AN ABSTRACT OF THE THESIS OF
Claudia Rafael Mariano Baigun for the degree of Master of Science in Fisheries
Science presented on November 14. 1994. Title: Characteristics of Pools Used by
Adult Summer Steelhead (Oncorhynchus mykiss) in the Steamboat Creek Basin.
North Umpgua River, Oregon.
Redacted for privacy
Abstract approved:
James R. Sedeil
This study examined features of deep pool (>0.8 m mean depth) used by
adult summer steelhead in Steamboat Creek (1991-1992). Steamboat Creek had
a heterogenous thermal profile, with some segments exceeding preferred
temperature of steelhead. Deep pools were scarce (4 % of the total habitat units)
and 39 % of them were identified as cool pools (mean bottom water temperature
19°C). Adult summer steelhead were found primarily in deep pools, avoiding
other habitats (glides,riffles) and even cold but shallow tributary junctions. Use of
odds ratio showed that use of cool pools use was estimated to be 11 times greater
than the odds of the use of warm pools (P <0.001). Discriminant analysis identified
mean bottom pool water temperature, riparian forest at the pool bank, proportion
of large boulders, maximum length and mean depth as the best subset of variables
that accounted for differences between pools occupied and not occupied by adult
steelhead. A total of 69 % of the variation was explained by differences in used and
not used groups. Classification accuracy was 89 %. Canton Creek, a tributary of
Steamboat Creek, were tested as validation site for the derived model, observing
that the classification function performed moderately, achieving a hit-ratio of 0.7.
Results of the study showed that, since bottom pool temperature was a major
factor but other ecological factors were also relevant, an integrated framework
would be required in determining pool used by this species. Moderate success of
the predictive model suggests that managers will want to check it before applying
in other basins.
@copyright by Claudio Rafael Mariano Baigün
November 14, 1994
All Rights Reserved
Characteristics of Pools Used by Adult Summer Steelhead
(Oncorhynchus mykiss)
in the Steamboat Creek Basin,
North Umpqua River, Oregon
by
Claudio Rafael Mariano BaigCin
A THESIS
submitted to
Oregon State University
in partial fulfillment of
the requirements for the
degree of
Master of Science
Completed November 14, 1994
Commencement June 1995
Master of Science thesis of Claudio R. M. BaigUn presented
on November 14. 1994
APPROVED:
Redacted for privacy
Professor, representing Fisheries Science
Redacted for privacy
,
Head of Deiartment of Fish and Wildlife
Redacted for privacy
Dean of GradIath School
I understand that my thesis will become part of the permanent collection of Oregon
State University libraries. My signature below authorizes release of my thesis to any
reader upon request.
Redacted for privacy
Claudlo Rafael Mariano Baigün
ACKNOWLEDGMENT
This section is not necessarily extensive but pleasant. Several people and
institutions have been involved in this research. Without their support this
dissertation would not have been completed.
I want to thank the personnel of the Department of Fish and Wildlife (OSU),
in particular Chris Sennet, Jean Smith, Charlotte Vickers and the past Department
Chairman, Dr. Richard Tubb, for providing me with a comfortable and friendly work
atmosphere. I am deeply indebted to the U.S. Forest Service, Pacific Northwest
Research Station (PNW), CorvalUs, for assistance with computer and other
facilities. Bruce Hansen provided me with all the necessary support and helped to
improve my field work. Cathie Quinn, Joe Lumianski, Mark Chambers and,
particularly, Mark Garner assisted me in the field and I want to thank them for their
help. Barbara Fontaine and Tim La Marr from the Umpqua National Forest, were
friendly people who became interested in my research, providing facilities and the
necessary support in the Steamboat Creek area. Jeffry Dambacher, Oregon
Department of Fish and Wildlife, provided data of fish number at Winchester Dam.
Dr. Terry Roelofs allowed me to analyzed his unpublished data from Steamboat
Creek basin and also provided me with valuable bibliographic material. Kevin
McGarigal provided statistical assistance.
Other people, not directly related with the project also provided valuable aid:
Paula Carter from the International Institute of Education (lIE, San Francisco) and
Marybeth Rowen from the Office of International Education (OSU). Partial financial
support were provided by the Fulbright Commission (Argentina) and Encyclopedia
Britannica Fellowship.
Special thanks to Dr. Jim Sedell, my advisor professor for many reasons. First,
he provided funds for my research and training program. Second, Jim provided
vital guidance for this study and contributed greatly to the development of my
research ideas. Third, and perhaps what is most relevant to me, he took the risk
of accepting me as part of his research team, without any prior knowledge of my
person, and deposited his confidence in my work. I hope I did not disappoint him.
I wish also to thank Dr. Carl Schreck, Dr. J. Lyford and Dr. Gordon (Gordie)
Reeves who served on my graduate committee, and Dr. Myron Schenk who
served as graduate council representative. Gordie spent much time discussing this
study with me this study and bravely answered the challenge to correct earlier
drafts of this thesis. Dr. Stan Gregory also made valuable suggestions.
Special thanks to our big family in Argentina (the Leff-Baigün-Minotti team),
who supported us in different ways and have patiently awaited our return, and of
course, to my family here in Corvallis. I dedicate this thesis to them. To Luci and
Ale, for accepting that I used much of their time for my work. To my lovely wife
Priscilla, who deserves enormous and invaluable credit for the completion of this
thesis. Her natural talent for ecology, that surpasses mine, was a precious source
of rich ideas and original suggestions. Thanks for all 'Patil!.
TABLE OF CONTENTS
I.INTRODUCTION
1.1. Objectives
3
II. MATERIAL AND METHODS
2.1. Description of the study area
2.2. Data collection
4
4
6
2.2.1. General sampling framework
2.2.2. Environmental sampling
2.2.2.1. Definition of deep pools
2.2.2.2. Deep pooi sampling
2.3. Basis for selected variables
2.4. Definition of cool and warm pools
2.5. Fish sampling
2.6. Statistical analysis
Ill. RESULTS
3.1. Steamboat Creek thermal regime
3.2. Distribution of deep pools
3.3. Pool characteristics
3.3.1. Morphometry
3.3.2. Water temperature
3.4. Fish abundance and distribution
3.4.1. Systematic sampling
3.4.2. Pool sampling
1
7
10
10
11
15
17
18
20
25
25
28
28
28
30
32
32
32
3.5. Temperature-abundance relationship
3.6. Relationship between poo1 environmental
and pool use
36
3.6.1. Selection of predictive variables
39
3.7. Pool use prediction
3.7.1. Square generalized distance
3.7.2. Development of a field key
39
44
44
45
TABLE OF CONTENTS (Continued)
IV. DISCUSSION
48
4.1. Thermal characteristics of the system
4.2. Fish distribution patterns
48
49
4.2.1. Use of cool and warm pools
50
4.3. Discriminant analysis
4.4. Validation of the discriminant model
4.5. Effects of population size
51
55
56
V. MANAGEMENT CONSIDERATIONS AND FUTURE DIRECTIONS
5.1. Deep pools protection
5.2. Considering water temperature as a
source of pool complexity
5.3. Model testing
5.4. Conduct studies on deep pool thermal dynamic
5.5. Evaluate the stream thermoregulatory capacity
5.6. Study of fish movements
5.7. Considering the riverscape perspective in deep
pool use evaluation
58
59
BIBLIOGRAPHY
69
APPENDICES
Appendix A
Appendix B
60
61
62
63
64
65
87
88
92
LIST OF FIGURES
Figure 1: Steamboat Creek basin location in Oregon and
expanded view.
5
Figure 2: Steamboat Creek and main tributaries.
8
Figure 3: Canton Creek and main tributaries.
Figure 4: Distribution of deep pools (mean depth > 0.8 m)
in Streamboat Creek, OR, sampled in this study.
12
Figure 5: Distribution of deep pools (mean depth > 0.8 m)
in Canton Creek, OR, samp(ed in this study.
13
Figure 6: Thermal profile of Steamboat Creek. Zero point
corresponds to the junction with the North Urnpqua
River. Arrows indicate main tributary junctions.
CA: Canton Creek; S: Steethead Creek; R: Reynolds
Creek; BB: Big Bend Creek; C: City Creek.
26
Figure 7: Distribution of cool pools ( 19°C mean bottom
water temperature) and warm pools (> 19°C mean
bottom water temperature) in Steamboat Creek.
Pools of similar category that were located close
together are shown as only one point. Zero point
corresponds to the junction with the North Umpqua
River.
32
Figure 8: Mean number of adult summer steelhead in deep pools
in Steamboat Creek during August-September 1991
and August-September 1992 (see figures 4 an 5 for
pool locations). Note: pools 1-10 are located in
Canton Creek (see Fig. 3 for pool locations).
35
Figure 9: Relationship between mean water temperature at
the bottom of the pool and number of adult summer
steelhead in study pools in Steamboat Creek in
A: August-September 1991; B: August -September 1992.
37
Figure 10: Plot of discriminant scores based on analysis in
Steamboat Creek. Vertical lines on the block and the
circle respectively indicate the centroid position.
Horizontal line at the extremes corresponds to the ranges.
43
LIST OF FIGURES (CONTINUED)
Figure 11: Number of adult summer steelhead at Winchester
Dam (North Umpqua River), counted between May
and November (1969-1992)(Oregon Fish and Wildlife
Department, unpublished data).
58
LIST OF TABLES
Table 1: Preferred water temperature of rainbow trout
reported in the literature. L.N.A: location not
available.
19
Table 2: Water temperature (°C) of Steamboat Creek tributaries.
Numbers in parenthesis represent the water temperature in
Steamboat Creek above the junction with the respective
tributary. Temperatures were measured between 1 pm and
4 pm. Tributaries are ordered in an upstream-downstream
position.
27
Table 3: Statistical parameters of measured variables in the
study pools in Steamboat Creek, OR, in 1991 and 1992.
29
Table 4: Correlation matrix among physical features of deep pools
in Steamboat Creek, OR, summer 1991-1992. LED: ledge
cover proportion; S:sand proportion; BR: bedrock proportion;
LB: large boulder proportion; C: cobble proportion; SB: small
boulder proportion; SO: small gravel proportion; LG: large
gravel proportion; A: Area; VOL: volume; MD: mean depth;
MAD: maximum depth; OR: depth ratio; MW mean width; LIT:
littoral area proportion; ML: maximum length; SSI: shore
sinuosity index; BO: bottom oxygen; TVC: temperature variation
coefficient; BT: mean bottom pool temperature (see text for
further explanation).
31
Table 5: Total number of adult summer steelhead in cool and
warm pools in Steamboat Creek during August-early
September 1991 and 1992. (S.D: standard deviation).
33
Table 6: Percent of cool pools occupied by adult summer steelhead
adults
in Steamboat Creek, percent of cool pools having
and mean density (fish/rn3) in cool pools (6) warm pools
(6w) and all deep pools (6) during 1991 and 1992.
Table 7: Results of odds ratio test of pools with different mean
bottom water temperatures that were use and not used
by adult summer steelhead in Steamboat Creek, OR,
August-September 1991 (A) and August-September
1992 (B). See text for further detail.
34
38
LIST OF TABLES (CONTINUED)
Table 8: Mean. standard deviation (S.D.) and range of
variables between USED and NOT USED pools by
adult summer steelhead in Steamboat Creek, OR,
August-September 1991 and 1992. See text for further
detail. RF: proportion of bank riparian forest; LB:
large boulder proportion; MD: mean depth (m); ML:
maximum length (m); BT: mean pool bottom
temperature (°C).
40
Table 9: Standarized canonical coefficients and structure
coefficients derived from discriminant analysis.
41
Table 10: Standarized canonical coefficients and structure
coefficients derived from discriminant analysis.
41
Table 11: Number of pools in Steamboat Creek correctly
classified by discriminant analysis after jacknife
resampling procedure.
44
Table 12: Number of pools in Canton Creek correctly
classified by discriminant analysis.
45
LIST OF APPENDIX FIGURES
Figure A.1: Number of fish observed in pools during the
systematic survey in Steamboat Creek (August 1991).
89
Figure A.2: Conceptual example of the dichotomous key development
according to Kendall (1975). See text for further
explanations.
90
Figure A.3: Proportion of habitat types in Steamboat Creek
observed in the systematic survey during 1991.
91
LIST OF APPENDIX TABLES
Table B.1: Percentages of bank riparian forest in pools USED
and NOT USED by adult summer steelhead in
Steamboat Creek, as utilized in the development
of the dichotomous key. Dashed lines defines cut-off
values.
93
Table B.2: Maximum length in pools USED and NOT USED by adult
summer steethead in Steamboat Creek, as utilized in the
development of the dichotomous key. Dashed lines defines
cut-off values.
94
Table B.3: Mean bottom temperature in pools USED and NOT USED
by adult summer steelhead in Steamboat Creek, as
utilized in the development of the dichotomous key.
Dashed lines defines cut-off values.
95
Table B.4: Maximum depth in pools USED and NOT USED by adult
summer steelhead in Steamboat Creek, as utilized
in the development of the dichotomous key. Dashed
lines defines cut-off values.
96
Table B.5: Comparative analysis of physical variables in pools
in Canton Creek and Steamboat Creek. X= mean; SD=
standard deviation; n.s: not significative at 0.05
level; s: significative at 0.05 level; power= 1- Type
II error.
97
Table B.6: Values measured in Steamboat Creek basin pools.
AREA: area (m2); VOL: volume (m3) ZD: mean depth (m);
ZX: maximum depth (m); DR: depth ratio; MW mean width
(m); LIT: littoral area proportion; PER: perimeter (m);
ML; maximum length (m); SSI: shore sinuosity index; LED:
ledge cover proportion; RF: proportion of bank riparian
forest; S:sand proportion; BR: bedrock proportion; LB:
large boulder proportion; SB: small boulder proportion;
C:cobble proportion; SG: small gravel proportion; LG:
large gravel proportion; BO: bottom oxygen (mg/L); TCV:
temperature coefficient of variation; BT: mean bottom
temperature (°C) (see text for further explanation).
99
Characteristics of Pools Used by Adult Summer Steelhead
(Oncorhynchus mykis.$) in the Steamboat Creek Basin,
North Umpqua River, Oregon
I. INTRODUCTION
Pools are important habitat for juvenile and adult salmonids. They offer cover,
protection from predators and rearing and holding opportunities (Binns 1982;
Oswood and Barber 1982; Campbell and Neuner 1985; Platts et al. 1983, 1987;
Beschta and Platts 1986; Sedell et al. 1986; Modde et at. 1991; Reeves et aL 1991;
Faush and Northcote 1992; Northcote 1992). Factors such as pool area, volume,
cover and depth influence the density and biomass of salmonids in pools (Lewis
1969; Rinne 1982; Plalls et at. 1983; 1987; Raleigh et aL 1984; Bowlby and Roff
1986; Wesche et at. 1987).
Water temperature can influence pooi use by salmonids (Bowiby and Roff
1986; Berman and Quinn 1991; Nielsen 1992: Nielsen et at. 1994). Temperature
is the dominant factor that controls the behavior and activities of aquatic
poikiloterms (Stauffer 1980). In general, temperature is a pervasive factor that
affects
several
physiological
processes,
such
as
metabolism,
diseases
susceptibility, growth and feeding (Lantz 1970; Adams et at. 1990; see also
Beschta et
al.
1987 for a review). Salmonids may seek cooler pools as a
mechanism for thermoregulation (Kaya et al. 1977; Clapp and Clark 1990; Meisner
1990; Berman and Quinn 1991), and residing in cool thermal refuges may allow
2
reported that steelhead inhabiting streams above 21 °C may have difficulties
extracting oxygen from the water. These temperature constraints have been noted
in different Pacific Northwest streams and rivers (Everest 1973; Dunn 1981; Nielsen
1992; Nielsen et al. 1994).
Summer steethead (Oncorhynchus mykiss) ascend rivers from May to October
and hold in pools in small to intermediate size streams until spawning between
January and March (Leider 1985; Barnhart 1986). Since these fish spend all
summer in freshwater, they may confront harsh ecological conditions particularly
in some California and Oregon streams. Water temperature has been identified as
a key ecological factor that influences adult summer steelhead in many streams in
California and western Oregon (Anderson and Miyajima 1975), but this tenet has
been supported by only a few quantitative studies (e.g. Adams et al. 1990; Nielsen
1992; Nielsen et at. 1994). On the other hand, ecological factors as depth and
cover have been identified in the literature as affecting salmonid abundance or
density in pools, but little effort has been made to determine the influence of these
and other factors in pool use by adult steelhead (e.g. Dunn 1981; Freese 1982).
An integrated framework is needed to describe the suite of factors and their
relative importance in governing pool use by adult summer steethead.
In the present study, I examined features of pools used by adult summer
steelhead in a mid-size Oregon stream. My general hypothesis was that pools
occupied by summer steethead are selected primarily on the basis of thermal
characteristics. I also examined the relevancy of geomorphic variables in defining
a second hierarchical level that accounts for the distribution of adult summer
steelhead in pools.
1.1. Objectives
The specific objectives were to:
a) Determine the relationship between pools used by adult summer steelhead and
the position of the pools in the thermal gradient of the stream.
b) Determine if adult summer steelhead use pools with cooler water temperature
more than pools with warmer water.
C)
Determine the relative importance of other ecological factors in pool use.
ri
II. MATERIAL AND METHODS
2.1. Description of the study area
The Steamboat Creek basin is located in the west Cascades, Douglas County,
Oregon (Fig. 1). The basin covers approximately 600 km2. Historical mean annual
flow for Steamboat Creek near its confluence with the North Umpqua River has
been reported as 20.48 m3/s, with a historical maximum of 1444 m3/s and a
minimum of 0.85 m3/s (Hubbard et al. 1990). Annual precipitation in the basin
averages 140 cm and 70 % of the precipitation occurs from November through
March (Dambacher 1991). Steamboat Creek, a fifth order stream at its mouth, is
the major stream in the basin. Canton and Big Bend Creeks are the most
important tributaries (Brown et al. 1971).
