F

I

Biological Fbqort 18

August 1993

Habitat Suitability Index Model for Brook Trout

in Streams of the Southern Blue Ridge Province:

Surrogate Variables, Model Evaluation, and

Suggested Improvements

BY

Christopher J. Schmitt, A. Dennis Lemly, and Parley V. Winger

U.S. Department of the Interior .

Fish and Wildlife Service

Washington, D.C. 20240
Contents

Page

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

Methods ................................................ . 3

Results ................................................ . 4

Winger et al. (1987) .......................................... 4

U.S. National Park Service ...................................... 5

Fowler (1985) and Lasier (1986) .................................. . 5

North Carolina Wildlife Resources Commission. .......................... 7

Commonahties and Cross-validation ................................. 7

Evaluating Competition Between Brook Trout and Rainbow Trout ................ 8

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

Limitations of the Regression-Correlation Approach and Comparison With

Principal Components Analysis. ..................................10

Problems in Application of HSI Models to Brook Trout in the Southern Blue Ridge

Province and to Stream-dwelling Fishes in General ........................11

Conclusions and Recommendations ..................................13

Acknowledgments ............................................15

CitedLiterature.............................................15

Appendix ............................................. . ..19

. . .

ill
Habitat Suitability Index Model for Brook Trout in Streams of

the Southern Blue Ridge Province: Surrogate Variables, Model

Evaluation, and Suggested Improvements

Christopher J. Schmitt

U.S. Fish and Wildlife Service

National Fisherks Contaminant Research Center

4200 New Haven Road

Columbia, Missouri 65201

A. Dennis Lemly

U.S. Forest Service

Coldwater Fisheries Research Unit

Department of Fisheries and Wildlife Sciences

Virginia Polytechnic Institute and State University

Blacksburg, Virginia 24061

and

Parley X Winger

U.S. Fish and Wildlife Service

National Fisheries Contaminant Research Center

Field Research Station

University of Georgia

Athens, Georgia 30602

Abetract. Data Corn several sources were collated and analyxed by correlation,

regression, and principal componenta analysis to define surrogate variables for use in

the brook trout (Salvelinus fontinalis) habitat suitability index (HSI) model, and to

evaluate the applicability of the model for assessing habitat in high-elevation streams of

the southern Blue Ridge Province (SBFW). In all data seta examined, pH and alkalinity

were highly correlated, and both declined with increasing elevation; however, the

magnitude of the decline varied with underlying rock formations and other factors,

thereby restricting the utility of elevation as a surrogate for pH. In the data seta that

contained biological information, brook trout abundance (as bibmass, density, or both)

tended to increase with elevation and decrease with the abundance of rainbow trout

(Onmrhynchus mykiss), and was not significantly correlated (P>O.O5) with the

1
2 SIOLAXICAL REP~W~ 18

abundance of most benthic macroinvertebrate taxa normally construed as important in

the diet of brook trout. Using multiple linear regression, we formulated an alternative

HSI model-based on point estimates of gradient, pH, elevation, stream width, and

rainbow trout density-which explained 40-50% of the variance in brook trout density

in 256 stream reaches. Although logically developed, the present U.S. Fish and Wildlife

Service HSI model, proposed in 1982, seems deficient. in several areas, especially when

applied to SBRF’streams. We recommend that the water quality component in the model

be updated and reevaluated, focusing on the differential sensitivities of each life stage,

the stochastic nature of the water quality variables, and the possible existence of habitat

requirements that differ among brook trout strains.

Key words: Alkalinity, benthos, brook trout, ecology, pH, rainbow trout, southeastern

United States, streams, substrate.

The southern Blue Ridge Province (SBRP), an

area of the Southern Appalachian Mountains

comprising parts of western North Carolina, eastern Tennessee, northeastern Georgia, and extreme northwestern South Carolina, represents

the southern limit of the original distribution of

the brook trout (Salvelinus fontinalis) in North

America (MacCrimmon and Campbell 1969; Fig.

1). The high-elevation streams of the SBRP constitute a peninsula of coldwater habitat that is

surrounded by warmer waters harboring a diverse

and potentially competitive ichthyofauna. CompeA

tition from introduced salmonids, particularly

rainbow trout (Oncorhynchus mykiss), is especially problematic for fishery managers, and has

been well documented (King 1937; Lennon and

Parker 1959, 1967; Kelly et al. 1980; Moore et al.

1983, 1986; Silsbee and Larson 1983; Larson and

Moore 1985; Fausch 1988). Studies completed to

date indicate that brook trout of the SBRP are

confined to high-elevation streams because of

higher temperatures and the presence of competing

species at lower elevations. The taxonomic status of

the Southern Appalachian strain of brook trout is

GA

Fig. 1. Geographic location of the Blue

Ridge Province (A) and southern Blue

Ridge Province (B) in the southeastern

United States. Areas covered in the

original studies that provided data

sets for this report are: l-3 = southem Blue Ridge Province (Fowler 1985;

Lasier 1986, Winger et al. 1987); 4 =

Great Smoky Mountains National

Park (C. R. Parker, unpublished manuscript, “Brook trout habitat in the

Great Smoky Mountains National

Park,” archived 1988 at. U.S. National

Park Service, Great Smoky Mountains

National Park, Gatlinburg, Tenn.

[Parker MS]); 5 = state of North Carolina (North Carolina Wildlife Resources Commission 1983).
HSI MODEL FOR B ROOK TROUT 3

also an issue. Some researchers contend that it is

taxonomically distinct, constituting a discrete

subspecies (Lennon and Parker 1967); however,

genetic studies (Stoneking et al. 1981; McCracken

et al. 1993) still have not resolved this question.

The U.S. Fish and Wildlife Service habitat suitability index (HSI) model for brook trout (Raleigh

1982) incorporates several variables that, when

applied to streams of the SBW are directly related

to elevation. These variables include temperature

(VI, average maximum water temperature during

the warmest period of the year; and VQ, average

maximum water temperature during embryo development), as well as variables related to gradient

and substrate (Vs, average water velocity over

spawning areas during embryo development; V7,

average substrate particle size in spawning areas;

Vg, dominant substrate type in riffle-run areas; and

V16, percent fmes in riffle-run and spawning areas

during average summer low flows), water chemistry 0713, annual maximal or minimal pH), and

stream size Cv,, average thalweg depth). Values for

some of these variables are difficult, if not ixnpossibl& to obtain without intensive sampling and frequent observations. Our major objective was to

explore relations among these variables and such

readily obtainable information as stream size, order, gradient, and elevation to determine whether

some of the more easily measured features can be

used as surrogate variables for application of the

brook trout HSI model in the SBRR Our second

objective was to evaluate the overall applicability

of the model for brook trout in SBFU’ streams.

Methods

Considerable data are available for the high-elevation streams of the SBRP (Fig.l). Winger et al.

(1987) surveyed physicochemical conditions in 30

SBRP headwater streams (point samples taken in

first- and third-order reaches in each stream) during 1983-84 to determine their sensitivity to acidic

deposition. The streams were selected on the basis

of elevation (third-order reaches above 600 m),

degree of human disturbance in the watershed,

and accessibility. Fowler (1985) and Lasier (1986)

studied a subset of the streams surveyed by Winger et al. (1987). They measured the physical and

’ Parker, C. R Unpublished manuscript. Brook trout habitat in

the Great Smoky Mountains National Ru-k. Archived 1088 at

U.S. National F’ark Service, Great Smoky Mountains National

Park, Gatlinburg, Tenn. 81 pp.

chemical attributes of several representative

reaches (100 m per reach) in each of seven brook

trout streams as determinants of benthic diversity

and biomass (number and biomass per square meter), and of the fish assemblages of the streams

(sampled with single-pass electrofishing). Staff of

the North Carolina Wildlife Resources Commission (NCWRC 1983) surveyed 265 streams (representative 100-m reaches) in the SBRP for physicochemical attributes, benthic organism density

(numbers of animals of major taxa per square foot),

and fish community composition (single-pass electrofishing); brook trout were present in 143 of

these streams. Biologists of the U.S. National Park

Service (Park Service) also surveyed coldwater

stream reaches in the Great Smoky Mountains

National Park. From these data, the Park Service

attempted to define regression models relating the

abundance of brook trout and rainbow trout (single-pass electrofishing) to several habitat variables

(Parker MS).’

For our investigation, existing data from the cited

studies were collated and standardized where possible. Exploratory regression and correlation analyses were performed on each data set to determine

the utility of the surrogate variables as predictors of

the more difficult-to-measure physicochemical attributes (maximum temperatures, water quality

variables, substrate particle size distributions, and

velocity) and the biological variables (benthic

biomass and brook trout abundance). Data for the

primary dependent variables (brook trout density

and biomass, alkalinity) were transformed (loglo) to

improve the linearity of predictions from the regression equations. At least two sets of regression analyses were performed for each data set that contained

fish data: The relation of brook trout abundance, as

density or biomass, was fast analyzed against

physicochemical variables for streams in which

brook trout, but no introduced salmonids, were present, to determine whether abundance can be predicted on the basis of habitat factors alone. Another

analysis included all variables to determine

whether additional effects can be predicted by information on the abundance of other salmonids and

whether predictive capability can be increased. A

forward-selection, stepwise multiple regression procedure was used to fit the models. Variables were

added to the model provided that the reduction in

the unexplained sum-of-squares resulting from the

addition was significant (P < 0.05). Other regression-correlation analyses were performed as

needed to identify specific relations within individ-
4 BIOLCXXCAL REFORT 18

ual data sets and among combined data sets. Tabu- group distinct from the remainder of the streams,

lar summaries of the data sets and of the statistical whereas those underlain by glacial alluvium did

analyses undertaken are presented in the Appen- not (Fig. 2). Accordingly, two sets of regression

dix. equations, one for Anakeesta streams and one for

the rest, were developed. For Anakeesta streams,

the relations

Results

Initial analyses focused on simple correlations

pH = 11.598 - 0.005 elevation, and

among biological and physicochemical variables. logic(alkalinity) = 5.018 - 0.003 elevation

For those sets that contained fish data, the analyses

then assessed predictive regression models that were highly significant (P < 0.01) and accounted for

were generated for brook trout density and stand- 85 and 74% of the variation in pH and alkalinity,

ing stock. For the remainder of the streams, the relations

Winger et al. (1987) pH = 6.982 - 0.0004 elevation, and

Winger et al. (1987) studied first- and third-orlogio(alkalinity) = 2.098 - 0.0003 elevation

der str&un reaches at elevations of 421-1,560 m,

pH values of 4.41-7.14, and alkalinities of O- were also highly significant (P < 0.01) and accounted

204 peq/ L (Table Al). These streams drain wafor 15 and 11% of the variability in pH and alkalinity

tersheds underlain or influenced by three major

(Fig. 2). These fmdings indicate that, for streams

classes of rock: (1) “crystalline complex” rocksaffected by the Anakeesta Formation, mean pH at

early Precambrian metamorphic and granitic

base flow (as reported by Winger et al. 1987) can be

gneisses and schists, including in some areas an

predicted satisfactorily with lmowledge of elevation.

overlay of later Precambrian sedimentary and

However, for the remainder of the streams, more

inf

metamorphic rock-which provide little buffering

ormation would be required, perhaps in the form of

capacity and underlie slightly acidic, low-alkalinmom precise data on bedrock geology.

ity streams; (2) glacial alluvium, which provides

Winger et al. (1987) reported that there was

relatively high buffering capacity and underlies

little difference in the acid-neutralizing capacity of

streams of moderate alkalinity and nearly neutral

the major formations constituting the crystalline

pH; and (3) the Anakeesta Formation, which is

complex. However, relations depicted in Fig. 2 inpyritic rock capable of producing acidity (Table

dicate that there are subgroups within this larger

Al). Because the underlying rock so profoundly

grouping-suggesting that, with more information

influences water quality (Winger et al. 1987),

on bedrock geology and soil type, more accurate

separate correlation and regression analyses were

regression models might be developed.

performed for each of the three groups of streams,

Accordingly, to determine if better predictive

as well as for all streams combined.

models for pH and alkalinity can be produced by

In the 52 stream reaches underlain by the

further stratification, the underlying rock formations and soil types constituting the watershed of

crystalline complex, pH and alkalinity were each stream surveyed by Winger et al. (1987) were

highly correlated (P < 0.01; Table A2). The cor- identified from information contained in geologic

relation between elevation and pH was also sig- and soil maps (Stuckyey 1958; Hardman et al.

nificant (P < 0.05), but not that between eleva- 1966; Tennessee Valley Authority 1968; Pickering

tion and alkalinity. In streams draining glacial and Murray 1976). Regression analysis produced

alluvium, elevation, pH, and alkalinity were not statistically significant (P < 0.05) relations for pH

significantly correlated; however, the sample versus elevation in streams underlain by four of the

size (n = 4) was small. In contrast, all correla- six rock formations (including Anakeesta) for which

tions were significant (P < 0.01) in streams on the data were adequate to warrant analysis (Table A3).

Anakeesta Formation, despite the small (n = 6) Three of the six regressions were significant for the

sample size. Stream order was not signifi- alkalinity-elevation regressions. Stratifying the

cantly correlated (P > 0.05) with any other streams according to their respective soil classificavariable in any of the three sets of streams. tions also produced significant relations for the

Inspection of the data revealed that the streams Porters-Ashe-Perkinsville and Ramsey-Rangerdraining the Anakeesta Formation constituted a Talledega soil groups, which together accounted for
HSI MODEL FOR BRINK TROUT 5

60 of the 62 reaches surveyed (Table A4). Further

stratification of the data to geologic and soil classifications provided little improvement over the regressions produced by the geologic stratification

alone (Table A5); in the strata with sufficient data,

the relations for the two stratification schemes

were essentially identical (cf. Tables A3 and A5).

U.S. National Park Service

Of the three data sets that contain information

on brook trout, the one based on survey data provided by the Park Service (Parker MS) summarized

the most information on physical attributes of

streams. The Park Service field records included

observations on variables associated with geophysical and chemical characteristics (elevation, gradient, temperature, and pH); canopy coverage; bank

composition (rocks, vegetation, and gravel); stream

size (mean depth, mean w&ed-area width, and

mean channel width); in-stream cover (turbulence,

rock, ledge, debris, vegetation, depth, bank, and

total); substrate composition (organic debris, orgarlic muck, sand, fine gravel, coarse gravel, small

rubble, large rubble, boulder, bedrock, and silt);

woody debris (stability, length, and volume); and

pool area. The data were collected during 1984 and

1986 (Table A6). The Park Service also determined

the density of brook trout and rainbow trout in each

stream reach.

