AN ANALYSIS OF THE EFFECTS OF TEMPERATURE ON 

SALMONIDS OF THE PACIFIC NORTHWEST 

WITH IMPLICATIONS FOR SELECTING TEMPERATURE CRITERIA

December 2000

Kathleen Sullivan 

Sustainable Ecosystems Institute 

Portland, Oregon 

Douglas J. Martin 

Martin Environmental 

Seattle, Washington

Richard D. Cardwell 

Parametrix Inc. 

Kirkland, Washington

John E. Toll 

Parametrix Inc. 

Kirkland, Washington 

Steven Duke 

Weyerhaeuser Company 

Tacoma, Washington

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TABLE OF CONTENTS

Table of Contents.......................................................................................................................................................II

Abstract .................................................................................................................................................................... IV

Acknowledgments ....................................................................................................................................................V

Section 1 Introduction and Objectives............................................................................................................. 1-1 

Section 2 Review of The Physiologic Response of Fish to Environmental Temperature ............................ 2-1 

ACUTE TEMPERATURE EFFECTS ........................................................................................................................... 2-3 

SUBLETHAL TEMPERATURE EFFECTS ...............................................................................................................................2-4

ASSOCIATING BIOLOGICAL EFFECTS AND STREAM TEMPERATURE .......................................................................... 2-8 

ESTABLISHING WATER QUALITY CRITERIA ........................................................................................................... 2-10 

CONCLUSIONS.................................................................................................................................................... 2-10 

Section 3 Temperature Characteristics of Streams Used in analysis............................................................. 3-1 

TEMPERATURE DATA............................................................................................................................................ 3-1 

TEMPERATUE CHARACTERISTICS .......................................................................................................................... 3-2 

COMPARISON OF STUDY SITES TO OTHER TEMPERATURE STUDIES IN WASHINGTON, OREGON, AND IDAHO............. 3-5 

TEMPORAL VARIATION AT A SITE ........................................................................................................................... 3-9 

TEMPERATURE INDICES ........................................................................................................................................ 3-9 

CONCLUSIONS.................................................................................................................................................... 3-11 

Section 4 Assessment of Risk of Salmon Species to Acute Temperatures in Streams and Rivers.............. 4-1 

INTRODUCTION..................................................................................................................................................... 4-1 

ACUTE THERMAL EFFECTS CURVES ASSOCIATED WITH 50% MORTALITY................................................................ 4-2 

COMPARISON OF LT50 AND LT10 MORTALITY RELATIONSHIPS .............................................................................. 4-4 

ACUTE THERMAL EFFECTS CURVES AT 10% MORTALITY ....................................................................................... 4-6 

ACUTE EXPOSURE CHARACTERIZATION................................................................................................................. 4-7 

ACUTE RISK CHARACTERIZATION .......................................................................................................................... 4-9 

DISCUSSION....................................................................................................................................................... 4-12 

CONCLUSIONS.................................................................................................................................................... 4-13 

Section 5 Development and Corroboration of a Bioenergetics-Based approach To Evaluating Salmon 

Growth in Relation to Environmental Temperatue.............................................................................................. 5-1 

INTRODUCTION..................................................................................................................................................... 5-2 

GROWTH MODEL .................................................................................................................................................. 5-4 

Consumption...................................................................................................................................5-5 

Specific Growth Rate..................................................................................................................... .5-11 

Growth Model................................................................................................................................5-14 

APPLICATION OF THE METHOD FOR PREDICTING GROWTH IN NATURAL STREAMS.................................................. 5-14 

COMPARISON OF MODEL PREDICTIONS TO OBSERVED FISH GROWTH................................................................... 5-22 

DISCUSSION....................................................................................................................................................... 5-30 

CONCLUSIONS.................................................................................................................................................... 5-33 

Section 6 Quantifying Growth Effects in Relation to Temperature Thresholds ........................................... 6-1 

INTRODUCTION..................................................................................................................................................... 6-2 

GROWTH SIMULATION METHOD............................................................................................................................. 6-3 

TEMPERATURE EFFECTS ON WEIGHT GAIN............................................................................................................ 6-5 

RELATIVE TEMPERATURE EFFECTS ON GROWTH ................................................................................................... 6-5 

