U.S. Army Corps of Engineers 

Portland District 

The Use of Electromyogram (EMG) Telemetry to Assess 

Swimming Activity and Energy Use of Adult Spring Chinook 

Salmon Migrating through the Tailraces, Fishways, and 

Forebays of Bonneville Dam, 2000 and 2001 

R.S. Brown and D.R. Geist 

(Pacific Northwest National Laboratory) 

M.G. Mesa 

(U.S. Geological Survey Biological Resources Division)

September 2002 

Prepared for 

the U.S. Army Corps of Engineers 

Portland District, Portland, Oregon 

under Contract DE-AC06 76RLO1830 

CENWP MIPR No. W66QK10432368 
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The Use of Electromyogram (EMG) Telemetry 

to Assess Swimming Activity and Energy Use 

of Adult Spring Chinook Salmon Migrating 

through the Tailraces, Fishways, and 

Forebays of Bonneville Dam, 2000 and 2001 

R.S. Brown 

D.R. Geist 

M.G. Mesa 

September 2002 

Prepared for 

the U.S. Army Corps of Engineers 

Portland District, Portland, Oregon 

under Contract DE-AC06 76RLO1830 

CENWP MIPR No. W66QK10432368 

PNNL-14080 

Prepared by 

Pacific Northwest National Laboratory 

P.O. Box 999 

Richland, WA 99352 

U.S. Geological Survey 

Biological Resources Discipline 

Western Fisheries Research Center 

Columbia River Research Laboratory 

5501-A Cook-Underwood Road 

Cook, WA 98605 

iii

Summary 

In 2000, the Pacific Northwest National Laboratory (PNNL) and the U.S. Geological Survey (USGS) 

initiated a two-year study for the U.S. Army Corps of Engineers to investigate the metabolic rate and 

swimming performance and to estimate the total energy used (i.e., aerobic and anaerobic) by adult spring 

chinook salmon (Oncorhynchus tshawystcha) migrating upstream through a large hydropower dam on the 

Columbia River. The investigation involved one year of laboratory study and one year of field study at 

Bonneville Dam. 

The objectives of the laboratory study were to 1) measure active rates of oxygen consumption of adult 

spring chinook salmon at three water temperatures over a range of swimming speeds; 2) estimate the 

upper critical swimming speed (Ucrit) of adult spring chinook salmon; and 3) monitor electromyograms 

(EMGs) of red and white muscle in the salmon over a range of swimming speeds. 

The objectives of the field study were to estimate the energetic costs incurred by salmon as they 

passed the tailraces, fishways, and forebays of Bonneville Dam. In addition we sought specifically to 

estimate and compare the swimming speeds and energetic costs of salmon as they passed through 

different sections of the dam’s fishways. 

Laboratory study results indicated that the rate of oxygen consumption and red and white muscle 

activity in adult spring chinook salmon were strongly correlated with swimming speed over a range of 

fish sizes and at three different temperatures. Active oxygen consumption (VO2) increased linearly with 

swim speed before leveling off at speeds at or above Ucrit. This pattern was similar at each water 

temperature and indicated that fish were approaching their maximal aerobic oxygen consumption at 

higher swim speeds. Modeling showed that temperature, but not size or sex, influenced the relation 

between V02 and swim speed, thus a V02-swim speed model based on temperature (but independent of 

sex and size) should be a biologically relevant way of estimating the energy use of fish in the wild. 

In the field studies at Bonneville Dam, EMG radiotelemetry was used to examine the amount of 

energy spring chinook salmon expend while migrating upstream past the dam’s tailraces, fishways, and 

forebays. Electromyogram transmitters were surgically implanted in the red muscle of the fish and 

calibrated to swim speeds for each fish prior to release. The fish were released below Bonneville Dam 

and tracked on 19 underwater and surface receivers through the dam’s tailrace, fishways, and forebays. 

Aerobic and anaerobic energy use rates were determined. The rates of energy used (i.e., kilo calories 

per kilogram per hour [kcal kg-1 h-1]) by spring chinook salmon were significantly higher in the tailraces 

(2.80 kcal kg-1 h-1) than in other parts of the dam, e.g., 26% higher than rates for the fishways (2.22 kcal 

kg-1 h-1) and 62% higher than for the forebays (1.73 kcal kg-1 h-1). Most of the energy used (i.e., kcal kg-1) 

by the fish was expended in the tailraces: 81% of the median energy used was expended in the tailrace 

(48.1 kcal kg-1), while only 18% (11.1 kcal kg-1) and 1.5% (0.9 kcal kg-1) were used in the fishways and 

forebays, respectively. A portion of the fish (the 90th percentile) used up much more energy passing these 

areas; e.g., ~300 kcal kg-1to pass the tailraces; 50 kcal kg-1 to pass the forebays, and ~40 kcal kg-1to pass 

the fishways. 

Among all fishway areas, Cascade Island fishway appears to be more energetically costly than other 

fishways. Also, section 12 of the Washington shore fishway appears costly. This is likely due to the 

diversion of fish to the Adult Facility, which occurs in this section. It appears from our data that there is 

an energetic cost associated with diverting fish from the Washington shore ladder to the adult facility. 
iv

Energy used during fallouts was substantial (11.5% to 18.8% of the amount of energy used for 

successful fishway passages). This percentage was highest in the Cascade Island fishway (18.8%) where 

energy use rates during fallout were even higher (3.67 kcal kg1

 h1) than energy use rates in the tailrace 

(2.8 kcal kg1

 h1). 
v

Acknowledgments 

Funding for this study was provided by the U.S. Army Corps of Engineers (USCOE) through the 

Anadromous Fish Evaluation Program. Michael Langeslay and David Clugston were the technical 

contracting officers from the USCOE. 

We would like to thank Scott Abernethy, Jessica Carter, Traci Degerman, Karen Dunmall, and Ian 

Welch of the Pacific Northwest National Laboratory for their field assistance and scientific advice. We 

wish to thank Israel Duran, Mike Gough, Jon Martin, Ann Postera, Sarah Rose, Chris Schafer, and Lisa 

Weiland of the U.S. Geological Survey Biological Resources Division for their field assistance. We 

would also like to thank Tammy Mackey of USCOE and Chuck Boggs, Megan Heinrich, Steve Lee, 

Chris Peery and Ken Tolotti of the University of Idaho for their assistance. Thank you to C. McKinstry 

and V. Cullinan of PNNL for statistical support. And thank you to the personnel at the Carson and Little 

White Salmon National Fish Hatcheries for providing fish for this study. 
vi
vii

Contents 

Summary iii 

Acknowledgments v 

1.0 Introduction 1.1 

1.1 Objectives 1.1 

1.2 Background 1.1 

1.3 Study Area 1.3 

1.3.1 Bradford Island Fish Ladder 1.4 

1.3.2 Cascade Island Fish Ladder 1.7 

1.3.3 Washington Shore Fish Ladder 1.7 

2.0 Methods 2.1 

2.1 Laboratory Studies 2.1 

2.1.1 Fish Collection and Holding 2.1 

2.1.2 Respirometry Trials 2.1 

2.1.3 Critical Swimming Speed Trials 2.4 

2.1.4 Electromyogram Swim Speed Trials 2.4 

2.2 Field Studies 2.6 

2.2.1 Fish Collection and Tagging 2.6 

2.2.2 Calibration of EMG Signals to Swim Speed 2.7 

2.2.3 EMG Signal Tracking 2.8 

2.2.4 Determination of Energy Use Rates 2.11 

2.2.5 Radio Tagging and Determination of Location in Fishways 2.12 

3.0 Results 3.1 

3.1 Laboratory Results 3.1 

3.1.1 Respirometry Trials 3.1 

3.1.2 Critical Swimming Speed 3.2 

3.1.3 Electromyogram-Swim Speed Relations 3.3 

3.2 Field Study Results 3.6 

3.2.1 Comparisons between Tailraces, Fishways, and Forebays 3.6 

3.2.2 Comparisons within Tailraces and Forebays 3.13 

3.2.3 Comparisons within Fishways 3.20 

4.0 Discussion 4.1 

4.1 Laboratory Studies 4.1 

4.2 Field Studies 4.2 

4.2.1 Swim Speed and Energy Use in the Tailraces, Fishways, and Forebays of 

Bonneville Dam 4.2 

4.2.2 Swim Speeds and Energy Use among Sections of the Fishways of Bonneville Dam 4.5 

5.0 Management Implications and Recommendations 5.1 

6.0 References 6.1 
viii
ix

Figures 

Figure 1.1. Bonneville Dam Powerhouses and Fishways ........................................................................1.4 

Figure 1.2. Reception Range of Antenna Arrays at Bonneville Dam. .....................................................1.5 

Figure 1.3. Mean Daily Water Temperature (heavy line) and Mean Daily Discharge (thin line) at 

Bonneville Dam, 2001.......................................................................................................................1.5 

Figure 1.4. Mean Daily Discharge (m3 s-1) Over the Spillway or Through Two Different Powerhouses 

at Bonneville Dam, 2001...................................................................................................................1.6 

Figure 1.5 Sections of the Bradford Island Fishway. Section 6 is immediately in front of the counting 

window..............................................................................................................................................1.6 

Figure 2.1. Process for Determining Swimming Speeds and Energy Use from Signals Received from 

EMG Transmitters Implanted in Spring Chinook Salmon..............................................................2.10 

Figure 2.2. The Cascade Island Fishway. ..............................................................................................2.13

Figure 2.3. The Washington Shore Fishway..........................................................................................2.13 

Figure 3.1. Observed Oxygen Consumption (data points) and Fitted Cubic Model by 45 Individual 

Spring Chinook Salmon (solid lines..................................................................................................3.1 

Figure 3.2. Observed Oxygen Consumption (data points) and Fitted Model 2 (solid lines) with Lower 

and Upper 95% Confidence Bounds (dashed lines)..........................................................................3.3 

Figure 3.3. Distribution of Upper Critical Swimming Speeds for 22 Adult Spring Chinook Salmon 

with Swimming Speeds Expressed as (A) cm s-1 and (B) body lengths per second (Bl s-1)...........3.3 

Figure 3.4. Red (circles) and White (squares) Muscle Electromyograms (EMGs; pulses min-1) by 

Swim Speed (cm s-1) for 10 Adult Spring Chinook Salmon. ............................................................3.4 

Figure 3.5. Fitted Models for Red and White Muscle Electromyograms by Swim Speed (cm s-1) for 

10 Adult Spring Chinook Salmon.. ...................................................................................................3.5 

Figure 3.6. Box Plots of Mean Swimming Speeds (in cm s-1(Panel A), and BL s-1 (Panel B)) of 85 

Spring Chinook Salmon at Three Different Areas during Passage of Bonneville Dam, April – 

June, 2001. ........................................................................................................................................3.9 

Figure 3.7. Box Plots of Mean Energy Use Rates (Panel A) and Energy Used (Panel B) by 

Spring Chinook Salmon at Three Different Areas during Passage of Bonneville Dam, April – 

June, 2001. ......................................................................................................................................3.10 

Figure 3.8. Box Plots of Total (aerobic and anaerobic combined) Energy Use Rates (Panel A) and 

Total (aerobic and anaerobic combined) Energy Used (Panel B) by Spring Chinook Salmon at 

Three Different Areas during Passage of Bonneville Dam, April – June, 2001.. ...........................3.11 
x

Figure 3.9. Percent Cumulative Distribution of Total (aerobic and anaerobic combined) Energy 

Used by Spring Chinook Salmon to Pass Three Areas of Bonneville Dam (A=tailraces, 

B=fishways, C=forebays) April – June 2001. .................................................................................3.12

Figure 3.10. Box Plots of Mean Swimming Speeds (in cm s-1(Panel A), and BL s-1 (Panel B)) of 76 

Spring Chinook Salmon in Three Different Tailraces during Passage of Bonneville Dam, April – 

June, 2001.. .....................................................................................................................................3.13 

Figure 3.11. Box Plots of Energy Use Rates in Three Different Tailraces (Panel A) and Three 

Different Forebays (Panel B) by Spring Chinook Salmon as They Passed Bonneville Dam, 

April – June, 2001. ..........................................................................................................................3.14 

Figure 3.12. Box Plots of Total (aerobic and anaerobic combined) Energy Use Rates in Three Different 

Tailraces (Panel A) and Three Different Forebays (Panel B) by Spring Chinook Salmon as They 

Passed Bonneville Dam April – June, 2001....................................................................................3.15 

Figure 3.13. Box Plots of Mean Swimming Speeds (in cm s-1(Panel A), and BL s-1 (Panel B)) of 45 

Spring Chinook Salmon in Three Different Forebays during Passage of Bonneville Dam, April – 

June, 2001. ......................................................................................................................................3.16 

Figure 3.14. Box Plots of Swimming Speed (Panel A) and Energy Use Rates (Panel B) of Spring 

Chinook Salmon at Three Different Areas of Dam Passage during Both Day and Night (shaded half) 

at Bonneville Dam, April – June, 2001...........................................................................................3.18 

Figure 3.15. Box Plots of Energy Use Rates of Spring Chinook Salmon at Three Different Areas of Dam 

Passage during both Day and Night (the shaded half of the figures) at Bonneville Dam, 

April – June, 2001. ..........................................................................................................................3.19 

Figure 3.17. The Time of Day when Fish Entered (Panel A) and Exited (Panel B) Fishways during 

their Successful Passage of Bonneville Dam, April – June, 2001...................................................3.20 

Figure 3.19. Box Plots of Time (h) Spring Chinook Salmon Took to Pass Three Fishways 

(Washington shore, Cascade Island, and Bradford Island) at Bonneville Dam, April – June, 2001.

.........................................................................................................................................................3.21 

Figure 3.20. Box Plots of Swimming Speeds (in cm s-1 in Panel A and in BL s-1 in Panel B) of Spring 

Chinook Salmon in the Washington Shore, Cascade Island, and Bradford Island Fishways as They 

Passed Bonneville Dam, April – June, 2001...................................................................................3.23 

Figure 3.21 Box Plots of Energy Use Rates of Spring Chinook Salmon in Washington Shore, Cascade 

Island, and Bradford Island Fishways as They Passed Bonneville Dam, April – June, 2001.........3.25 

Figure 3.22. Box Plots of Energy Use by Spring Chinook Salmon in Three Different Fishways 

(Washington Shore, Cascade Island, and Bradford Island) as They Passed Bonneville Dam, 

April – June, 2001.3. .......................................................................................................................3.26 

Figure 3.23. Plots of the Amount of Time (h) Spring Chinook Salmon Spent in Three Different 

Fishways During Fallout (Washington Shore, Cascade Island, and Bradford Island) as They 

Attempted to Pass Bonneville Dam, April – June, 2001.................................................................3.27 
xi

Figure 3.24. Box Plots of Swimming Speeds (in cm s-1 in panel A and in BL s-1 in panel B) of Spring 

Chinook Salmon during Fallout in Three Different Fishways (Washington Shore, Cascade Island, 

and Bradford Island) as they Attempted to Pass Bonneville Dam, April – June, 2001.. ................3.28 

Figure 3.25. Box Plots of Energy Use Rates of Spring Chinook Salmon during Fallout in Three 

Different Fishways (Washington shore, Cascade Island, and Bradford Island) as They 

Attempted to Pass Bonneville Dam, April – June, 2001.................................................................3.29 

Figure 3.26. Box Plots of Energy Use by Spring Chinook Salmon during Fallout in Three Different 

Fishways (Washington Shore, Cascade Island, and Bradford Island) as They Attempted to Pass 

Bonneville Dam, April – June, 2001...............................................................................................3.30 

Figure 3.27. Box Plots of Swimming Speeds (in cm s-1 in panel A and in BL s-1 in panel B) of 

Spring Chinook Salmon in Fishways during Day and Night as They Passed Bonneville Dam, 

April – June, 2001.. .........................................................................................................................3.38 

Figure 3.28. Box Plots of Energy Use Rates of Spring Chinook Salmon in Fishways during Day 

and Night as They Passed Bonneville Dam, April – June, 2001.....................................................3.39 

Figure 3.29. Plots of Swimming Speeds (Panel A) and Total Energy Use Rates (Panel B) of 

Migrating Spring Chinook Salmon in Fishways and the Number of Shad in Fishways at 

Bonneville Dam, April – June, 2001...............................................................................................3.40 

Figure 3.30. Plots of Swimming Speeds (Panel A), Total (aerobic and anaerobic combined) Energy 

Use Rates of Migrating Spring Chinook Salmon in Fishways (Panel B) and Number of Shad in 

Fishways (Panel C) and Water Temperature at Bonneville Dam, April – June, 2001....................3.41 
xii

Tables 

Table 3.1. Parameter Estimates for Two Linear Regression Models Relating Red and White Muscle 

EMGs to Swimming Speed (Bl s-1 ....................................................................................................3.5 

Table 3.2. Mean Swimming Speed (+SE), Energy Use Rates (+SE), and Median Energy Used by Spring 

Chinook Salmon as they Passed Tailraces, Fishways, and Forebays of Bonneville Dam, April – June 

2001..3..................................................................................................................................................7 

Table 3.3. Mean Percentage (and range) of EMG Signals (emitted from an individual transmitter) 

Collected from Individual Spring Chinook Salmon Passing Bonneville Dam April – June 2001 that 

Are over Either 70, 80, or 100% of the Critical Swimming Speed for Spring Chinook Salmon......3.9 

Table 3.4. Swimming Speeds during Day and Night of Spring Chinook Salmon Passing Bonneville 

Dam, April – June 2001.. ................................................................................................................3.17 

Table 3.5. Energy Use Rates during Day and Night of Spring Chinook Salmon Passing Bonneville Dam, 

April – June 2001.. ..........................................................................................................................3.17 

Table 3.6. Median Energy Used by Spring Chinook Salmon as they Successfully Passed Fishways or 

Fell Out of Fishways of Bonneville Dam, April – June 2001.........................................................3.22 

Table 3.7. Mean Swimming Speeds (+SE) and Energy Use Rates (+SE) of Spring Chinook Salmon as 

they Successfully Passed Three Different Fishways or Fell out of Fishways, April – June 2001. .3.24 

Table 3.8. Mean Swimming Speeds (+SE) and Energy Use Rates (+SE) of Spring Chinook Salmon as 

they Successfully Passed Three Different Fishways during Day or Night, April – June 2001.......3.31 

Table 3.9. Mean Swimming Speeds of Spring Chinook Salmon in Different Sections of Three Different 

Fishways of Bonneville Dam, April – June 2001. ..........................................................................3.32 

Table 3.10. Median Amount of Time Spring Chinook Salmon Spent in Different Sections of Three 

Different Fishways of Bonneville Dam, April – June 2001............................................................3.33 

Table 3.11. Mean Energy Use Rates of Spring Chinook Salmon in Different Sections of the Three 

Fishways of Bonneville Dam, April – June 2001. ..........................................................................3.34 

Table 3.12. Median Energy Used by Spring Chinook Salmon in Different Sections of Three Different 

Fishways of Bonneville Dam, April – June 2001 ...........................................................................3.35 

Table 3.13. Median Total Energy Used per Meter Length of Fishway by Spring Chinook Salmon in 

Different Sections of Three Different Fishways of Bonneville Dam, April – June 2001.. .............3.36 

Table 3.14. Total Energy Use Rates and Number of Hours Spent in Different Fishway Sections by 

Spring Chinook Salmon Passing Bonneville Dam, April – June 2001 ...........................................3.37 

Table 4.1. Energy Use Rates of Spring Chinook Salmon Passing Bonneville Dam, April – June 2001. 4.5 
1.1

1.0 Introduction 

In 2000, the Pacific Northwest National Laboratory (PNNL) and the U.S. Geological Survey (USGS) 

initiated a two-year study to investigate the metabolic rate and swimming performance and to estimate the 

total energy used (i.e., aerobic and anaerobic) by adult spring chinook salmon (Oncorhynchus 

tshawytscha) migrating upstream through Bonneville Dam, a large hydropower dam on the Columbia 

River. The investigation, conducted for the U.S. Army Corps of Engineers, involved one year of 

laboratory study and one year of field study. 

1.1 Objectives 

The objectives of the laboratory study were to 1) measure active rates of oxygen consumption of adult 

spring chinook salmon at three water temperatures over a range of swimming speeds; 2) estimate the 

upper critical swimming speed (Ucrit) of adult spring chinook salmon; and 3) monitor electromyograms 

(EMGs) of red and white muscle in adult spring chinook salmon over a range of swimming speeds. 

The objectives of the field study were to estimate the energetic costs incurred by salmon as they 

passed the tailraces, fishways, and forebays of the dam. We were particularly interested in the relative 

costs associated with passing different parts of fishways. Information on the activity and energy used by 

salmon as they migrate through these fishways will aide managers in maximizing fish passage while 

minimizing the amount of energy fish spend passing the dam. These measures will be helpful for 

improving recovery of Endangered Species Act-listed stocks. 

This study used electromyogram (EMG) radiotelemetry to examine the amount of energy spring 

chinook salmon expended while passing Bonneville Dam. Although EMG telemetry has been used to 

examine the behavior and energy use of Pacific salmon in free-flowing reaches of rivers (Hinch et al. 