As expected, there were significant correlations

among physicochemical measurements and many

of the habitat descriptors in the combined (allyears) data set (Table A7). Midsummer temperature, pH, canopy coverage, stream size, rock cover,

substrate, fine gravel, and substrate boulder abundance were all negatively correlated with elevation,

whereas in-stream debris cover, substrate sand and

bedrock, and total woody debris increased with

elevation. Brook trout density was positively correlated with elevation and bank vegetation and negatively correlated with measurements of rainbow

trout density, temperature, pH, in-stream bedrock,

width, and in-stream boulders. In contrast, rainbow trout density was negatively correlated with

elevation, bank vegetation, in-stream debris, and

substrate fine gravel and silt, and was positively

correlated with stream size and in-stream rocks

and boulders. Most of these associations support the

general assumptions of the brook trout HSI model.

The two sets of multiple regression analyses that

we performed on the combined (1984 and 1986)

Park Service data each produced models that provided excellent predictions of brook trout density

For streams containing brook trout but no introduced salmonids, statistically significant models

containing the variables elevation, pH, bank vegetation, stream width, pool area, and amount of

debris present explained as much as 87% of the

variation in brook trout density (Table A8). When

all streams containing brook trout were analyzed,

models that included physicochemical variables

and negative coefficients for rainbow trout density

explained 77% of the variance in brook trout density.

As noted by Parker (MS), the streams of the

Great Smoky Mountains National Park differed

substantially between 1984 and 1986; 1984 was a

“normal” water year, whereas the summer of 1986

was marked by near-record drought conditions and

unusually low water levels. Regression analyses

performed independently on the two sets of data

yielded different and often contradictory relations.

Although both sets of relations accurately predicted

brook trout density within the data sets from which

they were derived (R” = 0.91 to 0.99 for 1984, 0.87

to 0.97 for 1986; Table A8), cross-validation failed

to uphold the relations; the 1984 models could not

satisfactorily predict the 1986 abundance of brook

trout (based on 1986 data for the independent variables), and the 1986 model fared poorly when applied to the 1984 data. The models were contradictory in that different variables were selected for

each year, and the coefficients for some variables

had opposite signs in the two models. Parker (MS)

concluded that the two sets of samples were essentially drawn from different “populations” (in the

statistical sense).

Fowler (1985) and Lasier (1986)

Fowler (1985) and Lasier (1986) studied a subset

of the streams investigated by Winger et al. (1987)

throughout the SBRI? Although the data subset

assessed fewer physicochemical variables (stream

width, depth, flow, gradient, elevation, pH, and

alkalinity) than the Park Service analysis (Parker

MS), habitat coverage was expanded by incorporating extensive density and biomass estimates for

fishes and invertebrates (Table A9). In the expanded data subset, brook trout density and

biomass were negatively correlated with rainbow

trout density and biomass to about the same degree

as in the Park Service data (cf. Tables A7 and AlO).

Brook trout abundance was also negatively correlated with the abundance of sculpins (Cottus spp.)

and brown trout (SczLmo trutta), and with several

physicochemical variables (stream width, depth,

and flow). Width, depth, and flow were negatively
6 BIOLOGICALREPORT 1 8

7-

i

; '4

In fi

,'

_' +/

,' ,'

/'

5- C-'

'0 , ,'A'

\_/-'

43,

1 IO 100 1 J

Alkalinity, /feq/r

A

100

41, , , , , I I

4 5 6 7 a 9 10 11 12 13 14 15 1

Elevation, 100 m

Anakeesta Pyrite

Metagraywacke. Mica-schist-quatiite

Mica-schist. Gneiss

Biotite-gneiss

Metagraywacke. Mica-schist

Unicoi Formation

Great Smoky Formation

11’ + TMixed Gneisses & Schists I

4 5 6 7 a 9 10 11 12 13 14 15 1'

Elevation, 100 m

6

Fig 2. Relations among pH, alkalinity, and elevation in 62 headwater stream reaches of the southern Blue Ridge

&ovince surveyed by Winger et al. (1987) underlain by (1) the Anakeesta Formation (solid circles bounded by

s/u&d ellipses) and (2) other geologic formations and rock types.
HSI MODELFORBRCOKTROUT 7

correlated with elevation, whereas the correlation

of elevation with pH was positive (Table AlO). As in

the data from Winger et al. (1987), of which Fowler’s

(1985) data are a subset, pH and alkalinity were also

positively correlated (Table AlO).

Among the aquatic invertebrates included by

Lasier (1986), the abundance of those representing

the insect orders Odonata, Ephemeroptera, Plecoptera, Trichoptera, and Diptera (taxa important in

the diet of brook trout; Tebo and Hassler 1963; Reed

and Bear 1966; Cada et al. 1987) were positively

correlated with at least one measure of stream size

(order, width, or depth; Table AlO). Only the abundance of plecopterans was correlated with elevation, and the coefficient was negative. The abundance of coleopterans, which often are important in

salmonid diets, was positively correlated with elevation. Brook trout density and biomass were negatively correlated (albeit weakly) with the abundance of Odonata and Diptera, whereas the

biomass and density of the other fishes present in

the streams were positively correlated with the

abundance of invertebrates representing several

taxa. Collectively, these findings indicate that benthic invertebrates are less abundant in the upper

reaches of the streams, where the brook trout are

most abundant, than in the lower reaches, which

are more heavily populated by competing species.

The findings also indicate that benthic invertebrate

abundance has little effect on the distribution or

abundance of brook trout. However, the relations

among total available food, the specific organisms

actually being consumed (preferred food), and number of preferred feeding locations in the stream

reaches were not determined. The abundance of

visually isolated feeding lanes that deliver preferred food items in drift may be as important for

the production of sahnonids as the total amount of

invertebrate food available in the stream (Chapman 1966; Dolloff 1983). Thus, the neutral and

negative correlations between brook trout density

and benthic invertebrate abundance might simply

indicate that there are relatively few preferred

feeding areas in small, high-elevation streams.

For the data from Fowler (1985) and Lasier

(1986), stepwise multiple regression analysis using

only physicochemical variables produced models

that explained about 79% of the variation in brook

trout abundance (density and biomass) in streams

containing no competing salmonids Fable All).

When the analyses were expanded to include biological variables and streams containing competing

salmonids, models containing physicochemical

variables, a term for gastropod abundance, and

negative terms for rainbow trout and either sculpin

or brown trout abundance explained as much as

87% of the variance in brook trout abundance.

North Carolina Wildlife Resources

Commission

Stream survey data gathered by the North Carolina Wildlife Resources Commission (NCWRC

1983) also contained information on benthic invertebrates and fishes (Table A12). In this data set,

brook trout density was positively correlated with

elevation and in-stream cover, and negatively correlated with pH, stream length, width, and flow

(Table A13). Among the biological variables, brook

trout abundance was positively correlated with the

abundance of Odonata (opposite of the association

for Fowler’s data), and negatively correlated with

the abundance of rainbow trout and other fishes,

and three benthic taxa-Ephemeroptera, Coleoptera, and Trichoptera. Alkalinity and pH were positively correlated and, as reported for the data of

Winger et al. (1987), both variables declined with

increasing elevation. The abundance of Ephemeroptera and Coleoptera in the North Carolina

survey data was negatively correlated with elevation and positively correlated with pH and alkalinity, as reported for Fowler’s (1985) and Lasier’s

(1986) data. Other benthic taxa were positively

correlated with variables related to stream size,

corroborating the finding of greater benthic organism density in the lower reaches of these streams,

where brook trout coexist with other fishes.

Multiple regression analyses with the NCWRC

survey data yielded models that explained less

variability than models derived from the other data

sets. For brook trout streams only, a model containing measurements of width, flow, pH, and cover

explained 39% of the variance in brook trout density

(Table A14). For all streams, a model containing three

physicochemical variables and negative terms for

rainbow trout, brown trout, striped jumprock (Moxostoma rupiscartes), and Megaloptera density and a

positive term for Odonata density, explained about

59% of the variance in brook trout density.

Commonalities and Cross-validation

To provide some degree of cross-validation, we

combined all three data sets discussed above. Only

six variables-brook trout density, rainbow trout

density, gradient, elevation, pH, and stream

width-were common to all three data sets. Be-
8 BIOLCXXCAL REFQPT 18

cause many of the other variables in each data set

were correlated with the six common variables

(Tables A7, AlO, and A13), multiple regression

analysis was used to fit linear, quadratic, and cubic

terms in five independent variables for the combined data set. For all streams (n = 256), a model

containing positive terms for elevation, gradient

(quadratic), and stream width (cubic), and negative terms for gradient (linear and cubic) and width

(quadratic), explained 48% of the variation in

brook trout density (Table A15). Examination of

the residuals for this relation revealed that, although the fit of the model was good given the

heterogeneous data sources, there was strong positive bias on the low end. Moreover, the model failed

to predict the absence of brook trout in the presence of rainbow trout, and it overestimated brook

trout abundance despite the inclusion of a negative

cubic term for rainbow trout. When the analysis

was restricted to brook trout streams (those containing brook trout but no rainbow trout; n = 165),

a model that included positive terms for elevation

(linear) and gradient (quadratic), and negative

terms for pH (linear) and stream width (quadratic), explained 44% of the variability in brook

trout density. The model for brook trout only was

also biased positively on the low end despite the

deletion of the streams containing rainbow trout

and those not containing brook trout from the

analysis; however, there was no apparent pattern

to the deviations with respect to data source (i.e.,

none provided a discrete cluster). Therefore, there

was no demonstrated effect of locale and method

of data collection.

Evaluating Competition Between Brook

Trout and Rainbow Trout

Because brook trout have food and space requirements that are similar to those of other streamdwelling fishes, the other species are often viewed

as direct competitors in situations where the availability of food or other habitat components seems

to be limited. Competition, particularly with rainbow trout, is strongly implicated as a regulatory

mechanism for brook trout in the SBRP (Larson

and Moore 1985). Moreover, a reduction in density

and biomass of brook trout has been attributed to

summer food limitation in SRBP streams where

brook trout and rainbow trout occur (Ensign et al.

1990). To examine competitive displacement, we

computed the total abundance of fishes other than

brook trout for each stream reach in those data sets

containing species information (by NCWRC [ 19831

and Fowler [1985]). We determined correlations

among total fish abundance (all species other than

brook trout combined), brook trout abundance and

density, and rainbow trout abundance.

In the reaches studied by Fowler (1985), the

negative correlations between brook trout density

and biomass and the corresponding abundance of

all other species combined (Table A16) were virtually identical to the correlations between brook

trout and rainbow trout abundance (Table AlO).

Similarly, total fish abundance correlated to about

the same extent as rainbow trout abundance with

the physicochemical attributes of the streams (cf.

Tables A10 and A16). In the NCWRC data, the

correlations for total fish abundance (Table A17)

were slightly greater than for any single species,

including rainbow trout (Table A13). These slight

differences probably reflect the much broader array

of streams and habitats surveyed by the NCWRC.

Collectively, our results indicate that rainbow trout

exert as great an influence on brook trout as all

other species combined and lend support to the

contention that rainbow trout are key competitors

with brook trout in the SBRP However, the weak to

negative relation of brook trout biomass to abundance of food organisms (Tables AlO-A13) argues

against food limitation on a broad scale. The mechanisms by which rainbow trout and other species

exert competitive influences do not seem to center

on direct competition for food. Spatial factors regulating the availability of preferred foods, such as the

number of visually isolated feeding lanes per unit

of stream, may be a more important competitive

mechanism than the absolute amount of food available (Dolloff 1983). The HSI model considers dominant substrate type (Vs; assumption that substrate

containing most abundant aquatic insects is optimum), thalweg depth (Vd), pool class (Vis), and

in-stream cover (Vs), but does not include ameasure

of the spatial or temporal associations between

these or other variables that define preferred feeding habitat. The development and inclusion of a

variable describing the availability of preferred

feeding locations would be useful to assess the

potential foraging competition in streams where

rainbow trout are present.

Discussion

Some of the variables in the brook trout HSI

model (Raleigh 1982) are at best, nebulously defined. As noted by Trial et al. (1984), the where,

when, and how to measure and compute variables
HSI MODELFORBROOKTROUT 9

are not specified, such as for minimum dissolved

oxygen concentration (V3), average maximum

daily temperatures during different life stages (VI

and V,), average thalweg depth (v,), average velocity (Vs), and average annual minimum or maximum pH (VI& moreover, the methods used to

obtain these data may profoundly affect their values. Hence, the major hypothesis to be tested by

our investigation was that at least some of these

variables can be predicted on the basis of more

readily available information. Our findings indicate that for SBFU’ streams these predictions can

be made, but only to a limited extent. Many variables were correlated with elevation, as expected.

On the other hand, our analysis of the data from

Winger et al. (1987) showed that pH, an especially

significant variable, could not be predicted satisfactorily for streams draining all rock and soil

types. Therefore, we note that there are important

limitations in utility of the surrogate approach.

Another problem with pH is that minima in

streams are typically episodic, that is, associated

with runoff events (snowmelt and periods of intense rainfall) that typically depress the acid-neutralizing capacity of SBRP streams by 17-29%

(Messer et al. 1988). Mean and minimal values

are not independent, however, because buffering

capacity (i.e., alkalinity) declines with base pH

(Fig. 2A). Consequently, streams with low pH at

base flow characteristically experience the widest

pH excursions during the year. This phenomenon

is well illustrated in Table 1 of Winger et al.

(1987), which reveals a much greater coefficient of

variation for hydrogen ion activity and alkalinity

in streams of low pH. Other evidence indicates

that such pH excursions are toxic to brook trout

in streams based on the Anakeesta Formation and

that pH acts in concert with other water quality

constituents, especially metals, to exert its toxicity in acidic streams of the SBFE’ (Huckabee et al.

1975).

Despite the inherent problems outlined above,

our analysis of the individual and combined data

sets indicated that brook trout habitat quality can

be predicted on the basis of point estimates of pH

and variables that can be obtained from maps and

aerial photographs. This proposal reflects accumulated point estimates of population density

and, for Fowler’s (1985) data, standing stock estimates of biomass (Tables A8, Al 1, A14, and A15).

The combined data could be used to assign a

suitability index of 1.0 to streams with the greatest brook trout densities and zeros to those in

which no brook trout were found; the equations in

Table Al5 become, with all variables except pH

log10 transformed,

HSI = 0.22-0.78 (gradient) + 2.08 (gradient)2

- 1.21 (gradient)3 - 0.07 CpHJ + 0.11 (elevation)2

- 0.17 (width)2 - 0.007 (rainbow trout density)2

for all streams, and

HSI = 0.18 + 0.52 (elevation) - 0.15 (pH) - 0.19

(width)3 + 0.06 (gradient)2

for streams containing brook trout and no rainbow

trout. Despite the obvious bias of the models (Fig.

3), these “HSI” values are correlated to a greater

degree with brook trout abundance (? = 0.6 to 0.7)

in the 256 SBRP stream reaches included in this

exercise than were the values derived from the

original HSI model (Raleigh 1982) by Trial et al.

(1984) in their attempt to validate the HSI model

for Maine streams (2 < 0.5).