The basin has supported intensive timber harvest during the past 35 years,
with 34% of the basin harvested between 1955 and 1990 (Holaday 1992). Timber
harvest has modified stream temperatures, resulting in higher mean and daily high
temperatures (Hostetler 1991).
The Steamboat Creek basin supports a diverse fish community. Salmonids
include summer and winter steelhead, resident rainbow trout (0. mykiss), sea-run
and resident cutthroat trout (0. clarki), coho salmon (0. kisutch), chinook salmon
(0. tshawytscha), brown trout (Sa/mo trutta) and brook trout (Salvelinus fontinalis).
Other species include redside shinner (Richardsonius baleatus), suckers
(Catostomus spp.), Umpqua long-nose dace (Rhinichthys evermanni), Umpqua
Figure 1: Steamboat Creek basin location in Oregon and expanded view.
Ii
OHEGON
I
Roseburg
North Umpqua Fliver
I
South Umpqua River
I
cr1
squawfish (Ptychochei/us umpquae), sculpins (Cottus spp.), speckled dace
(Rhinichthys osculus)
and juvenile Pacific lamprey (Lampetra tridentata)
(Dambacher 1991). The basin has been recognized as the main spawning area for
summer steelhead in the North Umpqua River basin (Collins 1971), and fishing has
been closed since 1932 (Marlega 1971).
Adult steelhead enter the Umpqua River from May until December (J.
Dambacher pers. comm.). Only steelhead migrating from June to September are
considered to be summer steelhead. Some of these fish hold in the Steamboat
Creek basin until spawning, which occurs between December and March. Summer
water temperatures in areas of Steamboat Creek basin where adult summer
steelhead hold, may surpass the optimum physiological tolerance levels of
steelhead (Brown et al. 1971; Holaday 1992). Low flows may reduce suitable
resting and holding habitats. Previous studies on steelhead ecology in the area
have been focused mostly on juvenile stages: density and distribution were studied
by Roelof et al. (1983) and Dambacher (1991), species interactions by Reeves et
al. (1987), and influence of instream structures by Fontaine (1987). Adult studies
examined movements of adult summer ste&head (Wroble and Roelof 1985; Wroble
1990).
2.2. Data collection
Steamboat Creek was selected as the primary study area. The study area
extended from the junction of the North Umpqua River 32 km upstream to the
7
junction of the East Fork (Fig. 2). Canton Creek was also surveyed and habitat
characteristics and fish presence in deep pools (see below) were used to validate
results obtained in Steamboat Creek. For Canton Creek, the study area was from
the junction with Steamboat Creek to the junction with Pass Creek, 15 km
upstream (Fig. 3). In both streams, upper boundaries were considered the limits
of anadromous salmonid distribution during the summer.
2.2.1. General sampling framework
The sampling scheme was based on three stages: an initial systematic survey
in Steamboat Creek during 1991 was made to determine habitat types and
frequency and to identify characteristics of pool used by adult summer steelhead.
Twenty four sites were established every 800 m, located between the junction of
Steelhead Creek and the East Fork. Each site extended 200 m upstream from the
sampling site boundary. Glides, riffles, cascades and pools were identified
according to Helm (1985) and counted in the 200 m reach. The lower segment of
Steamboat Creek was not surveyed because of field limitations. All units were
sampled to determine the presence and abundance of adult summer steelhead.
All pools with the features identified in the systematic survey were sampled for
fish and selected physical features. In every case fish were sampled prior to
measuring physical variables. In an attempt to frame a general picture of ecological
factors that may govern the occupancy of the pools,
I
sampled the entire
population of deep pools. Thus the sampling scheme emphasized sampling
N
I
N
tause
0
___! n
CJ
-'
Figure 3: Canton Creek and main tributaries
c.
10
sites over observations per site (Matthews 1990). Such an approach was
considered appropriate because past observations performed in the basin were
focused only on a few pools (e.g. Roelof et al. 1983).
2.2.2. Environmental sampling
2.2.2.1. Definition of deep pools
A frequent problem in the analysis of environmental characteristics and
species abundance or presence is that some habitats are never used by species.
In single species studies is important to maximize those areas where the species
can be found (Bibby et al. 1992). In streams, physical limitations such as
shallowness, lack of accessibility or low flow are factors that may constrain habitat
availability and therefore the universe of potential sampling sites (see Gorman and
Karr 1978; Bartholow and Slauson 1990). Thus, a preliminary analysis was
conducted to identify those pools that did not hold adult fish. Using the systematic
sampling as reference, pool length, mean width, surface, and mean depth were
calculated and related to adult summer steelhead presence. A consistent pattern
was evident with mean depth. Pools having a mean depth <0.8 m. never held
adult fish and therefore these pools were defined as shallow pools. Pools
m mean depth were defined as deep pools.
0.8
11
2.2.2.2. Deep pool sampling
All deep pools were identified in Steamboat Creek and Canton Creek in
August and September 1991. Thirty eight pools were located in Steamboat Creek
(Fig. 4) and 10 deep pools were identified in Canton Creek (Fig. 5). Four pools in
Steamboat Creek were not sampled because of poor visibility or human
disturbances, such as swimming.
Transects were established every 3 m along the length of the pool in those
pools <20 m in length. Selected variables were measured every 2 m across a
transect, beginning at the bank in these pools. Transects were established every
5 and 7 m in pools 20-40 m and in pools >40 m, respectively. Variables were
measured every 3 m across a transect beginning at the bank in these pools also.
The following characteristics were measured in each pool:
a) Bank riparian forest, defined as the proportion of the pool bank occupied by
trees and high shrubs (>3 m) that were twice as high as the distance to the
water edge (Duff and Cooper 1976).
b) Substrate composition, defined as the substrate type located below a sampling
point. Categories were: sand (0.25-0.5 mm), small gravel (4-8 mm), large gravel
(8-64 mm), cobble (64-256 mm), small boulders (256-512 mm) and large
boulders (>512 mm) (Platts et al. 1983).
c) Bottom oxygen, defined as oxygen level at the bottom of the deepest point
in a pool. Measurements were made with a portable DO meter.
d) Mean bottom water temperature, estimated by taking the mean of all bottom
12
/
/
East FO,k
48
N
46
44-45
8usr& Cr.
43
Cdr Cr.
40.41-42
39
37.38
36
5
35
34
.9,
"04
31.32
30
29
27
Krt
Figure 4: Distribution of deep pools (mean depth > 0.8 m)
in Streamboat Creek, OR, sampled in this study.
13
a
N
2
Ct
C
3
4
9
Km
10
C)
Figure 5: Distribution of deep pools (mean depth > 0.8 m)
in Canton Creek, OR, sampled in this study.
14
temperature. Measures were made with a digital probe.
e) Temperature coefficient of variation, estimated as the grand mean average
temperature in the water column divided by its standard deviation.
f) Area, defined as the sum of areas between successive transects.
g) Mean depth, estimated by dividing the sum of depths at each transect point by
n+1 to account for zero depth at the margins (Welch 1948).
h) Volume, estimated by multiplying pool area by mean depth.
i) Maximum depth, the maximum depth within the pool.
j) Maximum length, defined as the length along the pooi axis that connected the
points located in the middle of the channel that corresponded to the first and last
transects respectively.
k) Shore sinuosity index, computed as the total shoreline length of the pooi divided
by twice the maximum length. A value of 1 implies a rectangular shape.
I) Depth ratio, defined as mean depth/maximum depth (Carpenter 1983).
m) Proportion of littoral area, defined as the proportion of sample points at each
transect having a depth
0.5 m.
n) Proportion of cover provided by ledges, defined as the percent of the perimeter
of a pool occupied by overhanging ledges > 30 cm in width.
o) Presence of waterfalls, defined as plunge at the head of the pool
1 m in
height.
The thermal gradient in Steamboat Creek was determined by measuring
water temperature in the middle of the water column at a point every 1.6 km
15
between 3 and 5 pm at the beginning of September 1991 and 1992. Temperatures
of selected tributaries were measured at the junction with Steamboat Creek.
2.3. Basis for selected variables
Physical characteristics of habitat play an important role for salmonid
distribution (Lanka and Hubert 1987; Scarnecchia and Bergersen 1987; Heggens
1988; Nelson et al. 1992). Several past studies have also used depth to assess
pool quality (e.g. Duff and Cooper 1976; Parsons et al. 1982; Binns 1982; Oswood
and Barber 1982; Helter et at. 1983; Plaffs et at. 1983; 1987). I considered depth
a major variable because deeper pools are expected to provide refuge from
predators (Skeesick 1989).
Pool length was used in this study despite its not being considered by other
researchers. I assumed that longer pools could provide better escapement from
predators. Area and volume were considered as relevant morphometric variables
because pools with larger areas and volumes could hold more individuals
(Nickelson et al. 1979; Dunn 1981; Grette 1985; Faush and Northcote 1992). The
depth ratio was also included in the analysis because it may represent the crossshape of a pool. This factor may be important for adult summer steelhead because
it is
representative of the general shape of the pool. Studies on bull-trout
(Sa/velinus con fluentus) in Idaho for example, have shown that this species
preferred pools with constrained sides provided by rock walls (A. Thurow,
Intermountain Research Station, Boise, Idaho, corn. pers.), but no such information
16
is available for steefhead. Although studies in streams have used other related
shape indices (see Olson-Rutz and Marlow 1992), such as width/depth ratio
(Beschta and Platts 1986), such measurements would be appropriate only in very
shallow channels and are not applicable for deep pools.
I used shore sinuosity to represent shore habitat diversity; more convoluted
shores indicate a greater array of different microhabitats in the pool and a larger
land-water interaction. This latter factor could be important in order to predict
potential sources of cold water provided by underground flows and seeps coming
from upland side slopes.
Substrate was considered another important variable that could influence the
presence of fish in pools. Adult summer steelhead have a preference for certain
substrate types (Hampton and Aceituno 1988). In addition, substrate may provide
cover and refuge (Everest and Chapman 1971; Binns 1982; Freese 1982). Pool
cover characteristics were also included in the analysis (Buttler and Hawthorne
1968; De Vore and White 1978; Mesick 1988). I considered two sources of cover:
cover by overhanging ledges because it provides an available permanent refuge
and cover by shade provided by the riparian forest.
Finally, water temperature and dissolved oxygen were included because they
influence metabolic processes of fish. The influence of temperature was considered
by two parameters: a) the coefficient of variation, which is appropriate to
summarize thermal heterogeneity and b) mean bottom temperature, which best
represents the presence of cool spots and cool bottoms.
17
2.4. Definition of cool and warm pools
Mean bottom water temperature was used to classify pools as either cool or
warm. Mean bottom temperature
is
a
better indicator
of pool thermal
characteristics than other parameters, such as mean water temperature, because
it is less likely to be influenced by air temperature variation. The mean bottom
temperature is also a suitable indicator of the volume affected by cool seeps or
stratification processes. Previous studies considered pools as cool when there was
at least 3 °C temperature difference between the pool and the mainstem in coastal
streams in California (Ozaki 1988; Nielsen 1992). Matthews et al. (1994) defined
stratified pools as those having a bottom-surface difference larger than 0.5 °C.
In this study
I
used an approach based on physiological requirements.
I
considered 19° C to be the limit between cool and warm pools. I believe that 19
o
is the upper limit of the preferred temperature of steelhead. The preferred
temperature is a key eco-physiological concept which defines the range of
temperature where most of organisms will congregate or spend most of their time
in a thermal gradient (Reynolds 1977; Reynolds and Casterlin 1979). Table 1 lists
a rather broad spectrum of preferred temperatures for rainbow trout. Most are for
luvenile rainbow trout, and despite the wide temperature range, 19-20 °C seems
to be the upper limit of the preferred temperatures. Since rainbow and steelhead
are the same taxonomic species,
I
assumed that they would have similar
physiological requirements.
Even if some discrepancy has been noted between field and laboratory results
18
(see Reynolds 1977 and references therein), there has been a reasonably close
correlation between laboratory and field data (Stauffer et at. 1980). A temperature
of 19 °C is also consistent with field observations made by McMichael and Kaya
(1991) who noted that rainbow trout strongly decreased in abundance in waters
with temperatures above > 19 °C. In turn, Matthews et al. (1994) found that
rainbow trout selected pool temperatures between 13 °C and 19
OC.
The U.S.
Environmental Protection Agency (1986) also recommended that summer water
temperature should not exceed 19 °C in order to avoid physiological stress of fish.
2.5. Fish sampling
Adult summer steelhead were counted by direct observation. One diver using
a mask and snorkel moved back and forth across a pooi in an attempt to locate
all fish. Pools were surveyed twice in August and early September of 1991 and
1992. Pools were defined as USED or NOT USEDbased on presence or absence
of adult summer steelhead and not on abundance. Although both measures are
related (Wright 1991), presence-absence was chosen for the following reasons: a)
binary data provide ecologically meaningful information with lower field cost (Green
1979); b) presence-absence data emphasize the limits of environmental tolerance
(Hinch et al. 1991) and would be preferable if variation in adult abundance were
important in determining pool occupancy (Rahel 1990);
C)
density may not be
19
Table 1: Reported preferred water temperature for rainbow trout.
L.N.A.: location not available.
Stage
Temperature
preference (°C)
Location
Author
Adult
13.0
laboratory
Garside & Tait 1958
Adult
16.5
lake Michigan
Spigarelli 1975
Adult
10.0-17.0
Adult
12.9-19.0
American R., CA
Matthews et al. 1994
Adult
17.7-19.0
Pit A., CA
Baltz et al. 1987
Juvenile
19.2-19.8
laboratory
Cherry et al. 1977
n.a
Coutant 1977
Juvenile
17.2
laboratory
Hokansson 1977
Juvenile
18.0-19.0
laboratory
McCauly & Pont 1971
Juvenile
13.6
laboratory
Fergurson 1958
Juvenile
18.0
laboratory
Cherry et al. 1975
Juveniles
10.0-13.0
n.a
Bell 1986
Juvenile
18.0
n.a
Schlesinger &
Regier 1983
20
necessarily correlated with habitat quality (Van Home 1983) and d) some pools in
Steamboat Creek were difficult to sample because of poor visibility and human
disturbances. Fish were considered present in a pool if at least one adult summer
steelhead was reported during two consecutive years.
2.6. Statistical analysis
Odds ratio (Kahn and Sempos 1989) based on a 2x2 contingency table was
used to measure the association between water temperature and use of deep
pool. Specifically, my hypothesis was that cool pools were occupied to a greater
extent by adult summer steelhead than were warm pools in August-September
1991 and 1992. Odds ratio is an alternative to traditional Chi-square statistics
because it is not affected by number of observations and can be used as a
directional test (Fleiss 1973). The null hypothesis was that the odds ratio of use of
deep pools was independent of water temperature and therefore the odds ratio
should be equal to one.
Differences in pool types used by fish and their relationship to ecological
characteristics were explored using a multivariate approach. The rational for such
an approach is based on the fact that a suite of potentially related factors influence
presence and abundance of species (Green 1978). Multivariate analysis captures
the Hutchinson's n-multidimensional niche concept, thus not surprisingly, in recent
years fish ecologists have shown a strong preference for multivariate methods, as
documented by the extensive literature on habitat suitability models (e.g., Bovee
21
and Zuboy 1988) and habitat-standing crop models (see Faush et al. 1989 for a
review).
In this study I used discriminant analysis (DA) to determine which physical
features were most strongly associated with presence of adult summer steelhead
in pools in Steamboat Creek. This statistical method has proven useful in relating
fish distribution and habitat use by others (e.g., Bowlby and Roff 1986; Baltz et al.
1987; Grossman and Freeman 1987; Layher et al. 1987; Doloff and Reeves 1990;
Jowett 1990; Capone and Kushlan 1991; Larscheid and Hubert 1992; Nelson et al.
1992).
Discriminant analysis has two goals (Williams 1983; Williams and Titus 1988).
One is to predict the group to which an observation belongs based on measured
variables; this procedure is called predictive discriminant analysis and uses a
discriminant function. A second purpose of DA is to exhibit an optimal separation
of groups. This approach, called descriptive discriminant analysis, is associated
with a linear function referred to as canonical variate or canonical function.
Descriptive analysis is a valuable method to for identifying a relatively small set of
variables that maximizes the difference among-groups. In this study, both methods
were applied.
To select potential discriminant variables, several steps were followed. First,
potential multicolinearity was reduced by eliminating highly correlated variables (r>
0.80). Second, a stepwise regression method (Proc stepdisc, SAS 1985) was used
but only as a first guideline. The use of stepwise regression is a controversial
process (Pimentel 1979). Differences in relationships among variables can yield
predictors that do not reflect population differences (Tabaschnick and Fidell 1989).
Although stepwise regression can produce a set of optimal discriminating variables,
it cannot guarantee that this set is the best combination (Klecka 1980). According
to Legendre and Legendre (1983), stepwise regression must be used to select
only the most important predictors from a very large number of descriptors.
Performance of discriminant analysis can be improved by examination of each
possible variable, as recommended by Cochran (1964) and McCabe (1975). Thus,
as a third step, a separate analysis was done by performing an univariate analysis
and observing the squared canonical correlation and number of entities correctly
classified. Those variables with the largest squared canonical correlations were
then combined sequentially to find which maximized the canonical correlation and
the number of pools correctly classified. Canonical scores were tested for normality
by examining their skewness and kurtosis. Equality of variance-covariance matrix
was checked by a Chi-square test (Morrison 1967). Wilks lambda statistics was
used to test differences between centroids. To interpret the discriminant function,
structure pattern (loadings) and standarized coefficients were used. Structure
pattern gives more substantive interpretation than standarized coefficients,
although these values are sometimes useful to indicate redundancy among
variables (Klecka 1980; Stevens 1986).
