Trophic performance of Oncorhynchus mykiss in tributaries of the South

Fork Trinity River, northern California

Sarah G. McCarthy

A thesis submitted in partial fulfillment of the

requirements for the degree of

Master of Science

University of Washington

2004

Program Authorized to Offer Degree:

School of Aquatic and Fishery Sciences

i

TABLE OF CONTENTS

LIST OF FIGURES.................................................................................................ii

LIST OF TABLES..................................................................................................iii

Chapter 1. Linking multiscale habitat associations with single species

performance: Oncorhynchus mykiss bioenergetics in small streams

INTRODUCTION....................................................................................................1

STUDY SITE...........................................................................................................4

PROJECT BACKGROUND....................................................................................4

OBJECTIVES..........................................................................................................7

APPROACH............................................................................................................7

NOTES TO CHAPTER 1......................................................................................16

Chapter 2. Factors affecting trophic performance of Oncorhynchus mykiss along

forest gradients in the South Fork Trinity River watershed, California

INTRODUCTION.................................................................................................19

STUDY SITE.........................................................................................................21

METHODS............................................................................................................22

Physical Attributes and Invertebrate Composition............................................23

Fish Sampling....................................................................................................26

Diet Analysis and Prey Electivity......................................................................26

Age and Growth Analysis..................................................................................27

Bioenergetics Modeling.....................................................................................28

RESULTS..............................................................................................................30

Physical Attributes and Invertebrate Composition............................................30

Diet Analysis and Prey Electivity......................................................................31

Age and Growth Analysis..................................................................................32

Bioenergetics Modeling.....................................................................................33

DISCUSSION........................................................................................................34

CONCLUSION......................................................................................................40

NOTES TO CHAPTER 2......................................................................................58

REFERENCES......................................................................................................63

ii

LIST OF FIGURES

Figure Number Page

1.1 Map of South Fork Trinity River watershed and study sites.................................11

1.2 Principle components analysis of 24 habitat variables..........................................12

1.3 Maximum consumption and respiration of Oncorhynchus mykiss........................13

2.1 Average daily temperature in 9 tributaries of the South Fork Trinity River.........42

2.2 Average invertebrate biovolume and mean stream discharge...............................43

2.3 Diet proportions for ages 0-2 O. mykiss................................................................44

2.4 Manly’s alpha preference index for ages 0-3 O. mykiss........................................45

2.5 Growth trajectories for ages 0-2 O. mykiss............................................................46

2.6 Bioenergetics model output of total consumption for ages 0-2 O. mykiss............49

2.7 Year-round growth of O. mykiss in two streams, as estimated by the

bioenergetics model...............................................................................................50

iii

LIST OF TABLES

Figure Number Page

2.1 2003 study design..................................................................................................49

2.2 Sample size, fork lengths, and regression analyses for O. mykiss in 2003............50

2.3 Mean relative weight of ages 0- 2 O. mykiss.........................................................51

2.4a Bioenergetics model input for individual streams in June, August, and October

2003.......................................................................................................................52

2.4b Bioenergetics model input for year-round modeling in two streams....................54

2.5a Bioenergetics model output for O. mykiss in individual streams during June,

August, and October 2003.....................................................................................55

2.5b Bioenergetics model output for year-round modeling in two streams..................57

iv

ACKNOWLEDGEMENTS

I would like to thank my committee - Dave Beauchamp, John Emlen, and Tom

Quinn - for all of their help and support over the last two years. Special thanks to Jeff

Duda who taught me all I know about field work, assisted with project planning and

implementation, and guided me in my data analysis and writing. Hart Welsh and Garth

Hodgson of the USDA Forest Service Redwood Sciences Laboratory established the

framework for this study and conducted all of the preliminary study design and

fieldwork. John Lang and Jim Fitzgerald of the USDA Forest Service ranger station in

Hayfork, CA assisted us immensely with local insight, field support, and most

importantly, missing data. I am very grateful for all of the field and laboratory assistance

I received from Evelyn Chia, Lorence Pascoe, Daniel O’Donnell, Adam Van Mason,

Catherine Chambers, Christina Galitsky, Jeremy Steinbacher, and Jim Matilla. Several

scientists from the USFS Redwood Sciences Lab and the US Geological Survey Western

Fisheries Research Center provided essential help and advice; including Brett Harvey,

Carl Ostberg, Reg Reisenbichler, Steve Rubin, Kimberley Larsen, and Stacey Dufrene.

Thank you to the Beauchamp lab – Chris Sergeant, Steve Damm, and Jim Matilla for

their back-breaking work on my ill-fated behavior experiment; Liz Duffy and Nathanael

Overman for giving me invaluable advice about sampling technique; Alison Cross and

Jamal Moss for teaching me all about fish scales and sharing their workspace with me;

Mike Mazur and Jen McIntyre for logistical advice and showing me the ropes when I

first arrived; and Susan Wang, Hans Berge, and Erik Schoen for editing and feedback.

Loving thanks to my family (Mom, Dad, Bri, and Jills) who continuously offer

me moral support and encouragement. I owe much gratitude to Michelle Marvier, Doug

Dey, Mary Moser, Alicia Matter, and Brian Burke for encouraging me to attend UW for

graduate school. Finally, thank you to Peter, Amy, Kristin, Stephanie, and Jen for putting

up with me over the last few quarters, encouraging me to have fun outside of school, and

putting things in perspective.

This research was made possible by funding and support from the US Geological

Survey Biological Resources Division Western Fisheries Research Center, the University

of Washington Cooperative Fish and Wildlife Research Unit, and the USDA Forest

Service Redwood Sciences Lab.

1

Chapter 1. Linking multiscale habitat associations with single species

performance: Oncorhynchus mykiss bioenergetics in small streams

INTRODUCTION

Stream-dwelling anadromous fishes in California have been declining as a result

of environmental and anthropogenic changes such as watershed degradation and

diversions (Moyle 1994). Steelhead, the anadromous form of rainbow trout

(Oncorhynchus mykiss) commonly rear in freshwater habitats for two years before

migrating to sea in populations south of Alaska (Busby et al. 1994). This extended

freshwater residence makes steelhead more reliant on seasonal patterns in stream

productivity than most other anadromous salmonids. Klamath Mountains Province

steelhead is the only evolutionary significant unit (ESU) of steelhead in California that is

not listed as threatened or endangered under the Endangered Species Act as of 2003

(Pautske 2001). However, this ESU was deemed likely to become endangered in the

foreseeable future (Pautske 2001). O. mykiss belonging to this ESU return to the South

Fork Trinity River and spawn in its tributaries. Accurate and extensive baseline data

describing this steelhead ESU and the environmental factors affecting it will be important

if the population becomes imperiled and requires recovery efforts in the future. In

addition, it is important to record and publish information on populations that are

relatively healthy in order to guide recovery plans for other populations.

Stream ecosystem health is highly influenced by the integrity of the surrounding

riparian forest. Riparian plants serve as buffers that stabilize stream conditions. They

provide shade and cover, filter and moderate runoff, stabilize banks to reduce erosion and

2

sedimentation, and provide habitat for invertebrates that are important to the stream food

web (Brosofske et al. 1997, Wallace et al. 1997, Naiman et al. 1998). Falling debris from

the forest alters streams by creating protective cover, retaining nutrients, and shaping

channel morphology (Murphy and Meehan 1991). In addition, the forest canopy reduces

solar input to streams, stabilizing water temperatures (Vannote et al. 1980).

Health of the riparian zone can be affected by environmental factors (slope,

elevation, flooding, fire, etc.) and anthropogenic factors (logging, road construction, etc.).

Logged streams often provide less suitable habitat for many aquatic organisms (Burns

1972, Welsh et al. 2000). Timber harvest in close proximity to stream channels destroys

root structure, destabilizing the soil and increasing sedimentation in the water (Murphy

and Meehan 1991). In some cases, removal of forest canopy that leads to increased

fluctuation in stream temperature can be lethal to some stream-dwelling organisms that

rely on a narrow temperature range (Vannote et al. 1980). However, previous research

has also shown increased growth of salmonids in logged streams due to increased

sunlight and therefore increased primary production (Bilby and Bisson 1987). Timber

harvest can also reduce allochthonous input of terrestrial insects to the stream (Murphy

and Meehan 1991).

Since salmonids can consume more than 80% of benthic prey production in

streams (Huryn 1996), food availability is a critical factor for survival during the stream-

rearing and subsequent life history stages. Juvenile stream-dwelling salmonids feed

primarily on aquatic invertebrates living in the substrate or drifting in the water column,

and terrestrial invertebrates that have fallen into the water and are drifting downstream

(Murphy and Meehan 1991, Huryn 1996). Therefore, riparian vegetation that fosters

3

terrestrial invertebrate input and contributes to aquatic invertebrate production should be

associated with higher fish productivity.

Stream-dwelling fishes must be able to adapt to changes in food availability

among seasons. Seasonal fluctuations between higher levels of in situ prey production

and allochthonous input are commonly seen in stream ecosystems (Nakano and

Murakami 2001). Aquatic invertebrate production tends to peak in the spring when

terrestrial insect production is low. Conversely, terrestrial insects contribute significantly

to the drift in the summer when aquatic invertebrate production is low (Kawaguchi and

Nakano 2001, Kawaguchi et al. 2003). Therefore, terrestrial subsidies to stream dwelling

fish can be important in some streams, particularly in low-productivity streams.

Since habitat can affect food availability and temperature, which directly affect

acquisition and expenditure of energy by stream-dwelling fish, a bioenergetic analysis of

seasonal energy gains and losses provides a conceptual framework for linking habitat

characteristics to growth and survival of steelhead. Bioenergetics modeling is a useful

tool for evaluating the importance of energy acquisition (food availability and energetic

quality) and expenditure (respiration, activity, and waste) to fish in response to

temperature, prey composition in the diet, and body size of the consumer. The purpose

of this study was to compare age-specific size and growth and to estimate food

consumption and growth efficiency of O. mykiss among streams differing along

environmental gradients to identify forest and stream conditions that were most

conducive to fish growth. Data were collected seasonally across an entire growing

season (April, June, August, and October) to record changes in temperature, growth, diet,

and prey availability. Streams were categorized according to temperature (warm vs.

4

cool) and forest cover type (conifer-dominated, late seral mixed forest, and lowland small

hardwood) and all comparisons were made with respect to these groups (Table 2.1).

Linkages between these differences in consumption, growth, and growth efficiency and

environmental gradients within the watershed will be used to make recommendations to

local forest managers and fisheries scientists.

STUDY SITE

The Trinity River is the largest tributary of the Klamath River. Construction of

the Trinity Dam in 1962 (Mills et al. 1997), blocked upstream passage of anadromous

fishes. The South Fork Trinity River watershed covers about 2538 km2 and drains into

the mainstem Trinity River, downstream of the dam (Figure 1.1). The entire reach of the

South Fork Trinity, designated as a wild and scenic river, remains undammed and

supports populations of spring and fall run chinook (O. tshawytscha), coho (O. kisutch),

and summer and winter steelhead. The coho salmon in this region were listed as

threatened under the Endangered Species Act in 1997 (Weitkamp et al. 1995), and the

spring chinook and summer steelhead have been termed sensitive species by the U.S.

Forest Service (John Lang, Hayfork Ranger Station, personal communication, 12 October

2002).

PROJECT BACKGROUND

The Northwest Forest Plan is a large scale forest management structure with the

goal of coordinating ecosystem management, monitoring, and adaptive management for

nearly 24 million acres of National Forest Lands in Washington, Oregon, and northern

5

California (Ringold et al. 1999). A key component to the monitoring strategies

developed for the Northwest Forest Plan is a transition from single-species approaches to

a more comprehensive habitat-based ecosystem approach. To better understand the

benefits and limitations of this approach to monitoring, basic research was initiated to

determine the ability to classify habitat structure, based upon data from multiple spatial

scales, and whether or not the habitat classifications were associated with variation in

species population dynamics (Noon 1999).

A project designed to describe the multiscale habitat relationships of herpetofauna

across the South Fork Trinity River watershed was initiated in 2000 (Hartwell Welsh and

Garth Hodgson, USDA Forest Service, Redwood Sciences Laboratory, personal

communication, 8 July 2002). The design and implementation of this project was based

upon results obtained from other studies in the same ecoregion (Welsh and Hodgson

1997, Welsh and Lind 2002). The goal of this research was to associate herpetofaunal

assemblages to aquatic and riparian habitat structure classified from variables measured

at three spatial scales: macroenvironmental, mesoenvironmental, and

microenvironmental (Tables 1.1, 1.2). The macroenvironmental scale refers to variables

at the sub-basin scale (1 km2 to 10 km2), such as sub-basin area, percent sub-basin

dominant vegetation, and road crossings above the sample reach. Mesoenvironmental

scale variables (10 m2 to 25 m2) refer to area adjacent to the sample reach that define

stand structure and age as well as aquatic habitat (e.g., stream temperature).

