In cooperation with the Wisconsin Department of Natural Resources

Nutrient Concentrations and Their Relations to the

Biotic Integrity of Nonwadeable Rivers in Wisconsin 

U.S. Department of the Interior

U.S. Geological Survey

Professional Paper 1754

Nutrient Concentrations and Their 

Relations to the Biotic Integrity of 

Nonwadeable Rivers in Wisconsin 

By Dale M. Robertson, Brian M. Weigel, and David J. Graczyk 

In cooperation with the Wisconsin Department of Natural Resources 

Professional Paper 1754 

U.S. Department of the Interior

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

DIRK KEMPTHORNE, Secretary

U.S. Geological Survey

Mark D. Myers, Director

U.S. Geological Survey, Reston, Virginia: 2008

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Suggested citation: 

Robertson, D.M., Weigel, B.M., and Graczyk, D.J., 2008, Nutrient concentrations and their relations to the biotic 

integrity of nonwadeable rivers in Wisconsin: U.S. Geological Survey Professional Paper 1754, 81 p. 

Library of Congress Cataloging-in-Publication Data

Nutrient concentrations and their relations to the biotic integrity of nonwadeable rivers 

 in Wisconsin / by Dale M. Robertson ... [et al.]; in cooperation with the Wisconsin 

 Department of Natural Resources.

p. cm. -- (Professional paper ; 1754)

Includes bibliographical references.

ISBN 978-1-4113-2171-7 (alk. paper)

1. Water quality biological assessment--Wisconsin. 2. Nutrient pollution of water--Wisconsin. 

 3. Rivers--Wisconsin. 4. Stream ecology--Wisconsin. I. Robertson, Dale M. 

 II. Series: Professional paper (Geological Survey (U.S.)) ; no. 1754. 

QH96.8.B5R63 2008

577.6’427--dc22

 2008023937

ISBN 978-1-4113-2171-7 
iii

Contents

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

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

Purpose and Scope ..................................................................................................................... 6

Approach....................................................................................................................................... 9

Methods of Data Collection and Analysis ........................................................................................ 9

Field Methods............................................................................................................................... 9

Discharge, Water Chemistry, and Suspended Chlorophyll a Concentrations.......... 9

Macroinvertebrates ......................................................................................................... 11

Fish ...................................................................................................................................... 11

Data Summaries......................................................................................................................... 11

Watershed Boundaries and Environmental Characteristics.............................................. 12

Statistical Methods.................................................................................................................... 12

Normalization..................................................................................................................... 14

Correlations and Regressions ........................................................................................ 14

Simultaneous Partial Residualization............................................................................ 14

Regression-Tree Analysis to Define Thresholds or Breakpoints.............................. 15

Redundancy Analysis....................................................................................................... 15

Statistical Differences among Groups .......................................................................... 17

Graphical Techniques ............................................................................................................... 17

Water Quality and Its Relations with Environmental Characteristics in the Watershed........ 18

Relations between Water Quality and Environmental Characteristics in 

the Watershed .............................................................................................................. 19

Correlations between Individual Characteristics........................................................ 19

Effects of Multiple Environmental Characteristics on Water Quality ...................... 25

Stepwise Regression .............................................................................................. 25

Redundancy Analysis.............................................................................................. 30

Environmental Characteristics Most Strongly Related to Water Quality ................ 33

Thresholds in Water-Quality Responses and Responses to Changes in Land Use........ 33

Reference Water Quality .......................................................................................................... 35

Macroinvertebrate Assemblages and Their Relations with Water-Quality and 

Environmental Characteristics............................................................................................ 39

Relations with Individual Characteristics.............................................................................. 42

Correlations........................................................................................................................ 42

Response to Changes in Nutrient Concentrations...................................................... 42

Effects of Multiple Characteristics on Macroinvertebrate Indices .................................. 47

Stepwise Regressions ..................................................................................................... 47

Redundancy Analysis....................................................................................................... 47

Reference Values for the Macroinvertebrate Indices......................................................... 49
iv

Fish Assemblages and Their Relations with Water-Quality and Environmental 

Characteristics....................................................................................................................... 53

Relations with Individual Characteristics.............................................................................. 53

Correlations........................................................................................................................ 53

Responses to Changes in Nutrient Concentrations.................................................... 57

Effects of Multiple Characteristics on Fish Indices ............................................................. 57

Stepwise Regressions ..................................................................................................... 57

Redundancy Analysis....................................................................................................... 61

Reference Values for the Fish Indices ................................................................................... 61

Multiparameter Biotic Indices to Estimate Nutrient Concentrations in 

Nonwadeable Rivers ............................................................................................................ 61

Summary of Results for Nonwadeable Rivers and Wadeable Streams .................................... 65

Water Quality.............................................................................................................................. 67

Response in the Biotic Communities...................................................................................... 68

Conclusions.......................................................................................................................................... 72

Literature Cited.................................................................................................................................... 72

Appendixes .......................................................................................................................................... 76

Figures

1. Maps showing two regionalization schemes considered for streams and 

rivers in Wisconsin: A, level III ecoregions with major land-use/land-cover 

categories and B, environmental phosphorus zones .................................................... 4

2. Map showing sites on nonwadeable rivers in Wisconsin included in this 

study. Water-quality and biological data for each site are given by siteidentification number in the appendixes ....................................................................... 10

3. Graphs showing definition of water-quality thresholds in responses of 

biotic indices to changes in water quality: A, biotic indicator with an 

abrupt threshold and B, biotic indicator with a broad threshold............................... 16

4–6. Maps showing:

4. Distributions for median monthly total phosphorus and dissolved 

phosphorus concentrations for the studied nonwadeable rivers 

in Wisconsin, 2003 .................................................................................................... 19

5. Distributions for median monthly total nitrogen total Keldahl nitrogen, 

dissolved nitrite plus nitrate nitrogen, and dissolved ammonia 

nitrogen for the studied nonwadeable rivers in Wisconsin, 2003 .................... 23

6. Distributions for median monthly suspended chlorophyll a and 

Secchi-tube depth for the studied nonwadeable rivers in 

Wisconsin, 2003 ........................................................................................................ 24

7–11. Graphs showing:

7. Percentages of variance in water quality described by land-use, 

basin, soil/surficial-deposit characteristics, interactions among 

categories, and unexplained variance for the studied nonwadeable 

rivers in Wisconsin................................................................................................... 31
v

8. Percentages of variance in A, suspended chlorophyll a

concentrations and B, Secchi-tube depths described by nutrients, 

environmental characteristics, interactions among categories, 

and unexplained variance for the studied nonwadeable rivers in 

Wisconsin .................................................................................................................. 32

9. Logarithmically transformed total phosphorus and total nitrogen 

concentrations as a function of the percentage of total agriculture 

in the watersheds of the studied nonwadeable rivers in Wisconsin, 

2003 ............................................................................................................................. 34

10. Logarithmically transformed suspended chlorophyll a

concentrations and Secchi-tube depths as a function of 

logarithmically transformed median total phosphorus and total 

nitrogen concentrations for the studied nonwadeable rivers in 

Wisconsin, 2003 ........................................................................................................ 36

11. Suspended chlorophyll a concentrations and Secchi-tube depths 

in Reference sites and High sites in the studied nonwadeable rivers 

in Wisconsin, 2003 .................................................................................................... 38

12. Maps showing distributions for four macroinvertebrate indices for the 

studied nonwadeable rivers in Wisconsin .................................................................... 41

13–17. Graphs showing:

13. Species richness, mean pollution tolerance value, percentage of 

individuals from the order Ephemeroptera, and Hilsenhoff Biotic 

Index values as a function of total phosphorus and total nitrogen 

concentration for the studied nonwadeable rivers in Wisconsin.................... 45

14. Species richness and mean pollution tolerance value as a function 

of dissolved phosphorus, dissolved nitrite plus nitrate nitrogen, 

dissolved ammonia nitrogen, and total Kjeldahl nitrogen 

concentrations for the studied nonwadeable rivers in Wisconsin.................. 46

15. Redundancy analysis results for macroinvertebrate indices 

and nutrients, other water-quality, and environmental characteristics: 

axis 2 scores are plotted as a function of axis 1 scores .................................... 50

16. Percentages of variance in 14 macroinvertebrate-index values 

described by nutrients, other water-quality characteristics, 

environmental characteristics, interactions), and unexplained 

variance for the studied nonwadeable rivers in Wisconsin.............................. 50

17. Species richness, mean pollution tolerance value, percentage of 

individuals from the order Ephemeroptera, and Hilsenhoff Biotic 

Index values in Reference sites and High sites in the studied 

nonwadeable rivers in Wisconsin ......................................................................... 52

18. Maps showing distributions for four fish indices for the studied 

nonwadeable rivers in Wisconsin .................................................................................. 54

19–24. Graphs showing:

19. Wisconsin large-river index of biotic integrity, percentage of suckers 

by weight, number of intolerant fish species, and percentage of 

individuals that are riverine species as a function of total 

phosphorus and total nitrogen concentration for the studied 

nonwadeable rivers in Wisconsin ......................................................................... 58
vi

Figures—Continued

20. Wisconsin large-river index of biotic integrity and percentage of 

suckers by weight as a function of dissolved phosphorus, dissolved 

nitrite plus nitrate nitrogen, dissolved ammonia nitrogen, and total 

Kjeldahl nitrogen concentrations for the studied nonwadeable rivers 

in Wisconsin .............................................................................................................. 59

21. Redundancy analysis results for fish indices and nutrients, other 

water-quality, and environmental characteristics: axis 2 scores are 

plotted as a function of axis 1 scores.................................................................... 62

22. Percentages of variance in 11 fish-index values described by 

nutrients, other water-quality characteristics, environmental 

characteristics, interactions, and unexplained variance for the 

studied nonwadeable rivers in Wisconsin ........................................................... 62

23. Wisconsin large-river index of biotic integrity, percentage of suckers 

by weight, number of intolerant fish species, and percentage of 

individuals that are riverine species in Reference sites and High sites 

in the studied nonwadeable rivers in Wisconsin................................................ 64

24. Measured and estimated A, total phosphorus and B, total nitrogen 

concentrations for the three-parameter regression models; 

C, measured phosphorus concentrations as a function of Biotic 

Index of total Phosphorus values; and D, measured nitrogen 

concentrations as a function Biotic Index of total Nitrogen values, 

with regression equations and coefficients of determination for 

the studied nonwadeable rivers in Wisconsin .................................................... 66 
vii

Tables

1. Reference concentrations for total phosphorus, total nitrogen, and 

suspended chlorophyll a concentrations, and turbidity in various 

ecoregions, Zones from the Upper Midwest study, and wadeable 

streams study ....................................................................................................................... 7

2. Summary statistics for median monthly water quality collected in 2003 

and environmental characteristics of the watersheds of the study sites 

in nonwadeable rivers in Wisconsin .............................................................................. 13

3. Median and average monthly concentrations for total, dissolved, and 

particulate phosphorus, suspended chlorophyll a, total nitrogen, nitrite plus 

nitrate, ammonia, Kjeldahl nitrogen, and Secchi-tube depth in the studied 

nonwadeable rivers in Wisconsin, 2003 ........................................................................ 20

4. Spearman correlation coefficients between median total, dissolved, and 

particulate phosphorus, total nitrogen, nitrite plus nitrate, ammonia, and 

total Kjeldahl nitrogen concentrations, median Secchi-tube depths, 

chlorophyll a concentrations, percentages of urban and agricultural areas, 

and specific environmental characteristics for the studied nonwadeable 

rivers in Wisconsin............................................................................................................ 21

5. Spearman correlation coefficients between residualized logarithmically 

transformed median total, dissolved, and particulate phosphorus, nitrogen, 

nitrite plus nitrate, total Kjeldahl nitrogen, ammonia, and chlorophyll a

concentrations, median Secchi-tube depths, and specific residualized 

environmental characteristics for the studied nonwadeable rivers in 

Wisconsin ........................................................................................................................... 26

6. Results from forward stepwise-regression analyses to explain variability 

in raw and residualized water-quality concentrations in the studied 

nonwadeable rivers in Wisconsin .................................................................................. 28

7. Results from redundancy analysis between water-quality and environmental 

characteristics for the studied nonwadeable rivers in Wisconsin........................... 31

8. Thresholds or breakpoints in the response in water quality to changes in 

the percentage of agricultural area in the basin for nonwadeable rivers 

in Wisconsin ....................................................................................................................... 35

9. Reference conditions for water-quality constituents for nonwadeable 

rivers in Wisconsin............................................................................................................ 37

10. Summary statistics for biotic indices for nonwadeable rivers in Wisconsin.......... 40

11. Spearman rank correlation coefficients between macroinvertebrateassemblage indices and median water-quality, land-use, basin, and 

soil/surficial-deposit characteristics for the studied nonwadeable rivers 

in Wisconsin ....................................................................................................................... 43

12. Thresholds or breakpoints in the responses in macroinvertebrate indices to 

changes in nutrient concentrations for nonwadeable rivers in Wisconsin ............ 48

13. Results from forward stepwise-regression analyses to explain variance in 

macroinvertebrate indices for the studied nonwadeable rivers in Wisconsin....... 48

14. Reference conditions for six macroinvertebrate indices for nonwadeable 

rivers in Wisconsin............................................................................................................ 51

15. Spearman rank correlation coefficients between fish-community 

indices and median water-quality, land-use, basin, and soil/surficialdeposit characteristics for the studied nonwadeable rivers in Wisconsin............. 55
viii

16. Thresholds or breakpoints in the responses of fish indices to changes 

in nutrient concentrations for nonwadeable rivers in Wisconsin............................. 60

17. Results from forward stepwise-regression analyses to explain variance 

in fish indices for the studied nonwadeable rivers in Wisconsin.............................. 60

18. Reference conditions for six fish indices for nonwadeable rivers in 

Wisconsin ........................................................................................................................... 63

19. Results from forward stepwise-regression models to explain variances 

in total phosphorus and total nitrogen concentrations with biotic indices 

for the studied nonwadeable rivers in Wisconsin ....................................................... 65

20. Reference conditions for water-quality constituents and biotic indices 

for nonwadeable rivers in Wisconsin ............................................................................ 69

21. Summary of thresholds or breakpoints in the responses of water quality 

and biotic indices to changes in nutrient concentrations for nonwadeable 

rivers in Wisconsin............................................................................................................ 71

Appendixes

1. Stream identification, location information, and summary statistics for 

flow and water-quality data collected in 2003 for each of the 42 studied 

nonwadeable rivers in Wisconsin .................................................................................. 76

2. Macroinvertebrate indices for each of the 41 studied nonwadeable rivers 

in Wisconsin ....................................................................................................................... 78

3. Fish indices and ratings for each of the 41 studied nonwadeable rivers in 

Wisconsin ........................................................................................................................... 80
ix

Conversion Factors

Multiply By To obtain

Length

micrometer (μm) 0.00003927 inch (in.)

millimeter (mm) 0.03927 inch (in.)

centimeter (cm) 0.3937 inch (in.)

meter (m) 3.281 foot (ft) 

kilometer (km) 0.6214 mile (mi)

Area

square kilometer (km2

) 247.1 acre

square kilometer (km2) 0.3861 square mile (mi2)

Volume

liter (L) 0.2642 gallon (gal)

cubic meter (m3

) 35.31 cubic foot (ft3)

Flow

cubic meter per second (m3

/s) 70.07 acre-foot per day (acre-ft/d) 

cubic meter per second per 

square kilometer ](m3

/s)/km2]

91.49 cubic foot per second per 

square mile [(ft3/s)/mi2]

Mass

gram (g) 0.03527 ounce, avoirdupois (oz)

kilogram (kg) 2.205 pound avoirdupois (lb)

microgram (μg) 0.00000003527 ounce, avoirdupois (oz)

milligram (mg) 0.00003527 ounce, avoirdupois (oz)

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

follows:

°F=(1.8×°C)+32

Specific conductance is given in microsiemens per centimeter at 25 degrees Celsius.

Concentrations of chemical constituents in water are given either in milligrams per liter 

(mg/L) or micrograms per liter (μg/L).
x

Abbreviations and Symbols

Ag % percentage of agricultural land in basin

AIC Akaike’s Information Criterion

BIN Biotic Index of total Nitrogen

BIP Biotic Index of total Phosphorus

DEM digital elevation model

DFA Driftless Area, level III ecoregion

DP dissolved phosphorus

EPT Ephemeroptera, Plecoptera, or Trichoptera (insect orders) 

EtOH ethanol

EV explained variance

EWI equal-width-increment

GIS geographic information system

N nitrogen

NCHF North Central Hardwood Forest, level III ecoregion

NLF Northern Lakes and Forests, level III ecoregion

NH4-N dissolved ammonia

NO3-N dissolved nitrite plus nitrate

NWIS National Water Information System

p probability

P phosphorus

PP particulate phosphorus

r Pearson correlation coefficient

r

s

 Spearman correlation coefficient

R2 coefficient of determination

Res residualized (X*)

RDA redundancy analysis

SCHL suspended chlorophyll a

SD Secchi-tube depth

SSC suspended sediment concentration

SWTP Southeastern Wisconsin Till Plains, level III ecoregion

STATSGO State Soil Geographic database

STORET Storage and Retrieval database

TKN total Kjeldahl nitrogen

TN total nitrogen

TP total phosphorus

Urb % percentage of urban land in basin
xi

USEPA U.S. Environmental Protection Agency

USGS U.S. Geological Survey

WDNR Wisconsin Department of Natural Resources

< less than

Abbreviations for Macroinvertebrate Indices

%CHIRON percentage of individuals from the family Chironomidae, within the order 

 Diptera, from the class Insecta 

%DEPOS percentage of individuals tolerant to depositional habitat

%DIPT percentage of individuals from the order Diptera, from the class Insecta

%EPHEM percentage of individuals from order Ephemeroptera, from the class 

 Insecta

%EPTN percentage of individuals that are EPT 

%EPTTX percentage of EPT taxa

%GATHER percentage of individuals that are gatherers (feeding function)

HBI Hilsenhoff Biotic Index

MPTV mean pollution tolerance value

%PLEC percentage of individuals from the order Plecoptera, from the class 

 Insecta

%SCRAP percentage of individuals that are scrapers (feeding function)

%SHRED percentage of individuals that are shredders (feeding function)

SPECIES species richness

%TRICHOP percentage of individuals from the order Trichoptera, from the class 

 Insecta

Abbreviations for Fish Indices

#INTOL number of species considered intolerant of degradation

#NATIVESP number of native species (not exotic)

#RIVERSP number of riverine species

#SUCKER number of sucker species in the family Catastomidae 

%DISEASE percentage of fish with disease or deformities

%INSECT percentage of total biomass accounted for by insectivores

%LITSPAWN percentage of individuals that are lithophilic spawners

%RIVERSP percentage of total biomass accounted for by individuals that are 

 riverine species

%SUCKER percentage of total biomass accounted for by round-bodied suckers 

 (genera Cycleptus, Hypentelium, Minytrema, and Moxostoma) 

IBI Wisconsin large-river index of biotic integrity

WPUE weight of fish caught per unit effort (excluding tolerant fish) 
xii

Acknowledgments

Technical Reviewers

J. Kent Crawford, Water-Quality Specialist, U.S. Geological Survey, 

New Cumberland, Penn.

Kenneth Schreiber, Watershed Expert, Wisconsin Department of Natural Resources, 

Eau Claire, Wis.

Editorial and Graphics

Mary S. Ashman, Technical Editor, U.S. Geological Survey, Northborough, Mass.

Jennifer L. Bruce, Geographer, U.S. Geological Survey, Middleton, Wis.

Michelle M. Greenwood, Reports Specialist, U.S. Geological Survey, Middleton, Wis.

James L. Kennedy, Geographer, U.S. Geological Survey, Middleton, Wis.

David A. Saad, Hydrologist, Geographer, U.S. Geological Survey, Middleton, Wis.

Approving Official 

Dorothy H. Tepper, Bureau Reports Approving Official, Eastern Region, U.S. Geological 

Survey, Reston, Va. 
1 Nutrient Concentrations and Their Relations to the Biotic Integrity of Nonwadeable Rivers in Wisconsin

Abstract

Excessive nutrient [phosphorus (P) and nitrogen (N)] 

input from point and nonpoint sources is frequently associated with degraded water quality in streams and rivers. 

Point-source discharges of nutrients are fairly constant and 

are controlled by the U.S. Environmental Protection Agency’s (USEPA) National Pollutant Discharge Elimination 

System. To reduce inputs from nonpoint sources, agricultural performance standards and regulations for croplands 

and livestock operations are being proposed by various 

States. In addition, the USEPA is establishing regionally 

based nutrient criteria that can be refined by each State to 

determine whether actions are needed to improve water 

quality. More confidence in the environmental benefits of 

the proposed performance standards and nutrient criteria 

would be possible with improved understanding of the 

biotic responses to a range of nutrient concentrations in 

different environmental settings. 

To achieve this general goal, the U.S. Geological Survey and the Wisconsin Department of Natural Resources 

collected data from 282 streams and rivers throughout 

Wisconsin during 2001 through 2003 to: (1) describe how 

nutrient concentrations and biotic-community structure 

differ throughout the State, (2) determine which environmental characteristics are most strongly related to the 

distribution of nutrient concentrations and biotic-community structure, (3) determine reference conditions for water 

quality and biotic indices for streams and rivers in the 

State, (4) determine how the biotic communities in streams 

and rivers in different areas of the State respond to differences in nutrient concentrations, (5) determine the best 

regionalization scheme to describe the patterns in reference conditions and the corresponding responses in water 

quality and the biotic communities (primarily for smaller 

streams), and (6) develop algorithms to estimate nutrient 

concentrations in streams and rivers from a combination of biotic indices. The ultimate goal of this study is to 

provide the information needed to guide the development 

of regionally based nutrient criteria for Wisconsin streams 

and rivers. In this report, data collected, primarily in 2003, 

from 42 nonwadeable rivers are used to describe nutrient 

concentrations and their relations to the biotic integrity 

of rivers in Wisconsin. In a separate report by Robertson 

and others (2006a), the data collected from 240 wadeable 

streams are used to describe these relations in streams in 

Wisconsin. 

Reference water-quality conditions for nonwadeable rivers were found to be similar throughout Wisconsin 

(approximately 0.035 milligrams per liter (mg/L) for total 

P (TP), 0.500 mg/L for total N (TN), 4 micrograms per 

liter for suspended chlorophyll a (SCHL), and greater than 

110 centimeters for Secchi-tube depth (SD)). For each 

category of the biotic community (SCHL, macroinvertebrates, and fish), a few indices were more strongly related 

to differences in nutrient concentrations than were others. 

For the indices most strongly related to nutrient concentrations, reference conditions were obtained with a regression 

approach, from values corresponding to the worst 75th-percentile value from a subset of minimally impacted streams 

(streams having reference nutrient concentrations), and 

from the best 25th-percentile value of all the data. 

Concentrations of TP and TN in nonwadeable rivers 

increased as the percentage of agricultural land in the basin 

increased; these increases resulted in increased SCHL concentrations and decreased SDs. The responses in SDs and 

SCHL concentrations to changes in nutrient concentrations 

were similar throughout most of the State except in rivers 

in the southeastern part, where SCHL concentrations were 

lower than would be expected given their nutrient concentrations. Rivers in the southeastern part of the State had 

high concentrations of total suspended sediment compared 

to the SCHL concentrations. 

Nutrient Concentrations and Their Relations to the 

Biotic Integrity of Nonwadeable Rivers in Wisconsin 

By Dale M. Robertson1, Brian M. Weigel 

2

, and David J. Graczyk1 

1

 U.S. Geological Survey, Middleton, Wisconsin

2

 Wisconsin Department of Natural Resources, Madison, Wisconsin 
2 Nutrient Concentrations and Their Relations to the Biotic Integrity of Nonwadeable Rivers in Wisconsin

Many biotic indices responded to increases in nutrient 

concentrations, which indicates that nutrients have direct 

or indirect effects on the composition of the biotic community. Higher nutrient concentrations and poorer biotic 

index scores, indicative of poorer water quality, were 

found in agricultural areas in the southern half of the State. 

Most of the biotic indices were more strongly related to 

changes in TP concentrations than to changes in TN concentrations. Many of the responses to changes in nutrient 

concentrations were nonlinear and, therefore, thresholds 

or breakpoints were identified where a small change in 

nutrient concentrations corresponded to a relatively large 

change in the biotic communities. The thresholds in the 

responses to changes in TP concentrations ranged from 

0.03 to 0.15 mg/L, whereas thresholds to changes in 

TN concentrations ranged from about 0.5 to 2.0 mg/L. 

The thresholds for many of the biotic responses were only 

slightly higher than the reference TP concentrations estimated for rivers throughout the State. 

The biotic communities in a river reflect its overall 

ecological integrity; they integrate the effects of many 

different stressors and thus provide a broad measure of 

the stressors’ aggregate effect. Nutrient concentrations by 

themselves, however, explained only 1–11 percent of the 

total variance in the components of the biotic communities 

or about 2–25 percent of the explained variance. Nutrient 

concentrations were most important in affecting SCHL 

concentrations. 

Three biotic indices were combined to create two new 

multiparameter indices [Biotic Index of total Phosphorus 

(BIP) and Biotic Index of total Nitrogen (BIN)] to estimate 

TP and TN concentrations from biotic data collected in the 

rivers. The BIP predicted TP concentrations better than the 

BIN predicted TN concentrations (63 and 51 percent of the 

variances, respectively). The difference in the accuracy of 

these indices was consistent with biotic indices that were 

more correlated with TP concentrations than with TN concentrations. This result indicates that P is more important 

than N in affecting most biotic communities in rivers.

Distributions of water quality and biotic indices for 

nonwadeable rivers, in general, were similar to those 

found for wadeable streams, with best conditions in the 

northern (forested) part of the State. The main differences between wadeable streams and nonwadeable rivers 

include: nonwadeable rivers had a smaller range in nutrient 

concentrations (less extreme concentrations, especially 

lower maximum concentrations), although median concentrations were similar; nonwadeable rivers had higher 

percentages of P and N in particulate forms; nonwadeable 

rivers had SCHL concentrations that were higher and had a 

stronger relation with nutrient concentrations; most biotic 

indices in nonwadeable rivers were more strongly related 

to nutrient concentrations; most biotic indices in nonwadeable rivers had a less consistent wedge-shaped response 

to changes in nutrient concentrations (the wedge-shaped 

response in wadeable streams resulted from biotic indices 

that ranged widely at low nutrient concentrations, but were 

consistently poor at high nutrient concentrations); and the 

biota in nonwadeable rivers had a slightly larger range in 

the thresholds in the responses to changes in TP concentrations.

Although specific mechanisms of how nutrients affect 

the biota in wadeable streams and nonwadeable rivers 

were not examined in this study, the results indicate that 

nutrients are important in controlling their biotic health. 

Although the biotic-community structure represents the 

overall ecological integrity of the stream or river, nutrients 

alone explained only a small part of the variance in the 

biotic community. Therefore, it is difficult to predict the 

exact result of reducing nutrient concentrations without 

also modifying the factors typically associated with high 

nutrient concentrations. Nutrient concentrations in many 

streams and rivers, especially in agricultural areas, are well 

above the threshold concentrations; therefore, small reductions in nutrient concentrations in these streams and rivers 

are not expected to have large effects on the biotic community. Even with these limitations, however, it is expected 

that reducing nutrient concentrations will improve the 

biotic communities of most streams and rivers, improve 

their beneficial ecological functioning, and improve the 

quality of downstream nutrient-limited receiving waters.

Introduction

Elevated concentrations of nutrients are some of the 

most common stressors (contaminants) affecting rivers and streams throughout the United States. Problems 

associated with elevated nutrient concentrations in surface 

water are not new, but they are among the most persistent. 

According to the National Water Quality Inventory: 1996 

Report to Congress by the U.S. Environmental Protection 

Agency (USEPA), 50 States, Tribes, and other jurisdictions surveyed water-quality conditions in 19 percent of 

the Nation’s 3.6 million miles of rivers and streams and 

found overenrichment of nutrients to be the second-most 

common reason for impairment after the combined effects 

of suspended sediment and siltation (U.S. Environmental 

Protection Agency, 1996). Excessive nutrients in rivers and streams can result in the overgrowth of benthic 
Introduction 3

algae in shallow areas and in areas with fast currents, and 

an overabundance of phytoplankton and macrophytes in 

deep areas with slow currents. High algal and macrophyte 

biomass can cause severe diurnal fluctuations in dissolved 

oxygen and pH associated with biotic production and respiration, and can cause low dissolved oxygen concentrations when part of the population dies (Welch and others, 

1992). Low dissolved oxygen concentrations, in turn, can 

cause an increase in the availability of toxic substances, 

reduction in available aquatic habitat, modifications to 

the composition of the biotic communities especially if 

fish die off, and a decrease in the overall usefulness of 

the stream (Miltner and Rankin, 1998; Dodds and Welch, 

2000). In addition to local effects, excessive transport of 

nutrients has also been linked to eutrophication of downstream lakes and impoundments, outbreaks of Pfiesteria in 

bays and estuaries in various Gulf and Mid-Atlantic States, 

and hypoxia in the Gulf of Mexico (U.S. Environmental 

Protection Agency, 2000a). 

Under recommendations of the Clean Water Action 

Plan released in 1998, the USEPA has developed a 

National strategy to develop waterbody-specific nutrient criteria for lakes and reservoirs, rivers and streams, 

wetlands, and estuaries (U.S. Environmental Protection 

Agency, 1998); this study is concerned with criteria for 

rivers and streams. The intention of this strategy is to get 

all States and Tribes to establish nutrient standards, that, if 

enforced, will reduce nutrient concentrations and improve 

the beneficial ecological uses of surface waters. The 

best way to control nutrient concentrations is to reduce 

the part contributed by humans, not the part contributed 

naturally (U.S. Environmental Protection Agency, 1998). 

Various environmental characteristics, such as land use, 

geology, soils, climate, and hydrology (including human 

modifications and hydrologic structures) are important in 

determining water quality (Monteith and Sonzogni, 1981; 

Clesceri and others, 1986; and Robertson, 1997). Because 

these characteristics differ greatly across the United States, 

the determination of regionally specific background or 

reference nutrient concentrations and the establishment of 

regionally specific nutrient criteria make scientific sense. 

