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lopment of a stream habitat index for use with an Index of Biotic Integrity in the St. Croix River 

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

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

Habitat analysis ...........................................................................................................................................................................................2

 Data collection..........................................................................................................................................................................................2

 Habitat variables.......................................................................................................................................................................................2

 Variable abbreviations and transformations.............................................................................................................................................2

 Data sets....................................................................................................................................................................................................2

 Core habitat variables...............................................................................................................................................................................5

Correlation of core habitat variables with IBI.............................................................................................................................................5

Correlation of core habitat variables with species richness......................................................................................................................... 6

Concurrence analysis with PCA..................................................................................................................................................................7

Habitat index ...............................................................................................................................................................................................7

Discussion....................................................................................................................................................................................................8

References ................................................................................................................................................................................................. 10

Appendix ................................................................................................................................................................................................... 13

6 .65&

Figure 1. Map showing location of the St. Croix River Basin and sampling sites in Minnesota...............................................................3

Figure 2. Schematic representation of principal components axes and important variables for site groups A and B .............................. .6

Figures 3-5 Plots showing:

3. Relation between the habitat index and IBI scores for the St. Croix River Basin for all sites sampled..............................9

 4. Observed and estimated IBI scores at sites with good habitat. The 45 degree line is the hypothetical 

perfect agreement between observed and estimated scores. Arrows indicate the potential range of IBI 

values for a single point .....................................................................................................................................................9

 5. Observed and estimated IBI scores at sites with good habitat throughout the full range of possible scores .................... 10

6.$7

 1. Habitat variables with transformations and variable abbreviations ....................................................................................................... 4

2. Core habitat variables identified by Principal Components Analysis..................................................................................................... 6

3. Comparisons of the variables determined by conventional principal components analysis of axes and the B4 selection method ........7

4. Habitat variables identified by two Principal Components Analysis methods listed by classification group .......................................8

5. Habitat variables with means and correlation to IBI scores.................................................................................................................... 8

5&27 5&/.65 

Multiply By To obtain

centimeter (cm) 0.3937 inch

meter (m) 3.281 foot

kilometer (km) .6214 mile

square kilometer (km2) .3861 square mile
 iv
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More than 70 streams in the St. Croix River Basin in Minnesota were sampled for fish community composition and physical habitat

during 1996–98. A habitat index was developed based on measurements, field observations, and land use. The objective was to 

develope a habitat index for use to evaluate water quality and the effects of nonpoint-source effects not associated with habitat 

degradation. Core habitat variables were determined with a concurrence analysis using principal components of two subsets of sites 

with pristine or least affected habitat. Although core habitat variables differed slightly between data sets, sufficient similarities allowed 

development of an index. The index (the sum of pluses or minuses dependent on the variable’s correlation to biotic integrity), 

composed of 12 core habitat variables in 5 classification groups (hydrology, geomorphology, substrate, instream habitat, and 

riparian/land use), was able to distinguish sites with low Index of Biotic Integrity scores not related to habitat degradation.


	




Development of biological criteria (biocriteria) for the evaluation of aquatic resource quality is mandated by the U.S. Environmental Protection Agency (EPA) under the Clean Water Act 

of 1972 (and its amendments). The most common biocriteria 

methods involve the use of an Index of Biotic Integrity (IBI) 

(Karr, 1981). An IBI is a multi-metric evaluation system based on 

the structure and function of a biological community in the 

stream. In many cases, the biological community that is evaluated is the fish community. IBIs usually consist of 10 to 15 metrics, which rate various aspects of the fish community 

investigated relative to a reference community from pristine or 

least affected streams. Metrics address species composition, 

trophic composition, abundance, and condition (Karr, 1981; Karr 

and others, 1986). 

Habitat is the principle determinant of the biological potential 

of a stream, and as such can be used to predict biological conditions, particularly the occurrence and abundance of fish (Gorman 

and Karr, 1978; Plafkin and others, 1989; and Rankin, 1989). 

Although fish community analyses have been accomplished with 

numerous approaches, analytical procedures for habitat data are 

still relatively new. Approaches to habitat data have involved 

using habitat indices (Fajen and Wehnes, 1982; Plafkin and others, 1989; Rankin, 1989; Petersen, 1992; and Wang and others, 

1998), habitat quantification models (Terrel and others, 1982; 

Nestler and others, 1989; Baker and Coon, 1997), examination of 

habitat gradients (Schlosser, 1982; Rahel and Hubert, 1991), or 

analyses of habitat preference (Rosenzweig, 1981; Nelson and 

others, 1992). Although habitat assessment protocols have 

attempted to produce a single number or index that can be used to 

rate stream habitat (for example, Rankin, 1989; Plafkin and others, 1989; Peterson, 1992), the indicies that have been developed 

are for the most part subjective and contain metrics identified a 

priori (Stauffer and Goldstein, 1997; Wang and others, 1998). 

Habitat data have a significant role in biocriteria development. Because the physical habitat of a stream has a major influence on the presence and abundance of fish, it may overshadow 

or confound the identification of other factors affecting the biotic 

integrity of fish communities. Therefore, the quantification of 

stream habitat is important.

Sources of stress can be physical, chemical, and biological. 

The use of a habitat evaluation system as an adjunct to an IBI 

provides a synergy of the information collected. Data are then 

available on the physical and biological components of the 

resource. When biocriteria scores are compared with physical 

habitat quality scores, discrepancies could be attributable to 

water quality. With the current control of most point-source discharges, the evaluation of habitat quality becomes more important to identify streams affected by nonpoint sources. 

An analysis was done to develop a habitat index for streams 

in the St. Croix River Basin in Minnesota. The index is a tool to 

evaluate stream-habitat data for use with biocriteria assessment. 