A problem typically encountered in DA is unequal group sample size. To
evaluate classification accuracy the Cohen's Kappa statistic (Cohen 1960; Titus et
23
al. 1984) was used. This statistic is used to express the proportion of pools
correctly classified by the discriminant function after the effect of correct
classification by chance is removed.
Predictive classification was achieved by two avenues. One attempt was
performed applying a classification criterion determined by a generalized squared
distance (Proc discrim SAS 1985). This criterion put each observation in the class
from which it has the smallest generalized squared distance. A second procedure
consisted of developing a non-parametric discriminant analysis, which has the form
of a dichotomous key (KendaN and Stuart 1966; Kendall 1980) (see Fig. A.2). The
method is not restricted by statistical assumptions of parametric statistics (Seber
1984). Use of discriminant methods in the form of a field key has been a
successful way to simpflfy the interpretation of the data and provide a suitable field
tool for managers and biologists unfamiliar with multivariate analysis (Tonn et al.
1983). To test the efficiency of the discriminant models outside Steamboat Creek,
pools in Canton Creek were used as a validation. As noted by several authors
(e.g., Reckhow et
al.
1987), when the data that are used to develop the
classification procedures are also used to estimate the accuracy of prediction, true
classification error rate is underestimated. Ideally, discriminant analysis should be
completed by checking the performance of the model with a different data set
(Picard and Berk 1990; Beauchamp et al. 1992).
Other general statistical procedures included the use of the Wilcoxon paired
test (Zar 1984) to compare fish abundance in deep pools between different years
24
(August-early September 1991 and 1992), and correlation coefficients to analyze
the association between environmental variables in pools. In turn, differences in
morphometric parameters and mean bottom water temperature between cool and
warm pools were examined by applying the normal approximation of the MannWhitney test (Zar 1984). Environmental variables between poois in Canton and
Steamboat streams were compared using a t-test followed by a power analysis
according to Cohen (1988).
25
Ill. RESULTS
3.1. Steamboat Creek thermal regime
Steamboat Creek had a similar thermal gradient in the summers of 1991 and
1992 (Fig. 6). Water temperature ranged from 15 °C to almost 22 °C
.
Only the
upper part of the stream and a short segment located in the middle course
exhibited a temperature well below 19 °C. Three segments were identified along
the Steamboat Creek based on thermal characteristics: a) from km 0 (Umpqua
River) to km 17, where water temperatures decreases slightly in the last 5 km; b)
from km 17 to km 20, where temperatures increased sharply and; c) from km 20
to km 32, where temperatures decreased (Fig. 6). The influence of Big Bend Creek
(km 17) made a thermal division in Steamboat Creek (Dambacher 1991). Other
cold tributaries such as Steelhead Creek, Reynolds Creek and Cedar Creek did not
have a major impact on thermal profile of Steamboat Creek. At km 25, the
temperature of Steamboat Creek sharply decreases becauser of the effect of the
riparian vegetation.
My results were consistent with Brown et al. (1971) and 1-loladay (1992) and
agree with extreme values reported by Reeves et al. (1987). Based on these
considerations, I concluded that water temperatures observed in 1991 and 1992
represented an average summer profile of Steamboat Creek.
26
fin
1)
t)
p
H
LU
4.5
7.7
10.4
13.9
17.0
20.5
23.8
27.0
30.2
Distance (km)
September 3/91 A-- September 2/92
Figure 6: Thermal profile of Steamboat Creek. Zero point corresponds
to the junction with the North Umpqua River. Arrows indicate
main tributary junctions. CA: Canton Creek; S: Steelhead Creek;
R: Reynolds Creek; BB: Big Bend Creek; C: City Creek.
Temperature differences between tributaries and Steamboat Creek increased
in a downstream direction (Table 2). Water temperatures of tributaries were
generally lower than those in Steamboat Creek in 1991 and 1992.
27
Table 2: Water temperature (°C) of Steamboat Creek tributaries.
Numbers in parenthesis represent the temperature in
Steamboat Creek above the junction with the respective
tributary. Temperatures were measured between 1 and
4 p.m. Tributaries are ordered in an upstream-downstream
position.
Tributary
August 7, 1991
September 8, 1992
Horseheaven
16.7
16.0 (17.1)
City
15.7
17.5 (18.7)
Little Rock
19.1
20.2 (18.7)
Long
13.7
14.8
Buster
15.0 (21.5)
Cedar
16.9 (20.8)
16.7 (21.2)
Big Bend
13.1 (21.2)
13.6 (20.0)
Johnson
Reynolds
Steelhead
16.7 (17.3)
17.4
14.5 (17)
Siwash
Spring
Grass
Canton
17.1 (17.3)
15.7 (18.5)
15.3
13.5
16.3
17.5
20.6 (19.9)
3.2. Distribution of deep pools
There were 0.83 deep pool/km in Steamboat Creek. Results of the
systematic sampling showed that deep pools were only 4 % of total number of
habitat units Deep pools were distributed in two main areas. One area was from
the junction with the North Umpqua River to near Cedar Creek (Fig. 4). This
segment contained 84 % of the deep pools. This area in turn, can be divided in
two segments: a) a lower segment from Canton Creek to Steelhead Creek, with
a pool density of 1.33 pool/km. This segment included the Black Gorge (from
pools 15-15 to Steelhead Creek); b) an upper segment from Steelhead Creek to
Cedar Creek. In the latter segment, pool density was 0.62 pools/km. Most of the
deep pools were found clustered between Long Creek and City Creek (Fig.4).
Upstream of City Creek Steamboat Creek no deep pools were found.
3.3. Pool characteristics
3.3.1. Morphometry
Main statistical parameters such as the mean, the standard desviation and
range of measured variables in Steamboat Creek are portrayed in Table 3.
29
Table 3: Statistical parameters of measured variables in study pools.
Variable
Ledge cover
Bank riparian forest
Sand
Bedrock
Small boulders
Large boulders
Small gravel
Large gravel
Area (m2)
Volume (m3)
Mean depth (m)
Maximum depth (m)
Depth ratio
Mean width (m)
Littoral area proportion
Maximum length (m)
Shore sinuosity index
Bottom oxygen (mg.02.L1)
Water temperature
coefficient of variation
Mean bottom water
temperature (°C)
Mean
0.31
0.37
0.06
0.45
0.16
0.14
0.06
0.05
565
856
1.57
3.5
0.50
13
Standard
desviation
0.39
0.29
0.09
0.20
0.09
0.14
0.06
0.04
389
684
0.47
3.15
0.50
14.2
Range
-
0.95
0.75
0.37
-
0.81
-
0
0
0
0
0
0
0
0.33
0.63
0.21
-
- 0.17
114- 1431
97 3035
3.46
5.00
- 0.77
- 24.40
- 0.47
11
77
1.08 2.64
0.86
1.75
0.30
4.85
0.12
0.25
30
1.40
8.75
0.29
37.4
0.34
1.91
1.5
1.70
1.66
0
19.69
1.95
-
-
-
12
5.44
16.59
Scour pools (Bisson et al. 1982) were the most common type (85%) of
22.68
p001
observed. Trench pools comprised 5% of the total pools, and plunge pools
formed by waterfalls represented 8% of the total. Plunge, dammed or backwater
pools formed by woody debris were not observed in either Steamboat Creek or
Canton Creek.
Results of the correlation analysis among the environmental variables are
30
shown in Table 4. Few variables showed a high correlation > 0.70. Mean
bottom pool temperature showed no statistical relation with morphometric
parameters such as area (r= -0.04; P< 0.50); volume (r= -0.02; P<0.50);
mean depth (r= -0.12; P<0.20); maximum depth (-0.06; P<0.20); depth ratio
(r= -0.18; P<0.50); mean width (r= -0.08; P<0.20) and maximum length
(r= -0.01; P<0.50).
3.3.2. Water temperature
Of the 38 deep pools surveyed, 39 % were classified cool pools. Warm
pools in Steamboat Creek were most abundant in the first 12 km, but also they
were located in the middle and upper part of Steamboat Creek. In turn, no cool
pools were detected in the lower segment. The uppest segment of Steamboat
Creek did not have deep pools (Fig. 7). Mean bottom temperature was 17.7 °C
and 20.9 °C in cool and warm pools, respectively and this difference was
significant (Z=5.13; P0.001).
Cool and warm pools did not differ in area (Z= 0.44; P0.69), volume (Z=
0.24; P> 0.81); mean depth (Z= -1.31; P0.19); maximum depth (Z= -0.91;
0.36), mean depth/maximum depth ratio (Z= -0.36; P> 0.72), mean width
(Z= 0.22; P 0.82) and maximum length (Z= 0.07; P0.94).
Table 4: Correlation matrix among physical features of deep pools in Steamboat Creek, OR, summer 1991-1992. LED:
ledge cover proportion; S:sand proportion; BR: bedrock proportion; LB: large boulder proportion; C:cobble
proportion; SB: small boulder proportion; SG: small gravel proportion; LG:large gravel proportion; A: Area;
VOL: volume; MD: mean depth; MAD: maximum depth; DR: depth ratio; MW mean width; LIT: littoral area
proportion; ML: maximum length; SSI: shore sinuosity index; BO: bottom oxygen; TVC: temperature variation
coefficient; BT: mean bottom pool temperature (see text for further explanation).
IC
RF
S
DR
LB
C
SB
SC
IG
A
P_c
1.00
RI'
0.13
1.00
S
0.04
-0.08
1.00
BR
-0.06
0.15
-0.40
1.00
LB
0.05
-0.35
0.18
-0.60
c
0.25
0.49
0.00
-0.37
0.34
1.00
SB
0.13
0.04
-0.15
-0.37
-0.08
0.26
1.00
SO
-0.33
0.00
-0.07
-0.30
-0.16
0.24
-0.05
10
-0.10
-0.05
-0.29
-0.13
-0.18
0.23
-0.03
0.38
1.00
A
0.59
0.40
0.05
0.05
-0.31
0.55
0.02
-0.08
0.08
1.00
0.01
0.90
VOL
ZP4
ZX
DR
P4W
LII
Ml
SI
DO
Br
1.00
1.00
VOL
0.64
0.36
0.02
0.00
-0.17
0.44
0.09
-0.17
ZM
0.66
0.07
0.07
-0.14
0.16
0.14
0.04
-0.22
0.04
0.44
0.67
1.00
2X
-0.24
-0.37
-0.18
0.01
0.17
-0.32
0.22
-0.14
-0.12
-0.55
-0.41
0.71
1.00
OR
0.41
-0.16
-0.10
-0.13
0.25
-0.11
0.26
-0.29
0.00
-0.02
0.27
0.70
0.48
1.00
MW
0.38
0.45
0.02
-0.07
-0.40
0.55
0.23
0.12
0.25
0.78
0.66
0.25
-0.40
-0.05
1.00
-0.06
0.10
0.00
0.28
-0.39
0.04
-0.01
0.01
-0.07
0.16
-0.10
-0.34
-0.44
-0.57
0.22
1.00
141
0.55
0.24
0.06
0.08
-0.09
0.32
-0.11
-0.20
-0.07
0.87
0.82
0.48
-0.51
0.02
0.42
0.11
1.00
SI
0.10
-0.09
-0.13
-0.14
0.15
-0.16
0.35
-0.11
0.00
-0.28
-0.07
0.36
0.41
0.67
-0.03
-0.39
-0.38
1.00
00
-0.33
-0.23
-0.09
-0.28
0.07
0.19
0.28
0.12
0.26
-0.23
-0.28
-0.25
0.17
-0.06
-0.22
0.00
-0.14
-0.03
1.00
LII
TVC
1.00
IVC
0.18
0.22
0.04
0.26
-0.22
0.09
-0.05
-0.22
-0.19
0.25
0.30
0.20
-0.05
0.07
0.21
-0.12
0.16
-0.13
-0.43
1.00
81
017
-0.28
0.73
-0.23
0.17
-0.06
0.12
0.02
-0.25
-0.04
-0.02
-0.12
-0.06
-0.18
-0.08
0.05
-0.01
-0.14
-0.09
-0.19
1OO
-s
32
', A
0
0
C
1)
C
C
o
2.5
5
7.5 10 12.5
15 17.5 20 22.5 25 27.5 30 32.5
Distance (km)
Figure 7: Distribution of cool pools ( 19°C mean bottom water
temperature) and warm pools (> 19°C mean bottom water
temperature) in Steamboat Creek. Pools of similar category
that were located close together are shown as only one point.
Zero point corresponds to the junction with the North Umpqua
River.
3.4. Fish abundance and distribution
3.4.1. Systematic sampling
Adult steelhead were found in deep pools. Almost no fish were observed in
shallow pools or other habitats such as glides and riffles. Cool tributary junctions
such as Johnson Creek, Steelhead Creek and Big Bend Creek, were all shaHow
and did not contain fish. Although I did not survey the tributaries, past studies
suggest that these tributaries, did not hold fish because of low summer flows
(Wroble and Roelof 1985). Two shallow pools (0.40-0.60 m mean depth)
harbored one fish during the summer of 1991, but not in 1992. These pools,
according to previous definitions (see 2.5), were not considered in the analysis.
3.4.2. Pool sampling
Number of cool pools occupied in August-early September 1991 and 1992
surpassed the number of warm pools occupied by adult summer steelhead
(Table 5). However not all cool pools were contained adult summer steelhead.
More than 50 % of deep pools remained unoccupied in both years (Table 6).
Table 5: Total number of adult summer steelhead in cool and warm deep pools
in Steamboat Creek during August-early September 1991 and 1992. (S.D:
standard desviation).
Year
Warm
pools
Cool
pools
Total
Mean
S.D
1991
2
238
240
8.57
22.37
1992
6
229
235
6.03
16.74
34
Table 6: Percent of cool pools occupied by adult summer steelhead in Steamboat
adultsand mean density (fish/rn3)
Creek, percent of cool pools having
in cool pools (6,), warm pools (6w) arid all deep pools (6)
Year
% occupied % cool pools
cool pools with
5 fish
C
6W
1991
43
5
0.018
0.002
0.008
1992
34
5
0.017
0.0005
0.007
There was no significative difference in the mean number of adult summer
steelhead in pools in Steamboat Creek in August-September 1991 and August-
September1992 (Wilcoxon paired-test: T= 20.5 >
=13; P>0.10). This result
suggests that abundance in each pool was similar between years.
Most of the adult summer steelhead were found in the middle part of
Steamboat Creek, but some adults were also detected in the upper segement.
Three pools located downstream from Big Bend Creek contained 72 % of the total
fish number in 1991 and 60 % in 1992 (Fig. 8).
35
[00-
80-
60-
1991
40
E
I..)
H
1992
40-
60-
80I
11
13
12
15
14
I
I
I
I
I
I
I
I
I
I
I
I
I
I
I
I
I
I
I
I
17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49
16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48
Pool No
Figure 8: Mean number of adult summer steelhead in deep pools in Steamboat
Creek during August-September 1991 and August-September 1992 (see
figure 4 for pool locations). Note: pools 1-10 are located in Canton
Creek (see Fig. 3 for pool locations).
The largest number of fish was observed in pools 33 and 36 in both years.
Pool 36 is located immediately downstream from the junction with Big Bend Creek.
The importance of this pool been already noted (Dambacher 1991). As was
36
previously pointed out, Big Bend Creek strongly influences water temperature of
Steamboat Creek. Very close to pool 36, pool 35 also had adult summer
steelhead. However, its small volume was probably a Hmiting factor. Big Bend
Creek affects Steamboat Creek for several kilometers downstream. Meanwhile pool
34 harbored few adult of summer steelhead, but pool 33 had a noticeable number
of adults. In general, I found that adult abundance in those pools agreed with
previous historical records (Roelof unpublished data).
On the other hand, a moderate number of adults were noted in pool 48. The
number of adults in this pool even surpassed the number of adults noted in pool
42. This pool has been considered as the final deep pool in Steamboat Creek by
previous surveys and usually harbored a moderate number of adult summer
steelhead (Roelof unpublished data). However, results of my study showed that
pool 48 must be considered as the last deep pool in the system that harbors adult
summer steelhead. Furthermore, pool 48 had a higher number of adult summer
steelhead than pool 42 according to past records.
3.5. Temperature-abundance relationship
Fish abundance decreased with increasing mean bottom temperature in 1991
and 1992. As shown in Figure 9, a clear pattern was observed: Adult summer
steelhead numbers were variable in those pools
never had high numbers of fish.
19 °C, but pools > 19 0C
37
ci)
-Q
E
U-
16
17
18
19
20
21
22
23
Pool mean bottom temperature (C)
1991+ 1992
Figure 9: Relationship between mean water temperature at the bottom of the pool
and number of adult summer steelhead in study pools in Steamboat Creek
in August-September 1991 and 1992.
Odds ratio results showed that during the summer of 1991, the estimated
odds that a cool pool was used by an adult (Q) was 1.66 (10/6). In turn, the odds
that a warm pool was used (O,) was estimated as 0.20 (2/10). As a measure of
association between the presence of cool pools and their use, the odds ratio OR
(O/O) was estimated as 8.33 (P <.007). For the summer of 1992 Q and O were
1.28 and 0.1 respectively and the OR was 12.85 (P'zO.004) (Table 7).
Table 7: Results of the odds ratio test of pools with different mean bottom
water temperature that were used and not used pools by adult summer
steelhead in Steamboat Creek, OR, in August-September 1991 (A) and
August-September 1992 (B). See text for further details.