Microenvironmental scale variables (1 m2 to 10 m2) were fine scale variables associated

with individual sample units, such as substrate composition and spatial dimensions of

habitat units. Additional taxa, such as birds and fish, were added to determine the

6

effectiveness of the multiscale habitat associations for other groups of animals.

Geographic information system (GIS) software was used to divide the watershed

into 15 polygons of roughly equal size. Four points were located at random within each

polygon. Field crews used maps to find the stream (at least 300 m of continuous above-

ground flow) closest to each point (Garth Hodgson, USDA Forest Service, Redwood

Sciences Laboratory, personal communication, 15 September 2003). If the stream

chosen was too deep to be surveyed or more than 2 km from an access point, another

random number was drawn. Using this method, 60 sites (within 60 separate streams) in

the South Fork Trinity River basin were selected.

In 2001, researchers and field crews from the USDA Forest Service Redwood

Sciences Laboratory in Arcata, CA, and the US Geological Survey Western Fisheries

Research Center in Seattle, WA conducted extensive stream surveys. They recorded

macro-environmental variables, such as slope, aspect, elevation, vegetation type, etc,

within one 300-m reach in each stream (Tables 1.1, 1.2). Principle components analysis

(Figure 1.2) and cluster analysis (Garth Hodgson, USDA Forest Service, Redwood

Sciences Lab, personal communication, 08 July 2002) were used to separate the streams

into four groups, characterized by dominant vegetation type. The four groups were

conifer-dominated, hardwood headwaters, late seral mixed forest, and low elevation

small hardwood. Data were collected in 2001 and 2002 such that relationships among

species and environmental parameters could be examined using an interaction assessment

(INTASS) model (Emlen et al. 2003).

O. mykiss were present in 23 of the 60 stream reaches, but were not found in any

of the hardwood headwaters streams. Otolith analysis of incidental mortalities in 2001

7

determined that the ages of O. mykiss in these streams ranged from 0 to 4 years (Jeffrey

Duda, USGS, Biological Resources Division, Western Fisheries Research Center,

personal communication, 25 June 2002). O. mykiss in the South Fork Trinity River

watershed exhibit two life history strategies: resident (rainbow trout) and anadromous

(steelhead) (Brett C. Harvey, USFS, Redwood Sciences Laboratory, personal

communication, 25 June 2002).

OBJECTIVES

In order to identify forest and stream conditions that were most conducive to fish growth

and productivity, our research objectives were to:

1. Compare physical stream characteristics, including stream temperature, discharge,

and invertebrate drift, among streams varying along an environmental gradient

from higher elevation conifer-dominated forest to mid-elevation mixed forest to

lower elevation small hardwood forest in the South Fork Trinity River watershed,

2. Compare size-at-age and growth of ages 0-2 O. mykiss among stream types,

3. Describe variation in O. mykiss diet and prey electivity among streams, and

4. Use bioenergetics modeling to compare consumption and growth efficiency of O.

mykiss among streams.

APPROACH

We sampled twenty-three fish-bearing streams in July and August 2002. Our

sampling protocol followed the INTASS project methods from 2001 (Emlen et al. 2003).

These streams fell into 3 of the 4 forest cover types (conifer-dominated, late seral mixed

8

forest, and low elevation small hardwood). We excluded streams in the hardwood

headwaters category because no fish were found in these streams in 2001.

Stream temperatures in 2002 were higher in the small hardwood category during

the late summer and early fall than in the conifer-dominated or late seral mixed forest

categories. As water temperatures approach and exceed 20°C, the specific rate of growth

(g/g/d) for O. mykiss begins to decrease (Hanson et al. 1997). Although temperatures did

not approach 20°C for the pooled forest cover categories, individual streams did reach

this temperature range during summer 2002. The bioenergetics modeling output showed

periods of diminished growth associated with these warm temperatures. These results

suggested that more research was necessary to determine the effects of seasonal variation

in diet, prey availability, and temperature.

In 2003, we reduced the number of sample streams to nine and repeated data

collection at each stream in April, June, August, and October to capture seasonal

differences in the streams. Three streams were located within each of the three forest

cover types (conifer-dominated, late seral mixed forest, and low elevation small

hardwood). We chose the three streams within each category to represent a range of

temperature regimes; thereby creating a gradient including five vegetative-thermal

categories: conifer-cool, conifer-warm, mixed-cool, mixed-warm, and hardwood-cool

(Table 2.1). We compared invertebrate drift, O. mykiss body condition (relative weight),

diet, prey electivity, consumption, and growth efficiency among these five categories

throughout the study.

We used a Wisconsin bioenergetics model to estimate O. mykiss consumption and

growth efficiency based on field measurements of growth, diet, and stream temperature.

9

(Hanson et al. 1997). The model equated consumption to growth minus losses from

metabolism and waste production (C = M + W + G, Hanson et al. 1997). Physiological

parameters, including costs associated with metabolism and waste production, for O.

mykiss were based on literature values (Rand et al. 1993). Thermal experience was input

as daily temperature averages from field measurements. Energetic quality (J/g wet

weight) of prey items was based on bomb calorimetry results from invertebrates in the

drift net samples (Tables 2.3a, 2.3b) and literature values (Cummins and Wuycheck

1971). Model runs were conducted such that consumption and growth efficiency could

be compared among vegetative-thermal categories, age classes, and seasons.

Seasonal consumption rates were estimated for individual O. mykiss of each age

class by the bioenergetics model. The incremental weight gain for each age was divided

by the corresponding consumption rate for each season to estimate growth efficiency. In

addition, the estimated consumption C was reported as a p-value (p=C/Cmax), the

proportion of the maximum consumption rate Cmax for a fish after accounting for the

effects of body mass and thermal experience. These p-values indicated whether feeding

was limited by access to food. Low p-values (food-limited conditions) reduce a fish’s

optimal feeding temperature and lower the scope for growth (Figure 1.3). Therefore, if

our streams were food-limited, we would expect to see lower or negative growth in the

warmer streams during the summer months. However, this expectation may be

confounded by the possibility that warmer streams receiving more sunlight may have

higher prey availability than cooler streams due to higher levels of primary production.

We also expected the higher elevation (conifer and mixed) streams to have lower

invertebrate production than the lower elevation (small hardwood) streams. Therefore,

10

O. mykiss in the higher elevation streams should have lower consumption rates and

therefore lower growth during warm periods than O. mykiss in the lower elevation

streams. Food quality should be higher in streams with greater input of aquatic adult and

terrestrial adult insects that have lower water contents than in streams with more

immature aquatic insects that have higher water contents. We expected to see higher

terrestrial insect input in the lower elevation small hardwood category and therefore

higher growth efficiencies in these streams than in the other two categories.

11

Figure 1.1. Map of California and the South Fork Trinity River watershed. Nine study

streams are labeled with triangles.

South Fork

Trinity River

Barker

Carrier

Potato

Ditch

Chanchellula

West Twin

Olsen

Monroe

Underwood

Barker

12

Figure 1.2. Principle components analysis of 24 habitat variables (Tables 1.1, 1.2) from

multiple spatial scales in the South Fork Trinity River watershed. The first two axes

explain 35% of the variance and separate forest habitat structure categories. A subset of

streams sampled from each category are labeled. Vectors indicate correlation of each

habitat variable with each principle components axis.

13

Temperature (oC)

0 5 10 15 20 25

Specific rate (g/g/d)

0.00

0.25

0.50

0.75

1.00

Cmax

25% Cmax

Respiration

50% Cmax

Figure 1.3. Maximum consumption (Cmax) and respiration of O. mykiss as a function of

temperature. Growth should be approximately equal to the distance between the

consumption curve and the respiration curve at a given temperature. 50% Cmax and 25%

Cmax curves represent reduction in growth potential and optimal feeding temperature

when prey rations are limited.

14

Table 1.1. Habitat variables, including percent stream canopy and upland canopy and abundance of conifers and hardwood trees,

for 60 streams in the South Fork Trinity River watershed. These variables were used to place streams into forest habitat structure

categories.

Forest Habitat

Structure Category

% Stream

Canopy

%

Upland

Canopy

L-R Upland

Canopy1

Conifer2

1

Conifer

2

Conifer

3

Conifer

4

Hdwd3

1

Hdwd

2

Hdwd

3

All 60 Streams

Headwaters (10) 91.7 (1.5) 81.8 (3.2) 13.7 (5.1) 24.1 (4.7) 22.6 (4.2) 17.6 (3.0) 1.3 (0.6) 3.9 (1.8) 3.1 (1.5) 0.4 (0.3)

Conifer (13) 88.2 (1.3) 69.5 (3.9) 26.6 (19.8) 19.2 (2.4) 15.2 (2.6) 13.6 (2.6) 0.92 (0.4) 20.1 (4.4) 5.3 (1.1) .08 (0.07)

Late Seral Mixed (23) 91.4 (1.1) 85.5 (1.8) 3.9 (0.7) 20.4 (2.0) 16.0 (1.9) 19.5 (1.7) 4.1 (0.6) 12.5 (1.8) 4.0 (0.9) 0.2 (0.1)

Small Hardwood (14) 95.9 (0.9) 90.6 (1.5) 8.0 (2.8) 6.8 (1.2) 4.5 (0.8) 8.0 (1.9) 1.4 (0.4) 29.4 (5.4) 18.8 (2.3) 4.1 (1.3)

Streams Sampled for Bioenergetics Study

Headwaters (0) ns ns ns ns ns ns ns ns ns ns

Conifer (3) 92.0 (2.3) 68.7 (5.4) 38.7 (11.0) 20.3 (1.2) 14.0 (3.6) 14.0 (6.4) 0.0 (0.0) 23.3 (6.8) 6.3 (2.7) 0.0 (0.0)

Late Seral Mixed (3) 89.7 (4.3) 90.3 (2.0) 5.3 (2.9) 16.0 (3.5) 14.7 (5.5) 15.0 (5.7) 5.0 (1.7) 10.0 (4.6) 3.7 (0.7) 0.0 (0.0)

Small Hardwood (3) 94.7 (3.8) 91.7 (2.0) 6.3 (4.4) 5.0 (3.0) 3.7 (2.7) 4.3 (3.3) 0.0 (0.0) 24.3 (5.8) 32.3 (1.2) 6.3 (3.9) 1Difference between left bank and right bank upland canopy cover readings

2 Abundance of conifers within 50 m

2 circular plot. Conifer size categories refer to DBH, where 1 = 15-27 cm, 2 = 28-60 cm, 3 =

61-120 cm, 4 = >120 cm 3 Abundance of hardwood trees within 50 m

2 circular plot. Hardwood size categories refer to DBH, where 1 = 15-27 cm, 2 = 28-

60 cm, 3 = >61 cm

15

Table 1.2. Habitat variables, including basin area, elevation, slope, aspect, stand age, and maximum weekly maximum

temperature, for 60 streams in the South Fork Trinity River watershed. These variables were used to place streams into forest

habitat structure categories.

Forest Habitat

Structure Category

Basin Area

(km2) Elevation (m) Slope (%) Aspect1 Age2

MWMT3

(˚C)

MWMT

Amplitude4

(˚C)

All 60 Streams

Headwaters (10) 55.8 (12.5) 1443.4 (98.8) 21.2 (2.0) 0.8 (0.3) 161.8 (18.9) 12.2 (0.4) 1.7 (0.2)

Conifer (13) 870.9 (200.8) 926.8 (87.2) 6.9 (1.3) 1.0 (0.2) 181.2 (23.0) 17.0 (0.5) 3.5 (0.4)

Late Seral Mixed (23) 387.4 (87.7) 1039.4 (39.4) 10.5 (1.4) 1.0 (0.2) 300.6 (20.5) 15.1 (0.4) 2.4 (0.2)

Small Hardwood (14) 245.6 (61.1) 628.8 (52.4) 18.3 (2.2) 0.9 (0.2) 196.9 (21.7) 15.0 (0.4) 1.4 (0.2)

Streams Sampled for Bioenergetics Study

Headwaters (0) ns ns ns ns ns ns ns

Conifer (3) 771.7 (257.7) 1016.7 (173.6) 5.7 (1.2) 1.0 (0.5) 175.0 (39.0) 15.7 (1.2) 2.8 (0.5)

Late Seral Mixed (3) 780.1 (246.6) 966.7 (17.6) 8.0 (1.2) 1.3 (0.6) 248.3 (17.7) 16.2 (0.4) 2.4 (0.6)

Small Hardwood (3) 532.1 (200.0) 594.3 (80.3) 17.3 (2.3) 0.6 (0.2) 158.7 (39.2) 14.8 (0.3) 2.0 (0.1) 1COS(3.14159*(45 - aspect) / 180), where 2 = north, 1 = west or east, 0 = south

2Determined by tree coring at least 1 tree in the dominant cohort of a stand

3Maximum Weekly Maximum Temperature is determined by averaging the maximum temperature during the hottest 7 day

period of summer low flows 4Average maximum – minimum daily temperature of MWMT

16

NOTES TO CHAPTER 1

Bilby, R. E., and P. A. Bisson. 1987. Emigration and production of hatchery coho salmon

(Oncorhynchus kisutch) stocked in streams draining an old-growth and clear-cut

watershed. Canadian Journal of Fisheries and Aquatic Sciences 45:1397-1407.