In studies of brook trout-habitat relations in

Wyoming, Chisolm and Hubert (1986) and Kozel

and Hubert (1989) found that brook trout abundance increased with elevation and decreased

with stream size. Kozel and Hubert (1989) also

reported that brook trout biomass declined as the

abundance of brown trout increased. These findings were corroborated in all of our analyses for

brook trout in the SBFE In our three sets of test

data containing information on brook trout (and

in the combined analysis), brook trout abundance

was positively correlated with elevation but declined with increasing stream size and rainbow

trout abundance. Kozel and Hubert (1989), citing

their own and other studies, also noted that such

determinants of habitat quality as overhead vegetation, width-to-depth ratio, and the abundance of

pools with in-stream cover increased with elevation but declined with stream size. Therefore, the

empirical data indicate that abiotic and biotic

factors must be considered when predicting brook

trout density and biomass. The surrogate variables identified in our analysis focus on environmental factors that determine habitat structure

and water quality (gradient, pH, elevation, and

width), as well as potential conipetitive interactions (rainbow trout density).
10 BIOLAXICAL REPXT 18

J

O-l

Broz Trout &sity, ‘~~$a

10,000

1.0

g 0.9

E 0.6

v* 0.7

= 0 6

.z?

z 0.5

.s 0.4

fz 0.3

iij

z 0.2

InJ 0.1

0.0I. I

o- 1

Brag Trout Ekkity , ‘~~~,ha

10,000

Fig. 3. Habitat suitability index (HSI = 100 x predicted standing stock / maximum standing stock) versus measured

standing stock in streams of the southern Blue Ridge Province. Diagonal lines represent perfect agreement

between HSI and measured standing stock; circles = surveys by Fowler (1985); squares = surveys by the North

Carolina Wildlife Resources Commission; asterislzs = surveys by the U.S. National Park Service (Parker MS).

Upper panel: all streams (n = 25tj; lower panel: brook trout (Salvelinus fontinalis) streams, no rainbow trout

(Oncorhyndus mykiss) present (n = 172).

Limitations of the

Regression-Correlation

Approach and Comparison

With Principal Components

Analysis

The use of multiple regression models that assume linear, additive relations among environmental (i.e., habitat) variables and animal abundance can be misleading, primarily because

species tend to have an optimum level for each

variable (Green 1977). Examination of the suitability curves in the brook trout HSI model illustrates clearly that this fact was recognized by

Raleigh (1982), and it is the reason that nonlinear

terms were considered in the combined regression analyses in our study. Similar studies of

salmonid abundance-habitat relations characteristically include only linear terms or, at best,

log-transformed variables (e.g., Binns and Eiserman 1979; Chisolm and Hubert 1986; Wesche and

Goertler 1987; Kozel and Hubert 1989).

The assumption of additivity in the multiple

regression approach usually precludes allowances

for interactions among habitat variables. Although such interactions are implicit in the brook

trout HSI model through its aggregation procedures, multiple regression assumes pure additivity. Other investigators have resolved this problem by testing combinations of selected pairs of

variables (e.g., Orth and Maughan 1982). Although useful for small numbers of variables, this

approach is not practicable for large numbers of

variables or where interactions are not suspected

a priori. Alternatively, a multivariate approach

that seeks out latent variables within the set of

environmental variables (e.g., by factor or principal components analysis), combined with regression, can sometimes resolve this complex problem

(e.g., Morrison 1967). To test this hypothesis, principal components (scores), as linear combinations

of the habitat variables, were computed for each

set of independent variables in the combined data

set. We then tested these values to determine their

ability to predict brook trout abundance. For the

combined data set, this approach produced HSI

values that were correlated to a lesser degree with

brook trout density than were the values produced

by regression analysis (cf. Tables A18-A20).

Another drawback of HSI validation by correlation-regression analysis is that the approach is

based on single point estimates of temporally fluctuating dependent and independent variables

(Platts and Nelson 1988). As noted by Cumbie and

Gnilka (1990), trout populations are influenced by

ecosystem productivity, interactions with other species, harvest, and other factors that lie beyond the
measurable physical habitat. Also implicit in the

regression-correlation approach is the assumption

that changes in habitat produce an instantaneous

population response; that is, there can be no time

lag or delayed response. The data analyzed in our

study, especially those of the Park Service (Parker

MS), illustrate these problems, The three brook

trout data sets showed the influence of other species

(especially rainbow trout) that, along with the gradient of habitat characteristics, also changed with

elevation. We can only speculate as to the extent

and significance of sport fishing on the brook trout

and rainbow trout densities measured in the studies from which our data came. We do know, however,

that the accessibility of SBRP streams generally

declines with increase in elevation, further confounding the habitat assessment.

Our cross-validation problems with the Park

Service data (models based on 1984 data did not

accurately predict conditions observed in 1986)

also clearly illustrated the effects of temporal

variation on apparent correlations between habitat and species abundance, and the possible influence of compensatory lag. Another example was

provided by a recent study of brook trout streams

in South Carolina (Cumbie and Gnilka 1990)

where, in two physically similar streams, HSI was

0.68-0.76 for brook trout and 0.74-0.76 for rainbow trout. Although these values indicate highquality habitat for both species in both streams,

one stream contained no fishes at all as a result of

logging and other past disturbances in the watershed from which the stream had not yet recovered.

Obviously, a correlation-regression validation

based on point estimates in such disturbed habitat

would prove futile, as would any other approach.

Problems in Application of HSI Models

to Brook Trout in the Southern Blue

Ridge Province and to Stream-dwelling

Fishes in General

Implicit in the HSI approach is the assumption

that high-quality habitat supports (or can support) a high biomass of the species in question and

low-quality habitat supports low biomass. Although appealing in its simplicity, the approach

presents several problems when applied to fishes,

especially in streams.

The change in the characteristics of trout populations with distance from the headwaters reflects

longitudinal zonation (Hynes 1972) or longitudinal

succession (Sheldon 1968; Ctmnnins 1972; Hawkes

HSI MODELFORBROOKTROUT 11

I975), for which streams are noted. This zonation

forms the basis of the river continuum concept

(Vannote et al. 1980). A stream, by defmition, is a

habitat continuum that, for brook trout, is often

reflected in a productivity continuum. This productivity continuum derives from the lower space requirements of small salmonids than of large salmonids (Chapman 1966; Allen 1969); the greater

efficiency of small fishes than of large fishes in the

conversion of food to biomass (Brett et al. 1969); the

presence of fewer competing species in upstream

reaches, especially in the SBRP; and the fact that a

unit area of small stream contains proportionately

more usable habitat and riparian vegetation than

a larger one (Kozel and Hubert 1989). The “steepness” of the habitat continuum (i.e., the rate of

longitudinal succession) is a property of the stream

that needs to be determined as part of the habitat

assessment process. Moreover, if biomass is the

index of species performance selected to assess

habitat suitability, two units of habitat in different

streams can be compared only if they are at equivalent points along their respective habitat continua.

Without accounting for position on the continuum,

a biomass-oriented index for salmonids will invariably lead upstream, toward small streams populated by large numbers of small trout. Resource

managers are not likely to accept such an index; a

recent survey showed that fisheries professionals

consider fish size to be an important determinant

of stream fishery value (Angermeier et al. 1991).

The steepness of the habitat continuum for

streams supporting brook trout is highly variable.

In streams of the SBR.P the habitat continuum

changes rapidly because of several factors. One

major force governing water quality and stream

ecology in this region is climate, which is itself a

component of the habitat continuum. Climate in

the SBRP varies greatly with elevation. In the

lowlands (elevation 450-500 m) it is warm and

humid, with an average rainfall of about

150 cm / year and an average temperature of

about 13.5” C. The climate is progressively cooler

and wetter with increase in elevation; at about

2,000 m it is classified as either “perhumid” or

“rain forest,” with an average annual temperature

of 7.6” C and average rainfall of >230 cm/ year

(Shanks 1954). Lennon and Parker (1967) noted

that climate had a profound effect on brook trout

in the Great Smoky Mountains National Park; at

higher elevations the streams flowed near bankfull for most of the rainy spring, summer, and fall,

but many redds were exposed and some froze
12 BIOLUGICAL REPORT 18

during the winter low-flow period. Anchor ice was

viewed as especially hazardous for developing embryos and overwintering adults and juveniles owing to the highly variable winter temperatures

and resultant freeze-thaw cycles, which periodically allow the ice to break loose and scour the

streambeds. In contrast, streams at lower elevations in the park are characterized by spring flow

maxima and late summer-early fall minima, and

streams do not generally freeze (Parker MS). The

increasing severity of the winter conditions with

elevation may also explain the corresponding decline-though not necessarily interdependencein benthic productivity and success of rainbow

trout noted in the data we analyzed. Rapid

changes in the habitat continuum make uniform

application of a single HSI model for brook trout

difficult in the SBR.P a factor true for many other

stream fish-habitat models that have been examined (Fausch et al. 1988).

To be used successfully, HSI models for stream

fishes must incorporate, and thereby predict, the

primary factors responsible for density, biomass,

and production (i.e., population regulators). The

identification of these limiting factors for brook

trout in the SBRP has been particularly elusive

because of the wide spatial and temporal fluctuations in habitat conditions (steepness of the habitat continuum), and the lack of empirical data

documenting the relative importance of individual

habitat components (e.g., large woody debris, thalweg depth, spawning substrates). In other areas

of the country, the success of stream habitat improvement programs in increasing standing stock

and production has demonstrated the importance

of “habitat” as a limiting factor in brook trout

populations (e.g., Hunt 1969,1976). At least some

of the success of these programs has been achieved

through an increase in the adult over-wintering

survival rate rather than through any increase in

recruitment or growth (Hunt 1969). Survival to

the adult stage is generally perceived as density

dependent and indirectly controlled by the habitat

(McFadden et al. 1967; McFadden 1969). Competition by adults for limited spawning areas may

result in fry survival rates that vary inversely

with spawning population density, having lower

fry survival in marginal spawning areas during

periods of high density than in more suitable

areas during periods of both low and high density

(McFadden et al. 1967). In other instances recruitment has been shown to be independent of parental stock density (McFadden 1969). Population

regulation, where it occurs, is often achieved

through emigration enforced through territoriality, with the size of the territory varying in proportion to the available food supply (primarily invertebrate drift) and the amount of visual isolation

provided by the habitat’s heterogeneity (Chapman 1966). These factors operate simuhaneously,

however, and the primary density regulator may

change seasonally.

Seasonal fluctuation in what seem to be primary density regulators is particularly problematic for accurate assessment of overall habitat

suitability. Food may be limiting through its effect

on territory size during spring, summer, and early

fall, whereas habitat suitability-in terms of protection from predation, displacement, and physical damage-may be limiting in winter (Chapman

1966). In regions where streams may have excessively high summer water temperatures, low

water levels, or both, the quantity of suitable

summer habitat (in the form of shaded seepages)

may be limiting. Although Vs (substrate size) of

the HSI model addresses winter cover for fry and

juveniles, variables Vd (thalweg depth) and Vs

(in-stream cover) specifically address “the late

growing season, low-water period,” thereby assuming warm-season habitat limitation for

adults. In the higher elevations of the SBI3.P however, winter habitat may be in short supply (Lennon and Parker 1967), as has been demonstrated

elsewhere (Hunt 1969). Moreover, it would be difficult to estimate the amount of winter (i.e., lowwater) habitat present based on a field recormaissance conducted during the warmer seasons of

higher flow; conducting low-water surveys and

estimating conditions at higher flows would be

easier. Winter conditions may also limit the reproductive success of brook trout at higher elevations

in the SBRP (Lennon and Parker 1967).

The HSI model of Raleigh (1982) represents a

logically constructed summary, based on recent

literature and expert opinion, of the mechanisms

and factors that control the abundance of brook

trout. No attempt was made to standardize methods of data acquisition for habitat variables in the

model. Consequently, as noted by others (Trial

et al. 19S4), some variables exist only in theory; in

practice, many either cannot be determined or are

at best stochastic, indicating that they would be

better addressed in probabilistic than in absolute

terms. Other variables may best be termed determinants of habitat, rather than habitat per se. In

this context, we equate habitat with what others
HSI MODELFORBROOKTROUT 13

term microhabitat (e.g., Orth andMaughan 1982).

Among the habitat determinants are such variables as discharge, stream size (e.g., width), gradient, cover, canopy, and substrate, which in turn

dictate water velocity, depth, turbulence, and incident light intensity. These variables define the

number of suitable, visually isolated territories

that a unit of stream contains. Stream fishes typically segregate along habitat continua formed by

these variables and their heterogeneity (e.g., Gibson and Keenleyside 1966), which are in turn

determined by primary factors such as stream size

and substrate (as well as by proportions typically

measured by stream biologists-woody debris,

rocks, boulders, vegetation, overhead cover, rifflepool ratio, etc.). The tendency of stream biologists

to focus on variables that are habitat determinants might at least partly explain why the models developed from Park Service data for two different water years (Parker MS) could not be

cross-validated, and why HSI models developed

for other fishes (e.g., Layher and Maughan 1985,

1988; Layher et al. 1987) have generally fared

poorly in tests of geographic cross-validation.

According to Chapman’s (1966) “grand speculation,” salmonids in streams are governed primarily by spatial requirements, with the size of the

required space varying indirectly with the food

supply and directly with the size of the individuals. Thus, the first task in quantifying habitat

would seem to be the determination of the habitat

requirements (in terms of space and characteristics) of the species in question; to some extent,

space requirements are a strict function of fish

size, even among different salmonid species (Allen

1969). The second task would be to determine the

amount of suitable space (i.e., meeting the requirements of the species) present in the stream

under consideration. This approach forms the basis of the “instream flow incremental methodology” (Stalnaker 1979; Orth and Maughan 1982),

another means of stream habitat quantitation employed by the U.S. Fish and Wildlife Service. The

habitat requirements also vary with the presence

or absence of other species (e.g., Fausch and White

1981, 1986), which also must be considered.

Conclusions and

Recommendations

1. Brook trout habitat assessment in the SBRP

needs to be scaled for the position of a particular

reach in the stream habitat continuum. Even

with the obvious limitations imposed by regression-correlation analysis, trends in habitat

with elevation were apparent. With increasing

elevation, SBRP streams generally decreased

in size, pH, alkalinity, average size of the brook

trout present, and benthic productivity. Conversely, brook trout standing stock and density

increased with elevation. Nevertheless, fishery

managers probably would not accept an index

that favored small streams supporting small

trout over large streams supporting large fish.

For this reason, and given the steepness of the

habitat continuum in the SBRP, position along

the continuum must be standardized by stream

order and elevation when comparisons are

made.

2. The present brook trout HSI model (Raleigh

1982) seems to be biased towards regions of the

United States where warm-season habitat is

limiting. In contrast, in the SBRP (and probably

elsewhere), survival through the winter may be

limiting (Lennon and Parker 1967). Although

recognized in the model documentation

(Raleigh 1982:4-5), the variables used in the

application of the model assume late growingseason extremes. Clearly, the HSI model user

should be instructed to identify and select appropriate limiting factors, which vary geographically and seasonally, as part of the

method of application.