A
USED
COOL
10
WARM
TOTAL
NOT USED
TOTAL
6
16
2
10
12
12
16
28
ODDS RATIO
8.33; Z = 2.43; P<0.007
B
USED
NOT USED
TOTAL
COOL
9
7
16
WARM
2
20
22
TOTAL
11
27
38
ODDS RATIO = 12.85; Z = 2.60; P<0.004
The Woolf test for interaction was applied to examine if both years could be
combined. A Chi-squared value of 0.13 (P<0.001) permitted the data set to be
merged and to estimate a common odds ratio of 11.35 (P <0.007). The result was
that the odds of cool deep pools being used by adult summer steelhead were
estimated to be 11 times as large as the odds of the use of warm pools.
39
3.6. Relationship between pool environmental characteristics
and pool use
3.6.1. Selection of predictive variables
No single variable was statistically significant as a predictor for discriminating
deep pool used or not used by adult summer steelhead in Steamboat Creek.
Square canonical correlations (SCC) derived from univariate analysis were highest
for mean bottom water temperature (SCC= 0.33), mean depth (SCC= 0.30) and
volume (SCC= 0.25). Use of an iterative solution for variable selection, produces
a parsimonious five variable solution. In other words, with five variables I found the
minimum number of variables that accounted for the largest proportion of
explained variance. Bank riparian forest, proportion of large boulder, mean depth,
mean bottom pool temperature and maximum length were identified as the best
subset of discriminating variables. Such variables differ between both the USED
and NOT USED pools (Table 8).
If a smaller subset of variables would be used, some amount of explained
variance must be sacrificed. For instance, if mean bottom pool temperature,
maximum length and mean depth would be the only used variables, only 62 % of
the variation between the groups would be explained.
40
Table 8: Mean, standard deviation (S.D) and range of variables discriminating
between USED and NOT USED pools by adult summer steelhead in
Steamboat Creek, OR. August-September 1991 and 1992. RF: bank riparian
forest; LB: proportion of large boulder proportion; MD: mean depth (m);
ML: maximum length (m); BT: mean pool bottom temperature (°C).
NOT USED
USED
Mean
S.D
RF
LB
MD
0.50
0.16
ML
BT
42.22
0.22
0.15
0.57
18.72
1.77
1.71
18.71
Range
0-0.75
0-0.63
1.25-3.46
11-77
16.6-21.2
Mean
S.D
Range
0.28
0.12
0.27
1.32
0.35
32.20
20.45
14.52
1.44
0.60
- 0.39
0.86-2.38
11-67
18.0-22.6
0.11
0
0
-
Wilkes Lambda was 0.30 (P>F 0.0001), which indicated that a significant
differences between groups (centroicis) was present relative to the amount of
dispersion within the groups. No significative differences were found for equality
of variance-covariance matrices between groups (Chi-square= 20.48; P<0.1).
Adjusted square canonical correlation was 0.82. In other words, 69 % of the
variation in the canonical function was explained by differences in means groups.
This value is a multiple correlation coefficient that represents the amount of the
canonical variation and is a measure of the discriminant performance.
Diagnosis of skewness and kurtosis performed on canonical scores, showed
that the NOT USED group had a normal distribution (skewness = -0.64; kurtosis
= 0.37), whereas the USED group showed a greater departure although not
serious enough to invalidate the analysis (skewness = -0.01; kurtosis = 0.49).
41
Analysis of structural canonical patterns and standarized coefficients showed that
mean depth, maximum length, bank riparian forest had positive values, while mean
bottom water temperature correlated negatively (Tables 9 and 10). In bath cases
the relative weight of the variables were similar: mean bottom water temperature
and mean depth were the most significative variables as shown by the largest
structure and standarized coefficients.
Table 9: Standarized canonical coefficients derived from the discriminant
analysis.
Variable
Bank riparian forest
Large boulder proportion
Mean depth
Maximum length
Mean bottom water temperature
Standarized coefficient
0.49
0.52
0.66
0.56
-1.09
Table 10: Structural patterns derived from the discriminant analysis
Variable
Bank riparian forest
Large boulder proportion
Mean depth
Maximum length
mean bottom water temperature
Structure pattern
0.45
0.32
0.66
0.54
-0.69
42
Proportion of large boulders appear to have a small influence, but removal of
this variable from the analysis, reduced the square correlation coefficient from 0.69
to 0.64.
The canonical function defined a gradient from long, shady, deep pools with
a coarse substrate and cooler bottoms (USED group) to pools that were shorter,
sunnier,
shallower,
with more fine particulate substrate, homogeneous in
temperature and with warmer bottom temperature (NOT USED group). Plot of the
canonical scores is presented in Figure 10. The NOT USED group had a wider
distributions and overlapped slightly with some pools from the USED group.
The obtained classification has a hit-ratio of 0.89 (34/38). A % of the USED
pools were correctly classified, whereas only a % of NOT USED pools were
incorrectly classified. After using the jackknife resampling procedure, only 4
samples were incorrectly classified (Table 11). Application of the Kappa statistics
showed that classification was 86% better than derived from random assignment
(Z= 4.77; P<0.001). This result was highly significative since priors possibilities
differed between the USED and NOT USED groups.
43
Warm
Shallow
Bottom temperature
Depth
Small
Substrate
Scarce
Shadow
Little _ '
Length
Cold
Deep
Coarse
Abundant
Much
Figure 10: Plot of discriminant scores based on analysis in Steamboat Creek.
Vertical lines on the block and the circle respectively indicate the centroid position.
Horizontal line at the extremes corresponds to the ranges.
44
Table 11: Number of pools in Steamboat Creek correctly classified by
discriminate analysis after jacknife resampling procedure.
NOT USED
NOT USED
USED
TOTAL
22
2
24
USED
2
12
14
TOTAL
24
16
38
PRIORS(%)
63
37
Since group sizes were unequal, these results may indicate a stable
classification. It should be noted that because 24 pools belong to one group
(NOT USED),
it would be possible get a hit ratio of 0.63 (24/38) by simply
assigning all the pools to the NOT USED group and yet obtain a significativ
classification due to chance (Morrison 1969; Hair et al. 1983).
3.7. Pool use prediction
3.7.1. Square generalized distance
This procedure showed that for Canton Creek pools, a hit rate of 0.7 was
obtained (Table 12). All NOT USED pools were correctly classified but 3 of 5
USED pools were classified as NOT USED pools. Use of the Kappa statistics
indicated an improvement over chance assignment of 40 % (Z= 1.26; P<0.10).
45
Table 12: Number of pools in Canton Creek correctly classified by discriminant
analysis.
NOT USED
USED
TOTAL
NOT USED
5
0
5
USED
3
2
5
TOTAL
8
2
10
50
50
50
PRIORS(%)
3.7.2. Development f
fjid
y
Discriminant analysis is a powerful technique to separate used from not used
deep pools based on major ecological variables, but it is sometimes difficult to use
directly in the field. Biologists wanting to predict the use of a deep pool based on
its characteristics, could apply DA developed in Steamboat Creek. This step
involves a computational procedure. Development of a field key is an alternative
for evaluating the potential of a pool to be used by adult summer steelhead. To
develop a key, two important conditions must be met: it should be based on
variables that can be easily measured in the field, and at the same time, the
variables need to be ecologically meaningful. Following the variable selection in the
DA,
I
used maximum length, bank riparian forest and mean bottom water
temperature since they performed well in the univariate discriminant analysis.
46
Presence of waterfall (a binary variable) was also included, since 20% of the pools
in Canton Creek had waterfalls (i.e., 1 m high). Mean depth was replaced by
maximum depth to facilitate the key use. The deepest spot in the pool can be used
as surrogate it a significant relationship exists between mean depth and maximum
depth (see table 3). In turn, proportion of large boulders was removed to facilitate
the key use. This parameter is difficult to measure from the bank.
One problem noted in the development of the key was the presence of
outliers". For instance, for the variable bank riparian forest" (RF), there was one
USED pool with a value of 0, that definitely departed from the rest of the
observations. In the case of the maximum length (ML), I found a USED pool as
short as the shortest NOT USED pool. Obviously, putting the cut-off point at FC =0
or ML= 11 would yield no possible discrimination, when in fact these cases are
rather isolated. After ranking mean bottom water temperature and maximum depth,
the following dichotomous key was derived from Steamboat Creek pools:
1. Maximum depth < 2.5 m .........................NOT USED
1'.Maximum depth
2.5 m ......................... 2
2. Bank riparian forest < 20% ....................... NOT USED
2'. Bank riparian forest
3. Maximum length
20 % .................... 3
25 m ..........................NOT USED
3'.Maximum length > 25 m ........................... 4
4. Bottom temperature
19°C ..................... USED
47
4'.Bottom temperature > 19°C. 5
5. Pools with waterfalls .................................... USED
5'.Pools without waterfalls............................... NOT USED
The key correctly identified 4 of the 5 USED pools and all the NOT USED pools
in Canton Creek. The misclassified pool was a short rounded pool with a waterfall.
IV. DISCUSSION
4.1. Thermal characteristics of the system
Steamboat Creek is a complex thermal system that exhibits a strong variation
in water temperature along its course. Big Bend Creek exerted a strong influence
in Steamboat Creek. Brown et al. (1971) emphasized the importance of this
tributary because of its relatively large flow and low water temperature. Although
other tributaries were colder than Steamboat Creek, their effect on temperature of
the main stem is negligible because of the relatively small amount of water in them
(Brown 1971). Local topographic and geological factors can also influence thermal
patterns (Smith and Lavis 1975). Bjornn et al. (1968) noted that thermal changes
can take place in very short distances.
In contrast, Canton Creek had a uniform profile where temperature surpassed
the optimum conditions for adult steelhead in the middle and lower course. Past
logging activities and road building have strongly reduced the riparian vegetation
and increased water temperature. Since riparian vegetation is an effective control
for solar energy (Beschta et al. 1987; Brown and Krygier 1970), direct solar
radiation input may be a major factor causing water temperature increases in this
stream (Brown and Krygier 1967; Moring 1975; Rishel et al. 1982; McGurck 1989;
Heller et al. 1983; Holtby 1988). Mean bottom water temperature of individual pools
was not associated with morphometric characteristics. For example depth has
been generally linked with existence of cool bottoms, but Ozaki (1988) found that
temperature differences between a pool and the mainstem were not related to
mean depth, maximum depth, or area. This relation was also verified in Steamboat
Creek, suggesting that even pools of moderate depth could be thermally stratified.
Consequently variables such as depth, area or volume can not be used as
potential indicators of thermal conditions in pools and therefore cannot be applied
as an indirect method to predict presence of adult steelhead in deep pools.
4.2. Fish distribution patterns
The use of deep pools by adult summer steelhead in Steamboat Creek was
consistent with results from other studies of pools used by adult steelhead.
Campbell and Neurier (1985) observed that in four western Washington Cascade
streams that rainbow trout selected deep pools over other habitats. Jones (1979)
found that only 7 % of adult summer steelhead in the upper Middle Fork Eel River
held in pools < 1.5 m maximum deep. This percentage was even lower (3%) in
the lower part of the Middle Fork Eel River (Jones 1992). Use of deep pools seems
also to be a general pattern in other California streams (Higgins and Kier 1992).
Nakamoto (1994) found that most pools used by summer steelhead in the New
River, California were > 1 m mean depth. These results agree with the pattern
found in the present study: adult summer steelhead in Steamboat Creek were
rarely found in shallow pools (<0.8 m mean depth) and therefore researchers
should use caution in estimating habitat suitability without considering habitat
availability.
50
4.2.1. jj
Qt cool
warm Qools
Water temperature influenced the distribution of adult summer stee)head and
quantity and quality of available habitat in Steamboat Creek. Cool pools with adult
summer steelhead average nearly 3 °C less than warm pools without adult
summer steelheacj. This differed from results of Nakamoto (1994), who showed
that cool pools holding summer steelhead in the New River, California, exhibited
an average temperature slightly lower than pools without fish.
Bowiby and lmhof (1989) proposed maximum temperature as the most
important water quality variable for modeling trout habitat quality. Baltz et al. (1987)
observed that temperature was an important factor in microhabitat choice and
claimed that this factor proved to be a good predictor of species location. Freese
(1982) suggested that water temperature should influence distribution of adult
summer steelhead in the New River, California. In the John Day River, Oregon,
Adams et
al.
(1990) demonstrated an inverse relationship between water
temperature and steelhead abundance. Berman and Quinn (1991) also found that
spring chinook salmon were associated with cold water sources in the Yakima
River. Although adult summer steelhead in Steamboat Creek clustered in few pools
cool pools, fish numbers were associated with mean bottom water temperature.
Adult summer steelhead varied in number in those pools
19 °C, but they were
never abundant in warmer pools. Such results support the argument that
physiological constraints are appropriate to separate cool from warm pools.
51
Through results of this study, I concluded that cool pools were used more
than warm pools as shown by odds ratio analysis. Since water temperature was
not related with morphological factors, it must be considered as a major driving
factor. In other words, fish used cooler pools because of the more suitable
temperature and not because they were deep. However, fish showed an uneven
distribution in cool pools. Some cool pools were never occupied and few warm
pools had fish. I noted that most used warm pools had waterfalls. The effect of
waterfalls may be associated with more oxygen in the water (oxygen solubility is
inversely proportional to water temperature), or with natural barriers for fish
movements.
4.3. Discriminant analysis
In this study, discriminant analysis provided a suitable method for identifying
variables most contributed to separating pools based on characteristics that
ultimately defined the habitat quality. Other analytical procedures have dealt with
habitat prediction of habitat use, but they are difficult to apply in the present study.
For example, a common avenue has been to construct habitat suitabllfty curves
(Bovee and Cochrtauer 1977) from which a composite habitat suitability index can
be derived. However, in the case of rainbow trout and steelhead most of these
studies were focused on juveniles stages (Bovee 1978; Shepard and Johnson
1985; Irvine et al. 1987; Waite and Barnhart 1992), and less frequently on adults
(Raleigh et aL 1984; Baltz et al. 1991). Other alternatives such as the habitat quality
52
index (Binns and Eisermann 1979) required several fixed variables. Whereas both
approaches are appropriate to determine habitat evaluation for reaches, segments,
or overall general stream conditions, they are inapplicable for specific habitats such
as deep pools.
The good pertormance of discriminant analysis in this study resulted because
assumptions associated with the method were not violated. Williams (1983) pointed
out that discriminant analysis permits moderate departures from the assumptions,
without affecting classification results. A key assumption of discriminant analysis
is that group dispersions (variance-covariance matrices) should be homogeneous
(Klecka 1980), and this condition was met in this study. Mutivariate normality is
another important restriction. Strong lack of multinormality affects the computation
of tests of significance and probabilities of group memberships (Klecka 1980).
Fortunately no serious departures were noted with the pool data.
Sample size also affects the accuracy of discriminant analysis. Williams and
Titus (1988) recommended that the sample size for each group used in
discriminant analysis should be equal or greater than three times the number of
variables used. In the present study if five variables were used, a minimum of 15
pools were included in each group. The NOT USED group largely exceeded the
minimum pool sample size, whereas the USED group would needed one additional
observation. Finally, discriminant analysis requires that no singularities may be
present. Highly correlated variables are not allowed in discriminant analysis; this
makes sense intuitively since variables highly correlated are redundant and do not
53
contain new information. As shown before, few variables were highly correlated.
Variable selection showed that mean bottom water temperature emerged as
the main factor that accounted for pool group separation, corroborating the
association found between temperature and pool use in the odds ratio analysis.
However, high structure values for selected morphometric variables could indicate
that pool occupancy is a sum of different factors and cannot be simplified only by
looking at thermal characteristics. Some of the morphometric variables selected in
the discriminant analysis have been identified previously as important features of
pools used by fish (Duff and Cooper 1976; Parsons et at. 1982; Binns 1982;
Oswood and Barber 1982; Heller et al. 1983; Platts et al. 1983; 1987). Jones
(1982) reviewed steelhead distribution in California streams and found that
populations appear to prefer deep, clear and cool pools. Lewis (1969) considered
cover, volume, area and depth as relevant factors in explaining trout density and
similar results were obtained by Rinne (1978). In the same vein, Heggens (1988)
reviewing physical habitat selection by brown trout, pointed out that water depth,
water velocity, streambed substrate and cover were the most important
characteristics. For rainbow trout, large and deep pools with more than 30 % of
the bottom obscured by factors such as depth, vegetation and boulders have been
classified as those with the highest suitability index (Raleigh et at. 1984).
Most of the identified features of pools used by adult summer steelhead in
Steamboat Creek, can probably be associated with cover, which has been related
to trout density (Stewart 1970; Fraley and Graham 1981), standing crop
54
(Harshbarger and Bhattacharya 1981; Wesche et at. 1987), population stability
(Elser 1968; Lewis 1969) or predator avoidance (Mesick 1988). As noted by Bjornn
and Reiser (1992), salmonids, like summer steelhead that enter freshwater streams
some months before spawning, require appropriate cover to avoid disturbance and
predation. Cover provided by riparian vegetation has been identified as a main
source of overhead shade (Boussu 1954; Gibson and Keenleyside 1966; Buttler
and Hawthorne 1968; Baldes and Vincent 1969; Lewis 1969; Harshbarger and
Bhattacharya 1981; Freese 1982; Wesche et at. 1985). Also, shaded waters, may
provide protection from predators (Helfman 1981). Advantages of streamside
vegetation can be extended also to thermal effects. Bilby and Wasserman (1989)
stated that 50-70 % of riparian conifer forest would be necessary to provide
thermal refugia for fish.
Physical dimensions of pools, as represented by length and depth, also
emerged as an important characteristic of pools used by adult summer steelhead
in Steamboat Creek. Past studies have addressed the importance of depth (e.g.
Raleigh et al. 1984), but length may also be relevant. It can provide an additional
source of escape from predators by allowing adult to swim back and forth. In the
Steamboat Creek basin, adult summer steelhead selected some deep pools that
were not very deep (1.25
1.75 m mean depth) but that were relatively long (e.g.,
pools 30, 33 and 42).