1996, Hinch and Rand 1998, Rand and Hinch 1998, Hinch and Bratty 2000, Brown and Geist 2002), this 

technology has not been used to examine behavior and energy use while they migrate past a hydroelectric 

dam. 

1.2 Background 

Pacific salmon (Oncorhynchus sp.) are commonly known for their long spawning migrations. They 

may enter streams several months before spawning (Groot and Margolis 1991) and, because feeding stops 

when they enter freshwater (Greene 1926; Raleigh et al. 1986; Brett 1995), they have a finite amount of 

energy reserves with which to migrate, produce gametes, and complete their life history. In that time they 

may pass through up to eight dams to reach their spawning grounds in the highly developed Columbia 

River Basin. 

A tremendous amount of research has been, and continues to be, conducted on the migrational 

behavior of Pacific salmon (Dauble and Mueller 2000). Within the Columbia River Basin, where 

hydroelectric development has significantly impacted this migrational behavior, thousands of adult 

salmon are implanted with radio transmitters during most years and their locations are noted at numerous 

points along their spawning migration. These studies have identified several possible ways in which the 

hydroelectric system has slowed or otherwise interfered with the migration of salmon. 
1.2

Two factors have emerged as major sources of delay at dams. The first is fallback, i.e., salmon 

ascend dams successfully, but later fall back over the spillway or other location (Liscom et al. 1985, Swan 

et al. 1994, Bjornn et al. 1999, Bjornn et al. 2000a), requiring a second ascent of the dam. Estimates of 

fallback rates for radio-tagged spring and summer chinook salmon, sockeye salmon (O. nerka), and 

steelhead (O. mykiss) at different dams on the Columbia River have ranged from 4% to over 20% (Bjornn 

et al. 1994, 1999a, 1999b, 2000a). 

A second source of delay involves salmon not being able to successfully find the entrances to 

fishways. There are numerous sources of water discharge in the tailraces of hydroelectric dams, any of 

which can confuse salmon who are attracted to flow. Salmon may spend many hours in the swift waters 

of dam tailraces before finding the entrance to the fishways (Bjornn et al. 1995, 1998). 

Migration delays at the dams, which can last for a few hours to several days per dam (Mendel et al. 

1992; Bjornn et al. 1994; Mendel and Milks 1997; Dauble and Mueller 2000), can have several negative 

effects. A major concern is that, since salmon have limited energy reserves available to expend before 

death, any delay increases the likelihood that the fish will run out of energy en route or at spawning areas 

before successfully spawning (Berman and Quinn 1991). Delaying salmon at dams can also increase the 

likelihood of pre-spawning mortality (Beiningen and Ebel 1970; Gray 1990; Snelling et al. 1992) through 

predation by marine mammals, harvest by anglers, or mortality due to other sources (Dauble and Mueller 

2000). Considering the depleted state of most wild salmon stocks, it is essential that the likelihood of 

successful spawning be maximized. 

Although the main impact of delay at dams relates to the energetic state of the fish, most research has 

instead focused on the amount of time the fish spend in tailraces, fishways, and forebays of dams (Bjornn 

et al. 2000b). However, the amount of time a salmon spends at a location in the vicinity of a dam cannot 

be directly related to the amount of energy it consumes during that period, e.g., a salmon that is slowly 

swimming at the margin of a dam tailrace uses a dramatically different amount of energy than a salmon 

bursting through high-velocity water attempting to locate a fishway entrance. 

Most of the salmon’s upstream migration is accomplished by sustained swimming, which uses 

aerobic metabolism produced in red muscle tissue (reviewed in Beamish 1978); this uses the majority of 

stored energy reserves contained within upstream migrating adult salmon. For example, 84% of the total 

energy used by upriver-migrating stocks of sockeye salmon in the Fraser River was attributed to 

swimming (Rand and Hinch 1998). 

Although the majority of energy used during upstream migration originates from aerobic pathways 

associated with sustained swimming, Pacific salmon use their tremendous anaerobic capacity in 

spectacular jumps and leaps through fishways and other natural barriers. The proportion of anaerobic 

metabolism in sockeye salmon was estimated to be 36% in very difficult passage areas in the Fraser River 

(Rand and Hinch 1998). These bursts of energy exceed the upper critical swimming speed (Ucrit) and 

likely result in exhaustive exercise with severe physiological consequences that may affect reproductive 

success (Driedzic and Kiceniuk 1976; Wood et al. 1983; Cameron and Cech 1990; Tufts et al. 1991; 

Pagnotta and Milligan 1991). Campbell et al. (1992) reported that exposure of female hatchery rainbow 

trout to acute stress (e.g., swimming to exhaustion) resulted in smaller egg size, delayed ovulation, and 

lower survival of larvae compared to unstressed fish. 

Further, recent evidence suggests that the most efficient migrational behavior incorporates a series of 

burst and coast strategies that take advantage of micro-scale features of river channels with fish likely 

exceeding upper critical swimming speeds for only very short periods without resulting in fatigue (Hinch 

and Bratty 2000). Therefore, incorporation of anaerobic and aerobic metabolism into models of energy 
1.3

use by anadromous salmonids is needed to accurately predict the total amount of energy used during 

upstream migration. 

Consequently, the use of EMG radio telemetry has become an effective method to estimate energy 

used by aerobic metabolism in adult salmon as they migrate to spawning areas (Hinch et al. 1996; Ǿkland 

et al. 1997; Hinch and Bratty 2000; Thorstad et al. 2000). EMG transmitters are surgically placed into the 

red muscle and EMG signals from these transmitters are calibrated against swim speed in a swim 

chamber. The calibration relationships are then used to convert EMG signals from free-swimming fish 

into estimates of swim speed and ultimately, if the relationship between oxygen consumption and swim 

speed is known, the swim speeds are expressed as measures of aerobic energy use (e.g., calories). 

Catabolism of stored glycogen in white muscle tissue is the primary source of anaerobisis in fishes 

(Connor et al. 1964; Burgetz et al. 1998). Therefore, bioenergetic models often incorporate anaerobic 

energy use as a “tax” that is levied against the energy reserves based on how often fish swim at speeds 

that involve the use of white muscle (Rand and Hinch 1998). As the swimming speeds of fish approach 

Ucrit, the firing of red muscle fibers becomes saturated and white muscle is increasingly recruited. 

Electromyography studies of muscle activity during sub-maximal swimming indicate that 80% of the 

Ucrit may be the threshold for when white muscle is recruited into swimming (Hudson 1973; Bone et al. 

1978; Brill and Dizon 1979; Rome et al. 1985). However, Burgetz et al. (1998) found that the anaerobic 

component of swimming at speeds above 70% Ucrit results in a significant contribution to total energy 

used while Beddow and McKinley (1999) first noted consistent recruitment of white muscle at 86% of 

Ucrit levels. Accurately determining the swimming speed at which white muscle is recruited is necessary 

to determine the relative contribution of aerobic and anaerobic metabolism in the total energy used by 

migrating salmon. 

Passage at each of the dams is facilitated by a variety of fishway designs. However, the behavior of 

fish and the amount of energy used during passage of these fishways is largely unknown. Bjornn et al. 

(1995) found that the amount of time from a salmon’s first entry to a fishway to its successful exit of a 

fishway varied from an average of ~13 to 16 hours on the Snake River. This is 34% to 64% of the 

median time it takes chinook salmon to pass lower Columbia River reservoirs (~20-47 hours). Thus 

finding and passing fishways is a major component of the spawning migration. Unlike the reservoir 

passage component, it is a component that is amenable to human manipulation and improvement. 

EMG telemetry can be useful for determining swimming behavior and its energetic consequences on 

a fine scale, making it possible to determine the swimming speeds (and consequently energy use) of fish 

throughout a dam’s fishways (Hinch et al. 1996; Hinch and Rand 1998; Rand and Hinch 1998; Hinch and 

Bratty 2000; Brown and Geist 2002). 

1.3 Study Area 

This study was conducted from April to July 1, 2001, at Bonneville Dam (Figure 1.1 and 1.2). 

Bonneville Dam was constructed in 1938 and is located 234.9 km upstream of the mouth of the Columbia 

River. During the study period, the mean daily discharge was 3,784 m3

 s-1 and daily averages ranged 

from ~2,500 to ~5,000 m3

 s-1 (Figure 1.3). These flows were much lower than the 10-year (1992–2001) 

average (8,141 m3

 s-1). The majority of water discharge past Bonneville Dam is directed through two 

powerhouses or one central spillway (Figure 1.1). Flow in these areas varies depending on the operation 

of the dam. Figure 1.4 shows the fluctuations in water discharged through the two powerhouses and over 
1.4

the spillway during the study period. Water temperatures increased over the study period from ~9°C to 

18°C (Figure 1.3). 

Figure 1.1. Bonneville Dam Powerhouses and Fishways 

1.3.1 Bradford Island Fish Ladder 

The Bradford Island fish ladder at Bonneville Dam has two branches (Figure 1.5), the A branch 

entrance is located along Powerhouse 1 and the B branch entrance is located at the south end of the 

spillway (USACE 1981). The length of the fishway is 373 m if fish pass through the A branch and 408 m 

if they pass through the B branch. The fishway has full-width overflow weirs (9.14 m wide) in the main 

stem (Section 5) of the fishway. The two branches have full-width overflow weirs in the lower ends (the 

lower 19 weirs in the A Branch and the lower 17 weirs in the B Branch) and have restricted overflow 

weirs in the remainder of the A and B branches. The restricted overflow weirs have 2.67-m-long 

overflow sections on each side. There is a height differential of 0.3 m between most weirs (weirs 10 to 

15, which have 0.38 m differentials) and weirs are 4.9 m apart. All weirs contain an orifice on each end. 

At the upper end of the main stem of the ladder is a fish counting station (USACE 1981). Between 

the counting slot (section 6) and the main stem of the fish ladder (part of section 6) is a triangular-shaped 

approach pool that is 1.5 to 2.1 m deep (part of section 6). This acts as a transition between the fish 

ladder and the counting slot. The counting slot (Section 6 in Figure 1.5) is 1.22 m wide, but can be 

adjusted down to 0.3 m wide to facilitate clear viewing of fish. 

Between the counting window and the fishway exit is a section of vertical slot weirs. The 18 vertical 

slots divide the section into 17 pools. Slots vary from 0.7 to 1.2 m in width. For further detail on design 

of fishways at Bonneville Dam contact the USACE Hydraulic Engineering District Office, Portland, 

Oregon. 
1.5

Figure 1.2. Reception Range of Antenna Arrays at Bonneville Dam. 

Underwater antenna reception areas are shown as dark circles; aerial 

antenna reception areas are shown as polygons with different patterns, 

depending on depth. Water depth is considerately deeper in some 

areas than in the reception zone. For bathymetry see Rakowski and 

Richmond (2000). 

8

10

12

14

16

18

20

18 April25 April

2 May9 May

16 May23 May30 May

6 June

13 June20 June27 June

Date

Water 

temperature (C)

2,000

2,500

3,000

3,500

4,000

4,500

5,000

5,500

Discharge (m3 

s

-1)

Figure 1.3. Mean Daily Water Temperature (heavy line) and Mean Daily Discharge 

(thin line) at Bonneville Dam, 2001 
1.6

Figure 1.4. Mean Daily Discharge (m3 s-1) Over the Spillway or Through Two 

Different Powerhouses at Bonneville Dam, 2001 

Figure 1.5 Sections of the Bradford Island Fishway. Section 6 is immediately in front of the counting 

window. 
1.7

1.3.2 Cascade Island Fish Ladder 

The Cascade Island fish ladder has been modified from its original construction (at a length of 400 

m). Originally fish were guided from its entrance at the tailrace of the spillway to the forebay of the 

spillway. However, this exit has been closed and fish are diverted through the underground migrant 

tunnel (UMT; which is ~ 156m long) to the Washington Shore fishway, through which they exit into the 

forebay of Powerhouse 1. The size, gradient, and spacing of the overflow weirs are similar to the 

Bradford Island fishway. All weirs contain an orifice on each end, and these orifices are 61 cm high and 

wide. 

1.3.3 Washington Shore Fish Ladder 

Most of the Washington shore fish ladder is composed of full overflow weirs. The size, gradient, and 

spacing of the overflow weirs are similar to those of the Bradford Island fishway. All weirs contain an 

orifice on each end; these orifices are 46 cm high and wide. At the upper end of the main stem of the 

ladder is a fish counting station that contains a counting window. Between the counting window and the 

fishway exit is a section of vertical slot weirs. The vertical slots divide the section into 17 pools. 
1.8
2.1

2.0 Methods 

2.1 Laboratory Studies 

The following sections describe respirometer trials of adult spring chinook salmon Ucrit and EMG 

swim speed trials conducted at the Pacific Northwest National Laboratory (PNNL), Richland, 

Washington, or the Columbia River Research Laboratory (CRRL), Cook, Washington 

2.1.1 Fish Collection and Holding 

We obtained 65 adult spring chinook salmon in May and June, 2000, from the Little White Salmon 

National Fish Hatchery, at Cook, Washington. These fish are normally spawned in the late summer to 

early fall and thus were relatively early in their reproductive development. All fish were transported in 

large, rectangular tanks (757 L) in pickup trucks to either PNNL or CRRL. For all transport, densities of 

fish were kept low (<12 fish per trip), bottled oxygen was used to maintain dissolved oxygen levels at 

>100% air-saturation, and the water was treated with polyaqua (160 ppm) and clove oil (ca.10 ppm) to 

reduce stress. Water temperature never increased more than 2 oC during any trip. 

Upon arrival, 3 or 4 fish were randomly placed in each of three covered, outdoor circular tanks 

receiving well water of similar temperature to that at the hatchery (7-9 oC). At PNNL, the tanks were 1.8 

m in diameter and had inflow rates of about 35 L min-1. At CRRL, tanks were 1.5 m in diameter and had 

flow rates of about 6 or 7 L min-1. All tanks had a water depth of about 50 cm and air stones to deliver 

atmospheric air from a compressor. Within 24 hours of arrival, fish were acclimated to either 8oC, 

12.5oC, or 17oC by increasing water temperature at a rate of 2 oC per day. All fish were allowed at least 

one week at their acclimation temperature before being used in experiments. All fish were treated for 1 h 

every 3 or 4 d with a static bath of formalin (37% formaldehyde) at a concentration of 0.17 mL L-1 to 

prevent fungal infections. 

2.1.2 Respirometry Trials 

We used swimming respirometry to determine the relation between metabolic rate (i.e., oxygen 

consumption) and swim speed. A total of 45 adult spring chinook salmon were exercised at the two 

laboratories (n = 24 at PNNL and n = 21 at CRRL). Fifteen fish were tested at each of the three 

temperatures (i.e., 8, 12.5, or 17 oC). Trials were conducted in either a 500-L Blazka (CRRL) or a 2,200-

L Brett-type (PNNL) respirometer. We used two respirometers to allow us to test a large number of fish 

in a fairly short period of time. The working section of the Blazka respirometer measured 142 cm long 

and 45.7 cm in diameter. The working section of the Brett respirometer measured 180 cm long and 39.4 

cm in diameter. Water velocities in both respirometers were created by a propeller driven by a variablespeed motor. 

We used linear regression to describe the relation between motor speed and water velocity (measured 

with flow meters inside the tunnel). The resulting equations were used to calculate the motor speed 

necessary to achieve a desired velocity. Because of the smaller diameter of the Brett respirometer, we 

used a correction factor to adjust velocities for solid blocking effects (Smit et al. 1971). During trials, 

both respirometers were sealed so that no gas exchange occurred. An electrified screen (Brett) or circular 

rings (Blazka) were used occasionally to keep fish from resting during trials. 
2.2

To start a trial, the respirometer was partially filled with water at the acclimation temperature. We 

then placed clove oil (11 to 12 ppm) into one randomly selected holding tank with the water flow on. 

This dose of clove oil served to calm fish prior to netting them out of the holding tank. When fish in the 

holding tank were quiescent, usually after about 5 min, one individual was gently netted and placed in a 

large container filled with water and clove oil at a concentration of 50 ppm. When the fish was fully 

anesthetized, it was weighed (to the nearest g), measured (fork length to the nearest mm), and placed in 

the respirometer. The respirometer was then closed, completely filled with water, and partially covered 

with black plastic to calm the fish and provide incentive for them to swim in the middle or anterior 

section of the respirometer. The fish remained overnight with the respirometer being continually flushed 

with fresh water (i.e., flow through) and velocity set at 30 cm s-1. 

The next morning, gas-impermeable tubing from a peristaltic pump was connected to two valves at 

the top or side of the respirometer. The pump served to sample water (60 mL min-1) in a continuous loop 

from the respirometer to a sealed Plexiglas chamber containing an oxygen electrode connected to an 

electronic meter (WTW Oxi 340 meter and CellOx 325 probe) and back to the respirometer. The oxygen 

meter and probe were calibrated in air (i.e., 100% air-saturated) every day according to manufacturer’s 

instructions, connected to a personal computer, and data (time, percent saturation of oxygen, and 

dissolved oxygen) were logged every 5 to 15 s using software supplied by WTW. After the oxygen 

monitoring equipment was set up, the respirometer was sealed by turning off the fresh water inflow and 

we started logging data from the first swim speed increment, 30 cm s-1. We recorded the starting time, 

percent saturation of oxygen, and dissolved oxygen (mg L-1) on a data sheet and logged data until the 

dissolved oxygen levels had dropped 0.15 to 0.30 mg L-1 from starting conditions. 

When the dissolved oxygen concentration had dropped sufficiently, we recorded the final time, 

percent saturation, and dissolved oxygen concentration. At the end of any speed increment, if the percent 

air saturation was > 75% and water temperature had not increased more than 2oC, we increased water 

velocity to the next speed increment and repeated the oxygen data logging procedure. If, however, the 

percent saturation was near 75% air-saturation or if water temperature had increased more than 2oC, we 

reduced water velocity to 30 cm s-1 and flushed the respirometer with fresh water until we achieved > 

90% air-saturation and reduced water temperature to starting conditions. We then sealed the respirometer 

and repeated the above procedures. 

Fish were tested at 30, 50, 70, 90, 110, 130, 150, 170, and 190 cm s-1; not every fish successfully 

completed all increments. Typically, at the lower velocities, fish were swum through about three 

increments before flushing the respirometer. As velocities increased (> 90 cm s-1), it was necessary to 

flush the respirometer with fresh water every one to two increments. 

When fish completed as many speed increments as they were capable of, or willing to, water velocity 

was reduced to 30 cm s-1 and the respirometer was flushed with fresh water. Data from each increment 

were plotted as the decrease in dissolved oxygen versus time and checked visually for a linear relation. If 

a relation from any increment was substantially non-linear or highly variable, we would attempt to swim 

the animal again at that speed, but this was rare. After this quick check on the data, the respirometer was 

again sealed and we introduced a sleep dose of either MS 222 (50 mg L-1) or clove oil (ca.10 ppm) to 

facilitate removal of the fish from the swim tunnel. Fish were removed from the respirometers, uniquely 

marked with a colored floy tag, and returned to their holding tank. A blank trial (30 min) was performed 

after each fish trial to determine the amount of microbial oxygen use. Microbial oxygen use was 

normally undetectable but when present was subtracted from the oxygen use estimate of the previous fish. 
2.3

The oxygen consumption rate (VO2 in mg O2·kg-1·h-1) for an individual fish during a velocity 

increment was determined using the following equation: 

W

O A V 1

2 = × ×

where V was the volume of the respirometer (L), W was the weight of the fish (kg), and A was the 

rate of oxygen consumed (mg O2·L-1·h-1), minus the blank, during a velocity increment as determined by 

linear regression analysis of the reduction in dissolved oxygen levels over time (t): 

A = bo + (t × b1)

Data analysis was accomplished by first comparing fish weights and lengths between laboratories 

(i.e., CRRL or PNNL), water temperatures, and sex using analysis of variance. A series of linear 

regression models were then compared to determine the best model for predicting oxygen consumption as 

a function of swimming speed (in cm sec-1 or body lengths sec-1) and temperature. For all analyses, the 

natural logarithms (loge) of oxygen consumption rates were used to reduce the residual variance and 

within-class heterogeneity. 

Since each fish was tested at only a single temperature, a third-order polynomial linear regression 

model of log oxygen consumption versus swim speed (X) was fit to each fish so that the third-order slope 

was common for all fish. That is, each fish had a separate intercept, separate slope associated with swim 

speed, separate slope associated with swim speed squared, and a common slope associated with swim 

speed cubed. A common third-order slope prevents distorted slope estimates for fish not tested at higher 

swim speeds. Analysis of variance of the estimated slopes and intercepts was then used to examine the 

differences between temperatures, laboratories, sex, and body size to determine the level of complexity in 

a regression model combining fish (e.g., common or separate slopes and intercept for each factor). 

Polynomial regression was used to remove patterns of curvature from the residuals. Regression was 

conducted using swim speed and swimming speed normalized by body length. 