Brosofske, K. D., J. Chen, R. J. Naiman, and J. F. Franklin. 1997. Harvesting effects on

microclimatic gradients from small streams to uplands in western Washington.

Ecological Applications 7:1188-1200.

Burns, J. W. 1972. Some effects of logging and associated road construction on northern

California streams. Transactions of the American Fisheries Society 101:1-17.

Busby, P. J., T. C. Wainwright, and R. S. Waples. 1994. Status review for Klamath

Mountains Province steelhead. NOAA Technical Memo NMFS-NWFSC-19, US

Department of Commerce.

Cummins, K. W., and J. C. Wuycheck. 1971. Caloric equivalents for investigations in

ecological energetics. International Association of Theoretical and Applied

Limnology Communications 18:1-158.

Emlen, J. M., D. C. Freeman, M. D. Kirchhoff, C. L. Alados, J. Escos, and J. J. Duda.

2003. Fitting population models from field data. Ecological Modelling 162:119-

143.

Hanson, P. C., T. B. Johnson, D. E. Schindler, and J. F. Kitchell. 1997. Fish

Bioenergetics 3.0. University of Wisconsin, Sea Grant Institute, Center for

Limnology.

Huryn, A. D. 1996. An appraisal of the Allen paradox in a New Zealand trout stream.

Limnological Oceanography 41:243-252.

Kawaguchi, Y., and S. Nakano. 2001. Contribution of terrestrial invertebrates to the

annual resource budget for salmonids in forest and grassland reaches of a

headwater stream. Freshwater Biology 46:303-316.

Kawaguchi, Y., Y. Taniguchi, and S. Nakano. 2003. Terrestrial invertebrate inputs

determine the local abundance of stream fishes in a forested stream. Ecology

84:701-708.

Mills, T. J., D. R. McEwan, and M. R. Jennings. 1997. California salmon and steelhead:

beyond the crossroads. Pages 91-111 in D. J. Stouder, P. A. Bisson, and R. J.

Naiman, editors. Pacific salmon & their ecosystems: status and future options.

Chapman & Hall, New York, NY.

17

Moyle, P. B. 1994. The decline of anadromous fishes in California. Conservation

Biology 8:869-870.

Murphy, M. L., and W. R. Meehan. 1991. Stream ecosystems. Pages 17-46 in W. R.

Meehan, editor. Influences of forest and rangeland management on salmonid

fishes and their habitats. American Fisheries Society, Bethseda, MD.

Naiman, R. J., K. L. Fetherston, S. J. McKay, and J. Chen. 1998. Riparian forests. Pages

289-323 in R. J. Naiman and R. E. Bilby, editors. River ecology and

management: Lessons from the Pacific coastal ecoregion. Springer-Verlag, New

York, NY.

Nakano, S., and M. Murakami. 2001. Reciprocal subsidies: dynamic interdependence

between terrestrial and aquatic food webs. Proceedings of the National Academy

of Science 98:166-170.

Noon, B. R. 1999. Scientific framework for effectiveness monitoring of the Northwest

Forest Plan. Pages 49-68 in B. S. Mulder, B. R. Noon, T. A. Spies, M. G.

Raphael, C. J. Palmer, A. R. Olsen, G. H. Reeves, and H. H. Welsh, Jr., editors.

The Strategy and Design of the Effectiveness Monitoring Program of the

Northwest Forest Plan. General Technical Report PNW-GTR-437, Pacific

Northwest Research Station.

Pautske, C. 2001. Endangered and threatened species: final listing determination for

Klamath Mountains Province steelhead. Federal Register 50 CFR Part 223, U.S.

Department of Commerce.

Rand, P. S., D. J. Stewart, P. W. Seelback, M. L. Jones, and L. R. Wedge. 1993.

Modeling steelhead population energetics in Lakes Michigan and Ontario.

Transactions of the American Fisheries Society 122:977-1001.

Ringold, P. L., B. S. Mulder, and J. Alegria. 1999. Establishing a regional monitoring

strategy: the Pacific Northwest Forest Plan. Environmental Management 23:179-

192.

Vannote, R. L., G. W. Minshall, K. W. Cummins, J. R. Sedel, and C. E. Cushing. 1980.

The river continuum concept. Canadian Journal of Fisheries and Aquatic Sciences

37:130-137.

Wallace, J. B., S. L. Eggert, J. L. Meyer, and J. R. Webster. 1997. Multiple trophic levels

of a forest stream linked to terrestrial litter inputs. Science 277:102-104.

Weitkamp, L. A., T. C. Wainwright, G. J. Bryant, G. B. Milner, D. J. Teel, R. G. Kope,

and R. S. Waples. 1995. Status review of coho salmon from Washington, Oregon,

and California. NOAA Technical Memo NMFS-NWFSC-24, U.S. Department of

Commerce.

18

Welsh, H. H., Jr., and G. R. Hodgson. 1997. A hierarchical strategy for sampling

herpetofauna assemblages along small streams in the western U.S., with an

example from northern California. Transactions of the Western Section of the

Wildlife Society 33:56-66.

Welsh, H. H., Jr., and A. J. Lind. 2002. Multiscale habitat relationships of stream

amphibians in the Klamath-Siskiyou region of California and Oregon. Journal of

Wildlife Management 66:581-602.

Welsh, H. H., Jr., T. D. Roelofs, and C. A. Frissel. 2000. Aquatic ecosystems of the

redwood region. Pages 165-199 in R. F. Noss, editor. The redwood forest: history,

ecology, and conservation of the coast redwoods. Island Press, Covelo, CA.

19

Chapter 2. Factors affecting trophic performance of Oncorhynchus

mykiss along forest gradients in the South Fork Trinity River

watershed, California

INTRODUCTION

Fitness of individual fish is directly related to efficient energy acquisition (Arrington

et al. 2002). Several factors affect the assimilation of energy into body mass,

including temperature, prey energy density, activity and metabolic rate, and waste

production (Hanson et al. 1997), and energy acquisition in streams can vary

dramatically in response to different environmental conditions that affect prey

availability, thermal regime, and access to cover from predation, current, or agonistic

interactions. The optimal temperature range for growth is species-specific, and this

optimal range generally shifts to cooler temperatures as food availability and daily

rations decline. Organisms may exhibit negative growth at temperatures well below

or above this optimal range. The energetic quality of prey also varies among life

stages, habitat, and body form of the organism. An energetics-based approach is

useful for determining how all these potentially interacting factors affect energy gains

and losses and how these effects are ultimately expressed as net growth (Hanson et al.

1997).

Stream-dwelling anadromous fishes in California have been declining as a result

of environmental changes such as watershed degradation, diversions, pollution, and other

environmental and anthropogenic impacts (Moyle 1994). Klamath Mountains Province

steelhead is the only evolutionary significant unit (ESU) of steelhead in California that

20

was not listed as endangered or threatened under the Endangered Species Act as of

2003 (Pautske 2001). However, this ESU was deemed likely to become threatened in the

foreseeable future. It is important to understand factors that limit production of this stock

to ascertain whether some critical thresholds in energy balance are in danger of being

reached. Bioenergetics modeling can determine whether these stocks are on the margin

of falling into negative growth regimes during critical periods of their life history. These

analyses may also be used to understand why other California stocks are currently listed.

The ability of streams to support fish populations depends on several factors

including: temperature, flow, seasonal variability, level of disturbance, predators, trophic

competitors, and food availability (Moyle 1994, Beecher et al. 1995, Arrington et al.

2002). Territorial drift-feeding fish must choose a stream position to maximize net

energy acquisition while minimizing costs associated with holding position, competition

with other fish for territories, and predator avoidance (Hill and Grossman 1993, Hughes

1998). Environmental changes related to seasonal temperature shifts, fluctuation in prey

availability, and flow variability can easily upset this delicate balance (Nakano and

Murakami 2001). These complex dynamics render the task of determining the

contribution of different environmental variables from multiple spatial scales to fish

fitness difficult.

Our objectives were to determine whether seasonal and age-specific growth of O.

mykiss varied among the three predominant forest types or thermal regimes, and whether

the seasonal patterns of prey availability, diet composition, and temperature associated

with these forest-temperature categories affected consumption, growth, and growth

efficiency of O. mykiss. We examined the variability in these factors among stream

21

categories that varied along a vegetative-thermal gradient from higher elevation

conifer-dominated forest to lower elevation small hardwood forest. We hypothesized

that fish condition, consumption, and growth efficiency should vary predictably along

this gradient. We selected nine streams within the South Fork Trinity River watershed

representing a range of forest cover types and temperature regimes that could be sampled

throughout the summer growing season. Seasonal consumption rates of major prey

categories were estimated to quantify the importance of each prey to the energy budget of

the different age classes of O. mykiss in each stream, and growth efficiency (change in

weight/consumption) was calculated to determine and compare the net effect of thermal

regime and prey quality on growth for each age class among the nine streams. These

comparisons were used to identify significant factors affecting efficient energy

acquisition for steelhead in this region.

STUDY SITE

The South Fork Trinity River watershed covers approximately 2538 km2 in

northwest California, about 95 km east of Eureka, CA. The South Fork Trinity River

enters the Trinity River downstream of a dam constructed on the mainstem in 1962 (Mills

et al. 1997), leaving the entire reach of the South Fork undammed and accessible to

anadromous fish populations such as spring and fall run Chinook salmon (O.

tshawytscha), coho salmon (O. kisutch), and summer and winter steelhead (Figure 1.1).

Based upon a pilot study in 2002 involving 23 streams, we selected nine

tributaries of the South Fork Trinity River for additional sampling in 2003. These

streams were categorized into conifer-dominated [conifer], late seral mixed forest

22

[mixed], and low elevation small hardwood [hardwood] based on ordination of

physical and environmental variables representing three spatial scales, including

dominant riparian vegetation type, elevation, aspect, canopy cover, soil and water

temperature, road density, fire disturbance, and geology (Welsh and Hodgson, USDA

Forest Service, Redwood Sciences Laboratory, unpublished data). Each stream within

these habitat structure categories was also designated as cool or warm, based upon 2002

and 2003 summer low flow temperature measurements (Table 2.1). O. mykiss in these

streams are suspected to be a mix of resident (rainbow trout) and anadromous (steelhead)

life history strategies (Brett C. Harvey, USFS, Redwood Sciences Laboratory, personal

communication, 25 June 2002). Other aquatic vertebrates in these tributaries include

Pacific giant salamanders (Dicamptodon tenebrosus), rough-skinned newts (Taricha

granulosa), yellow-legged frogs (Rana boylii), tailed frogs (Ascaphus truei), and pacific

coast aquatic garter snakes (Thamnophis atratus) (Welsh and Hodgson, USDA Forest

Service, Redwood Sciences Laboratory, unpublished data).

METHODS

To describe the role of various environmental variables in O. mykiss energy

acquisition and growth efficiency, we assigned streams to five vegetative-thermal

categories based on forest cover type (conifer, mixed, hardwood) and temperature regime

(cool, warm; Table 2.1). We used a bioenergetics model to estimate and compare

consumption and growth efficiency of juvenile O. mykiss among the five vegetative-

thermal categories using age-specific growth, diet, and environmental data from each

stream as inputs for the model simulations.

23

Physical Attributes and Invertebrate Composition

We sampled each stream within a 300-m reach selected by US Forest Service

researchers in 2001 (Garth Hodgson, USDA Forest Service, Redwood Sciences

Laboratory, personal communication).

We deployed HOBO® Temp temperature loggers in the nine study streams

during the growing season (April to October 2003) and year-round (April 2003 to May

2004) in three secure sites (Barker Creek, Carrier Gulch, and West Twin Creek). The

loggers, which were calibrated simultaneously, recorded temperature every 20 minutes

(accuracy: ±0.7°C at 21°C). We compared average daily temperatures between streams

in the three forest cover categories and among seasons. Maximum daily stream

temperatures were screened for periods of potential physiological stress on O. mykiss

(temperatures nearing and exceeding 20°C). We recorded the duration and magnitude of

all such periods for consideration in bioenergetics modeling and used average daily

temperatures during summer low flow (late June-early September) to assign streams to

the cool or warm temperature category. Streams with average daily summer

temperatures exceeding 17°C were categorized as warm and those below 17°C were

categorized as cool.