The USEPA has taken the initial step in developing a 

regional framework for nutrient criteria based on combining Omernik’s 84 level III ecoregions into 14 national 

nutrient ecoregions for the conterminous United States 

(U.S. Environmental Protection Agency, 1998; Rohm and 

others, 2002). On a subregional basis, such as a specific 

State, each of these 14 nutrient ecoregions can be further 

subdivided into the original level III ecoregions. Wisconsin 

is subdivided into two national nutrient ecoregions (ecoregions 7 and 8) that are further subdivided into four level 

III ecoregions: Northern Lakes and Forests (NLF; national 

nutrient ecoregion 8), and North Central Hardwood Forests 

(NCHF), Southeastern Wisconsin Till Plains (SWTP), and 

the Driftless Area (DFA) that are in national ecoregion 7 

(Omernik and others, 2000; fig. 1A). In addition, Wisconsin includes small parts of the Western Cornbelt Plains 

and the Central Cornbelt Plains ecoregions (not labeled in 

fig. 1A). The nutrient ecoregions provide an initial regionalization scheme for developing nutrient criteria; however, 

the USEPA expects individual States and Tribes to evaluate 

and possibly develop alternative regionalization schemes 

(U.S. Environmental Protection Agency, 2000b). Robertson and others (2006b) demonstrated that the ecoregions 

in the Midwest primarily reflect differences in land use 

and developed a regionalization technique that removed 

the effects of land use. They subdivided the Midwest into 

five environmental phosphorus zones that were delineated 

primarily on the basis of differences in soils and surficial 

deposits (primarily based on differences in the amount of 

till and clay in the soils; fig. 1B). 

After relatively homogenous geographic areas have 

been chosen, several approaches can be used to define 

quantitative nutrient criteria. The approach suggested 

by the USEPA to define possible criteria is based on the 

reference or potential water quality of each area—in other 

words, on the conditions that are attainable in the geographic location of each river or stream (U.S. Environmental Protection Agency, 2000a). Reference concentrations 

for total phosphorus (TP), total nitrogen (TN), suspended 

chlorophyll a (SCHL, also referred to as sestonic chlorophyll), and a measure of turbidity have been defined from 

the frequency distribution of all available data (from 

USEPA’s Storage and Retrieval, STORET, database) for 

each national nutrient ecoregion and most level III ecoregions. It has been suggested that the lower (best water 

quality) 25th percentile of all data for an area may represent this reference condition (25th-percentile approach; 

U.S. Environmental Protection Agency, 2000b); that 

is, 25 percent of all the sites have water quality at least 

as good as this reference condition. Defining reference 

conditions based on this approach can result in reference 

nutrient concentrations that are biased high in predominantly agricultural areas where more than 75 percent of 

the sites are impacted by nutrient influx, and biased low in 

predominantly forested areas where fewer than 75 percent 

of the sites are impacted. Therefore, it has also been suggested that the upper (worst water quality) 75th percentile 

of the data for a subset of rivers or streams thought to 

be minimally impacted for a defined area may represent 
4 Nutrient Concentrations and Their Relations to the Biotic Integrity of Nonwadeable Rivers in Wisconsin

Figure 1. Two regionalization schemes considered for streams and rivers in Wisconsin: 

A, level III ecoregions (Omernik and others, 2000) with major land-use/land-cover 

categories (Wisconsin Department of Natural Resources, 1998) and 

B, environmental phosphorus zones (Robertson and others, 2006b).

Environmental phosphorus zones

B. A.

1

Zones

2345

Mostly cropland

EXPLANATIONLand use/land cover EXPLANATION

NorthCentralHardwoodForests(NCHF)

Northern Lakes and Forests

(NLF)

SoutheasternWisconsinTill Plains(SWTP)

Driftless Area

(DFA)

Mixed croplandMixed forestMostly forestUrbanWaterEcoregion boundary

Level III ecoregions

00 20 40 60 KILOMETERS

20 40 60 MILES

43°

46°

92°

88°

43°

46°

92°

88°

Green Bay

Milwaukee

Bayfield Peninsula

Madison

Green Bay

Milwaukee

Madison

Bayfield Peninsula
Introduction 5

this reference condition (75th-percentile approach); that 

is, 75 percent of the minimally impacted sites have water 

quality at least as good as this reference condition. 

Defining reference conditions based upon a percentile approach is arbitrary. An alternative approach to 

estimate reference concentrations is to develop a multiple 

linear-regression model from data for a specific area that 

relates water quality to anthropogenic factors or characteristics such as the percentages of agricultural and urban 

area in the watershed (Dodds and Oakes, 2004). With this 

approach, the estimated concentration of a constituent 

or value of an index in the absence of human activities 

(with 0-percent agricultural and 0-percent urban areas) 

represents the reference or background condition. These 

relations or equations can also be used to place confidence 

intervals on the reference conditions.

A few studies have shown observational linkages 

between nutrient concentrations and the health of the 

biotic communities in streams. Nutrients have been shown 

to directly affect the productivity and species composition 

of primary producers, such as macrophytes and benthic 

and suspended algae, and indirectly affect the primary and 

secondary consumers in controlled nutrient-enrichment 

experiments (for example, Mundie and Simpson, 1991; 

Peterson and others, 1993; Perrin and Richardson, 1997). 

Miltner and Rankin (1998) reported that macroinvertebrate- and fish-assemblage indices were negatively correlated with TN and TP concentrations in wadeable streams 

in Ohio. Zorn (2003) reported that TP was one of the 

important variables for predicting the presence or absence 

of specific fish species in Michigan streams. Heiskary and 

Markus (2003) also reported significant negative correlations between macroinvertebrate- and fish-assemblage 

characteristics and TP and TN concentrations in nonwadeable rivers in Minnesota. 

Most empirical relations between nutrient concentrations and biotic indices, trophic status, primary production, 

and food-web dynamics, however, have generated mixed 

results (for example, Vannote and others, 1980; Junk and 

others, 1989; Sedell and others, 1989; Dodds and others, 

1998; and Thorp and Delong, 2002). These mixed results 

demonstrate that models developed in specific areas may 

not be applicable elsewhere, or that the biotic community 

reflects more than just the nutrient concentrations that 

are or were present in the stream. The biotic community 

represents the overall ecological integrity of the stream 

(physicochemical habitat and biotic integrity) and thus 

provides a broad measure of the aggregate effect of all 

stressors (Barbour and others, 1999). The physicochemical 

habitats of the stream where the biota live are in 

turn controlled by watershed characteristics such as 

geomorphology, geochemistry, and land use/land cover; 

therefore, watershed characteristics are also important 

factors affecting the biotic community (Frissell and others, 

1986; Poff, 1997).

Because biota respond to stresses from multiple 

spatial and (or) time scales and several pathways including 

habitat and water chemistry, monitoring macroinvertebrate 

and fish communities, in addition to water quality, is valuable for determining natural and anthropogenic influences 

on stream and river resources (Karr, 1981; Ohio Environmental Protection Agency, 1987; Rosenberg and Resh, 

1993). Macroinvertebrate-based assessments have become 

important tools in rapid bioassessment protocols for 

indicating ecological condition (Plafkin and others; 1989, 

Kerans and Karr, 1994; DeShon, 1995; Weigel, 2003). 

Likewise, protocols for assessing ecosystem health often 

use combinations of fish-assemblage measures as indices of biotic integrity (Karr and others, 1986; Simon and 

Lyons, 1995; Lyons and others, 2001). Macroinvertebrate 

and fish assessments complement each other because one 

may be more responsive to particular stressors or spatial 

scales than the other (Davis and Simon, 1995; Barbour 

and others, 2000; Davies and Jackson, 2006). One of the 

goals of establishing nutrient criteria is to protect the biotic 

community. An alternative approach to define nutrient 

criteria is to use biotic models or measured data to define 

thresholds in the response between nutrient concentrations 

and biotic indices such as the amount of suspended algae 

in a stream or river (as quantified by SCHL), water clarity 

(as quantified by SD), or macroinvertebrate or fish biotic 

indices (U.S. Environmental Protection Agency, 2000a).

One of the greatest impediments to understanding 

nutrient–biota relations is that biota may respond to nutrient enrichment in the same way that they respond to other 

stressors (Yoder and Rankin, 1995; Karr and Chu, 1999). 

Furthermore, generally the high correlations between 

environmental variables make it difficult to differentiate spurious correlations from cause-and-effect relations 

(Miltner and Rankin, 1998; Wang and others, 2003; Dodds 

and Oakes, 2004). 

If relations between nutrient concentrations and 

biotic integrity are used to define nutrient criteria, the final 

criteria should be chosen to minimize degradation in the 

biotic integrity of rivers and streams. In other words, the 

criteria should be the nutrient concentrations that would 

not result in high algal concentrations or values of biotic 

indices that reflect a degraded environment. One of the 

difficulties in defining nutrient criteria is determining the 

values of the biotic index (such as a SCHL concentration) 
6 Nutrient Concentrations and Their Relations to the Biotic Integrity of Nonwadeable Rivers in Wisconsin

for which a river or stream is considered degraded or 

impaired. Whichever approach is used, the final criteria 

must be stringent enough to protect the specific site and 

cause no adverse effects in downstream waters. 

The USEPA developed preliminary criteria based 

on the 25th-percentile approach and the distribution of 

median concentrations of all the data measured at each site 

within a specified region rather than the mean concentrations. Median values were used because they represent 

the concentration most frequently present in the stream, 

and because a statistical summary based on median values 

reduces the effects of outliers and values reported as less 

than their respective detection limits. The USEPA has 

provided preliminary criteria for TP, TN, SCHL, and turbidity for the national nutrient ecoregions and most level 

III ecoregions (table 1; U.S. Environmental Protection 

Agency, 2000b and 2001). Robertson and others (2006b) 

developed an alternative regionalization scheme for the 

Midwest (environmental phosphorus zones) and estimated 

reference concentrations for each zone by use of multiple 

linear-regression models that related TP, TN, SCHL, and 

SD to two anthropogenic characteristics (percentages 

of agricultural and urban areas in the watershed; Dodds 

and Oakes, 2004). Robertson and others (2006b) found 

similar reference TP concentrations for the entire State, but 

reference concentrations of TN and SCHL and SD were 

different in areas with high clay-content soils (Zone 3; 

from Green Bay to Milwaukee in southeastern Wisconsin 

and Bayfield Peninsula in northwestern Wisconsin; table 1 

and fig. 1B). 

There would be more confidence in the potential 

environmental benefits of enforcing nutrient criteria 

and standards for streams and rivers in Wisconsin if the 

criteria and standards were based on the most appropriate 

regionalization scheme and if the criteria and standards 

reflect appropriate regionally defined thresholds to biotic 

response. Defined nutrient criteria and thresholds for biotic 

indices would enable the use of monitoring data to identify 

rivers and streams affected by excessive nutrients and 

would be useful to water-resource managers in directing 

rehabilitation efforts.

Purpose and Scope

In 2001, the U.S. Geological Survey (USGS) and the 

Wisconsin Department of Natural Resources (WDNR) 

began a collaborative study to (1) describe how nutrient 

concentrations and biotic-community structure in rivers 

and streams differ throughout Wisconsin, (2) determine 

which environmental characteristics of watersheds are 

most strongly related to the distribution of nutrient concentrations and biotic-community structure, (3) determine 

reference water-quality and biotic conditions for different 

areas of the State, (4) determine how the biotic communities of rivers and streams in different areas of the 

State respond to differences in nutrient concentrations, 

(5) determine the best regionalization scheme to describe 

the patterns in reference conditions and the corresponding responses in water quality and the biotic communities (primarily for smaller streams), and (6) develop new 

algorithms to predict nutrient concentrations in rivers and 

streams from a combination of biotic indices. The ultimate 

goal of this study is to provide the information needed by 

water-resource managers to develop regionally based nutrient criteria for rivers and streams in Wisconsin.

This study was divided into two parts (nonwadeable rivers and wadeable streams) because (1) the natural 

physicochemical and biotic attributes are not comparable 

between nonwadeable rivers and wadeable streams, (2) the 

biotic response was expected to vary as a function of 

stream size, and (3) biota in wadeable streams are sampled 

by means of different techniques than nonwadeable rivers. 

The first part of the study involved sampling 240 wadeable 

streams in 2001–03, and the second part involved sampling 

42 nonwadeable rivers in 2003. The results of the first 

part of this study were described in the USGS publication 

entitled “Nutrient concentrations and their relations to the 

biotic integrity of wadeable streams in Wisconsin” by Robertson and others (2006a) and the biotic relations are summarized in an article entitled “Linkages between nutrients 

and assemblages of macroinvertebrates and fish in wadeable streams: implication to nutrient criteria development” 

by Wang and others (2007). The second part of the study, 

“Nutrient concentrations and their relations to the biotic 

integrity of nonwadeable rivers in Wisconsin,” is presented 

in this report, and the biotic relations are summarized in 

an article entitled “Identifying biotic integrity and water 

chemistry relations in nonwadeable rivers of Wisconsin: 

towards the development of nutrient criteria,” by Weigel 

and Robertson (2007).
Introduction 

7

Table 1. Reference concentrations for total phosphorus, total nitrogen, and suspended chlorophyll a concentrations, and turbidity in various ecoregions (U.S. Environmental 

Protection Agency, 2000b and 2001), Zones from the Upper Midwest study (Robertson and others, 2006b), and wadeable streams study (Robertson and others, 2006a; Wang and others, 2007).

[USEPA, U.S. Environmental Protection Agency; NCHF, North Central Hardwood Forests; DFA, Driftless Area; SWTP, Southeastern Wisconsin Till Plains; mg/L, milligram per liter; μg/L, microgram per liter; >, greater than; --, no data]

Region USEPA criteria Upper Midwest study Wadeable streams study Median Upper 95-percent confidence limit 25th percentile of all sites Median Upper 95percent confidence limit 25th percentile of all sites

Total phosphorus (mg/L)

Ecoregion 7 0.033 0.016 0.024 0.040 0.038 0.049 0.058

NCHF-51a .029 -- -- -- .041 .060 .054DFA-52a .070 -- -- -- .040 .057 .053SWTP-53a .080 -- -- -- .025 .044 .072

Ecoregion 8 .010 .015 .019 .010 .032 .036 .024

Areas with high clay-content soils (Zone 3) -- .012 .017 .020 .029 .043 .053

Rest of State (Zones 1, 2, 4, and 5) -- .021–.023 .026–.030 .030–.060 .032–.042 .043–.054 .027–.061

Total nitrogen (mg/L, calculated/reported) Total nitrogen (mg/L, calculated)

Ecoregion 7 0.54/0.54 -- -- -- 0.775 0.974 1.555

NCHF-51a .46/.71 -- -- -- 1.132 1.637 1.188DFA-52a 1.88/1.51 -- -- -- .643 .901 1.489SWTP-53a 1.59/1.30 -- -- -- .811 1.409 2.020

Ecoregion 8 .20/.38 -- -- -- .509 .587 .403

Areas with high clay-content soils (Zone 3) -- -- -- -- .367 .601 1.318

Rest of State (Zones 1, 2, 4, and 5) -- -- -- -- .557–.690 .676–1.050 .486–1.780

Turbidity (nephelometric/formazin turbidity units) Secchi-tube depth (cm) b

Ecoregion 7 1.7/2.32 -- -- -- >120 >120 66.0

NCHF-51a .84/2.14 -- -- -- 104.4 116.9 102.3DFA-52a 3.38/2.4 -- -- -- 118.6 >120 67.5SWTP-53a --/2.74 -- -- -- >120 >120 47.8

Ecoregion 8 .81/1.3 -- -- -- 116.5 119.8 >120

Areas with high clay-content soils (Zone 3) -- -- -- -- 109.5 >120 51.5

Rest of State (Zones 1, 2, 4, and 5) -- -- -- -- >115 >120 65.3–>120
8 Nutrient Concentrations and Their Relations to the Biotic Integrity of Nonwadeable Rivers in Wisconsin Region USEPA criteria Upper Midwest study Wadeable streams study Median Upper 95-percent confidence limit 25th percentile of all sites Median Upper 95-percent confidence limit 25th percentile of all sites Chlorophyll a (μg/L, fluorometric/spectrophotometric/trichromatic methods) Chlorophyll a (μg/L, trichromatic method) Ecoregion 7 1.54/3.50/5.8 -- -- -- 1.50 1.94 1.78 NCHF-51a 1.03/8.76/-- -- -- -- 1.73 2.65 1.49 DFA-52a 1.00/2.32/-- -- -- -- 1.48 2.02 1.75 SWTP-53a 0.55/3.52/-- -- -- -- 1.44 2.71 2.02 Ecoregion 8 0.60/2.60/4.3 -- -- -- 1.49 1.74 1.08 Areas with high clay-content soils (Zone 3) -- -- -- -- 1.03 1.55 1.62 Rest of State (Zones 1, 2, 4, and 5) -- -- -- -- 1.17–1.71 1.93–2.23 1.37–1.86

a U.S. Environmental Protection Agency level III ecoregion identification numbers.b A lower 95-percent confidence limit and 75th percentile of all data are given here because higher values represent better conditions for Secchi-tube depth.

Table 1. Reference concentrations for total phosphorus, total nitrogen, and suspended chlorophyll a concentrations, and turbidity in various ecoregions (U.S. Environmental 

Protection Agency, 2000b and 2001), Zones from the Upper Midwest study (Robertson and others, 2006b), and wadeable streams study (Robertson and others, 2006; Wang and others, 2007)—Continued.

[USEPA, U.S. Environmental Protection Agency; NCHF, North Central Hardwood Forests; DFA, Driftless Area; SWTP, Southeastern Wisconsin Till Plains; mg/L, milligram per liter; μg/L, microgram per liter; >, greater than; --, no data]
Methods of Data Collection and Analysis 9

Approach

Wisconsin has approximately 40 nonwadeable rivers 

with a combined length of more than 2,500 km as river 

and 1,500 km as impounded habitat (Weigel and others, 

2006). A river reach is considered nonwadeable if it has 

more than 3 km of continuous channel too deep to sample 

effectively by wading during summer base flow. Because 

simultaneously collected hydrological, water-quality, and 

biological data were not available to determine how the 

biotic integrity of Wisconsin’s rivers is related to differences in nutrient concentrations, a network of sites was 

selected to represent the nonwadeable rivers throughout 

the State. The locations of the 42 sites on 35 nonwadeable 

rivers are shown in figure 2 and listed in appendix 1. Sites 

were chosen from throughout the State and represented 

the types of rivers and the kinds and intensities of stress 

upon each river type. The drainage basins of these rivers represent approximately 88 percent of the area of the 

State. Watersheds of several basins extend into adjacent 

States. Multiple sampling sites were chosen on several 

of the major rivers in the State; however, the sites were 

widely separated. The discharge and water quality at each 

site were sampled monthly during a 6-month period (May 

through October, 2003). Macroinvertebrate data were collected during summer 2003. Fish-population data, however, were not necessarily collected as part of this study; 

most data were available from past surveys. All fish-population data were collected between 1996 and 2004. 

For each site, the drainage basin upstream of the 

sampling site was digitized and a geographic information system (GIS) was used to describe the environmental 

characteristics of the watershed. Several multivariate 

statistical approaches were then used to determine how 

the environmental characteristics of the watershed were 

related to water quality and biotic-community structure. 

For the wadeable streams, the water-quality and biotic data 

were used to evaluate different regionalization schemes. It 

was previously found that there were slight differences in 

reference conditions for TN, SCHL, and water clarity in 

wadeable streams; however, reference TP concentrations 

were similar throughout the State (table 1; Robertson and 

others, 2006a). Streams in areas with high clay-content 

soils (Zone 3 in fig. 1) had slightly lower reference concentrations of TN and SCHL and poorer clarity, and the TP 

concentrations were more responsive to differences in land 

use than in streams in the rest of the State. Despite variation in nutrient concentrations among wadeable streams 

in different regions (especially lower nutrient concentrations in the northern part of the State), the responses of all 

of the biotic indices to changes in nutrient concentrations 

were similar throughout the State. The watersheds of the 

nonwadeable rivers, however, usually crossed several 

ecoregions and environmental phosphorus zones; therefore, different regionalization schemes were not evaluated 

in detail for the nonwadeable rivers. 

Reference concentrations for TP, TN, SCHL, water 

clarity (SD), and biotic indices for the nonwadeable rivers 

were estimated by use of the multiple linear-regression 

and percentile approaches. Water-quality data in nonwadeable rivers were statistically compared with biotic indices 

describing SCHL, macroinvertebrates, and fish to determine (1) how the biotic integrity of rivers is related to differences in nutrient concentrations, (2) whether thresholds 

in TP and (or) TN concentrations can be defined above 

which the biota are adversely affected, and (3) the relations between environmental variables and the structure 

of macroinvertebrate and fish communities. Two indices 

were then developed to estimate TP and TN concentrations 

in rivers on the basis of a combination of three indices 

describing the structure of the biotic community. Results 

from the nonwadeable rivers were then compared with 

those from the wadeable streams (Robertson and others, 

2006a; Wang and others, 2007). 

Methods of Data Collection and 

Analysis

Field Methods

Discharge, Water Chemistry, and Suspended 

Chlorophyll a Concentrations

Discharge and water quality in each river were 

sampled monthly over a 6-month period (May through 

October 2003). This 6-month period represents the typical 

growing season for Wisconsin’s rivers and streams. Each 

site was sampled near the middle of the month regardless 

of flow conditions. If the site had an operating continuousrecord gage, discharge was determined on the basis of the 

current stage/discharge relation (Rantz and others, 1982). 

If the site had a discontinued continuous-record gage, 

discharge was determined on the basis of the last stage/

discharge relation that was in effect when the gage was 

operational. If the site never had a continuous-record gage, 

discharge was estimated on the basis of relations developed between previous discharge measurements at the site 

and measurements, adjusted for the drainage-area ratio, 

from a nearby continuous-record gage.
10 Nutrient Concentrations and Their Relations to the Biotic Integrity of Nonwadeable Rivers in Wisconsin

Figure 2. Sites on nonwadeable rivers in Wisconsin included in this study. Water-quality and biological data for each site are 

given by site-identification number in the appendixes. 

Base map from U.S. Geological Survey 

digital data, 1:2,000,000.

0

0

20 40 60 KILOMETERS

20 40 60 MILES

44°

45°

43°

46°

92°

91°

90°

89°

88°

87°

EXPLANATION

Site with identification number

Basin

Stream

City

2

4

17

1

25

7

5

28

12

8

32

18

6

37

24

16

10

40

19

42

23 9

35 14

33

27

20

31

39

3

22

11

41

38

15

29

30

13

26

21

34

36

42

Green Bay

Milwaukee

Madison

Stevens Point

Eau Claire
Methods of Data Collection and Analysis 11

During each visit, field parameters (specific conductance (SC), water temperature, dissolved oxygen, and pH) 

were measured at the time of sampling by use of a multiparameter meter. The meters were calibrated daily. Water 

clarity was also measured by use of a 120-cm Secchi tube 

[also referred to as a transparency tube (U.S. Environmental Protection Agency, 2006)]. The Secchi tube was 

filled with water collected from near the surface of the 

river. The tube was then held perpendicular to the ground 

and drained until the Secchi disk at the bottom of the tube 

became visible. The water level in the tube was read to the 

nearest centimeter and defined as the Secchi-tube depth 

(SD). If the disk was visible when the tube was full, the 

value was reported as greater than 120 cm.

During each visit, water samples were collected 

with depth-integrating samplers according to the equalwidth-increment (EWI) method (Edwards and Glysson, 

1999). If the river was wadeable at the time of sampling, 

a handheld sampler was used. When the river was too 

deep or velocities too high, a cable-suspended sampler 

was used. Samples from each location in a transect were 

composited into a churn splitter and split into appropriate 

bottles for laboratory analysis. Samples to be analyzed for 

dissolved constituents were filtered in the field through 

0.45-μm membrane filters. Samples to be analyzed for 

SCHL were obtained by filtering a known volume of water 

through a 5-μm membrane filter. The filter was then placed 

in a labeled petri dish and wrapped in aluminum foil. All 

samples were chilled until they were delivered to the Wisconsin State Laboratory of Hygiene for analysis, except 

samples to be analyzed for SCHL, which were frozen, 

kept in the dark, and delivered to the WDNR Research 

Laboratory. All samples were analyzed for TP, dissolved 

phosphorus (DP), total Kjeldahl nitrogen (TKN), dissolved 

nitrite plus nitrate nitrogen (NO3

-N), dissolved ammonia 

nitrogen (NH4-N), SCHL, and suspended sediment (SSC).

Total nitrogen was computed as the sum of TKN and 

NO3-N. Particulate phosphorus (PP) was computed as the 

difference between TP and DP. All chemical analyses of 

water samples (except SCHL) were done by the Wisconsin 

State Laboratory of Hygiene in accordance with standard 

analytical procedures described in the “Manual of Analytical Methods, Inorganic Chemistry Unit” (Wisconsin State 

Laboratory of Hygiene, 1993). At the WDNR Research 

Laboratory, the filters for SCHL analysis were placed 

in tubes containing 90 percent acetone, stored at least 

24 hours, sonicated for 15 minutes, and stored an additional 24 hours in a freezer. The trichromatic chlorophyll a

(SCHL) concentration in the samples was determined 

by means of a USEPA-approved method (Greenberg and 

others, 1992). Throughout this report, the water-chemistry, 

water-clarity, and SCHL data are collectively referred to as 

“water-quality data.” 

Macroinvertebrates 

Macroinvertebrates samples were collected at each 

site by the WDNR by use of modified Hester-Dendy artificial-substrate samplers during summer 2003. Sampling 

methods were based upon those of the Ohio Environmental 

Protection Agency (1987) regarding sampler construction and deployment. Three samplers were deployed at 

each site and macroinvertebrates were allowed to colonize 

them for 6 weeks starting in mid-June. Samplers were then 

retrieved, organisms were scraped off, sample contents 

were combined, and the organisms were preserved with 

ethanol (EtOH) for laboratory processing. Samples were 

sorted and identified by J. Dimick at the laboratory of 

Dr. Stanley Szczytko at the University of Wisconsin, Stevens Point. A randomized grid-pan subsorting procedure 

(Hilsenhoff, 1988) in combination with a large-and-rare 

organism search (Vinson and Hawkins, 1996) was used 

to select at least 500 individual organisms and reduce the 

sample to a manageable size. All macroinvertebrates were 

identified to the lowest possible taxon. 

Fish 

Standard WDNR methods were used for collecting 

fish-population data (Lyons and others, 2001). Sampling 

occurred once in each river during daylight between June 

and September from 1996 to 2004. An electrofishing 

boat was steered downstream along one randomly chosen 

shore for 1.6 km, a distance at which estimates of species 

richness were shown to be asymptotic and insensitive to 

variation in sampling effort (Lyons and others, 2001). One 

person used a 19-mm (3/8-in. stretch-mesh) dip net and 

tried to capture all of the fish seen. All captured fish were 

identified, counted, weighed in aggregate by species, and 

then released, except for a few specimens to confirm species identification. All sites had considerable flow during 

sampling.

Data Summaries

All of the water-quality data collected in this study 

were input into the USGS National Water-Quality Information System (NWIS) database (U.S. Geological Survey, 

1998) and are summarized by median values for each site 

in appendix 1. Median values were computed from the 
12 Nutrient Concentrations and Their Relations to the Biotic Integrity of Nonwadeable Rivers in Wisconsin

six individual monthly measurements. All data reported 

as less than the detection limit were set to one-half of the 

detection limit, and all SD data greater than 120 cm were 

set to 120 cm prior to any statistical and graphical analyses.

Fourteen assemblage measures were computed to 

summarize the macroinvertebrate data, including species richness (1 index), habitat (1) and pollution tolerance 

(2), feeding ecology (3), and insect order (7). Tolerance 

to depositional zones or substrates and feeding ecologies 

were largely estimated based upon Merritt and Cummins 

(1996). The Hilsenhoff Biotic Index (HBI; Hilsenhoff, 

1988) and mean pollution tolerance value index (MPTV; 

Lillie and Schlesser, 1994) represent the stress response 

of the macroinvertebrate assemblage to organic pollution. 

HBI and MPTV are on a 0–10 scale, with 0 representing no apparent organic pollution. The macroinvertebrate 

index values for each site are summarized in appendix 2.

Eleven assemblage measures were computed to 

summarize the fish data, including number of species and 

percentage of individuals of native or riverine fish (three 

indices), number of species in the sucker family Catastomidae, percentage of the biomass accounted for by roundbodied suckers of the genera Cylceptus, Hypentelium, 

Minytrema, and Moxostoma (Lyons and others, 2001), 

number of species intolerant of degradation, weight per 

unit effort (WPUE; one unit of effort equates to 1.6 km; 

this value excludes tolerant fish as defined by Lyons and 

others, 2001), percentage of fish that spawn in stony 

substrate (lithophilic), percentage of fish biomass that are 

insectivores, percentage of fish with diseases, and an overall index of biotic integrity (IBI; Lyons and others, 2001). 

All of the sites were in warm-water reaches (except one 

site, described below), where summer water temperatures 

excluded resident salmonids. Therefore a warm-water IBI 

was computed (Lyons and others, 2001) that ranges from 

0 to 100 with qualitative categories at 20-point increments 

(for example, 80 to 100 represents an excellent fishery). 

The fish metrics for each site are summarized in appendix 3. Biotic data from one of the sites (White River near 

Ashland) was not used in the analyses because it had cold 

water and a cold-water fish assemblage that was not comparable to the warm-water assemblages at the other sites. 

Therefore, only 41 sites were used to examine the response 

of the biotic communities to differences in water quality.

Watershed Boundaries and Environmental 

Characteristics

Watershed boundaries for the sampled rivers were 

manually digitized from 1:24,000-scale USGS topographic 

quadrangle maps (Henrich and Daniel, 1983). Coverages 

of the environmental characteristics thought to affect the 

water quality and biota in the rivers were compiled for 

each watershed used in this study: land use/land cover 

(based on data collected in 1992; Wisconsin Department 

of Natural Resources, 1998), soil characteristics (from the 

USSOILS digital coverage of the State Soil Geographic 

data base, STATSGO; Schwarz and Alexander, 1995), 

types of surficial deposits (Fullerton and others, 2003), 

annual average air temperature and annual total precipitation (National Climatic Data Center, 2002), annual 

evaporation (Farnsworth and others, 1982), annual runoff 

(Gebert and others, 1987), river length (length of the river 

between the sampling location and the most upstream 

location identified on the 1:24,000 hydrology GIS coverage; Wisconsin Department of Natural Resources, 2004), 

and mean basin slope [computed based on 30-m digitalelevation-model (DEM) data resampled to 100 m at the 

sampling site and at the most upstream location identified 

on the 1:24,000 hydrology GIS coverage and the river 

length; U.S. Geological Survey, 1999; Wisconsin Department of Natural Resources, 2004].

Coverages of all basin characteristics were compiled 

in digital form by use of a GIS and were then used to 

compute the average or percentage value for each environmental characteristic for each of the 42 watersheds. A 

summary of the environmental characteristics (with the 

specific metric describing each environmental characteristic) for all of the watersheds used in this study is given in 

table 2. 