Combined fish models were compared based on the principle that the simplest model (i.e., the model 

with the least number of parameters) that best explained the variability explained by the full model (i.e., 

the model with more parameters) and yet still made biological sense was the preferred model (Ramsey 

and Schafer 1996). The statistic (Fcalc) used to compare nested models was 

Full( )

Reduced( ) Full( )

Reduced( ) Full( )

EMS

edf edf

ESS ESS

Fcalc







 







−

−

=

and was compared to an Fα=0.05, reduced (edf) – full (edf), full(edf) where ESS, edf, and EMS refer to the error sum 

of squares, error degrees of freedom, and the error mean square (Ramsey and Schafer 1996). Competing 

nested models that were not found to be significantly different (α = 0.05) from the full model and 

competing models that were not nested were determined to be more appropriate based on either a 

minimum error mean square or the model’s biological relevance. All statistical analyses were done using 

JMP, version 4 (SAS Institute). 
2.4

2.1.3 Critical Swimming Speed Trials 

A total of 22 adult spring chinook salmon were tested at the two laboratories (n = 13 at PNNL and n = 

9 at CRRL). We used the same respirometers as previously described to estimate the Ucrit of adult spring 

chinook salmon. Fish used in these trials were either some of those used in the respirometry trials or were 

unused fish. In all cases, fish were allowed at least 12 to 18 h of recovery after a respirometry trial before 

being used in a Ucrit trial. Water temperature for all Ucrit trials was 12.5 oC. 

To start a trial, fish were anesthetized and placed in the swim tunnel as described for the respirometry 

experiments. After being placed in the respirometer, the fish were allowed at least 1 h of recovery under 

flow-through conditions (water velocity 30-50 cm s-1). After recovery, the water velocity was increased 

to 75 cm s-1 and the fish was swum for 30 min. Water velocity was then increased 25 cm s-1 every 30 min 

until the fish fatigued. Fatigue was defined as when the salmon rested or became impinged on the 

downstream screen and would not leave despite several electric shocks. After fatigue was confirmed, the 

fish was removed from the swim tunnel. Critical swimming speed was calculated in body lengths per 

second (BL s-1) and in water velocity (cm s-1) using the formula described by Beamish (1978). For fish 

swum in the Brett respirometer, velocities were corrected for solid blocking effects, as described 

previously. Analysis of variance was used to compare Ucrit values between laboratories. 

2.1.4 Electromyogram Swim Speed Trials 

To document the relationship between EMG activity and swimming performance, EMG radio 

transmitters were surgically implanted into 10 individuals who were swum at defined speeds in the Brett 

respirometer at PNNL. The fish used in these trials were a sub-set of the fish used in the Ucrit tests done 

at PNNL. In all cases, fish were allowed 48 h of recovery after surgery before being used in an EMG 

trial. Water temperature was 12.5 oC during all EMG swim trials. 

Prior to EMG transmitter implantation, fish were anesthetized in 35 L of water using 50 ppm of clove 

oil until they reached a stage 4 to 5 of anesthesia (Summerfelt and Smith 1990). Fish were then placed 

ventral side up on a surgical table in an adjustable sling and their gills were flushed continuously with an 

aerated, temperature-controlled clove oil anesthetic (30 ppm) using flexible tubing connected to a 

submersible pump. A 3-cm-long incision was made just off the mid-line about half-way between the 

posterior tip of the pectoral fin and the anterior edge of the pelvic girdle. A retractor was used to widen 

the incision and blunt forceps were used to create a space between the body wall and internal organs to 

safely complete the incision. 

To insert the transmitter and implant the electrodes, a 25-cm-long shielded needle was slid along the 

inside body wall until the tip of the needle was 5 to 10 cm posterior to the incision. The needle was used 

to puncture and send the catheter through the body wall. The needle was removed and the antenna from 

the transmitter was threaded through the catheter, which was then removed. The transmitter was inserted 

into the body cavity and pushed slightly anterior of the incision. 

The transmitters were epoxy-coated cylinders (length = 52 mm; diameter = 16 mm; weight in air = 

17.3 g; Lotek Engineering, Inc., Ontario, Canada) that had a single 25-cm antenna and two plastic-coated 

stainless steel wires (electrodes) extending from one end. Each electrode had a 7-mm-long, 1-mmdiameter 24-k gold rod attached to its end. The electrical impulses emitted by the muscles were detected 

and stored in a capacitor until a factory-set threshold (in μV) was reached, at which time a radio pulse 

was transmitted. Therefore, increasing muscle activity resulted in an increase in the pulse rate (in ms) of 

transmitted radio signals (i.e., time between radio transmissions decreases; see Kaseloo et al. 1992). 
2.5

The gold rods were loaded singly into 1.5 mm grooves cut in the tips of two custom-made 16-g 

needles and plungers. The needles were aligned, held in one hand, inserted deep in the body cavity, and 

pushed through the muscle going at a steep angle toward the lateral line. For placement of electrodes in 

the red muscle, the gold rods were discharged just under the skin at the lateral line; for placement in white 

muscle, the gold rods were discharged before reaching the red muscle at the lateral line. To discharge the 

gold rods, the plunger was pushed down while removing the needle, thus leaving the gold rod anchored in 

the muscle. We attempted to place the gold rods in alignment with one another and about 1 cm apart. To 

check for proper placement, we felt for the gold rods just under the skin (red muscle only) and, using a 

telemetry receiver, verified that the pulse rate of the transmitter had decreased, which would be expected 

in an anesthetized fish. Following placement of the transmitter, the incision was closed with 4 to 5 simple 

interrupted silk sutures and the fish was returned to its holding tank. Fish were allowed 48 h for recovery 

prior to starting the EMG swim trials. 

To document the EMG-swim speed relations, we first applied a light dose of clove oil to the tank 

containing a test fish, gently removed the fish, and placed it in a container that had a stronger dose of 

anesthetic (50 ppm clove oil). When the fish was completely anesthetized, we logged at least 30 EMG 

values using a telemetry receiver. We considered these values to be EMGs of fish at rest and recorded the 

values onto a computer spreadsheet as they appeared on the receiver screen. The fish was then removed 

from the anesthetic and placed in the Brett respirometer, where it was allowed to recover for 1 h under 

flow-through conditions and velocity set at 30 cm s-1

. 

To record EMGs while fish were swimming, we set up a video camera connected to a monitor that 

viewed simultaneously a side view of the fish in the swim chamber and the face of a telemetry receiver. 

An observer with a computer was positioned in front of the monitor to record data. Fish were swam at 30, 

50, 70, 90, 110, 130, 150, 170, and 190 cm s-1; again, not all fish swam at each velocity. At each velocity, 

the observer would watch the fish and, if the fish was swimming steadily, would record EMG data from 

the telemetry receiver by typing it into the spreadsheet. Data were not recorded if the fish was showing 

aberrant behavior such as burst swimming or resting. Fish would be encouraged to swim by briefly 

electrifying the downstream screen. 

Fish swam at each speed until at least 30 data points were recorded before the velocity was increased 

to the next speed. Generally, it would take a few minutes at each speed to collect 30 data points. At 

higher swim speeds, it was sometimes difficult for the observer to record data because of the erratic 

behavior of fish and relatively fast data transmission. Therefore, we occasionally used videotapes of fish 

(all fish were videotaped) to record data from them swimming at high speeds. 

After the last speed increment, the velocity was reduced to 30 cm s-1, and data were examined by 

plotting mean EMG output at each swim speed. For red muscle, we expected the time between pulses to 

decrease (indicating increasing muscle activity) linearly up to speeds approaching Ucrit. For white 

muscle, we expected minimal activity at low speeds and increasing muscle activity as speeds approached 

Ucrit and above. Fish were re-swum at any speed that was an obvious outlier on the observed trend line, 

but this was rare. When a trial was complete, the fish was removed from the respirometer, placed in a 

sleep dose of clove oil (50 ppm), and sacrificed by a blow to the head. The electrodes were dissected out 

to record the distance from the nose to the center of the electrodes, the distance between electrodes, the 

depth of the electrodes below the skin, and electrode orientation and location (red or white muscle). 

Data analysis was accomplished using linear regression which fit muscle EMG data to swimming 

speed expressed both in cm s-1 and body lengths sec-1 in separate models. The base model took a piecewise linear form on two model segments. 
2.6

EMG(x) = a1+ b1x x<x0 

2 2( ) 0 = a + b x − x x=>x0 

where 

x0 = (a2 − a1)/ b1

This was a generalization of a model form that has been used in toxicological threshold models and is 

sometimes referred to as the “hockey stick model” (Klepper and Bedaux 1997). These models were fit to 

red and white EMG muscle data separately. The fitting procedure estimates four model parameters 

(intercepts: a1, a2; slopes: b1, b2) with the breakpoint between model segments (x0) expressed as a 

function of a1, a2, and b1. The model is thus non-linear in x. The fitting procedure uses an Iteratively 

Re-weighted Least Squares (IRLS) methodology with a Gauss-Newton optimization algorithm. In this 

manner, both model segments are fit simultaneously and meet at the breakpoint (x0). The software used 

in this analysis was SAS ver. 8.2. 

2.2 Field Studies 

2.2.1 Fish Collection and Tagging 

Ninety-six spring chinook salmon were captured at Bonneville Dam between April 12 and June 8, 

2001. These fish had a mean weight of 6.0 kg (range 4.0 – 8.8; SD = 0.9) and a mean fork length (FL) of 

76 cm (range 68.0 - 85.5; SD = 3.1). Fish were captured in a trap inside the Washington shore fishway 

(Figure 1.1). While this trap was in operation, all fish were diverted from the fishway into a large tank 

and then into an aerated holding tank containing anesthetic (clove oil). 

After the fish were anesthetized, they were carried in a sling approximately 100 m to a holding tank. 

Three fish were randomly placed in each of four covered, indoor circular tanks receiving fresh river 

water. Tanks were 1.5 m in diameter and had flow rates of about 6 to 7 L min-1; all tanks had a water 

depth of about 50 cm. 

Shortly after capture, all fish were surgically implanted with EMG transmitters. Each transmitter 

emitted signals on a unique frequency between 148 and 151 MHz. The transmitters were epoxy-coated 

cylinders (length = 52 mm, diameter = 16 mm, weight in air = 17.3 g, and weight in water = 8 g; Lotek 

Engineering, Inc., Ontario, Canada) that had a single 25-cm antenna and two stainless steel, Teflon-coated 

wires (electrodes) extending from one end. Each electrode had a 7-mm-long, 1-mm-diameter, 24-k gold 

rod attached to its end. The EMG transmitter detected the electrical activity of the muscles. The 

electrical impulses emitted by the muscles were detected and stored in a capacitor until a factory-set 

threshold (in μV) was reached, at which time a radio pulse was transmitted. Therefore, increasing muscle 

activity resulted in an increase in the pulse rate of transmitted radio signals (i.e., the time between the 

radio transmissions decreased; see Kaseloo et al. 1992). 

Prior to EMG transmitter implantation, fish were anesthetized in 35 L of water using 50 ppm of clove 

oil until they reached a stage 4 to 5 of anesthetization. Fish were then placed ventral side up on a surgical 

table in a groove cut in a piece of foam rubber and their gills were flushed continuously with an aerated, 

temperature-controlled clove oil anesthetic mixture (30 ppm) using flexible tubing connected to a 
2.7

submersible pump. A 3-cm-long incision was made just off the mid-line about mid-way between the 

posterior tip of the pectoral fin and the anterior edge of the pelvic girdle. 

To insert the transmitter and implant the electrodes, a 25-cm-long shielded needle was slid along the 

inside body wall until the tip of the needle was 5 to 10 cm posterior to the incision. The needle was used 

to puncture and send the catheter through the body wall. The needle was removed and the antenna from 

the transmitter was threaded through the catheter, which was then removed. The transmitter was inserted 

into the body cavity and pushed slightly anterior of the incision. The gold rods were loaded singly into 

1.5-mm grooves cut in the tips of two custom-made 16-g needles and plungers. The needles were 

aligned, held in one hand, inserted deep in the body cavity, and pushed through the muscle going at a 

steep angle toward the lateral line. The gold rods were discharged just under the skin at the lateral line. 

To discharge the gold rods, the plunger was pushed down while removing the needle, thus leaving the 

gold rod anchored in the muscle. 

We attempted to place the gold rods in alignment with one another and about 1 cm apart. To check 

for proper placement, we felt for the gold rods just under the skin and, using a telemetry receiver, verified 

that the pulse rate of the transmitter had decreased, which would be expected in an anesthetized fish. 

Following placement of the transmitter, the incision was closed with 4 or 5 simple interrupted silk sutures 

and the fish was returned to its holding tank. Fish were allowed 48 h for recovery prior to calibrating 

EMG signals to swim speed. 

2.2.2 Calibration of EMG Signals to Swim Speed 

To document the EMG-swim speed relationship, we first applied a light dose of clove oil to the tank 

containing a test fish, gently removed the fish, and placed it in a container that had a stronger dose of 

anesthetic (50 ppm clove oil). When the fish was completely anesthetized, we logged at least 30 EMG 

values using a telemetry receiver (SRX 400, Lotek Engineering, Inc., Ontario, Canada). We considered 

these values to be EMGs of fish at rest and recorded the values onto a computer spreadsheet as they 

appeared on the receiver screen. The fish was then removed from the anesthetic and placed in the Blazka 

respirometer, where it was allowed to recover for 0.5 h under flow-through conditions and velocity set at 

30 cm s-1. 

The Blazka respirometer was used to calibrate the EMG tags to the swimming speed of each fish. 

The respirometer consisted of a tube containing 500 L of water and had a working section 142 cm long by 

45.7 cm in diameter. Water velocity in the respirometer was created by a propeller driven by a variablespeed motor. 

To record EMGs while fish were swimming, a video camera simultaneously filmed a side view of the 

fish in the swim chamber and the face of a telemetry receiver. An observer with a computer was 

positioned in front of a video monitor where the images of the fish and receiver were viewed. Fish were 

swam from 30 to 230 cm s-1 in 20 cm s-1 increments; not all fish swam at each velocity. At each velocity, 

the observer would watch the fish and, if the fish was swimming steadily, would record EMG data from 

the telemetry receiver by typing it into the spreadsheet. 

Data were not recorded if the fish was showing aberrant behavior such as burst swimming or resting. 

Fish could be encouraged to swim by briefly electrifying the downstream screen. Fish swam at each 

speed until at least 30 data points were recorded before the velocity was increased to the next speed. 

Generally, it would take a few minutes at each speed to collect 30 data points. Occasionally, at higher 
2.8

swim speeds, telemetry data from videotapes of the swimming fish, were used to add some data if the fish 

did not swim long enough to obtain 30 data points. 

After the last speed increment, the velocity was reduced to 30 cm s-1 and data were examined by 

plotting mean EMG output at each swim speed. We expected the time between pulses to decrease 

linearly (indicating increasing muscle activity) up to speeds approaching critical swimming speed (Ucrit, 

which is a measure of prolonged swimming performance; Beamish 1978, Webb 1995, Section 2.1 of this 

report). Fish were re-swum at any speed that was an obvious outlier on the observed trend line, but this 

was rare. 

An equation describing the relationship between swimming speed and EMG pulse interval was 

determined for each individual fish in-situ and was used to estimate swimming speeds of fish in-vivo. A 

simple linear regression was used to describe this relationship. This method is used since this relationship 

is usually linear (as seen in Section 2.1). However, if fish swim at higher speeds in the wild than they did 

during calibration in the lab, estimates of swimming at higher speeds may not be accurate. We examined 

this possible source of inaccuracy of swimming speeds by determining the number of signals collected 

that were above calibration speeds. To do this, we first determined the mean EMG signal interval emitted 

by the transmitter while each fish was swimming at the highest velocity increment in the lab. Then we 

compared that to the signal intervals for field-collected EMG signals and the percentage of shorter field 

intervals (indicating faster swim speeds) for each fish was reported. 

2.2.3 EMG Signal Tracking 

After EMG tags implanted in fish were calibrated to swimming speed, the fish were placed in a large 

cooler and transported approximately 10 minutes to the release site. During transport, a small air pump 

was used to maintain dissolved oxygen levels near saturation, and the water was treated with polyaqua 

(160 ppm) and clove oil (ca. 10 ppm) to reduce stress. Fish were released in one of two different 

locations; on the Washington shore 11.1 km downstream from Bonneville Dam and on the Oregon shore 

9.5 km downstream from Bonneville Dam. Fish were released during daylight between April 18 and June 

10. 

As fish approached and passed Bonneville Dam, EMG signals were logged on up to nineteen 

SRX400 receivers and antenna arrays (Lotek Engineering, Inc., Ontario, Canada; Figure 1.2). A single 

antenna array was used to receive EMG signals from fish passing the Bradford Island fishway and another 

was used for the Cascade Island fishway. Two antenna arrays were used to monitor the Washington shore 

fishway. These arrays were tested to ensure that emissions from transmitters would be received in all 

areas of the fishways, yet not pick up signals from transmitters in the tailrace or forebay. Arrays of up to 

eight antennas were used to receive signals from fish that were located in the tailrace, fishways, or 

forebays. 

Fishway antenna arrays consisted of multiple 4- or 6-element Yagi antennas (Model P150-4 or PLC 

6, Cushcraft Corporation, Manchester, NH) while forebay and tailrace antennas consisted of 6- or 9-

element Yagi antennas (PLC 1509 Cushcraft Corporation, Manchester, NH). In all cases, antennas were 

grouped together using an ASP-8 antenna switch box. Receivers were set to group all antennas together 

and treat them as one large antenna. The reception range (Figure 1.2) of tailrace and forebay antenna 

arrays was tested by placing an EMG transmitter in the water at different depths (10, 20, and 30 feet deep) 

while boating through the forebays and tailrace. Locations where reception range faded were noted using 

a GPS. 
2.9

EMG signals from implanted salmon were also received using an array of underwater antennas 

attached to a computerized receiving system (Multiprotocol Integrated Telemetry Acquisition System 

[MITAS], Grant System Engineering, King City, Ontario, Canada). Fifty underwater antennas were 

spaced along the shore of the forebays of Powerhouse 1 and the spillway; 18 dipole antennas were placed 

along the spillway, and 32 balanced loop vee antennas (Model LVB-150-B; dB labs, Gretna, Nebraska) 

were placed along the shores of Cascade and Bradford Islands (distribution shown in Figure 1.2). 

In addition to fixed reception arrays, manual trackers on foot and in trucks monitored EMG 

transmissions from implanted spring chinook salmon as they approached and passed the dam. Pick-up 

trucks were outfitted with 4- or 6-element Yagi antennas (models mentioned above). While trackers were 

walking they used three-element folding Yagi antennas (Advanced Telemetry Systems Inc., Isante, 

Minnesota). 

As can be seen from Figure 1.2, signals from transmitters could not be received within all areas of the 

tailraces and forebays. Signals could not be received in some areas because the fish were too far from the 

antennas. In other areas, fish were too deep in the water column and signals would be attenuated before 

they reached the surface. Even if a fish is within the range of a receiving array, all signals from the 

transmitter will not be recorded. Each receiver must scan through several frequencies. While other 

frequencies are being scanned, the tag within the reception range is not logged. Likewise, if multiple fish 

are in the reception range, the amount of time each fish is logged decreases, while other fish are logged. 

To estimate the percentage of time that signals were collected in each major area (tailraces, fishways, 

or forebays), EMG signals were summed and compared to the amount of time fish spent in the area. An 

EMG receiver logs the amount of time between consecutive radio signals. Thus, a sum of these signals 

provides an estimate of the amount of time that signals were received. In the fishways, it is estimated that 

signals were collected for a mean of 29% (SE = 4.6) of the time fish were present. In the forebays, this 

dropped to 22% (SE = 5.3) and it was only 7% (SE = 0.9) in the tailraces. Since fish are not constantly 

within the reception range of antennas in the forebays and tailraces, and fish were not constantly logged in 

fishways, data in this report do not give a full picture of fish behavior and energetics during dam passage. 

Instead, we attempted to provide an accurate representation of activity while fish are in each area. 

However, fish may consistently be missed in certain parts of tailraces or forebays (see Figure 1.2 for areas 

of reliable coverage) leading to a bias in reporting certain behaviors. 

The signals emitted from the EMG transmitters were logged in the memory of the radio receivers and 

swimming speeds were estimated from these values (Figure 2.1). Swimming speeds for each individual 

fish were calculated using the relationship between swimming speed and the signals emitted from the 

EMG transmitter during calibration (method described previously). Swimming speeds estimated during 

the field study were compared to the critical swimming speed (Ucrit) of Columbia River spring chinook 

salmon (see Section 2.1). Critical swimming speed (Ucrit) is the maximum sustained swimming speed of 

a fish and represents a transition point over which the fish starts to rely heavily upon burst swimming. 

Burst swimming involves a higher proportion of white muscle (and thus anaerobic metabolism) than does 

sustained swimming, which relies primarily on red muscle (i.e., aerobic metabolism; Beamish 1978). 
2.10

Figure 2.1. Process for Determining Swimming Speeds and Energy Use from Signals Received from 

EMG Transmitters Implanted in Spring Chinook Salmon 

Swimming speeds were assigned a temperature-specific value for oxygen consumption (mg kg-1 hr-1) 

using the relationship determined in Section 2.1 (Figure 2.1). Oxygen units (mg kg hr-1) were converted 

to aerobic energy units (kcal h-1) using the oxycalorific average of 3.25 cal mg O2

–1 (Brafield and 

Solomon 1972). 
2.11

2.2.4 Determination of Energy Use Rates 

During this study, EMG transmitters were only used to monitor the activity of the red muscle of 

spring chinook salmon. These muscles are fueled by aerobic, not anaerobic, pathways (Brett 1995). 