In each sampling reach, we measured a bank-full cross-sectional profile of depth

and water velocity using a meter stick and a Global Flow Probe® current meter in June,

August, and October 2003 (Rantz 1982a, 1982b). In April 2003, we attempted to

measure the cross-sectional profile of depth and water velocity with a meter stick and an

electromagnetic current meter in two streams. However, a malfunction forced us to

24

measure velocity in the five remaining streams by timing the travel distance of a

floating object drifting downstream (Gordon et al. 1992). Extremely high stream flows

during April 2003 made it unsafe to access two streams (Underwood and Chanchellula)

for velocity measurements. We calculated stream discharge for measurements taken with

both the electromagnetic current meter and the Global Flow Probe® current meter, using

the following equation:

nnn vdwvdwvdwQ +++= ...222111 ,

where w was subsection width, d was water depth, and v was water velocity (McMahon

et al. 1996). We used the Robins-Crawford method to estimate discharge for the April

stream velocity measurements taken with a floating object; following the equation:

Q =3

3333

2

2222

1

1111 *********

t

ladw

t

ladw

t

ladw++ ,

where w was the subsection width, d was the mean depth, a was the coefficient (0.8) that

converted surface velocity to mean velocity for a rough stream bottom, and t was the time

it took a floating object to travel a specified distance (l) (Orth 1983). We compared mean

discharge among streams in each vegetative-thermal category by season, and overall

mean discharge among seasons using ANOVA (Zar 1999). When a significant

difference was found, we used the Tukey multiple comparisons test to identify

relationships between all possible pairs of means (Zar 1999).

We sampled stream drift to determine the relative abundance of aquatic and

terrestrial invertebrates in the water column and to estimate the type and composition of

allochthonous input. Two drift nets were deployed in each stream, one in a riffle and one

in a pool. The drift samplers, constructed from two interlocking PVC tubes, had

25

openings with a cross-sectional area of 0.621 m2. The nets were set out (fully

submerged) before dusk and retrieved after dawn to capture evening and morning peaks

in invertebrate activity (Rabeni 1996). The volume of invertebrates captured in each drift

sample, the area of the stream and the drift sampler, and the stream discharge

measurement (m3/s) were used to quantify prey abundance per stream. The proportions

of each invertebrate category in the drift samples were multiplied by their energy density

to estimate prey availability in terms of energy density (J/g). We froze drift samples

immediately following retrieval to preserve specimens suitable for caloric content

analysis using bomb calorimetry (see Diet Analysis section below). We compared the

proportions of each invertebrate functional group in the drift net samples by season (June,

August, October), forest cover type (conifer, mixed, hardwood), and temperature regime

(cool, warm summer temperatures) using MANOVA on the square root-transformed

taxonomic proportions in each drift sample (Zar 1999).

Leaf litter and other organic material in the drift net samples were separated from

invertebrates, then biovolumes (mL) of available invertebrate prey and leaf litter/organic

material were measured in a graduated cylinder. Invertebrates found in the drift samples

were identified to order and blotted wet weights were measured for each order in the

sample. We dried samples of each order for three to six days at 55°C to obtain a constant

dry weight. When necessary to obtain sufficient dry weights (0.2 – 0.02 g) for bomb

calorimetry, we pooled orders into functional groups so that we could at least obtain

energy densities for these composite groups. Each functional group was ground into a

fine powder, redried at 55°C to a constant weight, pressed into small pellets, and burned

in the bomb calorimeter to obtain an energy density value (cal/g dry weight). We used

26

the wet weight to dry weight ratio for each sample to convert the calorimetry energetic

value to cal/g wet weight and then converted calories to Joules (4.185 J/cal) to obtain the

correct units (J/g wet weight) for bioenergetics modeling.

Fish Sampling

We sampled fish within each 300-m stream reach using a battery-powered Smith-

Root Model 12b backpack elecroshocker. Using a two-pass method, we electrofished

subsequent pools and riffles in the reach until a minimum of 20 fish were captured. We

minimized current and voltage while electrofishing to reduce trauma while facilitating

capture (Reynolds 1996). We anesthetized all O. mykiss over 30 mm in a bucket of

ambient water and dissolved Tricaine Methanesulfonate (MS-222) powder (Bowser

2001), measured fork length and weight (Anderson and Neumann 1996), removed

stomach contents using a non-lethal stomach lavage technique (Giles 1980), and

preserved the contents in 90% ethanol (Bowen 1996). Scales were collected from the

preferred region below and posterior to the dorsal fin of each O. mykiss captured, placed

on numbered gummed cards, and pressed into acetate impressions for age and growth

analysis (Devries and Frie 1996).

Diet Analysis and Prey Electivity

We identified invertebrates in the stomach contents to order and measured blotted

wet weight for each order represented in individual stomach samples. Diet was described

in terms of the percent composition by weight of each functional group (aquatic larvae,

aquatic nymphs, aquatic other, aquatic adults, and terrestrial insects) within each

27

stomach. We used MANOVA to compare how weight proportions of each

invertebrate functional group in the diet varied by season, forest cover, temperature

regime, and age class (Zar 1999).

We computed electivity indices for key taxa found in the stomach contents using

Manly’s α (Manly et al. 1972, Chesson 1983). This index indicated the consumer’s

preference for prey using the equation:

=

∑=

m

i ii

iii

nr

nr

1/

/α ,

where Manly’s preference index αi for prey type was calculated using the proportion of

prey type i in the diet (r), the proportion of that prey type (i) in the environment (n) from

drift samples, and the number of prey types possible (m). We divided one by m, the

number of prey types possible (three or four), to obtain a preference threshold for O.

mykiss.

Age and Growth Analysis

We determined age and back-calculated growth for O. mykiss from scales

(Devries and Frie 1996). Fish lacking an annulus were considered young-of-year (age 0).

Additional scale measurements included the distance from the focus to the scale edge and

the distance from the focus to each scale annulus (each measurement following the

original radius measurement). We back-calculated length-at-age using a regression

between fish length (mm) and scale radius (µm) at each annulus. A length-weight

regression converted back-calculated fork lengths (FL, mm) to weight (R2>0.94, p<0.001

for all streams; Table 2.2).

28

Growth and relative weights for the three age classes (age 0, 1, and 2) of O.

mykiss were compared among seasons, forest cover types, and temperature regimes. We

converted fork lengths to total lengths (mm; TL) for all O. mykiss from a regression of 10

pairs of fork and total lengths (R2=0.999; p<0.001):

TL = -0.6726 +1.0626*FL

We calculated length-specific standard weight Ws (Wege and Anderson 1978) for

lotic rainbow trout using the equation (Simpkins and Hubert 1996):

TLWs 1010 log024.3023.5log +−= ,

where TL was the converted total length. In order to compare fish condition between

seasons and forest cover types, we calculated relative weight (Wr) using the equation:

100*)/( sr WWW = ,

where W was the fish wet weight (g). For each season, we tested for significant

differences in relative weight among forest cover types and between temperature regimes

using ANOVA (Zar 1999).

Bioenergetics modeling

We used a Wisconsin bioenergetics model (Hanson et al. 1997) to estimate the

consumption necessary for O. mykiss to grow the amount observed over specified time

intervals (1-4 seasons). The model estimated the necessary energy consumed

(consumption; C) from the sum of somatic growth (G), activity, respiration, and specific

dynamic action (metabolism; M), and waste (W):

WMGC ++= .

29

The physiological parameters (including energetic costs associated with metabolism

and waste production) for O. mykiss were taken from Rand et al. (1993). Thermal

experience for O. mykiss was input as the average daily temperature from field

measurements from each stream during April-October 2003, and from just Barker Creek

and Carrier Gulch during October 2003-May 2004. Bomb calorimetry results from drift

net invertebrate samples provided input values for prey energy density (J/g wet weight).

We used literature values for the energy density of Dipteran larvae (Cummins and

Wuycheck 1971) because we had insufficient material to provide direct measures.

Bioenergetics model simulations were run for each age class in each stream

during the summer growing season to compare consumption and growth efficiency

among forest cover types and temperature regimes. For each stream, diet and body mass

data for each age class during June, August, and October were used as inputs for stream-

and age-specific simulations (Table 2.4a). We separated O. mykiss into age classes based

on scale data and estimated seasonal growth in each stream by taking the difference in

average weight between sampling events in June, August, and October 2003. We

excluded ages 3 and 4 O. mykiss, and in most cases age 0 in June and age 2 in October

from analysis due to low sample sizes. We also conducted year-round model runs for a

cool stream with conifer forest cover (Barker Creek) and a warm stream with conifer

forest cover (Carrier Gulch) that had year-round temperature measurements (Table 2.4b).

To determine the impact of increased summer stream temperatures on O. mykiss growth,

we added 2°C to the average daily summer low flow temperatures for Barker Creek and

Carrier Gulch and used the model to estimate growth based on consumption rates from

the previous model runs for these two streams.

30

Seasonal and annual consumption rates of each prey category were estimated

for individuals of each age class by the bioenergetics model. The estimated consumption

C was also reported as a p-value (p=C/Cmax), the proportion of the theoretical maximum

consumption rate Cmax for a fish after accounting for the effects of body mass and

thermal experience. These p-values indicated whether feeding was limited by access to

food. In addition, the incremental weight gain for each age was divided by the

corresponding consumption rate for each season to estimate seasonal or annual growth

efficiency. These metrics allowed comparisons of prey-specific consumption, feeding

rate, and growth efficiencies among age classes, seasons, and streams.

RESULTS

Physical Attributes and Invertebrate Composition

In the cool streams, mean daily temperatures were 6.4°C (SD=1.2) during winter

and 13.8°C (SD=1.8) during early June – early September, whereas the mean daily

temperatures in warm streams were both more variable and extreme (5.5° C; SD=1.5

during winter and 15.0°C; SD=2.2 during June-early September; Figure 2.1). Maximum

daily temperatures during July in the warm-mixed category exceeded 20°C for twelve

days in late July.

Within each forest cover category and month, mean stream discharge did not

differ among streams (ANOVA, p>0.58), nor did it differ between cool and warm

streams (two-sample t-test, p>0.11 for all months; Figure 2.2). However, overall mean

31

stream discharge in April was significantly higher than mean discharge in later months

(ANOVA, p<0.001; Tukey, p<0.001).

Overall, total invertebrate biovolume (mL/h) did not vary according to month

(ANOVA, p=0.208) or stream type (p=0.187; Figure 2.2). The biovolume of individual

prey types did not vary significantly among stream types or months, except during April

when the volume of aquatic nymphs was significantly higher than August or October

(ANOVA, p=0.034; Tukey, p<0.032).

Diet Analysis

Overall, the proportion of aquatic nymphs and adult insects (of both aquatic and

terrestrial origins) in the diet varied by month, forest cover, stream temperature, and fish

age, whereas other prey types showed no pattern (Figure 2.3). The proportion of aquatic

nymphs in the diet was significantly lower in August than June and October (ANOVA,

p<0.001; Tukey, p<0.002), and the proportion of adult insects (aquatic and terrestrial

origin) in the diet was significantly higher in August than in June and October (ANOVA,

p<0.006; Tukey, p<0.023) for comparisons across thermal-vegetative categories and

within cool streams for all forest cover categories. The proportion of adult insects was

significantly higher in mixed-cool streams than mixed-warm (ANOVA, p=0.001; Tukey,

p=0.039) and conifer-warm streams (ANOVA, p=0.001; Tukey, p=0.001). In general,

the proportion of aquatic nymphs in the diet decreased with age, while the proportion of

adult aquatic and terrestrial-origin invertebrates increased in the diet with age (Figure

2.3). For all comparisons, age 0 consumed a higher proportion of aquatic nymphs than

32

ages 1 and 2 (ANOVA, p<0.001; Tukey, p<0.007), and age 2 consumed a higher

proportion of adult insects than ages 0 and 1 (ANOVA, p<0.001; Tukey, p<0.021).

Overall, both drift and diet proportions were higher for aquatic nymphs in June

and October and higher for adult invertebrates in August (Figures 2.2, 2.3). Within cool

streams of all forest cover types, fish showed a general preference for aquatic larvae and

nymphs in August and for adult insects in October (Figure 2.4). We did not detect a

consistent pattern in prey electivity in the warmer streams, except during August when all

ages showed high electivity for aquatic larvae in conifer-warm streams and adult insects

in mixed-warm streams.

Age and Growth

The age distribution of O. mykiss in each month, forest cover category, and

temperature regime indicated that age-0 O. mykiss were not fully recruited in all streams

by June 2003. However, age 0-2 samples from all stream types were reasonably

represented in August (Table 2.2).

In general, body weights were higher in conifer-cool and hardwood-cool streams

for age 0 O. mykiss (ANOVA, p<0.007; Tukey, p<0.031; Figure 2.5). Body weights did

not differ among stream types by month (ANOVA, p>0.169), except age 1 fish had

higher June weights in the conifer-cool and conifer-warm streams than mixed-warm

(ANOVA, p=0.008; Tukey, p<0.042).