Statistical Methods

The SAS statistical software package (SAS Institute, 

Inc., 1989) was used for all statistical analyses except 

for the redundancy analyses, which were done with the 

CANOCO statistical software package (ter Braak and 

Smilauer, 2002), and the regression-tree analyses, which 

were done with the SPLUS statistical software package 

(Lam, 2001).
Methods of Data Collection and Analysis 13

Table 2. Summary statistics for median monthly water quality collected in 2003 and environmental characteristics of the 

watersheds of the study sites in nonwadeable rivers in Wisconsin.

[mg/L, milligram per liter; cm, centimeter; C, degrees Celsius; μS/cm, microsiemen per centimeter, %, percent; km2, square kilometer; mm, millimeter; 

mm/yr, millimeter per year; (m3/s)/km2, (cubic meter per second) per square kilometer; km, kilometer; mm/hr, millimeter per hour; >, greater than; 

--, unitless]

Characteristic Abbreviation Units Transformation Count Median Mean Standard 

deviation Minimum Maximum

Water-quality characteristics

Total phosphorus TP mg/L log 42 0.109 0.132 0.111 0.023 0.497

Dissolved phosphorus DP mg/L log 42 .041 .053 .042 .012 .156

Particulate phosphorus PP mg/L log 42 .053 .079 .077 .011 .391

Total nitrogen TN mg/L log 42 1.268 1.707 1.280 .266 5.485

Dissolved nitrite plus 

nitrate

NO3

-N mg/L log 42 .395 .837 1.036 .011 3.770

Dissolved ammonia NH4

-N mg/L log 42 .024 .034 .027 .007 .134

Total Kjeldahl nitrogen TKN mg/L log 42 .620 .848 .547 .255 2.850

Suspended 

chlorophyll a

SCHL μg/L log 42 7.31 18.47 25.3 1.74 130

Secchi-tube deptha SD cm none 42 60.5 71.8 40.0 12 >120

Suspended sediment SSC mg/L log 42 14.0 24.1 24.4 1.0 87.5

Water temperature WTemp C none 42 19.6 19.7 1.33 16.8 22.1

Specific conductance SC μS/cm none 42 295 364 234 78 904

pH pH standard none 42 8.18 8.15 .30 7.55 8.75

Color Color -- none 42 34.4 36.3 21.2 5 80

Land-use characteristics

Urban Urb % % none 42 0.005 0.013 0.023 0.000 0.109

Agriculture (row 

crops)

AgRow % % none 42 .122 .152 .134 .001 .479

Agriculture (other) AgOther % % none 42 .138 .146 .122 .004 .639

Total agriculture Ag % % none 42 .294 .298 .230 .007 .867

Grassland Grass % % none 42 .098 .095 .049 .002 .188

Wetland (open) WetO % % none 42 .074 .080 .046 .001 .211

Wetland (forested) WetF % % none 42 .079 .079 .057 .000 .222

Barren Barren % % none 42 .009 .011 .009 .000 .036

Forest (all) For % % none 42 .481 .471 .243 .067 .865

Basin characteristics

Watershed area Area km2 log 42 2,000 4,890 6,420 655 27,000

Air temperature ATemp C none 42 5.9 6.1 1.3 4.1 8.3

Precipitation PPT mm none 42 835 834 25.3 781 902

Evaporation Evap mm none 42 828 826 86.9 711 991

Runoff Runoff mm/yr none 42 255 260 49.1 356 181

River length Length km log 42 99.6 141 104 41.3 534

Basin slope BSlope degrees none 42 1.18 1.24 .737 .370 4.17

Flow per unit area Flow (m3

/s)/km2 log 42 .008 .011 .010 .003 .051
14 Nutrient Concentrations and Their Relations to the Biotic Integrity of Nonwadeable Rivers in Wisconsin

Characteristic Abbreviation Units Transformation Count Median Mean Standard 

deviation Minimum Maximum

Soil characteristics

Clay content SClay % none 42 12.4 13.8 6.71 5.69 30.6

Erodibility Erod -- none 42 .217 .218 .050 .146 .369

Organic-matter content OM % none 42 9.42 7.78 4.52 .393 15.2

Permeability Perm mm/hr none 42 4.67 4.60 1.62 1.24 8.28

Soil slope SSlope % none 42 6.59 7.44 3.17 3.76 15.1

Surficial deposits

Nonglacial deposits NonGlac % none 42 0.3 18.5 32.5 0.0 1.0

Clay SDClay % none 42 .1 6.7 9.9 .0 27.0

Loam Loam % none 42 .0 3.1 6.0 .0 30.8

Peat Peat % none 42 .0 .7 1.0 .0 3.8

Sand Sand % none 42 30.2 39.3 31.9 .0 97.1

Sand and gravel SG % none 42 24.0 31.7 28.4 .0 89.4

a

 All values greater than 120 cm were set to 120 centimeters for computation of summary statistics; as a result, the mean values were biased low.

Table 2. Summary statistics for median monthly water quality collected in 2003 and environmental characteristics of the 

watersheds of the study sites in nonwadeable rivers in Wisconsin—Continued.

[mg/L, milligram per liter; cm, centimeter; C, degrees Celsius; μS/cm, microsiemen per centimeter, %, percent; km2, square kilometer; mm, millimeter; 

mm/yr, millimeter per year; m3/s/km2, cubic meter per second per square kilometer; km, kilometer; mm/hr, millimeter per hour; >, greater than; 

--, unitless]

Normalization

Before statistical analyses were done, all waterquality data except the SD data were logarithmically 

transformed (base 10) to improve the normality of the data. 

The normality of the data improved, although not always 

to the 5-percent-significance level (Shapiro-Wilk normality test; SAS Institute, Inc., 1989). In addition, watershed 

areas were logarithmically transformed prior to statistical 

analyses.

Correlations and Regressions 

Spearman correlation analyses were used to determine the correlation (rs 

) between water-quality characteristics, biotic indices, and environmental characteristics. 

This nonparametric procedure was chosen to reduce the 

influence of the assumption of normal-data distributions. The statistical significance of each correlation was 

obtained by applying the Student’s t-test to the t statistic 

(Spiegel and others, 2000); correlations were also examined by accounting for the effects of the number of tests 

on the significance level by use of a Bonferroni correction 

(Tukey, 1977). Pearson correlation (r) analyses were also 

used to determine the relation between each water-quality 

characteristic and each environmental characteristic prior 

to the use of multiple regressions and forward stepwiseregression analyses [with a probability (p) less than 

(<) 0.05 as the critical level for entry]. This procedure was 

used to determine the magnitude of the interaction between 

environmental characteristics and water-quality characteristics, as well as to determine the best multivariate relation 

to estimate concentrations at a specific site as a function of 

the environmental characteristics in its watershed.

Simultaneous Partial Residualization

Other studies (such as Robertson and others, 2006b) 

have shown that land use in the watershed not only directly 

affects the water quality in a river, but it is often strongly 

correlated with the environmental characteristics used 

to define regions of similar water quality (through the 

indirect effects of land use). Therefore, in order to determine the relation between the water-quality characteristics 

and the nonanthropogenic or natural characteristics, a 

simultaneous partial-residualization approach (Robertson 

and others, 2006b), related to partial correlation, was used 

to remove agricultural and urban effects from the TP and 

TN concentrations and from the measures of each of the 

environmental characteristics. 
Methods of Data Collection and Analysis 15

In simple regression, the relation between the dependent variable Y (for example, logarithmically transformed 

TP) and a predictor variable X1 (for example, the clay 

content of the soil in the basin) can be measured by the 

sample correlation rYX1. If the variable X1 is regressed on 

the variable X2 (for example, the percentage of agricultural 

area), the estimated regression equation X1, 2 = β0 + β1X2

would be obtained. To adjust X1 for the effects of X2, a 

residualized X1, X1

*

, can be obtained by computing X1

*

 = 

X1 – X1, 2. In a manner similar to simple correlation, the 

strength of the relation between Y and X1 adjusted for X2

(in this case, land use) can be obtained by the correlation 

between the residuals for Y on X2 (Y*) and the residuals 

for X1 on X2 (X1

*

). The resulting correlation is the partial 

correlation of Y and X1 adjusted for X2; the strength of the 

relation between Y and X1

 has been adjusted for the effects 

of X2. This approach, described by Weisberg (1980), is 

easily extended to control for more than one variable; X2

can be replaced by an arbitrary set of variables. In this 

study, the residualization approach was used to remove the 

effects of the percentages of agricultural and urban areas to 

allow examinations of the relations between the dependent 

variables TP, TN, SCHL, and SD and all of the nonanthropogenic environmental characteristics. Residualizations of 

TP, TN, and SCHL were done on logarithmically transformed data to account for the nonlinear relations between 

water-quality concentrations and the percentages of agricultural and urban land uses. Spearman correlations and 

forward-stepwise regressions were done with raw data and 

with residualized data to determine which environmental characteristics best described the distribution of each 

water-quality characteristic. The residualization approach 

was not used to examine relations between environmental 

characteristics and the biotic indices.

Regression-Tree Analysis to Define 

Thresholds or Breakpoints

One approach to defining nutrient criteria is to 

identify thresholds or breakpoints in the response between 

nutrient concentrations and biotic indices (U.S. Environmental Protection Agency, 2000a). Defining a specific 

threshold or breakpoint in a response curve of a specific 

biotic index is straightforward if the curve is well defined 

and has an abrupt breakpoint (fig. 3A); however, the 

response curves in many biotic indices are poorly defined 

and have broad breakpoints (fig. 3B). In the case of indices 

with broad thresholds, it is very difficult to define the 

concentration at which the index first begins to change. 

For the biotic indices, the thresholds or breakpoints were 

defined as the concentrations at which the rate of response 

is greatest and, therefore, represents a critical concentration with ecological significance. Regression-tree analyses 

(Breiman and others, 1984) were used to determine thresholds or breakpoints (most abrupt responses) in the relations between nutrient concentrations and a biotic index. 

Regression-tree analysis sequentially partitions the data 

for each independent variable into two groups and examines the differences in the mean values of the dependent 

variable on the basis of the least-square-error criterion. 

The least-square-error criterion allows identification of 

breakpoints that maximize intergroup means relative to 

the intragroup variance. Only one independent variable 

(for example, TP or TN) and one dependent variable (for 

example, IBI) were used at a time; thus, the regression-tree 

analysis was forced to divide the data for the dependent 

variable into two groups with highly contrasting means 

relative to intragroup variances. To determine whether the 

intergroup means identified by the breakpoints in TP and 

TN concentrations were statistically different, two-sample 

student t-tests were done on the basis of assumed equal 

and unequal variances. 

Redundancy Analysis

Redundancy analysis (RDA) is a form of direct-gradient analysis that describes the variation between two multivariate data sets (for example, water-quality characteristics and biotic-assemblage data) as a function of multiple 

axes that are combinations of the explanatory characteristics (ter Braak and Prentice, 1988). The correlation of 

the explanatory characteristics with each axis indicates 

the strength of its relationship with the water-quality, fish, 

or macroinvertebrate characteristics. RDA is appropriate 

for data sets having short gradients and linear responses 

by the dependent variables. Detrended correspondence 

analysis demonstrated that gradient lengths were less than 

2.4, which indicated that RDA was the appropriate form of 

analysis to be used. RDA was used to determine the relative importance of specific explanatory characteristics to 

specific nutrient, fish, or macroinvertebrate characteristics 

and the most important characteristics within a specific 

category of characteristics (such as watershed characteristics). 
16 Nutrient Concentrations and Their Relations to the Biotic Integrity of Nonwadeable Rivers in Wisconsin

Figure 3. Definition of water-quality thresholds in responses of biotic indices to changes in water quality: A, biotic 

indicator with an abrupt threshold and B, biotic indicator with a broad threshold.

Low Moderate High

Low Moderate High

WATER-QUALITY CONSTITUENT (CONCENTRATION)

WATER-QUALITY CONSTITUENT (CONCENTRATION)

Defined threshold

ABRUPT 

THRESHOLD

Defined threshold

BROAD 

THRESHOLD

Excellent 

Moderate 

Poor 

Excellent 

Moderate 

Poor 

BIOTIC INDEX BIOTIC INDEX

EXPLANATION

First indication of biotic effects

Most rapid change in biotic index

Defined threshold/breakpoint

A.

B.
Methods of Data Collection and Analysis 17

In addition, partial RDA (Richards and others, 1996) 

was used to determine the fraction of the variance in the 

water-quality characteristics explained by specific categories of environmental characteristics (such as land-use, 

basin, and soil/surficial-deposit characteristics) and to 

determine the fraction of the variance in the macroinvertebrate- and fish-assemblage data explained by specific 

categories of environmental characteristics (such as 

nutrient characteristics and environmental characteristics). 

Monte Carlo permutation tests with 100 iterations, the 

default number of iterations in CANOCO, were used to 

determine the validity of the total and partial RDA results. 

Monte Carlo tests were done by randomly permutating the 

assignment of the independent (environmental) data to the 

dependent (water-quality or biological) data and repeating 

the ordinations (Richards and others, 1996; Johnson and 

others, 1997). 

Statistical Differences among Groups

To determine whether any apparent differences 

among groupings of data (such as sites with nutrient 

concentration near reference concentrations compared to 

sites with high nutrient concentrations) were statistically 

significant, a nonparametric Kruskal-Wallis rank-analysisof-variance test was used (SAS Institute, Inc., 1989). The 

probability of all statistically significant differences occurring by chance was less than 5 percent (p < 0.05), unless 

otherwise specified. 

Graphical Techniques

Bivariate scatterplots were used to demonstrate 

relations between specific variables graphically. On the 

bivariate scatterplots between nutrient concentrations 

and specific biotic indices, LOESS-smoothing lines with 

95-percent confidence intervals (computed with SAS) 

were used to highlight trends. LOESS is a nonparametric 

method of estimating regression surfaces while making 

no assumptions about the relation (Cleveland and others, 

1988). The default method within SAS was used to 

determine the value of the smoothing parameter in the 

LOESS fit, unless otherwise specified. The default method 

minimizes a bias-corrected Akaike’s Information Criterion 

(AIC) (Hurvich and others, 1998), which balances the 

residual sum of squares against the smoothness of the fit. 

A smoothing parameter larger than the default value was 

used in some cases to smooth the relation between two 

variables further. 
Water Quality and Its Relations with Environmental 

Characteristics in the Watershed 

U.S. Geological Survey personnel processing water samples and collecting streamflow measurements.

Median TP concentrations at the 42 sites ranged from 

0.023 to 0.497 mg/L (table 2, pages 13 and 14). The overall median and mean of the 42 individual medians were 

0.109 and 0.132 mg/L, respectively. Highest concentrations were measured in the southern and western parts of 

the State, and lowest concentrations were measured in the 

most northern rivers (fig. 4). There was a distinct seasonality in TP concentrations, with highest concentrations 

measured in July and lowest concentrations measured in 

October (table 3). 

Median DP concentrations ranged from 0.012 to 

0.156 mg/L (table 2). The overall median and mean were 

0.041 and 0.053 mg/L, respectively. Similar to TP, the 

highest concentrations were measured in the southern and 

western parts of the State, and lowest concentrations were 

measured in the most northern rivers (fig. 4). Median DP 

concentrations were strongly correlated to TP concentrations (rs

 = 0.89; table 4). However, unlike TP, there was 

little seasonality in DP concentrations (table 3). DP represented about 23 to 36 percent of the TP. 

Median TN concentrations ranged from 0.266 to 

5.485 mg/L (table 2). The overall median and mean were 

1.268 and 1.707 mg/L, respectively. Highest median TN 

concentrations were measured in the southern and western 

parts of the State and lowest concentrations were measured 

in the northern part of the State (fig. 5). Highest TN concentrations were measured in May and then concentrations 

slowly decreased as summer progressed (table 3). 

Median NO3

-N concentrations ranged from 0.011 

to 3.770 mg/L (table 2). The overall median and mean 

were 0.395 and 0.837 mg/L, respectively. Median NH4-N 

concentrations ranged from 0.007 mg/L (less than the 

0.013 mg/L detection limit) to 0.134 mg/L. The overall 

median and mean were 0.024 and 0.034 mg/L, respectively. Median TKN concentrations ranged from 0.255 to 

2.850 mg/L. The overall median and mean were 0.620 and 

0.848 mg/L, respectively. Nitrogen (N) was about equally 

divided between dissolved and particulate forms in all 

months. Highest NO3

-N, NH4-N, and TKN concentrations 

were generally found in southern parts of the State, and 

lowest concentrations were found in the northern part of 

the State except for NH4-N, for which lowest concentrations were found in the western and central parts of the 

State (fig. 5). 

18 Nutrient Concentrations and Their Relations to the Biotic Integrity of Nonwadeable Rivers in Wisconsin
Water Quality and Its Relations with Environmental Characteristics in the Watershed 19

Figure 4. Distributions (quintiles) for median monthly total phosphorus (TP) and dissolved phosphorus (DP) concentrations 

for the studied nonwadeable rivers in Wisconsin, 2003.

Total phosphorus (TP) Dissolved phosphorus (DP)

Concentration,

in milligrams per liter

Concentration,

in milligrams per liter

0.023 – 0.033

0.034 – 0.078

0.079 – 0.150

0.151 – 0.210

0.211 – 0.497

0.012 – 0.015

0.016 – 0.020

0.021 – 0.063

0.064 – 0.083

0.084 – 0.156

Median SCHL concentrations ranged from 1.74 to 

130 μg/L (table 2). The overall median and mean were 

7.31 and 18.47 μg/L, respectively. Highest SCHL concentrations were in the southern quarter of the State, and 

lowest concentrations were in northern part of the State 

(fig. 6). Highest SCHL concentrations were measured in 

August, and lowest concentrations were measured in May 

and October (table 3). 

Median SDs ranged from 12 cm to greater than 

120 cm (table 2). A few sites consistently had clarities 

greater than the length of the Secchi tube. The overall 

median and mean were 60.5 and 71.8 cm, respectively. 

Highest SDs (the best clarities) were in rivers in the 

northern quarter of the State, and lowest SDs (the worst 

clarities) were in rivers in the southern quarter of the State 

(fig. 6). Lowest SDs were measured during May through 

July, and the highest SDs were measured during September and October (table 3).

Relations between Water Quality and 

Environmental Characteristics in the 

Watershed 

Correlations between Individual 

Characteristics

Spearman correlation coefficients (rs values) between 

the water-quality characteristics are given in table 4. 

Concentrations of TP and DP were significantly correlated with TN, NO3

-N, and TKN (rs values ranged from 

0.51 to 0.89). Correlations between TP and the N species 

were slightly stronger than between DP and the N species. 

Concentrations of TN were strongly correlated to NO3-N 

and TKN (rs = 0.88 and 0.75, respectively) because each 

made up about 50 percent of the N. Concentrations of 

NH4

-N were more strongly correlated to TKN (rs = 0.40) 

than they were to TN and NO3-N (rs = 0.30 and 0.22, 

respectively). NH4-N is a larger part of TKN than of TN; 

therefore, a stronger correlation between NH4-N and TKN 

was expected.
20 Nutrient Concentrations and Their Relations to the Biotic Integrity of Nonwadeable Rivers in Wisconsin

Table 3. Median and average monthly concentrations for total, dissolved, and particulate phosphorus, suspended chlorophyll a, 

total nitrogen, nitrite plus nitrate, ammonia, Kjeldahl nitrogen, and Secchi-tube depth in the studied nonwadeable rivers in 

Wisconsin, 2003.

[All concentrations are in milligrams per liter, except chlorophyll a, which is in micrograms per liter, and Secchi-tube depth, which is in centimeters]

Month Total phosphorus Dissolved phosphorus Particulate phosphorus

Median Average Median Average Median Average

May 0.094 0.122 0.034 0.044 0.054 0.078

June .118 .142 .031 .052 .066 .090

July .136 .164 .045 .062 .064 .102

August .105 .145 .024 .060 .059 .086

September .110 .133 .039 .064 .048 .069

October .070 .099 .023 .043 .036 .057

May–October .109 .132 .041 .053 .053 .079

Month Suspended chlorophyll a Secchi-tube depth Total nitrogen

Median Average Median Average Median Average

May 6.95 17.82 57.0 67.6 1.443 1.956

June 8.15 24.97 56.0 64.5 1.369 1.653

July 7.71 27.83 55.5 66.1 1.376 1.983

August 11.3 28.71 59.0 73.1 1.133 1.538

September 7.71 14.53 76.0 74.8 1.260 1.790

October 5.74 16.10 94.5 85.2 1.193 1.525

May–October 7.31 18.47 60.5 71.8 1.268 1.707

Month Dissolved nitrite plus nitrate Dissolved ammonia Total Kjeldahl nitrogen

Median Average Median Average Median Average

May 0.435 0.997 0.036 0.054 0.840 0.959

June .337 .786 .043 .053 .710 .867

July .358 .984 .026 .052 .710 .999

August .199 .744 .019 .023 .520 .794

September .545 .975 .022 .057 .680 .814

October .358 .800 .007 .017 .555 .725

May–October .395 .837 .024 .034 .620 .848
Water Quality and Its Relations with Environmental Characteristics in the Watershed 21

Table 4. Spearman correlation coefficients (r

s ) between median total, dissolved, and particulate phosphorus, total nitrogen, nitrite plus nitrate, ammonia, and total Kjeldahl 

nitrogen concentrations, median Secchi-tube depths, chlorophyll a concentrations, percentages of urban and agricultural areas, and specific environmental (land-use, basin, soil, and surficial-deposit) characteristics for the studied nonwadeable rivers in Wisconsin.

[All r

s values with an absolute value greater than 0.41 are statistically significant (p < 0.05) with Bonferroni adjustment and r

s values with an absolute value greater than 0.30 are statistically significant with no 

Bonferroni adjustment; all r

s values with an absolute value greater than 0.6 are in bold]

Characteristic Total phosphorus Dissolved phosphorus Particulate phosphorus Total nitrogen Dissolved nitrite plus nitrate Dissolved ammonia Total Kjeldahl nitrogen Secchi-tube depth Suspended chlorophyll a concentration Percent urban Percent agriculture

Water-quality characteristics

Total phosphorus 1.00 0.89 0.95 0.89 0.74 0.20 0.69 -0.85 0.66 0.69 0.87

Dissolved phosphorus .89 1.00 .73 .79 .72 .28 .51 -.64 .42 .53 .77

Particulate phosphorus .95 .73 1.00 .84 .68 .12 .71 -.91 .75 .72 .83

Total nitrogen .89 .79 .84 1.00 .88 .30 .75 -.73 .59 .70 .92

Dissolved nitrite plus nitrate .74 .72 .68 .88 1.00 .22 .50 -.52 .37 .51 .79

Dissolved ammonia .20 .28 .12 .30 .22 1.00 .40 -.20 .16 .34 .27

Total Kjeldahl nitrogen .69 .51 .71 .75 .50 .40 1.00 -.75 .86 .87 .75

Suspended chlorophyll a .66 .42 .75 .59 .37 .16 .86 -.76 1.00 .83 .64

Secchi-tube depth -.85 -.64 -.91 -.73 -.52 -.20 -.75 1.00 -.76 -.75 -.75

Suspended sediment .90 .70 .94 .80 .65 .08 .63 -.89 .66 .62 .81

Water temperature .51 .34 .53 .43 .20 .09 .68 -.54 .70 .67 .56

Specific conductance .70 .60 .69 .81 .68 .40 .68 -.67 .51 .69 .85

pH .50 .32 .52 .46 .31 .03 .58 -.49 .63 .52 .58

Color -.61 -.56 -.57 -.52 -.61 .13 -.14 .48 -.23 -.28 -.54

Land-use characteristics

Urban .69 .53 .72 .70 .51 .34 .87 -.75 .83 1.00 .73

Agriculture (row crops) .85 .71 .82 .91 .74 .27 .80 -.76 .70 .77 .94

Agriculture (other) .85 .81 .77 .86 .81 .22 .62 -.66 .55 .59 .94

Total agriculture .87 .77 .83 .92 .79 .27 .75 -.75 .64 .73 1.00

Grassland .56 .44 .58 .54 .57 .05 .45 -.53 .45 .41 .50

Wetland (open) -.21 -.20 -.20 -.23 -.43 .11 .20 .07 .16 .05 -.27

Wetland (forested) -.82 -.68 -.82 -.68 -.62 .08 -.41 .76 -.47 -.48 -.73

Barren .31 .27 .27 .24 .13 .12 .47 -.29 .49 .47 .24

Forest (all) -.88 -.78 -.83 -.92 -.74 -.32 -.80 .78 -.68 -.76 -.95
22 Nutrient Concentrations and Their Relations to the Biotic Integrity of Nonwadeable Rivers in Wisconsin Characteristic Total phosphorus Dissolved phosphorus Particulate phosphorus Total nitrogen Dissolved nitrite plus nitrate Dissolved ammonia Total Kjeldahl nitrogen Secchi-tube depth Suspended chlorophyll a concentration Percent urban Percent agriculture Basin characteristics Watershed area -0.22 -0.26 -0.14 -0.23 -0.27 -0.23 0.06 0.11 0.20 0.04 -0.21 Air temperature .86 .71 .85 .87 .72 .24 .76 -.79 .67 .77 .93 Precipitation .76 .65 .76 .75 .69 .04 .47 -.66 .45 .51 .73 Evaporation .87 .76 .83 .81 .75 .08 .60 -.73 .57 .62 .85 Runoff -.90 -.80 -.87 -.87 -.77 -.20 -.70 .74 -.65 -.69 -.87 River length -.31 -.34 -.23 -.33 -.31 -.22 -.04 .23 .11 -.03 -.28 Basin slope -.28 -.18 -.32 -.23 -.03 -.01 -.41 .36 -.47 -.45 -.19 Flow per unit area -.49 -.44 -.46 -.44 -.33 -.10 -.34 .43 -.25 -.30 -.56 Soil characteristics Clay content .85 .73 .82 .80 .69 .16 .61 -.81 .56 .64 .92 Erodibility .73 .66 .68 .69 .59 .06 .50 -.65 .50 .51 .81 Organic-matter content -.68 -.59 -.66 -.60 -.67 .18 -.31 .48 -.42 -.34 -.64 Permeability -.64 -.63 -.56 -.57 -.52 -.01 -.29 .50 -.32 -.31 -.69 Soil slope -.08 -.09 -.05 -.18 .01 -.41 -.53 .13 -.37 -.33 -.23 Surficial-deposit characteristics Nonglacial deposits .58 .47 .59 .41 .45 -.34 .23 -.46 .34 .35 .45 Clay .09 -.04 .15 .27 .26 .48 .49 -.15 .24 .29 .24 Loam -.23 -.22 -.18 -.16 .00 -.07 -.13 .17 -.10 -.25 -.15 Peat -.41 -.27 -.44 -.51 -.46 -.15 -.27 .36 -.17 -.27 -.54

Sand -.07 -.03 -.08 -.15 -.30 -.01 .06 -.05 .17 .09 -.15

Sand and gravel -.44 -.45 -.39 -.17 -.18 .27 .04 .33 -.10 -.06 -.24

Table 4. Spearman correlation coefficients (r

s ) between median total, dissolved, and particulate phosphorus, total nitrogen, nitrite plus nitrate, ammonia, and total Kjeldahl 

nitrogen concentrations, median Secchi-tube depths, chlorophyll a concentrations, percentages of urban and agricultural areas, and specific environmental (land-use, basin, soil, and surficial-deposit) characteristics for the studied nonwadeable rivers in Wisconsin—Continued.

[All r

s values with an absolute value greater than 0.41 are statistically significant (p < 0.05) with Bonferroni adjustment and r

s values with an absolute value greater than 0.30 are statistically significant with no 

Bonferroni adjustment; all r

s values with an absolute value greater than 0.6 are in bold]
Water Quality and Its Relations with Environmental Characteristics in the Watershed 23

Figure 5. Distributions (quintiles) for median monthly total nitrogen (TN), total Keldahl nitrogen (TKN), dissolved nitrite plus 

nitrate nitrogen (NO3-N), and dissolved ammonia nitrogen (NH4-N) for the studied nonwadeable rivers in Wisconsin, 2003. 

Concentration,

in milligrams per liter

Concentration,

in milligrams per liter

0.266 – 0.571

0.572 – 1.152

1.153 – 1.790

1.791 – 2.810

2.811 – 5.485

Concentration,

in milligrams per liter

Concentration,

in milligrams per liter

0.011 – 0.085

0.086 – 0.288

0.289 – 0.609

0.610 – 1.275

1.276 – 3.770

0.255 – 0.490

0.491 – 0.580

0.581 – 0.715

0.716 – 1.100

1.101 – 2.850

0.007 – 0.019

0.020 – 0.024

0.025 – 0.038

0.039 – 0.048

0.049 – 0.134

Total nitrogen (TN) Kjeldahl nitrogen (TKN)

Dissolved nitrite plus nitrate-nitrogen

(NO3-N)

Dissolved ammonia-nitrogen

(NH4-N)
24 Nutrient Concentrations and Their Relations to the Biotic Integrity of Nonwadeable Rivers in Wisconsin

Figure 6. Distributions (quintiles) for median monthly suspended chlorophyll a (SCHL) and Secchi-tube depth (SD) for the 

studied nonwadeable rivers in Wisconsin, 2003.

Concentration,

in micrograms per liter

1.74 – 3.29

3.30 – 5.66

5.67 – 15.54

15.55 – 28.27

28.28 – 129.69

Depth, in centimeters

12.0 – 33.5

33.6 – 49.5

49.6 – 98.0

98.1 – 112.5

112.6 – >120

Suspended chlorophyll a (SCHL) Secchi-tube depth (SD)

All water-quality characteristics were significantly 

correlated with some environmental characteristics (landuse, basin, soil, and surficial-deposit characteristics; 

table 4); however, they were most strongly correlated with 

characteristics describing the land use (presence of agriculture or absence of forest), the basin (air temperature, precipitation, evaporation, and runoff), and the soil/surficial 

deposits (clay content, erodibility, organic-matter content, 

and permeability). In general, TN, NO3-N, TKN, TP, and 

DP were correlated with the same environmental characteristics. Concentrations of NH4

-N were less strongly 

correlated with the nonanthropogenic or natural environmental characteristics (basin and soil/surficial-deposit 

characteristics) than the other water-quality characteristics. 

Concentrations of NH4-N were most strongly correlated 

with soil slope and content of clay in the surficial deposits 

(rs = -0.41 and 0.48, respectively). 

Concentrations of SCHL were significantly correlated 

with most nutrient constituents: most strongly correlated 

with TKN and TP (rs = 0.86 and 0.66, respectively), less 

strongly correlated with TN, DP, and NO3-N (rs = 0.59 to 

rs = 0.37), and insignificantly correlated with NH4-N (rs = 

0.16). Concentrations of SCHL were significantly correlated with SDs (rs

 = -0.76), SSC concentrations (rs = 0.66), 

and pH (rs

 = 0.63). Concentrations of SCHL were significantly correlated with most environmental characteristics; 

however, they were most strongly correlated with land-use 

characteristics [positively correlated with the percentages 

of urban area (Urb %) and total agricultural area (Ag %), 

and negatively correlated with the percentage of forest 

(For %)], basin characteristics describing the air temperature, evaporation, and runoff from the watershed, and soil 

properties in the watershed (clay content and erodibility). 