Ideally, the habitat measures can be used to predict the quality of 

the fish community (IBI) such that deviations from the expected 

values will indicate other sources of perturbation. 

To develop the index, three primary objectives must be met: 

(1) Identify the features and variables that best describe physical 

habitat, which are the core habitat variables; (2) Determine which 

of the core habitat variables are correlated to fish community 

composition; and (3) Develop a basin specific index of fish community habitat quality from those variables, which is a single 

number that can be used in conjunction with IBI scores. The primary objectives may be met by the development of a habitat 

index that is directly related to fish community composition. The 

approach consists of determining the core habitat variables most 

correlated to IBI scores, or if IBI scores have not been developed 

or confirmed, to species richness. This approach assumes that, in 

reference-condition streams (streams not subjected to sources of 

external stress), the fish community composition and abundance 

are directly related to the quality of the physical habitat. This 

report describes the development of a habitat index for the St. 

Croix River Basin in Minnesota. 
 2 

8

.

The goal of the habitat analysis was to reduce the number of 

habitat variables for use in index development and to identify the 

core habitat variables that best describe the variability in St.Croix 

River Basin streams. Habitat data were collected, transformed to 

fit statistical assumptions, separated into data sets, and analyzed 

to determine the core variables. 







From 1996 through 1998, the Minnesota Pollution Control 

Agency collected data on fish community composition and 

stream habitat in the St. Croix River Basin in Minnesota (fig. 1) 

for development of biocriteria. Streams sampled had drainage 

areas that ranged in size from less than 2.59 km2

 to greater than 

12,950 km2. Under the EPA site selection guidelines (Whitter 

and Paulsen, 1992), 49 sampling sites were randomly selected 

within the basin. The EPA-Environmental Monitoring and 

Assessment Program (EMAP) site selection process uses a map 

grid and random selection process. Additionally, another 50 sites 

were specifically selected to encompass the extremes of habitat 

conditions. Therefore, the gradient of habitat conditions sampled 

included practically pristine streams to streams that were fully 

channelized and devoid of internal features. Sampling occurred 

during the summer, primarily under low flow conditions to 

provide comparable data relative to hydrologic conditions and to 

allow young-of-the-year fish to attain a size whereby they are 

more readily identified. In conjunction with electrofishing to 

collect a representative fish community sample, various 

measurements and determinations were made of the physical 

habitat of the stream. Habitat quantification generally followed 

Simonson and others (1995) who developed habitat 

quantification protocols for similar streams in Wisconsin. The 

principal deviation from Simonson and others (1995) was the use 

of 13 to 15 transects rather than transects every two mean 

channel widths. 

8

2	

The habitat variables were classified into five groups 

(Rankin, 1989; Stauffer and Goldstein, 1997) to facilitate 

analysis (Appendix, at the back of the report). The groups were: 

hydrology, geomorphology, instream habitat, substrate, and 

riparian zone/land use. Data were collected at one of three levels 

of scale (Frissel and others, 1986)—basin, segment, or reach—

depending on the variable. Within a reach, all transect data were 

summarized for each site. Where applicable, the number of 

transects was normalized to 13. The total number of habitat 

variables by classification group were: hydrology =4, 

geomorphology =10, instream habitat =13, substrate =11, and 

riparian zone/land use =11 (table 1).

2	.	



6	
	



Table 1 lists the variables and the abbreviations used in this 

analysis. Included are the transformations that were used to 

normalize the data. Many variables could not be used because 

they had many (more than 20 percent) ties, zeros, or 100 percent 

values, and therefore could not be transformed to approach a 

normal distribution for statistical analysis. For example, the 

variable bends per length of stream was deleted due to too many 

zeros. The zero value indicates no bends occurred in the reach. 

Although some of the deleted variables may have contributed to 

the index, the lack of normality precluded their use. 

Transformations of data were based largely on power series. 

Normality was confirmed with a Shapiro-Wilk test. For negatively skewed data, successive transformations of sqrt(x), 

sqrt(sqrt(x)), ln(x), -1/sqrt(x), and -1/x were taken to find an 

acceptable transformation to a normal distribution (sqrt is the 

square root and ln is the natural logarithm). The variable gradient 

was an exception where the transformation ln(sqrt(x)) was used.

The final count of normal variables for analysis was 25. Each 

of the major classification groups (hydrology, geomorphology, 

etc.) was represented. The basin land use variables were not used 

because they correlated highly (r=0.96) with the 100 meter land 

use data. 





 Initially, sites were designated as either “model” or “test” in 

terms of habitat quality. The “model” habitat sites were reference 

areas, characterized generally by undeveloped land use and no 

known discharges. Most model sites contained unaffected 

habitat. Some sites with relatively poor habitat but no other 

sources of perturbation were added to this group so that a range 

of habitat conditions was included. The criteria for inclusion as a 

model habitat site were: 

1. IBI score—for moderate and large streams (drainage 

area greater than 259 km2

) a score of about 45 or more but in 

smaller streams, a score greater than 35.

Species Richness—stream scored in the top one-third 

trisection of the species richness metric. 

Riparian corridor—contains native trees and vegetation from 

0 to 30 meters away from the stream. The corridor is mostly 

undisturbed.

Stream geomorphology—stream channel and banks are 

natural without riprap; stream has not been ditched, channelized, 

or snagged. 

Dams—no dams or impoundments within 5 km of the site. 

Point sources—none. 

Next, the model sites were divided into two groups, one for 

index development and the other for verification. A simple coin 

toss was used to randomly select sites for one group or the other. 

