Quality Assessment (NAWQA) Program (site numbers as in table 1; 

variable codes as in table 4).
Implications for Assessing Nutrient Conditions in Midwest Streams 17

The fish community composition among sites differed 

slightly, and the greatest differences between sites is evident at 

the extreme ends (sites 4 and 17) of the first CCA axis. Site 4 

was dominated by bluntnose minnow (Pimephales notatus, 

20.6 percent relative abundance), central stoneroller (Campostoma anomalum, 12.9 percent relative abundance), and longear 

sunfish (Lepomis megalotis, 12.7 percent relative abundance); 

site 17 was dominated by central stoneroller (Campostoma 

anomalum, 43.6 percent relative abundance), blackside darter 

(Percina maculata, 30.1 percent relative abundance) and 

bluntnose minnow (Pimephales notatus, 10.2 percent relative abundance). The large numbers of herbivores (central 

stoneroller) and omnivores (bluntnose minnow) at these 

sites suggests they are nutrient rich. Of the 32 taxa found 

at site 4, 22 taxa were not collected at site 17; these 22 taxa 

accounted for 51.2 percent relative abundance of individuals 

collected at site 4, including the third most dominant species 

(Lepomis megalotis) at site 4. Of the 13 taxa found at site 17, 

3 taxa were not collected at site 4; these 3 taxa accounted for 

30.5 percent relative abundance of individuals collected at site 

17, including the second most dominant species (blackside 

darter, Percina maculata) found at site 17 (appendix 4).

Implications for Assessing Nutrient 

Conditions in Midwest Streams 

A common sampling design used by states for estimating the percentage of impaired miles of streams for many 

stressors (including nutrients) is to randomly select sampling 

sites so that the findings at a limited number of sites can be 

extrapolated to all sites. This method works well to meet the 

objectives of the Clean Water Act in assessing the amount 

of impaired miles of streams. However, by randomly selecting sites, variables such as ecoregion, basin size, substrate, 

and canopy cover that could mask the relations between the 

stressor and the biological community cannot be controlled. 

Studies in Indiana used the randomly selected site design to 

assess the amount of impaired streams; however, results indicated that there were weak or no significant relations between 

nutrients and algal biomass (periphyton chlorophyll a or ash 

free dry mass) due to confounding variables (Caskey and 

others, 2007; Frey and others, 2007; Leer and others, 2007; 

Lowe and others, 2008). 

In this study, the algal diatom- (DTH and RTH), invertebrate-, and fish-community data that were collected at 30 sites 

within the Ecoregion 55 portion of the NAWQA WHMI 

Study Unit were examined to determine whether the response 

among biological communities reflected differences along a 

nutrient concentration gradient from low to high. Study sites 

were selected to minimize physical habitat differences while 

maximizing nutrient concentration differences along a nutrient 

(TP and TN) gradient. The biological community structure 

was related to nutrient and physical variables to determine the 

significant environmental variables for each community. Next, 

the significant nutrient gradients were examined to determine 

whether specific species could be identified as biological indicators of nutrient enrichment. 

Environmental Gradients

Several studies have shown that streams within Ecoregion 55 have elevated nutrient concentrations. Martin and 

others (1996), the USEPA (2000b), Reutter (2003), Caskey 

and others (2007), Frey and others (2007), and Leer and others 

(2007) have reported concentration ranges of total nitrogen 

from 0.56 to 9.26 mg/L and concentrations of total phosphorus 

from 0.015 to 0.285 mg/L throughout this region. Although 

many nutrient concentrations documented within the study 

area are elevated and many streams could be classified as 

nutrient-rich systems, it was thought there might be seasonal 

periods (such as August–September) during which evapotranspiration and algal uptake of nutrients would reduce nutrient 

concentrations to low levels, thereby permitting accurate 

definition of biological thresholds (Baker and others, 2006; 

Caskey and others, 2007; Frey and others, 2007; Leer and others, 2007; Lowe and others, 2008). 

Because a nutrient gradient was found at the 30 sites, the 

biological community data were related to the nutrient and 

physical data using a CCA. The results from the CCA show 

that two nutrient and two physical variables accounted for the 

most variability on the first CCA axis. Nutrients, specifically 

TP, accounted for the majority of the variation in the DTH 

algal-diatom community and the RTH invertebrate community, and habitat accounted for the most variability in the RTH 

algal-diatom community and the fish community. This relation 

suggests that the DTH algal-diatom and RTH invertebrate 

communities might best be used to assess the eutrophication 

of streams. However, the DTH samples are mostly composed of fine clay particles that tend to have high phosphorus 

concentrations, so the significant relations may be a function of sampling method. Additionally, all eigenvalues were 

low, suggesting the biological community composition and 

structure were similar among the low to high nutrient sites. 