For most age classes, relative weights did not differ significantly among forest

cover types and temperature regimes (ANOVA, p>0.078), except among thermal-

vegetative combinations in June (ANOVA, p=0.021) and among age classes in October

33

(p<0.001; Table 2.3). In June, relative weights in the cool-conifer streams were lower

than in the warm-conifer streams (Tukey, p=0.041) and both cool and warm mixed

streams (p<0.033). In October, relative weights for age 0 were significantly higher than

age 1 (Tukey, p=0.003) and age 2 (p=0.007) for all streams. Within cool streams,

relative weights in the mixed forest category were higher than the conifer and hardwood

categories (ANOVA, p=0.019; Tukey, p<0.001).

Bioenergetics Modeling

In model simulations, O. mykiss consumption ranged from 6-45% (p-

values=0.06-0.45) of maximum consumption and averaged <25% during June to October

(Table 2.5a). Model-derived growth efficiency was generally higher during June-August

and declined during the August-October simulations (Table 2.5a). Total model-derived

consumption was low (<50 g) in most cases, with the exception of age 2 O. mykiss in

Potato Creek and the hardwood-cool streams. O. mykiss generally consumed more in the

hardwood-cool streams than in other forest cover types (Figure 2.6). Terrestrial and adult

aquatic invertebrates represented important fractions of the prey biomass consumed for

most age classes in all streams during June-October (5-41% for age 0, 9-84% for age 1,

27-92% for age 2; Figure 2.6), and terrestrial insects represented 4-51% of the total

energy budget during this period. For age-2, the cool hardwood streams showed lower

reliance on terrestrial and aquatic adults (30-52%) than the conifer and mixed categories,

but exhibited higher overall consumption rates (>50 g).

According to field measurements, most of the growth happened outside the

presumed growing season of June-October simulated here; especially for age 0-1. The

34

higher observed weight gain between April and June was accompanied by higher

consumption rates and p-values (over 25%) in Barker Creek and 23-44% in Carrier

Gulch, and higher growth efficiencies, (Table 2.5b). Growth over October-June was

more dramatic in the conifer-warm stream (Carrier Gulch) than in the conifer-cool stream

(Barker Creek; Figure 2.7). When we added 2°C to the summer low flow temperatures

for Barker Creek and Carrier Gulch and modeled growth based on estimated

consumption rates (p-values) from the previous runs, average weight decreased 5.4-8.9%

for age 0 O. mykiss and 11.5-18.8% for age 1 (Figure 2.7).

DISCUSSION

The bioenergetic simulations indicated that summer growth of Oncorhynchus

mykiss in the nine study streams was limited primarily by food supply and secondarily by

elevated temperatures. Consumption estimates during June-October were routinely <25%

of the physiological maximum consumption rate for fish of comparable size under the

prevailing temperature regimes. Low ration accentuated the detrimental effects of high

stream temperatures, even though temperatures remained well below the lethal range.

For the two streams with year-round temperature data (Carrier Gulch and Barker

Creek), our modeling results showed rapid increase in growth during spring (April to

June), with higher proportions of maximum consumption during this period. During the

spring, these streams experienced both lower temperatures and higher flows. Higher

stream flow likely increased prey availability, and lower temperatures reduced the impact

of thermal stress on O. mykiss. The simulated reduction in growth for age 0 and 1 O.

mykiss under slightly warmer (+2°C) summer low flow conditions further emphasized the

35

effect of low prey availability on temperature-dependent growth. Though

temperatures did not exceed 20°C, low consumption rates caused the optimal temperature

for growth to decrease considerably, and the result was low or negative growth.

In addition to the differences in p-values between spring and our June-October

simulation period, our field measurements of O. mykiss growth indicated that age 0 fish

approximately tripled in weight between October and the following June. Size-selective

mortality often explains patterns such as this (Marschall and Crowder 1995). If the

smallest fish at the end of the summer do not survive through the winter, then the

apparent growth that resulted from sampling the larger surviving age 1 fish in June would

overestimate the true growth rate during October-June. However, we did not see

tightening of the length frequency curve for age 1 fish, which may indicate such pressure.

The more likely explanation for increased growth during winter and spring was improved

growing conditions due to lower stream temperatures and higher prey availability, and

this was supported by the higher growth efficiencies estimated during these periods. The

already low consumption rates and growth efficiencies during June-August generally

declined even further during the August-October simulation periods. As prey production

decreased through the summer (Nakano and Murakami 2001) and stream temperatures

increased, fish simultaneously experienced lower prey availability and higher thermal

stress which reduced growth efficiency.

The most marked declines in growth during late summer involved age 1 fish in

three streams: Ditch Gulch, Potato Creek, and Underwood Creek. Potato Creek had

maximum daily temperatures exceeding 20ºC for a two-week period (July 21 – August 4)

and average daily temperatures between 19ºC and 20ºC for that same period during

36

summer 2003. These temperatures can be stressful to salmonids when food is limited

and these confounding factors can cause weight loss (Dwyer and Kramer 1975). Based

on the low consumption rates estimated in the nine streams, the optimal temperature for

growth for these O. mykiss would have been considerably lower than the observed

temperatures.

Growth efficiency was higher in conifer-cool streams than in conifer-warm

streams. Possible explanations for this difference include lower daily and annual

temperature fluctuation, more moderate average temperatures overall, and higher

allochthonous invertebrate input due to thicker canopy cover.

O. mykiss in the South Fork Trinity River watershed ranged in age from 0 to 4

years. Most fish (45%) were in their first year of life, though recruitment of age-0 fish

was incomplete during the June samples in many streams. Stream temperatures stayed

low in 2003 due to unseasonably late snowstorms in April. These cool temperatures may

have delayed O. mykiss emergence, causing the fish in June to either be pre-emergent or

too small to capture.

The lack of strong patterns in prey electivity suggests that fish were feeding

somewhat opportunistically on seasonally available prey (Cloe and Garman 1996,

Nakano and Murakami 2001). O. mykiss in our study streams consumed more aquatic

nymphs in June and October, but more adult invertebrates (aquatic and terrestrial origin)

in August. Although not statistically significant, the drift samples indicated a higher

percentage of aquatic nymphs available in June and October and a higher percentage of

adult insects in August.

37

Overall, O. mykiss consumed a higher proportion of adult invertebrates in

mixed-cool streams than in conifer-warm and mixed-warm streams. In the cool streams,

the moderate temperatures may be a function of shade from thick forest cover. Previous

research has determined a link between thinner riparian cover and increased average

stream temperature and temperature fluctuation (Bourque and Pomeroy 2001, Macdonald

et al. 2003). More vegetation over the streambed would likely cause higher levels of

allochthonous input, including adult invertebrate prey sources (Edwards and Huryn 1996,

Nakano et al. 1999). However, fish growth may not be obviously higher in shaded

streams than in more exposed streams. Though thick forest cover offers many

advantages to stream-dwelling fish such as bank stabilization, large woody debris input,

and increased allochthonous input, previous research has noted that logged streams had

higher levels of primary production due to higher sunlight levels and therefore more

aquatic prey production (Bilby and Bisson 1987).

Stream-dwelling salmonids exhibit aggressive defense of feeding territories

(Chandler and Bjornn 1988, Harvey and Nakamoto 1997). Younger and smaller fish are

often relegated to suboptimal habitat, including riffles and tail ends of pools, whereas

older and larger fish often obtain more desirable habitat, such as the heads of pools

(Hughes 1998). Additionally, smaller fish that are vulnerable to predation from larger

fish seek shallow water as protection, while larger fish that are vulnerable to predation

from birds and mammals seek deeper water (Rosenfeld and Boss 2001). Though more

food is generally available in riffles than in pools, fish feeding in riffles will generally

have less time to react and eat food drifting by than fish in slower-flowing pools (Booker

et al. 2004). The fish in the pools have more time to react and can therefore choose

38

between food drifting on the surface and food suspended in the water column or living

on the stream bottom. In our study streams, younger O. mykiss consumed a higher

proportion of aquatic nymphs while older fish consumed a higher proportion of adult

invertebrates. Therefore, younger fish may be defending less desirable feeding habitats

than older fish.

Significant differences in diet among seasons should indicate differences in prey

availability; however we did not detect many significant differences in prey availability.

Our sampling technique may have over-represented immature aquatic invertebrates

because adult invertebrates (especially aquatic adults) were likely floating on the surface

and may have floated over or past the drift samplers.

Of the food choices experienced by fish in streams, aquatic invertebrates

including larvae and nymphs were less energy-rich than terrestrial or adult aquatic insects

due to differences in water content. Based on our bomb calorimetry results and on

previous research, we expected older fish that consumed more adult or terrestrial

invertebrates to exhibit better body condition than younger fish that consumed more

immature forms of aquatic invertebrates (Mason and Macdonald 1982, Filbert and

Hawkins 1995, Wipfli 1997). However, our results did not detect a significant difference

in relative weight between O. mykiss age classes. Filbert and Hawkins (1995) also

concluded that stream fish production may be food-limited.

Age 0 fish in conifer-cool and mixed-cool streams had higher initial weights in

June than age 0 fish in mixed-warm streams. One may expect the O. mykiss fry to

emerge earlier in warm streams because they were able to develop faster as eggs and

alevins in the gravel. However, the thermograph for our study streams suggested that the

39

“cool” streams experienced more moderate fluctuations from minimum to maximum

temperature over the year than the “warm” streams which showed sharper increases in

temperature from winter lows to summer highs. Since the cool streams appeared to stay

slightly warmer through the winter of 2002-2003, the fry in these streams likely

developed faster and were able to emerge earlier. The earlier emerging fish have the

opportunity to establish feeding territories sooner than the fish that emerge later (Jones et

al. 2003). Since growth conditions apparently degrade significantly during the summer,

earlier emergence during the higher growth period in the spring could confer a significant

selective advantage in these streams.

Overall, we found that season and stream type may not be accurate indicators of

prey availability. However, our drift sampling may have provided an incomplete

estimate for assessing total prey availability. In future studies, it may be useful to deploy

more replicates of rectangular drift samplers that are only partially submerged so that the

surface drift and submerged cross-section can be quantified and translated into unbiased

estimates of surface and water-column drift. These methods, along with diet analysis and

temperature monitoring, should be repeated over subsequent years to investigate how

interannual changes in temperature and flow affect productivity in the streams. In

addition to our focus on environmental factors that affected fish consumption, condition,

and growth efficiency, future research should include an investigation of the effects of

inter- and intraspecific competition for prey and feeding territories in the streams; with a

focus on whether these streams are food-limited. O. mykiss density or occurrence of

other species that compete for similar prey types may affect growth efficiency among

streams.

40

Our results suggest that interannual variation in temperature and/or climate

change could push this potentially food-limited population further into negative growth

patterns. The implications for a population that has been deemed likely to become

endangered or threatened in the foreseeable future are grave. Since the fish in these

streams are presumably eating just enough during the summer growing season to

maintain a productive stock, even slight reductions in ration or increases in temperature

could diminish the stock to dangerous levels.

These results could be extrapolated to predict how other West Coast steelhead

populations would react to a climate shift. Proper forest and stream management for

these stocks that are or may be on the brink of threatened status is essential. Since

consumption rates were limited in our study streams, forest practices that maximize

shading to stabilize and/or reduce stream temperatures may keep streams closer to the

reduced optimal feeding temperature.

CONCLUSION

Few differences were detected in O. mykiss growth, consumption, and growth

efficiency among stream categories. Fish appeared to grow faster during the spring and

possibly winter than during the summer growing season, when slow or negative growth

was measured. Our model results indicated low consumption rates across categories,

which suggest that our streams were food-limited. Though summer low flow

temperatures rarely reached sub-optimal levels, reduced rations caused the optimal

temperature for growth to decrease. Therefore, the summer did not prove to be the

“growing season” for O. mykiss in this watershed. Forest management practices in this

41

region should be designed to optimize stream temperatures for O. mykiss feeding at

low percentages of maximum consumption, and care should be taken to prevent further

reduction of prey availability in this watershed. Results from this study could be used to

predict the effects of climate shift or reduction in prey supply on this and other West

Coast steelhead stocks.

42

Warm

Temperature (

οο οο

C)

0

5

10

15

20

Conifer

Mixed

Cool

Day

0 50 100 150 200 250 300 350

0

5

10

15

20

Conifer

Mixed

Hardwood

Figure 2.1. Average daily temperature in 9 tributaries of the South Fork Trinity

River during 2003. Streams were categorized by forest cover and by temperature

regime.