A third data set (the “test” sites used for testing) included the 

non-reference sites with lower IBI scores so that the index could 

be tested in less-than-ideal environmental conditions. This third 

group was not included in either index development or 

verification because the lower IBI score could be attributed to 
 3 

River

St. Croix St. Croix

River

Kettle

River

SANDSTONE

River

Kettle

River

Snake

Apple

River

River

River

River

Kinnickinnic

Willow

Wood

Namekagon

River

River

River

Clam

Yellow

St. Croix St. Croix

River

St. Croix

River

STILLWATER

WYOMING

NORTH BRANCH

FOREST

LAKE

SANDSTONE

MORA

PINE CITY PINE CITY

91

92

93

45

46

0 50 Miles

0 50 Kilometers

Dase modified from U.S.

Geological Survey digital

data 1:100,000, 1972.

AlDers Equal-Area projection.

Standard parallels: 29 30 ́,

and 45 30 ́, central meridian:

-93 00 ́

EXPLANATION

Figure 1. Location of the St. Croix River Basin and sampling sites in Minnesota.

Sampling site

WISCONSIN

MINNESOTA

ST. CROIX

RIVER

BASIN

MINNESOTA

WISCONSIN
 4 

a

 The number of geomorphic units or features was standardized for station length and ln transformed. The resultant variables were 

based on the ln of the number of units plus 1 (to account for zero values) per 305 meters (1000 feet) of stream.

3)4	

		


			
	

[Variables with an abbreviation were use in the analysis. Definitions of variables are given in appendix A. ]

Classification groups and variables Transformation Abbreviation

Hydrology

drainage area ln (x) lndrainarea

mean annual runoff none runoff

percent of basin with pervious soils ln (x+1) lnperv1

storage ratio sqrt (x) sqrtstorage

Geomorphology: 

sinuosity ln (x-1) lnsinu

gradient ln (sqrt (x)) lnrtgrad

riffles per length of stream a ln (x+1) lnriffs/1000

pools per length of stream a ln (x+1) lnpools/1000

runs per length of stream a ln (x+1) lnruns/1000

bends per length of stream a

mean width sqrt (sqrt x) widthrtrt

mean depth none meandepth

coefficient of variation of depth sqrt (x) cvdepthrt

erosion frequency 

Instream Habitat: 

number of log jams

mean percent of algae 

mean percentage of macrophytes

mean percent of over hanging vegetation

mean percent of under cut banks

frequency of over hanging vegetation

frequency of under cut banks

 mean percent of woody debris none woody

mean percent of boulders ln (x+1) lnboulder

frequency of woody cover

frequency of boulders

frequency of submerged macrophyte cover

frequency of emergent macrophyte cover . 

Substrate:

frequency of bedrock 

frequency of boulders

frequency of cobble/rubble none cobble

frequency of gravel none gravel

frequency of sand sqrt (x) rtsand

frequency of silt

frequency of clay

frequency of detritus

sum of small particle frequencies sqrt(arcsin (x/100)) rtfines

mean depth of fines ln (x) lnfines

mean embeddedness 

Riparian Zone/Land Use: 

tree canopy (sum of four densiometer readings). sqrt (x) rtshade

agricultural within 100 m sqrt (%) rtag100

urban within 100 m -1/(%+1) turban100

forest within 100 m none forest100

wetlands within 100 m sqrt (x) rtwet100

water within 100 m sqrt (x) rtwater100

agriculture in basin sqrt (x) rtag

urban in basin sqrt (sqrt x) rurban

forest in basin none forest

wetland in basin sqrt (x) rtwet

water in basin
 5 

poor water quality, poor habitat, habitat alteration (ditching, 

dredging) or a combination of these conditions. Therefore, the 

low IBI score was not necessarily a function of habitat.

Three data sets were created from the original sites that 

contained only normally distributed variables from streams with 

basins >25.9 km2 and < 2,590 km2

. The smallest and largest 

streams were deleted from the analysis because the habitat was 

too variable in the smallest streams (either pristine or ditches), 

and the habitat in the largest rivers could not be adequately 

measured. The first group (Group A-development) had 25 sites 

while the second group (Group B-verification) had 17 sites. The 

third group (Group C-testing) had 26 sites. Some sites were 

sampled more than once. Replicate samples were treated as two 

different sets of data. 


	8

2	

The first objective was to identify the core habitat variables: 

the features that best describe physical habitat. This was 

accomplished by reducing the number of variables to a select few 

that are able to account for most of the variability in the habitat 

data set. Variable reduction is usually accomplished by principal 

components analysis (PCA)(for example, King and Jackson, 

1999). All statistical procedures were accomplished with Systat 

7.0 (SSPS, 1997).

Data for sites in groups A and B were analyzed with a PCA to 

identify those variables that accounted for the majority of the 

variability in each habitat data set. Conventional data reduction 

using PCA involves identifying the major factors in the data set 

represented by the PCA axes and defining surrogate variables for 

those factors. A correlation matrix is used to determine relations 

among variables, such that closely correlated variables group 

together on the PCA axes. Variables, or groups of related 

variables, that account for the most variability in the data occur at 

opposite ends of each factor (axis). The amount of variability 

associated with each factor (axis) is used to judge the number of 

factors needed to adequately describe the data set. 

As part of the PCA output in Systat, a scree plot is generated. 

A scree plot has the number of factors (axes) on the x axis and the 

cumulative sum of the eigenvalues (variation explained by the 

factor or axis) on the y axis. The sum of the eigenvalues equals 

the number of axes that equals the total variability in the data set. 

Because each successive factor (axis) explains less and less of the 

total variation from the data set, there is a point beyond which 

adding additional factors contributes little to the explanation of 

variance. The scree plots for groups A and B data sets each 

indicated that 4 or 5 factors explained the majority of the 

variability in the data sets. 