3. Our results indicate that benthic invertebrate

food organisms (abundance and biomass) may

not be an important limiting factor for brook

trout in SBRP streams. Conditions that determine the abundance of preferred foods and feeding locations (associations between V4, Vs, V11,

V15, and Vls) are probably more important than

the total amount of food available (implicit in

V7 and Vs). The aggregation procedure used in

the 1982 HSI model must be refined to include

new variables (and new combinations of existing variables) to more precisely define preferred

foraging habitat. Relations between total food

availability (terrestrial and aquatic origin),

food selection, and availability of preferred

feeding habitats (and their proximity to other

required habitat components) must be evaluated for use in the model. Integrated variables

that describe an optimal spatial arrangement

of habitat elements associated with feeding (as

well as spawning, rearing, and over-wintering)

would be more useful than several loosely asso-
14 B~OLLIGICAL REPORT 18

ciated determinants of foraging habitat, as is

now the case.

4. The results of this study and many others before

5.

6.

it (in the SBRP and elsewhere), as well as

laboratory studies (Bausch and White 1981,

1986), have shown that the carrying capacity of

streams for brook trout is reduced by the presence of other salmonids. Although a manager

can theoretically reduce or eliminate these competitors through a rigorous removal program,

such as that practiced in the Great Smoky

Mountains National Park @vIoore et al. 1983,

1986; Larson and Moore 1985), total eradication is seldom possible (e.g., Lennon and Parker

1959), repopulation by the unwanted species is

inevitable, and continuing control is necessary.

For streams of the SBRP, our regression analyses might be used to estimate the improvement

likely to result from a given level of rainbow

trout control. The technique might also be useful for determining the amount of effort necessary to mitigate the loss of brook trout from

streams where encroachment by rainbow trout

has occurred and restoration is desirable.

In the context of HSI model application, the

stochastic nature of the water quality variables

(i.e., dissolved oxygen, pH, and temperature),

as well as those related to water quantity (thalweg depth, velocity, riffle-pool structure, etc.),

is particularly problematic. These variables

fluctuate temporally, and absolute minima and

maxima are extremely difficult or impossible to

measure. Instead, they are often couched in

probabilistic terms such as “lo-year low flow”

and “lOO-year flood.” Yet within the present

brook trout HSI model, only V14 (flow variation)

is probabilistic; the other variables are expressed in absolute terms, to be modified by V14

(and the rest of the variables in the “other”

component) during the aggregation process.

Users of the model should be aware of the

inherent difficulty in obtaining accurate measurements for the variables that the model expresses in absolute terms (primarily V1 -V5 and

V13). In reality, the measurements may more

closely resemble point estimates rather than

absolute maxima or minima, which leads to

compounded error in the final HSI calculation.

The water quality component of the model

ignores several basic tenets of aquatic toxicology. The duration of exposure to potentially

harmful conditions (temperature, dissolved

oxygen, pH, etc.) is as important in determining the outcome of the exposure as the level of

the stress itself, as is the pre-exposure history

of the organism (i.e., the acclimation period),

which means that tolerance varies seasonally.

Although some of this time-related reasoning

is incorporated in current water quality regulations (e.g., U.S. Environmental Protection

Agency [EPA] 1985), the variables constituting

the water quality component of the HSI model

are stated only as annual absolutes (minimum,

maximum). A better approach might be to

adopt, where possible, existing levels and limits proposed as water quality criteria for the

protection of aquatic life (EPA 1985, 1986),

which are stated in statistical terms (mean

levels during specified periods). Of course, the

use of such an approach requires far more data

than the point estimates implicitly specified for

use in the present HSI model.

7. Another shortcoming of the water quality component of the brook trout HSI model is that it

is now badly out of date. Since publication of the

model in 1982, the interrelations of salmonids

and water quality (especially pH and related

variables) have been studied intensively in response to heightened public awareness of acidic

precipitation and its consequences. Among the

most important recent findings are the substantial differences in sensitivity among the life

stages of brook trout and other salmonids to

water quality conditions, and the interactions

between pH and other water constituents in

determining the outcome of exposure (e.g.,

Cleveland et al. 1986; Jagoe et al. 1986, 1987).

We recommend that more recent material be

reviewed and incorporated into a revised version of the water quality component of the HSI

model. The revised model also should evaluate

the utility of the approaches described previously for dealing with stochastic variables,

and should treat each life stage separately.

Such precise information would be especially

valuable to fishery managers contemplating

mitigation through watershed liming or habitat

improvement, where any increases in yield per

unit of improvement would depend on water

quality.

The HSI model was developed for uniform application to brook trout populations wherever

they occur or to habitats where they were historically. found. The model allows for spatial

variation in habitat components but assumes

one important constant-that there is no geo8.
HSI MODELFORBROOKTROUT 15

graphic variation in brook trout populations or

their response to habitat conditions. Information on life history characteristics and morphology (Lennon and Parker 1967; McGlade and

MacCrimmon 1979) and studies of population

genetics (Stoneking et al. 1981; McCracken

et al. 1993) indicate that the Southern Appalachian strain of brook trout is taxonomically

distinct. Lennon and Parker (1967) noted the

failure of attempts to repopulate Great Smoky

Mountains National Park streams with brook

trout derived from northern strains, whereas

fish of Southern Appalachian origin did well.

These findings indicate that water quality requirements of brook trout from the Southern

Appalachian strain differ from those of northern strains; however, requirements of northern

brook trout form the basis of literature and

laboratory standards 07 brook trout habitat

requirements. For cutthroat trout (Oncorhynthus darki), significant among-strain differences in sensitivity to pH and associated water

-quality variables have been demonstrated

(Woodward et al. 1991). In sum, the existence

of interstrain differences in the context of HSI

model application for brook trout may be important, but remains to be evaluated.

9. The five surrogate variables that were identified

in our study (gradient, pH, elevation, width,

and rainbow trout density) more precisely explained brook trout abundance in 256 stream

reaches in the SRBP than did the original HSI

model. Moreover, brook trout abundance was

more closely correlated with these surrogate

variables than were the values derived from the

original model when applied to streams in

Maine (Raleigh 1982). In practical terms, use of

the surrogate variables may provide substantial savings in costs of equipment and personnel

for gathering data necessary to evaluate brook

trout habitat in the SBRP; moreover, their use

would increase the precision of estimates. It is

important to stress, however, that accuracy of

the surrogate variables in predicting brook

trout abundance, biomass, or responses to habitat restoration or other management actions in

the SRBP must be validated in the field before

the approach can be widely implemented. We

recommend that the water quality component

of the original HSI be revised to reflect current

information on pH effects and related variables,

and that the refined model be compared with

surrogate variables by field-testing in the

SBRP.

Acknowledgments

The North Carolina Wildlife Resources Commission and the U.S. National Park Service (Great

Smoky Mountains National Park) allowed us access to their data; C. R. Parker was especially

helpful. Thanks also to F? Lasier, who coded the

data.

Cited Literature

Allen, K. R. 1969. Limitations on production in salmonid populations in streams. Pages 3-20 in ‘I! G.

Northcote, editor. Symposium on salmon and trout

in streams. Institute of Fisheries, University of British Columbia, Vancouver, B.C., Canada.

Angermeier, I? L., R. J. Neves, and L. A. Nielsen. 1991.

Assessing stream values: perspectives of aquatic resource professionals. North American Journal of

Fisheries Management 11: 1 - 10.

Binns, N. A., and E M. Eiserman. 1979. Quantification

of fluvial trout habitat in Wyoming. Transactions of

the American Fisheries Society lOS:215-223.

Brett, J. R., J. E. Shelboum, and C. ‘I! Shoop. 1969.

Growth rate and body composition of fingerling sockeye salmon, oncorhynchw rat&u, in relation to

temperature and ration size. Journal of the Fisheries

Research Board of Canada 26:2363-2394.

Cada, G. E, J. M. Loar, and D. K. Cox. 1987. Food and

feeding preference of rainbow trout and brown trout

in Southern Appalachian streams. American Midland Naturalist 117:374-385.

Chapman, D. W. 1966. Food and space as regulators of

salmonid populations in streams. American Naturalist 100345-357.

Chisolm, I. M., and W. A. Hubert. 1986. Influence of

stream gradient on standing stock of brook trout in

the Snowy Range, Wyoming. Northwest Science

60:137-139.

Cleveland, L., E. E. Little, S. J. Hamilton, D. R Buckler,

and J. B. Hunn. 1986. Interactive toxicity of aluminum and acidity to early life stages of brook trout.

Transactions of the American Fisheries Society

115:610-620.

Cumbie, E M., and A. Gnilka. 1990. Trout HEP applications to two South Carolina streams. Colorado

State University Habitat Evaluation Notes 3:1-2.

Cummins, K. W. 1972. Predicting variations in energy

flow through a semi-controlled lotic ecosystem.

Michigan State University Water Research Institute

Technical Report 19.21 pp.

Dolloff, C. A. 1983. The relationships of wood debris to

juvenile salmonid production and microhabitat se-
16 BIOLOGICAL REPORT 18

lection in small southeast Alaska streams. Ph.D.

dissertation, Montana State University, Bozeman.

100 PP.

Ensign, W. E., R J. Strange, and S. E. Moore. 1990.

Summer food limitation reduces brook and rainbow

trout biomass in a Southern Appalachian stream.

Transactions of the American Fisheries Society

119:894-901.

Fausch, K. D. 1988. Tests of competition between native

and introduced salmonids in streams: What have we

learned? Canadian Journal of Fisheries and Aquatic

Sciences 45:2238-2246.

Fausch, K. D., C. L. Hawkes, and M. G. Parsons. 1988.

Models that predict standing crop of stream fish from

habitat variables. U.S. Forest Service General Technical Report PNW-GTR-213.52 pp.

Fausch, K. D., and R. J. White. 1981. Competition

between brook trout (Salvelinus fontinalis) and

brown trout (Salmo trutta) for position in a Michigan

stream. Canadian Journal of Fisheries and Aquatic

Sciences 38:1220-1227.

Fausch, K. D., and R. J. White. 1986. Competition

among juveniles of coho salmon, brook trout, and

brown trout in a laboratory stream, and implications

for Great Lakes tributaries. Transactions of the

American Fisheries Society 115363-381.

Fowler, D. L. 1985. Fish assemblage characteristics of

acid-sensitive streams in the Southern Appalachian

Mountains. M.S. thesis, University of Georgia, Athens. 61 pp.

Gibson, R. J., and M. H.A. Keenleyside. 1966. Responses to light of young Atlantic salmon (Scdmo

salur) and brook trout (Salvelinus fontinalis). Journal of the Fisheries Research Board of Canada

23:1007-1024.

Green, R. H. 1977. Some methods for hypothesis testing

and analysis with biological monitoring data. Pages

200-211 in J. Cairns, Jr., K. L. Dixon, and G. E

Westlake, editors. Biological monitoring of effluent

and water quality. ASTM-STP 607-l. American Society for Testing and Materials, Philadelphia, Pa.

Hardman, W D., R. A. Miller, and G. D. Swingle. 1966.

Geological map of Tennessee-East. Division of Geology, Tennessee Department of Conservation, Nashville.

Hawkes, H. A. 1975. River zonation and classification.

Pages 312-374 in H. B. Whitton, editor. River ecology.

University of California Press, Berkeley.

Huckabee, J. W, C. F? Goodyear, and R. D. Jones. 1975.

Acid rock in the Great Smokies: unanticipated impact on aquatic biota of road construction in regions

of sulfide mineralization. nansactions of the American Fisheries Society 105677684.

Hunt, R. L. 1969. Effects of habitat alteration on production, standing crops and yield of brook trout in

Lawrence Creek, Wisconsin. Pages 281-312 in T G.

Northcote, editor. Symposium on salmon and trout

in streams. Institute of Fisheries, University of British Columbia, Vancouver, B.C., Canada.

Hunt, R L. 1976. Responses of a brook trout population

to habitat development in Lawrence Creek. Wisconsin Department of Natural Resources Technical Bulletin 48. 35 pp.

Hynes, H. B. N. 1972. The ecology of running waters.

University of Toronto Press, Toronto, Ont., Canada.

555 pp.

Jagoe, C. H., T A. Haines, and D. R. Buckler. 1987.

Abnormal gill development in Atlantic salmon

(Sdmo salar) h-y exposed to aluminum at low pH.

Annales de la Societi Royale Zoologique de Belgique

117 (Supplement 1):375-386.

Jagoe, C. H., T A. Haines, and E W. Kircheis. 1986.

Effects of reduced pH on three life stages of Sunapee

char Salvelinus alpinus. Bulletin of Environmental

Contamination and Toxicology 33:430-438.

Kelly, G. A., J. S. Griffith, andR. D. Jones. 1980. Changes

in distribution of trout in Great Smoky Mountains

National Park, 1900-1977. U.S. Fish and Wildlife

Service Technical Paper 102.10 pp.

King, W. 1937. Notes on the distribution of native speckled and rainbow trout in the streams of Great Smoky

Mountains National Park. Journal of the Tennessee

Academy of Science 12:351+X

Kozel, S. J., and W. A. Hubert. 1989. Factors influencing

the abundance of brook trout (Salvelinus fontinalis)

in forested mountain streams. Journal of Freshwater

Ecology 5:113-122.

Larson, G. L., and S. E. Moore. 1985. Encroachment of

exotic rainbow trout into stream populations of native brook trout in the Southern Appalachian Mountains. Transactions of the American Fisheries Society

114:195-203.

Lasier, P J. 1986. The use of benthic macroinvertebrates as indicators of acid sensitivity in headwater

streams of the southern Blue Ridge Province. M.S.

thesis, University of Georgia, Athens. 206 pp.

Layher, W. G., and 0. E. Maughan. 1985. Relations

between habitat variables and channel catfish populations in prairie streams. Transactions of the American Fisheries Society 114:771-781.

Layher, W G., and 0. E. Maughan. 1988. Using stream

survey data to predict directional change in fish populations following physicochemical stream perturbations. Environmental Toxicology and Chemistry

7689-699.

Layher, W. G., 0. E. Maughan, and W. D. Warde. 1987.

Spotted bass habitat suitability related to fish occurrence and biomass and measurements of

physicochemical variables. North American Journal

of Fisheries Management 7:238-251.

Lennon, R. E., and I? S. Parker. 1959. The reclamation

of Indian and Abrams creeks, Great Smoky Mountains National Park. U.S. Fish and Wildlife Service

Special Scientific Report-Fisheries 306.22 pp.

Lennon, R. E., and P S. Parker. 1967. Brook trout of

Great Smoky Mountains National Park. U.S. Bureau

of Sport Fisheries and Wildlife Technical Paper 15.

18 PP.
HSI MODEL FOR BROOK TROUT 17

MacCrimmon, H. R., and J. C. Campbell. 1969. World

distribution of brook trout, Salvelinus fontinalis.