Anaerobic energy was estimated using the results of Burgetz et al. (1998). They found that, at different 

percentages of their Ucrit, rainbow trout (O. mykiss) obtained different amounts of energy through 

anaerobic pathways producing varying levels of lactate. They then applied a calorific equivalent to allow 

anaerobic energy use to be estimated. Since the amount of lactate produced (representing anaerobic 

energy) by fish varied with swimming speed, and oxygen consumption also varies with swimming speed, 

Burgetz et al. (1998) expressed the anaerobic metabolism as a percentage of the rate of oxygen 

consumption at a given speed (i.e., an anaerobic tax). As the swimming speed increased from 70% to 

100% of the Ucrit, the anaerobic tax increased (65% at 70% Ucrit; 69.2% at 80% Ucrit; and 205.0% at 

100% Ucrit). Thus, when a fish is swimming at 100% Ucrit, the anaerobic costs of swimming are more 

than double the aerobic costs. 

We took the three anaerobic tax values of Burgetz et al. (1998) and applied them to all EMG signals 

that were over 70% Ucrit (using simple linear regression). Values over 100% Ucrit received the same tax 

as those at 100% Ucrit. This tax was applied to the calorific values determined for aerobic energy to 

provide calorific values for anaerobic energy (in kcal). 

Once aerobic and anaerobic energy use values were determined for each EMG signal, energy use of 

spring chinook salmon was summarized in the forebay, fishway, and tailrace areas of Bonneville Dam. 

Then, to identify a representative rate of energy use for each area, the mean of the energy use rate for each 

individual logged in each area was determined (Figure 2.1). The energy use rate within specific areas for 

each fish was multiplied by the amount of time that each fish spent in that area to determine total energy 

use in specific areas. Further comparisons were also made among the three different forebays and 

tailraces (Figure 1.2); more detailed analyses of fishways are reported in Section 3.2.3. 

To examine differences, data were checked for normality. Since swimming speed and energy use rate 

data were normally distributed, factor analysis ANOVA was used for comparisons. Factor analysis 

ANOVA was used to determine if swimming speeds and energy use rates differed among the three major 

areas (tailrace, fishway, and forebay) and by sex. Differences in sex were controlled for by factor 

analysis ANOVA. Two factors were used: the first factor had three levels (tailrace, fishway, or forebay); 

the second factor had a two-level factor for sex (male or female). 

Since data on time in areas and energy use were not normally distributed, Kruskal-Wallis tests were 

used. Throughout this report, when data are normally distributed, means are reported; otherwise, medians 

are reported. 

Differences in swim speed or energy use rates were compared between day and night; data from 

twilight periods were excluded since they represent a small proportion of the day and may dilute the 

contrast effects of maximum darkness vs. full daylight. Factor analysis ANOVA was used to determine if 

swimming speeds and energy use rates differed between day and night within tailraces or forebays. To 

determine if there was a diel difference in these factors, the variation attributed to differences in different 

locations (spillway, powerhouse 1 or 2) of forebays or spillways was controlled for by factor analysis 

ANOVA. 
2.12

Two factors were used: the first factor had three levels (powerhouse 1, spillway, or powerhouse 2); 

the second factor had a two-level factor for diel period (day or night). Total energy used between day and 

night was not compared since antenna arrays were not designed to make exact determinations of when 

fish left one forebay or tailrace and entered another. Since this transition could not be accurately 

assessed, the time fish spent in individual tailraces or forebays could not accurately be measured, making 

total energy use (energy use rates * time) impossible to calculate. 

Fish energy use in the tailrace was related to the spill over the spillway and the flow through the two 

powerhouses. The mean energy use rate was determined for each fish and related to the mean spill over 

the spillway or flow through the powerhouse while the fish was in a particular tailrace. However, since 

fish may not be actively attempting to ascend tailraces during the night, only data from the day were 

included in this analysis. To determine relationships between energy use in the tailraces and total energy 

use rates (aerobic and anaerobic combined), data were plotted and a general linear regression was applied. 

For all statistical tests, alpha = 0.05. 

2.2.5 Radio Tagging and Determination of Location in Fishways 

In addition to EMG tagging, each test fish was also tagged with a gastrically implanted radio tag. The 

radio signals were tracked on the University of Idaho’s antenna arrays to determine fine-scale locations of 

the fish in the fishways. The radio tags used were model MCFT 7A (Lotek Engineering, Inc., Ontario, 

Canada). These transmitters were 16 mm in diameter and 83 mm long. They weighed 29 g in air and 13 

g in water and had an estimated lifetime of 1,013 days. These transmitters emitted a coded pulse that 

allowed multiple transmitters to be received on a single frequency while retaining the ability to identify 

individual transmitters. Fish were placed in a container with anesthetic (~30 to 50 ppm clove oil). Once 

fish attained stage seven anesthesia, they were grasped by the lower jaw and a plastic tube or smooth 

wooden dowel was used to gently ease the transmitter into the stomach. 

The University of Idaho arrays consist of several underwater antennas placed at the entrances, exits, 

and throughout the fishways. These antennas consisted of a piece of coaxial cable with the terminal end 

stripped. The antennas were placed in the water and received signals from the coded transmitter in each 

spring chinook salmon, enabling small-scale changes in location to be noted. Antennas were linked to a 

receiver using digital-spectrum processors that scanned all transmitter frequencies almost simultaneously. 

Entrances to the fishway were defined by at least two signals from coded tags being logged on the 

receiver at the fishway entrance (though it was very uncommon to pick up only two signals at the 

entrance). There were two types of entrances, successful and unsuccessful. An entrance was 

unsuccessful if a fish was logged at the entrance to a fishway or higher in the fishway, but did not 

successfully pass (was later logged in the tailraces). This behavior is referred to as “fallout.” Successful 

fishway entries resulted in a fish progressing through the fishway and being logged at the fishway exit. 

Passage was also divided up among different sections within fishways. The three fishways were 

divided into several sections as is shown in Figures 1.5, 2.2, and 2.3. EMG data for an individual fish in a 

section were only used if at least 30 EMG signals were logged. 
2.13

Figure 2.2. The Cascade Island Fishway. From upper end, fish enter the underground 

migrant tunnel (UMT)and travel through Powerhouse 2 into the Washington shore 

fishway. 

Figure 2.3. The Washington Shore Fishway. The underground migrant tunnel (UMT) enters 

the fishway at section 14. Section 16 (arrow) is immediately in front of counting window. 

The fishways at Bonneville have several entrances. The fishways on each powerhouse have a south 

monolith entrance (not shown in figures). There is a channel that connects the south monoliths with the 

north monoliths. This channel runs along the face of the powerhouse and has several orifices through 

which fish can enter. However, it must be noted that during this study, we did not have enough equipment 

to monitor EMG signals from fish in the south monolith entrances to the Washington Shore or Bradford 

Island fishways. Also, we did not have enough equipment to record EMG signals from fish as they were 

in the fishways at the base of the two powerhouses. 

Using detailed drawings of fishways, the length of each fishway section was estimated. This allowed 

energy used to be expressed per unit length (m) of fishway. 

Entry and exit times were compared among fish that exited a fishway on the same day they entered it 

and fish that spent one or more nights in a fishway. When data were normally distributed and variances 
2.14

did not differ, a T-Test was used; otherwise, a Mann Whitney U test was used. These methods were also 

used to determine if there were differences in swimming speed, energy use rates, time spent in areas, and 

energy used in fishways between day and night, and also between fallouts and successful passages. Due 

to low sample sizes during twilight and night, only daytime data were used for this analysis. 

If data were normally distributed and variances did not differ, ANOVA was used to determine 

whether there were differences in swimming speeds, energy use rates, times spent in the three different 

fishways, or the amount of energy used in different fishways. If these assumptions were not met, a 

Kruskal-Wallis test was used. Since there were no significant (P<0.05) differences in speed or energy use 

rates among fishways, the different fishways were not included as a factor in more detailed analysis. 

Swimming speeds, energy use rates, energy use, and time spent in areas were ranked. In an effort to 

determine the relative energetic consequences of passage through each fishway section, rates of energy 

use and time spent in areas were combined into a single index. To accomplish this, the rankings of these 

two parameters were added. 
3.1

3.0 Results 

3.1 Laboratory Results 

Results from the laboratory studies conducted at PNNL and CRRL in 2000 are described in the 

sections below. 

3.1.1 Respirometry Trials 

Of the 45 fish used for the respirometry trials, 14 were males, 30 were females, and 1 was not 

identified. Fork lengths ranged from 67.0 to 80.4 cm and weights ranged from 3.7 to 6.4 kg. Mean 

lengths and weights of fish were not significantly different between laboratories, sexes, or temperatures 

(ANOVA P > 0.13 for all factors). 

As expected, oxygen consumption increased with increasing swim speed for all fish. The individual 

fish model explained 98.5% (corrected for intercepts) of the total variance in oxygen consumption 

(Figure 3.1). Depending on water temperature, the model suggested oxygen consumption flattened out, or 

slightly declined, at or about 1.9 to 2.6 BL s-1 (ca.135 to 260 cm s-1); note that our empirical data on 

oxygen consumption was only measured up to 190 cm s-1 and values beyond this are interpolated by the 

best-fit model. 

0

200

400

600

800

1000

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5

Swim speed (BL s-1)

VO2 (mg O2 kg-1 h-1)

Figure 3.1. Observed Oxygen Consumption (data points) and Fitted Cubic Model by 45 

Individual Spring Chinook Salmon (solid lines). All temperatures are combined. 

Analysis of variance (ANOVA) of the 45 individual cubic model parameters for each laboratory, sex, 

length class, and weight class (four classes based on quartiles of the length and weight distributions) 

indicated that these factors had little influence on the VO2-swim speed relations. Although there was not 

a statistical difference in the intercepts of each model based on water temperature (P = 0.11), there was a 

strong trend of increased base-line oxygen consumption with increased water temperature. Further, a 

model that incorporates temperature makes biological sense given the temperature range these fish could 
3.2

experience in the wild. The first- and second-order polynomial slopes were not significantly different 

between temperatures (ANOVA P > 0.60 both factors) and did not display a trend. Thus, fish were 

combined and fit to two competing regression models. The first model (Model 1) had a separate intercept 

for each temperature and common slopes for the first- through third-order polynomial of swim speed, and 

the second model (Model 2) had a common intercept and slope for temperature and common slopes for 

the first- through third-order polynomial of swim speed. 

Model 1 explained 93.2% of the total variance and can be expressed by the following fitted regression 

equations: 

LogVO2 (temp 8°C) = 4.38 + 0.770 * BL s-1 + 0.335 * (BL s-1)

2

 – 0.128 * (BL s-1)

3 ; 

LogVO2 (temp 12.5°C) = 4.50 + 0.770 * BL s-1 + 0.335 * (BL s-1)

2

 – 0.128 * (BL s-1)

 3 ; and 

LogVO2 (temp 17°C) = 4.62 + 0.770 * BL s-1 + 0.335 * (BL s-1)

2

 – 0.128 * (BL s-1)

3 

where BL s-1 is the swim speed in body lengths per second. Model 2 explained 92% of the total 

variance and can be expressed as: 

LogVO2 = 4.20376 + 0.026169 temp + 0.7103 * BL s-1 + 0.3699 * (BL s-1)

2

 – 0.1343 * (BL s-1)

3

. 

The variance explained by both models using swim speed in cm s-1 was 93.9% and 93.8% for Model 

1 and 2 respectively. However, the second- and third-order slope coefficients were extremely small (less 

than 6x10-5); thus, this unit of measure was not considered further. Note, Model 1 is written as three 

equations only for convenience; these equations were fit simultaneously. 

A reduced second-order polynomial was also fit to determine if the third order slopes could be 

removed. For both models, however, the second-order quadratic models were significantly different from 

the full model (P < 0.01). Also, without the third-order slope in the model, the residuals displayed a 

curvilinear pattern. Thus, the reduced models were rejected. Finally, even though Model 2 explained 

slightly less of the variance than Model 1, Model 2 was chosen as the final model due to its flexibility of 

estimation with alternative temperatures. Due to round-off error, all decimal places should be used. The 

predicted VO2-swim speed relations, raw data, and 95% confidence intervals at the three temperatures 

derived from Model 2 are presented in Figure 3.2. 

3.1.2 Critical Swimming Speed 

Of the 22 fish used for the swimming performance tests, 7 were males, 13 were females, and 2 were 

not identified. Fork lengths ranged from 69 to 82 cm (mean 75.5 cm) and weights ranged from 3.6 to 

6.4 kg (mean 5.1 kg). The Ucrits ranged from 125 to 196 cm s-1 (mean 154.7 cm s-1) and 1.6 to 2.7 BL s-1

(mean 2.05 BL s-1; Figure 3.3). Analysis of variance showed that the mean fork lengths, weights, and 

Ucrit values (BL s-1 and cm s-1) did not differ by sex (ANOVA P > 0.53 all factors). Mean weights of fish 

were not significantly different between laboratories (P = 0.41), but fork lengths of fish tested at CRRL 

were significantly greater (P = 0.02) than fish tested at PNNL (mean FL 77.7 vs. 73.9 cm) and the CRRL 

fish had significantly lower Ucrits measured by both BL s-1 (P = 0.0003; mean 1.80 vs. 2.23 BL s-1) and 

cm s-1 (ANOVA P = 0.005; mean 140.2 vs. 164.8 cm s-1). 
3.3

0

200

400

600

800

1000

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5

Sw im speed (BL s-1)

VO2 (mg O2 kg-1 h-1)

8 C

12.5 C

17 C

Figure 3.2. Observed Oxygen Consumption (data points) and Fitted Model 2 (solid lines) 

with Lower and Upper 95% Confidence Bounds (dashed lines) 

Figure 3.3. Distribution of Upper Critical Swimming Speeds (Ucrit) for 22 Adult Spring 

Chinook Salmon with Swimming Speeds Expressed as (A) cm s-1 and (B) body lengths per 

second (Bl s-1). For both plots, the line through the middle of the box represents the 

median, the upper and lower ends of the box represent the 75th and 25th percentiles of the 

distribution, and the upper and lower whiskers show the 90th and 10th percentiles of the 

distribution. Outliers (> 1.5 times the median) are shown by asterisks. 

3.1.3 Electromyogram-Swim Speed Relations 

Of the 10 fish used for the red and white EMG studies, 3 were males and 7 were females. Fork 

lengths ranged from 69 to 80 cm (mean 73.9 cm) and weights ranged from 3.6 to 6.4 kg (mean 5.0 kg). 

The Ucrits of these 10 fish ranged from 145 to 196 cm s-1 (mean 168.2 cm s-1) and 1.92 to 2.72 BL s-1

(mean 2.28 BL s-1). Analysis of variance showed that the mean fork lengths, weights, and Ucrit values 

(BL s-1 and cm s-1) did not differ significantly by sex (ANOVA P > 0.14 for all factors). 

As expected, EMG signals (pulse min-1) of red and white muscle increased with increasing swim 

speed for all fish (Figure 3.4). In all but one case (fish 10), the averaged EMG pulse rate of the red 
3.4

muscle was higher than the EMG pulse rate of white muscle at each swimming speed. The breakpoints at 

which the red and white muscle EMG pulse rates converged are nearly synchronized in their point 

estimates and confidence bounds, suggesting a nearly simultaneous shift from increasing red muscle 

activity (< 135 cm s-1) to increasing white muscle activity (> 137 cm s-1; Figure 3.5). 

Goodness-of-fit tests were highly significant (F-test; P < 0.0001) for both regression models 

(Table 3.1). This convergence point (135 to 137 cm s-1) is approximately 80% of the mean Ucrit for these 

fish (95% confidence interval, 73% to 88%). A similar analysis of the red and white muscle EMGs using 

a model constructed with swim speed in BL s-1 did not achieve convergence (not shown). For all fish, 

post-mortem dissections showed that all electrodes were firmly implanted in the appropriate muscle type. 

We did not note any malfunctioning transmitters during this phase of the study, but we did not re-swim 

fish to determine if transmitter drift occurred over time. 

Figure 3.4. Red (circles) and White (squares) Muscle Electromyograms (EMGs; pulses min-1) by Swim 

Speed (cm s-1) for 10 Adult Spring Chinook Salmon. Solid line shows best fit for EMG 

values; dashed line shows Ucrit. 
3.5

Figure 3.5. Fitted Models for Red and White Muscle Electromyograms by Swim Speed (cm s-1) for 10 

Adult Spring Chinook Salmon. An approximate 95% confidence interval is plotted for the 

estimated breakpoint (X0) between model segments. 

Table 3.1. Parameter Estimates for Two Linear Regression Models Relating Red and White Muscle 

EMGs to Swimming Speed (Bl s-1). The fitting procedure estimates four model parameters 

(intercepts: a1, a2; slopes b1, b2) with the breakpoint between model segments (x0) a 

function of a1, a2, and b1. All parameter estimates were highly significant based on t-values 

(p<0.001). 

Parameter Coefficient (SE) t-value 

Red EMG 

a1 32.106 (0.399) 80.530 

b1 0.141 (0.005) 29.780 

a2 51.174 (0.760) 67.380 

b2 0.072 (0.007) 9.910 

x0 135.000 (6.599) 20.450 

White EMG 

a1 28.132 (0.377) 74.650 

b1 0.037 (0.004) 8.210 

a2 33.174 (0.467) 71.090 

b2 0.114 (0.007) 16.630 

x0 137.000 (5.571) 24.590 
3.6

3.2 Field Study Results 

Ninety-six spring chinook salmon were implanted with EMG transmitters and released below 

Bonneville Dam. Signals from either the EMG transmitters or the coded transmitters were logged from 

88 (92%) of these fish. Useful EMG signals were logged from 85 fish; with 76 of these fish detected in 

the tailraces, 81 detected in the fishways, and 45 detected in the forebays. Most of these fish (79 or 82%) 

passed through the fishways successfully. Two fish entered fishways but did not successfully pass. Two 

fish that did not pass the fishways were logged in the navigation lock; one of these fish was later logged at 

several upstream dams, thus it likely passed the dam using the navigation lock. A mean of 8,858 (range 

59 – 78,072) EMG signals were received from each implanted spring chinook salmon. Of these, a mean 

of 2,578 (range 59 – 22,391) EMG signals were received in the tailrace, 6,142 (range 168 – 73,146) 

signals were received in the fishways, and 1,346 (range 35 – 48,490) signals were received in the forebay. 

3.2.1 Comparisons between Tailraces, Fishways, and Forebays 

3.2.1.1 Swimming Speeds 

Swimming speeds of fish varied considerably by the area of the dam they were passing, but were 

significantly (P<0.05) highest in the tailraces (Table 3.2; Figure 3.6). Mean swimming speeds of spring 

chinook salmon in the tailraces (1.40 BL s-1) were 23% faster than in the fishways (1.13 BL s-1) and 32% 

faster than in the forebays (1.06 BL s-1). Although swimming speeds in the fishways were slightly higher 

than in the forebays (~7%), there was no significant difference between areas. Swimming speeds also 

exceeded Ucrit much more (26% of the time) in tailraces than in either fishways (17%) or forebays (13%; 

Table 3.3). 

3.2.1.2 Time in Areas 

The time that spring chinook salmon spent in the tailraces was significantly higher than time spent in 

other areas (Table 3.2). For example, the median amount of time in the tailraces (21.1 h) was ~ four times 

higher than the median amount of time spent in the fishways (5.0 h) and over 30 times higher than the 

time spent in the forebays (0.7 h). In addition, spring chinook salmon spent significantly (~seven times) 

more time in the fishways than in the forebays. 

3.2.1.3 Energy Use Rates 

The rates of energy used (i.e., kcal kg-1 h-1) by spring chinook salmon were significantly higher in the 

tailraces than in any other part of the dam (Table 3.2; Figures 3.7 and 3.8). The mean energy use rate in 

the tailrace (2.80 kcal kg-1 h-1) was 26% higher than energy use rates for the fishways (2.22 kcal kg-1 h-1) 

and 62% higher than values recorded in the forebays (1.73 kcal kg-1 h-1). Although not significantly 

different, energy use rates in the fishways were 28% greater than in the forebays. 

These trends in differences were also observed when aerobic and anaerobic energy use rates were 

examined separately (Figure 3.7). The only exception was that the differences in the mean anaerobic 

energy use rate approached significance (P = 0.08) between the fishways (1.11 kcal kg-1 h-1) and forebays 

(0.73 kcal kg-1 h-1). 
3.7

Table 3.2. Mean Swimming Speed (+SE), Energy Use Rates (+SE), and Median Energy Used by Spring Chinook Salmon 

Tailraces, Fishways, and Forebays of Bonneville Dam, April – June 2001. The median amount of time they s

areas is also shown. Data are also provided for individual tailrace and forebays. N = number of fish. Variabl

with a capitalized letter in common indicate that there is no significant (P>0.05) difference among major areas 

forebays). Variables in the same column with a lower case letter in common indicate that there is no significa

within a particular tailrace or forebay. 