SDs were most strongly correlated with many of the 

same characteristics as SCHL; however, SDs were more 

strongly correlated with most characteristics, and were 

especially more correlated with TN, DP, NO3-N, SSC, and 

clay content. Similar results were obtained if only sites 

with SDs less than 120 cm were included in the analysis; 

however, the correlation coefficients were slightly smaller. 

Many of the natural characteristics were strongly correlated with the land-use characteristics, primarily Ag %, 

and less strongly with Urb % (table 4). For example, air 

temperature, precipitation, evaporation, and runoff were 

strongly correlated with Ag %. Therefore, even if the landuse characteristics were not included in further statistical analyses, their effects could be incorporated into the 

final results by use of natural characteristics such as air 

temperature. To examine the relations between the natural 

characteristics and the water-quality characteristics further, 
Water Quality and Its Relations with Environmental Characteristics in the Watershed 25

the relations between the land-use characteristics (Ag % 

and Urb %) and TP, TN, SCHL, and SD were removed by 

use of simultaneous partial-residualization. Residualized 

TP, TN, and SCHL concentrations and SDs were computed 

with equations 1–8 (the coefficient of determination (R2) 

is given for each characteristic):

Log TPRes = Log TPMeasured – Log TPPredicted , (1)

where

Log TPPredicted = -1.455 + 1.384 Ag % + 

 0.659 Urb %, and (2)

 R2 = 0.72;

Log TNRes = Log TNMeasured – Log TNPredicted , (3)

where

Log TNPredicted = -0.289 + 1.252 Ag % + 

 2.044 Urb %, and (4)

 R2 = 0.84;

Log SCHLRes = Log SCHLMeasured – Log SCHLPredicted , (5)

where

Log SCHLPredicted = 0.596 + 1.052 Ag % – 

 5.608 Urb %, and (6)

 R2 = 0.41;

SDRes = SDMeasured – SDPredicted , (7)

where

SDPredicted = 110.4 – 115.9 Ag % – 

 307.9 Urb %, and (8)

 R2

 = 0.54.

Residual transformations were also applied to all of the 

other water-quality and environmental characteristics.

Residualized concentrations and SDs were still 

significantly correlated with many residualized environmental characteristics; however, they were not as strongly 

correlated (table 5). Residualized nutrient concentrations 

were most strongly correlated with residualized runoff and 

evaporation. In addition to these characteristics, residualized TN was significantly correlated with residualized clay 

content. 

The correlation coefficients between the residualized 

variables revealed that highest TP and DP concentrations 

were measured in areas with low runoff and high evaporation. Highest TN, NO3

-N, and TKN concentrations were 

measured in areas with low runoff, and soils or surficial 

deposits with low clay content, low erodibility, high 

permeability, and high amounts of sand and gravel. Highest NH4

-N concentrations were measured in areas with 

low evaporation and high organic-matter content. Highest SCHL concentrations were measured in long rivers 

in large watersheds with low basin slopes. Highest SDs 

were measured in areas with low evaporation and cool air 

temperatures. 

Many of the natural characteristics, such as clay 

content, erodibility, and permeability of the soil were 

so strongly correlated with Ag % that their relations to 

water quality may have been removed by the residualization approach. Therefore, the use of the residualization 

approach may not provide a complete description of the 

characteristics affecting water quality. 

Effects of Multiple Environmental 

Characteristics on Water Quality

Stepwise Regression 

Forward stepwise regressions were done with all of 

the environmental characteristics to determine which three 

environmental characteristics best described the variance 

in TP, TN, and SCHL concentrations and SDs, then with 

only the natural (non-land-use) characteristics, and then 

with residualized characteristics (whose correlations with 

land-use characteristics had been removed). Models with 

more than three variables did not significantly increase the 

amount of variance explained (accumulative R2 values). 

Runoff was the first variable incorporated into the TP 

model; the second and third variables were the percentages of forested wetland (WetF %) and For %, respectively 

(table 6). Collectively, this model explained 89 percent of 

the variance in TP concentrations. If the land-use characteristics were omitted from the analysis, runoff remained 

the first variable incorporated into the model, evaporation 

was second, and the percentage of clay deposits was third; 

these three variables explained 86 percent of the variance. After the characteristics were adjusted to remove the 

land-use effects, residualized runoff was the first variable 

incorporated into the model, residualized clay deposits 

was second, and residualized air temperature was third; 

these three variables explained 56 percent of the variance 

in residualized total phosphorus concentrations (TPRes). 
26 Nutrient Concentrations and Their Relations to the Biotic Integrity of Nonwadeable Rivers in Wisconsin Table 5. Spearman correlation coefficients (r

s) between residualized logarithmically transformed median total, dissolved, and particulate phosphorus, nitrogen, nitrite plus 

nitrate, total Kjeldahl nitrogen, ammonia, and chlorophyll a concentrations, median Secchi-tube depths, and specific residualized environmental (basin, soil, and surficial-deposit) characteristics for the studied nonwadeable rivers in Wisconsin.

[All r

s values with an absolute value greater than 0.41 are statistically significant (p < 0.05) with Bonferroni adjustment and r

s values with an absolute value greater than 0.29 are statistically significant with no 

Bonferroni adjustment; all r

s values with an absolute value greater than 0.41 are in bold]

Characteristic Total phosphorus Dissolved phosphorus Particulate phosphorus Total nitrogen Dissolved nitrite plus nitrate Dissolved ammonia Total Kjeldahl nitrogen Secchi-tube depth Suspended chlorophyll a

Water-quality characteristics

Total phosphorus 1.00 0.67 0.91 0.34 0.17 -0.10 0.34 -0.78 0.47

Dissolved phosphorus .67 1.00 .35 .27 .33 .11 -.06 -.34 -.03

Particulate phosphorus .67 .35 1.00 .27 .16 -.23 .41 -.80 .58Total nitrogen .34 .27 .35 1.00 .77 .05 .29 -.17 .07Dissolved nitrite plus nitrate .17 .33 .16 .77 1.00 .06 -.09 .03 -.17Dissolved ammonia -.10 .11 -.23 .05 .06 1.00 .22 .12 -.11Total Kjeldahl nitrogen .34 -.06 .41 .29 -.09 .22 1.00 -.37 .76Suspended chlorophyll a .47 -.03 .58 .07 -.17 -.11 .76 -.48 1.00Secchi-tube depth -.78 -.34 -.80 -.17 .03 .12 -.37 1.00 -.48Suspended sediment .70 .26 .81 .28 .22 -.22 .21 -.75 .34Water temperature .18 -.19 .32 -.11 -.28 -.16 .56 -.19 .63Specific conductance -.22 -.13 -.24 -.08 -.06 .27 .07 .06 -.20

pH .07 -.11 .14 -.16 -.13 -.19 .25 -.14 .32Color -.28 -.30 -.25 -.03 -.22 .33 .44 .24 .15

Basin characteristics

Watershed area .06 -.17 .09 -.04 -.14 -.15 .33 -.04 .40

Air temperature .43 .14 .49 .03 .03 -.23 .14 -.58 .20Precipitation .22 .08 .32 .23 .27 -.30 -.27 -.27 -.08Evaporation .46 .31 .51 .25 .25 -.47 -.08 -.41 .13Runoff -.47 -.34 -.48 -.41 -.47 .16 -.33 .29 -.30River length .03 -.17 .07 -.10 -.09 -.10 .31 -.10 .41Basin slope -.36 -.05 -.38 -.14 .18 .11 -.43 .29 -.45Flow per unit area -.02 -.03 .01 .27 .27 .06 .02 .01 .03
Water Quality and Its Relations with Environmental Characteristics in the Watershed 27

Characteristic Total phosphorus Dissolved phosphorus Particulate phosphorus Total nitrogen Dissolved nitrite plus nitrate Dissolved ammonia Total Kjeldahl nitrogen Secchi-tube depth Suspended chlorophyll a

Soil characteristics

Clay content -0.12 -0.14 -0.07 -0.49 -0.31 -0.20 -0.37 -0.14 -0.18

Erodibility -.03 .01 -.05 -.40 -.21 -.37 -.35 -.02 -.06

Organic-matter content -.24 -.27 -.25 -.14 -.40 .51 .43 .09 .08Permeability .01 -.08 .05 .39 .16 .26 .41 -.04 .16Soil slope .06 .09 .10 -.09 .10 -.32 -.58 -.09 -.36

Surficial-deposit characteristics

Nonglacial deposits .22 .21 .23 -.07 .07 -.42 -.42 -.21 -.23

Clay -.07 -.16 -.03 .21 .30 .15 .25 -.05 .06

Loam -.02 -.12 .03 -.02 .14 -.10 -.08 .07 .13Peat .03 .12 -.03 -.09 -.17 -.05 .20 -.01 .24Sand .10 .06 .05 -.24 -.37 -.09 .09 -.03 .25Sand and gravel -.28 -.30 -.24 .21 .10 .38 .29 .17 .07

Table 5. Spearman correlation coefficients (r

s) between residualized logarithmically transformed median total, dissolved, and particulate phosphorus, nitrogen, nitrite plus 

nitrate, total Kjeldahl nitrogen, ammonia, and chlorophyll a concentrations, median Secchi-tube depths, and specific residualized environmental (basin, soil, and surficial-deposit) characteristics for the studied nonwadeable rivers in Wisconsin—Continued.

[All r

s values with an absolute value greater than 0.41 are statistically significant (p < 0.05) with Bonferroni adjustment and r

s values with an absolute value greater than 0.29 are statistically significant with no 

Bonferroni adjustment; all r

s values with an absolute value greater than 0.41 are in bold]
28 Nutrient Concentrations and Their Relations to the Biotic Integrity of Nonwadeable Rivers in Wisconsin

Table 6. Results from forward stepwise-regression analyses to explain variability in raw and residualized water-quality 

concentrations in the studied nonwadeable rivers in Wisconsin.

[All regressions were on log-transformed concentrations; r, Pearson correlation coefficient; R2, coefficient of determination for the one-, 

two-, and three-variable models; Res, residualized characteristics]

Dependent variable First variable Second variable Third variable

Total phosphorus (TP)

All environmental characteristics

TP Runoff Wetland (forested) Forest (all)

r -0.90 -0.81 -0.87

Accumulative R2 .81 .87 .89

No land-use characteristics

TP Runoff Evaporation Clay deposits

r -.90 .88 -.08

Accumulative R2 .81 .85 .86

Residualized characteristics

TPRes RunoffRes Clay depositsRes Air temperatureRes

r -.67 -.15 .53

Accumulative R2 .45 .53 .56

Total nitrogen (TN)

All environmental characteristics

TN Forest (all) Runoff Wetland (open)

r -.92 -.89 -.27

Accumulative R2 .86 .89 .90

No land-use characteristics

TN Runoff Precipitation Sand-and-gravel deposits

r -.89 .76 -.22

Accumulative R2 .80 .83 .87

Residualized characteristics

TNRes RunoffRes Clay contentRes PrecipitationRes

r -.57 -.53 .25

Accumulative R2 .32 .55 .65

Suspended chlorophyll a (SCHL)

All environmental characteristics

SCHL Total Kjeldahl nitrogen Dissolved ammonia River length

r .89 .21 .18

Accumulative R2 .79 .83 .84

No land-use characteristics

SCHL Total Kjeldahl nitrogen Dissolved ammonia River length

r .89 .21 .18

Accumulative R2 .79 .83 .84

Residualized characteristics

SCHLRes Total Kjeldahl nitrogen Total nitrogen Organic-matter contentRes

r .53 .03 .08

Accumulative R2 .28 .56 .71
Water Quality and Its Relations with Environmental Characteristics in the Watershed 29

Dependent variable First variable Second variable Third variable

Secchi-tube depth (SD)

All environmental characteristics

SD Particulate phosphorus Suspended sediment Organic-matter content

r -0.92 -0.90 0.48

Accumulative R2 .84 .86 .88

No land-use characteristics

SD Particulate phosphorus Suspended sediment Organic-matter content

r -.92 -.90 .48

Accumulative R2 .84 .86 .88

Residualized characteristics

SDRes Air TemperatureRes Suspended sediment Total nitrogen

r -.52 -.49 -.03

Accumulative R2 .27 .41 .65

Table 6. Results from forward stepwise-regression analyses to explain variability in raw and residualized water-quality 

concentrations in the studied nonwadeable rivers in Wisconsin—Continued.

[All regressions were on log-transformed concentrations; r, Pearson correlation coefficient; R2, coefficient of determination for the one-, 

two-, and three-variable models; Res, residualized characteristics]

The difference in the amount of variance explained by the 

first two regression models was caused by the removal 

of the effects of the land-use characteristics that were not 

correlated with other environmental characteristics in the 

second model. The large difference between the amounts 

of variance explained by the last two regressions was 

caused by the removal of all of the effects of the land-use 

characteristics, including the independent (direct) effects 

and correlated (indirect) effects on the other variables. 

For % was the first variable incorporated into the TN 

model, runoff was the second, and WetO % was the third; 

these three variables explained 90 percent of the variance 

in TN concentrations. If the land-use characteristics were 

omitted from the analysis, runoff was the first variable, 

precipitation was the second, and the percentage of sandand-gravel deposits was the third. After the characteristics 

were adjusted to remove the land-use effects, residualized runoff was the first variable included in the model, 

residualized clay content was the second, and residualized precipitation was the third. This model collectively 

explained 65 percent of the variance in residualized total 

nitrogen concentrations (TNRes). 

For both of these constituents, removing the effects 

of land use moderately reduced the amount of variability 

explained by the models. When the direct and indirect 

effects of the land-use characteristics were included in 

the models, 89 to 90 percent of the variance could be 

explained with three variables; however, when all of the 

land-use effects were removed, the models explained about 

55 to 65 percent of the variance.

To develop regression models to predict SCHL and 

SD, all of the water-quality characteristics and environmental characteristics were included. The TKN concentration 

was the first variable incorporated into the SCHL model, 

NH4

-N was the second, and stream length was the third; 

these three variables explained 84 percent of the variance in 

SCHL concentrations. If the land-use characteristics were 

omitted from the analysis, similar results were obtained. 

After the environmental characteristics were adjusted 

to remove the land-use effects, TKN remained the first 

variable incorporated in the model; however, TN was the 

second variable, and residualized organic-matter content 

was the third; these three variables explained 71 percent of 

the total variance. 

Particulate phosphorus (PP) was the first variable 

incorporated into the SD model, SSC was the second, and 

organic-matter content was the third; these three variables 

explained 88 percent of the variance in SDs. If the landuse characteristics were omitted from the analysis, similar 

results were obtained. After the environmental characteristics were adjusted to remove the land-use effects, residualized air temperature was the first variable included in the 

model, SSC was the second, and TN was the third; these 

three variables explained 65 percent of the total variance. 
30 Nutrient Concentrations and Their Relations to the Biotic Integrity of Nonwadeable Rivers in Wisconsin

Removing the land-use effects from the environmental characteristics included in the analysis had little effect 

on the amount of variability explained by the SCHL and 

SD models (second model). The land-use characteristics 

were strongly correlated with nutrient concentrations, and 

therefore, a similar amount of variability was explained 

whether or not the land-use characteristics or the nutrient 

concentrations were included. The models used to predict 

residualized SCHL and SD provided less predictability 

because much of the variability in SCHL and SD was 

removed in the residualization process.

Redundancy Analysis

Each of the four primary water-quality characteristics 

(TP, TN, SCHL, and SD) has been shown to be related to 

the land-use characteristics and other characteristics of the 

watershed upstream from the assessment site. Partial RDA 

was used to determine the relative importance of each of 

the general categories of environmental characteristics to 

the distribution of overall water quality, as defined by these 

four water-quality characteristics. 

The three main categories of environmental characteristics—land-use characteristics, basin characteristics, 

and soil/surficial-deposit characteristics (table 2)—were 

used in the partial RDA. A two-step process was used to 

select three characteristics to describe each category. The 

characteristics that were most significantly correlated 

with the individual water-quality characteristics in each 

category were initially chosen. The final characteristics in 

each category were then chosen to have minimal correlations among themselves. For example, Ag % and For % 

were both strongly correlated with water quality; however, 

both were not chosen for the land-use category because 

they were strongly correlated to one another. The land-use 

category was described by Ag %, Urb %, and WetO %. 

The basin characteristics were described by watershed 

area (logarithmically transformed), runoff, and basin 

slope. Soils/surficial deposits were described by the clay 

content of the soils, organic-matter content of the soils, 

and soil slope. The land-use category reflects the extent of 

human intervention—characteristics that may be altered. 

The basin and soil/surficial-deposit categories reflect the 

topographical and geological effects—characteristics that 

cannot be altered.

The total variance in the four water-quality characteristics was separated into five categories: (1) variance 

explained by the land-use characteristics alone, (2) variance explained by soil/surficial-deposit characteristics 

alone, (3) variance explained by the basin characteristics 

alone, (4) variance explained by the interactions of landuse, soil/surficial-deposit, and basin characteristics (variance that could not be assigned to a single category), and 

(5) variance not explained by these characteristics. Results 

from the partial RDA indicated that the nine characteristics 

collectively explained 74 percent of the variance (p < 0.01) 

in water quality (TP, TN, SCHL, and SD). Independently, 

the land-use characteristics explained 9 percent of the total 

variance (12 percent of the explained variance; p < 0.05; 

fig. 7), the soil/surficial-deposit characteristics explained 

12 percent of the total variance (16 percent of the 

explained variance; p < 0.01), and the basin characteristics 

explained 10 percent of the total variance (14 percent of 

the explained variance; p < 0.01). The shared contribution 

or interactions of all three general categories of environmental characteristics explained 43 of the total variance 

(58 percent of the explained variance). Therefore, much of 

the variance in water quality could not be explained by a 

single category of environmental characteristics. 

RDA was also used to determine which of the environmental characteristics explained the most variance in 

overall water quality (TP, TN, SCHL, and SD). In RDA, 

as in principal-component analysis, the explained variance 

is separated into a series of ordination (canonical) axes. 

Almost all of the variance in this analysis was explained 

on the first canonical axis. The scores on the first axis 

(table 7) indicate the water-quality characteristics with 

the most explained variance and the importance of the 

individual environmental characteristics in explaining this 

variance. SD had the highest scores (absolute values) on 

the first canonical axis; these scores indicate that more of 

its variance was explained by the environmental characteristics than were the variances in TP, TN, and SCHL. The 

most important characteristics explaining the variance in 

these four water-quality characteristics in descending order 

of axis score were Ag %, runoff, clay content, organicmatter content, and Urb %. The relations between the 

environmental characteristics and water-quality characteristics can be determined by comparing their respective axis 

scores. Areas with low runoff, high Ag %, high Urb %, 

and soils with high clay content and low organic-matter 

content had the highest nutrient and SCHL concentrations 

and the worst water clarity. These results agree with the 

findings of the correlation and regression analyses.
Water Quality and Its Relations with Environmental Characteristics in the Watershed 31

Figure 7. Percentages of variance in water quality [total phosphorus (TP), total nitrogen (TN), and suspended chlorophyll a

concentrations (SCHL) and Secchi-tube depth (SD)] described by land-use, basin, soil/surficial-deposit characteristics, 

interactions among categories (variance that cannot be explained by a single category), and unexplained variance for the 

studied nonwadeable rivers in Wisconsin. [%, percentage of total variance]

9%

10%

12%

43%

26%

Land-use characteristics

General categories

Soil/surficial-deposit characteristics

Interactions among categories

Unexplained

Basin characteristics

Table 7. Results from redundancy analysis between waterquality and environmental (land-use, basin, and soil/surficial 

deposit) characteristics for the studied nonwadeable rivers in 

Wisconsin.

[log, logarithm to base 10 transformation]

First canonical 

axis score

Water-quality constituents

Total phosphorus (log) -0.79

Total nitrogen (log) -.79

Suspended chlorophyll a (log) -.67

Secchi-tube depth .86

Land-use characteristics

Total agriculture -.85

Urban -.52

Wetland (open) .17

Basin characteristics

Watershed area (log) .11

Runoff .85

Basin slope .27

Soil/surficial-deposit characteristics

Clay content -.84

Organic-matter content .56

Soil slope -.09

A multiple-regression approach (similar to partial 

RDA) was done to determine how the total variances in 

SDs and SCHL, independently, could be separated into 

four categories: (1) variance explained by nutrients alone 

(TP, DP, NO3-N, NH4-N, and TKN; PP and TN were not 

included because they were computed from the other 

constituents), (2) variance explained by environmental characteristics alone (the same nine environmental 

characteristics that were used in the partial RDA for 

water quality), (3) variance explained by the interactions 

between nutrients and environmental characteristics, and 

(4) variance not explained by these characteristics. In 

this approach, three regressions were done for SD and 

for SCHL: multiple regressions with all 15 variables, 

with only the 6 nutrient constituents, and with only the 

9 environmental characteristics. The first regression with 

all 15 variables was used to determine the total variance 

explained by all of the variables. The other two regressions 

were used to partition the variance among the three categories. The amount of variance explained by the interaction 

of the two categories was determined by equation 9:

EVInteractions = EVNutrients + EVEnvironmental – EVAll Variables , (9)

where EV is the variance explained by the specified group 

of variables. The variance explained by each subset of variables alone was then determined by subtracting the variance explained by the interactions between the variables 

from the total variance explained by a subset of variables. 
32 Nutrient Concentrations and Their Relations to the Biotic Integrity of Nonwadeable Rivers in Wisconsin

For example, the variance explained by the nutrients alone 

is equal to the explained variance from the nutrient regression minus the variance explained by the interactions:

EVNutrients Alone = EVNutrients – EVInteraction . (10)

Results from this analysis indicated that these 

15 characteristics collectively explained 90 and 94 percent 

of the variance in SCHL and SDs, respectively (fig. 8). 

Nutrients alone explained 22 and 19 percent of the total 

variance in SCHL and SDs, respectively. The environmental characteristics alone explained 6 to 7 percent of the 

variance in SDs and SCHL concentrations, and the interactions between the nutrients and environmental characteristics explained 62 to 68 percent of the total variance in both 

characteristics. Again, most of the total variability in these 

two parameters could not be explained by the nutrients 

alone. 

Figure 8. Percentages of variance in A, suspended chlorophyll a concentrations (SCHL) and B, Secchi-tube depths (SD) 

described by nutrients, environmental characteristics (land-use, basin, and soil/surficial-deposit characteristics), interactions 

among categories (variance that cannot be explained by a single category), and unexplained variance for the studied 

nonwadeable rivers in Wisconsin. [%, percentage of total variance]

19%

7%

68%

6%

22%

6%

62%

10%

Nutrients

General categories

Environmental characteristics

Interactions among categories

Unexplained

Variation in Secchi-tube depth (SD)

A. Variation in suspended chlorophyll a (SCHL)

B.
Water Quality and Its Relations with Environmental Characteristics in the Watershed 33

Environmental Characteristics Most Strongly 

Related to Water Quality 

Correlations, stepwise regressions, and redundancy 

analyses all indicated that the land-use characteristics 

(primarily For % and Ag %) were the characteristics most 

strongly related to water quality. Simply omitting the landuse characteristics and reanalyzing the data, however, may 

not provide a true indication of what other factors affect 

water quality because some of the remaining factors were 

strongly correlated with the land-use characteristics of the 

basins. For example, air temperature and clay content of 

the soil were both strongly correlated with many waterquality characteristics and with Ag % (table 4). Therefore, 

it is difficult to determine whether it was these factors or 

the indirect effects of agriculture that affected water quality. The clay content of the soil has been demonstrated to 

have a strong effect on the water quality of Midwestern 

streams (Robertson, 1997; Robertson and others, 2006b); 

however, the effects of air temperature seem questionable 

and may be indirectly related to the land-use characteristics. 

Various approaches (RDA and residualization analyses) were used to determine which environmental characteristics other than land-use characteristics were most 

strongly related to water quality. The results of partial RDA 

indicated that soil characteristics were important; however, 

much of the variance explained by soil characteristics was 

also explained by the land-use characteristics. Results of 

RDA indicated that the most important soil characteristics 

were the clay content and organic-matter content of the 

soils; however, many of the soil characteristics were correlated with one another. The results also indicated that the 

amount of runoff was strongly related to water quality. 

Results of the residualization analyses indicated that 

the natural (non-land-use) environmental characteristics 

most strongly related to the distribution of TP concentrations were runoff and basin slope. Air temperature and 

evaporation were also found to be important, but these 

factors may simply reflect their north/south gradients that 

also occurs in TP concentrations. The natural environmental characteristics most strongly related to the distribution 

of TN concentrations were runoff and the clay content and 

erodibility of the soils. In all cases, high nutrient concentrations were related to low annual runoff values (including 

residualized values). Lower amounts of total annual runoff 

and higher nutrient concentrations occurred in the southern 

part of the State than in the northern part. High nutrient 

concentrations typically occur during runoff events; however, the concentrations used in this study represent the 

typical (base-flow) condition.

The natural environmental characteristics most 

strongly related to the distribution of SDs were air temperature and evaporation, which was similar to that for TP. 

In addition to the land-use characteristics, results from the 

stepwise regressions indicated that the distribution of SDs 

was strongly related to SSC concentrations. The natural 

environmental characteristics most strongly related to the 

distribution of SCHL were the size and slope of the basin 

and the length of the river. All of these factors would affect 

the travel time of the water in the basin and allow different 

amounts of time for the algal community to consume nutrients. A few of the natural characteristics, however, such as 

clay content and erodibility of the soil, were so strongly 

correlated with Ag % that their relations to water quality 

may have been reduced by the residualization approach. 

Thresholds in Water-Quality Responses and 

Responses to Changes in Land Use

Concentrations of TP and TN were significantly 

correlated with Ag %. To define these relations better, 

Log TP and Log TN concentrations were plotted against 

Ag % (fig. 9), and regression-tree analyses were done to 

determine the percentages of agriculture that were the best 

breakpoints or thresholds in the responses. Regressiontree results indicate that the best statistically significant 

(p < 0.001) breakpoints in the responses of Log TP and 

Log TN to changes in Ag % were at 24.7 and 18.1 percent, 

respectively (table 8). In both cases, however, the relations 

between Log TP and Log TN concentrations and Ag % 

appear linear; the line determined with linear regression 

better defined the response than a step change in values 

(on the basis of a mean-square-error criterion).

Thresholds or breakpoints in the responses to changes 

in Ag % were also determined for the other logarithmically 

transformed water-quality constituents, including SSC 

(table 8). Thresholds ranged from as low as 8.8 percent 

agriculture for NO3

-N to 43.7 percent for NH4-N. In almost 

all cases, the relations between the concentrations and 

Ag % appear linear; however, for all of these additional 

constituents a step change better defined the response (on 

the basis of a mean-square-error criterion). 
34 Nutrient Concentrations and Their Relations to the Biotic Integrity of Nonwadeable Rivers in Wisconsin

Figure 9. Logarithmically transformed total phosphorus (TP) and total nitrogen (TN) concentrations as a function of the 

percentage of total agriculture (Ag %) in the watersheds of the studied nonwadeable rivers in Wisconsin, 2003. Computed 

thresholds in the response are identified by vertical lines. LOESS-smoothing lines and linear-regression lines with the 

coefficients of determination (R 

2

) are given on each graph.

y = 0.014x - 1.457

R2

 = 0.72

-2.0

-1.8

-1.6

-1.4

-1.2

-1.0

-0.8

-0.6

-0.4

-0.2

0.0

0 10 20 30 40 50 60 70 80 90 100

PERCENTAGE OF TOTAL AGRICULTURAL LAND USE IN BASIN 

y = 0.013x - 0.286

R2

 = 0.83

-1.0

-0.8

-0.6

-0.4

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

0 10 20 30 40 50 60 70 80 90 100

PERCENTAGE OF TOTAL AGRICULTURAL LAND USE IN BASIN 

LOG (TOTAL PHOSPHORUS, IN MILLIGRAMS PER LITER) LOG (TOTAL NITROGEN, IN MILLIGRAMS PER LITER)

Measured concentration

Threshold/breakpoint

LOESS-smoothing line

Linear-regression line
Water Quality and Its Relations with Environmental Characteristics in the Watershed 35

Concentrations of SCHL were significantly correlated 

with TP and TN concentrations (table 4). To better define 

these relations, Log SCHL concentrations were plotted 

against Log TP and Log TN concentrations (fig. 10), and 

regression-tree analyses were done. Log TP explained 

45 percent of the variance in Log SCHL concentrations 

and Log TN explained 35 percent of the variance. If the 

sites with the highest SSC to SCHL ratios in drainage 

areas primarily in the Driftless Area ecoregion (southwest 

part of the State) were omitted, then Log TP and Log TN 

explained 79 and 59 percent of the variance in Log SCHL 

concentrations, respectively. Regression-tree results indicated that the best breakpoint in the response of SCHL to 

changes in TP concentrations was at 0.064 mg/L (Log TP 

= -1.19), and at 0.927 mg/L (Log TN = -0.03) for changes 

in TN concentrations; both breakpoints were statistically 

significant at p < 0.001. The relations between SCHL and 

TP and TN concentrations appear linear; however, the 

step-change response better defines these relations (on the 

basis of a mean-square-error criterion). A similar response 

was found between TP and SCHL in temperate streams 

throughout North America, with SCHL concentrations 

increasing most rapidly at TP concentrations less than 

0.1 mg/L(Van Nieuwenhuyse and Jones, 1996).

To define the relations between SDs and TP and TN 

concentrations better, SDs were plotted against Log TP 

and Log TN concentrations (fig. 10) and regression-tree 

analyses were done. There was little apparent relation 

between SDs and TP and between SDs and TN at lower 

nutrient concentrations because of the limited length 

of the Secchi tube; however, as nutrient concentrations 

increased, SDs decreased. Overall concentrations of TP 

and TN explained 77 and 50 percent of the variance in 

SDs, respectively. Regression-tree results indicate that the 

best breakpoint in the response of SDs to changes in TP 

concentrations was at 0.091 mg/L (Log TP = -1.04) and to 

changes in TN concentrations was at 1.097 mg/L (Log TN 

= 0.04); both breakpoints were statistically significant at 

p < 0.001. A regression line defines the response for TP 

better than a step change; however, a step change defines 

the response better for TN (on the basis of a mean-squareerror criterion). The reduction in SDs with increasing 

nutrient concentrations may have been caused by other 

factors that are correlated to TP and TN concentrations, 

such as the SSC concentrations (table 4).