43

Conifer-warm

Month

April June August October

Average invertebrate biovolume/day

0.00

0.25

0.50

0.75

1.00

1.25

1.50

1.75

2.00

2.25

0

1

Mixed-warm

Month

April June August October

Average invertebrate biovolume/day

0.0

0.5

1.0

1.5

2.0

Stream discharge (m

3

/h)

0

1

Conifer-cool

Month

April June August October

0.0

0.5

1.0

1.5

2.0

X Axis 2

April June August October

Aquatic larvae

Aquatic nymphs

Other

Aquatic adults

Terrestrial adults

Mixed-cool

Month

April June August October

Average invertebrate biovolume (mL/h)

0.0

0.5

1.0

1.5

2.0

Hardwood-cool

Month

April June August October

0.0

0.5

1.0

1.5

2.0

0

1

Figure 2.2. Average invertebrate biovolume (mL/h) from drift net sampling and stream

discharge measurements (m3/h; 2SE bars) during April, June, August, and October 2003.

44

Conifer-warm

Month

Jun Aug Oct Jun Aug Oct Jun Aug Oct

Diet Proportions

0.00

0.25

0.50

0.75

1.00

Aquatic larvae

Aquatic nymphs

Other

Aquatic adults

Terrestrial adults

Conifer-cool

Month

Jun Aug Oct Jun Aug Oct Jun Aug Oct

0.00

0.25

0.50

0.75

1.00

Mixed-warm

Month

Jun Aug Oct Jun Aug Oct Jun Aug Oct Diet Proportions

0.00

0.25

0.50

0.75

1.00

Mixed-cool

Month

Jun Aug Oct Jun Aug Oct Jun Aug

Diet Proportions

0.00

0.25

0.50

0.75

1.00

Hardwood-cool

Month

Jun Aug Oct Jun Aug Oct Jun Aug Oct

0.00

0.25

0.50

0.75

1.00

Age 0

Age 0

Age 1

Age 1

Age 2

Age 2

12 11 16 5 2 2 1 5 2 26 23 16 8 14 66 2

14 19 7 3 2 6 1 8 28 22 25 11 12 6 8 4

3 16 15 10 17 21 10 21 22

Figure 2.3. Diet proportions for ages 0, 1, and 2 O. mykiss during June, August, and

October 2003. Streams were categorized by forest cover and temperature regime. Prey

items were pooled to create 5 broad prey types: aquatic larvae (Diptera, Trichoptera,

Coleoptera), aquatic nymphs (Ephemeroptera, Plecoptera, Diptera), aquatic other

(Ostracoda, Gastropoda, Isopoda, Crustacea, Acarina), aquatic adults (Ephemeroptera,

Plecoptera, Trichoptera, Diptera, Neuroptera), and terrestrial adult invertebrates

(Hymenoptera, Coleoptera, Orthoptera, Arachnida).

45

August August

October October

Hardwood-cool

June

Age

0 1 2

Manly's αα αα

0.0

0.5

1.0

Aquatic larvae

Aquatic nymphs

Aquatic other

Adult insects

no preference

Age

0 1 2

Manly's αα αα

0.0

0.5

1.0

0 1 2

Manly's αα αα

0.0

0.5

1.0

Mixed-warm

June

Age

0 1 2

Manly's αα αα

0.0

0.5

1.0

August

October

AugustAugust

OctoberOctober

August

Age

0 1 2

Manly's αα αα

0.0

0.5

1.0

October

0 1 2

Manly's αα αα

0.0

0.5

1.0

Mixed-cool

June

Age

0 1 2

Manly's αα αα

0.0

0.5

1.0

August

Age

0 1 2

Manly's αα αα

0.0

0.5

1.0

October

Age

0 1 2

Manly's αα αα

0.0

0.5

1.0

Conifer-warm

June

Age

0 1 2

0.0

0.5

1.0

August

Age

0 1 2

Manly's αα αα

0.0

0.5

1.0

October

0 1 2

0.0

0.5

1.0

Conifer-cool

June

Age

0 1 2

0.0

0.5

1.0

August

Age

0 1 2

Manly's αα αα

0.0

0.5

1.0

October

0 1 2

0.0

0.5

1.0

Figure 2.4. Manly’s alpha, as calculated for age 0, 1, 2, and 3 O. mykiss during June, August, and October 2003 in streams divided

into three forest cover categories and two temperature regimes. Calculations were based on average diet for each age class and on

prey availability from the results of drift net sample analysis. Alpha values that indicate no preference fall on the dotted reference

line, which is placed according to the number of prey types (1/m). Points that fall above the line indicate prey preference and

points below the line indicate prey avoidance.

46

Age 2

June August October

0

10

20

30

40

50Age 1

Month

June August October

0

5

10

15

20Age 0

June August October

Weight (g)

0

1

2

3

4

5Conifer-cool

Conifer-warm

Mixed-cool

Mixed-warm

Hardwood-cool

Figure 2.5. Mean weight (2SE) of age 0, 1, and 2 O. mykiss in nine streams during June, August, and October 2003. Streams were

categorized by forest cover and temperature regime.

47

Ditch Gulch (W)

Simulation Period/Age

Jun-Aug Aug-Oct Jun-Aug Aug-Oct Jun-Aug

Consumption (g)

0102030405060708090

100110120130140Conifer

Carrier Gulch (W)

Simulation Period/Age

Jun-Aug Aug-Oct Jun-Aug Aug-Oct

Consumption (g)

0102030405060708090

100110120130140

Barker Creek (C)

Simulation Period/Age

Jun-Aug Aug-Oct Jun-Aug Aug-Oct Jun-Aug Aug-Oct

0

25

50

75

100

125

Aquatic larvae

Aquatic nymphs

Other

Aquatic adults

Terrestrial insects

West Twin Creek (W)

Simulation Period/Age

Jun-Aug Aug-Oct Jun-Aug Aug-Oct Jun-Aug Aug-Oct

Consumption (g)

0102030405060708090

100110120130140Mixed

Potato Creek (W)

Simulation Period/Age

Jun-Aug Aug-Oct Jun-Aug Aug-Oct Jun-Aug Aug-Oct

Consumption (g)

0102030405060708090

100110120130140

Chanchellula Creek (C)

Simulation Period/Age

Jun-Aug Aug-Oct Jun-Aug Aug-Oct Jun-Aug

Total Consumption (g)

0

25

50

75

100

125

Underwood Creek (C)

Jun-Aug Aug-Oct Jun-Aug Aug-Oct Jun-Aug Aug-Oct

Consumption (g)

Hardwood

Olsen Creek (C)

Simulation Period/Age

Jun-Aug Aug-Oct Jun-Aug Aug-Oct Jun-Aug Aug-Oct

Consumption (g)

Monroe Creek (C)

Jun-Aug Aug-Oct Jun-Aug Aug-Oct Jun-Aug Aug-Oct

0

25

50

75

100

125

Age 0 Age 0Age 0Age 1 Age 1 Age 1Age 2 Age 2 Age 2

Figure 2.6. Bioenergetics model estimation of total consumption (biomass; g) of 5 prey types over 2-month simulation periods

(June-August, August-October) for age 0, 1, and 2 O. mykiss in cool (C) and warm (W) streams in three forest cover categories.

48

Figure 2.7. Bioenergetics model output of daily growth for ages 0, 1, and 2 O. mykiss in two streams (Barker Creek and Carrier

Gulch) with year-round (April 2003 to May 2004) field measurements of temperature (dotted line). Average daily summer low

flow temperature was increased by 2°C and growth was estimated using consumption rates (p-values) from the initial modeling

(solid line).

Simulation Day

100 200 300 400 500 600 700

Field Temperature

Temperature +2oC

0 100 200 300 400 500 600 700

Weight (g)

0

5

10

15

20

Field Temperature

Temperature +2oC

Aug

Jun

Jun

Oct

Oct

Aug

Aug

Aug

Jun

Jun

Oct

Oct

Aug

Barker Creek

Cool (3.1 - 14.4oC)

Carrier Gulch

Warm (1.3 - 18.4oC)

49

Table 2.1. 2003 study design. Nine streams were categorized by forest cover (conifer-

dominated, late seral mixed forest, low elevation small hardwood) and temperature

regime (cool and warm). Study analyses examine differences among these five

vegetative-thermal categories and among individual streams.

Cool Warm

Conifer-cool

N=1 (Barker Creek)

Conifer-warm

N=2 (Carrier Gulch, Ditch Gulch)

Conifer-dominated

Late Seral

Mixed Forest

Small Hardwood

Mixed-cool

N=1 (Chanchellula Creek)

Mixed-warm

N=2 (West Twin Creek, Potato

Creek)

Hardwood-cool

N=3 (Monroe, Olsen, and

Underwood Creeks)

N/A

50

Table 2.2. Sample size (N), range of fork lengths (FL), y-intercept, slope, R2, and

significance values (p) for O. mykiss in individual streams in June, August, and October

2003. Values are based on log(weight) versus log(length) regression analyses.

N FL range intercept slope R2

p

Conifer-cool

Barker Creek

June 20 43 - 200 -5.10 3.09 0.99 <0.001

August 21 42 - 169 -5.36 3.20 0.99 <0.001

October 20 51 - 137 -4.53 2.80 0.97 <0.001

Conifer-warm

Carrier Gulch

June 17 89 - 156 -4.83 2.98 0.96 <0.001

August 20 50 - 119 -4.59 2.82 0.96 <0.001

October 21 53 - 162 -4.46 2.75 0.98 <0.001

Ditch Gulch

June 21 28 - 210 -4.64 2.88 0.99 <0.001

August 20 49 - 135 -5.01 3.03 0.99 <0.001

October 20 46 - 144 -4.10 2.57 0.98 <0.001

Mixed-cool

Chanchellula Creek

June 16 70 - 158 -4.63 2.90 0.97 <0.001

August 20 34 - 183 -5.00 3.03 0.98 <0.001

October 21 43 - 99 -5.00 3.02 0.96 <0.001

Mixed-warm

Potato Creek

June 22 30 - 143 -4.76 2.93 0.99 <0.001

August 28 33 - 170 -5.17 3.12 1.00 <0.001

October 20 43 - 170 -4.70 2.89 0.99 <0.001

West Twin Creek

June 20 37 - 116 -5.42 3.29 0.94 <0.001

August 20 46 - 111 -4.62 2.84 0.98 <0.001

October 19 62 - 118 -4.21 2.64 0.95 <0.001

Hardwood-cool

Monroe Creek

June 11 83 - 149 -4.61 2.88 0.98 <0.001

August 18 60 - 150 -5.05 3.07 0.98 <0.001

October 20 60 - 168 -5.05 3.06 0.99 <0.001

Olsen Creek

June 18 42 - 152 -5.30 3.20 0.98 <0.001

August 20 57 - 159 -4.95 3.01 0.99 <0.001

October 20 70 - 185 -4.60 2.83 0.99 <0.001

Underwood Creek

June 12 33 - 165 -5.16 3.13 0.99 <0.001

August 22 59 - 189 -4.88 2.97 1.00 <0.001

October 20 56 - 161 -5.06 3.06 0.98 <0.001

51

Table 2.3. Mean relative weight (Wr), standard deviation of Wr, and sample size of age

0, 1, and 2 O. mykiss during June, August, and October 2003 in streams categorized by

forest cover and temperature regime.