Swimming Speed Energy Use Rate (kcal kg-1 h-1) Ene

Area N 

cm s-1 BL s-1 Aerobic Anaerobic Total 

Time (h) 

Aerob

Tailraces

Powerhouse 1 22 108.0 (9.1) bc 1.43 (0.1)bc 1.31 (0.10)bc 1.65 (0.22)bc 3.00 (0.3)bcSpillway 41 91.5 (4.1) b 1.21 (0.1)b 1.32 (0.05)b 1.05 (0.11)b 2.18 (0.15)bPowerhouse 2 49 119.2 (6.5) ac 1.58 (0.1)ac 1.39 (0.07)ac 1.86 (0.16)ac 3.26 (0.22)acCombined 76 106.4 (4.2)A 1.40 (0.1)A 1.27 (0.04)A 1.53 (0.10)A 2.8 (0.15)A 21.1A 25.5A FishwaysCombined 79 84.9 (4.2)B 1.13 (0.1)B 1.11 (0.05)B 1.11 (0.09)B 2.22 (0.13)B 5.0B 5.6B ForebaysPowerhouse 1 35 82.5 (7.4)a 1.09 (0.1)a 1.02 (0.08)a 0.79 (0.18)a 1.81 (0.26)aSpillway 10 89.3 (13.9)a 1.17 (0.2)a 1.02 (0.53)a 0.76 (1.05)a 0.78 (1.55)aPowerhouse 2 13 70.6 (10.8)a 0.96 (0.2)a 0.90 (0.12)a 0.56 (0.20)a 1.46 (0.31)aCombined 45 80.2 (6.2)B 1.06 (0.1)B 1.00 (0.07)B 0.73 (0.14)B 1.73 (0.21)B 0.7C 0.6C 
3.8
3.9

Figure 3.6. Box Plots of Mean Swimming Speeds (in cm s-1(Panel A), and BL s-1 (Panel B)) of 85 Spring 

Chinook Salmon at Three Different Areas during Passage of Bonneville Dam, April – June, 

2001. Boxes indicate the 25th and 75th percentile of data, line inside box indicates median of 

data. Whiskers indicate the 95% confidence intervals for the data, asterisks are outliers. 

Ucrit of Columbia River spring chinook salmon is shown for reference. 

Table 3.3. Mean Percentage (and range) of EMG Signals (emitted from an individual transmitter) 

Collected from Individual Spring Chinook Salmon Passing Bonneville Dam April – June 

2001 that Are over Either 70, 80, or 100% of the Critical Swimming Speed for Spring 

Chinook Salmon. Also shown is the percentage of signals that are over the highest 

swimming speed that the fish were calibrated. 

 Percentage of Swimming Speeds 

Location > 70% Ucrit > 80% Ucrit > 100% Ucrit > Highest Calibration Speed 

Tailrace 44 (2-90) 38 (1-88) 26 (0-72) 20 (0-64) 

Fishway 33 (1-91) 26 (1-86) 16 (0-66) 11 (0-57) 

Forebay 33 (4-88) 23 (2-86) 14 (1-66) 10 (1-69) 
3.10

Figure 3.7. Box Plots of Mean Energy Use Rates (Panel A) and Energy Used (Panel B) by Spring 

Chinook Salmon at Three Different Areas during Passage of Bonneville Dam, April – June, 

2001. The left side of each panel shows values for aerobic energy use, while the right side 

shows values for anaerobic energy use. Boxes indicate the 25th and 75th percentile of data, 

while the line inside the box indicates the median of the data. Whiskers indicate the 95% 

confidence intervals for the data, asterisks are outliers. One outlier (763 kcal kg-1) was 

removed from the aerobic energy use data (Panel B) for the tailrace and one outlier (992 kcal 

kg-1) was removed from the anaerobic energy use data (Panel B) for the tailrace. 
3.11

Figure 3.8. Box Plots of Total (aerobic and anaerobic combined) Energy Use Rates (Panel A) and Total 

(aerobic and anaerobic combined) Energy Used (Panel B) by Spring Chinook Salmon at 

Three Different Areas during Passage of Bonneville Dam, April – June, 2001. Boxes 

indicate the 25th and 75th percentile of data, line in box is median of data. Whiskers indicate 

95% confidence intervals, and asterisks are outliers. For purposes of graphing, one outlier of 

1,755 kcal was removed from the energy use data (Panel B) for the tailrace. 
3.12

3.2.1.4 Energy Used 

The energy used (i.e., kcal kg-1) by spring chinook salmon while migrating past Bonneville Dam was 

primarily expended in the tailraces (Figure 3.7 and 3.8). Eighty-one percent of the median energy used by 

spring chinook salmon passing the dam was used in the tailrace (48.1 kcal), while only 18% was used in 

the fishways (11.1 kcal) and 1.5% was used in the forebays (0.9 kcal). The total energy that fish used was 

significantly different among the three areas of dam passage (Table 3.2; Figure 3.8). Similar trends in 

differences were observed for aerobic and anaerobic energy use (Table 3.2). 

Although the median energy (50th percentile) used to pass the tailraces was 48.1 kcal (Table 3.2), an 

additional 41% of the fish (i.e., the 90th percentile) used up to ~300 kcal to pass the tailraces, suggesting 

the energy use data were highly skewed (Figure 3.9). Similar results occurred in the forebays where the 

median energy use was 0.9 kcal and the 90th percentile was ~50 kcal. The data were also skewed, albeit 

to a lesser degree, in the fishways where the median was 11.1 kcal as compared to the 90th percentile at 

~40 kcal (Figure 3.9). 

Figure 3.9. Percent Cumulative Distribution of Total (aerobic and anaerobic combined) Energy Used by 

Spring Chinook Salmon to Pass Three Areas of Bonneville Dam (A=tailraces, B=fishways, 

C=forebays) April – June 2001. For purposes of graphing, one outlier of 1,755 kcal was 

removed from the energy use data for the tailrace (Panel A). 
3.13

3.2.2 Comparisons within Tailraces and Forebays 

3.2.2.1 Tailraces 

Within the tailraces, the highest swimming speeds were observed in the tailrace of Powerhouse 2 

(Table 3.2; Figure 3.10); mean swimming speeds there (1.58 BL s-1) were 31% faster than mean 

swimming speeds in the tailrace of the spillway (1.21 BL s-1; significantly different) and 10% faster than 

values in the tailrace of Powerhouse 1 (1.43 BL s-1; not significantly different). There was no significant 

difference in the swimming speeds through Powerhouse 1 and the spillway (Table 3.2). Interestingly, 

daily average discharge through Powerhouse 2 was consistently higher than through Powerhouse 1 and 

over the spillway at all times during the study (Figure 1.4). 

Figure 3.10. Box Plots of Mean Swimming Speeds (in cm s-1(Panel A), and BL s-1 (Panel B)) of 76 

Spring Chinook Salmon in Three Different Tailraces during Passage of Bonneville Dam, 

April – June, 2001. Boxes indicate the 25th and 75th percentile of data, line in box is median 

of data. Whiskers indicate 95% confidence intervals for the data. Ucrit (critical swimming 

speed) measured on Columbia River spring chinook salmon in 2000 is shown for reference. 
3.14

The mean rates of energy used (total aerobic and anaerobic) by spring chinook salmon were 

significantly higher in the tailrace of Powerhouse 2 (3.26 kcal kg-1 h-1) than in the spillway tailrace (2.18 

kcal kg-1 h-1; Table 3.2; Figures 3.11 and 3.12). Despite the large differences in the daily average 

discharge between the two powerhouses (Figure 1.4), there was only a 9% difference in energy use rates, 

which was not significantly different. There was also no significant difference in total energy use rates in 

the spillway and Powerhouse 1 tailraces. Both aerobic and anaerobic energy use rates showed the same 

trends as the total energy use rate data (Figure 3.11). The exception was that the differences in anaerobic 

energy use rates approached significance (P=0.063) between the Powerhouse 1 and spillway tailraces, 

with anaerobic rates being 77% higher in the Powerhouse 1 tailrace than in the spillway tailrace. 

Figure 3.11. Box Plots of Energy Use Rates in Three Different Tailraces (Panel A) and Three Different 

Forebays (Panel B) by Spring Chinook Salmon as They Passed Bonneville Dam, April – 

June, 2001. The left side of each panel shows values for aerobic energy use, while the right 

side shows values for anaerobic energy use. Boxes indicate the 25th and 75th percentile of 

data; line in box indicates median. Whiskers indicate 95% confidence intervals for the data, 

asterisks are outliers. 
3.15

Figure 3.12. Box Plots of Total (aerobic and anaerobic combined) Energy Use Rates in Three Different 

Tailraces (Panel A) and Three Different Forebays (Panel B) by Spring Chinook Salmon as 

They Passed Bonneville Dam April – June, 2001. Boxes indicate the 25th and 75th percentile 

of data, line inside box indicates median of the data. Whiskers indicate the 95% confidence 

intervals for the data, asterisks are outliers. 

3.2.2.2 Forebays 

There was no significant difference in swimming speed among the three forebays (Table 3.2, 

Figure 3.13). There was also no significant difference in aerobic, anaerobic, or total energy use rates 

among the three forebays (Figures 3.11 and 3.12). 
3.16

Figure 3.13. Box Plots of Mean Swimming Speeds (in cm s-1(Panel A), and BL s-1 (Panel B)) of 45 

Spring Chinook Salmon in Three Different Forebays during Passage of Bonneville Dam, 

April – June, 2001. Boxes indicate the 25th and 75th percentile of data, while the line inside 

the box indicates the median of the data. Whiskers indicate the 95% confidence intervals for 

the data, asterisks are outliers. Ucrit (critical swimming speed) measured on Columbia River 

spring chinook salmon in 2000 is shown for reference. 

3.2.2.3 Variables Influencing Activity 

3.2.2.3.1 Diel Period 

The influence of diel period on swimming speed and energy use depended on area. In the forebays, 

there was no significant difference in either swimming speed or energy use rates between day and night 

(Tables 3.4 and 3.5, Figures 3.14 and 3.15). This was true not only for total (aerobic and anaerobic 

combined) energy use rates, but for both aerobic and anaerobic energy use rates. 
3.17

Table 3.4. Swimming Speeds during Day and Night of Spring Chinook Salmon Passing Bonneville 

Dam, April – June 2001. Variables in the same row with a letter in common indicate that 

there is no significant (P>0.05) difference between day and night. 

Swimming Speed (cm s-1) Swimming Speed (BL s-1) Area 

Day Night Day Night 

Tailraces 109.0 (4.5)a 87.6 (7.1)a 1.44 (0.1)a 1.16 (0.1)a

Forebays 88.0 (6.5)a 62.4 (32.0)a 1.17 (0.9)a 0.84 (0.4)a

Table 3.5. Energy Use Rates during Day and Night of Spring Chinook Salmon Passing Bonneville Dam, 

April – June 2001. Total energy rates are aerobic and anaerobic rates combined. Variables 

in the same row with a letter in common indicate that there is no significant (P>0.05) 

difference between day and night. 

Aerobic Anaerobic Total Area 

Day Night Day Night Day Night 

Tailraces 1.31 (0.0)a 1.12 (0.1)b 1.59 (0.1)a 1.16 (0.2)a 2.91 (0.1)a 2.29 (0.3)a

Forebays 1.04 (0.5)a 0.97 (0.3)a 0.85 (1.2)a 1.19 (0.7)a 1.89 (1.7)a 2.15 (1.0)a

Although there was no significant difference between swimming speeds in tailraces between day 

(1.44 BL s-1; Table 3.4, Figure 3.14) and night (1.16 BL s-1), there were differences in energy use between 

these two periods (Table 3.5, Figure 3.15). The aerobic energy use rates of fish in tailraces were 

significantly higher during the day (1.31 kcal kg-1 h-1) than during the night (1.12 kcal kg-1 h-1). There 

was no significant difference between day and night in either swimming speed, total, or anaerobic energy 

use rates. However, the alpha for both values approached significance (P=0.057 for swimming speed in 

BL s-1; P=0.056 for anaerobic and P=0.052 for total energy use rates). 

3.2.2.3.2 Sex 

The sex of the fish did not make a significant contribution to the differences in swimming speed or 

energy use rates used in any of the three main parts of the dam (tailraces, fishways, or forebays). 

3.2.2.3.3 Project Operations 

There was no significant linear relationship between total energy use rates and the discharge from the 

tailrace of either the spillway or powerhouse 1 or 2 (Figure 3.16). If any relationship between these two 

variables exists, it was not a linear relationship. 
3.18

Figure 3.14. Box Plots of Swimming Speed (Panel A) and Energy Use Rates (Panel B) of Spring 

Chinook Salmon at Three Different Areas of Dam Passage during Both Day and Night (shaded 

half) at Bonneville Dam, April – June, 2001. Boxes indicate the 25th and 75th percentile of data; 

line inside box indicates median. Whiskers indicate 95% confidence intervals for the data. Ucrit 

(critical swimming speed) measured on Columbia River spring chinook salmon in 2000 is shown 

for reference. 
3.19

Figure 3.15. Box Plots of Energy Use Rates (aerobic in Panel A, anaerobic in Panel B, and combined in 

panel C) of Spring Chinook Salmon at Three Different Areas of Dam Passage during both 

Day and Night (the shaded half of the figures) at Bonneville Dam, April – June, 2001. 

Boxes indicate the 25th and 75th percentile of data, line inside box indicates median of data. 

Whiskers indicate the 95% confidence intervals for the data, asterisks are outliers. 
3.20

3.2.3 Comparisons within Fishways 

The mean time of the day that fish entered fishways was 14:56 Pacific Standard Time. The mean 

time that fish exited fishways (14:34) was very similar to the mean time fish entered. All entries and exits 

of fishways occurred between approximately 05:00 and 22:00 (Figure 3.17). 

Of the 79 fish that successfully passed fishways, 55 (70%) exited on the same day, while the other 24 

(30%) exited the fishway on a different day than the day they first entered the fishway. Fish that 

successfully passed the fishway the day they entered, entered a fishway significantly (P<0.05) earlier (a 

mean time of 12:41 vs. a mean time of 17:15) than fish that spent the night in the fishways. Fish that 

spent the night in fishways exited fishways significantly earlier (a mean time of 9:24) than fish that 

successfully passed on the same day they entered (a mean time of 16:50). The morning exit of fish is an 

obvious peak in the histogram of exit times shown in Figure 3.17. 

Figure 3.17. The Time of Day when Fish Entered (Panel A) and Exited (Panel B) Fishways during their 

Successful Passage of Bonneville Dam, April – June, 2001 
3.21

Fish that successfully passed fishways spent a median of 5.0 hours and a mean (SE = 1.1) of 9.2 hours 

in fishways (Figure 3.18). Some fish spent as much as 53 hours in fishways during a successful passage. 

The amount of time it took a fish to successfully pass a fishway depended on which fishway it passed 

(Figure 3.19). It took fish significantly less time to pass the Bradford Island fishway (3.10 h, Table 3.6) 

than the Cascade Island (5.55 h) or Washington shore (6.35 h) fishways. There was no significant 

difference in the amount of time it took fish to pass the Cascade Island and Washington shore fishways. 

Figure 3.18. A Frequency Histogram Indicating the Number of Hours Spent in a Fishway by Spring 

Chinook Salmon that Successfully Passed Bonneville Dam, April – June, 2001 

Figure 3.19. Box Plots of Time (h) Spring Chinook Salmon Took to Pass Three Fishways (Washington 

shore, Cascade Island, and Bradford Island) at Bonneville Dam, April – June, 2001. Boxes 

indicate the 25th and 75th percentile of data, line in box is median. Whiskers indicate the 

95% confidence intervals for the data, asterisks are outliers. One outlier at 53.2 hours for 

Washington shore fishway is not shown. 
3.22

Table 3.6. Median Energy Used by Spring Chinook Salmon as they Successfully Passed Fishways or 

Fell Out of Fishways of Bonneville Dam, April – June 2001. The median amount of time 

they spent in the different fishways is also shown. N = number of fish. Variables in the 

same column with a letter in common indicate no significant (P>0.05) difference between 

the fishways. 

Energy Used (kcal kg-1) 

 N Time (h) Aerobic Anaerobic Total 

Successful passage 

Washington 34 6.35a 6.33ab 4.90a 11.89ab

Cascade 8 5.55a 7.45b 8.05a 15.50b

Bradford 37 3.10b 3.75a 4.00a 7.97a

Unsuccessful passage (fallout) 

Washington 19 0.52a 0.71a 0.80a 1.51a

Cascade 5 0.61a 1.10a 1.81a 2.92a

Bradford 13 0.38a 0.52a 0.39a 0.92a

3.2.3.1 Swimming Speed and Energetics 

Some fish behavior appeared to be fairly uniform among the three different fishways at Bonneville 

Dam. Although there were differences in the amount of time spent in the fishways, there was no 

significant difference among the three fishways in either swimming speed (Figure 3.20) or energy use 

rates (Table 3.7, Figure 3.21). However, due to the different amounts of time fish spent in the fishways, 

there were differences in the amounts of energy used in the three fishways (Figure 3.22; Table 3.6). The 

amount of aerobic (7.45 kcal kg-1) and total energy used (15.5 kcal kg-1) in the Cascade Island fishway 

was significantly higher than that used in the Bradford Island fishway (aerobic = 3.75 kcal kg-1, total = 

7.97 kcal kg-1). There were no significant differences between the aerobic or total energy used in the 

Washington shore fishway and the other two fishways. There were also no significant differences among 

the three fishways in anaerobic energy used. 

3.2.3.2 Fallout 

Several spring chinook salmon (25 of 79, or 32%) entered fishways more than one time and fell back 

out of fishways. Some fish entered and fell back out of fishways up to 21 times. However, the mean 

number of fallouts for each fish was 3.3 (SE=0.8) while the median was 2.0. 

The total amount of time fish spent in fishways (Figure 3.22) during unsuccessful entries was a 

median of 0.48 hours (range 0.016 – 24.82 hours; the median is shown since data are not normally 

distributed). This is 9.6% of the median time it took fish to successfully pass a fishway. There was no 

significant difference among fishways in the amount of time fish spent during fallout events (Table 3.6; 

medians ranged from 0.38 h in the Bradford Island fishway to 0.61 h in the Cascade Island Fishway). 
3.23

Figure 3.20. Box Plots of Swimming Speeds (in cm s-1 in Panel A and in BL s-1 in Panel B) of Spring 

Chinook Salmon in the Washington Shore, Cascade Island, and Bradford Island Fishways as 

They Passed Bonneville Dam, April – June, 2001. Swimming speeds are related to critical 

swimming speed in panel B. Boxes indicate 25th and 75th percentile of data; line in box is 

median. Whiskers show 95% confidence intervals, asterisks are outliers. 
3.24

Table 3.7. Mean Swimming Speeds (+SE) and Energy Use Rates (+SE) of Spring Chinook Salmon as 

they Successfully Passed Three Different Fishways or Fell out of Fishways, April – June 

2001. N = number of fish. Variables in the same column with a letter in common indicate 

no significant (P>0.05) difference among fishways. 

Swimming Speed Energy Use Rate (kcal kg-1 h-1) Area N 

cm s-1 BL s-1 Aerobic Anaerobic Total 

Successful Passage

Washington 34 81.3 (7.3)a 1.08 (0.1)a 1.06 (0.1)a 1.09 (0.2)a 2.15 (0.2)a

Cascade 8 100.3 (14.2)a 1.33 (0.2)a 1.32 (0.2)a 1.43 (0.4)a 2.75 (0.5)a

Bradford 37 85.1 (5.5)a 1.12 (0.1)a 1.12 (0.1)a 1.10 (0.1)a 2.22 (0.2)a

Unsuccessful Passage (fallout)

Washington 19 121.7 (12.4)a

 1.63 (0.2)a 1.43 (0.1)a 1.80 (0.3)a 3.24 (0.4)a

Cascade 5 119.8 (11.3)a

 1.62 (0.2)a 1.58 (0.1)a 2.09 (0.5)a 3.67 (0.7)a

Bradford 13 86.9 (8.2)a

 1.14 (0.1)a 1.17 (0.1)a 0.99 (0.2)a 2.16 (0.3)a
3.25

Figure 3.21 Box Plots of Energy Use Rates of Spring Chinook Salmon in Washington Shore, Cascade 

Island, and Bradford Island Fishways as They Passed Bonneville Dam, April – June, 2001. 

Aerobic energy use rates are shown in panel A, anaerobic rates in panel B, and total rates in 

panel C. Boxes indicate the 25th and 75th percentile of data; line inside box indicates median. 

Whiskers indicate 95% confidence intervals, outliers are shown as asterisks. 
3.26

Figure 3.22. Box Plots of Energy Use by Spring Chinook Salmon in Three Different Fishways 

(Washington Shore, Cascade Island, and Bradford Island) as They Passed Bonneville Dam, 

April – June, 2001. Aerobic energy use is shown in panel A, anaerobic use in panel B, and 

total use in panel C. Boxes indicate the 25th and 75th percentile of data; line inside box 

indicates median. Whiskers indicate 95% confidence intervals, outliers are shown as 

asterisks. 
3.27

Figure 3.23. Plots of the Amount of Time (h) Spring Chinook Salmon Spent in Three Different Fishways 

During Fallout (Washington Shore, Cascade Island, and Bradford Island) as They Attempted 

to Pass Bonneville Dam, April – June, 2001. Boxes indicate the 25th and 75th percentile of 

data; line inside box indicates median. Whiskers indicate 95% confidence intervals, 

asterisks are outliers. One outlier of 23.7 hours from the Washington shore fishway is not 

shown. 