In groups A and B, the first three axes explain 58% and 65%, 

respectively, of the total variability in each data group. The core 

variables identified by the first two axes are almost the same for 

both data groups (fig. 2); on the first axis the core variable in 

group A was riffles per length of stream but in group B it was 

frequency of cobble/rubble substrate. Although the underlying 

factors were the same, there were differences in the core 

variables from the two data sets. Combining the core variables 

from both yields a set of variables (table 2) that are able to 

explain the majority of the variability in the habitat data set. 

The 12 core variables represent the majority of the variation 

in groups A and B. The representation in each of the five 

classification groups is poor. Most of the variability is associated 

with the substrate and geomorphology classes. These would be 

considered to explain the majority of the variability and provide 

the most differentiation among sites. 


		



	8



2	


The second objective was to correlate the core habitat 

variables with IBI scores or other fish community statistics. Even 

though a habitat variable is very useful in explaining variability 

among streams, that does not necessarily mean it is important or 

correlated to the fish community. Therefore, it was necessary to 

determine which of the core habitat variables were correlated to 

IBI scores or fish community composition. A regression model of 

habitat and IBI scores was developed to identify the best set of 

core habitat variables to use in the habitat index.

The variables identified by PCA were used in a stepwise 

multiple regression to determine the best set of core habitat 

variables to explain as much of the variability in the fish 

community data as possible. Once the regression model was 

constructed, the data set containing the verification sites was used 

to verify variable selection by comparison of observed and 

estimated fish community statistics. Finally, testing of the 

regression model consisted of determining how well the model 

estimated the fish community statistics at the other (nonreference) sites. The quality of the model can be determined by 

examination of three aspects of the results: (1) The mean of the 

difference between the observed and estimated values should be 

close to zero; (2) The standard error of the estimate should be 

close to zero; (3) The observed and estimated values should 

correlate highly. A graph of the correlation of the observed to the 

estimated IBI values should have a y intercept of zero, and a 

slope of 1. 

The core habitat variables (table 2) and group A data 

produced the following results (with appropriately transformed 

and normal data, table 1):

IBI = 36.25 + 2.0 (lndrainarea) + 5.95 (lnpools/1000) –1.011 

(rtwet100) – 0.22 (rtshade)

The r2

 = 0.623 and the standard error of the estimate was 3.8. 

The correlation was highly significant (p<0.0001). This would 

seem to be a good model. However, the application of the 

equation to the data from group B gave these results: (1) The 

mean difference between the estimated IBI and the observed IBI 

was –3.3; (2) The standard error of the estimate was 7.2; and (3) 

There was no significant correlation between observed and 

estimated IBI values. The r2 was 0.004. As an alternative 

approach, species richness was used.
 6 

3 4	

		
		
5		


,
	

[Definitions of variables are given in Appendix A]

Classification group Variable

Hydrology drainage area,

mean annual runoff,

percent of basin with pervious 

soils

Geomorphology riffles per length of stream 

runs per length of stream

coefficient of variation of depth

mean depth

Substrate frequency of cobble or rubble 

mean depth of fines

Instream habitat tree canopy

mean percent of woody debris

Riparian zone/land use wetlands within 100 m 

Figure 2. Schematic representation of principal components axes and important variables for site groups A and B.

(The variables with the largest positive and negative loadings are indicated (negative on the left,

positive on the right); secondary variables in parentheses.)

DATA GROUP A PRINCIPAL COMPONENTS AXES

axis 1

axis 2

axis 3

axis 4

mean depth of fines

(frequency of sand)

drainage area

(agriculture within 100 m)

mean annual runoff

riffles per length of stream

(frequency of rubble/cobble)

runs per stream length

(tree canopy)

coefficient of variation

mean percent woody debris

of depth

DATA GROUP B PRINCIPAL COMPONENTS AXES

axis 1

axis 2

axis 3

axis 4

mean depth of fines

wetlands within 100 m

(agriculture within 100 m)

percent of basin with pervious soils

frequency of rubble/cobble

runs per length of stream

(pools per length of stream)

wetlands within 100 m

mean depth


		



	8



2	
 

The same PCA and multiple regression approach was used 

with species richness replacing IBI scores. Species richness may 

be more precisely replicated than IBI score, and species richness 

was highly correlated with IBI score (r2

=0.6).

The forward stepwise multiple regression of the core habitat 

variables (table 2) determined this model using data from group A: 

Species Richness = -3.94 + 3.725 (lndrainarea) + 4.96 

(lnpools/1000) + 0.17 (woody) – 1.07 (rtwet 100)

The r2 was 0.78, the regression was highly significant, 

p<0.0001. When the model was applied to the data from group B, 

the mean of the differences between the observed and estimated 

species richness values was –1.04, which seems an acceptable 

value close to zero. However, the standard error of the estimate 

(5.1) was relatively high. The correlation between the observed 

and estimated species richness values was better than the 

correlations with IBI score (r2 = 0.058); however, the results were 

not significant (p>0.1).

The results are analogous to the results achieved with the IBI 

scores. The linear regression model of habitat was not able to 

consistently estimate either IBI scores or species richness. The 

correlations that were generated for individual data sets were 
 7 

good. However, the linear regression model developed from one 

data set did not adequately describe another data set. 


		.
-.

 A recent article by King and Jackson (1999) describes an 

alternative and perhaps statistically more rigorous approach to 

variable reduction and the identification of core variables. They 

demonstrate a procedure using PCA to extract the most 

variability with the fewest variables. The procedure is based on 

using a “broken stick” model and the results of comparisons of 

methods made by Jolliffe (1972). It involves using the one 

variable with the highest loading from each of the first x number 

of axes as the core variables, where x is one-third the number of 

samples (Grossman and others, 1991). So, in the case of group A 

with 26 sites, variable reduction has the goal of determining 8 

core variables or less. 