Mean SD N Mean SD N Mean SD N

Conifer-cool

June 94.30 13.03 12 94.32 13.94 5 124.33 1

August 80.22 8.99 11 97.55 5.48 2 86.57 7.38 5

October 104.74 19.61 16 93.79 6.14 2 86.01 4.51 2

Conifer-warm

June 110.25 9.64 16 106.09 8.81 14

August 96.19 14.61 26.00 93.01 9.29 6.00 89.99 11.09 6

October 110.89 15.67 23.00 82.48 10.09 8.00 96.73 16.55 2

Mixed-cool

June 122.04 17.04 7 129.12 18.02 6

August 95.53 15.9079 14 100.40 8.34191 3 95.78 1

October 90.3017 13.4103 19 83.3767 4.77026 2

Mixed-warm

June 110.30 27.50 8 110.61 12.99 25 94.76 12.19 6

August 93.81 11.66 28 97.13 8.59 11 92.11 7.23 8

October 108.63 11.67 22 102.05 7.73 12 84.82 11.72 4

Hardwood-cool

June 98.16 18.55 3.00 105.85 12.28 10.00 105.20 16.72 10

August 98.59 8.47 16 95.71 12.76 17 94.26 7.37 21

October 93.86 9.10 15 93.74 7.23 21 92.69 12.67 22

Age 0 Age 1 Age 2

52

Table 2.4a. Bioenergetics model input for individual streams in June, August, and

October 2003. Stomach content proportions (by weight) were grouped into functional

prey groups, based on our bomb calorimetry results. Prey items were also characterized

as aquatic origin (A) or terrestrial (T). Energy density of prey items (J/g wet weight;

noted under each prey item) was obtained from bomb calorimetry analysis of drifting

invertebrates and from literature values for soft-bodied larvae (Diptera; Cummins and

Wuycheck 1971). Prey Items:

Larvae

(soft)

Larvae

(rigid) Nymphs Other

Winged

Insects

Coleop.

adults

Hymenop.

adults

Orthop.

adults

N SD 2746 (A) 4272 (A) 3076 (A) 2788 (A) 4224 (A) 6387 (A/T) 5133 (T) 4228 (T)

Conifer-cool

Barker Creek

Age 0

June 1 12 1.40 0.41 0.047 0.000 0.745 0.066 0.127 0.015 0.000 0.000

August 60 11 2.21 1.03 0.182 0.084 0.503 0.077 0.005 0.079 0.071 0.000

October 120 16 3.04 1.03 0.110 0.089 0.493 0.001 0.155 0.065 0.087 0.000

Age 1

June 1 5 11.10 1.98 0.054 0.000 0.612 0.000 0.047 0.223 0.065 0.000

August 60 2 13.00 4.24 0.020 0.189 0.030 0.000 0.199 0.233 0.329 0.000

October 120 2 13.75 6.01 0.000 0.000 0.075 0.000 0.620 0.000 0.305 0.000

Age 2

June 1 1 18.00 0.533 0.000 0.107 0.000 0.195 0.105 0.059 0.000

August 60 5 21.80 9.22 0.025 0.034 0.040 0.022 0.227 0.092 0.560 0.000

October 120 2 23.50 7.78 0.000 0.000 0.030 0.000 0.696 0.020 0.254 0.000

Conifer-warm

Carrier Gulch

Age 0

August 60 17 3.03 2.23 0.234 0.065 0.506 0.130 0.004 0.006 0.055 0.000

October 120 12 2.95 0.62 0.239 0.030 0.678 0.029 0.002 0.000 0.022 0.000

Age 1

June 1 5 11.30 1.15 0.020 0.000 0.666 0.000 0.280 0.034 0.000 0.000

August 60 2 10.65 10.39 0.025 0.000 0.471 0.005 0.000 0.500 0.000 0.000

October 120 6 11.52 4.13 0.058 0.024 0.720 0.030 0.000 0.167 0.000 0.000

Age 2

June 1 7 17.50 3.64 0.008 0.100 0.516 0.142 0.047 0.148 0.039 0.000

October 120 2 37.00 12.73 0.003 0.000 0.589 0.000 0.000 0.000 0.000 0.408

Ditch Gulch

Age 0

August 60 9 1.63 0.25 0.344 0.080 0.350 0.000 0.017 0.157 0.052 0.000

October 120 11 2.32 0.57 0.216 0.030 0.628 0.002 0.105 0.000 0.019 0.000

Age 1

June 1 11 8.80 2.93 0.080 0.045 0.434 0.000 0.111 0.097 0.233 0.001

August 60 4 8.88 0.51 0.047 0.290 0.227 0.002 0.077 0.051 0.306 0.000

October 120 2 7.65 1.20 0.462 0.052 0.424 0.000 0.000 0.063 0.000 0.000

Age 2

June 1 6 21.33 3.44 0.108 0.091 0.154 0.000 0.208 0.161 0.278 0.000

August 60 6 21.17 6.42 0.018 0.233 0.071 0.015 0.274 0.122 0.178 0.090

Mixed-cool

Chanchellula Creek

Age 0

August 60 14 1.66 1.01 0.203 0.069 0.250 0.002 0.298 0.048 0.082 0.049

October 120 19 1.96 0.94 0.108 0.087 0.455 0.000 0.257 0.051 0.042 0.000

Age 1

June 1 7 7.24 2.69 0.047 0.243 0.537 0.003 0.136 0.030 0.004 0.000

August 60 3 10.00 1.80 0.005 0.000 0.010 0.000 0.583 0.068 0.334 0.000

October 120 2 9.80 0.99 0.022 0.013 0.602 0.000 0.284 0.000 0.079 0.000

Age 2

June 1 6 20.25 8.53 0.004 0.087 0.116 0.005 0.434 0.283 0.071 0.000

August 60 1 23.00 0.000 0.074 0.000 0.000 0.459 0.118 0.349 0.000

Simulation

Day

Weight

(g)

53

Table 2.4a, cont’d. Prey Items:

Larvae

(soft)

Larvae

(rigid) Nymphs Other

Winged

Insects

Coleop.

adults

Hymenop.

adults

Orthop.

adults

N SD 2746 (A) 4272 (A) 3076 (A) 2788 (A) 4224 (A) 6387 (A/T) 5133 (T) 4228 (T)

Mixed-warm

Potato Creek

Age 0

June 1 7 0.60 0.12 0.086 0.008 0.774 0.000 0.070 0.024 0.037 0.000

August 60 19 1.42 0.68 0.285 0.163 0.363 0.001 0.105 0.034 0.049 0.000

October 120 14 1.67 0.58 0.366 0.022 0.477 0.001 0.097 0.020 0.017 0.000

Age 1

June 1 7 7.68 2.28 0.103 0.000 0.478 0.001 0.309 0.070 0.040 0.000

August 60 3 10.17 2.75 0.307 0.000 0.160 0.007 0.090 0.000 0.436 0.000

October 120 2 6.60 1.56 0.251 0.102 0.093 0.000 0.196 0.007 0.002 0.349

Age 2

June 1 5 13.90 2.07 0.179 0.091 0.417 0.000 0.019 0.278 0.016 0.000

August 60 5 35.50 11.72 0.461 0.027 0.000 0.005 0.175 0.169 0.032 0.132

October 120 3 40.83 18.25 0.000 0.310 0.000 0.000 0.188 0.076 0.426 0.000

West Twin Creek

Age 0

June 1 1 0.40 0.000 0.000 0.481 0.000 0.519 0.000 0.000 0.000

August 60 9 2.54 0.75 0.067 0.026 0.731 0.000 0.064 0.054 0.058 0.000

October 120 8 3.66 0.38 0.038 0.120 0.611 0.005 0.126 0.034 0.066 0.000

Age 1

June 1 18 6.89 2.53 0.163 0.070 0.416 0.009 0.145 0.132 0.058 0.008

August 60 8 8.00 1.47 0.017 0.338 0.371 0.000 0.102 0.026 0.147 0.000

October 120 10 8.73 3.01 0.004 0.073 0.382 0.001 0.417 0.011 0.111 0.000

Age 2

June 1 1 14.50 0.011 0.185 0.000 0.000 0.798 0.000 0.006 0.000

August 60 3 13.33 3.06 0.002 0.115 0.275 0.000 0.277 0.074 0.257 0.000

October 120 1 14.00 0.021 0.000 0.216 0.000 0.053 0.673 0.038 0.000

Hardwood-cool

Monroe Creek

Age 0

August 60 3 3.20 0.72 0.029 0.050 0.114 0.029 0.075 0.000 0.704 0.000

October 120 9 3.80 0.99 0.162 0.016 0.531 0.014 0.133 0.102 0.041 0.000

Age 1

June 1 4 10.80 4.14 0.100 0.176 0.291 0.012 0.222 0.090 0.108 0.000

August 60 7 12.81 5.36 0.161 0.105 0.036 0.016 0.000 0.119 0.562 0.000

October 120 6 14.42 3.53 0.196 0.042 0.531 0.026 0.052 0.032 0.121 0.000

Age 2

June 1 3 23.50 1.32 0.457 0.199 0.028 0.024 0.137 0.060 0.095 0.000

August 60 6 25.92 7.68 0.213 0.181 0.035 0.080 0.099 0.101 0.291 0.000

October 120 5 32.00 13.51 0.049 0.000 0.639 0.257 0.005 0.025 0.025 0.000

Olsen Creek

Age 0

June 1 1 0.75 0.000 0.000 1.000 0.000 0.000 0.000 0.000 0.000

August 60 3 2.63 0.50 0.029 0.000 0.747 0.000 0.000 0.000 0.224 0.000

October 120 1 4.10 0.079 0.523 0.397 0.000 0.000 0.000 0.000 0.000

Age 1

June 1 6 7.15 1.79 0.184 0.001 0.775 0.000 0.010 0.023 0.007 0.000

August 60 8 9.81 2.59 0.143 0.056 0.394 0.001 0.189 0.033 0.186 0.000

October 120 12 10.23 3.07 0.215 0.112 0.255 0.011 0.119 0.086 0.203 0.000

Age 2

June 1 6 18.25 5.90 0.177 0.000 0.328 0.000 0.075 0.203 0.217 0.000

August 60 6 25.83 10.15 0.385 0.074 0.105 0.005 0.143 0.041 0.248 0.000

October 120 5 24.60 5.63 0.038 0.000 0.262 0.074 0.225 0.193 0.208 0.000

Underwood Creek

Age 0

June 1 2 0.68 0.25 0.012 0.000 0.963 0.000 0.000 0.000 0.024 0.000

August 60 10 3.16 0.78 0.216 0.127 0.108 0.001 0.048 0.024 0.477 0.000

October 120 5 3.06 0.85 0.229 0.025 0.715 0.002 0.000 0.000 0.029 0.000

Age 1

August 60 2 8.45 5.73 0.472 0.031 0.496 0.000 0.000 0.000 0.000 0.000

October 120 3 6.27 1.93 0.042 0.015 0.708 0.042 0.192 0.000 0.000 0.000

Age 2

June 1 1 23.00 0.000 0.150 0.800 0.000 0.000 0.000 0.050 0.000

August 60 9 33.94 10.88 0.126 0.033 0.187 0.001 0.140 0.055 0.382 0.076

October 120 12 33.42 15.96 0.133 0.006 0.511 0.160 0.103 0.041 0.045 0.000

Simulation

Day

Weight

(g)

54

Table 2.4b. Bioenergetics model input for O. mykiss in two streams (Barker Creek and

Carrier Gulch). Temperature measurements from April 2003 to April 2004 were used to

model consumption for two years of growth (August age 0 to August age 2). Stomach

content proportions (by weight) were grouped into functional prey groups, based on our

bomb calorimetry results. Prey items were also characterized as aquatic origin (A) or

terrestrial (T). Energy density of prey items (J/g wet weight; noted under each prey item)

was obtained from bomb calorimetry analysis of drifting invertebrates and from literature

values for soft-bodied larvae (Diptera; Cummins and Wuycheck 1971).

Prey Items:

Larvae

(soft)

Larvae

(rigid) Nymphs Other

Winged

Insects

Coleop.

adults

Hymenop.

adults

Orthop.

adults

N SD 2746 (A) 4272 (A) 3076 (A) 2788 (A) 4224 (A) 6387 (A/T) 5133 (T) 4228 (T)

Conifer-cool

Barker Creek

Age 0 August 1 11 2.21 1.03 0.18 0.08 0.50 0.08 0.01 0.08 0.07 0.00

October 60 16 3.04 1.03 0.11 0.09 0.49 0.00 0.15 0.06 0.09 0.00

Age 1 June 300 5 11.10 1.98 0.05 0.00 0.61 0.00 0.05 0.22 0.06 0.00

August 365 2 13.00 4.24 0.02 0.19 0.03 0.00 0.20 0.23 0.33 0.00

October 425 2 13.75 6.01 0.00 0.00 0.07 0.00 0.62 0.00 0.31 0.00

Age 2 June 665 1 18.00 0.53 0.00 0.11 0.00 0.20 0.11 0.06 0.00

August 730 5 21.80 9.22 0.02 0.03 0.04 0.02 0.23 0.09 0.56 0.00

Conifer-warm

Carrier Gulch

Age 0 August 1 17 3.03 2.23 0.23 0.06 0.51 0.13 0.00 0.01 0.06 0.00

October 60 12 2.95 0.62 0.24 0.03 0.68 0.03 0.00 0.00 0.02 0.00

Age 1 April 240 3 4.57 0.74 0.23 0.03 0.52 0.00 0.09 0.14 0.00 0.00

June 300 5 11.30 1.15 0.02 0.00 0.67 0.00 0.28 0.03 0.00 0.00

August 365 2 10.65 10.39 0.02 0.00 0.47 0.00 0.00 0.50 0.00 0.00

October 425 6 11.52 4.13 0.06 0.02 0.72 0.03 0.00 0.17 0.00 0.00

Age 2 June 665 7 17.50 3.64 0.01 0.10 0.52 0.14 0.05 0.15 0.04 0.00

Simulation

Day

Weight

(g)

55

Table 2.5a. Bioenergetics model output for O. mykiss in individual streams during

June, August, and October 2003. Growth efficiency (GE) was calculated by dividing

average growth over each simulation period by the model estimate of consumption (C).

The p-value is the proportion of maximum consumption C/Cmax.