During fallouts, there was no significant difference among the three fishways in either the swimming 

speeds, the energy use rates, or the amount of energy used (Figure 3.24, 3.25, and 3.26; Table 3.6, 3.7, 

and 3.8). However, both swimming speeds and rates of energy use during fallout events were 

significantly higher than during successful fishway passage. This was true for swimming speeds 

expressed in cm s-1 (a mean of 85.0 cm s-1 during successful passage and a mean of 109.2 cm s-1 during 

fallout) and BL s-1 (a mean of 1.12 BL s-1 during successful passage and a mean of 1.46 BL s-1 during 

fallout). The significant differences were also apparent for energy use rates: aerobic (a mean of 1.12 kcal 

kg1

 h1 during successful passage and a mean of 1.36 kcal kg1 h1 during fallout), anaerobic (a mean of 1.13 

kcal kg1

 h1 during successful passage and a mean of 1.56 kcal kg1 h1 during fallout), and total (a mean of 

2.24 kcal kg1

 h1 during successful passage and a mean of 2.92 kcal kg1 h1 during fallout). 
3.28

Figure 3.24. Box Plots of Swimming Speeds (in cm s-1 in panel A and in BL s-1 in panel B) of Spring 

Chinook Salmon during Fallout in Three Different Fishways (Washington Shore, Cascade 

Island, and Bradford Island) as they Attempted to Pass Bonneville Dam, April – June, 2001. 

Swimming speeds are related to critical swimming speed in panel B. Boxes indicate the 25th

and 75th percentile of data; line inside box indicates median. Whiskers indicate 95% 

confidence intervals. 
3.29

Figure 3.25. Box Plots of Energy Use Rates of Spring Chinook Salmon during Fallout in Three Different 

Fishways (Washington shore, Cascade Island, and Bradford Island) as They Attempted to 

Pass Bonneville Dam, April – June, 2001. Aerobic energy use rates are shown in panel A, 

anaerobic rates in panel B, and total rates in panel C. Boxes indicate the 25th and 75th

percentile of data; line inside box indicates median. Whiskers indicate 95% confidence 

intervals; outliers are shown by asterisks. 
3.30

Figure 3.26. Box Plots of Energy Use by Spring Chinook Salmon during Fallout in Three Different 

Fishways (Washington Shore, Cascade Island, and Bradford Island) as They Attempted to 

Pass Bonneville Dam, April – June, 2001. Aerobic energy use is shown in panel A, 

anaerobic use in panel B, and total use in panel C. Boxes indicate the 25th and 75th percentile 

of data; line inside box indicates median. Whiskers indicate 95% confidence intervals; 

outliers are shown by asterisks. One outlier of 28.6 is omitted from the aerobic, one outlier 

of 22.1 is omitted from anaerobic data, and one outlier of 50.7 is omitted from total energy 

data. 
3.31

Table 3.8. Mean Swimming Speeds (+SE) and Energy Use Rates (+SE) of Spring Chinook Salmon 

as they Successfully Passed Three Different Fishways during Day or Night, April – June 

2001. N = number of fish. Variables in the same row with a letter in common indicate that 

there is no significant (P>0.05) difference between day and night. 

 Day Night 

Swimming Speed 

cm s-1 84.9 (3.8)a 65.4 (11.8)b

BL s-1 1.12 (0.1)a 0.86 (0.2)b

Energy Use Rates 

aerobic 1.12 (0.4)a 0.85 (0.1)b

anaerobic 1.10 (0.1)a 0.63 (0.2)b

total 2.22 (0.1)a 1.48 (0.3)b

The amount of time spent by fish during fallout events varied from 8.2% to 12.3% of the time that it 

took fish to successfully pass fishways. These percentages generally reflected the comparative percent of 

energy used during fallout events (Figure 3.26, Table 3.6). Energy used during fallout attempts was 

11.5% to 18.8 % of the amount of energy used during successful fishway passage. This percentage was 

highest in the Cascade Island fishway (18.8%). 

3.2.3.3 Differences among Section of Fishways in Swim Speed and Energy Use 

The behavior of fish in different sections of fishways (Figures 1.5, 2.2, and 2.3) was examined based 

on several different parameters - swimming speed, energy use rates, types of energy use, time spent in 

areas, the amount of energy used, and the energy use per unit length of fishway. 

The mean swimming speeds of the fish were well under the critical swimming speed in all fishway 

parts, with the highest mean being 118 cm s-1 in the entrance to the Cascade Island fishway (Table 3.9). 

However, there were large differences in swimming speed among fishway sections. As would be 

expected, the slowest swimming speeds were observed in the junction pool in the Washington shore 

fishway (section 14; mean 66 cm s-1). However, in a similar junction pool in the Bradford Island fishway, 

swimming speeds were much higher (section 4; mean 109 cm s-1) and actually ranked third highest out of 

all the fishway sections at Bonneville Dam, and highest in the Bradford Island fishway. This indicates 

that, although this pool was likely designed to provide an area for fish to rest, fish are not using it for that 

purpose and are instead moving through it quickly. This is substantiated by the relatively short amount of 

time spent in this section - a median of 0.16 hours or 9.6 minutes (ranking 6th out of 7 parts of the 

fishway; with a rank of 7 being the shortest amount of time in an area; Table 3.10). For comparison, fish 

spent over twice as long (0.34 hours or 20.4 minutes) in the junction pool of the Washington Shore 

fishway. 
3.32

Table 3.9. Mean Swimming Speeds of Spring Chinook Salmon in Different Sections of Three Different 

Fishways of Bonneville Dam, April – June 2001. Ranks of swimming speed are also shown 

(with 1 being the highest swimming speed) both within individual fishways, and among all 

fishway sections throughout the dam. N = number of fish. Fishway section numbers 

correspond to Figures 1.5, 2.2, and 2.3. 

Swimming Speed Rank Fishway Section N

cm s-1 Bl s-1 Within Among 

Bradford 1 6 77 1.0 5 12 

Bradford 2 15 81 1.1 4 11 

Bradford 3 14 68 0.9 6 16 

Bradford 4 19 109 1.4 1 3 

Bradford 5 26 91 1.2 3 10 

Bradford 6 14 67 0.9 7 17 

Bradford 7 34 96 1.3 2 7 

Cascade 8 7 118 1.6 1 1 

Cascade 9 8 97 1.3 2 5 

Washington 10 30 99 1.3 2 4 

Washington 11 30 92 1.2 5 9 

Washington 12 5 94 1.2 4 8 

Washington 13 9 75 1.0 7 13 

Washington 14 36 66 0.9 9 18 

Washington 15 18 75 1.0 6 14 

Washington 16 7 70 0.9 8 15 

Washington 17 34 96 1.3 3 6 

Washington 18 25 111 1.5 1 2 
3.33

Table 3.10. Median Amount of Time Spring Chinook Salmon Spent in Different Sections of Three 

Different Fishways of Bonneville Dam, April – June 2001. Ranks of time spent are also 

shown (with 1 being the longest amount of time) both within individual fishways, and 

among all fishway sections throughout the dam. N = number of fish. Fishway section 

numbers correspond to Figures 1.5, 2.2, and 2.3. 

Ranks 

Fishway Section N 

Hours 

Within Among 

Bradford 1 6 0.09 7 16 

Bradford 2 15 0.92 2 4 

Bradford 3 14 0.97 1 3 

Bradford 4 19 0.16 6 15 

Bradford 5 26 0.50 3 8 

Bradford 6 14 0.41 4 9 

Bradford 7 34 0.33 5 12 

Cascade 8 7 0.56 2 7 

Cascade 9 8 1.89 1 1 

Washington 10 30 0.25 6 13 

Washington 11 30 0.77 2 5 

Washington 12 5 1.09 1 2 

Washington 13 9 0.58 3 6 

Washington 14 36 0.34 5 11 

Washington 15 18 0.06 8 17 

Washington 16 7 0.05 9 18 

Washington 17 34 0.38 4 10 

Washington 18 25 0.19 7 14 

Another section in which swimming speed ranking is higher than may be expected is the exit to the 

Washington Shore fishway (section 18). It ranks second highest among all fishway areas (Table 3.9). 

This section is mostly comprised of a long, relatively low water velocity channel. However, manual 

trackers noted that fish appeared to be swimming very quickly at the lower end of this section, near the 

exit of the vertical slot weirs. Unfortunately, placement of antennas did not allow this section to be 

broken up into subsections. 

In both fishways (since there are no vertical slot fishways in the Cascade Island Fishway), swimming 

speeds were remarkably similar in the vertical slot weirs (sections 7 and 17). The mean swimming speeds 
3.34

in both sections were 96 cm s-1 and 1.3 BL s-1. This fishway type also ranked relatively high for 

swimming speeds (ranked 2nd highest within the Bradford Island fishway and 3rd highest within the 

Washington Shore fishway). 

The entrances to the Cascade Island and Washington shore fishways also ranked relatively high, at 1st

and 4th highest respectively for swimming speeds among all fishway areas, with mean swimming speeds 

of 118 and 99 cm s-1. The A branch entrance to the Bradford Island fishway; however, ranked relatively 

low in swimming speeds, having a mean swimming speed of 77 cm s-1. 

The rankings of energy use rates (Table 3.11) in different sections of the fishways generally reflected 

ranks of swimming speeds. Rankings were exactly the same in both the Bradford Island and Cascade 

Island fishways, although two sections switched ranks within the Washington shore fishway. 

Table 3.11. Mean Energy Use Rates of Spring Chinook Salmon in Different Sections of the Three 

Fishways of Bonneville Dam, April – June 2001. Ranks of energy use rates are also shown (1 = 

highest) within individual fishways and among all fishway sections throughout the dam. N = 

number of fish. Fishway section numbers correspond to Figures 1.5, 2.2, and 2.3. Aerobic, 

anaerobic, and total (aerobic + anaerobic) energy use rates are shown. 

Energy Use Rate (kcal kg-1 h-1) Ranks 

Fishway Section N Aerobic Anaerobic Total Within Among 

Bradford 1 6 0.96 0.87 1.83 5 13 

Bradford 2 15 1.04 1.01 2.04 4 12 

Bradford 3 14 0.95 0.73 1.67 6 14 

Bradford 4 19 1.29 1.62 2.91 1 2 

Bradford 5 26 1.19 1.23 2.42 3 10 

Bradford 6 14 0.92 0.67 1.60 7 15 

Bradford 7 34 1.25 1.39 2.65 2 7 

Cascade 8 7 1.52 1.90 3.42 1 1 

Cascade 9 8 1.29 1.40 2.69 2 5 

Washington 10 30 1.28 1.48 2.77 2 4 

Washington 11 30 1.19 1.33 2.52 5 9 

Washington 12 5 1.24 1.30 2.53 4 8 

Washington 13 9 1.02 1.03 2.05 6 11 

Washington 14 36 0.89 0.43 1.32 9 18 

Washington 15 18 1.00 0.54 1.54 7 16 

Washington 16 7 0.96 0.55 1.51 8 17 

Washington 17 34 1.25 1.42 2.68 3 6 

Washington 18 25 1.36 1.54 2.90 1 3 
3.35

Fish generally spent the longest periods of time (Table 3.10) in long sections of the fishway, such as 

the Bradford Island A and B branches (sections 2 and 3), the upper section of the Cascade fishway 

(section 9), and two long sections of the Washington shore fishway (sections 11 and 12). However, fish 

also spent a relatively long period of time in the adult facility (a median of 0.58 h, or 34.8 min.). 

Spending long periods of time in sections of the fishway resulted in fish using a relatively large 

amount of energy in these areas (Table 3.12). One exception is that, although the amount of time spent in 

the adult facility was in the upper half of the rankings, the amount of energy used in the facility was in the 

lower half, due to the relatively low swimming speeds and energy use rates there. However, it should be 

noted that, although the mean swimming speeds in the adult facility were identical to those in section 15 

(the junction pool in the Washington shore fishway), the fish used much more anaerobic energy in the 

adult facility, likely jumping at the false weir. This resulted in the energy use rates in the adult facility 

being 33% higher than in the junction pool of the Washington shore fishway. 

Table 3.12. Median Energy Used by Spring Chinook Salmon in Different Sections of Three Different 

Fishways of Bonneville Dam, April – June 2001. Ranks of energy used also shown (1 = 

highest) both within individual fishways and among all fishway sections. N = number of 

fish. Fishway section numbers correspond to Figures 1.5, 2.2, and 2.3. 

Energy Use (kcal kg-1) Ranks Fishway Section N

Aerobic Anaerobic Total Within Among 

Bradford 1 6 0.07 0.05 0.11 7 17 

Bradford 2 15 0.75 0.60 1.48 2 6 

Bradford 3 14 1.03 0.63 1.53 1 5 

Bradford 4 19 0.18 0.16 0.34 5 14 

Bradford 5 26 0.55 0.45 1.02 3 9 

Bradford 6 14 0.22 0.04 0.32 6 15 

Bradford 7 34 0.40 0.47 0.88 4 10 

Cascade 8 7 0.91 0.76 1.62 2 4 

Cascade 9 8 1.92 1.38 3.24 1 2 

Washington 10 30 0.27 0.26 0.50 5 11 

Washington 11 30 0.90 0.90 1.78 2 3 

Washington 12 5 1.71 1.78 3.62 1 1 

Washington 13 9 0.68 0.65 1.32 3 7 

Washington 14 36 0.30 0.13 0.46 6 12 

Washington 15 18 0.07 0.03 0.12 8 16 

Washington 16 7 0.03 0.01 0.04 9 18 

Washington 17 34 0.51 0.63 1.18 4 8 

Washington 18 25 0.17 0.17 0.37 7 13 
3.36

It is obvious that in many cases the energy used in fishway sections is relatively higher in longer 

sections than in short sections. Thus, energy use was looked at by unit length (Table 3.13). When the 

data are presented in this manner, two relatively short sections, where a relatively small amount of energy 

was used, rank highest - the adult facility (5.2 kcal kg-1 m-1) and the Bradford Island junction pool (3.3 

kcal kg-1 h-1). 

Table 3.13. Median Total Energy Used per Meter Length of Fishway by Spring Chinook Salmon in 

Different Sections of Three Different Fishways of Bonneville Dam, April – June 2001. 

Ranks of energy use per meter are also shown (with 1 being the highest) both within 

individual fishways and among all fishway sections throughout the dam. Fishway section 

numbers correspond to Figures 1.5, 2.2, and 2.3. 

Total Energy Used Ranks 

Fishway Section Length (m) (kcal kg-1 m-1) per 100 m Within Among

Bradford 1 50 0.2 7 16 

Bradford 2 171 0.9 5 12 

Bradford 3 200 0.8 6 13 

Bradford 4 10 3.3 1 2 

Bradford 5 71 1.4 4 10 

Bradford 6 15 2.1 2 4 

Bradford 7 55 1.6 3 7 

Cascade 8 71 2.3 1 3 

Cascade 9 258 1.3 2 11 

Washington 10 202 0.2 7 15 

Washington 11 115 1.5 4 8 

Washington 12 172 1.5 5 9 

Washington 13 26 5.2 1 1 

Washington 14 27 1.7 3 6 

Washington 15 75 0.2 9 18 

Washington 16 23 0.2 8 17 

Washington 17 56 2.1 2 5 

Washington 18 108 0.3 6 14 

Another section that is relatively short but ranked high was the viewing window of the Bradford 

Island fishway (section 5; 2.1 kcal kg-1 m-1). It is interesting to compare this section to the viewing 

window section of the Washington shore fishway (0.2 kcal kg-1 m-1) where energy use was ~10 times 

lower. 

Similar to the swimming speed results, the amount of energy use per unit length was fairly similar 

between the vertical slot fishways in the Bradford Island (1.6 kcal kg-1 m-1) and Washington shore 

fishways (2.1 kcal kg-1 m-1). Also, the rankings were very similar to those seen among the swimming 

speed data, remaining among the most energetically costly. 
3.37

Many fishway sections have similar designs, for example sections 2, 3, 5, 9, and 11 have weirs that 

are 5 m apart with a 0.3 m rise between the weirs. Several of these sections have very similar energy use 

per unit length. Fish passing the A and B branches of the Bradford Island fishway used 0.9 and 0.8 kcal 

kg-1 m-1 respectively; and fish passing section 5 in the Bradford Island fishway, section 9 in the Cascade 

fishway, and section 11 in the Washington shore fishway used 1.4, 1.3, and 1.5 kcal kg-1 m-1, respectively. 

By combining the ranking for energy use rates and time spent in an area, a meaningful comparison 

may be made among areas relative to the difficulty of fish passage (Table 3.14). If the ranking of a 

fishway section for energy use rate is added to the ranking for amount of time spent in that section (see 

Table 3.14), it can be seen that passage of the Cascade fishway not only takes a relatively long period of 

time, but the time spent in those areas is relatively energetically costly. Two lower sections of the 

Washington shore fishway (sections 11 and 12), the A branch of the Bradford Island fishway and the 

vertical slot weirs of the Washington Shore fishway lead the list in areas that are more energetically costly 

than other sections. However, there was very little difference in the rankings of a large number of 

fishway sections. 

Table 3.14. Total Energy Use Rates and Number of Hours Spent in Different Fishway Sections by 

Spring Chinook Salmon Passing Bonneville Dam, April – June 2001. Ranks of energy use 

rates and time spent in areas are also shown (1 = highest) among all fishway sections 

throughout the dam. Ranks of energy use and hours spent in areas are summed and presented as a cumulative score. These scores are ranked from lowest (1= highest combined rank 

of energy use rate and time). Fishway section numbers correspond to Figures 1.5, 2.2, 2.3. 

Fishway Section N 

Total Energy Use 

Rate (kcal kg-1 h-1)

Ranks 

Among Hours

Ranks 

Among 

Cumulative 

Scores 

Rank of 

Cum. Scores

Bradford 1 6 1.83 13 0.09 16 29 10 

Bradford 2 15 2.04 12 0.92 4 16 5 

Bradford 3 14 1.67 14 0.97 3 17 6 

Bradford 4 19 2.91 2 0.16 15 17 6 

Bradford 5 26 2.42 10 0.50 8 18 7 

Bradford 6 14 1.60 15 0.41 9 24 9 

Bradford 7 34 2.65 7 0.33 12 19 8 

Cascade 8 7 3.42 1 0.56 7 8 2 

Cascade 9 8 2.69 5 1.89 1 6 1 

Washington 10 30 2.77 4 0.25 13 17 6 

Washington 11 30 2.52 9 0.77 5 14 4 

Washington 12 5 2.53 8 1.09 2 10 3 

Washington 13 9 2.05 11 0.58 6 17 6 

Washington 14 36 1.32 18 0.34 11 29 10 

Washington 15 18 1.54 16 0.06 17 33 11 

Washington 16 7 1.51 17 0.05 18 35 12 

Washington 17 34 2.68 6 0.38 10 16 5 

Washington 18 25 2.90 3 0.19 14 17 6 
3.38

3.2.3.4 Variables Influencing Activity in Fishways 

3.2.3.4.1 Diel Period 

Swimming speeds of spring chinook salmon passing fishways were 30% (and significantly) higher 

during the day (mean 1.12 BL s-1; Figure 3.27) than during the night (mean 0.86 BL s-1). Energy use rates 

were also significantly higher during the day than during the night with the difference being largest in anaerobic energy use (Figure 3.28). Anaerobic energy use rates were 75% (and significantly) higher during 

the day (1.1 kcal kg-1 h-1) than during the night (0.63 kcal kg-1 h-1). This indicates that the amount of burst 

swimming decreased dramatically at night. The aerobic and total (aerobic and anaerobic combined) energy use rates were also significantly higher during the day than at night (32% and 50% higher 

respectively). 

Figure 3.27. Box Plots of Swimming Speeds (in cm s-1 in panel A and in BL s-1 in panel B) of Spring 

Chinook Salmon in Fishways during Day and Night as They Passed Bonneville Dam, April 

– June, 2001. Swimming speeds are related to critical swimming speed in panel B. Boxes 

indicate the 25th and 75th percentile of data; line inside box is median. Whiskers indicate the 

95% confidence intervals for the data, asterisks are outliers. 
3.39

Figure 3.28. Box Plots of Energy Use Rates of Spring Chinook Salmon in Fishways during Day and 

Night as They Passed Bonneville Dam, April – June, 2001. Aerobic energy use rates are 

shown in panel A, anaerobic rates in panel B, and total rates in panel C. Boxes indicate the 

25th and 75th percentile of data; line in box shows median. Whiskers indicate the 95% 

confidence intervals for the data; asterisks are outliers. 