Using this procedure, which is referred to as B4 by Jolliffe 

(1972), allows for confirming the variable selection process that 

was done using the previous core variable identification method. 

There is a high level of concurrence (agreement) between the two 

PCA analyses (table 3). Based on conventional and B4 methods 

together, there are 17 variables (table 4) that are the most important core variables to measure during habitat quantification or 

monitoring in the St. Croix River Basin. 

8



The third objective was the development of a basin-specific 

habitat index. Although IBI scores and species richness could not 

be fit to a correlation model of habitat quality, an index of habitat 

quality can be developed using the core variables. The habitat 

analysis identified 17 core variables (table 4); however, some of 

the variables were not used in the index either to reduce redundancy within the classification groups (pools per length of 

stream, percent sand, percent gravel, and urban land use), or to 

increase the ease of data aggregation (percent of basin with pervious soils). The final index includes 12 variables (table 5).

The index rates each habitat variable relative to the mean 

values in the data set and assigns a plus or minus to each factor. 

Each variable is adjusted to insure a plus sign corresponds to 

increased habitat quality and a minus sign to decreased habitat 

quality as derived from the St. Croix River Basin data relative to 

IBI scores and species richness. For example, high numbers of 

riffles receive a plus, while high numbers of runs receive a 

minus. To assign a plus or minus to the variable, any value 

exceeding the mean by about 5 percent receives a plus. Variables 

with values about 5 percent less than the mean receive a minus. 

Comparison factors that are similar to the mean (within about 5 

percent of the mean) are not rated. The net sum of the pluses and 

minuses produces the numerical value of the index, which ranges 

from –12 (poorest habitat) to +12 (best habitat). 

Verification and testing of the index follows a procedure similar to that used for the regression models. The index values are 

generated for each site (in this case development and verification 

data sets are used together) and then correlated with both IBI 

scores and species richness. 

The regression of the combined data sets (groups A and B) 

with IBI scores produced a significant correlation (p=0.028). The 

significance may be due more to the number of observations 

(n=47) than the relation (a function of the number of degrees of 

freedom in the statistical test); however, the correlation was positive and the correlation coefficient was r=0.32. 

The group C test data set contained 31 sites. Only 13 sites, 

however, had IBI scores. The sites sampled in 1998 have not yet 

36	
	
	

			

	7
2
	


&8	)19 (

[Definitions of variables are given in appendix A.]

Data Set Conventional method B4 method 

Group A mean depth of fines riffles per length of stream

riffles per length of stream drainage area

drainage area mean annual runoff

runs per length of stream mean percent of woody debris

mean annual runoff forest within 100 m

coefficient of variation of depth storage ratio

mean percent of woody debris

Group B mean depth of fines mean depth of fines

frequency of cobble substrate runs per length of stream

wetlands within 100 m percent of basin with pervious soils 

runs per length of stream mean depth

percent of basin with pervious soils frequency of gravel 

mean depth mean annual runoff
 8 

324	

		
		

5		


,
	
	

		
	#

[Definitions of variables are given in appendix A.] 

Classification group Variable

Hydrology drainage area,

mean annual runoff,

storage ratio

percent of basin with pervious 

soils

Geomorphology riffles per length of stream 

runs per length of stream

pools per length of stream

Instream habitat mean percent of woody debris

coefficient of variation of depth

tree canopy

Substrate frequency of cobble/rubble 

mean depth of fines

frequency of sand

frequency of gravel

Riparian zone/land use forest within 100 m 

agriculture within 100 m

urban with 100 m

had IBI scores determined. The correlation coefficients were very 

low and the regressions were not significant between the habitat 

index and IBI scores or species richness. 

If the index does in fact reflect the quality of the habitat and is 

related to the IBI score, then the addition of the third data set to 

the first two should increase the resolution of the correlation 

because the range of conditions has been increased. No increase in 

the correlation coefficient would indicate that the index could not 

distinguish poor habitat sites from good habitat sites, using IBI 

scores as the measure of “good” and “poor”.

The regression of the habitat index and IBI scores for all 60 

sites (fig. 3) improved the correlation. The correlation coefficient 

increased to 0.44, and the regression was highly significant 

(p=0.0004). 

Two sites, an unnamed tributary and Brown’s Creek, had very 

low IBI scores and species richness but moderate habitat indices. 

The deletion of these two sites increased the correlation. The coefficient of determination increased (r2

 = 0.25), indicating about a 5 

percent increase in the variance explained, and the standard error 

of the estimate decreased from 8.4 to 7.4. 

The habitat index can be used in conjunction with the IBI to 

identify streams where low IBI scores may not be due to the quality of the habitat. The approximate relation is:

3:4	

		

	
00

[Definitions and units of variables are given in appendix A.

a Total cover is the sum of percentages of boulders, woody 

debris, over hangs, undercuts, and macrophytes.

Habitat Variable

Correlation to 

IBI score or 

species 

richness Mean value

Hydrology

drainage area positive 130

mean annual runoff positive 9.4

storage ratio negative 30

Geomorphology

riffles per length of stream positive 10

runs per length of stream positive 16

Instream habitat

percent total covera positive 45

tree canopy (total densiometer) positive 210

coefficient variation of depth positive 50

Substrate

frequency of cobble/rubble positive 25

mean depth fines negative 0.6

Riparian zone/land use

forest within 100 m positive 40

agriculture within 100 m negative 20

IBI Score = 46 + Habitat Index

An observed IBI score for the stream more than 15 points (2 

standard errors) below the calculated value (as was the case with 

the unnamed tributary and Brown’s Creek), may be indicative of 

water-quality problems. The low IBI score may not be due to 

quantity or quality of habitat. 