The composition of the different biological communities were 

dominated by species found in nutrient-rich waters. Despite 

the low-end nutrient gradient reflected by the August samples, 

the biological communities reflect higher nutrient conditions 

found throughout the year. This finding suggests that a single 

nutrient sample collected during the low-nutrient period of 

July–September may not accurately reflect the nutrient condition of a stream and could be misleading. A better measure 

of the nutrient condition within a stream would be one that 

encompasses the amount of nutrients that affect the biological communities throughout the year, such as annual mean 

or median concentrations. It also suggests that even though 

a low-end nutrient gradient was found, the annual nutrient 

concentrations may not be low enough to cause changes in the 

biological communities; that is, fewer algivores and omnivores. Dodds and others (2002) found significant breakpoints 
18 Biological-Community Composition in Small Streams in Indiana and Ohio, 2004

for TP (0.03 mg/L) and TN (0.04 mg/L) with periphyton 

chlorophyll a. Almost all of the TP data were higher than 

0.03 mg/L, and all of the TN data were well above the 

0.04 mg/L threshold. Additionally, the CCA results indicate 

that habitat is important when assessing streams for all four 

biological communities. 

Biological Indicators

Findings from the gradient analysis showed that the 

biological communities were similar (as indicated by the low 

eigenvalues. The observed changes in the biological community composition were often in response to non-nutrient gradients. Consequently, the most significant gradients from each 

biological community were examined to determine whether 

changes could be detected in species composition and structure along each gradient. In all cases, the biological communities were dominated by taxa found in nutrient-rich systems 

(Ward, 1992; Feminella and Hawkins, 1995; Petersen and 

Femmer, 2002; Lowe, 2003). For example, in this study more 

than 65 percent of the DTH algal-diatom communities and 

more than 66 percent of the RTH algal-diatom communities 

were dominated by three diatom genera (Amphora, Navicula, 

and Nitzschia), the invertebrate communities were dominated 

by the order of Ephemeroptera (almost 50 percent), and the 

fish communities were dominated by Campostoma and Pimephales species (almost 37 percent). Mueller and Spahr (2006) 

showed that streams within Ecoregion 55, which includes 

a part of the study area, have some of the highest N and P 

loadings within the United States. Within these streams, about 

25 percent of the TN and TP concentrations were greater than 

the USEPA proposed criteria, so it was not surprising to find 

communities dominated by eutrophic species. It was surprising that corresponding shifts in community composition and 

structure along the observed nutrient gradient were not apparent, these findings support the idea that streams sampled in the 

study were nutrient-saturated, resulting in communities dominated by eutrophic species. Furthermore, even though nutrient 

and biological samples were collected in periods known to 

have lower nutrients, the biological community reflected the 

periods of higher nutrient concentrations often associated with 

runoff from spring and winter periods within the study area.

Because there were no differences in the biological 

communities along the nutrient gradient, biological communities were assessed along the physical habitat gradients to see 

whether those gradients could account for the differences in 

the community composition and structure. The changes in 

the invertebrate communities were related to changes within 

instream (channel) substrate types, and changes in the fish 

community were related to stream/basin size. One dominant 

invertebrate group was Dipterans, which are often found in 

freshwater habitats with eutrophic conditions; consequently, 

Dipterans have been widely used as a pollution indicator 

(Ward, 1992). In the current study, as the pool-to-riffle ratio 

increased, the number of Dipterans decreased. Because the 

invertebrate samples were collected from riffle habitats with 

gravel/cobble substrates, it may be that as the pool-to-riffle 

ratio increases, restricting substrate availability, the number 

of Dipteran taxa would decrease because, in part, of the loss 

of substrate variability (Ward, 1992). The fish communities, as mentioned earlier, were also dominated by eutrophic 

species. In a study of Ozark streams, Petersen and Femmer 

(2002) found that Campostoma species, which are common 

in eutrophic environments, can be the dominant species in 

nutrient-rich streams because they are able to make use of the 

increased algal growth associated with increased nutrients. 

However, if nutrients are at saturation levels, other species 

requirements such as substrate type and water clarity (all 

shown to be associated with stream/basin size) can account for 

species abundances. No specific species were identified that 

suggest nutrient enrichment because the biological communities were similar and dominated by eutrophic species. Changes 

in composition and structure were most often the response of 

physical habitat changes.

Assessing Nutrient Rich Midwest Streams 

The sites in this study, which are typical of Midwest 

streams, are in agriculturally dominated landscapes with 

elevated concentrations of nutrients (Mueller and Spahr, 

2006). Understanding the complex interrelations among nutrients, environmental variables, and biological communities is 

difficult for these streams because these waters often are nutrient saturated, resulting in communities that are dominated by 

similar eutrophic species. Because all the streams in this study 

are dominated by eutrophic species, the small differences in 

community composition and structure tended to be a function 

of physical habitat rather than nutrients. 