3.2.3.4.2 Shad and Water Temperature 

It is difficult to determine what the relationship is between the numbers of shad in the fishway and the 

swimming activity and energy use of spring chinook salmon. These relationships are confounded by the 

relationship between both shad abundance and fish behavior with water temperature. Although there was 

a significant, weak positive relationship between both swimming speed (R2

 = 0.15) and total energy use 

rates (R2

 = 0.13) and the number of shad passing fishways (Figure 3.29), there are stronger, and also 

significant, relationships between water temperature and these variables (fish swimming speeds, energy 
3.40

use rates, and shad abundance; Figure 3.30). The relationship between water temperature and the number 

of shad in fishways is stronger (R2

=0.42) than both the relationship between swimming speed and water 

temperature (R2

 = 0.26) and the relationship between total energy use rates and water temperature (R2 = 

0.24; Figure 3.30). 

Figure 3.29. Plots of Swimming Speeds (Panel A) and Total (aerobic and anaerobic combined) Energy 

Use Rates (Panel B) of Migrating Spring Chinook Salmon in Fishways and the Number of 

Shad in Fishways at Bonneville Dam, April – June, 2001. 
3.41

Figure 3.30. Plots of Swimming Speeds (Panel A), Total (aerobic and anaerobic combined) Energy Use 

Rates of Migrating Spring Chinook Salmon in Fishways (Panel B) and Number of Shad in 

Fishways (Panel C) and Water Temperature at Bonneville Dam, April – June, 2001. 
3.42
4.1

4.0 Discussion 

4.1 Laboratory Studies 

Our results indicate that the rate of oxygen consumption and red and white muscle activity in adult 

spring chinook salmon were strongly correlated with swimming speed over a range of fish sizes and at 

three different temperatures. Active oxygen consumption increased linearly with swim speed before 

leveling off at speeds at or above Ucrit. This pattern was similar at each water temperature and indicates 

that fish were approaching their maximal aerobic oxygen consumption at higher swim speeds. 

Our estimates of active oxygen consumption of spring chinook salmon compare favorably with those 

of other large salmonids swum at similar speeds and temperatures (see Geist et al. 2000). At most swim 

speeds, oxygen consumption was lowest at 8 oC, highest at 17 oC, and intermediate at 12.5 oC, which 

corresponds to the general model of a positive relation between metabolic rate and temperature in 

poikilotherms (Schmidt-Nielsen 1990). 

In our modeling of the relation between VO2 and swim speed, temperature—but not laboratory, sex, 

or size—was the only factor that influenced this relation. Although the differences between the intercepts 

and linear slopes of the models at 8, 12, and 17.5 oC were minor, there was a strong trend of increased 

oxygen consumption at increased water temperatures, which probably reflects some underlying biological 

significance. Thus, for predictive purposes, we chose a polynomial regression model incorporating 

temperature that had common intercepts and linear slopes for temperature and common slopes for the 

first- through third-order polynomials for swim speed. Using VO2-swim speed models based on 

temperature and independent of factors such as sex or size should be a powerful and biologically relevant 

way of estimating the energy use of fish in the wild. 

Our estimates of Ucrit for adult spring chinook salmon indicate that these fish are strong swimmers 

and the Ucrit represents an important measure of swimming performance relevant to estimating the 

energy use of fish in the wild. Although Ucrit may depend on several biotic and abiotic factors (for a 

review see Hammer 1995), it is thought to be the swimming velocity where maximum oxygen uptake 

occurs and thus represents an important threshold between aerobic and anaerobic metabolism. 

Our data on VO2, EMG activity, and blood chemistries (unpublished physiology data) indicate that at 

speeds approaching Ucrit, there was an increasing contribution of anaerobic metabolism to the swimming 

performance of spring chinook salmon. Specifically, anaerobiosis at higher swim speeds was evidenced 

in our results by 1) the leveling off of VO2; 2) the leveling off of red muscle activity; 3) the increase in 

white muscle activity; and 4) the appearance of lactic acid in the bloodstream at the time of fatigue (data 

not shown). Thus, the Ucrit represents velocities that fish have difficulty swimming against for moderate 

or long periods of time and that can have substantial energetic costs associated with them. 

Recent evidence by Hinch and Bratty (2000) indicated that passage success of sockeye salmon 

migrating through the notoriously difficult Hell’s Gate reach on the Fraser River was dependent on the 

duration of time fish spent swimming at speeds at or greater than Ucrit. A knowledge of when and how 

long fish are swimming at speeds near Ucrit will clearly be important to estimating the energy use of fish 

in the wild. 

Within the population of spring chinook salmon we tested, EMG activity in the red muscle was 

strongly and positively correlated with swim speed, a finding consistent with other studies on different 

species (McKinley and Power 1992; Booth et al. 1997; Thorstad et al. 2000; Geist et al. 2002). In 
4.2

contrast, EMG activity in the white muscle was relatively low at speeds below Ucrit and increased in all 

individuals as swim speeds approached or surpassed Ucrit. These results indicate that swimming at 

speeds below Ucrit is mostly powered by red muscle and swimming at speeds near or beyond Ucrit 

requires an increasing contribution of white muscle fibers. These conclusions conform well to the 

commonly reported function and physiology of red and white muscle fibers in fish (see Webb 1993 for a 

review). When combined with the oxygen consumption-swim speed relations, electromyogram telemetry 

has considerable potential for estimating the metabolic rates and swimming activity of fish in the wild. 

This conclusion is consistent with EMG studies conducted on other species (Kaseloo et al. 1992; 

McKinley and Power 1992; Demers et al. 1996; Hinch et al. 1996). 

Using a hockey stick modeling approach, we determined that the convergence point at which red 

muscle EMGs began to decrease and white muscle EMGs began to increase in our fish was approximately 

80% of the Ucrit (95% confidence interval, 73% to 88% Ucrit). This finding is very consistent with most 

studies of muscle electromyography (Hudson 1973; Bone et al. 1978; Brill and Dizon 1979; Rome et al. 

1985; Beddow and McKinley 1999). The implication of this could be significant in estimating total 

energy used during upstream migration. For example, Burgetz et al. (1998) expressed the anaerobic 

metabolism as a percentage of the rate of oxygen consumption at a given speed, or an anaerobic tax. As 

the swimming speed increased from 70% to 100% of the Ucrit, the anaerobic tax increased such that 

anaerobic energy used at 70%, 80%, and 100% of the Ucrit would be an additional 65%, 69.2%, and 

205.0% of the aerobic (oxygen consumption) energy use. Our results suggest that future modeling of 

total energy of adult spring chinook salmon migrating up the Columbia River must incorporate anaerobic 

metabolism at swimming speeds greater than approximately 80% of the Ucrit. 

In summary, we have documented strong and consistent relations between oxygen consumption, 

electromyogram activity, and swim speed in adult spring chinook salmon. These relations will serve as 

the foundation for estimating the metabolic costs of upstream migration of fish in the wild. Such 

information will help determine the energetic costs associated with activities like fallback and delay at 

dams and whether excessive energy use is a problem for adult salmonids migrating in today’s altered 

ecosystem. Ultimately, we hope to better define the relation between energy use and reproductive success 

in these fish. Physiological telemetry appears to be a useful tool to accomplish this goal. 

4.2 Field Studies 

4.2.1 Swim Speed and Energy Use in the Tailraces, Fishways, and Forebays of 

Bonneville Dam 

This study showed that adult spring chinook salmon that passed Bonneville Dam in 2001 spent the 

most time, swam hardest, and used the most energy in the tailrace areas as compared to fishways or 

forebays. Of all energy used during dam passage, 81% was used in the tailraces. 

Fish behavior during passage of the fishways and forebays was similar in several aspects. Neither 

swimming speeds or their correlate, energy use rates, differed between these two areas. However, as 

would be expected in an area where burst swimming occurs frequently, the differences between anaerobic 

energy rates approached significance between fishways and forebays, with fishway rates appearing to be 

higher than forebay rates. The main difference between fishways and forebays was that it took much 

longer (7.14 times longer) for fish to pass fishways than forebays. Despite having similar energy use 

rates, spending more time in fishways led to a greater use of energy, thus fishways were the second most 

energetically costly portion using 18% of total energy (compared to 1.5% used in the forebays). 
4.3

To apply these energy use data to the population of spring chinook salmon passing Bonneville Dam, 

results for the median fish have been presented. The median is presented since data are not normally 

distributed. Most fish exhibited energy use on the lower end of the scale; however, another segment of 

fish used a much larger amount of energy (Figure 3.9). The median fish took 26.8 hours to pass 

Bonneville Dam and expended 60.1 kcal kg-1. Since the average weight of fish is 6 kg, a representative 

fish would use approximately 361 kcal to pass Bonneville Dam. However, this median energy use does 

not reflect the energy used by a portion of the population. For example, ~8% of the spring chinook 

salmon tracked in our study used more than the median (i.e., 361 kcal) just to pass the tailraces of 

Bonneville Dam. As this use of energy to pass the dam increases, the risk of prespawning mortality also 

increases. 

There was no difference in either the swimming speeds or the energy use rates among the three 

forebays. It appears that the migrating spring chinook salmon sampled during this low-spill year were 

able to avoid high-velocity areas and maintain a rather uniform swimming behavior, avoiding fallback. 

This may not hold true, however, during a more normal year with higher water discharge when spill 

would be higher. 

Unlike forebays, there were differences in behavior among tailraces. The only difference was 

between the tailrace of Powerhouse 2 and the spillway tailrace, with swimming speeds and energy use 

rates being higher in the tailrace of Powerhouse 2. It may be expected that this behavior would be present 

since the daily average discharge through Powerhouse 2 was higher than through Powerhouse 1 or the 

spillway (Figure 1.4). In fact, there was very little discharge over the spillway for a majority of the study 

period. However, there was also no significant difference between swimming speeds and energy use rates 

between the tailraces of the two powerhouses, despite the much higher water discharge through 

Powerhouse 2. 

Discharges can vary drastically at Bonneville Dam (Figure 1.4) through the powerhouse or over the 

spillway with flow rates changing gradually or even stopping abruptly. Despite this large variability in 

dam operations, there was no relationship between the water discharge in the tailraces and the swimming 

speeds or energetic use rates of the fish. However, in a normal water year (with higher discharge), results 

may differ. 

Fish exhibited varied behavior within tailraces. Periods of low energy use rates and high energy use 

were seen when discharge was both high and low (Figure 3.16). Thus, fish swimming speeds appear at 

least partially independent of discharge in tailraces. Fish activity is likely associated with water velocity 

on a smaller scale, whereby fish are likely resting in low-velocity areas between attempts at finding 

fishway entrances. Even when a tailrace has low discharge, fish may swim through low-velocity areas at 

a high rate of speed. Also, in an attempt to attract salmon, water velocities at fishway entrances are high 

during both periods of high and low discharge through the powerhouses. 

The aerobic energy use rates of migrating spring chinook salmon were higher during the day than at 

night in tailraces of Bonneville Dam. This is consistent with other studies, which show that activity in 

fishways decreases during the night. For example, some researchers have found that relatively few 

migrating fish (e.g., Atlantic salmon, Salmo salar, and brown trout, Salmo trutta) ascend fish ladders at 

dams or attempt to pass waterfalls during the night (Gowans et al. 1999). Sonic tracking of three fall 

chinook salmon in the Bonneville pool showed that fish either slowed their pace or stopped moving as it 

grew dark (Johnson 1960). Similarly, migrations of chum salmon (O. keta) migrating in coastal waters 

have been observed swimming slower at night (between 21:00 and 03:00) than during the day (09:00 and 

15:00; Tanaka et al. 2001). 
4.4

No relationship was found between the sex of the fish and swimming speeds or energy use rates for 

the spring chinook salmon as they passed Bonneville Dam. This is contrary to the findings of Hinch and 

Rand (1998) who suggest that female sockeye salmon (O. nerka) migrating in the Fraser and Nechako 

rivers generally swam slower and used less energy per unit distance than males. They suggest that female 

sockeye salmon were more energetically efficient despite generally having the same sizes and shapes (i.e., 

identical length-mass relationships); they suggest, however, that males may have more drag due to the 

development of a humped back and kype. 

This is the first study examining the energetics of salmon passing a hydroelectric dam. Consequently, 

comparative data at hydroelectric facilities do not exist. However, there are some data sets pertaining to 

migration in free-flowing rivers that can be used as a general comparison. When compared to energy use 

rates estimated from proximate analysis studies (reviewed by Brett 1995, Table 4.1), our values for spring 

chinook salmon passing Bonneville Dam were generally higher. The rates of energy use we found in the 

fishways (2.22 kcal kg-1 h-1) and tailraces (2.80 kcal kg-1 h-1) are higher than any energy use rate estimated 

for male salmon of several species mentioned in Brett (1995). Because females allocate large amounts of 

energy to developing the gonads during migration and males complete most of their maturation in the 

marine environment (Brett 1995), our results - which represent the metabolic cost of swimming only - 

more accurately reflect the energy use of males than of females. The rates collected by proximate 

analysis, however, were for salmon migrating in free-flowing rivers (without dams to pass). 

The rates in the forebays of Bonneville Dam (1.73 kcal kg-1 h-1) are lower than those estimated for 

sockeye salmon (1.84 kcal kg-1 h-1) and closer to rates seen for several other species in free-flowing rivers 

estimated by proximate analysis (Table 4.1). The rates of energy use in the forebay area are also similar 

to those found by Rand and Hinch (1998) for sockeye salmon migrating in the Fraser River, British 

Columbia. Rand and Hinch (1998) used swimming speeds of adult sockeye salmon estimated from EMG 

radio telemetry (reported by Hinch and Rand 1998) to estimate the energetic costs of spawning 

migrations. They estimated that the costs of spawning migrations for sockeye salmon varied between 

approximately 1.46 and 5.42 kcal kg-1 h-1 in certain river sections (converted from their estimate of ~5 to 

18 W d-1 for fish with a mean weight of 2.88 kg). Difficult passage areas were on the higher end of this 

range while other areas were similar to rates exhibited by spring chinook salmon passing Bonneville 

Dam, ranging from 1.45 to 3.0 kcal kg-1 h-1. 

The energy use values estimated by Rand and Hinch (1998) agree more with estimates of energy use 

at Bonneville Dam than many of the values obtained by proximate analysis studies. This may be due to 

the fact that energy use values collected in proximate analysis studies may be biased toward lower values. 

Proximate analysis studies estimate the energy contained in the bodies of salmon at the start and end of 

migrations and sometimes at several points along the migration. However, mortality of salmon occurs 

along the migration, and the fittest salmon reach the spawning grounds. Since fish collected at the end of 

the migration reflect the fittest individuals in the population, having either higher energy reserves at the 

start of migrations, or using energy more efficiently than the fish that die along the migration, estimates of 

energy use via proximate analysis are likely biased toward low energy use values (Peter Rand, personal 

communication, July 2002, Vancouver, BC). 
4.5

Table 4.1. Energy Use Rates of Spring Chinook Salmon Passing Bonneville Dam, April – June 2001. 

For comparison, energy use rates during spawning migration of male salmon of several 

species are shown (from Brett 1995). 

Area Aerobic 

 kcal kg-1 h-1

Anaerobic

kcal kg-1 h-1

Total 

kcal kg-1 h-1

Tailraces 1.27 1.53 2.80 

Fishways 1.11 1.11 2.22 

Forebays 1.00 0.73 1.73 

Other Species 

 chinook (Greene 1926) 1.38 

 chum (Pentegoff et al. 1928) 1.13 

 pink (Williams et al. 1986) 0.93 

 sockeye (Newman & Collins 1967) 1.14 

 sockeye (Gilhousen 1980) 0.88-1.42 

 sockeye (Idler & Clemens 1959) 1.84 

4.2.2 Swim Speeds and Energy Use among Sections of the Fishways of 

Bonneville Dam 

4.2.2.1 Swimming Speed and Energetics 

Although there was no significant difference in swimming speeds or energy use rates among the three 

fishways, the sample size of fish in the Cascade Island fishway was low (N=8). This was likely 

associated with the relatively little flow over the spillway during the study period. Fish only entered the 

Cascade Island fishway while there was discharge over the tailrace. Examining swimming behavior of 

fish in this area when there is spill would likely increase use of the Cascade Island fishway and improve 

the quantity and quality of data in this area. 

It is not unusual that mean swimming speeds and energy use were similar among the fishways. These 

fishways are very similar in design. The pool and weir fishways that dominate all fishways have similar 

size, spacing, and gradient. This will be discussed further in the section discussing individual fishway 

sections. 

The amount of time that it took fish to pass fishways varied with the fishway they passed. However, 

since swimming speeds did not differ among the three fishways, other factors are involved. One of these 

factors is that the length of these fishways varies. A fish passing the Bradford fishway through the A 

branch would only need to travel less than 400 m (Table 3.13) to exit the fishway. However, a fish 

passing the Washington shore fishway may travel over 800 m. Fish passing the Cascade fishway must 

pass through ~300 m of fishway in the Cascade fishway proper, then pass through the UMT channel 

(156 m long) and then another ~300 m of the upper Washington shore fishway. These longer distances 

are factors in the differences in travel time and energy expenditure among fishways. 
4.6

4.2.2.2 Fallout 

The percentage of spring chinook salmon tracked during this study that fell out of fishways was less 

(32%) than observed at other dams in the Columbia Basin. A study of chinook salmon that fell out of 

Snake River Fishways in the summer of 1993 found 57% fell out at Ice Harbor Dam, 65% at Lower 

Monumental Dam, 58% at Little Goose Dam, and 63% at Lower Granite Dam (Bjornn et al. 1995). 

However, it must be noted that during the current study, we did not have enough equipment to monitor 

EMG signals from fish in the south monolith entrances to the Washington Shore or Bradford Island 

fishways or from the fishways at the base of the two powerhouses. Because of this, we likely 

underestimate the number of fallouts, and the energetic consequences of fallout. 

4.2.2.3 Differences among Section of Fishways 

Throughout the passage of different fishway areas, mean swimming speeds were well under critical 

swimming speed. This indicates that fish did not appear to have excessive difficulty passing any of the 

fishway sections. However, one section that did stand out was the exit to the Washington shore fishway. 

Although this section has relatively low water velocities and does not contain any weirs for fish to pass, 

the ranking of swimming speed in this section is very high. Also, manual trackers noted that fish 

appeared to be swimming very quickly at the lower end of this section, near the exit of the vertical slot 

weirs. Further examination of salmon behavior in this area is recommended. 

Among all areas of fishways, although swimming speeds and energy use rates varied, no serious 

hindrance to passage was obvious. The reason for elevated swimming speeds or energy use rates can 

differ, both of these rates were elevated in the junction pool of the Bradford Island Fishway. However, 

this area does not likely pose an area of difficult passage. Instead, the junction pool of the Bradford 

Island fishway is traveled through at a speed (a mean of 109 cm s-1) that is near the optimum cruising 

speed for salmon (1 Bl s-1; Webb 1995) and is likely moved through quickly because it is low velocity, 

and there are no weirs within it. 

However, when swimming speed and energy use rates are elevated for long periods of time in an area, 

it may be worth doing further examination of fish behavior there to ease and reduce the energy 

consumption of passage. When the ranks of energy use rates and time spent in an area were combined 

(Table 3.14), the Cascade Island fishway stands out as the most energetically costly route for dam 

passage. Not only do fish have to travel a longer distance than if passing the other two fishways, and fish 

spend a relatively long period of time there, but energy use rates are relatively high. One possible source 

of delay in this area is the presence of bubbles in the entrances to some fishways. There are problems 

with bubbles in the diffusers in both the Bradford Island and Cascade Island fishways (Tammy Mackey, 

Personal communication September 12, 2002). 

Another possible source of delay in fishways is the presence of sharp bends or corners. Although 

several sections have very similar design (sections 2, 3, 5, 9, and 11), the energy used per m varied among 

some of these sections. Part of this may be due to bends, corners, or other structures in these fishway 

sections. Spring chinook salmon passing the A and B branches of the Bradford Island fishway had very 

similar (0.9 and 0.8 kcal kg-1 m-1) and lower energy use rates than fish passing section 9 or 11 (where use 

was 1.3 and 1.5 kcal kg-1 m-1). Weaver (1963) reported significantly longer passage times through corner 

and bend pools. Section 2 and 3 have relatively gentle bends in them, while section 9 and 11 either have 

a fairly sharp bend (section 9) or a corner (section 11). 
4.7

4.2.2.4 Variables Influencing Activity 

4.2.2.4.1 Diel period 

There was a relationship between the time of entry to fishways by spring chinook salmon and the 

amount of time they spent in fishways, and this relationship relates to their diel behavior. Fish that 

successfully passed the dam did not enter fishways after 22:00 and generally those that entered fishways 

without adequate time to pass before dark (or ~ 22:00) spent the night in fishways. Also, swimming 

speeds and energy use rates of spring chinook salmon in fishways were lower during the night than during 

the day. 

All of these factors indicate less activity at night and less movement through fishways at night. This 

decrease in movement through fishways at night has been previously documented. Bjornn et al. (1995) 

found that in Snake River dams, salmon temporarily discontinue upstream migration at night while in 

fishways. Gowans et al. (1999) found that relatively few migrating Atlantic salmon (Salmo salar) or 

brown trout (Salmo trutta) ascend fish ladders at dams or attempt to pass waterfalls during the night. 

However, if artificial light is present, the behavior of migrating salmon increases during the night. 

Brett and MacKinnon (1954) found that, when artificial light was present, the passage of pink salmon (O. 

gorbuscha) through fishways was higher at night. Collins et al. (1962) also found that fish continued to 

move through an endless fishway 24 hours a day if artificial light was present. Even entry into fishways 

by chinook, sockeye, and coho salmon and steelhead increases during the night when artificial light is 

present (Gauley 1967). 