The analysis went through numerous steps. The original 

procedure did not produce and index. It may be that the streams 

were too similar for the PCA/multiple regression program to be 

successful. The method that did lead to the index was a 

concurrence method; whereby, the data sets were examined for 

similarities. The similar features were used to develop the index. 

The original approach may be appropriate in another basin. There 

is no way, however, to determine beforehand which habitat 

analysis method to use. 

Our planned analytical approach proved to be inadequate. 

There was no correlation between the observed and estimated IBI 

scores or species richness in the verification data set. These results 

indicate that the regression model of core habitat features and IBI 

scores or species richness cannot be used. Is habitat irrelevant to 

the quality of the fish community? How can there be no 

correlation between IBI scores and habitat or species richness? 
 9 

There are at least two different answers. The first is that IBI scores 

have poor statistical properties. The second is that not enough, or 

not the correct, habitat variables were used. 

IBI scores may not have great precision (reproducibility) and 

therefore may be poor variables for regression analysis. The 

confidence interval on an IBI score can be 12 points, although 

most confidence intervals are 6 points (Fore and others, 1994). 

Assuming a conservative confidence interval of 6 points, every 

point could range three points to the left or right (fig. 4). If the 

estimated score (estimated from the habitat variables) had similar 

confidence limits, it too could shift the data point a similar 

distance in the other directions (up or down). The arrows in figure 

4 indicate the range of possible locations for the data point in the 

center. Therefore, the location of the data point may not be 

precisely determined. Normally, one characteristic of the 

independent variable (observed IBI score in this instance) is that it 

can be accurately measured; this may not be the case with IBI 

scores. 

The range of the values of the data points used for model 

development further confounds the relation between observed and 

estimated scores. If the range used is only one-half the total range 

of possible IBI scores, as it is here (one of the selection criteria for 

sites for model development was an IBI score above 35 or 45 

depending on drainage area), the relation between observed and 

estimated is obscured. Only one-half of the relation is under consideration. When the scale approximates the full range of potential 

-12 -8 -4 0 4 8 12

60

50

40

30

20

10

0

HABITAT INDEX

IBI SCORES

Figure 3. Relation between the habitat index and the

IBI scores for the St. Croix River Basin for all

sites sampled.

IBI scores from 12 to 60, the effects of scale become more apparent (fig. 5). The 45-degree line indicates the correspondence line 

that would occur with perfect agreement between the observed 

and estimated IBI scores. If the full range of IBI scores had been 

used, then perhaps points at the lower end of the range of score 

values could have better defined the relation between observed 

and estimated scores. 

The lack of resolution and correlation appears to be, at least 

partially, the result of the imprecise value of the IBI used for this 

analysis. An analysis of the statistical properties of the IBI indicated that the index is in fact variable and that at least 8.5 points 

are necessary to distinguish a difference between two sites (Fore 

and others, 1994). The same study also used power analysis to 

determine that 5 or 6 distinct scoring classes could be determined. 

Several earlier analyses of the IBI indicated that the mean value 

has a standard deviation of about 4 units (Angermeier and Karr, 

1986). Therefore, using IBI score as an independent variable in a 

regression analysis may not be appropriate. 

Shields and others (1995) found little correlation between IBI 

scores and habitat degradation in Mississippi streams. They attributed the lack of correlation to the temporal variability in IBI 

scores at degraded sites where they had sampled. They also indicated that the lack of reference sites may have affected the ability 

of the IBI to adequately incorporate a full gradient of habitat quality. This was not the case with the St. Croix River Basin data. The 

60

55

50

45

40

35

35 40 45 50 55 60

OBSERVED IBI

ESTIMATED IBI

Figure 4. Observed and estimated IBI scores at sites with good

habitat. The 45 degree line is the hypothetical perfect

agreement between observed and estimated scores. Arrows

indicate the potential range of IBI values for a single point.
 10 

sampled sites included almost pristine streams in forested settings 

at one end of the gradient, to channelized ditches in agricultural 

landscapes at the other end. A gradient of habitat quality that 

lacked chemical, landscape, or other sources of degradation might 

have produced a more distinct relation. The loss of resolution due 

to the presence of sources of degradation other than habitat is a 

real complicating factor. The relation between IBI and habitat is 

obscured as IBI score decreases because the IBI score responds to 

sources of degradation other than habitat quality. It may even be 

reasonable to conclude that streams that are subject to the greatest 

amount of habitat disruption are also subject to the greatest 

amount of chemical and landscape sources of degradation in urban 

areas and areas of agriculture and forestry. The precision of the IBI 

decreases with biotic integrity (Fore and others, 1994): the lower 

the IBI score, the wider the confidence interval. With other 

sources of degradation, the habitat and IBI relation would disintegrate at the low end, and the utility of the habitat index would 

decrease. 

Although the habitat index is only able to explain about 20 percent of the variability in IBI scores, it does account for the majority of the variability in the habitat data. One difficulty with this 

analysis is that low IBI scores may not be strictly attributable to 

habitat quality. For example the two lowest IBI scores (fig. 5) 

came from two very small streams, an unnamed tributary and 

Brown’s Creek (drainage areas 27.4 km2

 and 35.2 km2, respectively). While both sites received the same IBI score of 22, the 

unnamed tributary had a habitat index of 0, and Brown’s Creek 

had a habitat index of –3. The unnamed tributary contained 2 species, but Brown’s Creek had 5. It certainly is possible that factors 

other than habitat could be contributing to the variance. In fact, 

when these two sites are deleted from the analysis the results were 

significantly changed. Some additional testing would be necessary 

to determine the behavior of the index over a range of conditions 

and how it behaves mathematically and statistically.