Reference Water Quality

Several approaches have been used to define reference water quality (also referred to as background or 

potential water quality in some publications) for specific 

areas. In defining reference conditions for national nutrient criteria, the USEPA has suggested that the frequency 

distribution of the data available for a specific area could 

be used to define a reference condition: the lower (or best) 

25th percentile of all the data for a specific area or the 

upper (or worst) 75th percentile of the data for a subset of 

streams thought to be minimally impacted (U.S. Environmental Protection Agency, 2000a). Because it is difficult to 

determine which sites are minimally impacted, the lower 

(the best) 25th percentile of all the data is the more common approach and the one used by the USEPA to define 

their proposed water-quality criteria (table 1; U.S. Environmental Protection Agency, 2000b; and 2001). 

The watersheds of the nonwadeable rivers in Wisconsin usually covered large areas of the State and extended 

across several ecoregions and environmental phosphorus 

zones (fig. 1); therefore, reference water-quality conditions were not examined for different areas or individual 

ecoregions in the State, but rather for the entire State. 

Reference TP and TN concentrations for the nonwadeable 

rivers of Wisconsin based on the 25th-percentile approach 

were 0.034 and 0.670 mg/L, respectively (table 9). The 

reference conditions for SCHL and SD were 3.83 μg/L and 

greater than 120 cm, respectively. The reference conditions for the other constituents are also listed in table 9. 

Table 8. Thresholds or breakpoints in the response in water 

quality to changes in the percentage of agricultural area in the 

basin for nonwadeable rivers in Wisconsin.

Constituent Threshold percentage 

of agriculture

Total phosphorus 24.7

Dissolved phosphorus 24.7

Particulate phosphorus 31.8

Total nitrogen 18.1

Dissolved nitrite plus nitrate 8.8

Dissolved ammonia 43.7

Total Kjeldahl nitrogen 34.6

Suspended chlorophyll a 18.1

Secchi-tube depth 32.7

Suspended sediment 18.1
36 Nutrient Concentrations and Their Relations to the Biotic Integrity of Nonwadeable Rivers in Wisconsin

Figure 10. Logarithmically transformed suspended chlorophyll a (SCHL) concentrations and Secchi-tube depths (SDs) as a function of logarithmically transformed median total 

phosphorus and total nitrogen concentrations for the studied nonwadeable rivers in Wisconsin, 2003. Linear-regression lines and coefficients of determination (

R 2 ) are given on 

each graph. Sites in the Driftless Area ecoregion (southwestern part of Wisconsin) are identified separately for SCHL.

2.52.01.51.00.50.0

-1.8 -1.6 -1.4 -1.2 -1.0 -0.8 -0.6 -0.4 -0.2

LOG (TOTAL PHOSPHORUS, IN MILLIGRAMS PER LITER)

2.52.01.51.00.50.0

-0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8

LOG (TOTAL NITROGEN, IN MILLIGRAMS PER LITER)

y = -91.3x - 22.7

R2 = 0.77

0

80604020

120100

-1.8 -1.6 -1.4 -1.2 -1.0 -0.8 -0.6 -0.4 -0.2

LOG (TOTAL PHOSPHORUS, IN MILLIGRAMS PER LITER)

SECCHI-TUBE DEPTH, IN CENTIMETERS

 IN MICROGRAMS A, LOG (SUSPENDED CHLOROPHYLL 

PER LITER)

 IN MICROGRAMS A, LOG (SUSPENDED CHLOROPHYLL 

PER LITER)

y = -83.9x + 81.1

R2 = 0.50

0

80604020

120100

-0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8

LOG (TOTAL NITROGEN, IN MILLIGRAMS PER LITER)

SECCHI-TUBE DEPTH, IN CENTIMETERS

Threshold/breakpoint Data from sites outside the Driftless Area

Data from sites within the Driftless AreaData from all sites throughout the study area

Linear-regression line (without data from the Driftless Area)Linear-regression line

y = 1.24x + 2.39

R2 = 0.79

Without Driftless Area

All data

y = 0.86x + 1.87

R2 = 0.45

Without Driftless Area

y = 1.16x + 0.93

R2 = 0.59

All data

y = 0.86x + 0.89

R2 = 0.35

EXPLANATION
Water Quality and Its Relations with Environmental Characteristics in the Watershed 37

Defining reference conditions based upon the percentile 

approach is arbitrary in nature because the percentages 

of agricultural and urban areas in the region can strongly 

affect the results for characteristics correlated with land 

use, such as the water-quality characteristics examined in 

this study. The 25th-percentile approach usually results in 

areas with extensive agriculture and urban development 

having relatively poor reference conditions; therefore, 

other approaches were examined.

Another approach to estimate reference concentrations is a multiple linear-regression model (regression 

approach) that relates water quality to anthropogenic characteristics of the watershed (Dodds and Oakes, 2004): 

Log PPredicted = a + b Ag % + c Urb %, (11)

where a, b, and c are empirical coefficients based on data 

for all nonwadeable rivers. After calibrating the model 

with data from a specific area, an estimate of reference 

conditions in the absence of anthropogenic activities 

can be obtained by setting the variables describing the 

anthropogenic characteristics to 0 (in this study, setting 

Ag % and Urb % to 0). The general form of this model is 

similar to that used to estimate residualized concentrations 

in equations 2, 4, 6, and 8 (page 25). These relations can 

also be used to place confidence intervals on the estimated 

reference concentrations. Because this type of model estimates the logarithm of the reference concentration (except 

for SD), the median reference concentrations were estimated as 10a

. The median reference condition, the standard 

error of the reference condition, and the upper bound of 

the 95-percent confidence interval of the reference condition for each water-quality constituent are given in table 9. 

A bias correction is typically applied to results for mean 

values obtained by logarithmic regression; however, the 

bias correction was not used here because median values 

were determined rather than mean values.

On the basis of the results of the regression approach,

the reference concentration for TP was 0.035 mg/L, with 

an upper 95-percent confidence limit of 0.045 mg/L 

(table 9). The reference concentration for TN was 

0.514 mg/L, with an upper 95-percent confidence limit of 

0.604 mg/L. The reference concentration for SCHL was 

3.95 μg/L, with an upper 95-percent confidence limit of 

6.20 μg/L. The reference SD was 110 cm, with a lower 

95-percent confidence limit of 96 cm (a lower limit is 

given because higher values represent better conditions). 

Reference conditions for the other water-quality characteristics are given in table 9.

A reference SCHL concentration was also estimated 

by examining the SCHL concentrations in sites with both 

reference TP and reference TN concentrations (considered to be minimally impacted sites). For this analysis, 

the 42 sites were divided into three categories: reference 

sites (Reference, fig. 11)—6 sites with both TP concentrations at or below the 0.035-mg/L reference concentration and TN concentrations at or below the 0.514-mg/L 

reference concentration; high nutrient-concentration 

sites (High)—29 sites with both TP and TN concentrations 

above their respective upper 95-percent confidence limits 

for reference concentrations (TP concentrations above 

Table 9. Reference conditions for water-quality constituents for nonwadeable rivers in Wisconsin. Median reference values, 

standard errors, and upper 95-percent confidence limits were estimated with the multiple linear-regression approach.

[mg/L, milligram per liter; cm, centimeter; μg/L, microgram per liter; >, greater than; --, insufficient data to estimate]

Constituent

Regression approach Percentile approach

Median 

reference

Standard 

error

Upper 95-percent 

confidence limit

Best 25th percentile 

of all data

Worst 75th percentile 

of Reference sites

Total phosphorus (mg/L) 0.035 0.005 0.045 0.034 --

Dissolved phosphorus (mg/L) .016 .002 .021 .017 --

Particulate phosphorus (mg/L) .018 .003 .025 .018 --

Total nitrogen (mg/L) .514 .043 .604 .670 --

Dissolved nitrite plus nitrate (mg/L) .061 .020 .107 .132 --

Dissolved ammonia (mg/L) .022 .000 .022 .019 --

Kjeldahl nitrogen (mg/L) .434 .043 .524 .500 --

Suspended chlorophyll a (μg/L) 3.95 1.00 6.20 3.83 3.85

Secchi-tube depth (cm) 110 7 96a >120 >120

Suspended sediment (mg/L) 3.2 .8 4.9 4.0 2.8

a

 A lower 95-percent confidence limit is given here because higher values represent better conditions for Secchi-tube depth.
38 Nutrient Concentrations and Their Relations to the Biotic Integrity of Nonwadeable Rivers in Wisconsin

0.045 mg/L and TN concentrations above 0.604 mg/L); 

and nonclassified sites—7 sites with either TP or TN concentrations above their respective reference concentrations 

but below their upper 95-percent confidence limits (these 

sites were not included in this analysis and not included in 

fig. 11). 

The median SCHL concentration of the Reference 

sites was 3.4 μg/L (Log (3.4) = 0.54), with the upper 

75th percentile being 3.8 μg/L (Log (3.8) = 0.59), which 

was significantly less than the median concentration of 

18.6 μg/L (Log (18.6) = 1.3) measured at the High sites 

(fig. 11). It has been suggested by the USEPA that the 

upper (worse) 75th percentile of a subset of streams thought 

to be minimally impacted (Reference sites) may represent 

the reference condition; therefore, an alternative reference SCHL concentration for the entire State would be 

3.8 μg/L. The reference values (3.8 to 3.9 μg/L) estimated 

with the approaches used in this study are slightly less than 

those defined by the USEPA for nutrient ecoregions 7 and 

8 (5.8 and 4.3 μg/L, respectively; values obtained when 

the trichromatic method is used for chlorophyll a analysis; 

table 1). 

A reference SD was also determined by examining 

the sites at which both TP and TN concentrations were at 

or below their respective reference concentrations. The 

median SD measured at the Reference sites was greater 

than 120 cm, which was significantly greater than the 

median SD measured at the High sites (47 cm; fig. 11). 

The lower 25th percentile of SDs at the Reference sites 

(equivalent to the worst 75th percentile of the minimally 

impacted sites) was also greater than 120 cm; therefore, an 

alternative reference SD value for the entire State would 

be greater than 120 cm. Because this length exceeds the 

length of the Secchi tube, a specific reference condition 

was not able to be obtained. 

Figure 11. Suspended chlorophyll a (SCHL) concentrations and Secchi-tube depths (SDs) in Reference sites and High 

(nonreference) sites in the studied nonwadeable rivers in Wisconsin, 2003.

REFERENCE HIGH

0.0

0.5

1.0

1.5

2.0

2.5

REFERENCE HIGH

0

30

60

90

120

SECCHI-TUBE DEPTH, IN CENTIMETERS

EXPLANATION

Outlier

Interquartile

range (IR)

75th percentile + 1.5 * IR

25th percentile – 1.5 * IR

75th percentile 

25th percentile 

Median

LOG (SUSPENDED CHLOROPHYLL A, IN MICROGRAMS PER LITER)
39 Nutrient Concentrations and Their Relations to the Biotic Integrity of Nonwadeable Rivers in Wisconsin

Macroinvertebrate Assemblages and Their Relations with 

Water-Quality and Environmental Characteristics 

Wisconsin Department of Natural Resources personnel preparing Hester-Dendy artificial substrate samplers for deployment and collection of 

macroinvertebrates. Macroinvertebrate photos provided by Stanley Szczytko (University of Wisconsin–Stevens Point). 

Macroinvertebrate Assemblages and Their Relations with Water-Quality and Environmental Characteristics 39

Fourteen indices were used to describe the 

macroinvertebrate communities in the nonwadeable rivers 

in Wisconsin. These indices describe species richness 

(1 index: SPECIES), depositional substrate (1: %DEPOS) 

and pollution tolerance (2: MPTV and HBI), feeding 

ecology (3), and insect order (7) (table 10). SPECIES, 

HBI, MPTV, and both Ephemeroptera, Plecoptera, and 

Trichoptera (EPT) indices indicated that sites spanned 

from very poor to excellent conditions. The number of 

species (SPECIES) ranged from 10 to 51 (median = 32; 

table 10; fig. 12). The median percentage of individuals 

from EPT orders (%EPTN) was about 50 percent (ranged 

from 2.6 to 94.7 percent) and about 45 percent of the 

individuals were from the order Diptera (%DIPT; ranged 

from 3.3 to 92.7 percent). The percentage of individuals 

from the order Ephemeroptera (%EPHEM) ranged 

from 0.0 to 69.1 percent (table 10, fig. 12; median = 

16.7 percent). Two indices described the assemblage’s 

stress response to organic pollution: MPTV, which ranged 

from about 3.5 to 6.8; and HBI, which ranged from about 

2.8 to 9.6 (table 10, fig. 12). For most macroinvertebrate 

indices, higher values are representative of better water 

quality except for MPTV and HBI for which lower 

index values are representative of better water quality. In 

general, macroinvertebrate communities in rivers in the 

southeast part of the State would normally be considered 

representative of poorer water quality, with fewer species, 

fewer EPT individuals and taxa (shown only for %EPHEM 

in fig. 12), and higher MPTV and HBI values than those of 

rivers in the rest of the State. 
40 Nutrient Concentrations and Their Relations to the Biotic Integrity of Nonwadeable Rivers in Wisconsin Table 10. Summary statistics for biotic indices for nonwadeable rivers in Wisconsin. [Ephemeroptera, Plecoptera, and Trichoptera (EPT); %, percent; #, number] Index Abbreviation Units Count Median Mean 25th percentile of all data 75th percentile of all data Standard deviation Minimum Maximum Macroinvertebrates Species richness SPECIES # 41 32 32.2 28 38 8.7 10 51 Percentage of individuals from order Ephemeroptera %EPHEM

% 41 16.7 21.1 5.6 31.4 18.1 .0 69.1

Percentage of individuals from order Plecoptera %PLEC

% 41 .2 .9 .0 .8 1.9 .0 11.2

Percentage of individuals from order Trichoptera %TRICHOP

% 41 24.0 26.0 9.4 35.3 20.7 .4 84.3

Percentage of individuals from order Diptera %DIPT

% 41 44.7 46.6 27.2 65.8 23.3 3.3 92.7

Percentage of individuals from Diptera: Chironomidae %CHIRON

% 41 41.9 44.3 25.6 59.4 23.0 3.3 92.7

Percentage of EPT number %EPTN

% 41 49.4 48.0 28.4 62.9 24.6 2.6 94.7

Percentage of EPT taxa %EPTTX

% 41 40.0 38.6 31.6 47.1 12.2 12.5 65.0

Percentage of individuals that are scrapers %SCRAP

% 41 7.2 10.8 1.8 12.5 13.3 .0 55.9

Percentage of individuals that are shredders %SHRED

% 41 8.6 10.7 4.8 14.8 9.0 .4 46.6

Percentage of individuals that are gatherers %GATHER

% 41 23.3 28.9 12.3 37.9 22.3 3.2 94.8

Percentage of individuals tolerant to depositional 

habitat

%DEPOS

% 41 8.7 10.7 4.6 16.0 9.7 .6 58.0

Mean pollution tolerance value MPTV # 41 5.290 5.225 4.760 5.640 .740 3.520 6.769

Hilsenhoff Biotic Index HBI # 41 5.386 5.500 4.865 5.821 1.192 2.772 9.578

Fish

Total number of native species (not exotic) #NATIVESP # 41 12 13.4 10 16 5.0 4 27

Number of river species #RIVERSP # 41 4 4.9 4 7 2.9 0 23

Number of sucker (Catostomidae) species #SUCKER # 41 4 4.0 3 5 1.6 1 8

Number of species considered intolerant of degradation #INTOL # 41 3 2.7 1 3 1.6 0 7

Weight of fish collected per unit effort WPUE kg 41 24.5 30.2 11.8 36.5 26.3 3.9 151

Percentage of individuals that are river species %RIVERSP

% 41 26.4 28.3 14.3 39.4 19.5 .0 79.0

Percentage of individuals that are lithophilic spawners %LITSPAWN

% 41 52.9 54.3 37.0 73.1 24.7 1.5 93.5

Percentage of suckers by weight %SUCKER

% 41 32.0 41.9 15.7 71.8 31.4 .6 97.4

Percentage of insectivores by weight %INSECT

% 41 40.5 49.1 29.1 76.6 29.7 .9 97.8

Percentage of fish with disease or deformities %DISEASE

% 41 .0 .5 .0 .0 1.2 .0 6.7

Wisconsin large-river index of biotic integrity IBI # 41 75 67.1 50 90 27.1 5 100
Macroinvertebrate Assemblages and Their Relations with Water-Quality and Environmental Characteristics 41

Figure 12. Distributions (quintiles) for four macroinvertebrate indices [species richness (SPECIES), mean pollution tolerance 

value (MPTV), percentage of individuals from the order Ephemeroptera (%EPHEM), and Hilsenhoff Biotic Index (HBI)] for the 

studied nonwadeable rivers in Wisconsin. Better macroinvertebrate communities are indicated by lower MPTV and HBI values. 

Number

10 – 27

28 – 30

31 – 34

35 – 39

40 – 51

Species richness (SPECIES) Mean Pollution Tolerance Value (MPTV)

Percentage of Ephemeroptera (%EPHEM) Hilsenhoff Biotic Index (HBI)

Tolerance value

3.52 – 4.64

4.65 – 5.03

5.04 – 5.50

5.51 – 5.66

5.67 – 6.77

Percentage

0 – 4.3

4.4 – 11.2

11.3 – 21.1

21.2 – 39.5

39.6 – 69.1

Index value

2.77 – 4.71

4.72 – 5.12

5.13 – 5.56

5.57 – 6.05

6.06 – 9.58

16 15

17

19

20

1

18

21 22 24

23

25

26

27

28 29

30

33 32

34

31 2

40 41

38

39

35

37

36

42

14

13

7

11 10 12

9

8 6 5

4

16 15

17

19

20

1

18

21 22 24

23

25

26

27

28 29

30

33 32

34

31 2

40 41

38

39

35

37

36

42

14

13

7

11 10 12

9

8 6 5

4

16 15

17

19

20

1

18

21 22 24

23

25

26

27

28 29

30

33 32

34

31 2

40 41

38

39

35

37

36

42

14

13

7

11 10 12

9

8 6 5

4

16 15

17

19

20

1

18

21 22 24

23

25

26

27

28 29

30

33 32

34

31 2

40 41

38

39

35

37

36

42

14

13

7

11 10 12

9

8 6 5

4
42 Nutrient Concentrations and Their Relations to the Biotic Integrity of Nonwadeable Rivers in Wisconsin

Relations with Individual Characteristics 

Correlations 

Most of the macroinvertebrate indices were significantly correlated (rs

 values) with several nutrient constituents except %DIPT, percentage of the individuals that are 

from the family chironomidae (%CHIRON), %EPTN, and 

gathers (%GATHER; table 11). Six of the indices were 

more strongly correlated with the nutrient constituents 

than the other indices [SPECIES, MPTV, %EPHEM, 

HBI, percentage of the individuals from the order Plecoptera (%PLEC), and percentage of the individuals that are 

scrapers (%SCRAP)]; these are listed in decreasing order 

of the strength of these relations. These indices were most 

strongly correlated with TP, TN, and TKN. SPECIES, 

%EPHEM, %PLEC, and %SCRAP were negatively correlated with most nutrient concentrations, although some 

of the correlations were not statistically significant; MPTV 

and HBI were positively correlated with all nutrient concentrations. Lower MPTV and HBI values are representative of better macroinvertebrate communities and better 

water quality. Unexpectedly, the percentage of individuals 

from the order Trichoptera (%TRICHOP) and percentage 

of individuals that are shredders (%SHRED) were positively correlated with all nutrient concentrations. There 

are, however, a few Trichoptera species, such as Hydropsyche, that are relatively pollution tolerant.

In general, the six indices most strongly correlated 

with the nutrient concentrations were also the indices most 

strongly correlated with SCHL, SD, SSC, water temperature, SC, pH, and the land-use characteristics, especially 

Ag %, percentage of row-crop agriculture (AgRow %), 

For %, and Urb %. Better macroinvertebrate index scores 

(higher SPECIES, %EPHEM, %PLEC, and %SCRAP 

scores and lower MPTV and HBI scores) generally were 

correlated with lower SCHL, SSC, SC, pH, Ag %, Urb %, 

and with higher SDs and For %. These six indices were 

also more strongly correlated with a few basin characteristics (air temperature, runoff, and basin slope) and the 

soil/surficial-deposit characteristics (clay content and soil 

slope) than were the other indices. In general, better macroinvertebrate assemblages were found in rivers in areas 

with cool air temperatures, high runoff, and soils with low 

clay content and steep slopes; these characteristics are generally found in the northern part of the State with mixed 

and mostly forested areas (figs. 1A and 12).

Response to Changes in Nutrient 

Concentrations 

Responses of the four macroinvertebrate-assemblage 

indices most strongly correlated with nutrients (SPECIES, 

MPTV, EPHEM%, and HBI) are shown with respect to 

median TP and TN concentrations in figure 13 and the 

two indices most strongly related with nutrients (SPECIES 

and MPTV) are shown with respect to median DP, NO3

-N, 

NH4-N, and TKN concentrations in figure 14. SPECIES 

and MPTV were chosen as the best macroinvertebrate 

measures and used for additional detailed investigation 

because correlation analyses and scatterplots indicated that 

they were the most responsive to differences in nutrient 

concentrations and they appeared to best represent the 

other macroinvertebrate indices. In general, SPECIES and 

EPHEM% decreased as nutrient concentrations increased, 

whereas MPTV and HBI values increased as nutrient 

concentrations increased. The strongest relations were 

found between SPECIES and MPTV values and TP, TN, 

and TKN concentrations. The other relations with nutrient concentrations ranged more widely; little relation was 

found with DP, NO3

-N, and NH4-N concentrations for any 

of the indices. The wide range in biotic index values at any 

nutrient concentration in these graphs may indicate the 

effects of factors other than nutrient concentrations.
Macroinvertebrate Assemblages and Their Relations with Water-Quality and Environmental Characteristics 43

Table 11. Spearman rank correlation coefficients (rs) between macroinvertebrate-assemblage indices and median water-quality, land-use, basin, and soil/surficial-deposit 

characteristics for the studied nonwadeable rivers in Wisconsin.

[All absolute values greater than 0.30 (Student’s t-test; bold black values) and 0.48 (Bonferroni correction; bold red values) were significant at p < 0.05. All characteristics are defined in table 2 except macroinvertebrate indices, which are defined in table 10.]

Characteristic SPECIES %EPHEM %PLEC %TRICHOP %DIPT %CHIRON %EPTN %EPTTX %SCRAP %SHRED %GATHER %DEPOS MPTVa HBIa

Water-quality characteristics

TP -0.55 -0.39 -0.34 0.27 0.00 0.06 -0.09 -0.25 -0.24 0.18 -0.06 -0.24 0.42 0.39

DP -.34 -.23 -.26 .25 -.05 .01 -.04 -.20 -.06 .14 -.06 -.13 .26 .25

PP -.61 -.45 -.31 .26 .04 .10 -.11 -.26 -.33 .20 -.05 -.28 .47 .43

TN -.44 -.33 -.37 .40 -.09 -.04 .04 -.10 -.31 .27 -.03 -.16 .36 .27

NO3-N -.18 -.13 -.15 .33 -.06 -.03 .02 .03 -.10 .13 .04 -.02 .11 .13

NH4-N -.21 -.07 -.45 .24 -.16 -.13 .08 -.04 -.24 .22 .19 -.05 .28 .15TKN -.58 -.59 -.69 .34 -.01 .06 -.11 -.42 -.56 .47 .07 -.31 .72 .51

SCHL -.69 -.71 -.42 .17 .22 .28 -.31 -.45 -.64 .42 .01 -.39 .59 .51

SD .66 .52 .44 -.35 .05 -.01 .07 .26 .38 -.23 .04 .34 -.58 -.45

SSC -.52 -.46 -.29 .29 -.02 .05 -.07 -.22 -.27 .16 -.08 -.27 .43 .41

WTemp -.53 -.42 -.44 -.02 .10 .18 -.23 -.40 -.34 .39 .01 -.23 .58 .43

SC -.35 -.35 -.50 .34 -.10 -.03 .00 -.22 -.36 .23 .12 -.11 .45 .27

pH -.39 -.48 -.34 .07 .16 .19 -.21 -.32 -.37 .33 .03 -.16 .38 .29

Color .24 .10 -.03 -.27 .05 .06 -.04 -.04 -.04 -.05 .06 -.03 .11 .01

Land-use characteristics

Urb % -.68 -.52 -.61 .28 .03 .11 -.16 -.38 -.50 .33 .04 -.31 .60 .46

AgRow % -.52 -.55 -.45 .38 .01 .06 -.09 -.28 -.49 .31 .02 -.26 .48 .33

AgOther % -.35 -.32 -.26 .33 -.04 .01 -.01 -.18 -.17 .17 -.03 -.20 .25 .20

Ag % -.50 -.42 -.40 .40 -.09 -.02 .01 -.20 -.32 .23 -.06 -.26 .40 .26

Grass % -.21 -.44 -.19 .23 .14 .15 -.18 -.14 -.31 .17 .06 -.14 .14 .25

WetO % -.13 -.22 -.28 -.19 .21 .22 -.23 -.24 -.33 .13 .07 -.14 .35 .25

WetF % .48 .26 .15 -.25 .01 -.01 .03 .13 .15 -.03 .04 .16 -.18 -.20

Barren % -.29 -.18 -.37 .01 .10 .13 -.18 -.44 -.33 .36 .35 .12 .35 .24

For % .55 .47 .47 -.37 .00 -.06 .07 .26 .43 -.30 .01 .24 -.44 -.33
44 Nutrient Concentrations and Their Relations to the Biotic Integrity of Nonwadeable Rivers in Wisconsin Characteristic SPECIES %EPHEM %PLEC %TRICHOP %DIPT %CHIRON %EPTN %EPTTX %SCRAP %SHRED %GATHER %DEPOS MPTVa HBIa Basin characteristics Area -0.15 -0.07 0.08 -0.01 0.05 0.07 0.00 -0.07 -0.11 0.21 -0.17 -0.12 0.11 -0.02 ATemp -.46 -.47 -.48 .37 .01 .06 -.10 -.32 -.37 .27 .05 -.22 .45 .36 PPT -.34 -.23 -.21 .19 .07 .10 -.09 -.18 -.17 .11 .09 -.06 .22 .26 Evap -.41 -.34 -.27 .38 -.06 -.01 -.01 -.20 -.14 .13 -.08 -.21 .25 .23 Runoff .38 .49 .33 -.17 -.17 -.22 .26 .37 .35 -.21 -.10 .19 -.43 -.47 Length -.09 -.02 .11 -.09 .06 .07 -.02 -.02 -.05 .07 -.17 -.17 .06 -.06 BSlope .45 .32 .28 -.20 -.06 -.10 .08 .26 .42 -.30 .04 .19 -.33 -.21 Flow .14 .00 .20 -.03 .16 .12 -.08 .19 -.01 .04 -.09 -.01 -.27 -.19 Soil/surficial-deposit characteristics SClay -.46 -.37 -.31 .28 -.03 .03 -.04 -.21 -.23 .15 -.05 -.20 .36 .28 Erod -.43 -.31 -.18 .16 .09 .11 -.11 -.13 -.24 .15 .00 -.15 .19 .17 OM .20 .19 -.05 -.16 -.08 -.11 .09 .14 .01 -.08 .02 -.01 .00 -.11 Perm .34 .11 .01 -.03 -.05 -.07 .02 .04 .02 -.01 .10 .10 -.03 -.07 SSlope .22 .39 .47 .06 -.20 -.24 .23 .39 .50 -.31 -.10 .23 -.45 -.31 NonGlac -.21 -.13 -.01 .11 .05 .11 -.10 -.21 .17 .02 -.19 -.10 .12 .23 SDClay .05 -.13 -.45 .27 -.13 -.07 .06 -.16 -.21 .23 .23 -.02 .43 .22 Loam .22 .16 .26 .12 -.19 -.18 .21 .15 .16 .10 -.16 .01 -.10 -.20 Peat .15 .07 .16 -.33 .26 .23 -.25 -.07 .10 -.10 .01 -.10 -.15 .01 Sand -.29 -.28 -.01 -.20 .32 .27 -.29 -.11 -.37 .09 .00 -.26 -.02 .08 SG .12 .20 -.12 .04 -.23 -.22 .26 .21 -.08 .11 .05 .16 .06 -.12

a For MPTV and HBI, lower index values are typically thought to represent better water quality.

Table 11. Spearman rank correlation coefficients (rs) between macroinvertebrate-assemblage indices and median water-quality, land-use, basin, and soil/surficial-deposit 

characteristics for the studied nonwadeable rivers in Wisconsin—Continued.

[All absolute values greater than 0.30 (Student’s t-test; bold black values) and 0.48 (Bonferroni correction; bold red values) were significant at p < 0.05. All characteristics are defined in table 2 except macroinvertebrate indices, which are defined in table 10.]
Macroinvertebrate Assemblages and Their Relations with Water-Quality and Environmental Characteristics 45

Figure 13. Species richness (SPECIES), mean pollution tolerance value (MPTV), percentage of individuals from the order 

Ephemeroptera (%EPHEM), and Hilsenhoff Biotic Index (HBI) values as a function of total phosphorus (TP) and total nitrogen (TN)

concentration for the studied nonwadeable rivers in Wisconsin. LOESS-smoothing lines with 95-percent confidence limits and 

computed thresholds in the response, identified by vertical lines, are given on each graph.

0

10

20

30

40

50

60

0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 0.50

SPECIES RICHNESS

0

10

20

30

40

50

60

0 1 2 3 4 5 6

SPECIES RICHNESS

3

4

5

6

7

0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 0.50

MEAN POLLUTION TOLERANCE VALUE

3

4

5

6

7

0 1 2 3 4 5 6

MEAN POLLUTION TOLERANCE VALUE

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 0.50

PERCENTAGE OF EPHEMEROPTERA

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0 1 2 3 4 5 6

PERCENTAGE OF EPHEMEROPTERA

2

3

4

5

6

7

8

9

10

0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 0.50

MEDIAN TOTAL PHOSPHORUS, IN MILLIGRAMS PER LITER MEDIAN TOTAL NITROGEN, IN MILLIGRAMS PER LITER

MEDIAN TOTAL PHOSPHORUS, IN MILLIGRAMS PER LITER MEDIAN TOTAL NITROGEN, IN MILLIGRAMS PER LITER

MEDIAN TOTAL PHOSPHORUS, IN MILLIGRAMS PER LITER MEDIAN TOTAL NITROGEN, IN MILLIGRAMS PER LITER

MEDIAN TOTAL PHOSPHORUS, IN MILLIGRAMS PER LITER MEDIAN TOTAL NITROGEN, IN MILLIGRAMS PER LITER

HILSENHOFF BIOTIC INDEX

2

3

4

5

6

7

8

9

10

0 1 2 3 4 5 6

HILSENHOFF BIOTIC INDEX

LOESS-smoothing line

Lower 95-percent confidence limit

Upper 95-percent confidence limit

Threshold/breakpoint
46 Nutrient Concentrations and Their Relations to the Biotic Integrity of Nonwadeable Rivers in Wisconsin

Figure 14. Species richness (SPECIES) and mean pollution tolerance value (MPTV) as a function of dissolved phosphorus 

(DP), dissolved nitrite plus nitrate nitrogen (NO3-N), dissolved ammonia nitrogen (NH4-N), and total Kjeldahl nitrogen (TKN) 

concentrations for the studied nonwadeable rivers in Wisconsin. LOESS-smoothing lines with 95-percent confidence limits and 

computed thresholds in the response, identified by vertical lines, are given on each graph.