C p-value GE (%)

Conifer-cool

Barker Creek

Age 0

June-August 9.13 0.20 8.90

August-October 10.80 0.18 7.73

Age 1

June-August 27.73 0.16 6.85

August-October 26.00 0.14 2.89

Age 2

June-August 47.88 0.20 7.94

August-October 42.71 0.16 3.98

Conifer-warm

Carrier Gulch

Age 0

August-October 10.06 0.15 -0.79

Age 1

June-August 28.17 0.15 -2.31

August-October 26.48 0.16 3.27

Age 2

June-October 201.01 0.32 9.70

Ditch Gulch

Age 0

August-October 9.49 0.19 7.22

Age 1

June-August 24.28 0.16 0.31

August-October 16.83 0.12 -7.28

Age 2

June-August 46.75 0.16 -0.36

Mixed-coolChanchellula Creek

Age 0

August-October 6.21 0.14 4.93Age 1

June-August 24.98 0.20 11.04

August-October 19.03 0.13 -1.05Age 2

June-August 39.04 0.16 7.04

56

Table 2.5a, cont’d. C p-value GE (%)

Mixed-warm

Potato Creek

Age 0

June-August 8.41 0.25 9.76

August-October 6.81 0.16 3.67

Age 1

June-August 38.22 0.24 6.51

August-October 8.51 0.06 -41.95

Age 2

June-August 141.94 0.45 15.22

August-October 83.86 0.21 6.36

West Twin Creek

Age 0

June-August 13.94 0.35 15.38

August-October 13.32 0.20 8.40

Age 1

June-August 26.06 0.19 4.24

August-October 21.48 0.16 3.40

Age 2

June-August 30.93 0.15 -3.77

August-October 26.01 0.13 2.56

Hardwood-cool

Monroe Creek

Age 0

August-October 11.34 0.15 5.29

Age 1

June-August 33.83 0.18 5.95

August-October 35.42 0.18 4.53

Age 2

June-August 65.54 0.21 3.69

August-October 86.17 0.25 7.06

Olsen Creek

Age 0

June-August 14.24 0.32 13.22

August-October 15.45 0.21 9.49

Age 1

June-August 36.65 0.25 7.27

August-October 25.11 0.16 1.64

Age 2

June-August 78.28 0.27 9.69

August-October 45.40 0.15 -2.72

Underwood Creek

Age 0

June-August 15.58 0.33 15.95

August-October 11.06 0.25 -0.90

Age 1

June-August 29.12 0.18 -2.14

August-October 17.95 0.13 -12.16

Age 2

June-August 112.49 0.32 9.73

August-October 82.72 0.21 -0.64

57

Table 2.5b. Bioenergetics model output for O. mykiss in two streams (Barker Creek

and Carrier Gulch). Temperature measurements from April 2003 to April 2004 were

used to model consumption for two years of growth (August age 0 to August age 2).

Growth efficiency (GE) was calculated by dividing average growth over each simulation

period by the model estimate of consumption (C). The p-value is the proportion of

maximum consumption C/Cmax.

C p-value GE (%)

Conifer-cool

Barker Creek

Age 0 August-October 10.82 0.18 7.67

October-June 65.68 0.26 16.50

June-August 35.01 0.19 5.42

August-October 26.14 0.14 2.87

October-June 95.88 0.20 4.43

June-August 51.20 0.19 7.42Conifer-warm

Carrier Gulch

Age 0 August-October 10.13 0.15 -0.79

October-April 25.10 0.24 6.45

April-June 40.57 0.44 16.59

June-August 30.40 0.15 -2.14

August-October 26.67 0.16 3.26

Age 2 October-June 95.31 0.23 6.21

Age 1

Age 2

Age 1

58

NOTES TO CHAPTER 2

Anderson, R. O., and R. M. Neumann. 1996. Length, weight, and associated structural

indices. Pages 447-481 in B. R. Murphy and D. W. Willis, editors. Fisheries

Techniques. American Fisheries Society, Bethseda.

Arrington, A. A., K. O. Winemiller, W. F. Loftus, and S. Akin. 2002. How often do

fishes "run on empty"? Ecology 83:2145-2151.

Beecher, H. A., J. P. Carleton, and T. H. Johnson. 1995. Utility of depth and velocity

preferences for predicting steelhead parr distribution at different flows.

Transactions of the American Fisheries Society 124:935-938.

Bilby, R. E., and P. A. Bisson. 1987. Emigration and production of hatchery coho salmon

(Oncorhynchus kisutch) stocked in streams draining an old-growth and clear-cut

watershed. Canadian Journal of Fisheries and Aquatic Sciences 45:1397-1407.

Booker, D. J., M. J. Dunbar, and A. Ibbotson. 2004. Predicting juvenile salmonid drift-

feeding habitat quality using a three-dimensional hydraulic-bioenergetic model.

Ecological Modelling 177:157-177.

Bourque, C. P. A., and J. H. Pomeroy. 2001. Effects of forest harvesting on summer

stream temperatures in New Brunswick, Canada: an inter-catchment, multiple-

year comparison. Hydrology and Earth System Sciences 5:599-613.

Bowen, S. H. 1996. Quantitative description of the diet. Pages 513-531 in B. R. Murphy

and D. W. Willis, editors. Fisheries techniques. American Fisheries Society,

Bethseda.

Bowser, P. R. 2001. Anesthetic options for fish. in R. D. Gleed and J. W. Ludders,

editors. Recent Advances in Veterinary Anesthesia and Analgesia: Companion

Animals. International Veterinary Information Service, Ithaca NY.

Brosofske, K. D., J. Chen, R. J. Naiman, and J. F. Franklin. 1997. Harvesting effects on

microclimatic gradients from small streams to uplands in western Washington.

Ecological Applications 7:1188-1200.

Burns, J. W. 1972. Some effects of logging and associated road construction on northern

California streams. Transactions of the American Fisheries Society 101:1-17.

59

Busby, P. J., T. C. Wainwright, and R. S. Waples. 1994. Status review for Klamath

Mountains Province steelhead. NOAA Technical Memo NMFS-NWFSC-19, US

Department of Commerce.

Chandler, G. L., and T. C. Bjornn. 1988. Abundance, growth, and interactions of juvenile

steelhead relative to time of emergence. Transactions of the American Fisheries

Society 117:432-443.

Chesson, J. 1983. The Estimation and Analysis of Preference and Its Relationship to

Foraging Models. Ecology 64:1297-1304.

Cloe, W. W., and G. C. Garman. 1996. The energetic importance of terrestrial arthropod

inputs to three warm-water streams. Freshwater Biology 36:105-114.

Cummins, K. W., and J. C. Wuycheck. 1971. Caloric equivalents for investigations in

ecological energetics. International Association of Theoretical and Applied

Limnology Communications 18:1-158.

Devries, D. R., and R. V. Frie. 1996. Determination of age and growth. Pages 483-512 in

B. R. Murphy and D. W. Willis, editors. Fisheries techniques. American Fisheries

Society, Bethseda, MD.

Dwyer, W. P., and R. H. Kramer. 1975. Influence of Temperature on Scope for Activity

in Cutthroat Trout, Salmo-Clarki. Transactions of the American Fisheries Society

104:552-554.

Edwards, E. D., and A. D. Huryn. 1996. Effect of riparian land use on contributions of

terrestrial invertebrates to streams. Hydrobiologia 337:151-159.

Emlen, J. M., D. C. Freeman, M. D. Kirchhoff, C. L. Alados, J. Escos, and J. J. Duda.

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Filbert, R. B., and C. P. Hawkins. 1995. Variation in condition of rainbow trout in

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Transactions of the American Fisheries Society 124:824-835.

Giles, N. 1980. A stomach sampler for use on live fish. Journal of Fisheries Biology

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Gordon, N. D., T. A. McMahon, and B. L. Finlayson. 1992. Stream hydrology: an

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Hanson, P. C., T. B. Johnson, D. E. Schindler, and J. F. Kitchell. 1997. Fish

Bioenergetics 3.0. University of Wisconsin, Sea Grant Institute, Center for

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Harvey, B. C., and R. J. Nakamoto. 1997. Habitat-dependent interactions between two

size-classes of juvenile steelhead in a small stream. Canadian Journal of Fisheries

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Hill, J., and G. D. Grossman. 1993. An Energetic Model of Microhabitat Use for

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Hughes, N. F. 1998. A model of habitat selection by drift-feeding stream salmonids at

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Anderson, R. O., and R. M. Neumann. 1996. Length, weight, and associated structural

indices. Pages 447-481 in B. R. Murphy and D. W. Willis, editors. Fisheries

Techniques. American Fisheries Society, Bethseda.

Arrington, A. A., K. O. Winemiller, W. F. Loftus, and S. Akin. 2002. How often do

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Beecher, H. A., J. P. Carleton, and T. H. Johnson. 1995. Utility of depth and velocity

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Transactions of the American Fisheries Society 124:935-938.

Bilby, R. E., and P. A. Bisson. 1987. Emigration and production of hatchery coho salmon

(Oncorhynchus kisutch) stocked in streams draining an old-growth and clear-cut

watershed. Canadian Journal of Fisheries and Aquatic Sciences 45:1397-1407.

Booker, D. J., M. J. Dunbar, and A. Ibbotson. 2004. Predicting juvenile salmonid drift-

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Ecological Modelling 177:157-177.

Bourque, C. P. A., and J. H. Pomeroy. 2001. Effects of forest harvesting on summer

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year comparison. Hydrology and Earth System Sciences 5:599-613.

Bowen, S. H. 1996. Quantitative description of the diet. Pages 513-531 in B. R. Murphy

and D. W. Willis, editors. Fisheries techniques. American Fisheries Society,

Bethseda.

Bowser, P. R. 2001. Anesthetic options for fish. in R. D. Gleed and J. W. Ludders,

editors. Recent Advances in Veterinary Anesthesia and Analgesia: Companion

Animals. International Veterinary Information Service, Ithaca NY.

Brosofske, K. D., J. Chen, R. J. Naiman, and J. F. Franklin. 1997. Harvesting effects on

microclimatic gradients from small streams to uplands in western Washington.

Ecological Applications 7:1188-1200.

64

Burns, J. W. 1972. Some effects of logging and associated road construction on

northern California streams. Transactions of the American Fisheries Society

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Busby, P. J., T. C. Wainwright, and R. S. Waples. 1994. Status review for Klamath

Mountains Province steelhead. NOAA Technical Memo NMFS-NWFSC-19, US

Department of Commerce.

Chandler, G. L., and T. C. Bjornn. 1988. Abundance, growth, and interactions of juvenile

steelhead relative to time of emergence. Transactions of the American Fisheries

Society 117:432-443.

Chesson, J. 1983. The Estimation and Analysis of Preference and Its Relationship to

Foraging Models. Ecology 64:1297-1304.

Cloe, W. W., and G. C. Garman. 1996. The energetic importance of terrestrial arthropod

inputs to three warm-water streams. Freshwater Biology 36:105-114.

Cummins, K. W., and J. C. Wuycheck. 1971. Caloric equivalents for investigations in

ecological energetics. International Association of Theoretical and Applied

Limnology Communications 18:1-158.

Devries, D. R., and R. V. Frie. 1996. Determination of age and growth. Pages 483-512 in

B. R. Murphy and D. W. Willis, editors. Fisheries techniques. American Fisheries

Society, Bethseda, MD.

Dwyer, W. P., and R. H. Kramer. 1975. Influence of Temperature on Scope for Activity

in Cutthroat Trout, Salmo-Clarki. Transactions of the American Fisheries Society

104:552-554.

Edwards, E. D., and A. D. Huryn. 1996. Effect of riparian land use on contributions of

terrestrial invertebrates to streams. Hydrobiologia 337:151-159.

Emlen, J. M., D. C. Freeman, M. D. Kirchhoff, C. L. Alados, J. Escos, and J. J. Duda.

2003. Fitting population models from field data. Ecological Modelling 162:119-

143.

65

Filbert, R. B., and C. P. Hawkins. 1995. Variation in condition of rainbow trout in

relation to food, temperature, and individual length in the Green River, Utah.

Transactions of the American Fisheries Society 124:824-835.

Giles, N. 1980. A stomach sampler for use on live fish. Journal of Fisheries Biology

16:441-444.

Gordon, N. D., T. A. McMahon, and B. L. Finlayson. 1992. Stream hydrology: an

introduction for ecologists. Wiley, NY.

Hanson, P. C., T. B. Johnson, D. E. Schindler, and J. F. Kitchell. 1997. Fish

Bioenergetics 3.0. University of Wisconsin, Sea Grant Institute, Center for

Limnology.

Harvey, B. C., and R. J. Nakamoto. 1997. Habitat-dependent interactions between two

size-classes of juvenile steelhead in a small stream. Canadian Journal of Fisheries

and Aquatic Sciences 54:27-31.

Hill, J., and G. D. Grossman. 1993. An Energetic Model of Microhabitat Use for

Rainbow-Trout and Rosyside Dace. Ecology 74:685-698.

Hughes, N. F. 1998. A model of habitat selection by drift-feeding stream salmonids at

different scales. Ecology 79:281-294.

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