The analytical procedure that led to the index may not have 

included the proper variables to define the habitat and IBI relation. 

A number of the initial variables could not be used because they 

were almost categorical. Many of the variables had a large percentage of zero values and could not be successfully transformed 

to fit a normal distribution. While these variables could not be 

used in this analysis, they certainly contain relevant information. 

Variables included in the analysis however, did represent the major 

habitat variable groups (hydrology, geomorphology, instream habitat, riparian zone, and substrates that have been used in other habitat indices (Stauffer and Goldstein, 1997). When about 60 percent 

of the variability in habitat and as 80 percent of the variability in 

species richness can be explained with the variables identified 

from the original habitat data collection, no new habitat variables 

would seem needed. 

A greater change occurred to IBI scores as habitat quality 

decreased. The range of estimated IBI scores was greater with 

habitat index values that were negative than habitat scores that 

were positive. When IBI scores from average sites were compared 

to IBI scores from high quality sites, there was not a great deal of 

difference, the scores were similar. However, when IBI scores 

from average habitat sites were compared to sites with low habitat 

scores, the IBI values decreased. It appears that “good” habitat is 

20

12

28

36

44

52

60

12 20 28 36 44 52 60

OBSERVED IBI

ESTIMATED IBI

Figure 5. Observed and estimated IBI scores at sites with good

habitat throughout the full range of possible scores. (The 45

degree line is the hypothetical perfect agreement between

observed and estimated scores.)

an amalgamation of conditions. The conditions can be better, but 

the community response is minimal and difficult to determine. 

Conversely, when the habitat conditions degrade, the effect is 

more pronounced and more readily reflected in the IBI scores. 

The application of the habitat index to field work would 

allow a reduced number of variables to be measured in the field 

and therefore increase the cost efficiency of the habitat data collection, while maintaining sufficient information to evaluate the 

biocriteria for a site. 

 	

Angermeier, P.L., and Karr, J.R., 1986. Applying an index of 

biotic integrity based on stream-fish communities—

Considerations in sampling and interpretation: North 

American Journal of Fisheries Management, v.6, p. 418–429.

Baker, E.A., and Coon, T.G., 1997, Development and evaluation 

of alternative habitat suitability criteria for brook trout: 

Transactions of the American Fisheries Society, v. 127, 

p. 65–76.

Fajen, O.F., and Wehnes, R.E., 1982, Missouri’s method of 

evaluating stream habitat, in N. Armantrout, ed., Acquisition 

and utilization of aquatic habitat inventory information: 

American Fisheries Society, Western Division, Bethesda, 

Maryland, p. 117–123.
 11 

Fore, L.S., Karr, J.R., and Conquest, L.L., 1994, Statistical 

properties of an index of biological integrity used to evaluate 

water resources: Canadian Journal of Fisheries and Aquatic 

Sciences, v. 51, p. 1077–1087.

Frissel, C.A., Liss, W.S., Warren, C.E., and Hurley, M.D., 1986, 

A heirarchical framework for stream habitat classification—

Viewing streams in a watershed context: Environmental 

Management, v. 10, p. 199–214.

Gorman, O.T. and Karr, J.R., 1978, Habitat structure and stream 

fish communities: Ecology, v. 59, p. 507–515.

Grossman, G.D., Nickerson, D.M., and Freeman, M.C., 1991, 

Principal component analyses of assemblage structure data—

Utility of tests based on eigenvalues: Ecology, v. 72, p. 341–

347.

Harrel, R.C., Davis, B.J., and Dorris, T.C., 1967, Stream order 

and species diversity of fishes in an intermittent stream: 

American Midland Naturalist, v. 78, p. 728–436.

Karr, J.R., 1981, Assessment of biotic integrity using fish 

communities: Fisheries, v. 6, p. 21–27.

Karr, J.R., Fausch, K.D., Angermeier, P.L., Yant, P.R., and 

Schlosser, I.J., 1986, Assessing biological integrity in running 

waters—A method and its rationale: Illinois Natural History 

Survey Special Publication Number 5, 28 p. 

King, J.R., and Jackson, D.A., 1999, Variable selection in large 

environmental data sets using principal components analysis: 

Environmetrics, v. 10, p. 67–77.

Jolliffe, I.T., 1972, Discarding variables in a principal 

components analysis, I. Artificial data: Applied Statistics, 

v. 21, p. 160–173.

Matthews, W.J., 1986, Fish faunal structure in an Ozark stream—

Stability, persistence, and a catastrophic flood: Copeia, p. 

388–397.

Nelson, R.L., Platts, W.S., Larsen, D.P., and Jensen, S.E., 1992, 

Trout distribution and habitat in relation to geology and 

geomorphology in the North Fork Humboldt River drainage, 

northeastern Nevada: Transactions of the American Fisheries 

Society, v. 121, p. 405–426.

Nestler, J.M., Milhous, R.T., and Layzer, J.B., 1989, Instream 

habitat modeling techniques, in Gore, J.A., and Petts, G.E., 

eds., Alternatives in regulated river management: CRC Press, 

Boca Raton, Florida, p. 295–315 

Pearson, T.N, Li, H.W., and Lamberti, G.A., 1992, Influence of 

habitat complexity on resistance to flooding and resilience of 

stream fish assemblages: Transactions American Fisheries 

Society, v. 121, p. 427–436.

 Petersen, R.C., Jr., 1992, The RCE—A riparian, channel, and 

environmental inventory for small streams in the agricultural 

landscape: Freshwater Biology, v. 27, p. 295–306.

Plafkin, J.L., Barbour, M.T., Porter, K.D., Gross, S.K., and 

Hughes, R.M., 1989, Rapid bioassessment protocols for use in 

streams and rivers—Benthic macroinvertebrates and fish: 

U.S. Environmental Protection Agency, Assessment and 

Watershed Protection Division, EPA–440–4–89–001, 

Washington, D.C., variously paged.