3.0

3.5

4.0

4.5

5.0

5.5

6.0

6.5

7.0

0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16

MEDIAN DISSOLVED PHOSPHORUS, IN MILLIGRAMS PER LITER

MEAN POLLUTION TOLERANCE VALUE

0

10

20

30

40

50

60

0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16

MEDIAN DISSOLVED PHOSPHORUS, IN MILLIGRAMS PER LITER

SPECIES RICHNESS

3.0

3.5

4.0

4.5

5.0

5.5

6.0

6.5

7.0

0 1 2 3 4

MEDIAN DISSOLVED NITRITE PLUS NITRATE, IN MILLIGRAMS PER LITER

MEAN POLLUTION TOLERANCE VALUE

0

10

20

30

40

50

60

0 1 2 3 4

MEDIAN DISSOLVED NITRITE PLUS NITRATE, IN MILLIGRAMS PER LITER

SPECIES RICHNESS

3.0

3.5

4.0

4.5

5.0

5.5

6.0

6.5

7.0

0 0.02 0.04 0.06 0.08 0.1 0.12 0.14

MEDIAN DISSOLVED AMMONIA, IN MILLIGRAMS PER LITER

MEAN POLLUTION TOLERANCE VALUE

0

10

20

30

40

50

60

0 0.02 0.04 0.06 0.08 0.1 0.12 0.14

MEDIAN DISSOLVED AMMONIA, IN MILLIGRAMS PER LITER

SPECIES RICHNESS

3.0

3.5

4.0

4.5

5.0

5.5

6.0

6.5

7.0

0 0.5 1 1.5 2 2.5 3

MEDIAN TOTAL KJELDAHL NITROGEN, IN MILLIGRAMS PER LITER

MEAN POLLUTION TOLERANCE VALUE

0

10

20

30

40

50

60

0.0 0.5 1.0 1.5 2.0 2.5 3.0

MEDIAN TOTAL KJELDAHL NITROGEN, IN MILLIGRAMS PER LITER

SPECIES RICHNESS

LOESS-smoothing line

Lower 95-percent confidence limit

Upper 95-percent confidence limit

Threshold/breakpoint
Macroinvertebrate Assemblages and Their Relations with Water-Quality and Environmental Characteristics 47

The LOESS-smoothing lines on the scatterplots 

between nutrient concentrations and macroinvertebrate 

index values indicate that, for most nutrient constituents, 

there were nonlinear relations (figs. 13 and 14). Regression-tree analyses were then used to define the thresholds 

or largest breakpoints in the responses of the six macroinvertebrate indices most strongly correlated with nutrient 

concentrations (table 12). The ranges in the significant 

breakpoints in the responses to changes in nutrient concentrations were: 0.034 to 0.150 mg/L for TP; 0.014 to 

0.066 mg/L for DP; 0.016 to 0.101 mg/L for PP; 0.527 to 

1.990 mg/L for TN; 0.052 to 0.147 mg/L for NO3-N; 0.035 

to 0.055 mg/L for NH4-N; and 0.603 to 0.928 mg/L for 

TKN. All of the breakpoints for TP and TN were statistically significant at p < 0.05 except those identified for 

%PLEC. These analyses indicated that the values of these 

six macroinvertebrate indices changed the most over a 

relatively narrow range in concentrations for each constituent. None of the breakpoints exceeded 0.15 mg/L for TP or 

2.0 mg/L for TN. In general, for macroinvertebrate indices 

with relatively low breakpoint values (such as for MPTV 

and EPHEM%, with breakpoint values of about 0.03 and 

0.04 mg/L for TP, respectively), the indices continue to 

degrade with increasing nutrient concentrations (fig. 13); 

however, there was little change in the index values with 

increasing nutrient concentrations above the relatively 

high breakpoint values (such as for HBI, with a breakpoint 

value for TP of 0.150 mg/L).

Effects of Multiple Characteristics on 

Macroinvertebrate Indices

Stepwise Regressions

Forward stepwise regressions were done with waterquality (median values) and environmental characteristics 

to determine which characteristics best described the 

variance in the six macroinvertebrate indices most strongly 

correlated with nutrient concentrations (table 13). Models 

with more than three variables did not substantially 

increase the amount of variance explained. For all of the 

indices, TKN was the first or second variable incorporated 

in the models. TKN concentrations alone explained 29 to 

52 percent of the variance in the four indices most strongly 

correlated to nutrient concentrations. The percentage of 

clay deposits was the only other variable in more than 

one model. With three variables, the models explained 

between 26 and 66 percent of the variance in the indices. 

The models for HBI and %PLEC only explained 29 and 

26 percent of the variance, respectively. Plecopterans 

were not commonly found in this study; they were absent 

at 17 sites and composed less than 4 percent of the 

abundance at 23 other sites.

Redundancy Analysis

One of the greatest impediments to understanding the relations between nutrient concentrations and 

biotic response is that the biota may respond to nutrient 

enrichment in the same way that they react to other stressors (Yoder and Rankin, 1995; Karr and Chu, 1999). In 

addition, environmental characteristics are often highly 

correlated, making it difficult to differentiate spurious 

correlations from cause-and-effect relations (Miltner and 

Rankin, 1998; Wang and others, 2003; Dodds and Oakes, 

2004). The approach used for RDA in this study is similar 

to that of Wang and others (2003) and Weigel (2003) and 

had three main components. First, a forward-selection 

procedure in RDA was used to identify the most important (key) characteristics to include from each of three 

categories: nutrients (the seven nutrient constituents in 

table 2), other water-quality characteristics (the other seven 

water-quality characteristics in table 2), and environmental 

characteristics (all remaining land-use, basin, soil, and 

surficial-deposit characteristics). RDA was then used to 

determine the influence of each of these categories on the 

macroinvertebrate assemblages (the 14 macroinvertebrate 

indices). Finally, partial RDA was used to determine the 

relative importance of the nutrients, other water-quality 

characteristics, environmental characteristics, and interactions among categories (variability that could not be 

attributed to a specific category) in affecting the macroinvertebrate assemblages (the 14 macroinvertebrate indices). 

The same characteristics found with the forward variableselection procedure were used to describe each category of 

characteristics.
48 Nutrient Concentrations and Their Relations to the Biotic Integrity of Nonwadeable Rivers in Wisconsin Table 13. Results from forward stepwise-regression analyses to explain variance in macroinvertebrate indices for the studied nonwadeable rivers in Wisconsin.

[rs, Spearman correlation coefficient; 

R2, coefficient of determination for the one-, two-, and three-variable models; no additional, no statistically significant (p < 0.05) terms]

Dependent variable First variable Second variable Third variable

Species Richness (SPECIES) Total Kjeldahl nitrogen Clay deposits Basin slope

rs -0.58 0.05 0.45Accumulative 

R2 .45 .58 .61

Mean pollution tolerance value (MPTV) Total Kjeldahl nitrogen Sand Total nitrogen

rs .72 -.02 .36

Accumulative 

R2 .52 .57 .66

Percent of individuals from order Ephemeroptera (% EPHEM) Total Kjeldahl nitrogen Grassland Flow per unit area

rs -.59 -.44 .00Accumulative 

R2 .29 .38 .43

Hilsenhoff Biotic Index (HBI) Total Kjeldahl nitrogen (no additional) (no additional)

rs .51Accumulative 

R2 .29

Percent of individuals from order Plecoptera (% PLEC) Loam deposits Total Kjeldahl nitrogen Clay deposits

rs .26 -.69 -.45

Accumulative 

R2 .14 .21 .26

Percent of individuals that are scrapers (% SCRAP) Total Kjeldahl nitrogen Permeability Agriculture (row crop)

rs -.56 .02 -.49Accumulative 

R2 .16 .27 .65

Table 12. Thresholds or breakpoints in the responses in macroinvertebrate indices to changes in nutrient concentrations for nonwadeable rivers in Wisconsin.[All concentrations in milligrams per liter; nss, not statistically significant at p < 0.05]

Index Total phosphorus Dissolved phosphorus Particulate phosphorus Total nitrogen Dissolved nitrite plus nitrate Dissolved ammonia Total Kjeldahl nitrogen

Species richness (SPECIES) 0.150 0.066 0.101 1.925 0.030 (nss) 0.051 (nss) 0.770

Mean pollution tolerance value (MPTV) .064 .014 .051 .634 .147 .055 .658

Percentage of individuals from order Ephemeroptera (%EPHEM) .040 .024 .023 .527 .052 .055 (nss) .875

Hilsenhoff Biotic Index (HBI) .150 .014 (nss) .082 1.990 .134 .035 (nss) .928

Percentage of individuals from order Plecoptera (%PLEC) .148 (nss) .076 (nss) .051 (nss) 1.965 (nss) 1.235 (nss) .035 .603

Percentage of individuals that are scrapers (%SCRAP) .034 .014 (nss) .016 .527 .030 (nss) .016 (nss) .875
Macroinvertebrate Assemblages and Their Relations with Water-Quality and Environmental Characteristics 49

RDA retained PP and TKN from the nutrient characteristics, SD and SCHL from the other water-quality 

characteristics, and AgRow % and basin slope from 

the environmental characteristics. RDA was then run 

again with only those key characteristics. RDA based on 

these six key characteristics and the use of multiple axes 

explained 61 percent of the variance in the macroinvertebrate indices (p < 0.01); however, the first axis alone 

accounted for 99 percent of the total explained variance. 

The strongest relations (farthest from a value of 0 on 

RDA axis 1) were between SPECIES and SD and basin 

slope (positive relations) and between SPECIES and PP, 

SCHL, TKN, and AgRow % (negative relations); strong 

but opposite relations were found between MPTV and HBI 

and these constituents (fig. 15). The percent forest (For %) 

had the opposite response as AgRow %. TP and TN, which 

were not included in the analysis, are displayed in a similar 

fashion to the selected variables on this figure without 

affecting the results: TP and TN corresponded strongly 

with the first RDA axis, similar to PP and TKN. Therefore, 

if these characteristics were used instead of PP and TKN, 

similar results would have been obtained.

Partial RDA was then used to determine the relative 

importance of nutrients, other water-quality characteristics, environmental characteristics, and interactions among 

categories in affecting the macroinvertebrate assemblages. 

These four categories explained 61 percent of the variance 

in the 14 macroinvertebrate-assemblage indices (fig. 16). 

Nearly all of the variance was explained by the interactions among variable categories (55 percent of the total 

variance or 89 percent of the explained variance). Environmental characteristics explained 4 percent of total variance 

(7 percent of the explained variance), and nutrients and 

other water-quality characteristics each explained only 

1 percent of the total variance (2 percent of the explained 

variance for each). Therefore, nutrient concentrations by 

themselves explained only a small part of the total variance 

in the macroinvertebrate assemblages. About 39 percent of 

the total variance could not be explained by the characteristics in this study, and an additional 55 percent of the total 

variance could not be separated into a single category of 

characteristics. 

Reference Values for the Macroinvertebrate 

Indices

The use of different approaches provides a range in 

estimated reference values for each of the macroinvertebrate indices. Reference values for the six macroinvertebrate indices most strongly related to nutrient concentrations (SPECIES, MPTV, %EPHEM, HBI, %PLEC, and 

%SCRAP) were determined by using the best 25th percentile based on data from all of the sites, the median value for 

sites considered minimally impacted (Reference sites with 

both TP and TN concentrations at or below the estimated 

reference concentrations), the worst 75th percentile for 

minimally impacted sites, and two variants of the regression approach (table 14). Six Reference sites had median 

TP concentrations at or below the 0.035-mg/L reference 

concentration and median TN concentrations at or below 

the 0.514-mg/L reference concentration (table 9). 

The distributions of index values for the Reference 

sites are compared with the distribution of values for the 

29 High sites (with median TP and TN concentrations 

above their respective upper 95-percent confidence limits 

for reference concentrations, 0.045 mg/L and 0.604 mg/L, 

respectively) (fig. 17 and tables 9 and 14). For all of these 

indices, there was a statistically significant (p < 0.05) 

difference between the medians from the Reference and 

High sites, except for %PLEC (p = 0.08) and SPECIES. 

Reference values based on the worst 75th percentile of the 

Reference sites were similar to the median of the Reference sites except for EPHEM%. The regression approach, 

based on the relation between water quality and Ag % and 

Urb %, gave estimated reference values similar to those 

from the 25th-percentile approach. %EPHEM and HBI, 

however, were only weakly related to changes in Ag % and 

Urb % (0.06 > p < 0.08) and %PLEC and %SCRAP were 

not significantly related (p > 0.1) to changes in Ag % and 

Urb %. 
50 Nutrient Concentrations and Their Relations to the Biotic Integrity of Nonwadeable Rivers in Wisconsin

Figure 15. Redundancy analysis (RDA) results for macroinvertebrate indices and nutrients, other water-quality, and 

environmental characteristics: axis 2 scores are plotted as a function of axis 1 scores. Parameters describing each category 

were determined by forward-selection procedures in RDA. Water-quality and land-use abbreviations are defined in table 2 and 

macroinvertebrate abbreviations are defined in table 10.

-1

0

1

-1 0 1

AgRow %

TKN

SCHL

PP

SD

BSlope

TP

TN FOR %

SPECIES

MPTV HBI

%EPTX %EPHEM

%DEPOS

%SCRAP

%GATHER

%PLEC

%EPTN %DIPT

%CHIRON

%TRICOP %SHRED

 R2 = 0.61

RDA AXIS 2

RDA AXIS 1

Macroinvertebrate indices

Other characteristics 

not included in analysis

EXPLANATION

Figure 16. Percentages of variance in 14 macroinvertebrate-index values described by nutrients, other water-quality 

characteristics, environmental (land-use, soil, and surficial-deposit) characteristics, interactions (variance that cannot be 

explained by a single category), and unexplained variance for the studied nonwadeable rivers in Wisconsin. [%, percentage of 

total variance]

1%

4%

55%

39%

1%

Nutrients

General categories

Environmental characteristics

Interactions among categories

Unexplained

Other water-quality characteristics
Macroinvertebrate Assemblages and Their Relations with Water-Quality and Environmental Characteristics 51

Table 14. Reference conditions for six macroinvertebrate indices for nonwadeable rivers in Wisconsin.[nss, not statistically significant at p < 0.1]

Index Abbreviation

Best 25th

percentile 

for all dataa

Median for 

Reference sites

Worst 75th

percentile for 

Reference sitesa

Regression 

approach

(mean)

Regression 

approach (worst 

95-percent 

confidence limit) a

Species richness SPECIES 38 30 (nss) 29 38 34Mean pollution tolerance value MPTV 4.8 4.6 4.7 4.8 5.2Percentage of individuals from order Ephemeroptera %EPHEM 31.4 45.0 30.8 29.1 (p = 0.08) 20.0 (p = 0.08)Hilsenhoff Biotic Index HBI 4.9 4.5 4.7 5.0 (p = 0.06) 5.6 (p = 0.06)Percentage of individuals from order Plecoptera %PLEC .8 .7 (p = 0.08) .3 1.3 (nss) .3 (nss)Percentage of individuals that are scrapers %SCRAP 12.5 17.4 11.8 13.7 (nss) 7.0 (nss)

a Higher values for all of these indices except for MPTV and HBI were shown to represent better water quality; therefore, for all indices except MPTV and HBI, the best 25th percentile for all of the data was 

obtained from the 75th percentile of all of the data, the worst 75th percentile for the Reference sites was obtained from the 25th percentile for the Reference sites, and the worst 95-percent confidence limit was 

obtained from the lower 95-percent confidence limits from the regression approach. 
52 Nutrient Concentrations and Their Relations to the Biotic Integrity of Nonwadeable Rivers in Wisconsin

0

10

20

30

40

50

SPECIES RICHNESS

REFERENCE HIGH REFERENCE HIGH

REFERENCE HIGH REFERENCE HIGH

3

4

5

6

7

MEAN POLLUTION TOLERANCE VALUE

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

PERCENTAGE OF EPHEMEROPTERA

0

2

4

6

8

10

HILSENHOFF BIOTIC INDEX

EXPLANATION

Outlier

Interquartile

range (IR)

75th percentile + 1.5 * IR

25th percentile – 1.5 * IR

75th percentile 

25th percentile 

Median

Figure 17. Species richness (SPECIES), mean pollution tolerance value (MPTV), percentage of individuals from the 

order Ephemeroptera (%EPHEM), and Hilsenhoff Biotic Index (HBI) values in Reference sites and High (nonreference) 

sites in the studied nonwadeable rivers in Wisconsin.
53 Nutrient Concentrations and Their Relations to the Biotic Integrity of Nonwadeable Rivers in Wisconsin

Fish Assemblages and Their Relations with Water-Quality 

and Environmental Characteristics 

Wisconsin Department of Natural Resources personnel collecting fish with an electrofishing boat. Photos provided by John Lyons (Wisconsin Department 

of Natural Resources).

Eleven indices were computed to summarize the fish 

data, including indices describing the number of nonexotic 

species (1 index: #NATIVESP), number and percentage 

of riverine fish (2: #RIVERSP and %RIVERSP, respectively), number of sucker species and percentage by 

weight of round-bodied sucker species (2: #SUCKER and 

%SUCKER, respectively), number of species intolerant of 

degradation (1: #INTOL), weight per unit sampling effort 

(1: WPUE), percentage of fish that spawn over stony environments (1: %LITSPAWN), percentage of biomass that 

is composed of insectivores (1: %INSECT), percentage of 

fish with diseases or deformities (1: %DISEASE), and one 

overall index of biotic integrity for large rivers in Wisconsin (IBI; Lyons and others, 2001). The fish indices indicated a wide range of environmental quality for the 41 sites 

in the study (table 10, page 40). Fish IBI scores spanned a 

range from very poor to excellent (appendix 3), with most 

sites scoring good or better (IBI value of 60 or greater). All 

of the fish indices had a wide range of values; for example, 

#NATIVESP ranged from 4 to 27, #RIVERSP ranged from 

0 to 23, WPUE ranged from 3.9 to 151 kg, and IBI ranged 

from 5 to 100. For all fish indices except %DISEASE, 

higher values are thought to represent better water quality. 

In general, fish assemblages in rivers in the southeast part 

of the State would normally be considered representative 

of poorer water quality than in rivers in other parts of the 

State; these rivers had lower IBI, %SUCKER, %RIVERSP, 

and #INTOL values (fig. 18). Many of the other fish indices did not exhibit strong regional patterns. 

Relations with Individual Characteristics 

Correlations 

All of the fish indices were significantly correlated 

(rs ) with several nutrient constituents except WPUE 

(table 15). Six of the indices, however, were more strongly 

correlated with nutrient concentrations than the others 

(IBI, %SUCKER, #INTOL, %RIVERSP, #RIVERSP, and 

%LITSPAWN; listed in decreasing order of the strength 

of their relations). These indices were most significantly 

correlated with the three P constituents, TN, and TKN. 

Fish Assemblages and Their Relations with Water-Quality and Environmental Characteristics 53
54 Nutrient Concentrations and Their Relations to the Biotic Integrity of Nonwadeable Rivers in Wisconsin

Figure 18. Distributions (quintiles) for four fish indices [Wisconsin large-river index of biotic integrity (IBI), percentage of 

suckers by weight (%SUCKER), number of intolerant fish species (#INTOL), and percentage of individuals that are riverine species 

(%RIVERSP)] for the studied nonwadeable rivers in Wisconsin.

Index value

5.0 – 43.7

43.8 – 65.0

65.1 – 80.0

80.1 – 95.0

95.1 – 100

Index of Biotic Integrity (IBI) Percentage of suckers by weight (%SUCKER)

Number of intolerant species (#INTOL) Percentage of riverine species (%RIVERSP)

Number

0

1

2

3

4 – 7

Percentage

0.6 – 10.8

10.9 – 28.3

28.4 – 42.4

42.5 – 75.7

75.8 – 97.4

Percentage

0 – 10.7

10.8 – 20.8

20.9 – 32.8

32.9 – 40.7

40.8 – 79.0

16 15

17

19

20

1

18

21 22 24

23

25

26

27

28 29

30

33 32

34

31 2

40 41

38

39

35

37

36

42

14

13

7

11 10 12

9

8 6 5

4

16 15

17

19

20

1

18

21 22 24

23

25

26

27

28 29

30

33 32

34

31 2

40 41

38

39

35

37

36

42

14

13

7

11 10 12

9

8 6 5

4

16 15

17

19

20

1

18

21 22 24

23

25

26

27

28 29

30

33 32

34

31 2

40 41

38

39

35

37

36

42

14

13

7

11 10 12

9

8 6 5

4

16 15

17

19

20

1

18

21 22 24

23

25

26

27

28 29

30

33 32

34

31 2

40 41

38

39

35

37

36

42

14

13

7

11 10 12

9

8 6 5

4
Table 15. Spearman rank correlation coefficients (rs) between fish-community indices and median water-quality, land-use, basin, and soil/surficial-deposit characteristics for 

the studied nonwadeable rivers in Wisconsin.

[All absolute values greater than 0.30 (Student’s t-test; bold black values) and 0.48 (Bonferroni correction; bold red values) were significant at p < 0.05. All characteristics are defined in table 2 except fish indices, which are defined in table 10.]

Characteristic #NATIVESP #RIVERSP #SUCKER #INTOL WPUE %RIVERSP %LITSPAWN %SUCKER %INSECT %DISEASEa IBI

Water-quality characteristics

TP -0.25 -0.42 -0.32 -0.61 -0.05 -0.44 -0.43 -0.50 -0.41 0.30 -0.51

DP -.30 -.36 -.33 -.63 -.19 -.39 -.30 -.41 -.35 .37 -.47

PP -.22 -.40 -.30 -.56 .00 -.43 -.46 -.49 -.41 .25 -.50

TN -.09 -.30 -.20 -.49 -.08 -.33 -.49 -.53 -.42 .24 -.43

NO3-N .07 -.03 -.08 -.36 -.09 -.17 -.35 -.46 -.37 .31 -.28

NH4-N -.31 -.44 -.45 -.24 -.22 -.31 -.37 -.43 -.44 .13 -.46TKN -.20 -.51 -.32 -.44 .02 -.43 -.68 -.51 -.45 .20 -.50

SCHL -.27 -.44 -.29 -.41 .22 -.35 -.51 -.28 -.24 .16 -.37

SD .27 .51 .31 .60 .04 .42 .52 .47 .38 -.23 .53

SSC -.15 -.33 -.23 -.51 -.05 -.43 -.46 -.49 -.39 .18 -.46

WTemp -.38 -.46 -.28 -.37 .10 -.28 -.40 -.24 -.20 .19 -.42

SC -.20 -.42 -.31 -.49 -.34 -.45 -.60 -.63 -.54 .18 -.59

pH -.11 -.19 -.14 -.20 .12 -.25 -.35 -.23 -.11 .03 -.20

Color -.07 -.13 -.02 .20 .04 -.01 .04 .14 .02 -.14 .08

Land-use characteristics

Urb % -.27 -.48 -.33 -.46 -.02 -.34 -.53 -.39 -.37 .17 -.49

AgRow % -.22 -.45 -.34 -.53 -.14 -.43 -.62 -.53 -.42 .26 -.55

AgOther % -.18 -.32 -.28 -.52 -.14 -.41 -.52 -.54 -.44 .28 -.50

Ag % -.24 -.42 -.30 -.55 -.19 -.43 -.62 -.58 -.48 .24 -.57

Grass % .06 -.12 -.16 -.29 .08 -.15 -.21 -.17 -.13 .27 -.14

WetO % -.20 -.28 -.11 -.07 .16 .01 .03 .21 .13 .03 .04

WetF % .15 .21 .14 .38 -.03 .16 .26 .30 .24 -.14 .30

Barren % -.15 -.23 -.26 -.16 .33 .01 -.09 .05 .03 -.02 -.09

For % .29 .50 .36 .60 .10 .40 .55 .49 .41 -.25 .53

Fish Assemblages and Their Relations with Water-Quality and Environmental Characteristics 55
56 Nutrient Concentrations and Their Relations to the Biotic Integrity of Nonwadeable Rivers in Wisconsin Table 15. Spearman rank correlation coefficients (rs) between fish-community indices and median water-quality, land-use, basin, and soil/surficial-deposit characteristics for the studied nonwadeable rivers in Wisconsin—Continued. [All absolute values greater than 0.30 (Student’s t-test; bold black values) and 0.48 (Bonferroni correction; bold red values) were significant at p < 0.05. All characteristics are defined in table 2 except fish indices, which are defined in table 10.] Characteristic #NATIVESP #RIVERSP #SUCKER #INTOL WPUE %RIVERSP %LITSPAWN %SUCKER %INSECT %DISEASEa IBI Basin characteristics Area 0.11 0.18 0.24 0.17 -0.14 0.06 -0.01 0.05 0.05 0.06 0.11 ATemp -.25 -.45 -.34 -.58 -.15 -.44 -.59 -.51 -.45 .20 -.57 PPT -.14 -.26 -.17 -.47 .04 -.20 -.21 -.25 -.19 .09 -.31 Evap -.18 -.30 -.24 -.56 -.03 -.29 -.38 -.36 -.29 .24 -.42 Runoff .26 .45 .36 .61 .08 .47 .51 .52 .49 -.32 .55 Length .15 .24 .24 .31 -.16 .07 -.01 .02 -.03 .03 .14 BSlope .15 .18 .04 .23 -.06 -.03 .03 -.08 -.06 -.06 .05 Flow .30 .41 .20 .31 -.08 .19 .26 .23 .23 -.06 .33 Soil/surficial-deposit characteristics SClay -.27 -.38 -.24 -.55 -.14 -.37 -.51 -.52 -.43 .23 -.53 Erod -.27 -.31 -.18 -.42 -.06 -.21 -.33 -.34 -.28 .15 -.37 OM .02 .03 .12 .24 -.08 .17 .23 .25 .18 -.05 .17 Perm .16 .16 .05 .31 .02 .20 .15 .29 .19 -.18 .24 SSlope .16 .38 .25 .14 -.01 .24 .37 .17 .19 -.24 .23 NonGlac -.08 -.03 .03 -.44 .06 -.09 -.11 -.17 -.13 .16 -.20 SDClay .03 -.22 -.23 -.09 -.21 -.33 -.59 -.49 -.50 .02 -.33 Loam .23 .37 .31 .27 .02 .17 -.22 -.07 -.04 .02 .16 Peat -.06 .17 .22 .12 .23 .38 .38 .46 .33 .16 .34 Sand -.17 -.11 .03 .14 .28 .22 .20 .33 .24 .19 .18 SG .29 .20 .15 .40 -.12 .10 .01 -.04 -.01 -.12 .16

a For all of these fish indices except %DISEASE, higher index values are thought to represent better water quality than lower index values.
Fish Assemblages and Their Relations with Water-Quality and Environmental Characteristics 57

All of the indices (except %DISEASE) were negatively 

correlated with most nutrient constituents, although some 

of the correlations were not statistically significant. The 

#NATIVESP was only weakly correlated with the nutrient 

constituents, and WPUE was not statistically correlated 

with any nutrient constituent. Of the nutrient constituents, 

NO3-N was significantly correlated with the fewest fish 

indices.

The six fish indices most strongly related to nutrient 

concentrations were also the indices most strongly correlated with SCHL, SD, and the land-use characteristics. 

These fish indices were most strongly correlated with 

Ag %, AgRow %, Urb %, and For %. High index values 

(better fish index values) were correlated with low Ag % 

and Urb % and high For %. IBI, #INTOL, %LITSPAWN, 

and %SUCKER were more strongly correlated with 

several basin characteristics (air temperature, runoff, and 

evaporation) and soil characteristics (clay content in the 

soils and surficial deposits) than were the other indices. In 

general, rivers with better fish index values had cool air 

temperatures, high runoff, and soils with low clay content; 

these characteristics are generally found in the northern 

part of the State with mixed and mostly forested areas 

(figs. 1A and 18).

Responses to Changes in Nutrient 

Concentrations 

Responses of the four fish-assemblage indices most 

strongly correlated with nutrients (IBI, %SUCKER, 

#INTOL, %RIVERSP) are shown with respect to median 

TP and TN concentrations in figure 19, and the responses 

of the two indices most strongly related with nutrients (IBI 

and %SUCKER) are shown with respect to median DP, 

NO3

-N, NH4-N, and TKN concentrations in figure 20. IBI 

and %SUCKER were chosen as the best fish indices and 

used for additional detailed investigation because correlation analyses indicated that they were the most responsive 

to differences in nutrient concentrations, and because 

they appeared to be most representative of the other fish 

indices. The LOESS-smoothing lines indicate a consistent 

decrease in all of these indices as nutrient concentrations 

increased (except for TN). All of the graphs show a wide 

range in index values at any nutrient concentration; however, at low nutrient concentrations, the measured indices 

had a slightly larger range in values than at higher nutrient 

concentrations. The variability at any nutrient concentration may indicate that factors in addition to nutrients are 

affecting the fish assemblages.

The LOESS-smoothing lines on the scatterplots 

between nutrient concentrations and fish-index values 

(figs. 19 and 20) indicate that, for some nutrient constituents, there is a nonlinear relation. To define the thresholds 

or largest breakpoints in these responses, regression-tree 

analyses were done (table 16). The ranges in the breakpoints in response to changes in nutrient concentrations 

were: 0.055 to 0.147 mg/L for TP, 0.035 to 0.081 mg/L for 

DP, 0.010 to 0.064 mg/L for PP, 0.634 to 1.965 mg/L for 

TN, 0.030 to 0.241 mg/L for NO3

-N, 0.016 to 0.051 mg/L 

NH4-N, and 0.505 to 1.075 mg/L for TKN. All of the 

breakpoints for TP and TN were statistically significant at 

p < 0.05. These analyses indicated that the values of these 

six fish indices changed the most over a relatively narrow 

range in concentrations for each constituent. None of the 

breakpoints exceeded 0.15 mg/L for TP or 2.0 mg/L for 

TN, similar to the results for the macroinvertebrate indices. 

However, unlike the trends in the macroinvertebrate indices, at concentrations above even the highest threshold or 

breakpoint values, the biotic indices usually continued to 

degrade.