Large boulders are an important component of pool substrate and may
provide shelter for trout by forming protected areas, caves and crevices (Binns
55
1982; Freese 1982; Nakamoto 1994). De Vore and White (1978) found that brown
trout used cover provided by artificial structures that were placed close to the bed
stream. Nakamoto (1994) indicated that lateral scour pools with boulders were
heavily used in the New River, California by adult summer steelhead. In the John
Day river basin, Oregon, bull trout showed a preference for deep pools with
boulder-rubble substrate in the summer (Skeesick 1989). Dambacher (1991) noted
that the abundance of age >1 + steelhead was associated with large boulders in
Steamboat Creek. Baltz et al. (1991) determined that 40 % of juvenile and adult
rainbow trout in Rock Creek, California, were associated with boulders during the
summer.
4.4. Validation of the discriminant model
The use of parametric discriminant analysis appears to be a suitable method
to identify physical features of deep pools used by adult summer steelhead in
Steamboat Creek. However, its application in Canton Creek was only moderately
successful. The classification was only slightly better than if was done by chance
alone. Sampling size was probably a major reason for this result. Canton Creek
contained only 10 deep pools and with such small sample size, natural variability
produced by potential factors other than those considered in this study may be
difficult to control. Also, Canton Creek is a
4th
order stream, which carries
approximately half of the water volume of Steamboat Creek. This may imply
different bedforms and geomorphic characteristic (Platts 1979). Although the test
56
for difference between geomorphic characteristic between deep pools of Canton
and Steamboat did not show significative statistical differences for most of study
variables, conclusive results are not possible due to the low power observed in the
t-test.
4.5. Effect of population size
One potential limitation in this study is the influence of adult summer steelhead
density on pool occupancy. It is reasonable to assume that pools may have some
carrying capacity and agonistic behavior among adult summer steelhead may
affect pooi use. This feature may be evident if adult summer steelhead follow an
ideal free distribution pattern (see Fretwell and Lucas 1970; Fretwell 1972). Under
such models, a dispersive fish species will occupy a pool depending on its quality,
which is a function of intrinsic environmental characteristics and fish numbers in
the pool (see Fraser and Sise 1980).
In
other words, pools with good
environmental conditions will be occupied until high fish density is high enough to
force some fish to move to other pools with poorer environmental conditions. The
process may be repeated and lower quality pools may be become occupied by
fewer fish along the stream. Unfortunately, no studies on summer steelhead have
focused on this issue and those based on raibow trout seem to be contradictory.
Campbell and Neuner (1985) noted a hierarchical relationship in rainbow trout,
where the largest fish occupied the head of the pool, but Matthews et al. (1994)
noted that thermally stratified poois rainbow trout did not interact for space in the
57
cooler areas of pools. In my study, fish abundance was relastvely low compared
with past years (1. Roelof unpublished data). Thus I assumed that most of the
pools was under their carrying capacity. However, such effects cannot be
disregarded if run size increased.
['ISI
V. MANAGEMENT CONSIDERATIONS AND FUTURE DIRECTIONS
In the last decade a progressive depletion of salmonid stocks through the
Pacific Northwest has occurred as a result of several factors (Bisson et al. 1992).
In the North Umpqua River, the number of adult summer steelhead has declined
in recent years (Fig. 11).
a)
_o
L_.
C
C
C
I
I
I
1969
1971
1970
1972
I
I
1973
1975
1974
I
1978
1977
1980
1979
1982
1981
1984
1983
1986
1985
1988
1987
1990
1989
1992
1991
Year
Figure 11: Number of adult summer steelhead at Winchester Dam
(North Umpqua River, OR), counted between May and
November (1969-1992)(Oregon Fish and Wildlife Department,
unpublished data).
59
Escapement of steelhead populations have decreased along the Oregon coast
during this time (Everest et al. 1984; Konkel and McIntyre 1987). I believe that in
order to improve that current status of summer steelhead in the Steamboat Creek
basin, not only cool deep pools need to be managed and preserved, but also a
broader perspective
is required.
I
believe that the following management
considerations are needed to improve or maintain pool conditions in Steamboat
Creek.
5.1. Deep pools protection
Efforts should be developed to protect deep cool pools that act as thermal
refugia, even if they are marginal habitats. Loss of thermal refugia could have
deleterious impact on summer steelhead populations. Coutant (1985; 1987)
provided examples of how fish confined to small thermal refugia during the
summer became stressed, had increased mortality and decreased reproductive
potential. This has important consequences for salmonids which appear to have
evolved through the selective pressure of bioenergetic efficiency (Brett 1971).
Minimizing energetic costs has been recognized as an important goal of salmonids
(Bernatchez and Dodson 1987). Fish must divert energy from growth and
reproduction into survival strategies in stressful environments (Berman and Quinn
1992; Hall et al. 1992). Areas with water temperature above the optimum will
increase metabolic costs (Dickson and Kramer 1971; Brett and Glass 1973; Smith
and Li 1982), which may reduce energy available for reproduction (Gilhousen
1980). These potential constraints may be found during the summer in the
Steamboat Creek basin.
On the other hand, heavy use of pools by people should be of major concern.
Some deep and critical pools for adult summer steelhead in Steamboat Creek are
heavily used for swimming or diving. The potential impact of such activities must
be investigated. In particular, I recommend a ban on any activity on pool 23 (see
Fig.4). This pool is located immediately below the fish ladder at Steamboat Falls
and may be used as a resting pools before fish ascend the ladder.
5.2. Considering water temperature as a source of pool complexity
Faush and Bramblett (1991) stated that complex pools should be deeper
than 1 m, have coarse substrate, vegetation cover or undercuts banks. Moore and
Gregory (1989) found that pool complexity was determined mainly by boulders and
woody debris. Other studies advocated hydraulic retention (Parsons et al. 1992),
as source of habitat complexity or mixed structural variables with flow (Theurer et
al. 1985). Because water temperature is a not a structural factor, its contribution
to system complexity has often been neglected or not sufficiently considered in
past studies.
Previous research has described and measured proportion of habitat types,
quantity of large woody debris, constrained and unconstrained reaches, habitat
proportions and different geomorphological characteristics. Whereas these factors
61
affect fish abundance and distribution (e.g. Bisson et al. 1987), water temperature
may be the most influential component during critical winter and summer periods
(Adams et al. 1990). In the case of pools, seeps and thermal stratification offer a
wider spectrum of microhabitat choices providing thermal complexity. However,
maintenance of cool suitable temperature in reaches or habitat units cannot be
isolated from watershed management (Heller et al 1983). Fish and land managers
should work together to avoid erosion processes and temperature increases by
protecting upper watersheds and riparian areas from logging practices and grazing
effects.
5.3. Model testing
I suggest caution be exercised in using results of the discrimiriant model in of
pool use by adult summer steelhead in Steamboat Creek mother areas. Different
authors noted that empirical models have very limited success when applied
outside the geographical area where they were developed. For example, Lahyer
et al. (1987) found that discriminant analysis failed in some cases to predict
presence or absence of spotted bass
(Micropterus
punctatus) when applied
outside the original region where the model was developed. These constraints are
not only restricted to the use of multivariate analysis in stream ecology. Other
approaches such as the habitat suitability curves, may also present problems.
Kinzie and Ford (1988) concluded that habitat utilization curves cannot be
transferred among streams. Morhardt (1987) reviewed the success of empirical
models application for aquatic resources. My re-analysis of his data shows that
only 18 % of the tested models agree with the expected results. This agrees with
the poor success of extrapolated models in fisheries found by Fausch et al. (1989).
Managers should be aware of the limited applicability of empirical models to
regions outside the original areas. As stated by Box (1979) all models are wrong
but some are more useful' and this principle fit into ecological studies. Finding the
best model to predict pool use may imply a basin-by-basin study which is a costly
alternative, but will yield more useful results.
5.4. Conduct studies on deep pool thermal dynamic
Past results suggest that thermal characteristics of pools are still not well
understood. Keller and Hofstra (1983), Bowlby and Roff (1984) and Matthews et
al. (1994) identified cold groundwater and morphology as the main source of cold
pool formation. Ozaki (1988) stated that stratification appeared to depend on
hydrological and geomorphic characteristics. In turn, Neel (1951) found evidence
that stratification could also be explained by the inlet temperature in the pool. My
study in Steamboat Creek demonstrated that morphometry is a poor predictor of
thermal stratification.
I propose that more effort must be made to understand factors that determine
thermal stratification in pools. Interesting progresses has been recently made by
Moses (1984). Managers can apply this information to minimize human impact or
to improve habitat conditions. For example, increasing riparian vegetation may
63
promote stratification during the day and provide colder habitats for trout. Cold
water from springs can be diverted to pools to improve thermal conditions. In
addition, some pools with good thermal conditions but poor cover, can be
improved by increasing the depth via impounding the tail of the pool. Such
procedure will in turn affect the water turnover ratio. This factor, in addition to width
and depth has been identfied as a key parameter to promote thermal stratification.
5.5. Evaluate the stream thermoregulatory capacity
Existence of cool spots in Steamboat Creek could be considered a limiting
factor for adult summer steelhead. Thus, viewing thermal refuges in a spatialtemporal context could yield a more appropriate evaluation of how species can
exploit their resources (Tracy and Christian 1986).
Distribution of thermal refugia and reaches with unfavorable water
temperature
reaches would provide
a
valuable
picture of
a
stream's
thermoregulatory capability. Ultimately this information can be combined with other
features such as cover, flow, depth and gradient on a geographic information
system (GIS) platform to orientate management alternatives and evaluate the
length of the stream offering suitable thermal and cover conditions. To accomplish
this goal, managers need to be aware of routine analysis procedures (see
Bartolow 1989) and check for appropriate sampling designs to achieve reliable
information. Smith and Lavis (1975) pointed out that during the summer period
marked differences can exist in water temperature along a stream course because
of local topography and geological factors. Thermal changes are also not
uncommon in very short distances (Bjornn et al. 1968). A difficulty often ignored
in the literature is that standard statistical analysis might be inappropriate if spatial
dependence is suspected for temperature processes.
5.6. Study of fish movements
Questions dealing with pattern of pools occupancy when adults arrive in the
basin, pooi carrying capacity and how possible past experience of individuals
affect the use of pools, should receive further attention. Repeat spawners are not
uncommon in summer steelhead stocks (Freese 1982). The percentage of repeat
spawner varies among rivers systems (Roelof 1983). Since first spawners may
have a different time of return than repeat spawners (Leider 1985), it would be
interesting to see if earlier migrants select pools in a different way than do later
migrants.
It is still unknown if fish remain in the same pools over the summer. Dunn
(1981) reported high pool fidelity for adults surveyed in Wooley Creek, California,
but movement during summer storms were not uncommon (Freese 1982). It was
interesting to note that some cool pools in Steamboat Creek were used
considerably more than others. They probably had better conditions and high
suitability. If fish follow an ideal free distribution, in years of large runs, managers
should be prepared to protect optimal and marginal cool pools against poaching
and other human disturbances. This protection should encompass the entire
summer period.
Tagging fish
would be allow managers and researchers to gain an
understanding of fish movements. Biotelemetry has detected fish movements and
seasonal shifts in habitat used by chinook (Gray and Haynes 1979; Milligan et al.
1985) and Atlantic salmon
(Atlantic salmon)
(Webb 1992). Furthermore, the
technique allows one to relate movement patterns and thermal habitats of
salmonids (Haynes and Gerber 1989; Berman and Quinn 1991). Valuable
information on life history patterns of steelhead has been collected by Everest
(1973) in the Rogue River and Schroeder and Smith (1989) in the Deshutes River
working on steelhead and rainbow trout, respectively. Tagged adult fish also
allowed Freese (1982) to relate adult residence in pools with storms events in the
North Fork Trinity River.
In Steamboat Creek, past tagging experiments performed by Wroble (1990)
have yield important information and should be continued. Furthermore, this study
identified that pools use by adult summer steelhead appears to be a dynamic
process, but followed different patterns depending of stream temperature. Since
fish enter the basin from July to December it is virtually impossible to distinguish
early and late migrants without marking techniques. Roelof (1983) recommended
tagging experiments to answer similar questions in California.
5.7. Considering the riverscape perspective in deep pool use evaluation
I suggest that a perspective that encompasses the riparian and aquatic river
rT
systems together is needed to understand why adult steethead are found in
certain stream segments, and use or avoid certain deep pools within these
streams. This approach can be termed the riverscape perspective. This approach
considers the absolute and relative positions of each pool within a reach, a
segment, a stream, and even the whole basin, and is not solely based on pool
characteristics. Spatial (geographical) location implies not only inter-pools
distances as an obvious parameter, but also inter-pools segments with their
geomorphic characteristics.
A traditional approach in stream fish ecology has been to consider fish
abundance patterns based on habitat quality conditions while ignoring the
potential effect of adjacent or surrounding habitats. In other words, studies on
'focal factors' predict the habitat use in time and space, but fail to consider the
effect of habitat connectivity. This effect could be important in analyzing habitat
use or preference by migratory fish. A riverscape approach takes this into
account. Parsons et at. (1982) and Lanka et at. (1987) point out that trout habitat
quality is related to the geomorphology of the basin; Freese (1982) reports that
the distance to the closest downstream pool accounts for steelhead distribution
in the New River, California.
I believe that deep cool pools in Steamboat Creek are the most used habitats.
Deep cool pools influence the distribution pattern of adult summer steelhead in the
stream. Deep cool pools are embedded in a longitudinal matrix of unfavorable
habitats, so that they appear as patches of suitable water temperature and depth.
67
In desert streams, pool patches were found to play a key role regulating fish
survival (Deacon Williams and Williams 1989; Mundhal 1990). Fish movements
have also been associated with patchy environments in large rivers (Welcomme
1985). Unfortunately, this approach has not been fully explored and applied by
fisheries managers to explain fish distribution and migratory movements in small
and moderate order streams (but see Fraser and Sise 1980).
A riverscape framework can be useful to evaluate and predict fish movements
and relate stream ecological processes within a landscape-watershed perspective
(Shlosser 1991). This broad river perspective would be useful to analyze patterns
of fish movements in relation to water temperature. Salmonids respond to natural
temperature patterns in streams by moving upstream and downstream when
temperature becomes unsuitable (Bjornn and Reisner 1992). Lack of riparian
vegetation generates warm corridors that act as barriers between different stream
segments, decreasing the connectivity among them. Because corridors exhibit a
high edge/area ratio (Hobbs 1992), narrow streams like Steamboat or Canton
Creek may be strongly affected by edge features such as shading. During the
summer, development of such corridors are quasi instantaneous since direct
insolation affects water temperature in a short time (Brown 1969; Rishel et al.
1982). Wroble and Roelof (1985) noted that some warm reaches with extensive
bedrock exposed directly to sunlight are major reasons for increased water
temperatures in Steamboat Creek. In the John Day basin, Oregon, Adams et al.
(1990) found that the general passage of fish up and down a stream may be
limited by reduced flows.
In Steamboat Creek, exposed reaches lacking vegetation cover and with low
flow fit the definition of warm corridors. Steamboat Creek can be interpreted as
fragmented pattern of suitable habitat. The more fragmented a stream by warm
segments, the less connectivity there is among suitable pools. In this sense, water
temperature defines different subsystems in the stream; some pools act as cool
patches, glides and riffles can be identified as warm corridors. Movement patterns
within these subsystems may vary, and fish abundance may depend of
environmental conditions in adjacent segments. In other words, fish may perceive
deep pool features in the stream at different scales. At a minor scale, ecological
attributes found within the pool, such as cover, substrate, flow and temperature
(microhabitat features), define its quality. The fish response is to use or not use
the pool. At a larger scale, pool selection can be governed by a comparative
perspective among pool characteristics. Fish may be able to choose between
pools, occupying the most suitable one. Finally, dispersion ability and inter-pool
corridors characteristics will define pool connectivity, and therefore affect
movement among pools. In summary, managers should realize that a hierarchical
framework at a broad scale is required to enhance steelhead stock conditions in
Steamboat Creek and other basins with identified water temperature problems.
BIBLIOGRAPHY
Adams, R.M., P.C. Kingeman and H.W. Li. 1990. A bioeeconomic analysis of
water allocations and fish habitat enhancements, John Day basin, Oregon.
Oregon State University, U.S. Geological Survey Grant Number 14-68-000G1479, 168 p.
Anderson, J.W. and L. Miyajima. 1975. Analysis of the Vincent Creek fish rearing
pool project. U.S. Department of the Interior-Bureau of Land Management,
Technical Note 274, 25 p.
Baldes, R.J. and R.E. 1969. Physical parameters of microhabitats occupied by
brown trout in a experimental flume. Transactions of the American Fisheries
Society 98: 230-238.
Baltz, D.M., B. Vondracek, L.R. Brown and P.B. Moyle. 1987. Influence of
temperature on microhabitat choice by fishes in a California stream.
Transactions of the American Fisheries Society 116: 12-20.
Baltz, D.M., B. Vondracek, L.R. Brown and P.B. Moyle. 1991. Seasonal changes
in microhabitat selection by rainbow trout in a small stream. Transactions
of the American Fisheries Society 120: 166-176.
Barnhart, R. A. 1986. Species profiles: life histories and environmental requirements
of coastal fishes and invertebrates (Pacific Southwest)--steelhead. U.S. Fish
WildI. Serv. Biol. Rep. 82 (11.60). U. S. Army Corps of Engineers, TR EL-824, 21 p.
Bartholow, J.M. 1989. Stream temperature investigations: Field and analytical
methods. Iristream Flow Information Paper No 13. United States of Fish and
Wildlife Services Biological Report 89 (17), 139 p.
Bartholow, J.M. and W. Slauson. 1990. Questions on habitat preference. North
American Journal of Fisheries Management 10: 362-363.
Beauchamp, J.J, S.W. Christensen and E.P. Smith. 1992. Selection of factors
affecting the presence of brook trout (Salvelinus fontinalis). Canadian
Journal of Fisheries and Aquatic Sciences 49: 597-608.