Journal of the Fisheries Research Board of Canada

26:1699-1725.

McCracken, G. I?, C. R. Parker, and S. Z. Guffey. 1993.

Genetic differences and hybridization between

hatchery stock and native brook trout in Great

Smoky Mountains National Park. Transactions of

the American Fisheries Society. In press.

McFadden, J. T. 1969. Dynamics and regulation of

salmonid populations in streams. Pages 313-329

in T. G. Northcote, editor. Symposium on salmon

and trout in streams. Institute of Fisheries, University of British Columbia, Vancouver, B.C.,

Canada.

McFadden, J. T, G. R. Alexander, and D. S. Shetter.

1967. Numerical changes and population regulation

in brook trout Salvelinus fontinalis. Journal of the

Fisheries Research Board of Canada 24:1425-1459.

McGlade, J., and H. MacCrimmon. 1979. Taxonomic

congruence in three populations of Quebec brook

trout, Salvelinus fontinalis (Mitchill). Canadian

Journal of Zoology 57: 1998-2009.

Messer, J. J., C. W. Ariss, J. R. Baker, S. K. Drouse, K.

N. Eshleman, A. J. Kinney, W. S. Over-ton, M. J. Sale,

and R. D. Schonbrod. 1988. Stream chemistry in the

southern Blue Ridge: feasibility of a regional synoptic

sampling approach. Water Resources Bulletin

24:821-829.

Moore, S. E., G. L. Larson, and B. RidIey. 1986. Population control of exotic rainbow trout in streams of a

natural area park. Environmental Management

10:215-219.

Moore, S. E., B. Ridley, and G. L. Larson. 1983. Standing crops of brook trout concurrent with removal of

rainbow trout from selected streams in Great Smoky

Mountains National Park. North American Journal

of Fisheries Management 3:72-80.

Morrison, D. E 1967. Multivariate statistical methods.

McGraw-Hill, New York. 415 pp.

North Carolina Wildlife Resources Commission. 1983.

Survey and classification of state-managed trout

streams. Federal Aid in Fisheries Project F24-5. Division of Inland Fisheries, North Carolina Wildlife

Resources Commission, Raleigh. Volumes l-3.

Orth, D. J., and 0. E. Maughan. 1982. Evaluation of the

incremental methodology for recommending instream flows for fishes. Transactions of the American

Fisheries Society 111:413-445.

Pickering, S. M., Jr., and J. B. Murray. 1976. Geological

map of Georgia. Geologic and Water Resources Division, Georgia Department of Natural Resources, Atlanta.

Platts, W. S., and R. L. Nelson. 1988. Fluctuations in

trout populations and their implications for land-use

evaluation. North American Journal of Fisheries

Management 8:333-345.

Raleigh, R. E 1982. Habitat suitability index models:

brook trout. U.S. Fish and Wildlife Service

FWS/OBS-8v10.24.42 pp.

Reed, E. B., and G. Bear 1966. Benthic animals and

foods eaten by brook trout in Archuleta Creek, Colorado. Hydrobiologia 27:227-237.

Shanks, R. E. 1954. Climates of the Great Smoky Mountains. Ecology 35:354-361.

Sheldon, A. 1968. Species diversity and longitudinal

succession in fish. Ecology 49:193-198.

Silsbee, D. G., and G. L. Larson. 1983. A comparison of

streams in logged and unlogged areas of Great

Smoky Mountains National Park. Hydrobiologia

102:99-111.

Stalnaker, C.B. 1979. The use of habitat structure preferenda for establishing flow regimes necessary for

maintenance of fish habitat. Pages 321-337 in J. V

Ward and J. A. Stanford, editors. The ecology of

regulated streams. Plenum, New York.

Stoneking, M. D., J. Wagner, and A. C. Hildebrand.

1981. Genetic evidence suggesting subspecific differences between northern and southern populations of

brook trout (Salvelinus fontinalis). Copei a

1981:810-819.

Stuckyey, J. L. 1958. Geological map of North Carolina.

Division of Mineral Resources, North Carolina Department of Conservation and Development,

Raleigh.

Tebo, L. B., Jr., and A. W. Hassler. 1963. Food of brook,

brown, and rainbow trout from streams of western

North Carolina. Journal of the Elisha Mitchell Scientific Society 7944-53.

Tennessee Valley Authority. 1968. Soils in the Tennessee Valley-map CAR-44 P 165. Soils and Fertilizer

Branch, National Fertilizer Development Branch,

Tennessee Valley Authority, Muscle Shoals, Ala.

Trial, J. G., C. S. Wade, and J. G. Stanley. 1984. HSI

models for northeastern fishes. Pages 17-56 in J. W.

Terrell, editor. Proceedings of a workshop on fish

habitat suitability indexmodels. U.S. Fish and Wildlife Service Biological Report 85(6).

U.S. Environmental Protection Agency. 1985. Technical

support document for water-quality-based toxic control. EPA-440/4-85-032. Office of Water Regulations

and Standards, U.S. Environmental Protection

Agency, Washington, D.C. 74 pp.

U.S. Environmental Protection Agency, 1986. Quality

criteria for water, 1986. EPA-440/5-86-001. Office of

Water Regulations and Standards, U.S. Environmental Protection Agency, Washington, D.C. n.p.

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

Sedell, and C. E. Cushing. 1980. The river continuum

concept. Canadian Journal of Fisheries and Aquatic

Sciences 37:130-137.

Wesche, T A., and C. M. Goertler. 1987. Modified habitat

suitability index model for brown trout in southern

Wyoming. North American Journal of Fisheries Management 7:232-237.
18 BIOLOGICAL REPORT 18

Winger, F! V, F! J. Lasier, M. Hudy, D. Fowler, and M. Woodward, D. E, A. M. Farag, E. E. Little, B. Steadman,

J. Van Den Avyle. 1987. Sensitivity of high-eleva- and R. Yanick. 1991. Sensitivity of greenback cuttion streams in the southern Blue Ridge Province throat trout to acidic pH and elevated aluminum.

to acid deposition. Water Resources Bulletin Transactions of the American Fisheries Society

23:379-386. 12034-42.
HSI M ODEL FOR B ROOK TROUT 19

Appendix. Statistical Tables

Table Al. Mean (z), standard deviation (SD), minimum, and maximum values of variables determined

by Winger et al. (1987) for reaches of 30 streams of the southern Blue Ridge Province (SBW)

underlain by three types of rock.

_ ___.

Stream Elevation Alkalinity

Bedrock geology and statistic order (m> pHa @es/ J.Y

Crystalline complex (n = 52)b

x 2.0 876.1 6.62 59.02

SD 1.0 208.2 0.20 32.92

Minimum 1 536.0 5.88 9.50

Maximum 3 1,560.O 7.13 204.52

Glacial alluvium (n = 4)

x 2.0 550.3 6.95 154.88

SD 1.1 109.4 0.16 4.16

Minimum 1 421.0 6.79 150.00

Maximum 3 677.0 7.14 159.40

Anakeesta pyrite (n = 6)

x 1.8 1,259.3 5.54 9.49

SD 1.0 152.9 0.80 16.14

Minimum 1 1,073.o 4.41 0.0

Maximum 3 1,439.0 6.62 46.00

All types (n = 62)

r 2.0 892.1 6.54 52.60

SD 1.0 244.7 0.45 41.14

Minimum 1 421.0 4.41 0.0

MaximUll 3 1,560.O 7.14 204.52

‘Values for pH and alkalinity are based on the means of 5-6 observations per reach.

bCont.ains early Precambrian metamorphic gneisses and schists overlain in some areas by later Precambrian sedimentary and

metamorphic rock.

’ Geometric mean.

Table A2. Statistically significanta correlations

(Pearson product-moment, r) between variables

in reaches of 30 SBRP streams underlain by

three types of rock. Data from Winger et al.

(1987).

Bedrock geology and variable pH Alkalinityb

Crystalline complexC (n = 52)

Elevation -0.31 l -0.18

PH 0.81”

Glacial alluvium (n = 4)

Elevation 0.68 -0.80

PH -0.70

Anakeesta pyrite (n = 6)

Elevation -0.92’. -0.86’.

PH 0.97. *

All (n = 62)

Elevation -0.60” -0.57. l

PH 0.93 ”

a l *P 50.01; ‘P 5 0.05.

b As loglo of value.

’F’er Table Al, footnote b
Table A3. Results of regression analyses fitting equations of the form yi = m + fl lxi, where yi = either pH or alkalinity and xi = elevation, to the E

data of Winger et al. (1987) after grouping the streams according to underlying rock formations (numbers in parentheses represent key to

data and headings in Tables A4 and A5). E

8

Variable and formation

PH

Anakeesta pyrite (0)

Glacial alluvium (1)

Crystalline complexb

Mica schist-gneiss (2)

Biotite-gneiss (3)

Unicoi FormationC (6)

Great Smoky conglomeratesd (8)

Mixed gneisses and achi& (9)

All except Anakeesta Formation

Alkalinity, pm/L (loglo)

Anakeesta pyrite (0)

Glacial alluvium (1)

Crystalline complex

Mica schist-gneiss (2)

Biotite-gneiss (3)

Unicoi Formation (6)

Great Smoky conglomerates (8)

Mixed gneisaes and schists (9)

All extent Anakeesta Formation

6 23.17” 0.85 11.599 -0.0048

4 1.9 0.49 6.386 0.0010

52 5.32’ 0.10 s 6.892 -0.0003

6 25.34” 0.86 7.198 -0.0012

4 119.85” 0.98 7.133 -0.WO6

10 2.47 0.24 6.891 -0.0004

11 0.01 co.01 6.743 -0.0001

21 15.32” 0.45 7.410 -0.0008

56 9.88” 0.15 6.982 -0.0004

6 11.19’

4 3.49

52 1.74

6 16.44”

4 2.36

10 0.29

11 1.08

21 14.21”

56 6.70’

0.74

0.64

0.03

0.80

0.54

0.03

0.11

0.43

0.11

5.018 -0.0032 0.98

2.237 -0.0001 2.19

1.933 -0.0001 1.77

2.728 -0.0012 1.89

2.429 -0.0008 1.85

1.867 -0.0002 1.69

1.595 -0.0903 1.89

2.461 -0.0007 1.78

2.098 -0.0003 1.80

5.54 2

6.95 E

6.24

6.64

6.70

6.54

6.71

6.67

6.65

‘df = 1, n - 1; ‘F’5 0.05, “PS 0.01.

b Includes all except glacial alluvium and Anskeests Formation.

’ Sandstones, quart&es, conglomerates, shales, and slates.

d Grsywacks sandstone and conglomerate with slats in&beds.

e Mica gneiss, mica schist, and other gneisses and schists.
HSI MODELFORBRCOKTF~OUT 21

Table A4. Results of regression analyses fitting equations of the form y; = 60 + plxi, where yi = either

pH or alkalinity &d Zi = elevation, to the data of Winger et al. (1987) after grouping the streams

according to watershed soil type (codes in parentheses for formations and soil types as in Table A3).

Variable and soil type n F”

PH

Porters-Ashe-F’erkinsville (l)b 32 25.69”

Ramsey-Ranger-Talladega (2)’23 13.12”

Alkalinity (logic)

Rx-&s-Ashe-Perkiesville (1) 32 27.83’.

Ramsey-Ranger-Talladega (2) 28 6.85’

*df= 1, n -1; ‘PS 0.05, "PS 0.03.

bLevel terrain; parent material alluvium.

‘Mountainous terrain; parent materials gneiss, schist, and granite.

2 PO Pl ii

0.46 7345 -0.0009 6.60

0.34 7.856 -0.0014 6.46

0.48 2.420 -0.0008 1.74

0.21 2.475 -0.0009 1.66

Table A5. Results of regression analyses fitting equations of the form yi = 00 + plxi, where yi = either

pH or alkalinity and 2; = elevation, to the data of Winger et al. (1987) after grouping the streams

according to underlying rock formations and watershed soil types

Variable and geologic

formation/ soil codesb n F

PH

o/2 5 24.16”

2/l 4 2.08

3/l _ 4 119.85”

6/2 10 23.97”

B/2 11 2.47

9/l 19 0.01

Alkalinity

O/2 5 10.20*

2/l 4 0.30

3/l 4 2.36

6/2 10 0.29

B/2 11 1.08

9/l 19 20.27’.

adf = 1, n -1; ‘P _( 0.05, l . P 5 0.01.

bCodes for formations and soil types as in Tables A3 and A4.

R2 PO

0.89

0.51

0.98

0.59

0.24

12.044

7.318

7.133

7.423

6.891

6.743

0.77 5.335

0.13 2.215

0.54 2.429

0.04 1.867

0.11 1.595

0.54 2.483

___~~____

Pl

-0.0051

-0.0009

-0.0006

-0.0009

-0.0004

-0.0001

-0.0034

-0.0006

-0.0007

-0.0002

-0.0003

-0.0008

r

5.12

6.54

6.70

6.63

6.54

6.71

0.96

1.74

1.85

1.69

1.89

1.75
22 BIOUXXCAL REFWT 18

Table A6. Mean (I%), standard deviation (SD), median, minimum, and maximum values of variables

measured in 71 stream reaches (n = 70 for debris characteristics) in Great Smoky Mountains National

Park by the U.S. National Park Service (Parker MS).’

Variable f S D Median Minimum Maximum

Physicochemical

Elevation (m)

Gradient (%)

Temperature (” C)

PH

Mean depth (cm)

Stream width (m)

Channel width (m)

canopy (%)

Bank composition (%)

Rock

Vegetation

Gravel

In-stream cover (%)

Turbulence

Bock

Ledge

Debris

Vegetation

Depth

Bank

Totd

Substrate composition (“Yo)

Organic debris

Organic muck

Sand

Fine gravel

Coarse gravel

Small rubble

Large rubble

Boulder

Bedrock

Silt

Debris characteristics

Stability indexb

Total length (km / ha)

Total in-stream (km/ ha)

Log volume <m3/ ha)

Associated debris (m3/ ha)

Total volume (m3/ ha)

Fish density (no. / ha)

Brook trout’

Bainbow troutd

Total pool area (%)

lO51.7 249.2 1,037.o 433 1,488

8.9 4.9 8.0 2.0 24.0

14.8 2.3 15.0 9.0 18.0

6.1 0.31 6.1 5.6 6.8

16.9 9.9 12.9 4.1 49.1

5.0 2.0 4.7 2.0 11.3

8.8 3.4 8.0 3.4 18.9

69.9 18.9 75.0 0 100.0

51.0 29.4 50.0 0 95.0

40.0 28.9 45.0 0 91.0

6.5 8.8 5.0 0 30.0

15.2 9.0 13.9 1.2 38.6

9.3 6.0 8.1 0.6 30.2

1.4 2.2 0.5 0 10.5

2.1 2.6 1.3 0 13.3

1.8 4.5 0.1 0 31.9

2.1 3.3 1.0 0 19.9

0.5 1.1 0 0 6.2

32.3 14.9 30.6 9.4 76.2

2.6 2.8 1.9 0 15.6

0.3 0.7 0 0 3.8

7.7 7.0 5.4 0 25.2

16.2 11.0 15.4 0 59.2

10.9 6.4 9.6 0 26.8

12.0 6.3 11.1 0.5 30.0

10.7 7.2 9.4 0 39.4

29.0 15.0 26.3 2.2 66.3

9.2 17.4 1.1 0 71.6

1.2 2.0 0.6 0 11.9

12.4 2.8 12.6 4.9 17.0

6.3 5.3 4.6 0 19.8

1.5 1.6 0.8 0 6.9

31.0 53.9 10.7 0 287.0

345.0 781.4 73.0 0 4,073.o

376.0 782.2 103.0 0 4,087.O

l136.5 1,244.l 740.4 0 5,538.g

352.3 524.6 0 0 2,141.2

38.8 16.0 38.0 6.0 74.0

‘Parker, C. FL Unpublished manuscript. Brook trout habitat in the Great Smoky Mountains National Park. Archived 1988 at

U.S. National Park Service, Great Smoky Mountains National Park, Gatlinburg, Tenn. 81 pp.

bDimensionless indicator of permanence.