Effects of Multiple Characteristics on Fish 

Indices

Stepwise Regressions

Forward stepwise regressions were done with the 

median water-quality and environmental characteristics to 

determine which three characteristics best described the 

variance in the six fish indices most strongly correlated 

with nutrient concentrations (table 17). Models with more 

than three variables did not significantly increase the 

amount of variance explained. TP, TKN, air temperature, 

and runoff were the first variables incorporated into these 

models. Runoff is highly correlated with all of the nutrient 

constituents; when runoff was omitted from the analyses, 

TP or Ag % were the first variables incorporated into the 

models. Several other variables, mostly describing nutrient 

concentrations, land use, or soil type, were incorporated 

into the models as the second and third variables. With 

three variables, the models explained between 37 and 

63 percent of the variance in the indices. 
58 Nutrient Concentrations and Their Relations to the Biotic Integrity of Nonwadeable Rivers in Wisconsin

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LOESS-smoothing

Lower 95-percent confidence limit

Upper 95-percent confidence limit

Threshold/breakpoint

Figure 19. Wisconsin large-river index of biotic integrity (IBI), percentage of suckers by weight (%SUCKER), number of intolerant 

fish species (#INTOL), and percentage of individuals that are riverine species (%RIVERSP) as a function of total phosphorus (TP) 

and total nitrogen (TN) concentration for the studied nonwadeable rivers in Wisconsin. LOESS-smoothing lines with 95-percent 

confidence limits and computed thresholds in the response, identified by vertical lines, are given on each graph. The default 

parameter for the LOESS-smoothing lines was changed to 0.6 for the relation between TN and %SUCKER.
Fish Assemblages and Their Relations with Water-Quality and Environmental Characteristics 59

Figure 20. Wisconsin large-river index of biotic integrity (IBI) and percentage of suckers by weight (%SUCKER) as a function 

of dissolved phosphorus (DP), dissolved nitrite plus nitrate nitrogen (NO3-N), dissolved ammonia nitrogen (NH4-N), and total 

Kjeldahl nitrogen (TKN) concentrations for the studied nonwadeable rivers in Wisconsin. LOESS-smoothing lines with 95-percent 

confidence limits and computed thresholds in the response, identified by vertical lines, are given on each graph. The default 

parameter was changed to 0.6 for the LOESS-smoothing lines for the relation between dissolved nitrite plus nitrate (NO3

-N) and 

%SUCKER.

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60 Nutrient Concentrations and Their Relations to the Biotic Integrity of Nonwadeable Rivers in Wisconsin Table 16. Thresholds or breakpoints in the responses of fish indices to changes in nutrient concentrations for nonwadeable rivers in Wisconsin. [All concentrations in milligrams per liter; nss, not statistically significant at p < 0.05] Index Total phosphorus Dissolved phosphorus Particulate phosphorus Total nitrogen Dissolved nitrite plus nitrate Dissolved ammonia Total Kjeldahl nitrogen Wisconsin large-river index of biotic integrity (IBI) 0.139 0.045 0.051 0.635 0.191 0.051 1.075 Percentage of suckers by weight (%SUCKER) .091 .045 .051 .634 .134 .043 .505 Number of intolerant species (#INTOL) .139 .051 .051 1.125 .241 .051 .613 Percentage of individuals that are river species (%RIVERSP) .079 .035 .015 .556 .030 .026 .505 Number of river species (#RIVERSP) .147 .081 .064 1.965 .078 (nss) .016 .993 Percentage of individuals that are lithophilic spawners (%LITSPAWN) .055 .035 .019 .634 .147 .043 .505 Table 17. Results from forward stepwise-regression analyses to explain variance in fish indices for the studied nonwadeable rivers in Wisconsin.

[rs, Spearman correlation coefficient; 

R2, coefficient of determination for the one-, two-, and three-variable models]

Dependent variable First variable Second variable Third variable

Wisconsin large-river index of biotic integrity (IBI) Air temperature Ammonia Total nitrogen

rs -0.57 -0.46 -0.43Accumulative 

R2 .34 .51 .59

Percentage of suckers by weight (%SUCKER) Runoff Sand and gravel deposits Grassland

rs .36 .03 -.16Accumulative 

R2 .35 .48 .57

Number of species considered intolerant of degradation (%INTOL) Total phosphorus Wetland (open) Nonglacial deposits

rs -.61 -.07 -.44Accumulative 

R2 .34 .41 .49

Percentage of individuals that are river species (%RIVERSP) Runoff Wetland (forested) Particulate phosphorus

rs .47 .16 -.43Accumulative 

R2 .22 .32 .37

Number of river species (#RIVERSP) Total Kjeldahl nitrogen Loam deposits Flow per unit area

rs -.51 .37 .41Accumulative 

R2 .28 .35 .42

Percentage of individuals that are lithophilic spawners (%LITSPAWN) Total Kjeldahl nitrogen Clay deposits Total agriculture

rs -.68 -.59 -.62Accumulative 

R2 .38 .54 .63
Multiparameter Biotic Indices to Estimate Nutrient Concentrations in Nonwadeable Rivers 61

Redundancy Analysis 

The forward-selection procedures in RDA were used 

to identify the key characteristics to include from each of 

three categories: nutrients, other water-quality characteristics, and environmental characteristics. The RDA analysis 

retained DP and TKN from the nutrient category; SC, SD, 

SCHL, and water color from the other water-quality category; and For % from the environmental-characteristics 

category. RDA was then run again with only these seven 

key characteristics. RDA based on these key characteristics 

and the use of multiple axes explained 44 percent of the 

variance in the fish indices (p < 0.01). The first two RDA 

axes accounted for 91 percent of the variance explained 

by the full model (all of the axes). The first axis alone 

accounted for 72 percent of the total explained variance. 

On RDA axis 1, almost all of the fish indices (except 

%DISEASE) were related positively with SD and For % 

and negatively with SCHL, TKN, DP, and SC (fig. 21). 

RDA axis 2 accounted for 19 percent of the total explained 

variance. On RDA axis 2, WPUE was related positively 

with SCHL, TKN, and water color. The other nutrient 

characteristics and AgRow %, when plotted in a similar 

manner, corresponded strongly with RDA axis 1 and were 

related to the fish indices similarly to DP, SCHL, SC, and 

TKN. Therefore, if these characteristics were used instead 

of DP, SCHL, and TKN, the results would have been 

similar.

Partial RDA was then used to determine the relative 

importance of nutrients, other water-quality characteristics, 

environmental characteristics, and interactions among categories in affecting the fish assemblages (11 fish indices). 

The same characteristics found with the forward variableselection procedure were used to describe each category 

of characteristics. These 4 categories explained 45 percent 

of the variance in the 11 fish indices (fig. 22). Of the total 

variance, 11 percent was described by the nutrients alone 

(25 percent of the explained variance), 17 percent by the 

other water-quality characteristics alone, and 3 percent by 

the environmental characteristics alone. About 55 percent 

of the total variance could not be explained with the characteristics in this study, and an additional 14 percent of the 

total variance could not be separated into a single category 

of characteristics. Although the selected characteristics 

explained less of the variance in the fish indices than in 

the macroinvertebrate indices, nutrients by themselves 

explained much more of the total variance in the fish indices than the macroinvertebrate indices. 

Reference Values for the Fish Indices

The use of different approaches provides a range in 

estimated reference conditions for the fish indices. Reference values for the six fish indices most related to nutrient 

concentrations (IBI, %SUCKER, #INTOL, %RIVERSP, 

#RIVERSP, and %LITSPAWN; table 18) were determined 

by using the same approaches that were used to determine 

reference conditions for the macroinvertebrate indices. 

Median values for the Reference sites or minimally 

impacted sites (sites with both reference TP and reference 

TN concentrations) were higher than those estimated by 

the other approaches for all of the indices except for 

#RIVERSP. The distributions of index values for the 

6 Reference sites are compared with the distribution of 

values for the 29 High sites for the 4 fish indices most 

strongly related to nutrient concentrations in figure 23. For 

each of these indices, the median values for the Reference 

sites were significantly higher than those for the High 

sites. The other approaches provided similar reference 

conditions for all of the fish indices (table 18). 

Multiparameter Biotic Indices to 

Estimate Nutrient Concentrations in 

Nonwadeable Rivers 

One goal of this study was to estimate nutrient 

concentrations in rivers from the biotic data. Individual 

relations between specific biotic indices and TP and TN 

concentrations explained 45 and 35 percent of the variance 

or less, respectively (SCHL explained the most variance 

in TP and TN concentrations). Combining several biotic 

indices, however, was expected to improve these relations. 

To develop multiparameter indices to estimate TP and TN 

concentrations in nonwadeable rivers, the biotic indices 

found to be most strongly related to differences in nutrient 

concentrations were input into forward stepwise-regression 

analyses. Thirteen biotic indices were included in this 

analysis: one describing the amount of suspended algae in 

the stream (chlorophyll a concentrations: Log SCHL), six 

describing the macroinvertebrate assemblages (table 12), 

and six describing the fish assemblages (table 16). 
62 Nutrient Concentrations and Their Relations to the Biotic Integrity of Nonwadeable Rivers in Wisconsin

Figure 21. Redundancy analysis (RDA) results for fish indices and nutrients, other water-quality, and environmental 

characteristics: axis 2 scores are plotted as a function of axis 1 scores. Parameters describing each category were determined 

by forward-selection procedures in RDA. Water-quality and land-use abbreviations are defined in table 2 and fish abbreviations 

are defined in table 10.

-1

0

1

-1 0 1

FOR %

TKN

SCHL

DP

SD SC

TP

TN AgRow %

%RIVERSP

WPUE

IBI

#SUCKER 

#NATIVESP

%INSECT

%DISEASE

%RIVERSP

%LITSPAWN

#INTOL

%SUCKER

R2 = 0.44

Color

RDA AXIS 1

RDA AXIS 2

PP

Macroinvertebrate indices

Other characteristics 

not included in analysis

EXPLANATION

Figure 22. Percentages of variance in 11 fish-index values described by nutrients, other water-quality characteristics, 

environmental (land-use, soil, and surficial-deposit) characteristics, interactions (variance that cannot be explained by a single 

category), and unexplained variance for the studied nonwadeable rivers in Wisconsin. [%, percentage of total variance]

11%

17%

3%

14%

55%

Nutrients

General categories

Environmental characteristics

Interactions among categories

Unexplained

Other water-quality characteristics
Table 18. Reference conditions for six fish indices for nonwadeable rivers in Wisconsin.

[nss, not statistically significant at p < 0.1]

Index Abbreviation Best 25th percentile for all dataa Median of Reference sites Worst 75th percentile for Reference sitesa Regression approach (mean) Regression approach (worst 95-percent confidence limit)a

Wisconsin large-river index of biotic integrity IBI 90.0 95.0 87.5 87.0 74.9Percentage of suckers by weight %SUCKER 71.8 83.5 76.6 66.6 53.1Number of species considered intolerant of degradation #INTOL 3 4 3.3 3.7 3.0Percentage of individuals that are river species %RIVERSP 39.4 43.8 29.7 38.9 29.4Number of river species #RIVERSP 7 6 (nss) 4.3 6.4 5.0Percentage of individuals that are lithophilic spawners %LITSPAWN 73.1 87.6 84.5 73.5 62.7

a Higher values for all of these indices were shown to represent better water quality; therefore, the best 25th percentile for all of the data was obtained from the 75th percentile of all of the data, and the worst 

75th percentile for the Reference sites was obtained from the 25th percentile for the Reference sites, and the worst 95-percent confidence limits was obtained from the lower 95-percent confidence limits from the 

regression approach.

Multiparameter Biotic Indices to Estimate Nutrient Concentrations in Nonwadeable Rivers 63
64 Nutrient Concentrations and Their Relations to the Biotic Integrity of Nonwadeable Rivers in Wisconsin

Figure 23. Wisconsin large-river index of biotic integrity (IBI), percentage of suckers by weight (%SUCKER), number of 

intolerant fish species (#INTOL), and percentage of individuals that are riverine species (%RIVERSP) in Reference sites 

and High (nonreference) sites in the studied nonwadeable rivers in Wisconsin.

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REFERENCE HIGH REFERENCE HIGH

REFERENCE HIGH REFERENCE HIGH

EXPLANATION

Outlier

Interquartile

range (IR)

75th percentile + 1.5 * IR

25th percentile – 1.5 * IR

75th percentile 

25th percentile 

Median
Summary of Results for Nonwadeable Rivers and Wadeable Streams 65

The three-parameter model to estimate TP concentrations in nonwadeable rivers included indices describing the 

amount of suspended algae (Log SCHL), the fish assemblage (#INTOL), and the macroinvertebrate assemblage 

(%PLEC). This model explained 63 percent of the variance 

in TP concentrations (table 19, fig. 24A). The three-parameter model to estimate TN concentrations included indices 

describing the amount of suspended algae (Log SCHL), 

the fish assemblage (%SUCKER), and the macroinvertebrate assemblage (MPTV). This model explained 

51 percent of the variance in TN concentrations (table 19, 

fig. 24B). Models with more than three variables did not 

significantly increase the amount of explained variance. 

The regression equations described in table 19 were 

then used to develop multiparameter biotic indices to 

estimate TP and TN concentrations in nonwadeable rivers. The indices were developed (by including additional 

coefficients) to provide values ranging from 1 to 10, with 1 

representing the lowest TP and TN concentrations and 10 

representing the highest concentrations. 

The Biotic Index of total P (BIP) is computed as

BIP = 5.0 x (-1.167 + 0.428 Log SCHL – 

 0.125 #INTOL + 5.601 %PLEC) + 

 10.0, and (12) 

the Biotic Index of total N (BIN) is computed as

BIN = 6.67 x (0.764 + 0.394 Log SCHL – 

 0.578 %SUCKER – 0.151 MPTV) + 

 4.0. (13)

BIP and BIN estimated the measured Log median 

TP and TN concentrations equally well over the range of 

concentrations measured in this study (fig. 24); however, 

BIP estimated TP concentrations better than the BIN 

estimated TN concentrations (63 percent of the variance 

in TP explained by the BIP compared to 51 percent of 

the variance in TN explained by the BIN). The difference 

in the predictability of these indices was consistent with 

most of the biotic indices being more strongly correlated 

with TP concentrations than with TN concentrations. This 

difference in predictability indicates that TP concentrations 

are more important than TN concentrations in affecting the 

biotic communities over the range in nutrient concentrations measured in this study. 

Summary of Results for 

Nonwadeable Rivers and Wadeable 

Streams

Excessive nutrient input from point and nonpoint 

sources into streams and rivers is frequently associated 

with degraded water quality. Point-source discharges of 

nutrients are fairly constant and are controlled by the 

U.S. Environmental Protection Agency’s (USEPA’s) 

National Pollutant Discharge Elimination System. To 

reduce input from agricultural areas, performance standards and regulations for croplands and livestock operations are being proposed by various States. In addition, the 

USEPA is establishing regionally based nutrient criteria 

that can be refined by each State to determine whether 

actions are needed to improve water quality. More confidence in the environmental benefits of the proposed 

standards and nutrient criteria would be possible with an 

improved understanding of the biotic responses to a range 

of nutrient concentrations in different environmental settings. 

Table 19. Results from forward stepwise-regression models to explain variances in total phosphorus and total nitrogen 

concentrations with biotic indices for the studied nonwadeable rivers in Wisconsin.

[All regressions were on log-transformed concentrations; r, Pearson correlation coefficient; R2, coefficient of determination for the one-, two-, and 

three-variable models; SCHL, suspended chlorophyll a concentration; see table 10 for definitions of abbreviations and units for each biotic index]

Constant First variable Second variable Third variable

Total phosphorus (TP) Log SCHL #INTOL %PLEC

Coefficient -1.167 0.428 -0.125 5.601

r .67 -.58 -.08

Accumulative R2 .44 .58 .63

Total nitrogen (TN) Log SCHL %SUCKER MPTV

Coefficient 0.764 .394 -.578 -.151

r .59 -.55 .37

Accumulative R2 .32 .46 .51
66 Nutrient Concentrations and Their Relations to the Biotic Integrity of Nonwadeable Rivers in Wisconsin

Figure 24. Measured and estimated 

A, total phosphorus and 

B, total nitrogen concentrations (logarithm to base 10 transformation) for the three-parameter regression models; 

C, measured phosphorus concentrations as a function of Biotic Index of total Phosphorus (BIP) values; and 

D, measured nitrogen concentrations as a function of Biotic Index of 

total Nitrogen (BIN) values, with regression equations and coefficients of determination (R2) for the studied nonwadeable rivers in Wisconsin.

y = 0.63x - 0.38

R2 = 0.63

-0.5

-1.0

-1.5

-2.0

0.0

-2.0 -1.5 -1.0 -0.5 0.0

MEASURED LOG (TOTAL PHOSPHORUS, IN MILLIGRAMS PER LITER)

ESTIMATED LOG (TOTAL PHOSPHORUS,

IN MILLIGRAMS PER LITER) y = 0.51x + 0.06

R2 = 0.51

-0.1-0.3-0.5

0.70.50.30.1

-0.5 -0.3 -0.1 0.1 0.3 0.5 0.7

MEASURED LOG (TOTAL NITROGEN,IN MILLIGRAMS PER LITER)

ESTIMATED LOG (TOTAL NITROGEN,

IN MILLIGRAMS PER LITER)

y = 0.20x - 2.00

R 2 = 0.63

-0.5-1.0-1.5-2.0

0.0

0 1 2 3 4 5 6 7 8 9

BIOTIC INDEX OF TOTAL PHOSPHORUS (BIP)

MEASURED LOG (TOTAL PHOSPHORUS,

IN MILLIGRAMS PER LITER)

y = 0.15x - 0.60

R 2 = 0.51

-0.5-1.0

1.00.50.0

0 1 2 3 4 5 6 7 8

BIOTIC INDEX OF TOTAL NITROGEN (BIN)

MEASURED LOG (TOTAL NITROGEN,

IN MILLIGRAMS PER LITER)

A. B.C. D.
Summary of Results for Nonwadeable Rivers and Wadeable Streams 67

To provide the information needed by water-resource 

managers to develop regionally based nutrient criteria 

for Wisconsin’s streams and rivers, the U.S. Geological 

Survey (USGS) and Wisconsin Department of Natural 

Resources (WDNR) collected water-quality and biotic 

data in 240 wadeable streams and 42 nonwadeable rivers 

throughout Wisconsin to (1) describe how nutrient concentrations and biotic-community structure differ throughout 

the State, (2) determine which environmental characteristics are most strongly related to the distribution of nutrient 

concentrations, (3) determine reference water-quality and 

biotic conditions for streams and rivers throughout the 

State, (4) determine how biotic communities respond to 

differences in nutrient concentrations, (5) determine the 

best regionalization scheme to describe the patterns in 

reference conditions and the corresponding responses in 

water quality and the biotic communities (primarily for 

smaller streams), and (6) develop new indices to estimate 

nutrient concentrations from a combination of biotic 

indices. This report primarily describes the results for the 

nonwadeable rivers, but in this section, the results are compared with the results for the wadeable streams. 

Water Quality

Nutrient concentrations were consistently highest in 

the nonwadeable rivers in the southern and western parts 

of the State. In general, nutrient concentrations in the 

wadeable streams also had this same pattern; however, the 

wadeable streams, such as those in the central part of the 

State, had many isolated areas with high concentrations. 

Nutrient concentrations in the nonwadeable rivers typically 

had a smaller range in concentrations (primarily because 

of lower maximum values) than the wadeable rivers. These 

differences reflect the fact that the basins of the larger 

rivers integrate many small streams and the more predominant land uses, and that wadeable streams are more likely 

to be affected by local point sources. The basins of the 

nonwadeable rivers may have included more point sources, 

but their effects were diluted by inflow from many other 

tributaries in the basin.

The overall median and mean TP and TN concentrations were similar in the nonwadeable rivers and wadeable 

streams. The median TP and TN concentrations in the 

nonwadeable rivers were 0.109 and 1.268 mg/L, respectively (table 2), compared to 0.085 and 1.695 mg/L in the 

wadeable streams (table 2 in Robertson and others, 2006a). 

The proportion in different forms of the nutrients was quite 

different. In the nonwadeable rivers, most of the nutrients 

were in particulate forms (approximately 55–75 percent for 

P and 45–80 percent for N), whereas in wadeable streams 

approximately 30–45 percent of the P and 20–45 percent 

of the N were in particulate forms. In nonwadeable rivers, 

N was about equally partitioned between NO3-N and TKN; 

however, in the wadeable streams, there was about twice as 

much NO3-N as TKN. 

The nonwadeable rivers had much higher SCHL concentrations and lower clarity than the wadeable streams. 

The overall median and mean SCHL concentrations in the 

nonwadeable rivers were 7.31 and 18.47 μg/L (table 2), 

respectively, compared to only 2.27 and 3.23 μg/L, respectively, in the wadeable streams (table 2 in Robertson and 

others, 2006a). The overall median and mean SDs in the 

nonwadeable rivers were 60.5 and 71.8 cm, respectively, 

compared to 112 and 97.3 cm, respectively, in the wadeable streams. The lower concentrations of dissolved nutrients in the nonwadeable streams, higher SCHL, and lower 

clarity indicate that more of the nutrients are taken up by 

the algae in the larger rivers.

The watersheds of the nonwadeable rivers usually 

continued across several areas defined by the regionalization schemes evaluated for the wadeable streams (environmental phosphorus zones and Omernik ecoregions); for 

this reason, regional differences in the relations between 

nutrient concentrations and biotic responses were not 

examined in detail. The only regional response found in 

large rivers was for SCHL. Concentrations of SCHL in 

rivers in the southwestern part of the State (Driftless Area 

ecoregion) were lower than would be expected given the 

nutrient concentrations, possibly because of the relatively 

high SSC concentrations in rivers in that area.

The water quality of the nonwadeable rivers was significantly related to many of the same environmental characteristics as the water quality in the wadeable streams; 

however, in general, the correlations were much stronger 

for the nonwadeable rivers. The water quality of small 

streams and large rivers was strongly related to the land 

use/land cover in the basin [percentage of forested (For %) 

and agricultural (Ag %) land], runoff from the basin, and 

the clay content, erodibility, and permeability of the soil. 
68 Nutrient Concentrations and Their Relations to the Biotic Integrity of Nonwadeable Rivers in Wisconsin

More variability in the water-quality characteristics 

(TP, TN, SCHL, and SD) was explained by the environmental characteristics for the nonwadeable rivers (74 percent; fig. 7) than for the wadeable streams (43 percent). 

Concentrations of TP and TN in the nonwadeable rivers 

explained 45 and 35 percent, respectively, of the variance 

in Log SCHL concentrations and 77 and 50 percent of the 

variance in SDs, respectively (fig. 10). These percentages 

are higher than the percentages explained for small streams 

(10 to 28 percent for SCHL and SD; fig. 7 in Robertson 

and others, 2006a). Although nutrient concentrations by 

themselves explained only a small part of the variance in 

SCHL concentrations and SDs in the nonwadeable rivers 

and wadeable streams (based on redundancy analyses), 

nutrients by themselves explained more of the total variance in the nonwadeable rivers (about 20 percent of the 

variance in SCHL and SDs; fig. 8) than in the wadeable 

streams (only about 12 percent). This, again, indicates that 

nutrients are more important for larger nonwadeable rivers 

than for smaller wadeable streams. 

The reference water-quality conditions in the nonwadeable rivers (table 20) are similar to those in the 

wadeable streams (table 22 in Robertson and others, 

2006a). The best estimates of median reference TP and 

TN concentrations in the nonwadeable rivers (regression 

approach) were 0.035 and 0.514 mg/L, respectively, compared to 0.03–0.04 and 0.4–0.7, respectively, for the wadeable streams. The range in reference concentrations for the 

wadeable streams was the result of subdividing the State 

into areas with high clay-content soils (Zone 3; fig. 1) 

and the rest of the State. The best estimate of a median 

reference SCHL concentration for the nonwadeable rivers 

was 3.95 μg/L, which is higher than that for the wadeable 

streams (1.0 to 1.7 μg/L). The best estimate of a median 

reference SD for rivers and streams was about 110 cm.

Reference values from this study are similar to those 

defined by the USEPA for nutrient ecoregion 7 (southern 

part of the State), but higher than those defined for nutrient 

ecoregion 8 (northern part of the State). The USEPA 

defined reference TP and TN concentrations for nutrient 

ecoregion 7 as 0.033 and 0.54 mg/L, respectively, and 

for nutrient ecoregion 8 as 0.010 and 0.20–0.38 mg/L, 

respectively. The lower values defined by the USEPA 

for the northern part of the State were probably a result 

of their values being estimated with the 25th-percentile 

approach, whereas most of the watersheds of streams 

and rivers in those areas are dominated by forest, and 

probably less than 25 percent of the streams and rivers 

were substantially affected by anthropogenic factors. 

The reference SCHL concentration found in this study 

(3.8–3.9 μg/L) is close to those defined by the USEPA 

using the Trichromatic Method of analysis: 5.8 μg/L for 

the southern part of the State and 4.3 μg/L for the northern 

part of the State.

Response in the Biotic Communities 

In general, biotic communities in wadeable streams 

and nonwadeable rivers in the southern half of the State, 

especially the southeastern part, were representative 

of poorer water quality than in the streams and rivers 

throughout the rest of the State. Nutrient concentrations 

and probably other factors associated with agriculture and 

urbanization that affect the biotic community were highest 

in these southern streams and rivers. 

Reference conditions for the macroinvertebrate and 

fish indices that were most strongly related to differences 

in nutrient concentrations are listed in table 20. For each 

biotic category, the indices are listed in decreasing order 

of the strength of the relation with differences in nutrient 

concentrations [for macroinvertebrates, species richness 

(SPECIES) was most strongly related to nutrients, and 

for fish, the IBI was the most strongly related]. It has 

also been suggested that best 25th percentile of all data or 

the upper 75th percentile for a subset of sites thought to 

be minimally impacted for a defined area may represent 

the reference condition (U.S. Environmental Protection 

Agency, 2000a). The values for each biotic index at 

the best 25th percentile of all the data and the worst 

75th percentile of the subset of streams thought to be 

minimally impacted are listed in table 20. 

Changes in biotic indices as nutrient concentrations 

increase or decrease indicate that nutrients in wadeable 

streams and nonwadeable rivers have direct or indirect 

effects on the composition of the biotic community. The 

responses of most biotic indices to increases in nutrient 

concentrations in large nonwadeable rivers were nonlinear, 

with a broad range of values at all nutrient concentrations. The ranges of values for most macroinvertebrate 

indices were broad and of similar magnitude at all nutrient 

concentrations; in contrast, the ranges of values of many 

fish indices were broader at low nutrient concentrations 

and narrower, indicative of consistently poor conditions, 

at high nutrient concentrations (wedge-shaped response). 

In comparison, the responses of most biotic indices in 

small wadeable streams were broad at low nutrient concentrations and narrow at high nutrient concentrations. 
Summary of Results for Nonwadeable Rivers and Wadeable Streams 69

Table 20. Reference conditions for water-quality constituents and biotic indices for nonwadeable rivers in Wisconsin.

[nss, not statistically significant at p < 0.1; mg/L, milligram per liter; μg/L, microgram per liter; cm, centimeter; >, greater than]

Constituent/index Abbreviations

Regression approach Percentile approach

Median 

reference

Worst 

95-percent 

confidence limit

Best 25th

percentile 

for all data

Worst 75th

percentile for 

Reference sites

Water-quality constituents

Total phosphorus (mg/L) TP 0.035 0.045 0.034 --

Dissolved phosphorus (mg/L) DP .016 .021 .017 --

Particulate phosphorus (mg/L) PP .018 .025 .018 --

Total nitrogen (mg/L) TN .514 .604 .670 --

Dissolved nitrite plus nitrate (mg/L) NO3

-N .061 .107 .132 --

Dissolved ammonia (mg/L) NH4

-N .022 .022 .019 --

Total Kjeldahl nitrogen (mg/L) TKN .434 .524 .500 --

Suspended chlorophyll a (μg/L) SCHL 3.9 6.2 3.8 3.8

Secchi-tube depth (cm) a SD 110 96 >120 >120

Suspended sediment (mg/L) SSC 3.2 4.9 4.0 2.8

Macroinvertebrate indices

Species richness a SPECIES 38 34 38 29

Mean pollution tolerance value MPTV 4.8 5.2 4.8 4.7

Percentage of individuals from order 

Ephemeropteraa

%EPHEM 29.1

(p = 0.08)

20.0

(p = 0.08)

31.4 30.8

Hilsenhoff Biotic Index HBI 5.0

(p = 0.06)

5.6

(p = 0.06)

4.9 4.7

Percentage of individuals from order 

Plecopteraa

%PLEC 1.3 (nss) .3 (nss) .8 .3

Percentage of individuals that are scrapers a %SCRAP 13.7 (nss) 7.0 (nss) 12.5 11.8

Fish indices

Wisconsin large-river index of biotic 

integritya

IBI 87.0 74.9 90.0 87.5

Percentage of suckers by weight a %SUCKER 66.6 53.1 71.8 76.6

Number of species considered intolerant 

of degradationa

#INTOL 3.7 3.0 3.0 3.3

Percentage of individuals that are river 

species a

%RIVERSP 38.9 29.4 39.4 29.7

Number of river species a #RIVERSP 6.4 5.0 7.0 4.3

Percentage of individuals that are lithophilic 

spawners a

%LITSPAWN 73.5 62.7 73.1 84.5

a

 Higher values for this constituent/index was shown to represent better water quality; therefore, the worst 95-percent confidence limit was obtained 

from the lower 95-percent confidence limit from the regression approach, the best 25th percentile for all of the data was obtained from the 75th percentile 

of all of the data, and the worst 75th percentile for the Reference sites was obtained from the 25th percentile for the Reference sites.
70 Nutrient Concentrations and Their Relations to the Biotic Integrity of Nonwadeable Rivers in Wisconsin

This wedge-shaped response to increases in nutrient concentrations is common for relations between biotic indices 

and anthropogenic factors, such as the percentage of urban 

land use in an area (Wang and others, 2001; Wang, 2003). 

The wedge-shaped response indicates that at low nutrient concentrations, factors in addition to nutrients limit 

the health of the biotic communities, whereas in streams 

and rivers with high nutrient concentrations, nutrients or 

factors correlated with high nutrient concentrations may be 

the predominant factors affecting the biotic communities 

(Cade and others, 1999).