4.2.2.4.2 Shad and Temperature 

The fishways on the Columbia River dams were not initially designed for shad passage, consequently 

during shad runs, which coincided with salmon runs, the accumulation and eventual death of shad 

resulted in the blockage of salmon passage (Bates 2000). Part of the reason shad were delayed at 

Columbia river dams was that, unlike Pacific salmon, shad would not pass through the orifices in the 

weirs of fish ladders and several sections of fishways (such as the flow control sections) had orifices with 

no surface overflow (Bates 2000). 

We examined the relationships between the number of shad passing Bonneville Dam and the 

swimming speed and energy use rates of migrating spring chinook salmon. Unfortunately, water 

temperature was a correlate to both shad numbers and the energy use rates of migrating salmon. Due to 

this, no conclusive statements can be made concerning the interactions of shad and migrating salmon. To 

determine whether shad influence the energy use of fish in the fishway, experiments would be needed to 

examine the energy use of salmon when the water temperatures were constant and shad abundance could 

be varied. 
4.8
5.1

5.0 Management Implications and Recommendations 

The higher swimming speeds and energy use rates we found in the Bonneville Dam tailraces are 

likely associated with generally higher water velocities in the tailraces than other areas and with problems 

finding the openings of fishways. The high energy use rates in tailraces may be partially due to a lack of 

resting areas along the approach to the fishway entrances. In comparison, the fishways themselves are 

designed to allow fish to rest at each step and between other high-velocity areas. 

Spring chinook salmon used burst swimming more in the tailraces than in other parts of the dam. 

This is indicated by the higher anaerobic energy use rates in the tailraces than in the fishways or forebays. 

Burst swimming is used by salmon to pass high velocity areas (Webb 1995). However, fish are unable to 

sustain burst swimming speeds for long periods of time. Brett (1995) states that jumps or bursts of 

swimming at falls or obstacles in streams probably do not exceed 20 to 30 seconds. Similarly, Webb 

(1995) states that burst swimming can be maintained less than about 30 seconds. Brown and Geist (2002) 

found fall chinook salmon attempting to pass waterfalls used burst swimming for periods with a mean of 

only 20 seconds at a time. Thus, fish may use excess energy and be delayed at dams if water velocities 

exceed critical swimming speeds for long distances without areas to rest. Fish can spend long periods of 

time in tailraces searching for entrances to fishways. When attempting to pass hydroelectric dams, 

chinook salmon can make up to 30 approaches before successfully entering fishways (Bjornn et al. 1995). 

Adding structures in tailraces that allow fish to rest as they approach the dam may decrease energy 

use and delay. Also, these structures could be placed in a way that guide fish toward the entrances to fish 

ladders. 

Additional use of EMG telemetry would be helpful to examine the behavior of fish in the highvelocity waters near the powerhouses and spillway, especially near the entrances to the fishways. We 

suggest experimenting with structures placed in the tailraces to allow resting and guidance along the path 

to fishway entrances. 

Also, since 2001 was a dry year, and there was very little spill, few fish fell back over the spillway. 

An additional year of study would be helpful to determine the relationships between forebay flow 

characteristics and the behavior associated with fallback under more typical flow conditions. 

Among all fishway areas, Cascade Island fishway appears to be the most energetically costly. Also, 

section 12 of the Washington shore fishway appears costly. It appears from our data that there is an 

energetic cost associated with diverting fish from the Washington shore ladder to the adult facility. These 

costs should be compared with the benefits of the research that uses the adult facility. 

Further research in fishways should concentrate on fish behavior in the exit of the Washington shore 

fishway near the vertical slot weirs. Also, an evaluation of the relationship between bubbles in the 

fishway entrances and delays of passage in these areas is recommended. 

Although the Cascade Island fishway appears to be more energetically costly than other fishways, the 

number of fish sampled in this fishway was limited. Only 8 fish went through this fishway while 34 to 37 

went through the other two. This is likely due to the lack of spill attracting fish to the tailrace of the 

spillway during the low flow year of the study. Further research in a year with more normal flows would 

likely improve the quantity and quality of data in this area. 
5.2

Energy used during fallouts was substantial (11.5% to 18.8 % of the amount of energy used for 

successful fishway passages). This percentage was highest in the Cascade Island fishway (18.8%) where 

energy use rates during fallout were even higher (3.67 kcal kg1

 h1) than energy use rates in the tailrace 

(2.8 kcal kg1

 h1). If management agencies are interested, existing data on individual salmon could be 

examined further, possibly leading to a reduction in this behavior and a decrease in energy expenditures 

of passage. 
6.1

6.0 References 

Bates, K. 2000. Fishway Guidelines for Washington State. Washington Department of Fish and 

Wildlife. Available at http://www.wa.gov/wdfw/hab/ahg/fishguid.pdf.

Beamish, F.W.H. 1978. “Swimming Capacity.” Chapter 2 in Fish Physiology (W.H. Hoar and 

D.J. Randall, eds.), Volume 7. Academic Press, New York. 

Beddow, T.A., and R.S. McKinley. 1999. “Importance of Electrode Positioning in Biotelemetry 

Studies Estimating Muscle Activity in Fish.” Journal of Fish Biology 54:819-831. 

Beiningen, K.T., and W.J. Ebel. 1970. “Effects of John Day Dam on Dissolved Nitrogen 

Concentrations and Salmon in the Columbia River.” Transactions of the American 

Fisheries Society 99:664-671. 

Berman, C.H., and T.P. Quinn. 1991. “Behavioral Thermoregulation and Homing by Spring 

Chinook Salmon, Oncorhynchus tshawytscha (Walbaum), in the Yakima River”. J. Fish 

Biol. 39: 301-312. 

Bjornn, T.C., J.P. Hunt, K.R. Tolotti, P.J. Kneiry, and R.R. Ringe. 1994. Migration of Adult 

Chinook Salmon and Steelhead Past Dams and through Reservoirs in the Lower Snake 

River and into Tributaries - 1992. U.S. Army Corps of Engineers, Walla Walla, 

Washington. 

Bjornn, T.C., J.P. Hunt, K.R. Tolotti, P.J. Keniry, and R.R. Ringe. 1995. Migration of Adult 

Chinook Salmon and Steelhead Past Dams and through Reservoirs in the Lower Snake 

River and into Tributaries - 1993. Technical Report 95-1, Idaho Cooperative Fish and 

Wildlife Research Unit, University of Idaho, Moscow. 

Bjornn, T.C., M.L. Keefer, C.A. Peery, K.R. Tolotti, R.R. Ringe, and L.C. Stuehrenberg. 1999b. 

Adult Chinook and Sockeye Salmon, and Steelhead Fallback Rates at the Dalles Dam – 

1996, 1997, and 1998. Draft Report to the U.S. Army Corps of Engineers, Portland, 

Oregon, and Walla Walla, Washington, and to the Bonneville Power Administration, 

Portland, Oregon. 

Bjornn, T.C., T.S. Reischel, R.R. Ringe, K.R. Tolotti, and L.S. Stuehrenberg. 1999. Radio 

Telemetry Assessments of Migration Patterns and Fallbacks of Adult Salmon and 

Steelhead in the Forebay of Bonneville Dam, 1997-1998. Technical Report 99-1. U.S. 

Army Corps of Engineers, Portland, Oregon. 

Bjornn, T.C., M.L. Keefer, C.A. Peery, M. A. Jepson, K.R. Tolotti, R.R. Ringe, and L.C. 

Stuehrenberg. 2000a. Adult Chinook and Sockeye Salmon, and Steelhead Fallback Rates 

at the Dalles Dam – 1996, 1997, and 1998. Report to the U.S. Army Corps of Engineers, 

Portland, Oregon, and Walla Walla, Washington, and to the Bonneville Power 

Administration, Portland, Oregon. 
6.2

Bjornn, T. C., M.L. Keefer, C.A. Peery, K.R. Tolotti, R.R. Ringe, P.J. Keniry and, L.C. 

Stuehrenberg. 2000b. “Migration of Adult Spring and Summer Chinook Salmon Past 

Columbia and Snake River Dams, Through Reservoirs and Distribution into Tributaries, 

1996.” Report to the U.S. Army Corps of Engineers, Portland, Oregon, and Walla Walla, 

Washington, and to the Bonneville Power Administration, Portland, Oregon.

Bone, Q., J. Kiceniuk, and D.R. Jones. 1978. “On the Role of the Different Fibre Types in Fish 

Myotomes at Intermediate Swimming Speeds.” Fishery Bulletin of the US Fish and 

Wildlife Service 76:691-699. 

Booth, R. K., R. S. McKinley, F. Økland, and M. M. Sisak. 1997. “In situ Measurement of 

Swimming Performance of Wild Atlantic Salmon (Salmo salar) Using Radio Transmitted 

Electromyogram Signals.” Aquatic Living Resources 10:213-219. 

Brafield, A. E., and D. J. Solomon. 1972. “Oxy-Calorific Coefficients for Animals Respiring 

Nitrogenous Substrates.” Comparative Biochemistry and Physiology A, Comparative 

Physiology 43:837-841. 

Brett, J.R. and D. MacKinnon. 1954. “Some Aspects of Olfactory Perception in Migrating 

Adult Coho and Spring Salmon.” J. Fish. Res. Bd. Can. 11(3):310-318. 

Brett, J.R. 1995. “Energetics.” In Physiological Ecology of Pacific Salmon, ed. C. Groot, L. 

Margolis, and W. C. Clarke. pp.3-68. UBC Press, Vancouver, British Columbia. 

Brill, R.W., and A.E. Dizon. 1979. “Red and White Muscle Fibre Activity in Swimming 

Skipjack Tuna, Katsuwonus pelamis (L.)”. Journal of Fish Biology 15: 670-685. 

Brown, R. S., and D. R. Geist. 2002. Determination of Swimming Speeds and Energetic 

Demands of Upriver Migrating Fall Chinook Salmon (Oncorhynchus tshawytscha) in the 

Klickitat River. Project Number 22063. Prepared for Bonneville Power Administration 

by Pacific Northwest National Laboratory. 

Burgetz, I.J.; A. Rojas-Vargas; S.G. Hinch, and D.J. Randall. 1998. “Initial Recruitment of 

Anaerobic Metabolism during Sub-Maximal Swimming in Rainbow Trout 

(Oncorhynchus mykiss).” J. Exp. Biol. 201(19):2711-2721. 

Cameron, J.N., and J.J. Cech. 1990. “Lactate Kinetics in Exercised Channel Catfish, Ictalurus 

punctatus.” Physiological Zoology 63:909-920. 

Campbell, P.M., T.G. Pottinger, and J.P. Sumpter. 1992. Stress Reduces the Quality of Gametes 

Produced by Rainbow Trout. Biology of Reproduction 47:1140-1150. 

Collins, G.B., J.R. Gauley, and C.H. Elling. 1962. “Ability of Salmonids to Ascend High 

Fishways.” Trans. Am. Fish. Soc. 91: 1-7. 

Connor, A.R., C.H. Elling, E.C. Black, G.B. Collins, J.R. Gauley, and E. Trevor-Smith. 1964. 

“Changes in Glycogen and Lactate Levels in Migrating Salmonid Fishes Ascending 
6.3

Experimental ‘Endless’ Fishways.” Journal of the Fisheries Research Board of Canada

21:255-290. 

Dauble, D.D., and R.P. Mueller. 2000. “Upstream Passage Monitoring: Difficulties in 

Estimating Survival for Adult Chinook Salmon in the Columbia and Snake Rivers.” 

Fisheries 25(8):24-34. 

Demers, E., R. S. McKinley, A. H. Weatherly, and D. J. McQueen. 1996. Activity Patterns of 

Largemouth and Smallmouth Bass Determined with Electromyogram Biotelemetry. 

Transactions of the American Fisheries Society 125:434-439. 

Driedzic, W.R., and J.W. Kiceniuk. 1976. “Blood Lactate Levels in Free-Swimming Rainbow 

Trout (Salmo gairdneri) Before and After Strenuous Exercise Resulting in Fatigue.” 

Journal of the Fisheries Research Board of Canada 33:173-176. 

Gauley, J. R. 1967. “Effect of Water Velocity on Passage of Salmonids in a Transportation 

Channel.” USFWS Fishery Bulletin, 66(1):59-63. 

Geist, D.R., C.S. Abernethy, S.L. Blanton, and V.I. Cullinan. 2000. “The use of 

electromyogram telemetry to estimate energy expenditure of adult fall chinook salmon.” 

Transactions of the American Fisheries Society 129:126-135. 

Geist, D. R., R. S. Brown, K. Lepla, and J. Chandler. 2002. “Practical application of 

electromyogram radiotelemetry: the suitability of applying laboratory-acquired 

calibration data to field data.” North American Journal of Fisheries Management

22:474-479. 

Gowans, A.R.D., J.D. Armstrong, and I.G. Priede. 1999. “Movements of Adult Atlantic Salmon 

in Relation to a Hydroelectric Dam and Fish Ladder.” J. Fish Biol. 54: 713-726. 

Gray, R.H. 1990. “Fish behavior and environmental assessment.” Environmental Toxicology 

and Chemistry 9:53-67. 

Greene, C.W. 1926. “The Physiology of the Spawning Migration.” Physiological Reviews 6(2): 

201-241. 

Groot, C., and L. Margolis, editors. 1991. Pacific Salmon Life Histories. University of British 

Columbia Press, Vancouver, BC. 

Hammer, C. 1995. “Fatigue and exercise tests with fish.” Comparative Biochemistry and 

Physiology 112A:1-20. 

Hinch, S.G., R.E. Diewert, T.J. Lissimore, A.M. Prince, M.C. Healey, and M.A. Henderson. 

1996. “Use of Electromyogram Telemetry to Assess Difficult Passage Areas for RiverMigrating Adult Sockeye Salmon.” Transactions of the American Fisheries Society 

125:253-260. 
6.4

Hinch, S.G., and P.S. Rand. 1998. “Swim Speeds and Energy Use of Upriver-Migrating 

Sockeye Salmon (Oncorhynchus nerka): Role of Local Environment and Fish 

Characteristics.” Can. J. Fish. Aquat. Sci. 55: 1821-1831. 

Hinch, S.G., and J. Bratty. 2000. “Effects of Swim Speed and Activity Pattern on Success of 

Adult Sockeye Salmon Migration through an Area of Difficult Passage.” Transactions of 

the American Fisheries Society 129:598-606. 

Hudson, R.C. 1973. “On the function of the white muscles in teleosts at intermediate swimming 

speeds.” Journal of Experimental Biology 58:509-522. 

Johnson, J. H. 1960. “Sonic Tracking of Adult Salmon at Bonneville Dam, 1957.” Fishery 

Bulletin 176 Vol. 60: 471-484. 

Kaseloo, P.A.; A.H. Weatherley, J. Lotimer, and M.D. Farina. 1992. “A Biotelemetry System 

Recording Fish Activity.” J. Fish Biol. 40:165-179. 

Klepper, O., and Bedaux, J.J.M. 1997. “Nonlinear parameter estimation for toxicological 

threshold models.” Ecological Modeling 102:315-324. 

Liscom, K. L., G. E. Monan, L. C. Stuehrenberg, and P. J. Wilder. 1985. “Radio-tracking 

Studies on Adult Chinook Salmon and Steelhead Trout at Lower Columbia River 

Hydroelectric Dams, 1971-77.” NOAA Tech. Memo. NMFS F/NWC-81, 225 p. 

(Available from NMFS/NOAA Northwest Fisheries Science Center, Seattle WA) 

McKinley, R.S., and G. Power. 1992. Measurement of activity and oxygen consumption for 

adult lake sturgeon (Acipenser fulvescens) in the wild using radio transmitted EMG 

signals. In Wildlife Telemetry: Remote Monitoring and Tracking of Animals (I.G. Priede 

and S.M. Swift, ed.) pp. 307-318. Ellis Horwood, New York. 

Mendel, G. and D. Milks. 1997. “Upstream passage and spawning of fall chinook salmon in the 

Snake River.” Chapter 1 in Upstream Passage, Spawning, and Stock Identification of 

Fall Chinook Salmon in the Snake River, 1992 and 1993 (H.L. Blankenship and G. 

Mendel, eds.) Final Report. Bonneville Power Administration, Portland, Oregon. 

Mendel, G., D. Milks, R. Bugert, and K. Petersen. 1992. Upstream Passage and Spawning of 

Fall Chinook Salmon in the Snake River, 1991. Washington Department of Fisheries, 

Olympia, Washington. 

Ǿkland, F., E.B. Thorstad, R.S. McKinley, B. Finstad, and R.K. Booth. 1997. “Radio 

transmitted electromyogram (EMG) signals as indicators of physical activity in Altlantic 

salmon (Salmo salar).” Journal of Fish Biology 51:476-488. 

Pagnotta, A., and C.L. Milligan. 1991. “The role of blood glucose in the restoration of muscle 

glycogen during recovery from exhaustive exercise in rainbow trout (Oncorhynchus 

mykiss) and winter flounder (Pseudopleuronectes americanus).” Journal of Experimental 

Biology 161:489-508. 
6.5

Rakowski, C. L., and M. C. Richmond. 2000. Bonneville Project Adult Migrant Fallback Study. 

Letter Report. U.S. Army Portland District Corps of Engineers, Portland, Oregon. 

Raleigh, R.F., W.J. Miller, and P.C. Nelson. 1986. “Habitat Suitability Index Models and 

Instream Flow Suitability Curves: Chinook Salmon.” U.S. Fish and Wildlife Service 

Biological Report 82(10.122), USFWS, Washington, D.C. 

Ramsey, F.L., and D.W. Schafer. 1996. The statistical Sleuth: a Course in Methods of Data 

Analysis. Duxbury Press, New York. 

Rand, P.S, and S.G. Hinch. 1998. “Swim Speeds and Energy Use of Upriver Migrating Sockeye 

Salmon (Oncorhynchus nerka): Simulating Metabolic Power and Assessing Risk of 

Energy Depletion.” Can. J. Fish. Aquat. Sci. 55: 1832-1841. 

Rome, L.C. P.T. Loughna, and G. Goldspink. 1985. Temperature Acclimation: Improved 

Sustained Swimming Performance in Carp at Low Temperature. Science 228, 194-196. 

Schmidt-Nielsen, K. 1991. Animal Physiology: Adaptation and Environment. Cambridge 

University Press, New York. 

Smit, H., J.M. Amelink-Koutstaal, J. Vijverberg, and J.C. Von Vaupel-Klein. 1971. “Oxygen 

consumption and efficiency of swimming goldfish.” Comparative Biochemistry and 

Physiology 39A:1-28. 

Snelling, J.C., and 6 co-authors. 1992. Migratory Characteristics of Spring Chinook Salmon in 

the Willamette River. Oregon Cooperative Fishery Unit, Oregon State University, 

Corvallis, Oregon. 

Sumerfelt, R.C., and L.S. Smith. 1990. “Anesthesia, surgery, and related techniques.” In 

Methods for Fish Biology (C.B. Schreck and P.B. Moyle, eds.) pp. 213-273, American 

Fisheries Society, Bethesda, Maryland. 

Swan, G. A., L. K. Timme, R. N. Iwamoto, L.C. Stuehrenberg, E. E. Hockersmith, B. L. Iverson, 

and B. J. Sandford. 1994. Wells Dam Radio-telemetry Study 1992. NMFS/NOAA 

Northwest Fisheries Science Center. Report to Douglas County PUD, East Wenatchee 

Washington. 

Tanaka, H., Y. Takagi, and Y. Naito. 2001. “Swimming Speeds and Buoyancy Compensation 

of Migrating Adult Chum Salmon Oncorhynchus keta Revealed by 

Speed/Depth/Acceleration Data Logger.” J. Exp. Biol. 204:3895-3904. 

Thorstad, E.B., F. Ǿkland, A. Koed, and R.S. McKinley. (2000). Radio-transmitted 

electromyogram signals as indicators of swimming speed in lake trout and brown trout. 

Journal of Fish Biology 57, 547-561. 

Tufts, B.L., Y. Tang, K. Tufts, and R. G. Boutilier. (1991). Exhaustive exercise in wild Atlantic 

salmon (Salmo salar): acid-base regulation and blood gas transport. Canadian Journal 

of Fisheries and Aquatic Sciences 48:868-874. 
6.6

Weaver, C. 1963. “Influence of Water Velocity upon Orientation and Performance of Adult 

Migrating Salmonids.” Fish. Bull. 63:97-121. U.S. Fish Wildl. Serv., Washington, D.C. 

Webb, P.W. 1993. “Swimming.” Chapter 2 in The Physiology of Fishes (D.H. Evans, ed.) 

Boca Raton, Florida: CRC Press. 

Webb, P. W. 1995. “Locomotion.” In Physiological Ecology of Pacific Salmon, ed. C.Groot, L. 

Margolis, and W.C. Clarke. pp. 79-99. UBC Press, Vancouver, British Columbia. 

Wood, C.M., J.D. Turner, and M.S. Graham. (1983). Why do fish die after severe exercise? 

Journal of Fish Biology 22:189-201.