’Salvelinus fontinalis.

d Oncwhynchus mykiss.
HSI MODELFORBROOKTROUT 23
24 BIO~ICAL REPORT 18

Table A7. Statistically significanta correlations between variables measured by the U.S. National Park

coefficients above the principal diagonal (indicated by bullets), Spearman rank coefficients below.

Ele- GraGeneral Bank composition

Temper- Vegevation dient ature PI-I canopy Bock tation

General

Elevation

Gradient

Temperature

PI-I

canopy

Bank composition

Bock

Vegetation

Gravel

Stream size

Depth

Wetted width

Channel width

In-stream cover

Turbulence

Bock

Ledge

Debris

Vegetation

Depth

l - -0.61

- b -

-0.60 -

l

-0.36 - -

-0.27 - -

- - -

- - -

- -0.43 -

- -0.33 -

-0.25 -0.34 -

- -0.22 -

- - -

-0.21 - -

- - 0.22

0.25 0.32 -

- - -

- - 0.20

-0.31 -0.24 - -

- - -

- - - -

l - - -

- 0 - -

-

- a -0.78

- - 0.86 l

- - - -

- 0.37 -0.48

-

- 0.44 -0.44

-

- 0.51 -0.55

-

- -

0.21 -

- -

- -

- -

Bank - - - - -

Substrate composition

Organic debris - - - - -0.28

Organic muck - - - - -

Sand 0.21 0.24 - - -

Fine gravel -0.26 - 0.26 0.45 -

Coarse gravel - - - 0.28 0.22

Small rubble - -0.25 - - 0.21

Large rubble - -0.22 - -0.22 -

Boulder -0.26 - - - -

Bedrock 0.31 - - - -

Silt - -0.20 - - -0.22

Miscellaneous habitat

Total cover - - - -

Debris stability - -0.22 - -0.37 -

Total debris 0.24 - - -0.38 -

In-stream debris -

- - - -

Log volume - 0.36 - - -

Assoc. debris volume -

- - -0.26 -

Total debris volume - - - - -

Fish density

Brook trout 0.63 - -0.39 -0.22 -

-0.30

-0.22

-0.29

-0.34

-

-0.21

-0.28

0.38

-

-

0.25

-

0.21

0.50

0.33

-

-0.20

-

-

-0.42

-

-0.26

-0.33

-0.23

0.34

-

-

-

0.25

-

-

-0.23

-0.25

-

-

- -

-0.25 0.43

- 0.23

0.21 -

0.39 -0.46

- -

- -

-0.32

0.40

0.43 -

-0.51 - 0.29 0.36 - -0.34 -

- - - - - - 0.25 -0.21 -

- -

-

- 0.27

-0.32 0.39

-0.36 0.27

- 0.27

- -

Gravel

-___

-

-0.40

-0.02

-

-

-0.23

.

0.30

0.31

0.34

Rainbow trout

Total pool srea

*If Irl >0.30,P<0.01;if0.30> Irl >0.23,P<0.05;if0.23> Irl >0.2O,P<O.10.
HSI MODEL FOR BRINK TROUT 25

Service (Parker I@) in 71 streams of Great Smoky Mountains National Park. Pearson product-moment

&&es indicate pairs of variables not significantly correlated. Variables as in Table A6.

Stream size In-stream ccwer __

Wetted Channel Turbu- VegeDepth width width lence Rock Ledge Debris tation Depth Bank

-0.24 -0.28 _ - - - 0.23 - - -

-0.31 -0.38 -0.23 - - - 0.27 - - -

- - - - - 0.24 - - - -

- - - - 0.27 - - - - -

- - - - - - - - - -

0.35 0.44 0.50 - - - -0.33 -0.29 -

-0.43 -0.47 -0.58 - - 0.32 0.39 - 0.27

0.24 0.30 0.30 - - -0.42 -0.34 - -0.44

___ _

l 0.69 0.61 0.24 - - -0.41 - -

0.59 a 0.87 - - - -0.37 -0.20 -

0.57 0.85 a - - - -0.37 -0.25 -0.22

0.26 0.24 0.24 a 0.33 - -

-

- - 0.33 a 0.28 -

-

- - - 0.26 a -

-0.43 -0.31 -0.34 - - - l

-z - -0.29 -0.25 -0.26 - 0.24

- - - 0.34 - 0.37 -

- - - - - - 0.23

-0.32 -0.24 -0.26 - - 0.28 0.54

- -0.33 -0.23 - - - 0.35

-0.32 -0.40 -0.51 - -0.34 - 0.52

-0.46 -0.37 -0.42 - 0.34 0.30 -

-0.59 -0.33 -0.31 - 0.29 - 0.28

- - - - - -0.27 -

0.22 - - - - -

0.38 0.27 0.45 0.21 0.44 - -0.43

- - - - - - -

0.21 - - - - - -

-

- - 0.75 0.61 0.31 0.22

0.52 - - - -0.47 -0.40 -

0.21 -0.22 - - -0.38 -0.27 -

-

- -0.31 - -0.32 - 0.22

0.41 -0.38 -0.54 -0.26 - - 0.61

-

- -0.23 - -0.30 - 0.33

- -0.36 -0.33 -0.21 -0.26 - 0.45

0.51 -0.48 -0.48 - - - 0.35

-0.22 0.34

-0.21 -

- 0.27

0.20 0.23

a -

- .

0.27 -

-

-

-

-

-

-

-

-

-

0.20

0.20

-

a

0.35 - -

0.34 - -

0.33 - -

- - -

- - -

* -

-0.37 -

-

0.25

-0.33 - -

- - -

- -

-

- 0.55 -

-

-0.24 -

- - -

- - -

- 0.24 -

- - -

- - -

- - -

0.25 0.43 0.45 - 0.32 - -0.26 - -

0.31 - - 0.27 - - -0.29 - -0.28 -
26 BIOLOGICAL REPORT 18

Table A7. Continued.

Substrate composition

organic organic Fine Coarse Small hge Ehll- Bed

debris muck Sand gravel gravel rubble rubble der rock Silt

- - 0.20 -0.23 - - -0.23 -0.27 0.34 -

- - 0.20 - - -0.28 -0.25 - - -

- - - 0.35 0.23 - - - - -

- - 0.42 0.27 - - - - -

-0.20 -0.29 - - - - - - - _

-0.32 -0.25 -0.29 -0.27 - -0.24 -0.28 0.37 - -

0.23 - 0.21 0.49 0.27 - - -0.45 - -0.22

- - -

-

-0.20 0.34 - - - 0.24

-0.34 - -0.32 -0.42 -0.44 - 0.26 0.34 - -

-0.24 -0.26 -0.39 -0.30 -0.23 - - 0.30 - -

-0.21 -0.23 -0.47 -0.31 - - - 0.51 - -

-0.20 - - -0.21 -

-

- -0.30 0.40 0.36

0.23 - - 0.33 -

0.52 0.34 0.50 - 0.23

0.31 0.34 0.34 - -

- - - - -

- - - - -

l 0.42 0.46 0.22 -

- l 0.28 - -

0.48 0.28 0 - -

0.27 - - 0 0.37

0.22 - - 0.32 0

-

- - -0.23 0.20

-

- -0.23 - -

-0.43 -0.23 -0.64 - -

- - 0.21 -0.28 -0.36

- - - - -

- - - - -

-

- - -0.50 -0.54

-

- 0.21 -0.35 -

- - - - -

0.43 - 0.46 0.31 0.30

-

- 0.28 - -

0.27 0.20 0.36 - -

0.22 - 0.26 0.25 -

-

- -0.37 - -

-0.27 - -0.24 - -0.26

- -

-0.21 -

-0.27 -

- -

- 0.21

-0.36 -

- -

- -

- -

- -0.24

-0.25 -

-

0 0.32

0.25 l

- 0.24

-0.31 -0.23

- -

- -

0.25 0.32

0.23 -

- -

- -

-

-

- -0.20

- -

- -

- - -

0.39 -0.34 -

-0.39 - -

-0.26 - -

- -

-0.3 8

-

-0.6 4

-

-

-

-

l

-0.35

-

-

-

-

0.42

-

-0.23

-0.47

0.47

0.20

- -

- -

0.22 -

-0.45 -

-0.55 -0.23

-0.35 -

-0.41 -

-0.47 -

0 -

- l

- -

0.21 0.29

- -

- -

- -

- -

- -

- -

-0.28 -

- -
HSI MODEL FOR BROOK TROUT 27

Total

cover

Miscellaneous habitat variables Fish density

Debris In- ASSOC. Total Rain- Total

stabil- Total stream Log debris debris Brook bow poolity debris debris volume volume volume trout trout area

- - 0.26 - - - - 0.62 -02.8 -

- - - 0.25 - - - -0.23 - -

- - - - - - -

-0.30 - -

- -0.34 -0.32 - 0.24 - - -0.25 0.24 -

- - - - - - - - - -

- - - - -

-0.27 -0.28 -0.36 0.42 0.25

- -0.27 - - 0.25 - - 0.40 -0.44 -

-0.21 0.40 - - -0.28 0.23 0.26 - - -

- 0.43 - - -0.32 - - -0.38 0.27 0.29

- - -0.29 -0.22 -0.24 - - -0.44 0.49 -

- - -0.21 -0.23 -0.26 - - -0.41 0.54 -

0.76 - - - -0.24 - - - - 0.29

0.66 -0.49 -0.34 -0.26 - - - - 0.23 -

0.32 -0.48 -0.22 - - - - - - _

0.27 - - - 0.44 0.22 0.25 0.20 -0.24 -0.33

- - - - - - - 0.20 - -

0.57 -0.20 - - - - - - - -

- - - - - - - - - -

l

-0.29

-

-0.23

-

-

- -

- -

- -

-0.46 -0.33

-0.50 -

0.23 -

0.26 -

- -

- -

0.33 -

-

-

-

-

-0.21

-

-

-

-

-

-0.27 -0.23 -0.22

a 0.61 0.49

0.67 0 0.82

0.47 0.77 s

- 0.31 0.61

0.31 0.71 0.60

0.24 0.70 0.66

0.33

-

0.38

-

0.29

-

-

-0.31

-

-

0.23

-

-

-

-

0.24

-

-

-

- -

0.24 0.24

- -

-

0.21

- -

- -

- -0.45

- 0.21

- -

0.30

0.30

0

0.43

0.61

-

0.41

0.42

-

0

0.96

-0.21 -0.27

- -

-0.29 -0.21

- -

- -0.20

- -

- -

0.47 -

- -

- -

- 0.21

-0.20 -

-0.26 -

-0.31 -

- -0.29

- -

- -

- - -

- -0.31 -0.30

0.20 - -

- 0.29 - - l -0.48 -

-0.42 -0.30 -0.22 -0.25 -0.61 0 -

- - - - - - l
Table AS. Results of multiple regression analysis, as regression coefficientsa and other statistics,b relating brook trout abundance (no. / ha) to

habitat variables (including the abundance of rainbow trout) in streams of Great Smoky Mountains National Park (Parker MS). Variables 8

as defined in Table A6.

Regression

statistics and variables

/

1984 1984 and 1986 g

All 1986’ All 6

Brook trout Trout streams Brook trout Trout Brook trout Trout streams ij

streamsd streamse and data streams streams streams streams and data F

z

ii2

I;b

Intercept @o)

Elevation

Canopy

Bank rock

Small rubble

Large rubble

Total debris length

Ledge cover

Sand

Boulder

Debris stability

Log volume

Depth cover

Rainbow trout

Gradient

Coarse gravel

. Total pool area

z Depth

g

Fine gravel

2 PH

:

Bank vegetation

r

Channel width

_(0

Bank gravel

16

0.99

129.23

.880.670

1.288

-10.585

-10.665

48.566

30.592

0.079

27 34

0.93 0.91

73.01 45.58

-359.672 -799.262

1.500 1.925

- -

-15.252 -

17 36

0.97 0.87

55.21 32.88

-9,457.060 -3,299.962

7.237 3.665

153.251 32.887

- -

- -

- -

- -

- -

-52.375 -

28.969 -

323.027 -

-6.684 -

- -

- -1.363

- -87.185

- -58.288

- 20.330

- -

- -

- -

- -

- -

- -

33 63 70 . 8

0.85 0.76 0.76 3 .

25.15 29.33 33.86

7,395.073 -1.566343 -1491.316

%

2.666 2.880 2.880

-

-

-

-

-

-

-

-

-

-

-

-0.712

-86.987

-

22.372

-31.775

20.036

-

-

-

-

-

- - - -

- - - -

0.090 - -0.190 -

- - - - -

- - - - -

- - - - -

- - - - -

- - - - -

- -211.655 -

-0.525

-52.375

78.934

-

- -

-0.757

-86.606

-

23.678

-33.950

21.971

- - -

- - -

- - -

- - 44.483

- - - -

- - - -

- 1,709.468

25.917

- - - -

- - -

-63.995

25.751

-

- -

- - - -

a All listed coefficients statistically significant (P < 0.05); dashes indicate pairs of variables not significantly con-elated.

bAll regressions highly significant overall (P < 0.01).

’ All streams surveyed in 1986 contained trout

dDistinguished by absence of rainbow trout

e Distinguished by presence of rainbow trout
HSI MODEL FOR BROOK TRQUT 29

Table A9. Mean (IT), standard deviation (SD), median, minimum, and maximum values of variables as

measured in seven SBRP streams (n = 56) by Fowler (1985).