Changes in the biotic communities of wadeable 

streams and nonwadeable rivers were more strongly 

related to changes in TP concentrations than to changes in 

TN concentrations. Because the relations between nutrient concentrations and most biotic indices were nonlinear, 

thresholds or breakpoints in the responses were determined (table 21). A threshold represents the concentration at which the biotic community changes most rapidly 

and, therefore, represents a critical concentration with 

ecological significance. The thresholds in the responses 

to changes in TP concentrations were at slightly higher 

concentrations for nonwadeable rivers (0.034–0.150 mg/L) 

than for wadeable streams (0.04–0.09 mg/L; table 23 in 

Robertson and others, 2006a). These thresholds are only 

slightly higher than the estimated reference TP concentrations. The thresholds in the responses to TN concentrations ranged more widely for nonwadeable rivers 

(0.527–1.990 mg/L) and wadeable streams (0.5–1.2 mg/L). 

Thresholds for macroinvertebrate and fish indices fluctuated over these ranges. 

The biotic communities in a river reflect the overall 

ecological integrity (physical, chemical, and biological 

integrity). The biotic communities integrate the effects 

of many different stressors (such as extreme hydrologic 

conditions, extreme sedimentation rates, pesticides, and 

nutrients) over timespans of days to years and thus provide 

a broad measure of their aggregate effect. In addition, the 

geomorphologic and geochemical regimes, and land use/

land cover in the watershed control the physicochemical 

habitat in which the biota live. Results of redundancy 

analyses for nonwadeable rivers and wadeable streams 

indicated that nutrient concentrations alone explained 

only a small part of the variance in the biotic indices. For 

small streams and large rivers, nutrient concentrations by 

themselves explained only about 1 to 11 percent of the 

total variance in the biotic indices (about 2 to 25 percent of 

the explained variance), and were most strongly related to 

SCHL concentrations.

Through a combination of the biotic indices, two 

new multiparameter indices (BIP and BIN) were developed for streams and rivers. For both streams and rivers, the BIP estimated TP concentrations better than the 

BIN estimated TN concentrations. Both of these indices, 

however, predicted the nutrient concentrations better for 

nonwadeable rivers than for wadeable streams. The BIP 

explained 63 percent of the variance in TP concentrations 

for the rivers, but only 54 percent for the streams. The BIN 

explained 51 percent of the variance in TN concentrations 

for the rivers, but only 41 percent for the streams. The differences in the predictability of these indices are consistent 

with the stronger correlations between the biotic indices 

and TP concentrations than between the biotic indices and 

TN concentrations. These differences indicate that TP concentrations are more important than TN concentrations in 

affecting the biotic communities.
Summary of Results for Nonwadeable Rivers and Wadeable Streams 71

Table 21. Summary of thresholds or breakpoints in the responses of water quality and biotic indices to changes in nutrient concentrations for nonwadeable rivers in Wisconsin.[All concentrations given in milligrams per liter]

Biotic indices Abbreviation Total phosphorus Dissolved phosphorus Particulate phosphorus Total nitrogen Dissolved nitrite plus nitrate Dissolved ammonia Total Kjeldahl nitrogen

Water quality

Secchi-tube depth SD 0.091 0.045 0.048 1.097 0.241 0.051 0.658

Suspended chlorophyll a (log) SCHL .064 .041 .064 .927 .030 .026 (nss) .833

Macroinvertebrates

Species richness SPECIES .150 .066 .101 1.925 .030 (nss) .051 (nss) .770

Mean pollution tolerance index MPTV .064 .014 .051 .634 .147 .055 .658

Percentage of individuals from order 

Ephemeroptera

%EPHEM .040 .024 .023 .527 .052 .055 (nss) .875

Hilsenhoff Biotic Index HBI .150 .014 (nss) .082 1.990 .134 .035 (nss) .928

Percentage of individuals from order 

Plecoptera

%PLEC .148 (nss) .076 (nss) .051 (nss) 1.965 (nss) 1.235 (nss) .035 (nss) .603

Percentage of individuals that are scrapers %SCRAP .034 .014 .016 .527 .030 (nss) .016 .875

Fish

Wisconsin large-river index of biotic 

integrity

IBI .139 .045 .051 .635 .191 .051 1.075

Percentage of suckers by weight %SUCKER .091 .045 .051 .634 .134 .043 .505

Number of intolerant species #INTOL .139 .051 .051 1.125 .241 .051 .613

Percentage of individuals that are river 

species

%RIVERSP .079 .035 .015 .556 .030 .026 .505

Number of river species #RIVERSP .147 .081 .064 1.965 .078 (nss) .016 .993

Percentage of individuals that are 

lithophilic spawners

%LITSPAWN .055 .035 .019 .634 .147 .043 .505
72 Nutrient Concentrations and Their Relations to the Biotic Integrity of Nonwadeable Rivers in Wisconsin

Conclusions

The ultimate goal of this study was to provide the 

information needed by water resource managers to develop 

regionally based nutrient criteria for rivers and streams in 

Wisconsin by refining reference or background nutrient 

and biotic conditions and describing the responses (thresholds) of the biotic community to differences in nutrient 

concentrations. Meaningful changes in macroinvertebrate 

and fish assemblages were correlated with changes in 

nutrient concentrations, and most of the assemblages 

changed at concentrations only slightly above the refined 

reference concentrations. Nutrient concentrations in many 

streams and rivers, especially those in agricultural areas, 

are well above the response thresholds for many biotic 

indices; therefore, large reductions in nutrient concentrations in these systems would be needed to have large 

effects on the biotic community. Differences in nutrient 

concentrations alone explained only a small part of the 

variability in the biotic community because the biotic community represents the overall ecological integrity of the 

river or stream. Therefore, it is difficult to predict the exact 

result of reducing nutrient concentrations without also 

modifying the factors typically associated with high nutrient concentrations. Actions taken to reduce the input of 

nutrients from the watersheds will likely not only reduce 

nutrient concentrations, but could also mitigate the effects 

of many other correlated stressors on the biotic community, such as suspended sediment and siltation. Reductions 

in nutrient input from the watersheds as a result of the 

establishment and implementation of nutrient criteria and 

standards would not only reduce nutrient concentrations in 

streams and rivers, but would also improve riparian habitat, 

the ecological functioning of streams and rivers, and the 

quality of downstream nutrient-limited receiving waters.

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76 Nutrient Concentrations and Their Relations to the Biotic Integrity of Nonwadeable Rivers in Wisconsin

Appendix 1. Stream identification, location information, and summary statistics for flow and water-quality data collected in 2003 

for each of the 42 studied nonwadeable rivers in Wisconsin.

[ID, stream identifier; USGS, U.S. Geological Survey; km2, square kilometer; (m3/s)/km2, (cubic meter per second) per square kilometer; mg/L, milligram 

per liter; μg/L, microgram per liter; cm, centimeter; °C, degrees Celsius; μS/cm, microsiemen per centimeter. Location of stream sites shown on figure 2.]

ID Stream name

USGS 

site 

number

Ecoregion ID

Longitude

(decimal 

degrees)

Latitude

(decimal 

degrees)

Watershed 

area 

(km2

)

Flow per 

unit area 

[(m3

/s)/km2)]

Total 

phosphorus 

(mg/L)

1 Wisconsin River at Grandfather Dam 05393705 3-NLF-09 89.7761 45.3233 5,870 0.010 0.054 

2 Wisconsin River at Portage 05404024 3-NCHF-10 89.4733 43.5361 21,100 .011 .072 

3 White River near Ashland 04027500 3-NLF-04 90.9042 46.4972 781 .010 .045 

4 Menominee River near McAllister 04067500 3-NLF-12 87.6633 45.3258 10,100 .008 .027 

5 Peshtigo River at Peshtigo 04069500 3-NLF-11 87.7444 45.0469 2,870 .009 .030 

6 Oconto River near Gillett 04071000 3-NLF-10 88.3000 44.8647 1,770 .009 .031 

7 Fox River at Berlin 04073500 3-SWTP-10 88.9522 43.9539 3,450 .008 .133 

8 Wolf River near Shawano 04077400 3-NCHF-18 88.6250 44.8358 2,190 .008 .034 

9 Embarrass River near Embarrass 04078500 3-NCHF-16 88.7361 44.7247 1,010 .008 .057 

10 Wolf River at New London 04079000 3-NCHF-12 88.7403 44.3922 5,870 .020 .102 

11 Little Wolf River at Royalton 04080000 3-NCHF-13 88.8653 44.4125 1,330 .010 .046 

12 Fox River near Wrightstown 04084500 3-NCHF-14 88.1972 44.3175 15,600 .023 .156 

13 Sheboygan River at Sheboygan 04086000 3-SWTP-09 87.7539 43.7417 1,110 .004 .151 

14 Milwaukee River at Milwaukee 04087000 3-SWTP-08 87.9089 43.1000 1,810 .003 .149 

15 Namekagon River at Trego 05332500 3-NLF-03 91.8881 45.9481 1,260 .012 .032 

16 St. Croix River near Danbury 05333500 3-NLF-02 92.2472 46.0750 4,000 .014 .023 

17 St. Croix River at St. Croix Falls 05340500 3-NLF-01 92.6469 45.4069 16,200 .012 .047 

18 Chippewa River near Bruce 05356500 3-NLF-08 91.2608 45.4522 4,250 .012 .029 

19 North Fork Flambeau River at Oxbo 05358330 3-NLF-05 90.7081 45.8592 2,480 .007 .031 

20 South Fork Flambeau River near Phillips 05359500 3-NLF-06 90.6153 45.7042 1,600 .010 .028 

21 Flambeau River near Bruce 05360500 3-NLF-07 91.2094 45.3725 4,900 .051 .033 

22 Jump River at Sheldon 05362000 3-NCHF-01 90.9564 45.3081 1,490 .005 .034 

23 Eau Claire River near Fall Creek 05366500 3-NCHF-03 91.2806 44.8097 1,970 .004 .080 

24 Red Cedar River at Colfax 05367500 3-NCHF-02 91.7111 45.0525 2,840 .009 .125 

25 Chippewa River at Durand 05369500 3-NCHF-04 91.9689 44.6283 23,400 .016 .078 

26 Buffalo River near Tell 05372000 3-DFA-01 91.8492 44.3917 1,050 .009 .330 

27 Trempealeau River at Dodge 05379500 3-DFA-02 91.5533 44.1317 1,670 .007 .399 

28 Black River near Galesville 05382000 3-NCHF-05 91.2872 44.0603 5,390 .006 .150 

29 La Crosse River at La Crosse 05383075 3-DFA-03 91.2103 43.8608 1,230 .006 .195 

30 Lemonweir River near New Lisbon 05403500 3-NCHF-08 90.1775 43.9292 1,110 .008 .115 

31 Baraboo River near Baraboo 05405000 3-DFA-07 89.6358 43.4808 1,570 .006 .204 

32 Wisconsin River at Muscoda 05407000 3-NCHF-09 90.4433 43.1981 27,000 .044 .078 

33 Kickapoo River at Steuben 05410490 3-DFA-04 90.8583 43.1828 1,780 .006 .146 

34 Grant River at Burton 05413500 3-DFA-05 90.8192 42.7203 710 .004 .216 

35 Crawfish River at Milford 05426000 3-SWTP-04 88.8494 43.1000 1,960 .003 .497 

36 Bark River at State Highway D 05426460 3-SWTP-06 88.7014 42.8942 655 .004 .182 

37 Rock River at Fort Atkinson 05427085 3-SWTP-05 88.8428 42.9275 5,810 .007 .311 

38 Yahara River near Fulton 05430175 3-SWTP-01 89.1719 42.8264 1,280 .007 .210 

39 Rock River at Afton 05430500 3-SWTP-03 89.0706 42.6092 8,650 .024 .261 

40 Pecatonica River at Martintown 05434500 3-DFA-06 89.7994 42.5094 2,680 .004 .269 

41 Sugar River near Brodhead 05436500 3-SWTP-02 89.3981 42.6117 1,360 .005 .199 

42 Fox River near New Munster 05545750 3-SWTP-07 88.2258 42.6108 2,020 .004 .151 
Dissolved 

phosphorus 

(mg/L)

Total 

nitrogen 

(mg/L)

Dissolved 

nitrite 

plus 

nitrate 

(mg/L)

Dissolved 

ammonia 

(mg/L)

Total 

Kjeldahl 

nitrogen 

(mg/L)

Suspended 

sediment 

(mg/L)

Suspended 

chlorophyll a

(μg/L)

Secchitube 

depth 

(cm)

Average 

water 

temperature 

(°C)

Specific 

conductance 

(μS/cm)

pH

Color 

(standard 

units)

Dissolved 

oxygen 

minimum 

(mg/L)

0.018 0.598 0.033 0.034 0.565 4.0 9.6 103 19.6 90.5 7.75 77.5 5.10

.018 1.085 .159 .024 .910 11.0 24.4 64.5 19.9 154 7.80 40.0 8.00

.017 .266 .011 .025 .255 13.5 1.7 37.5 16.8 180 7.95 23.8 8.30

.012 .441 .026 .015 .370 4.0 3.9 120 19.6 280 8.15 40.0 8.10

.015 .769 .239 .032 .550 1.0 2.7 120 19.3 274 8.00 52.5 6.60

.012 .670 .204 .024 .510 7.0 4.5 120 19.2 280 7.95 75.0 8.30

.015 1.841 .288 .019 1.650 53.0 28.3 25.5 20.4 362 8.30 45.0 6.40

.016 .714 .178 .047 .555 5.5 3.0 120 19.2 261 8.10 38.8 5.30

.040 1.790 1.110 .048 .650 6.0 3.3 120 17.6 417 8.20 52.5 6.30

.054 1.382 .559 .044 .665 22.5 6.7 49.5 19.3 421 8.15 37.5 5.70

.028 1.910 1.195 .043 .580 4.0 3.5 120 19.4 428 8.20 32.5 8.80

.073 1.321 .244 .074 1.100 22.5 27.2 42.0 20.9 403 8.50 16.3 8.30

.049 1.991 .615 .046 1.400 36.5 35.7 47.0 20.4 651 8.60 47.5 8.70

.086 1.713 .555 .044 1.100 14.5 24.8 42.5 21.5 844 8.50 33.8 7.20

.014 .416 .085 .038 .340 2.0 5.2 120 19.4 170 8.30 23.8 7.40

.012 .411 .011 .007 .365 1.0 1.8 120 17.6 135 8.00 32.5 8.00

.020 .701 .136 .021 .565 5.5 5.7 113 19.8 190 7.80 60.0 7.30

.015 .571 .132 .020 .430 2.5 2.9 120 18.8 106 7.90 55.0 8.10

.019 .470 .021 .020 .420 2.0 2.4 120 18.7 135 7.55 60.0 7.20

.014 .542 .018 .024 .500 3.0 4.2 120 17.7 78.0 7.60 72.5 7.90

.018 .513 .092 .021 .440 3.0 3.8 120 19.4 108 7.75 67.5 8.10

.020 .501 .011 .024 .490 1.0 3.1 120 20.6 150 8.30 62.5 5.30

.042 1.095 .435 .023 .610 6.0 13.3 110 20.2 110 8.15 32.5 8.60

.075 1.940 1.275 .025 .595 11.0 7.9 98.0 17.3 175 7.70 12.5 7.00

.030 1.198 .609 .012 .625 11.5 18.6 79.0 19.2 181 8.35 32.5 8.60

.129 3.020 2.420 .012 .545 74.0 6.1 48.5 18.4 345 8.10 8.8 8.20

.156 2.415 1.975 .017 .500 53.0 3.9 41.5 19.6 311 8.05 8.8 8.20

.061 1.099 .413 .010 .695 15.5 19.8 56.5 21.3 157 8.65 27.5 9.10

.071 1.376 .368 .019 1.040 54.5 45.8 33.5 22.2 341 8.75 7.5 8.70

.063 1.152 .362 .061 .715 4.5 6.0 64.0 18.8 191 7.55 80.0 6.50

.077 1.990 1.145 .046 .840 69.0 18.3 20.0 18.0 382 7.95 15.0 6.80

.015 1.215 .376 .012 .945 20.0 34.4 55.5 20.6 243 8.35 35.0 8.80

.064 1.170 .791 .013 .355 51.0 5.5 57.0 17.8 486 8.20 8.8 7.60

.137 3.665 3.020 .019 .615 22.0 2.8 66.5 19.9 638 8.20 5.0 8.10

.106 3.326 .070 .027 2.850 87.5 129.7 12.0 22.0 664 8.60 40.0 5.60

.083 2.810 1.410 .129 1.400 43.0 15.5 27.0 20.5 673 8.10 42.5 6.50

.106 3.000 1.000 .037 2.200 51.5 98.5 16.0 21.8 700 8.50 36.3 6.10

.080 5.485 3.770 .057 1.400 48.0 19.6 34.5 20.2 680 8.40 15.0 8.80

.146 3.775 1.925 .134 1.850 22.5 37.0 36.5 20.4 769 8.30 18.8 8.20

.104 4.475 3.680 .054 .825 74.0 8.1 29.5 20.1 634 8.20 8.8 7.60

.068 4.150 3.245 .023 1.050 39.5 36.1 19.5 20.6 596 8.25 15.0 8.10

.017 2.740 .963 .023 1.550 29.5 40.5 26.5 21.9 904 8.45 30.0 9.40

Appendixes 77
78 Nutrient Concentrations and Their Relations to the Biotic Integrity of Nonwadeable Rivers in Wisconsin

Appendix 2. Macroinvertebrate indices for each of the 41 studied nonwadeable rivers in Wisconsin. (All data were collected by 

the Wisconsin Department of Natural Resources.)

[ID, stream identifier; EPT, Ephemeroptera, Plecoptera, Trichoptera. Location of sites shown on figure 2.]

ID Stream name Species 

richness

Percent

Ephemeroptera

Percent

Plecoptera

Percent

Trichoptera

Percent 

Diptera

Percent 

Chironomidae

1 Wisconsin River at Grandfather Dam 28 31.4 2.9 19.0 43.6 38.1 

2 Wisconsin River at Portage 28 6.0 .0 43.5 44.7 44.1 

4 Menominee River near McAllister 30 69.1 3.9 8.2 15.9 15.5 

5 Peshtigo River at Peshtigo 37 32.1 .2 45.8 20.4 19.3 

6 Oconto River near Gillett 45 29.2 1.2 4.5 45.8 45.8 

7 Fox River at Berlin 18 1.1 .0 84.3 13.8 13.6 

8 Wolf River near Shawano 44 54.0 .0 8.4 36.4 36.4 

9 Embarrass River near Embarrass 51 16.7 .2 33.2 47.6 41.9 

10 Wolf River at New London 34 16.9 .0 67.3 13.2 13.0 

11 Little Wolf River at Royalton 47 20.7 .4 41.8 36.5 35.8 

12 Fox River near Wrightstown 23 2.5 .0 24.0 53.9 53.5 

13 Sheboygan River at Sheboygan 24 2.2 .0 .4 92.7 92.7 

14 Milwaukee River at Milwaukee 37 4.3 .0 28.2 56.4 54.4 

15 Namekagon River at Trego 29 14.4 .6 24.8 59.9 43.9 

16 St. Croix River near Danbury 39 26.7 .8 29.3 41.9 25.6 

17 St. Croix River at St. Croix Falls 37 7.9 .5 23.3 67.0 66.3 

18 Chippewa River near Bruce 44 21.1 2.1 5.3 70.8 58.8 

19 North Fork Flambeau River at Oxbo 42 46.8 .0 2.6 41.0 40.7 

20 South Fork Flambeau River near Phillips 43 10.5 .8 8.6 79.5 73.9 

21 Flambeau River near Bruce 28 43.2 .2 11.9 44.7 42.9 

22 Jump River at Sheldon 30 60.6 1.3 .6 35.4 35.4 

23 Eau Claire River near Fall Creek 32 5.2 .3 9.1 84.5 83.6 

24 Red Cedar River at Colfax 30 39.5 11.2 31.3 17.4 12.4 

25 Chippewa River at Durand 33 5.6 .4 24.5 68.9 64.8 

26 Buffalo River near Tell 30 8.2 1.7 3.8 85.5 84.8 

27 Trempealeau River at Dodge 34 25.5 1.3 10.6 60.8 57.4 

28 Black River near Galesville 39 44.1 .9 14.7 27.2 27.2 

29 La Crosse River at La Crosse 31 2.1 .0 10.5 61.3 59.4 

30 Lemonweir River near New Lisbon 38 20.7 .0 25.8 40.2 39.7 

31 Baraboo River near Baraboo 21 31.4 .2 63.2 3.3 3.3 

32 Wisconsin River at Muscoda 33 4.1 .2 12.1 76.5 75.2 

33 Kickapoo River at Steuben 38 8.5 3.0 33.6 51.0 49.9 

34 Grant River at Burton 34 44.2 .0 37.6 15.8 15.0 

35 Crawfish River at Milford 10 .0 .0 69.7 30.1 29.9 

36 Bark River at State Highway D 33 11.2 .0 31.0 23.1 22.9 

37 Rock River at Fort Atkinson 16 .0 .0 9.4 78.8 78.8 

38 Yahara River near Fulton 26 22.8 .0 36.0 39.1 36.8 

39 Rock River at Afton 19 4.2 .0 23.4 65.8 65.7 

40 Pecatonica River at Martintown 29 41.0 .6 35.3 22.5 22.5 

41 Sugar River near Brodhead 28 15.2 .5 61.0 23.3 22.7 

42 Fox River near New Munster 27 15.6 .0 9.7 74.3 74.1 
Percent 

EPT number

Percent

EPT taxa

Percent 

scrapers

Percent 

shredders

Percent 

gatherers

Percent from a 

depositional habitat

Mean pollution 

tolerance value

Hilsenhoff 

Biotic Index

53.3 48.0 4.2 19.0 28.8 17.7 4.88 4.86

49.4 23.1 1.9 9.2 18.8 4.6 5.57 5.53

81.2 46.2 55.9 4.0 7.9 6.8 4.65 3.93

78.1 54.3 15.7 14.6 12.3 8.6 5.06 4.22

34.8 39.0 5.7 5.1 71.7 58.0 5.42 5.31

85.4 41.2 .6 8.8 3.8 .8 6.27 5.39

62.4 29.3 10.3 6.0 62.1 23.1 5.65 6.05

50.1 46.7 8.3 17.6 23.3 8.7 4.45 4.96

84.3 46.9 8.5 3.4 14.4 6.3 5.75 5.22

62.9 48.8 6.7 11.6 26.5 16.2 4.37 4.70

26.5 30.4 1.7 15.6 34.0 9.5 6.55 6.47

2.6 21.7 .0 2.0 94.8 4.4 6.57 9.58

32.5 21.2 .8 27.2 27.8 14.1 5.60 5.75

39.7 50.0 5.3 2.5 16.8 3.2 3.65 4.76

56.8 47.1 10.7 2.1 34.2 9.0 4.12 4.31

31.7 34.4 7.4 8.1 6.1 4.4 4.84 5.67

28.4 46.2 9.9 15.6 43.2 16.0 4.32 4.98

49.4 31.6 15.1 .7 53.9 21.5 5.64 6.07

19.9 40.0 1.6 10.9 22.2 20.7 4.88 5.58

55.3 56.0 19.8 7.0 6.1 3.0 4.76 4.47

62.5 48.1 55.1 1.0 10.1 8.9 4.54 4.59

14.6 26.7 4.6 8.6 13.7 4.4 5.03 5.73

82.0 50.0 7.2 10.3 37.9 9.1 3.52 2.77

30.5 40.0 4.6 7.6 9.7 9.7 5.29 5.51

13.7 39.3 7.6 2.8 5.7 3.8 4.64 5.77

37.4 41.2 12.6 3.8 27.6 18.4 5.15 5.87

59.7 36.1 33.0 13.2 20.3 9.0 5.52 5.13

12.6 17.2 12.5 19.8 51.5 11.1 5.50 7.17

46.5 14.3 11.2 4.8 41.0 7.5 5.89 5.82

94.7 65.0 27.9 .4 6.0 1.4 4.80 4.94

16.4 33.3 1.8 17.7 30.8 4.8 5.53 6.66

45.1 37.8 6.2 7.5 31.2 11.8 4.64 4.64

81.8 40.0 18.3 4.8 23.2 17.2 5.35 4.71

69.7 33.3 .0 26.9 3.2 .6 6.11 5.43

42.2 40.6 7.5 9.3 43.9 5.5 6.19 6.18

9.4 12.5 1.1 6.5 80.7 1.1 6.77 8.85

58.8 40.0 1.0 21.5 33.3 18.6 5.14 4.97

27.6 22.2 .0 46.6 15.6 6.3 5.56 5.57

76.9 59.3 35.6 12.3 11.3 7.8 5.00 5.32

76.7 50.0 5.8 5.6 12.3 7.4 5.42 4.92

25.4 32.0 .2 14.8 67.7 16.4 5.67 7.15

Appendixes 79
80 Nutrient Concentrations and Their Relations to the Biotic Integrity of Nonwadeable Rivers in Wisconsin

Appendix 3. Fish indices and ratings for each of the 41 studied nonwadeable rivers in Wisconsin. (All data were collected by 

the Wisconsin Department of Natural Resources.)

[ID, stream identifier; kg, kilogram. Location of sites shown on figure 2.]

ID Stream name

Number 

of native 

species

Number 

of riverine 

species

Number 

of sucker 

species

Number of 

intolerant 

species

Weight per 

unit effort 

(kg)

Percent 

river 

species

1 Wisconsin River at Grandfather Dam 8 3 3 3 151,000 25.3 

2 Wisconsin River at Portage 11 4 4 3 22,300 45.5 

4 Menominee River near McAllister 17 7 5 5 7,340 26.4 

5 Peshtigo River at Peshtigo 18 6 3 3 4,830 20.8 

6 Oconto River near Gillett 8 2 1 4 14,400 5.1 

7 Fox River at Berlin 19 4 4 3 25,700 15.1 

8 Wolf River near Shawano 27 8 6 6 21,100 35.9 

9 Embarrass River near Embarrass 25 7 6 3 32,100 19.4 

10 Wolf River at New London 18 5 4 2 10,800 15.8 

11 Little Wolf River at Royalton 14 7 4 3 32,000 38.8 

12 Fox River near Wrightstown 5 1 2 1 3,890 4.5 

13 Sheboygan River at Sheboygan 10 0 2 3 33,600 .0 

14 Milwaukee River at Milwaukee 4 1 3 1 4,010 9.1 

15 Namekagon River at Trego 12 7 4 4 57,800 48.2 

16 St. Croix River near Danbury 15 5 5 5 11,800 39.4 

17 St. Croix River at St. Croix Falls 12 7 7 2 24,500 38.6 

18 Chippewa River near Bruce 16 7 5 4 21,600 34.2 

19 North Fork Flambeau River at Oxbo 9 4 5 3 38,000 79.0 

20 South Fork Flambeau River near Phillips 11 6 5 2 33,300 65.5 

21 Flambeau River near Bruce 18 7 4 3 15,900 15.8 

22 Jump River at Sheldon 10 4 4 4 52,000 60.5 

23 Eau Claire River near Fall Creek 14 4 3 3 79,300 20.4 

24 Red Cedar River at Colfax 23 13 5 7 32,700 54.5 

25 Chippewa River at Durand 16 10 7 3 47,900 40.6 

26 Buffalo River near Tell 19 7 5 3 41,300 26.8 

27 Trempealeau River at Dodge 10 4 3 0 10,200 10.7 

28 Black River near Galesville 15 8 6 3 34,900 38.4 

29 La Crosse River at La Crosse 12 4 2 1 61,100 11.4 

30 Lemonweir River near New Lisbon 11 1 1 0 17,100 2.7 

31 Baraboo River near Baraboo 14 4 3 3 21,900 14.3 

32 Wisconsin River at Muscoda 16 10 8 5 58,900 48.0 

33 Kickapoo River at Steuben 11 6 4 1 7,720 31.1 

34 Grant River at Burton 14 5 4 2 8,230 24.3 

35 Crawfish River at Milford 8 0 3 1 30,800 .0 

36 Bark River at State Highway D 8 3 4 1 11,700 32.8 

37 Rock River at Fort Atkinson 12 1 2 1 13,000 3.9 

38 Yahara River near Fulton 16 6 3 4 36,500 61.3 

39 Rock River at Afton 9 1 2 1 9,800 2.2 

40 Pecatonica River at Martintown 8 4 5 1 20,000 22.2 

41 Sugar River near Brodhead 11 4 5 1 35,900 40.7 

42 Fox River near New Munster 15 4 5 2 42,500 29.4 
Percent 

lithophilic 

spawners

Percent 

suckers by 

weight

Percent 

insectivores 

by weight

Percent with 

disease

Large-river 

index of biotic 

integrity

Fish rating based on 

the large-river index 

of biotic integrity

91.9 83.8 84.1 0.0 80 Excellent

41.4 32.0 32.7 .0 65 Good

59.8 69.5 72.6 .0 80 Excellent

24.8 1.6 3.7 .0 50 Fair

6.1 6.7 6.7 .0 44 Fair

19.8 32.0 42.3 .0 70 Good

73.1 10.8 11.8 .0 80 Excellent

50.4 26.0 40.5 .0 95 Excellent

43.9 15.7 40.0 .0 70 Good

74.1 94.7 97.8 1.7 85 Excellent

22.7 .6 10.4 .0 10 Very poor

1.5 .8 .9 .0 30 Poor

36.4 35.5 35.5 .0 35 Poor

91.2 97.4 97.5 .0 95 Excellent

83.9 87.6 91.3 .0 95 Excellent

64.4 34.8 36.5 2.0 75 Good

83.6 71.8 72.4 .0 90 Excellent

93.5 95.8 95.9 .0 95 Excellent

91.4 87.7 87.8 .0 94 Excellent

88.8 75.7 81.8 .0 85 Excellent

86.4 79.4 79.4 .0 95 Excellent

47.3 65.1 67.0 .6 75 Good

53.3 77.2 79.6 .0 100 Excellent

69.2 65.4 76.6 .0 100 Excellent

72.2 36.2 38.4 .0 95 Excellent

76.8 21.9 37.6 1.8 35 Poor

60.5 49.5 56.7 1.2 90 Excellent

64.0 74.7 88.2 .0 60 Good

32.4 28.3 29.1 .0 35 Poor

47.6 12.1 25.3 1.2 55 Fair

43.9 42.4 63.7 .0 100 Excellent

62.2 28.5 38.7 .0 55 Fair

37.8 5.2 21.3 .0 50 Fair

37.0 29.3 46.2 .0 50 Fair

34.3 8.9 9.0 1.5 30 Poor

23.5 3.7 14.6 2.0 15 Very poor

70.4 55.3 57.7 .0 90 Excellent

28.9 2.1 6.9 6.7 5 Very poor

40.0 17.8 36.7 .0 50 Fair

42.4 28.3 60.3 .0 70 Good

52.9 26.6 36.9 .0 75 Good

Appendixes 81


Robertson, D.M., Weigel, B.M., and Graczyk, D.J.—

Nutrient Concentrations and Their Relations to the Biotic Integrity of Nonwadeable Rivers in Wisconsin—Professional Paper 1754

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