In this study, the ranges of nutrients found during the 

low-nutrient August period resulted in a gradient that should 

have been suitable for a nutrient gradient study. However, the 

biological communities reflected the higher nutrient conditions 

present throughout the year. Multiple nutrient samples at a site 

would more accurately assess the annual nutrient condition of 

a stream. Single nutrient samples collected during the lownutrient period of June–September could be misleading. For 

example, in this study, if the biological community data had 

not been included in the study design, the nutrient data alone 

would have suggested that many streams are not nutrient-rich. 

However, biological communities reflect the long-term effects 

within an aquatic ecosystem, and the biological community 

data are therefore useful in assessing potential nutrient enrichment in the nutrient-rich Midwest.

For gradient studies to work effectively in the nutrientrich Midwest, reference sites with low-annual nutrient 

concentrations need to be found. In the Midwest, reference 

sites are difficult to find because most of the land is flat, has 

fertile soils, and is conducive to agriculture. This results in 

application of fertilizer to fields that subsequently is transported into streams through several pathways. A possible 
References Cited 19

target for low-nutrient conditions may be the 0.03 mg/L for 

TP and 0.04 mg/L for TN found by Dodds and others (2002). 

Although these low-end nutrient levels were not found at the 

30 sites in this study, they have been found in other areas of 

the Midwest.

It was noteworthy that the most influential nutrients were 

also significantly related to physical-habitat characteristics. 

Caskey (2003) found that in agriculturally-dominated landscapes, relations among physical habitat can mask or affect the 

response of anthropogenic variables that could influence biological conditions. Often, habitat drives which species can live 

in a stream. The intent of this study, however, was to select 

sites that were as physically similar as possible to minimize 

the effects of habitat along a nutrient gradient. Despite the 

physical similarity between sites, the physical variables still 

explained the differences among the biological communities 

of streams better than the nutrient concentrations because the 

study sites were nutrient saturated.

Whether increases in nutrients have a negative impact on 

biological-community dynamics depends, in part, on the environmental setting. For example, in a headwater stream with 

elevated nutrient concentrations and a closed canopy, light 

attenuation may be limited to the extent that changes in algaldiatom community composition may not occur regardless of 

nutrient concentrations. This study demonstrates that many 

Midwestern agricultural streams are more habitat limited than 

nutrient limited and that the biological communities contain 

numerous eutrophic indicators that could be important for 

tracking future changes in nutrient conditions. It also suggests 

that if habitat is not included as part of stream assessments for 

nutrient criteria, even if nutrient reductions are made, habitat 

limitations could preclude improvements in the biological 

communities. 

Conclusions

The objective of this study was to relate algal-, invertebrate-, and fish-community composition in small streams 

to habitat, nutrients, and land-use variables in agriculturallydominated landscapes of the Midwest in Indiana and Ohio. 

To minimize the variability associated with non-nutrient 

variables, thirty sample locations were selected from a single 

ecoregion (Ecoregion 55-USEPA Level III Eastern Corn Belt 

Plains) in wadable streams, and when possible, with similar 

substrate and canopy cover, along a previously determined 

nutrient-concentration gradient. Biological and nutrient 

samples were collected during stable flow conditions in 

August 2004. Canonical correspondence analysis was used to 

determine which variables most influenced each community. 

Total phosphorus concentrations significantly influenced the 

depositional-targeted habitat (DTH) algal-diatom community 

and the richest-targeted habitat (RTH) invertebrate community, 

although the significant relations with the DTH algal-diatom 

community may be related to sampling the fine sediments as 

part of the DTH sampling methods. Multivariate statistical 

analysis showed that habitat variables were more influential to 

the richest-targeted habitat algal-diatom and fish communities 

than nutrient concentrations. Although the nutrient concentrations measured during the stable low flow period of August 

indicate that most streams were not eutrophic, the biological 

communities were dominated by eutrophic species, suggesting 

streams sampled were eutrophic. This also suggests that the 

biological community reflected the periods of higher nutrient 

concentrations often associated with runoff from spring and 

winter periods within the study area. This study demonstrates 

that many Midwestern agricultural streams are more habitat 

limited than nutrient limited. Furthermore, if habitat is not 

included as part of stream assessments for nutrient criteria, 

even if nutrient reductions are made, habitat limitations could 

preclude improvements in the biological communities.

Acknowledgments

Fish community data was collected in cooperation with 

the Indiana Department of Environmental Management, 

Division of Water, Assessment Branch. The authors thank 

U.S. Geological Survey personnel for collecting the data. 

We also thank Mark Munn of the U.S. Geological Survey for 

technical advice during this project. Eric Smith of the Virginia 

Polytechnic Institute and State University and Karen Murray 

of the U.S. Geological Survey, New York Water Science Center are thanked for serving as colleague reviewers. 

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