Rahel, F.J., and Hubert, W.A., 1991, Fish assemblages and 

habitat gradients in a Rocky Mountain-Great Plains stream—

Biotic zonation and additive patterns of community change: 

Transactions of the American Fisheries Society, v. 120, p. 

319–332.

Rankin, E.T., 1989, The qualitative habitat evaluation index 

[QHEI]—Rationale, methods, and application: Ohio 

Environmental Protection Agency, Ecological Assessment 

Section, Division of Water Quality and Assessment, 

Columbus, 54 p.

Rosenzweig, M.L., 1981, A theory of habitat selection: Ecology, 

v. 62, p. 327–335.

Schlosser, I.J., 1982, Fish community structure and function 

along two habitat gradients in a headwater stream: Ecological 

Monographs, v. 52, p. 395–414.

Shields, F.D., Jr., Knight, S.S., and Cooper, C.M., 1995, Use of 

the index of biotic integrity to assess physical habitat 

degradation in warmwater streams: Hydrobiologia, v. 312, 

p. 191–208.

Simonson, T.D., Lyons, John, and Kanehl, P.D., 1995, Guidelines 

for evaluating fish habitat in Wisconsin: U.S. Department of 

Agriculture, Forest Service, North Central Forest Experiment 

Station, General Technical Report NC-164. St. Paul, Minn., 

36 p. 

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qualitative habitat indices and their applicability to prairie 

streams: North American Journal of Fisheries Management, 

v. 17, p. 348–361.

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

Relation of wooded riparian zones and soil runoff potential to 

fish community composition in agricultural basins: Canadian 

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Chicago, Ill. 

 Terrel, J.W., McMahon, T.E., Inskip, P.D., Raleigh, R.F., and 

Williamson, K.L., 1982, Habitat suitability index models—

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82/10.A. Washington, D.C., variously paged.

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1492, Fort Worth, Tex., 88 p.

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Management, v. 18, p. 775–785.
 12 

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Ecosystem Health, v. 1, p. 119–126.
 13 

.
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Hydrology (basin-level variables):

1. Size of the drainage basin in square miles

2. Mean annual runoff in inches per square mile per year

Even though sampling should have occurred during stable 

low flow conditions, that was not always the case. The historical 

hydrologic conditions can have a profound effect on the fish 

community of a stream (Harrel and others, 1967; Matthews, 

1986; Pearson and others, 1992). Hydrologic history was 

quantified for each major subbasin with the mean annual runoff 

statistic that was used for all the sites within the same subbasin. 

Although this variable ignores evapotranspiration (which may be 

significant in forested or highly agricultural basins), it still gave 

an order-of-magnitude approximation of the total amount of 

water in the stream during the year prior to sampling. 

3. Percent of the basin area with pervious soils (derived from 

the U.S. Department of Agriculture, 1991, STASTGO data base). 

The type of soil in the basin may be significant in affecting the 

quantity of water in a stream and how rapidly runoff reaches a 

stream. The soil hydrologic group, which is a measure of the 

runoff potential, was retrieved from the U.S. Department of 

Agriculture’s STATSGO data base, and the proportion of the 

basin with pervious soils was calculated for the basin upstream of 

each site (Stauffer and others, 2000).

4. Storage ratio the proportion of the total basin area in water 

or wetland

Geomorphology (reach-level variables):

1. Sinuosity (segment level)

2. Gradient in feet per mile (segment level)

3. Total number of riffles

4. Total number of pools

5. Total number of runs

6. Total number of bends

7. Mean channel width, based on 5 points on each of 13 

transects, in feet

8. Mean channel depth, based on 5 points on each of 13 

transects, in feet

9. Coefficient of variability of depth 

10. Erosion frequency, the number of times erosion was 

observed at the two ends of each transect divided by the total 

number of observations 

Instream habitat (developed into reach-level variables from 

transect data):

1. Number of log jams in the reach 

2. Mean percent of algae observed at a point on a transect, 

based on 5 points on each of 13 transects

3. Mean percent of macrophytes observed at a point on a 

transect, based on 5 points on each of 13 transects 

4. Mean percent of overhanging vegetation, based on the 

amount (percent) of overhanging vegetation observed on both 

sides of 13 transects 

5. Mean percent of undercut banks, based on the amount 

(percent) of undercut banks observed on both sides of 13 

transects

6. Frequency of overhanging vegetation, based on the 

number of observations of overhanging vegetation at all the 

transects

7. Frequency of undercut banks, based on the number of 

observation of undercut banks at all the transects

8. Mean percent of woody cover, based on the amount 

(percent) of woody cover at each transect

9. Mean percent of boulders, based on the amount (percent) 

of boulders at each transect

10. Frequency of woody cover at all the transects

11. Frequency of boulders at all the transects

12. Frequency of submerged macrophyte cover at all the 

transects

13. Frequency of emergent macrophyte cover at all the 

transects 

Substrate (converted to frequency of occurrence for the reach; 

each point on each transect was treated as one of the total number 

of observations, 5 points per transect). 

1. Frequency of each substrate type (bedrock, boulders, 

cobble/rubble, gravel, sand, silt, clay, and detritus)

2. Mean depth of fines 

3. Mean embeddedness, based on quartiles 

4. Small particles, the sum of the frequencies of sand, silt, 

clay, and detritus

Riparian zone/land use (land use was classified as forest, agriculture, urban, wetland, water, or other): 

1. Tree canopy (total amount of shade, based on the sum of 

all four densiometer readings)

2. Percent land use within 100 meters of the stream 

3. Percent land use in the basin