Bell, M.C. 1986. Fisheries handbook of engineering requirements and biological
criteria. U.S. Army Corps of Engineers, Office of the Chief of Engineers, Fish
Passage Development and Evaluation Program, Portland, Oregon, 290 p.
70
Berman, C.H. and T.P. Quinn. 1991. Behavioral thermoregulation and homing by
spring chinook salmon, Oncorhynchus tshawytscha (Walbaum), in the
Yaquima river. Journal of Fish Biology 39: 301 -31 2.
Bernatchez, L. and J.J. Dodson. 1987. Relationship between bloenergetics and
behavior in anadromous fish migrations. Canadian Journal of Fisheries and
Aquatic Sciences 44: 399-497.
Beschta, R. L. and W. S. Platts. 1986. Morphological features of small streams:
significance and function. Water Research Bulletin 22: 369-379.
Beschta, R.L, R.E. Bilby, G.W. Brown, L.B. Hoitby and T.D. Hofstra. 1987. Stream
temperature and aquatic habitat: fisheries and forestry interactions. Pages
191-232 in Salo E.O. and T.W. Cundy (eds.) Streamside management:
forestry and fishery interactions. Institute of Forest Resources, University of
Washington, Contribution No 57.
Bibby, C., N.D. Burgess and D.A. Hill. 1992. Bird census techniques. Academic
Press, London, 257 p.
Bilby, R.E. and L.J. Wasserman. 1989. Forest practices and riparian management
in Washington state: Data based regulation development. Pages 87-94 in
Gresswell R., B.A. Barton and J. L. Kershner (eds.) Practical approaches to
riparian resource management. An educational workshop. U.S. Bureau of
Land Management, Billings, Montana, 193 p.
Binns, N. A. 1982. Habitat quality index procedures manual. Wyoming Game and
Fish Department, 209 p.
Binns, N. A and F. M. Eiserman. 1979. Quantification of fluvial trout habitat in
Wyoming. Transactions of the American Fisheries Society 108: 21 5-228.
Bisson, P.A., J.L. Nielsen, R.A. Plamason and L.E. Grove. 1982. A system of
naming habitat types in small streams, with examples of habitat utilization
by salmonids during low streamfiow. Pages 62-73 in N. Armantrout (ed.)
Acquisition and utilization of aquatic habitat inventory information. American
Fisheries Society, 375 p.
Bisson, P.A., R.E. Bilby, M.D. Bryant, C.A.. Dollof, G.B. Grette, R.A. House, M.L.
Murphy, K.V. Koski and J.R. Sedell. 1987. Large woody debris in forested
streams in the Pacific Northwest: Past, present and future. Pages 143-190
in Salo E.O. and T.W. Cundy (eds.) Streamside management: forestry and
71
fishery interactions. Institute of Forest Resources, University of Washington,
Contribution No 57.
Bisson, P., T. A. Quinn, G. H. Reeves and S. V. Gregory. 1992. Best management
practices, cumulative effects and long-term trends in fish abundance in
Pacific Northwest river systems. Pages 189-232 in R. J. Naiman (ed.)
Watershed management: balancing sustainability and environmental change.
Springer-Verlag, New York, 542 p.
Bjornn, T.C., D.R. Craddock and D.R. Corley. 1968. Migration and survival of
Redfish lake, Idaho, sockeye salmon, Oncorhynchus keta. Transactions of
American Fisheries Society 97: 360-373.
Bjornn, T.C. and D.W. Reiser. 1992. Habitat requirements of salmonids in streams.
Pages 83-138 in W. A. Meehan (ed.)Influences of forest and rangeland
management on salmonids fishes and their habitats. American Fisheries
Society Special Publication 19, Bethesda, Maryland.
Boussu, M.F. 1954. Relationship between trout populations and cover on a small
stream. Journal of Wildlife Management 18: 229-239.
Bovee, K. D. 1978. Probability of use criteria for the familia Salmonidae. U.S. Fish
and Wildlife Service Biological Services Program FWS/OBS-78/07.
Bovee, K. and 1. Cochnauer. 1977. Development and evaluation of weighted
criteria, probability-of-use- curves for instream flow assessment: fisheries.
U.S. Fish and Wildlife Services Biological Services Program FWS/OBS77/63.
Bovee, K. and J.R. Zuboy. 1986. Proceedings of a workshop on the development
and evaluation of habitat suitability criteria. U.S. Fish and Wildlife Services
Biological Report 88(11), 407 p.
Bowlby, J.N and J.G. Imhof. 1989. Alternative approaches in predicting trout
populations from habitat in streams. Pages 318-332 in Gore J.A. and G.E.
Petts (eds.) Alternatives in regulated river management CRC Press Inc.,
Boca Raton, Florida.
Bowiby, J.N. and J.C. Roff. 1986. Trout biomass and habitat relationship in
southern Ontario streams. Transactions of the American Fisheries Society
115: 503-513.
Box, C.E. 1979. Robustness in the strategy of scientific model building. Pages 201-
236 in Launer R.L.and
G.N. Wilkinson (eds.) Robustness in statistics,
72
Academic Press, New York, 296 p.
Brett, J.R. 1971. Energetic response of salmon to temperature. A study of some
thermal relations in the physiology and freshwater ecology of the sockeye
salmon (Oncorhynchus nerka). American Zoologist 11: 99-113.
Brett, J.R. and N.R. Glass. 1973. Metabolic rates and critical swimming speeds of
sockeye salmon (Oncorhynchus nerka) in relation to size and temperature.
Journal of Fisheries Research Board of Canada 30: 379-387.
Brown, G.W. 1969. Predicting temperatures of small streams. Water Resources
Research 5: 68-75.
Brown, G. W. 1971. Water temperature in small streams as influenced by
environmental factors and logging. Pages 175-181 in Krygier, J.T. and
J.D.Hall (eds.) Proceedings of a symposium forest land uses and stream
environment, Oregon State University, 252 p.
Brown, G.W. and J.T. Krygier. 1967. Changing water temperatures in small
mountain streams. Journal of Soil and Water Conservation 22: 242-244.
Brown, G.W. and J.T. Krygier. 1970. Effect of clearcutting on stream temperature.
Water Resources Research 6: 1133-1139.
Brown, G., G.W. Swank and J. Rothacher. 1971. Water temperature in the
Steamboat drainage. Pacific Northwest Forest and Range Experimental
Station, Portland Oregon, 17 p.
Buttler, R.L. and V.M. Hawthorne. 1968. The reactions of dominant trout to
changes in overhead artificial cover. Transactions of the American Fisheries
Society 97: 37-41.
Campbell, R.F. and J.H. Neuner. 1985. Seasonal and diurnal shifts in habitat
utilized by resident rainbow trout in Western Washington Cascade mountain
streams. Pages 39-48 in Olsen F.W., R.G. White and R.H. Hamre (eds.)
Proceedings of the symposium on small hydropower and fisheries.
American Fisheries Society, Western Division, Bethesda, Maryland.
Capone, T.A. and J.A. Kushlan. 1991. Fish community structure in dry-season
stream pools. Ecology 72: 983-992.
Carpenter, S.R. 1983. Lake geometry: implications for production and sediment
accretion rates. Journal of Theoretical Biology 105: 273-286.
73
Cherry, D.S., K L. Dickson and J. Cairns Jr. 1975. Temperature selected and
avoided by fish at various acclimation temperature. Journal of Fisheries
Research Board of Canada 32: 485-491.
Cherry, D.S., K.L. Dickson, J.Cairns Jr. and J. Stauffer. 1977. Preferred, avoided
and lethal temperatures of fish during rising temperature conditions. Journal
of Fisheries Research Board of Canada 34: 239-246.
Clapp, D.F. and R.D. Clark Jr. 1990. Range, activity and habitat of large,
free-ranging brown trout in a Michigan stream. Transactions of the American
Fisheries Society 119:1022-1034.
Cochran, W. G. 1964. On the performance of the linear discriminant function.
Technometrics 6: 179-189.
Cohen, J. 1960. A coefficient of agreement for nominal scales. Education and
Psychological Measurements 20: 37-46.
Cohen, J. 1988. Statistical power analysis for the behavioral sciences, Lawrence
Erlbaum Assoc. PubI., Hillsdale, 566 p.
Collins, C. S. 1971. North Umpqua case study - an introduction. Pages 225-226
in Krygier, J.T. and J.D.Hall (eds.) Proceedings of a symposium forest land
uses and stream environment, Oregon State University, 252 p.
Coutant, C.C. 1985. Striped bass, temperature and dissolved oxygen: A
speculative hypothesis for environmental risk. Transactions of the American
Fisheries Society 114: 31-61.
Coutant, C.C. 1987. Thermal preferences: when does an asset become a liability.
Environmental Biology of Fishes 18: 161-172.
Dambacher, J. M. 1991. Distribution, abundance and emigration ofjuvenile
steelhead (Oncorhynchus mykiss) and analysis of stream habitats in the
Steamboat Creek basin, Oregon. M.S. Thesis, Oregon State University, 129
p.
De Vore, P.W. and R.J. White. 1978. Daytime responses of brown trout (Salmo
trutta) to cover stimuli in stream channels. Transactions of the American
Fisheries Society 107: 763-771.
Deacon Williams, C. and J.E. Williams. 1989. Refuge management for the
threatened Railroad Valley springfish in Nevada. North American Journal of
Fisheries Management 9: 465-470.
74
Dickson, LW. and R.H. Kramer. 1973. Factors influencing scope for activity and
active and standard metabollsm of rainbow trout (Salmo gairdneri). Journal
of Fisheries Research Board of Canada 28: 587-596.
Dolloff, A.C. and G.H. Reeves. 1990. Microhabitat partitioning among streamdwelling juvenile coho salmon, Oncorhynchus kisutch and Dolly Varden,
Salvelinus malma. Canadian Journal of Fisheries and Aquatic Sciences 47:
2297-2306.
Duff, 0. A. and J. L. Cooper. 1976. Techniques for conducting stream habitat
survey on national resource land. T/N 283, U.S. Department of the Interior,
Bureau of Inland Management, 72 p.
Dunn, P.L. 1981. Adult summer steelhead in Wooley Creek, California: migration
behavior and holding habitat. M.S. Thesis, Humboldt State University,
Arcata, 83 p.
Elser, A.A. 1968. Fish populations of a trout stream in relation to major habitat
zones and channel alterations. Transactions of the American Fisheries
Society 97: 389-397.
Everest, F.H. 1973. Ecology and management of summer steelhead in the Rogue
River. Fisheries Research Report 7, Oregon State Game Commission, 48 p.
Everest, Ff1. and D.W. Chapman. 1971. Habitat selection and spatial interaction
by juvenile chinook salmon and steelhead trout in two Idaho streams.
Journal of Fisheries Research Board of Canada 29: 91-100.
Everest, F.l-L, J.R. Sedell, G.H. Reeves and J. Wolfe. 1984. Fisheries enhancement
in the Fish Creek basin--an evaluation of in-channel and off- channel
projects, 1984. U. S. Department of Energy Boneville Power Administration
Division of Fish and Wildlife, Annual Report, 228 p.
Fausch, K.D. and R.G. Bramblett. 1991. Disturbance and fish communities in
intermittent tributaries of a western great plains river. Copeia 1991: 659-674.
Faush, K.D. and T.G. Northcote. 1992. Large woody debris and salmonid habitat
in a small coastal British Columbia stream. Canadian Journal of Fisheries
and Aquatic Sciences 49: 682-693.
Fausch, K.D., C.L. Hawkes, M.G. Parsons 1989. Models that predict standing crop
of stream fish from habitat variables:1950-1985. USDA Forest Services,
Portland, Oregon, Pacific Northwest Research Station.
75
Fleiss, J.L. 1977. Statistical methods for rates and proportions, John Wiley and
Sons, New York, 321 p.
Fontaine, B. 1987. An evaluation of the effectiveness of instream structure for
steelhead trout rearing habitat in the Steamboat Creek basin. M.S. Thesis,
Oregon State University, 68 p.
Fraley, J.J. and P.J. Graham. 1982. Physical habitat, geologic bedrock types and
trout densities in tributaries of the Flathead river drainage, Montana. Pages
178-190 in Armantrout N. (ed.) Acquisition and utilization of aquatic habitat
inventory information. American Fisheries Society, 375 p.
Fraser, D. F and 1. E. Sise. 1980. Observations on stream minnows in a patchy
environment: a test of a theory of habitat distribution. Ecology 61: 790-797.
Freese, J.L. 1982. Selected aspects of the ecology of adult summer steelhead in
Trinity River, California. M.S. Thesis, Humboldt State University, Arcata, CA,
96 p.
Fretwell, S.D. 1972. Population in a seasonal environment. Princeton University
Press, New Jersey, 217 p.
Fretwell, S. D. and H. L. Lucas, Jr. 1970. On territorial behavior and other factors
influencing habitat distribution in birds. I. Theoretical development. Acta
Biotheoretica XIX: 16-36.
Garside, E.T. and J.S. Tait. 1958. Preferred temperature of rainbow trout (Salmo
gairdneri) and its unusual relationship to acclimation temperature. Canadian
Journal of Zoology 36: 536-567.
Gibson, R.J. and M.H. Keenleyside. 1966. Responses to light of young Atlantic
salmon (Salmo salar) and brook trout (Salvellnus fontinalis). Journal of
Fisheries Research Board of Canada 32: 1007-1024.
Gilhousen, P. 1980. Energy sources and expenditures in Fraser River sockeye
salmon during their spawning migration. International Pacific Salmon
Fisheries Commission Bulletin X)(ll, 48 pp.
Gorman O.T. and J.R. Karr. 1978. Habitat structure and stream fish communities.
Ecology 59: 507-515.
Gray, J.C. and J.M. Edington. 1969. Effect of woodland clearance on stream
temperature. Journal of Fisheries Research Board of Canada 26: 399-403.
Green, R.H. 1979. Sampling design and statistical methods for environmental
biologists, John Wiley and Sons, New York, 257 p.
Grette, G. B. 1985. The role of large organic debris in juvenile salmonid rearing
habitat in small streams. M.S. Thesis, University of Washington, 105 p.
Grossman, GD. and M.C. Freeman. 1987. Microhabitat use in a stream fish
assemblage. Journal of Zoology of London 212: 151-176.
Hair, J. F., Jr., P. E. Anderson and R. L. Tatham. 1987. Multivariate data analysis,
Mcmillan Pubi. Co., New York, 449 p.
Hall, C.A., J.A. Standford and F.R. Hauer. 1992. The distribution and abundance
of organisms as a consequence of energy balances along multiple
environmental gradients. Oikos 65: 377-390.
Hampton, M. and M. Aceituno. 1988. Direct observation techniques for habitat
use criteria development ont the Trinity River, Trinity county, California.
Pages 159-180 in Bovee K. and J.R. Zuboy (eds.) Proceedings of a
workshop on the development and evaluation of habitat suitability criteria.
U.S. Fish and Wildlife Services Biological Report 88(11), 407 p.
Harshbarger, T. J. and H. Bhattacharya. 1981. An application of factor analysis in
an aquatic habitat study. Pages 180-184 in Capen D.E. (ed.) The use of
multivariate statistics in studies of wildlife habitat, USDA Forest Services
General Technical Report, RM-87, 231 p.
Haynes,
J.M.
and G.P. Gerber. 1989. Movements and temperatures of
radiotagged salmonines in lake Ontario and comparisons with other large
aquatic systems. Journal of Freshwater Ecology 5: 197-204.
Heggens, J.A. 1988. Physical habitat selection by brown trout (Salmo trutta) in
riverine systems. Nordic Journal of Freshwater Research 64: 74-90
Hokkanson, K.E. 1977. Temperature requirements od some percids and
adaptations to the seasonal temperature cycle. Journal of Fisheries
Research Board of Canada 34: 1524-1550.
Heggens, J.A. 1988. Effects of short-term flow fluctuations on displacement of,
and habitat use by, brown trout in a small stream. Transactions of the
American Fisheries Society 117: 336-344.
Helfman, G.S. 1981. The advantage of fishes of hovering in shade. Copela 1981:
392-400.
Heller, D.A., J.R. Maxwell and M. Parsons. 1983. Modeling the effects of forest
management on salmonid habitat. Forest Services, Pacific Northwest region,
Siuslaw National Forest, 63 p.
Helm, W. T. ed. 1985. Aquatic habitat inventory: glossary and standards methods.
American Fisheries Society, Western Division, Bethesda, Maryland, 35 p.
Higgins, P. and W.M. Kier. 1992. A plan for restoring biodiversity of anadromous
fish resources of the Klamath River. Pages 217-225 in R. Harris and D.
Erman (eds.) Proceeding of the symposium of biodiversity of Northwestern
California, October 28-30, 1991, Santa Rosa, California, University of
California, Berkeley.
Hinch, S.G., N.C. Collins and H.H. Harvey. 1991. Relative abundance of littoral
zone fishes: biotic interactions, abiotic factors and postglacial colonization.
Ecology 72: 314-1324.
Hobbs, R. J. 1992. The role of corridors in conservation: solution or bandwagon.
Trends in Ecology and Evolution 7: 389-391.
Holaday, S.A. 1992. Summertime water temperature trends in Steamboat Creek
basin, Umpqua National Forest. M.S. thesis, Oregon State University, 128
p.
Hoitby, L.B. 1988. Effect of logging on stream temperature in Carnation Creek,
British Columbia and associated impacts on the coho salmon
(Oncorhynchus kisutch). Canadian Journal of Fisheries and Aquatic
Sciences 45: 502-515.
Hostetler, S.W. 1991. Analysis and modeling of long-term stream temperatures on
the Steamboat Creek basin, Oregon: implications for land use and fish
habitat. Water Research Bulletin 27: 637-647.