Variable It S D Median Minimum Maximum

Physicochemical

Order 1.9

Width (m) 3.8

Depth (cm) 22.0

Flow (cubic feet / s) 4.8

Gradient (%) 10.8

Elevation (m) 900.6

PH 6.64

Alkalinity (mg/ L) 2.95

Fish abundance*

Brook trout

Adult density 660.5

Adult biomass 13.6

Y-0-p density 179.0

Y-O-Y biomass 0.4

Rainbow trout

Adult density 222.6

Adult biomass 6.4

Y-O-Y density 42.8

Y-O-Y biomass 0.2

Brown trout” density 5.5

Brown trout biomass 0.9

Longnose dated density 31.0

Longnose date biomass 0.4

Creek chube density 4.1

Creek chub biomass 0.1

Sculpin’ density 308.8

Invertebrate densi@

Oligochaeta 9.0

Gastropoda 0.02

Odonata 3.2

Ephemeroptera 50.6

Plecoptera 20.3

Coleoptera 3.4

Megaloptera 0.02

Trichoptera 12.6

Diptera 30.3

a Fish density = no. / ha; biomass = kg / ha.

b Young of the year,

cSalmo trutta.

dRhinichthys catamctae.

e Semotilw atromaculatus.

f Cottus spp.

g Invertebrate density = no. / m2.

1.0 1.0

2.1 3.2

7.9 21.5

8.0 2.0

3.7 10.9

137.5 878.0

0.18 6.6

1.43 2.4

1.0

;::

0.1

2.4

658.0

6.4

1.5

3.0

10.1

35.0

32.2

17.1

1,170.o

7.0

5.6

559.5 532.5 15 2,693.0

12.0 10.5 0.1 46.0

729.0 0 0 5,427.0

1.5 0 0 10.9

326.4 0 0 1,173.0

10.0 0 0 38.7

118.6 0 0 661.0

0.6 0 0 3.6

26.0 0 0 151.0

3.2 0 0 15.2

95.8 0 0 478.0

1.3 0 0 5.9

30.7 0 0 230.0

0.3 0 0 2.3

575.7 0 0 2,115.0

8.9 5.0 0 36.0

0.13 0 0 1.0

3.1 2.0 0 14.0

47.6 35.5 3.0 173.0

10.1 20.0 6.0 38.0

2.9 3.0 0 11.0

0.13 0 0 1.0

7.7 11.0 3.0 31.0

24.9 20.5 4.0 121.0
30 BIOLOGICAL REPORT 18

Table AlO. Statistically significant* correlations between variables in seven SBRP streams (n = 56)

(indicated by bullets), rank coefficients below. Dashes indicate pairs of

Physicochemical variables

Gra- Ele- AlkaVariable Order Width Depth Flow dient vation PH linity

Physicochemical

Order

Width

Depth

Flow

Gradient

Elevation

PH

Alkalinity

Fish abundance

Brook trout

Adult density

Adult biomass

Y-O-Y density

Y-O-Y biomass

Rainbow trout

Adult density

Adult biomass

Y-O-Y density

Y-O-Y biomass

Brown trout density

Brown trout biomass

Longnose date density

l 0.71

0.77 s

0.72 0.84

0.87 0.82

-0.48 -0.27

-0.56 -0.68

- -

-0.26 -0.46

-0.52

-0.50

-

-

-0.57 -0.46

-0.59 -0.42

- -

- -

,

0.54

0.58

0.45

0.46

0.31

0.31

0.42

0.45 0.37

0.49 0.41

0.35 0.28

0.36 0.30

0.31 0.25

0.31 0.25

0.45 0.46

0.45 0.47

- -

- -

0.34 0.25

0.36 0.28

Longnose date biomass 0.42

Creek chub density -

Creek chub biomass -

Sculpin density 0.45

Sculpin biomass 0.47

Invertebrate density

Oligochaeta 0.53

Gastropoda -

Odonata -

Ephemeroptera 0.24

Plecoptera 0.38

Coleoptera -

Megaloptera -

Trichoptera 0.44

Dip&a 0.35

0.72

0.78

e

0.76

-

-0.64

-

0.39

0.24 -

- -

- -

0.37 0.38

0.38 0.31

- -

- -

- -

0.32 0.25

0.55

0.85

0.57

0

-

-0.58

0.29

-0.30

-0.46

-0.41

-

-

0.38

0.45

0.40

0.42

0.36

0.36

0.47

0.47

-

-

0.24

0.26

0.42

-

-

0.36

0.44

-

-

0.32

0.27

-0.55 -0.5 8

- -0.70

- -0.65

- -0.58

0 -

- a

- -

-0.23 0.66

-

-

-

-

-

0.29

0

0.51

0.49 0.50

0.61 0.37

- -

- -

-

-

0.34

0.34

-0.45 -0.57 -0.36

-0.42 -0.60 -0.34

-0.39 -0.45 -0.30

-0.38 -0.46 -0.30

- -0.37 -0.32

-0.25 -0.38 -0.32

- -0.57 -0.24

- -0.57 -0.24

-0.23 - -

-0.23 - -

-0.63 -0.28 -0.37

-0.62 -0.28 -0.36

-0.37 -

- -

- -

0.27 -

- -0.38

- -

- -

-0.23 -

-0.25 -

-

-

-0.26

-

-

-

-

-

-

aIfIrl >0.33,P<O.Ol;if0.33> Irl >0.27,PC0.05;if0.27> Irl >0.23,P<O.l0.

-

-0.27

-

-

-

0.52

0.86

0

-

-

-

-

-0.38

-0.40

-0.29

-0.29

-

-0.23

-0.35

-0.35

-

-

-0.28

-0.28

-

-

-

-0.25

-0.39

-

-

-
HSI MODEL FOR BROOK TROUT 31

studied by Fowler (1985). Pearson product-moment coefficients arrayed above the principal diagonal

variables not significantly correlated. Variables as defined in Table A9.

Fish abundance

Brook Trout Rainbow Trout

Adult Y-O-Y Adult Y-O-Y

Bio- Den- Bio- Den- Den- Bio- Den- Biomass sitv mass sity sity mass sity mass

0.48 -0.44

-0.51 -0.53

-0.47 -0.44

-0.40 -0.33

0.39 0.53

0.46 0.40

- -

- -

i.89

0.43

0.44

0.33

i.44

0.43

-0.73 -0.71

-0.73 -0.72

-0.54 -0.56

-0.54 -0.56

-0.40 -0.39

-0.40 -0.39

-0.23 -0.25

-0.27 -0.24

- -

- -

-0.68 -0.70

-0.69 -0.70

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

- - 0.43

- - 0.50

- - 0.30

- - 0.62

- - -0.47

- - -0.53

0.34 0.34 -0.34

0.30 0.31 -0.35

-

-

i.99

- -0.61

- -0.63

0.99 -

. -

-0.43 -0.49

-0.47 -0.48

-0.40 -0.40

-0.40 -0.40

-0.23 -0.23

-0.23 -0.23

- -

- -

- -

- -

-0.49 -0.49

-0.49 -0.49

i.98

0.69

0.69

0.40

0.40

0.49

0.48

-

- -

0.76 0.72

0.76 0.76

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

0.29 0.32

- -

0.39 0.37

- -

0.23 0.25

- -

- -

0.36 0.37

0.23 0.25

0.50 0.37 0.33

0.61 - 0.31

0.40 - -

0.74 - 0.42

-0.42 -0.41 -0.25

-0.58 -0.24 -0.30

-0.32 - -

-0.36 -0.23 -

-0.53

-0.60

-

-

-0.32 -0.29

-0.35 -0.29

- -

- -

0.95

i.70

0.70

0.45

0.45

0.53

0.52

-

0.53

0.52

i.99

0.56

0.56

0.45

0.44

0.23

0.23

0.62

0.60

0.52

0.58

0.75

.

0.57

0.57

0.48

0.47

0.23

0.23

0.62

0.61

0.33

-

0.57

-

0.29

-

-

0.37

-

0.32

-

0.57

-

0.27

-

-

0.35

-
32 BIOLCCICAL REPORT 18

Table AlO. Continued.

Fish abundance (continued)

Brown trout

Den- BioLongnose date

Den- BioCreek chubDen- Biosity mass sity mass sity

0.23

-

-

-

-0.37

-0.18

-

-

-0.22

-0.24

-

-

-

0.30

0.45

0.31

l _

-

0.26

0.28

-

-

0.45

0.43

0.24

-

0.35

-

-

-

-

-

-

0.30

0.34

0.24

0.43

-0.31

-0.33

-

-

-0.29

-0.30

-

-

0.44

0.57

0.44

0.53

0.85

.

0.26

0.23

-

-

0.45

0.43

0.24

-

0.35

-

-

-

-

-

-

0.36 0.36

0.69 0.61

0.42 0.40

0.83 0.69

- -

-0.51 -0.48

- -

-0.26 -0.25

-0.25

-0.25

-

-

-

-

-

-

0.49

0.57

-

-

-

-

.

0.99

-

-

-

0.24

0.38

0.47

-

-

-

-

0.93

.

-

-

-

0.23

-

-

0.23

-

-

-

-

-

-

-

-

0.23

-

-

-

-

-

-

-

-

-

-

-0.31

-

-

-

-

-

-

-

-

0.24

-

-

-

-

-

-

.

-

-

-

-

-

-

-

-

-

-

-

-

mass

-

-

-

-

-0.31

-

-

-

-

-

-

-

-

0.24

-

-

-

-

-

-

-

.

-

-

-

-

-

-

-

-

-

-

-

Sculpins

Den- Biosity mass

-

0.48 0.49

0.36 0.39

0.27 0.30

0.32 0.37

-0.61 -0.57

-0.26 -0.27

-0.27 -0.24

-0.30 -0.29

-0.52

-0.55

-

-

-0.51

-0.54

-

-

0.64

0.57

0.56

0.47

0.41

0.42

-

-

-

-

.

0.99

0.61

0.55

0.51

0.47

0.24

0.30

-

-

-

-

0.95

.

-

-

0.29

-0.28

-

-

-

0.30

-

-

-

0.27

-0.23

-

-

-

0.27

-
HSI MODEL FOR BROOK Rrou~ 33

Invertebrate density

Oligochaeta Gastropoda Odonata Ephemeroptera Plecoptera Coleoptera Megaloptera Trichoptera Diptera

0.49

-

-

-

-0.47

-

-

-

-

-

-

-

-

-

0.39

-

0.56

0.56

0.47

-

-

-

-

-

-

0.40

0.32

0.61

0.52

-

0.64

0.71

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

0

-

-

-

-

-

-

-

0.24

-

-

-

-0.3 8

-

-

-

0.36 -

0.23 -

0.29 -

- -

-0.30 0.27

- -

-0.25 -

-0.27

-0.24

-

-

-

-

-

-

0.29

-

-

-

- - -

-

-

-

-

-

-

0.46 -0.25

0.42 -

0.76 -

0.52 -

0.37 -

0.37 -

0.34 -

- -

- -

- -

- -

- -

- -

- -

0.33 -

- -

- -

- 0.23

- -

- -

- -

- -

- -

- -

- - - -

- - - -

0 - - -

- l - 0.33

0.30 0.55 l 0.35

0.44 0.54 0.50 0

- - - -

0.70 - 0.38 0.43

- 0.62 0.61 0.66

-

-

-

-

0.39

-

-

-

-0.30

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

0.28

-

-

.

-

-

-

-

-

0.24

-

0.56

0.27

0.30

0.30

-

-

-

-

-

-

-

-

-

0.46

0.42

-

0

-

0.39

0.23

-

-

-0.30

-

-

-

-0.22

-

-

0.33

-

0.53

0.24

-

-

-

-

-

-

-

-

-

-

-

0.71

0.62

0.49

-

0.46

.
34 BIOLOGICAL REPOFLT 18

Table All. Fksults of multiple regression analysis, as regression coefficients and other statisticqa

relating brook trout abundance (as loglo density, no./ha, and log10 biomass, kg/ ha) to

physicochemical and biological variables in seven SBRF’ streams surveyed by Fowler (1985).

Variable or statistic

Intercept (PO)

Length

Width

Plow

Gradient

Elevation

PI-I

Alkalinity

Abundanceb

Rainhow trout

Brown trout

Sculpins

Gastropoda

Plecoptera

Odonata

Physicochetnical

variables only

Density Biomass

7.90 2.13

0.017 -

-0.086 -0.24

0.077 0.10

- -

0.00083 -

-1.11 -

- -0.11

- -

- -

- -

- -

- -

- -

12.03 21.74

0.70 0.70

-

0.069

-

Physicochemical and

biological variables

Density Biomass

7.5 0.30

- -

-0.161 -

0.027

-

-

-0.647 -

-0.016

-0.0048

-

-0.6629 -

0.0118

-0.0347

-

-0.0502

-

47.23

0.87

-

0.0384

39.88

0.76

a All l&ted coefficients statistically significant (P S 0.05); dashes indicate pairs of variables not significantly correlated.

b For fishes, abundance in same units as brook trout (no. / ha.); for invertebrates, no. / m2.

’ All regressions highly significant overall (P << 0.01).
I

HSI MODEI, FOR BROOK TROUT 35

Table A12. Mean (z), standard deviation (SD), median, minimum, and maximum values of variables

measured in 138 streams (n = 136 for invertebrate densities, n = 129 for pH, alkalinity, and hardness)

by the North Carolina Wildlife Resources Commission (1983).

Variable f SD Median Minimum Maximum

Physicochemical

Width (m)

Flow (cubic feet / s)

Gradient (%)

Cover (Oh)

Elevation (m)

PH

Alkalinity (mg/ L)

Hardness (mg / L)

Fish density (no. / ha)

Brook trout

Bainbow trout

Total Y-O-Y*

Brown trout

Longnose date

Blacknose daceb

Rosyside daceC

Creek chub

Bluehead chubd

Sculpina

Stonerollere

Greenaide darter’

Striped jumprockg

Invertebrate density

(no. / square foot)

Oligochaeta

Gastropoda

Hemiptera

Odonata

Ephemeroptera

Plecoptera

Coleoptera

Megaloptera

Trichoptera

Diptera

*Salmonids.

bRhinichthys atmtulus.

’Clinostomus fhduloides.

d Nocomis leptocephalus.

e Camposioma anomalum.

f Ethostoma bknnioides.

g Moxostoma rupiscartes.

4.1 1.6 3.7 1.5 9.8

10.9 6.7 8.0 3.0 35.0

2.1 0.67 2.0 1.0 3.0

3.0 0.7 3.0 1.0 4.0

964.1 232.2 948.5 427.0 1647.0

6.91 0.34 7.0 6.5 8.0

11.7 4.9 8.6 4.3 34.2

19.0 5.8 17.1 17.1 51.3

477.3 546.8 301.5 7.0 2662.0

105.6 289.0 0 0 1,892.0

344.9 416.8 212.5 0 2,372.0

33.9 98.8 0 0 560.0

42.7 143.4 0 0 891.0

213.4 600.9 0 0 4,429.0

83.1 282.2 0 0 1,972.0

24.0 88.0 0 0 678.0

29.8 138.1 0 0 1,105.o

20.1 381.9 0 0 2,141.0

30.9 173.6 0 0 1,630.O

0.1 1.7 0 0 20.0

2.1 14.7 0 0 129.0

0.25 0.66 0 0 4.0

0.63 2.13 0 0 19.0

0.07 0.31 0 0 2.0

0.26 0.77 0 0 6.0

21.56 24.22 13.0 1.0 160.0

6.71 6.64 4.5 0 33.0

1.38 2.89 0 * 0 18.0

0.09 0.45 0 0 3.0

17.0 26.26 8.0 0 193.0

7.39 11.69 5.0 0 115.0
36 BIOLQG~CAL REFYXT 18

Table Al3. Statistically significant* correlations between variables measured in 138 SBRP streams by

arrayed above the principal diagonal (indicated by bullets), Spearman rank coefficents below.