Hubbard, L.E., T. Herrett, R. Kraus and C. Kroll. 1990. Water resources data for
Oregon, water year 1990. Vol 2. Western Oregon. USGS/WRD/HD-91/250.
Irvine, J.R., 1G. Jowett and D. Scott. 1987. A test of the instream flow incremental
methodology for underyearling rainbow trout, Salmo gairdneri in
experimental New Zealand streams. New Zealand Journal of Marine and
Freshwater Research 21: 35-90.
Jones, W.E. 1979. Summer steelhead (Sa/mo gairdneri) in the Middle Fork Eel
River and their relationship to environmental changes, 1966 through 1978.
California Department of Fish and Game, Anadromous Fisheries Branch
Administration, Report 80-2, 25 p.
Jones, WE. 1982. Summer steelhead survey Middle Fork Eel River Trinity and
Mendocino counties. Region 3, California Department of Fish and Game,
November 1992 (mimeo).
Jones, W.E. 1992. Historical distribution and recent trends of summer steelhead,
Oncorhynchus mykiss, in the Eel River, California. Region 3, California
Department of Fish and Game, January 1992 (mimeo).
Jowett, I.G. 1990. Factors related to the distribution and abundance of brown trout
and rainbow trout in New Zealand clear-water rivers. New Zealand Journal
of Marine and Freshwater Research 24: 429-440.
Kahn, H. and C.T. Sempos. 1989. Statistical methods in epidemiology. Oxford
University Press, New York, 293 p.
Kaya, C.M., L.R. Kaeding and D.E. Burkhalter. 1977. Use of cold-water refuge by
rainbow trout and brown trout in a geothermally heated stream. Progressive
Fish Culturist 39: 37-39.
Keller, E.A. and T.D. Hofstra. 1983. Summer cold pools in Redwood Creek near
Orick, California and their importance as habitat for anadromous salmonids.
Pages 221-224 in Van Riper C. Ill, L. Whiting and M. Murphy (eds.)
Proceedings of the First Biennial Conference of Research in California's
National Parks. University of California, Davies, 310 p.
Kendall, M. 1980. Multivariate analysis. McMillan Pubi. Co., Inc., New York, 210 p.
Kendall, M.G. and A. Stuart. 1966. The advanced theory of statistics, vol Ill. Griffin
ed., London, 552 p.
Klecka, W.R. 1980. Discriminant analysis. Sage University paper series. Quantitative
Applications in Social Sciences, series no. 07-019, Beverly Hills, London, 71
p.
Konkel, G.W. and J.D. McIntyre. 1987. Trends in spawning populations of Pacific
anadromous salmonids. Fish and Wildlife Technical Report 9, 25 p.
Lanka, ftP., W.A. Hubert and T.A. Wesche. 1987. Relations of geomorphology to
stream habitat and trout standing stock in small Rocky mountain streams.
Transactions of the American Fisheries Society 116: 21-28.
Lantz, R. L. 1970. Influence of water temperature on fish survival, growth and
79
behavior. Pages 182-193
J. Morris (ed.) Proceedings of symposium on
forest land uses and the stream environment. Oregon State University,
in
Corvallis.
Larscheid, J.G. and W.A. Hubert. 1992. Factors influencing the size structure of
brook trout and brown trout in southeastern Wyoming mountain streams.
North American Journal of Fisheries Management 12: 109-117.
Layher, W.G. and O.E. Maughan. 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.
Legendre, L. and P. Legendre. 1983. Numerical ecology. Elsevier Scientific
Publication Company, Amsterdam, 419 p.
Leider, S.A. 1985. Precise timing of upstream migrations by repeat steelhead
spawners. Transactions of the American Fisheries Society 114: 906-908.
Lewis, S. 1969. Physical factors influencing fish populations in pools of a trout
stream. Transactions of the American Fisheries Society 98: 14-19.
Marlega, R.R. 1971. Streamside strips and management problems. Pages 228-229
in Krygier, J.T. and J.D. Hall (eds.) Proceedings of a symposium forest land
uses and stream environment, Oregon State University, 252 p.
Matthews, W.J. 1990. Spatial and temporal variation in fishes of riffle habitats: a
comparison of analytical approaches for the Roanoke River. American
Midland Naturalist 124: 31-45.
Matthews, K.R., N.H. Berg and D.L. Azuma and 1. R. Lambert 1994. Cool water
formation and trout habitat use in a deep pool in the Sierra Nevada,
California. Transactions of the American Fisheries Society 123: 549-564.
McCabe, G.P. Jr. 1975. Computations for variable selection in discriminant
analysis. Technometrics 17: 103-109.
McCauley, R.W. and N.W. Huggins. 1979. Ontogenetic and non-thermal seasonal
effects on thermal preferenda of fish. American Zoologist 19: 267-271.
McCauley, R. W. and W. L. Pont 1971. Temperature selection of rainbow trout
fingerlings in vertical and horizontal gradients. Journal of Fisheries Research
Board of Canada 28: 1801-1804.
McMichael, G. and C.M. Kaya. 1991. Relations among stream temperature, angling
success for rainbow trout and brown trout, and fisherman satisfaction. North
American Journal of Fisheries Management 11: 190-199.
Meisner, J.D. 1990. Potential loss of thermal habitat for brook trout, due to climatic
warming in two southern Ontario streams. Transactions of the American
Fisheries Society 119: 282-291.
Mesick, C.F. 1988. Effects of food and cover on numbers of Apache and brown
trout establishing residency in artificial stream channels. Transactions of the
American Fisheries Society 117: 431-431.
Milligan, P.A., W.O. Publee, D.D. Cornett and R.A. Johnston. 1985. The
distribution and abundance of chinook salmon (Oncorhynchus
in the upper Yukon river basin as determined by a radiotagging and spaghetti tagging program: 1982-1982. Canadian Technical
tshawytscha)
Report in Fisheries Aquatic Sciences 1352, 157 p.
Modde, T., R.C. Ford and M.G. Parsons. 1991. Use of habitat-based stream
classification system for categorizing trout biomass. North American Journal
of Fisheries Management 11:305-311.
Moore, K. M. and S. V. Gregory. 1989. Geomorphic and riparian influences on the
distribution and abundance of salmonids in a cascade mountain stream.
Pages 256-261 in Proceedings of the California riparian systems conference
September 22-24, 1988. Protection, management and restoration for the
1990's. U.S. Department of Agriculture, Washington DC.
Morhardt, J. E. 1986. Iristream flow methodologies. EA-4819, Research Project
2194-2, EAEngineering, Science and Technology, Inc., Lafayette, California.
Morhardt, J.E. and D.F. Hanson. 1988. Habitat availability considerations in the
development of suitable criteria. Pages 292-407 in Bovee K. and J. R.
Zuboy. (eds.) Proceedings of workshop on the development and evaluation
of habitat suitability criteria. U.S. Fish and Wildlife Services Biological Report
88(11), 407 p.
Moring, J. R. 1975. The Alsea watershed study: Effects of logging on the aquatic
resources on three headwater streams of the Alsea River, Oregon. Fishery
Research Report 9, Oregon Department Fish & Wildlife, Corvallis, Oregon,
39 p.
Morrison, D.F. 1967. Multivariate statistical methods. McGraw-Hill, New York.
Morrison, D.F. 1969. On the interpretation of discriminant analysis. Journal of
[..
Marketing Research 6:156-163.
Moses, C.G. 1984. Pool morphology of Redwood Creek, California. M.S. Thesis,
University of California, Santa Barbara, 117 p.
Mundahl, N. D. 1990. Heat death of fish in shrinking stream pools. American
Midland Naturalist 123: 40-46.
Nakamoto, R. 1994. Characteristics of pools used by adult summer steelhead
oversummering in the New River, California. Transaction of the American
Fisheries Society 123: 757-765.
Neel, J.K. 1951. Interrelations of certain physical and chemical features in a
headwater limestone stream. Ecology 32: 368-391.
Nelson, R.L., W.S. Platts, D.P. Larsen and S.E. Jensen. 1992. Trout distribution
and habitat in relation to geology and geomorphology in the North Fork
Humboldt river drainage, Northeastern Nevada. Transaction of the American
Fisheries Society 121: 405-426.
Nickelson, T.E., W.M. Beidler and M.J. Willis. 1979. Streamflow requirements of
salmonids. Federal Aid Progress Report Fisheries, Oregon Department of
Fish and Wildlife, AFS-62, 29 p.
Nielsen, J.L. 1992. The role of cold-pool refugia in the freshwater fish assemblage
in Northern California rivers. Pages 79-87 in Kerner, H.M (ed.) Proceeding
of the symposium of biodiversity of Northwestern California, October 28-30,
1991, Santa Rosa, CA. University of California, Wildiand Resources Center
Report No 29, 316 p.
Nielsen, J.L, T.E Lisle and V. Ozaki. 1994. Thermally stratified pools and their use
by steelhead in northern California stream. Transaction of the American
Fisheries Society 123: 613-626.
Northcote, T.G. 1992. Migratory and residency in stream salmonids- some
ecological considerations and evolutionary consequences. Nordic Journal
of Freshwater Research 67: 5-17.
Olson-Rutz, K.M. and C.B. Marlow. 1992. Analysis and interpretation of stream
channel cross-sectional data. North American Journal of Fisheries
Management 12: 55-61.
Oswood, M. E. and W. E. Barber. 1982. Assessment of fish habitat in streams:
goals, constraints and a new technique. Fisheries 7: 8-11.
Ozaki, V. L. 1988. Geomorphic and hydrologic conditions for cold pool formation
on Redwood Creek, California. Reedwood National Park Research and
Development Technical Report. Reedwood National Park, Arcata, CA. 57 p
+ appendices.
Parsons, M., J.R. Maxwell and D. Helter. 1982. A predictive fish habitat index model
using geomorphic parameters. Pages 85-91 in Armantrout N. (ed.)
Acquisition and utilization of aquatic habitat inventory information. American
Fisheries Society, 375 p.
Parsons, T. N., H. W. Li and 0. A. Lamberti. 1992. Influence of habitat complexity
on resistance to flooding and resilience of stream fish assemblages.
Transactions of the American Fisheries Society 121: 427-436.
Picard, R.R. and K.N. Berk. 1990. Data splitting. American Statistics 44: 140-147.
Pimentel, R.A. 1979. Morphometrics, the multivariate analysis of biological data.
Kendall/Hunt Publishing Company, Dubuque, Iowa, 276 p.
Platts, W.S. 1979. Relationships among stream order, fish populations and aquatic
geomorphology in an Idaho river drainage. Fisheries 4(2): 5-9.
Platts, W.S., W.F. Megahan and G.W. Minshall, 1983. Methods for evaluating
stream, riparian and biotic conditions. General Technical Report INT-138.
Ogden, UT:U.S. Department of Agriculture, Forest Service, Intermountain
Forest and Range experimental Station, 70 p.
Platts, W.S. et at. 1987. Methods for evaluating riparian habitats with applications
to management. General Technical Report INT-221. Ogden, UT: U.S.
Department of Agriculture, Forest Services, Intermountain Forest and Range
experimental Station, 177 p.
Rahel, F.J.
1984.
Factors structuring fish assemblages along a big lake
successional gradient. Ecology 65: 1276-1289.
Raleigh, R. F., 1. Hickman, R. Solomon and P. Nelson. 1984. Habitat suitability
information: Rainbow trout. Fish and Wildlife Services, FWS/OBS-82/10.60,
64 p.
Reeves, G.H., G.H. Everest and J.D. Hall. 1987. Interactions between the redside
shinner (Richardsonius baleatus) and the steelhead trout (Salmo gairdneri)
on eastern Oregon. Canadian Journal of Fisheries and Aquatic Sciences 44:
1603-1613.
Reeves, G.H., J.D. Hall, T.D. Roelof, T.L. Hickman and C.O. Baker. 1991.
Rehabilitating and modifying stream habitats. Pages 519-556 in W.R.
Meehan (ed.) Influence of forest and rangeland management on salmonid
fishes and their habitats. American Fisheries Society, Special Publication 19.
Reynolds, W.W. 1977. Temperature as a proximate factor in orientation behavior.
Journal of Fisheries Research Board of Canada 34: 734-739.
Reynolds, W.W. and M.E. Casterlin. 1979. Behavioral thermoregulation and the
"final preferendum" paradigm. American Zoologist 19: 211-224.
Rinne, J. N. 1982. Problems associated with habitat evaluation of an endangered
fish in headwater environments. Pages 202-209 in N. B. Armantrout (ed.)
Acquisition and utilization of aquatic habitat inventory information. American
Fisheries Society Western Division, Bethesda, Maryland.
Rinne, J. N. 1978. Development of methods of population estimation and habitat
evaluation for management of the Arizona and Gila trouts. Pages 113-123
in J.R. Moring (ed.) Proceedings wild trout-catchable trout symposium,
Oregon Department of Fisheries and Wildlife, Corvallis.
Rishel, G.B., J.A. Lynch and E.S. Corbett. 1982. Seasonal stream temperature
changes following forest harvesting. Journal of Environmental Quality 11:
112-116.
Roelof, T.D. 1983. Current status of California summer steelhead (Salmo gairdneri)
stocks and habitat and recommendations for their management. USDA
Forest Services, Region 5, San Francisco, California, 77 p.
Roelof, T.D., M.S. Hill and J.J. Wroble. 1983. Juvenile salmonid densities in eight
north Umpqua River tributaries, summer 1983. Report presented to
Steamboat Ranger District, Umpqua National Forest, Oregon Department
Fish and Wildlife, l6p.
SAS, 1985. SAS users's guide: statistics. SAS Institute, Gary, North Carolina, USA.
Sedell, J.R., G.H. Reeves and F.H. Everest. 1986. Reoccupying our habitat losses
with a riparian vegetation strategy. Pages 30-39 in D. Gutrie (ed.)
Proceedings of wild trout, steelhead and salmon in the 21 at century,
Oregon Sea Grant No. ORESU-W-86-002, Oregon State University.
Scarnecchia, D.L. and E.P. Bergersen. 1987. Trout production and standing crop
in Colorado's small streams, as related to environmental features. North
American Journal of Fisheries Management 7: 315-330.
Schiessinger, D.A. and H.A. Regier. 1983. Relationship between environmental
temperature and yields of subartic and temperate zone fish species.
Canadian Journal of Fisheries Research Board of Canada 34: 1784-1791.
Schiosser, l.J. 1991. Stream fish ecology: a landscape perspective. Bioscience 41:
704-712.
Seber, G.A. 1984. Multivariate observations. John Wiley & Sons, New York, 686 p.
Sheppard, J.D. and J.H. Johnson. 1985. Probability-of-use for depth, velocity, and
substrate by subyearling coho salmon and steelhead in lake Ontario
tributary streams. North American Journal of Fisheries Management 5: 277282.
Skeesick, D. 1989. Biology of the Bull trout, Salvelinus confluetitus. Willamette
National Forest, Eugene Oregon, 53 p.
Smith, J.J. and H.W. Li. 1973. Energetic factors foraging tactics of juvenile
steelhead trout, Salmo gairdneri. Pages 173-180 in 0. L. Noakes (ed.)
Predators and prey in fishes. Dr. W. Junk Publishers, The Hague,
Netherlands.
Smith, K.and M. E. Lavis. 1975. Environmental influences on the temperature of a
small upland stream. Oikos 26: 228-236.
Spigarelli, S.A., G.P. Romberg, W. Prepejchal and M.M. Thommes. 1974. Bodytemperature characteristics of fish at thermal discharge on lake Michigan.
Pages 119-132 in J.W. Gibbons and R.R. Sharitz (eds.) Thermal ecology,
U.S. Atomic Energy Commission, Oakridge.
Stauffer, J.R. Jr. 1980. Influence of temperature on fish behavior. Pages 103-141
in C.H. Hocutt, J.R. Stauffer, J.E. Edinger, L.W. Hall Jr. and R.P. Morgan II
(eds.) Power Plants. Effects on fish and shellfish behavior, Academic Press,
New York, 346 p.
Stevens, J. 1986. Applied multivariate statistics for the social sciences. Lawrence
Erlbaum Assoc., Pubi., Hilisdale, New Jersey, 515 p.
Stewart, P. A. 1970. Physical factors influencing trout density in a small stream
PhD Thesis, Colorado State University, Fort Collins, Colorado.
Tabachnick, B. G. and L. S. Fidell. 1989. Using rnu(tivariate statistics. Harper &
Row, Pubi., New York, 746 P.
Theurer, F. 0., I. Lines and T. Nelson. 1985. Interaction between riparian
vegetation, water temperature and salmonid habitat in the Tucannon river.
Water Research Bulletin 27: 53-64.
Titus, K., J.A. Mosher and B.K. Williams. 1984. Chance-corrected classification for
use in discriminant analysis: ecological applications. American Midland
Naturalist 111: 1-11.
Tonn, W.M., J.J. Magnuson and A. Forbes. 1983. Community analysis in fishery
management: an application with northern Wisconsin lakes. Transaction of
the American Fisheries Society 112: 368-377.
Tracy, R. C. and K. A. Christian. 1986. Ecological relations among space, time and
thermal niche axis. Ecology 67: 609-615.
U.S. Environmental protection agency. 1986. Quality criteria for water 1986. EPA
440/5-86-001. Government Printing Office, Washington D.C.
Waite, l.R. and R.A. Barnhart. 1992. Habitat criteria for rearing steelhead: a
comparison of site-specific and standard curves for use in the instream flow
methodology. North American Journal of Fisheries Management 12: 40-46.
Van Home, B. 1983. Density as a misleading indicator of habitat quality. Journal
of Wildlife Management 47: 893-901.
Webb, J. 1992. The behavior of adult salmon (Salmon salar L.) in the River Tay
as determined by radio telemetry. Scottish Fisheries Report 52, 18 p.