Variable Width Flow

Physicochemical variables

Gradient Cover Elevation pH Alkalinity Hardness

Physicochemical

Width

Flow

Gradient

Cover

Elevation

PH

Alkalinity

Hardness

Fish density

Brook trout

Bainhow trout

Y-0-9

Brown trout

Longnose date

Blacknose date

Bosyside date

Creek chub

Bluehead chub

sculpins

Stoneroller

Greenside darter

Striped jumprock

Invertebrate density

Oligcchaeta

Gastropoda

Hemiptera

Odonata

Ephemeroptera

Plecoptera

Coleoptera

Megaloptera

‘Xchoptera

Diptera

l

0.78

-

-0.81

a

-

-

0.21

-

-

0.24

-0.14

0.15

-

- -

-0.45 -0.35

- -

-0.32 -0.26

0.45 0.36

0.39 0.36

0.16 -

- -

- -

- -

- -

0.27 0.29

- -

- -

0.21

-

-

-0.18

0.22

-

-

-

-

-

0.19

-

-

-0.19

0.22

-

-

-

-

-

-

-

0

0.32

-

-

-0.20

-

-

0.15

0.30

0

0.19

-0.16

-

-

-

-

-

-

-

-0.26

-0.28

-0.28

-0.26

-

-

-

-

0.21 0.36

- -

- -

- -0.18

- -

-0.25 -0.22

-0.26 -0.34

-0.25 -0.40

-0.23 -0.35

-0.23 -

- -

-0.16 -

- -0.22

- -

- -

- -

- -

- -

- 0.15

-0.14 -0.16

- -

-0.16 -0.23

-0.16 -

-

-

0.21

0.22

- -

0.15 -0.17

l -0.31

-0.34 l

-0.33 0.50

- 0.32

-0.24 -0.20

- -

- -

0.25 0.15

0.31 0.31

0.45 0.49

0.33 0.30

0.23 0.24

0.26 0.29

0.33 0.42

0.38 0.33

- -

-

-

-

-

-

-0.20

-

-0.26

-

-0.17

-

-

0.36

-

- -

0.42 0.54

0.21 0.24

0.41 0.28

0.19 0.30

0.45 0.48

0.27 0.41

-

-

-

-

-0.30

0.55

0

0.31

-

0.17

0.39

-

*If Irl >0.2l,P<O.O1; if0.21 > IPI > O.l6,P< 0.05; if0.16 20.14, P<O.lO.

b Salmonids.

-

-

-

-

-

-

0.32

0.49

a

-

-

-

-

0.21

0.34

-

-

-

0.33

0.27

-

-

-

0.21

-

-

0.18

-

-

-

0.26

-
HSI MODEL FOR BRINK TROUT 37

The North Carolina Wildlife Resources Commission (1983). Pearson product-moment coefficients

Dashes indicate pairs of variables not significantly correlated. Variables as defined in Table A12.

Fish density

Brook R.iIhOW Br0WI-l Longnose Blacknose Rosyside Creek Bluehead Scul- Stonetrout trout Y-O-Y trout date date date chub chubTpins roller

-0.41

-0.31

-

0.17

0.32

-0.24

-0.16

-

l

-0.34

0.65

-0.44

-0.31

-0.25

-0.29

-0.27

-0.30

-

-0.33

-

-0.20

-

-

-

0.25

-0.22

-

-0.22

-0.21

-

-

- -0.31 0.16

- -0.27 0.14

- - -

- - -

-

- -0.17

- - 0.23

- - -

0.19 - -

-0.19 0.69 -0.21

0 0.19 -

- l -

- -0.21 l

0.15 - 0.42

0.14 - 0.19

- -0.20 0.18

- -0.18 0.22

-

- 0.23

-

- 0.18

0.11 -0.23 -

- - -

- -0.19 -

- 0.22

0.15

1

0.19

- - -

- 0.23 -

- -0.16 0.22

- - -

- - 0.21

- - -

- - -

- - -

0.29

0.39

-

-

-

0.20

0.28

0.21

-

-

-0.19

-0.27

-

0.31

0.47

0.27

-0.17

-

-

-

- -

-

l

0.34

-

-

-

-

0.28

-

0.21

-

-

0

0.52

0.35

0.27

0.52

-

-

-

0.19 -

0.23 0.35

- -

- -

0.23 0.22

- -

0.15 0.21

0.28 0.25

0.19 0.46

0.20 0.23

- -

- -

-0.18 -0.19

-0.14 -0.29

-0.28 -0.24

0.22 -

0.36 -

- -

- -0.17

- -

- -0.14

- -

- -

0.46 0.34

0 0.52

0.78 l

0.58 0.66

0.18 -

0.31 -

- -

0.23 0.20

- -

0.30 -

0.16 -

- -

0.24 0.23

0.17 0.15

0.33 0.32

0.31 -

0.41 0.28

0.25 0.17

- -

- -

-0.17 -

-0.18 -0.17

-0.24 -

- 0.39

- 0.58

- 0.46

-

-

-

-

-

0.41

0.41

0

-

0.26

-

0.30

-

-

-

-

0.26

0.50

0.18

-

-

l

-

-

-

-

-

-

-

0.25

0.20

0.27

0.16

0.31

0.21

-

0.28

-

-

0.15

-

-

0.20

0.35

0.17

0.25

0.40

-

-

-

0.29

0.31

-

-

-

-

0.15

0.45

-

-

-

-

-

0

0.29

-

0.14

0.31

0.16

-

0.22

-

0.16

0.32

0.20

0.18
38 BIOLOGICAL REPORT 18

Table A13. Continued

Invertebrate density

Greenside Striped Oligo- Gastro- Hemip- Ephemer- Pleco- Coleo- Megalo- Trichodarter jumprock chaeta poda tera Odonata optera ptera ptera ptera ptera Diptera

0.15

0.25

-

-0.24

-

-

-

-

-

-

-

-

0.15

-

-

-

-

-

-

a -

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-0.27

-

-

-

-

-

-

-

-

-

0.17

-

0.15

-

-

-

0

-

-

-

-

-

-

-

-

-

-

0.20

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

0

-

-

-

0.16

-

-

-

0.17

0.24

-

-

-

-

-

0.27

0.54

0.27

-

-

-

-

-

-

0.21

-

-

-

-

-

-

0.48

0.44

.-

-

0.42

-

-

-

-

-

-

-

-

0.30

0.28

0.18

-

0.20

-

-

-

-

0

-

-

0.35

l

- -

0.32 0.16

0.19 0.22

0.30 -

0.33 -

0.30 0.16

0.24 -

-0.16

-0.17

-0.16

-

-

-

-

-

0.32

-

0.32

-

-

-

-

-

-

-

-

-

-

0.27

0.17

-

.

-

-

-

-

-

-

0.22 -

0.21 -

- -

- 0.18

-0.30 -

0.36 0.17

0.50 0.16

- -

-0.116 -

- -

- -

0.20 -

- -

-

0.19 i.18

0.15 -

- -

0.20 -

- -

- -

- -

- -

0.32 0.25

0.28 0.22

- -

0 0.46

0.47 l

0.39 0.34

0.28 0.23

0.59 0.40

0.44 0.40

-

-

-

-

-0.2 4

0.45

0.39

0.15

-0.16

-

-

-

0.16

0.27

0.46

-

-

-

-

-

-

-

0.27

-

-

0.40

0.30

0

0.33

0.39

0.28

- 0.17 -

- - -

- - -

- -0.23 -

- - -

0.22 0.49 0.18

0.39 0.57 0.34

- 0.38 -

- -0.16 -

- - -

- 0.15 -

- - -

- 0.15 -

0.36 0.46 0.28

0.39 0.28 -

- - -

- -

0.32 0.52 i.28

- - -

- - -

- - -

- - -

0.57 0.49 0.27

0.27 0.23 -

- - -

0.32 0.46 0.29

0.26 0.29 0.32

0.31 0.40 0.19

0 0.35 0.73

0.27 l 0.33

0.32 0.47 l

u

Table A16. Statistically significanta correlations between brook trout abundance, the abundance of all other fishes combined, and

physicochemical attributes of seven SBRP streams (n = 56) studied by Fowlel; (1985). Pearson product-moment coefficients arrayed above

$

the principal diagonal (indicated by bulleti), Spearman rank coefficients below. Dashes indicate pairs of variables not significantly correlated.

Variables as defined in Table A9. ;

Brook trout Other species (total) Stream size Other 8

Density Biomass Density Biomass Width &Pth Flow Gradient Elevation pH Alkalinity %

Brook trout

Density

Biomass

Other species (total)

Density

Biomass

Stream size

Width

Depth

Flow

Other

Gradient

Elevation

PH

Alkalinity

-

-

iii

-0.36

-0.38

-0.27

-

-

-

0.52

0.86

l

l 0.88 -0.61 -0.59 -0.51 -0.47 -0.40 0.39 0.46

0.89 0-0.65 -0.61 -0.53 -0.44 -0.38 0.53 0.40

-

-

-0.33

-0.33

-

-

-

-

-

.

0.51

-0.6 0 -0.45

-0.49 -0.57

-0.72 -0.73 a

-0.74 -0.74 0.98

0.89

l

0.51 0.35 0.54

0.63 0.43 0.73

-0.57 -0.59 0.48 0.51 0 0.79 0.85 - -0.70

-0.46 -0.42 0.39 0.43 0.84 l 0.57 - -0.65

-0.46 -0.41 0.40 0.46 0.82 0.76 l - -0.58

0.49 0.61

0.50 0.37

- -

- -

-0.51

-0.53

-0.35

-0.41

-0.48

-0.5 8

-0.3 5

-0.41

-0.25 - -

-0.67 -0.64 0.59

-

- 0.29

-0.46 -0.39 -0.30

l -

- 0

- -

-0.23 0.66

*If Irl >0.33,P<O.Ol;if0.33> Irl 20.27,P<O.O5;ifO.27> (r/ >0.23,P<O.10.
Table A17. Statistically significanta correlations between brook trout abundance, the abundance of all other fishes combined, and

physicochemical attributes in 138 SBW streams surveyed by the North Carolina Wildlife Resources Commission (1983). Pearson

product-moment coefficients arrayed above the principal diagonal (indicated by bullets), Spearman rank coefficients below. Dudes indicate

pairs of variables not significantly correlated. Va.riables as defined in Table A12.

Other

Fish densitv Stream size Ele- Alka- HardVariable Brook trout Others Width Flow Gradient vation PH linity ness

Fish density ,

Brook trout l -0.20 -0.41 -0.32 - 0.32 -0.23 -0.16 -

Other species (total) -0.49 l - > - -0.19 -0.22 0.47 0.63 0.38

Stream size

Width -0.45 0.17 l 0.81 - - 0.21 - -

Flow -0.35 - 0.78 l - - 0.22 - -

Other

Gradient - -0.19 - - l - - - -

Elevation 0.36 -0.34 - - - l -0.31 -0.30 -

PH -0.27 0.49 0.21 0.15 - -0.34 l 0.55 0.31

Alkalinity -0.20 0.53 - - -0.21 -0.34 0.50 l 0.49

Hardness - 0.35 - - - - 0.32 0.31 l

‘If Irl ~0.21,P<O.Ol;if0.21> Irl 20.16,P<0.05;if0.16> Irl 20.14,P<O.10.
42 BIOLOGICAL REFORT 18

Table Al& Statistically significanta correlations

(n = 56) between mean brook trout weight

(biomass and density) and other variables in

seven SBRP streams surveyed by Fowler (1985).

Dashes indicate pairs of variables not

significantly correlated. Variables as defined in

Table A9.

Variable

Flow

Gradient

Elevation

Brook trout density

Brook trout biomass (adult)

Rainbow trout biomass (adult)

Rainbow trout biomass (Y-O-Y)

Long-nose date density

Longnose date biomass

Gastropod abundance

Coefficientb

‘P’ m

0.3 4 -

0.30 0.33

-0.23 -0.29

-0.22 -

0.23 -

- 0.23

0.35 -

0.23 -

- 0.32

0.32 -

*If I rl 2 0.33, P 5 0.01; if 0.33 > I n j L 0.27, P < 0.05; if 0.27 >

b I rl 2 0.23, P < 0.10.

y, Pearson product-moment; rs~, Spearman rank.

Table Al9. Results of principal components regression analyisa relating brook trout abundance to

physicochemical variables and rainbow trout abundance in SBFLP streams. Data from North

Carolina Wildlife Resources Commission (1983), Fowler (19&j), and U.S. National Park Service

(Parker MS).

Factor

Variables and statistics 1 2 3 4 5

Standardized scoring coeffcientsc

Width 0.371 0.203 0.645 -0.554 0.642

Elevation 0.072 -0.621 0.440 0.653 0.405

Gradient 0.370 -0.134 -0.791 <O.OOl 0.729

PH -0.431 0.263 -0.063 0.216 1.142

Rainbow trout density 0.269 0.492 0.075 0.938 -0.166

Variability explained

Proportion

Cumulative total

Regression coefficients (PO = 0.645)

0.37 0.26

0.37 0.64

- -0.126

0.16

0.80

-0.028

0.12

0.92

-

0.08

1.00

-0.015

-=n = 256, F = 49.00 (P < O.Ol), R* = 0.37.

bAll variables except pH loglo transformed.

’ Weightings of the variables in each factor after transformation to standard deviation units.
HSI MODEL FOR B ROOK TRQUT 43

Table A!Uh Fiesults of princigg components regression analysisa relating brook trout abundance to

physicochemical variables in SBRP streams. Data from North Carolina Wildlife Resources

Commission (1983), Fowler (1985), and US. National Park Service (Parker MS).

Variables and statistics 1

Factor

2 3 4

Standardized scoring co&icientsC

Width 0.503 0.821 -0.036

Elevation 0.579 -0.325 0.707

Gradient 0.602 -0.421 -0.617

PH -0.855 -0.028 0.024

Variability explained

Proportion 0.42 0.24 0.22

Cumulative total 0.42 0.66 0.88

Regression coefficients (PO = 0.714) 0.099 -0.069 0.024

‘n = 165, F= 30.34 (P O.Ol),< Z? = 0.43.

b All variables except pH loglo transformed.

’ Weightings of the variables in each factor after transformation to standard deviation units.

0.259

0.243

0.233

0.517

0.12

1.00

-0.029