Welcomme, A. L. 1985. River fisheries. FAO Technical Paper 262. Food and
Agriculture Organization of the United Nations, Rome, Italy.
Welch, P.S 1948. Limnological methods, Blakiston Co., Phadelphia, 538 p.
Wesche, T., C. Goertler and C. Frye. 1985. Importance and evaluation of instream
and riparian cover in smaller trout streams. Pages 325-328 in Johnson R.
et al. (eds.) Riparlan ecosystems and their management: reconciling
conflicting uses. USDA Forest Services General Technical Report RM-120,
Fort Collins, Colorado, 523 p.
Wesche, T., C. M. Goertler and W. A. Hubert. 1987 a. Modified habitat suitability
index model for brown trout in southeastern Wyoming. North American
Journal of Fisheries Management 7: 232-237.
Williams, B. K. 1983. Some observations on the use of discriminant analysis in
ecology. Ecology 68: 1283-1291.
Williams, B.K. and K. Titus. 1988. Assessment of sampling stability in ecological
applications of discriminant analysis. Ecology 69: 1275-1285.
Wroble, J. 1990. Seasonal movements and spawning of adult summer steelhead
trout (Salmo gairdneri) in the North Umpqua River and Steamboat Creek
basin. USD1 Forest Services, Umpqua National Forest and North Umpqua
Ranger District, 86 p.
Wroble, J.J. and T.D. Roelof. 1985. Seasonal movements of adult steelhead in
Steamboat and Canton Creeks, North Umpqua River, Oregon. A report
presented to North Umpqua Ranger District, Umpqua National Forest,
Oregon Department of Fish and Wildlife and Bureau of Inland Management,
41 p.
Zar, J.H. 1984. Biostatistical analysis, Prentice-Hall, Inc, New Jersey, 718 p.
APPENDICES
APPENDIX A
C
C
C
I-.
E
0-0.19
0.4-0.bY
0.2-0.39
0.8-0.99
0.6-0.79
1.2-1.39
2-2.19
1.6-1.79
1.4-1.59
1-1.19
1.8-1.99
Mean depth (m)
Pools without fish
Pools with fish
Figure A.1: Number of fish observed in pools during the systematic survey in
Steamboat Creek (August 1991).
Vi V2
V2
2 } LAhocatebovi
4
41
5
5
6
6
7
7
8
8
9
9
10
10
V3
I
Allocate to V2
Introduc.e V3
Allocate to V3
10
10
} Allocate to Vi
Figure A.2: Conceptual example of the dichotomous key development according
to Kendall (1975). See text for further explanations.
(_.._____J___.. IA flfll'
- "' 8%)
tallow pools (18.4%)
Riffles (33.9%)
Glides (39.7%)
Figure A.3: Proportion of habitat types in Steamboat Creek observed in
the systematic survey during 1991.
APPENDIX B
93
Table B.1: Percentages of bank riparian forest in pools USED
and NOT USED by adult summer steelhead in
Steamboat Creek, as utilized in the development
of the dichotomous key. Dashed lines defines cut-off
values.
USED
NOT USED
0
0
0
0
0
0
0
5
10
10
10
20
25
30
50
50
50
60
60
70
75
75
75
75
25
25
25
50
50
50
50
50
60
60
60
60
100
Table B.2: Maximum length in pools USED and NOT USED by adult
summer ste&head in Steamboat Creek, as utilized in the
development of the dichotomous key. Dashed lines defines
cut-Off values.
USED
NOT USED
13
14
17
18
20
22
22
24
24
25
27
28
- 28
30
29
30
30
32
39
40
40
43
44
50
44
44
47
58
60
62
67
69
77
95
Table B.3: Mean bottom temperature in pools USED and NOT USED by
adult summer steelhead in Steamboat Creek, as utilized
in the development of the dichotomous key. Dashed lines
defines cut-off values.
USED
NOT USED
16.59
16.60
16.63
16.69
17.00
17.08
17.46
17.73
17.95
18.00
18.50
18.58
18.90
18.83
19.86
19.90
19.90
19.02
17
19.93
19.93
19.94
20.21
20.45
20.82
21.08
21.12
21.42
21.72
22.07
22.25
22.50
22.61
22.61
22.62
22.68
Table B.4: Maximum depth in pools USED and NOT USED by adult
summer steelhead in Steamboat Creek, as utilized
in the development of the dichotomous key. Dashed
lines defines cut-off values.
USED
2.50
2.75
2.75
NOT USED
1.75
2.00
2.25
2.25
2.25
2.50
2.50
2.50
2.75
2.75
2.75
2.75
2.75
2.75
2.75
3.00
3.00
3.00
3.25
3,25
3.50
3.50
3.50
3.30
3.40
3.50
3.50
3.80
4.00
4.50
4.50
4.30
4.50
5.00
97
Table B.5: Comparative analysis of physical variables in pools
in Canton Creek and Steamboat Creek. X= mean; SD=
standard deviation; n.s: not significative at 0.05
level; s: significative at 0.05 level; power= 1- type
II error.
Variable
Canton Creek
Steamboat Creek
Difference
X= 0.29
SD= 0.222
SD = 0.291
X= 0.06
SD= 0.040
= 0.06
SD 0.098
n.s (P<0.99)
= 0.39
SD= 0.12
X= 0.45
SD= 0.195
n.s (P<0.39)
power= 0.31
LB
X= 0.12
SD= 0.083
X= 0.14
SD= 0.135
n.s (P<0.58)
power= 0.08
C
X= 0.14
SD= 0.098
SD= 0.048
s (P<0.004)
power= 0.80
SG
X= 0.05
SD= 0.034
X= 0.06
SD= 0.06
n.s (P<0.75)
power= 0.17
LG
X= 0.09
SD= 0.070
SD= 0.043
X= 328
SD= 182
SD= 389
X= 502
SD= 344
SD= 684
LED
S
BR
A
VOL
MD
MAD
DR
MW
5= 1.53
X= 0.31
=
0.07
= 0.05
= 565
= 856
n.s (P<0.79)
power= 0.06
n.s (P<0.06)
power= 0.59
n.s (P<0.07)
power= 0.45
n.s (P<0.12)
power= 0.35
SD= 0.369
SD= 0.518
n.s (P< 0.69)
power= 0.17
5= 3.40
SD= 0.546
X= 3.15
SD= 0.789
n.s (P<0.95)
power= 0.35
= 0.45
= 0.46
SD= 0.095
SD= 0.097
n.s (P<0.75)
power= 0.06
= 9.79
3= 14.05
SD= 3.336
= 1.45
SD= 4.994
s (P<0.002)
power= 0.70
Table B.5 (Continued)
Variable
LIT
ML
SI
Canton Creek
X= 0.28
SD= 0.095
X= 31.1
SD= 12.740
= 1.40
SD= 0.414
BO
5(= 7.73
SD= 0.869
TVC
5=
1.53
SD= 1.250
BT
3(=
18.75
SD= 0.300
Steamboat Creek
Difference
= 0.28
n.s (P<0.99)
SD= 0.099
5= 37.24
SD= 18.01
n.s (P<0.32)
power= 0.17
X= 1.39
SD= 0.337
n.s (P<0.99)
X= 8.75
SD= 1.906
n.s (P<0.11)
power= 0.37
X= 2.71
SD= 1.640
n.s (P<0.74)
power= 0.50
X= 19.52
SD= 2.113
n.s (P<0.25)
power= 0.19
Table B.6: Values measured in Steamboat Creek basin pools. REA: area (m2);
VOL: volume (m3) ZD: mean depth (m); ZX: maximum depth (m); DR: depth
ratio; MW mean width (m); LIT: littoral area proportion; PER: perimeter (m);
ML; maximum length (m); SSI: shore sinuosity index; LED:Iedge cover
proportion; RF: proportion of bank riparian forest; S:sand proportion; BR:
bedrock proportion; LB: large boulder proportion; SB: small boulder
proportion; C:cobble proportion; SG: small gravel proportion; LG: large
gravel proportion; BO: bottom oxygen (mg/L); TCV: temperature coefficient
of variation; BT: mean bottom water temperature (°C) (see text for further
explanation).
Poo'
LED
S
BR
SB
LB
C
SQ
LG
1
0,31
0.09
0.18
0.09
0.15
0.12
0.12
0.24
2
0.74
0.10
0.40
0.16
0.11
0.20
0.05
0.07
3
0.15
0.1Q
0.33
013
0.13
0.15
0.05
0.10
4
0.26
0.00
0.37
0.14
0.26
0.14
0.03
006
5
6
0.17
0.03
0.57
0.20
0.07
0.07
0.03
0.03
0.40
0.05
0.32
0.08
0.24
0.26
0,00
0.05
7
0.20
0.00
026
0.21
0.05
0.32
0.05
0,11
8
0.10
0.08
0.52
0.20
0.12
0.04
0.04
0,00
9
0.00
0.02
0.50
0.14
0.00
0.10
0.08
016
10
0.55
0.09
0.48
0.26
0.04
0.00
0.09
0.04
11
0.95
0.03
0.38
0.17
0.24
0.09
0.03
0.06
12
0.75
0.22
0.40
0.10
0.06
0.17
0.01
0.02
13
0.00
0.06
0.26
0.26
0.17
0.06
0.13
0.06
14
0.00
0.02
0.33
0.20
0.02
0.12
018
0.13
15
0.37
0.00
0.31
0.33
0.20
0.09
0.02
0.04
16
0.05
0.25
0.14
0.19
0.14
0.15
0,07
0.07
17
0.02
0.16
0.26
0.11
0.16
0.21
0,07
18
0.00
0.40
0.05
0.51
0,10
0.13
0.10
0.05
0.05
19
0.65
0.03
0.37
0.29
0.13
0.11
0.01
0.08
20
0.30
0.55
0.00
0.47
0.24
0.03
0.12
0.03
0.12
0.04
0.60
0.13
0.02
0.09
0.11
0.00
22
23
24
25
26
27
28
29
30
0.10
0.02
0.51
0.14
0.10
0.04
0,12
0.06
0.75
0.00
0.55
0.20
0.13
0.05
0.00
0.07
0.50
0.11
0.24
029
021
0.03
0.11
0.03
0.05
0.00
0.30
0.04
0.35
0.06
0.13
0.09
0.45
0.37
0.00
0.00
0.63
0.00
0.00
0.00
0.60
0.02
0.27
0.22
0.39
0.02
0.05
0.00
0.25
0.06
0.41
0.11
0.30
0.06
0.07
0.00
0.00
0.64
0.06
0.17
0.03
0.06
0.06
0.10
0.00
0.00
0.59
0.16
0,12
0.08
0.02
31
0.00
0.00
0.75
0.13
0.06
0.00
32
33
34
35
36
37
38
39
40
0.00
0.00
0.56
0.12
0.08
0.20
0.02
0.81
0.12
0.10
0.14
0.22
0.08
0.10
0.00
0.73
0.00
0.10
0.05
0.21
0.35
0.00
0.25
0.55
0.07
0.25
0.13
0.90
41
42
43
44
45
46
47
48
21
A
VOL
459
1287
566
448
367
339
137
205
564
329
0.02
435
512
484
293
203
290
70
160
655
202
976
1297
443
320
279
986
326
610
1174
987
893
272
1122
510
184
230
254
404
594
639
0.00
0.06
121
0,04
0.12
0.08
168
0.02
0.03
0.00
0.00
807
0.10
0.10
0.24
0.12
0.20
0.00
0.00
0.07
867
217
0.26
0.29
0.10
0.01
0.08
0.19
0.38
0,13
0.00
0.06
0.44
0.14
0.00
0.16
0.09
0.09
0.49
0.33
0.00
0.03
0.03
0.00
735
252
1118
277
0.03
0.68
010
0.03
0.10
0.08
0.00
1431
0.25
0.03
0.70
0.16
0.00
0.03
0.03
0.05
0.70
0.04
0.64
0.07
0.01
0.11
0.03
0.10
0.00
0.08
0.67
0.08
0.10
0.05
0.03
0.00
0.37
0.04
0.60
0.02
0.04
0.02
0.11
0.17
0.70
0.00
0.32
0.32
0.20
0.08
0.00
0.08
0.00
0.00
0.60
0.08
0.16
0.08
0.08
0.00
0.00
0.27
0.59
0.14
0.00
0.00
0.00
0.00
0.55
0.18
0.37
0.18
0.08
0.12
0.08
0.02
432
1190
230
270
169
120
114
460
1161
2055
470
410
315
1297
365
738
3035
1234
1125
478
2278
796
248
444
667
1305
647
971
310
227
928
1179
364
897
333
1465
277
2275
445
1664
250
332
602
97
108
727
100
Table B.6 (Continued)
MW
ZO
ZX
OR
LIT
ML
SI
Rff
80
TCV
IF
1.30
3.50
0.37
15.40
0.40
1.36
0.50
5.80
1.69
18.22
2
2.33
4.20
0.55
16.83
0.15
29
32
1.19
0.60
9.00
4.75
17.94
3
1.41
4.00
0.35
11.33
0.38
4?
135
0.50
7.00
1.08
19.43
4
1.61
3,20
0.50
11.57
0.20
1.12
0.50
7.00
2.26
18.80
5
1.87
4.00
0.47
6.14
0.28
1.53
0.00
7.80
0.91
20.50
6
1.38
2.75
0.50
8.28
0.38
25
33
35
1.23
0.50
7.80
0.90
20.50
7
1.84
3.50
0.53
7.00
0.16
10
2.50
0.20
9.20
0.79
21.60
8
1.45
2.75
0.53
8.00
0.40
1.08
0.25
6.80
0.76
21.60
9
1.39
3.40
0.41
11.40
0.30
20
58
1.16
0.20
7.70
1.13
20,10
10
1.87
2.75
0.68
7.60
0.29
0.00
8.20
0.71
20.10
1.33
3.50
0.38
16.83
0.47
1.19
0.00
8.80
0.00
22.50
12
13
14
1.30
4.30
0.30
20.31
0.25
28
58
67
1.45
11
1.27
0.60
8.80
2.49
22.62
1.17
2.50
0.47
16.40
0.27
148
0.60
11.00
0.06
19.86
1.40
2.75
0.51
10.00
0.19
1.41
0.50
11.00
0.00
19.90
15
1.68
2.75
0.61
12.67
0.42
1.57
0.00
12.00
0.00
19.90
16
1.52
2.75
0,55
22.71
0.28
12.00
1.15
19.93
1.28
2.75
0.47
10.85
0.36
132
117
0.25
17
0.10
10.50
0.71
20.45
18
1.35
3.00
0.45
8.60
0.28
1.25
0.50
1000
0.70
21.42
19
2.27
4.50
0.50
17.80
0.13
1,32
0.70
8.50
1.22
3.00
0.41
21.00
0.34
1.29
0.50
10.00
3.08
0.74
21.12
20
21
1.43
3.30
0.43
14.80
0.45
27
32
22
39
30
44
66
47
60
20
77
30
17
49
29
40
44
50
1,14
0.60
11.50
0.47
20.82
20.45
Poal
22
1.42
3.00
'0.47
18.10
0.35
23
24
2.47
5.00
0.49
14.91
0.30
1.63
2.75
0.59
16.67
0.18
25
26
27
28
29
30
1.39
2.25
0.62
10.80
0.22
1,86
4.00
0.47
4.85
0.17
2.25
3.50
0.64
7.88
0.12
1.94
4.50
0.43
10.41
0.18
1.25
2.50
0.50
13.83
0.39
1.50
2.75
0.55
12.11
0.30
1.48
0.50
7.80
0.25
22.25
1.17
0.20
8.00
2.52
19.02
1.83
0.05
8.00
0.87
21.72
1.21
0.00
8.00
0.31
22.61
1.98
0.00
7,00
0.74
22.81
1.91
0.00
8.60
1.32
21.08
2.13
0.30
7.80
1.99
18.58
1.13
0.00
10.00
0.46
18.00
1.25
0.50
9.20
5.16
17.00
31
2,38
3.80
0.63
8.68
0.19
14
2.21
0.00
9.20
1.58
18.50
32
1,44
2.50
0.58
0.15
0.25
9.00
2.50
0.54
1.13
0.60
8.20
0.00
0.30
19.00
1.34
22
69
1.14
33
7.80
13.30
34
35
36
37
38
1.50
3.50
0.43
21.66
0.34
1.39
0.50
.7.70
2.11
17.08
1.55
3.25
0.48
9.50
0.24
1.14
0.25
9.80
1.91
16.6.
1.25
3.25
0.38
17.92
0.29
1.42
0.75
9.80
2.36
16.59
1.51
2.75
0.55
10.50
0.28
1.44
0.50
9.80
2.11
18.90
1.52
3.50
0.43
0.31
1.28
0.75
6.50
3.31
16.60
39
40
1.18
2.25
0.52
24.40
14.40
1.11
0.10
8.20
3.68
22.68
1.73
4.50
0.38
23.86
0.29
1.38
0.75
1.50
5.18
19.57
41
1.15
2.50
0.48
15.08
0.44
1.39
0.60
5.50
5.03
22.07
42
43
44
1.37
2.75
0.50
19.20
0.30
1.30
0.60
7.50
0.93
18.83
1.51
0.76
9.00
0.15
1.12
0.10
8.80
5.44
20.21
1.44
2.00
3.40
0.42
11.64
0.30
40
28
44
24
43
19
60
28
62
25
24
1.35
0.00
9.60
2,65
17.95
45
46
3.46
4.50
0.77
16.00
0.12
11
2.64
6.80
0.46
17.73
0.86
1.75
0.49
10.50
0.36
11
1.18
0.50
1.00
7.50
0.27
19.93
47
48
1.15
2.25
0.51
8.75
0.50
13
1.54
0.25
7.00
0.00
19.94
1,75
3.50
0.50
13.00
0.25
30
1.27
0.75
9.60
4.09
16.69
0.40
0.38
17.46