RISK ASSOCIATED WITH GROWTH LIMITATION ..................................................................................................... 6-10 

GROWTH LOSS AND TEMPERATURE CRITERIA...................................................................................................... 6-12 

DISCUSSION....................................................................................................................................................... 6-12 

CONCLUSIONS.................................................................................................................................................... 6-14 
 iv 

Section 7 Relationship between Existing and Proposed Temperature Criteria and Risk Assessment 

Findings.................................................................................................................................................................. 7-1 

CRITERIA AND METHODS TO DERIVE THEM............................................................................................................ 7-2 

Experimental Information-based Method (EPA).......................................................................................7-2 

Field Observation Methods.................................................................................................................7-5 

Risk assessment Approach.............................................................................................................. .7-6 

Review Approach.............................................................................................................................7-8 

TEMPERATURE CRITERIA ..................................................................................................................................... 7-9 

DISCUSSION....................................................................................................................................................... 7-13 

CONCLUSIONS.................................................................................................................................................... 7-14 

Section 8 A Discussion of the Scientific and Management Implications of Findings 

THE REGULATORY CONTEXT OF TEMPERATURE CRITERIA...................................................................................... 8-1 

THE CHARACTERISTICS OF TEMPERATURE CRITERIA ............................................................................................. 8-2 

THE BASIS FOR DERIVATION OF TEMPERATURE CRITERIA ...................................................................................... 8-3 

QUANTITATIVE ANALYSES TO ASSESS THE EFFECTS OF TEMPERATURE ON FISH IN NATURAL ENVIRONMENTS.......... 8-4 

THE SCIENTIFIC BASIS FOR TRANSLATING QUANTITATIVE BIOLOGICAL ANALYSES TO TEMPERATURE CRITERIA ........ 8-8 

TEMPERATURE THRESHOLDS BASED ON RISK ASSESSMENT .................................................................................. 8-9 

TEMPERATURE CRITERIA DERIVED FROM RISK ASSESSMENT............................................................................... 8-11 

UNCERTAINTIES IN APPLYING CRITERIA IN NATURAL ENVIRONMENTS.................................................................... 8-13 

CONCLUSIONS.................................................................................................................................................... 8-14 

Section 9 Literature Cited.................................................................................................................................. 9-1 

Section 10 Appendices..................................................................................................................................... 10-1 

APPENDIX A.. DATA USED TO DEVELOP RELATIONSHIPS BETWEEN GROWTH RATE, TEMPERATURE, AND CONSUMPTION

 DETERMINED FROM LABORATORY STUDIES. ...............................................................................A-1 

APPENDIX B..... TEMPERATURE DATA FOR THE PACIFIC NORTHWEST REGION, FROM U.S. GEOLOGICAL SURVEY WATER 

 RESOURCES DATA.................................................................................................................B-1 

APPENDIX C ACUTE EFFECTS OF TEMPERATURE ON SALMON AND TROUT: DATA USED, ANALYSES 

 AND ASSUMPTIONS...................................................................................UNDER SEPARATE COVER
 v 

ABSTRACT

To administer the Clean Water Act, the U.S. Environmental Protection Agency and state water quality 

agencies throughout the nation have adopted numeric and qualitative criteria that establish environmental 

conditions known to protect aquatic life from adverse effects. Pacific Northwest states have adopted 

temperature criteria designed specifically to protect fish with emphasis on salmonid species because water 

temperature plays a role in virtually every aspect of salmon life. Adverse levels of temperature can affect 

growth, behavior, disease resistance, and mortality. In recent years, the EPA and National Academies of 

Science and Engineering have promoted risk assessment techniques to develop water quality criteria, 

including formal protocols that have been peer reviewed nationally. Risk assessment is designed to 

combine the information from biological studies with an analysis of each population’s exposure to 

quantified effects. Risk occurs when the stress’ magnitude, frequency and duration exceed the species’ 

ability to deal with that stress. A risk-based approach seems ideally suited to developing criteria for and 

assessing temperature risk to fish because exposure has been well documented through temperature 

monitoring and extensive research on the lethal and sublethal effects on salmon physiology has been 

conducted over the past 40 years. Nevertheless, risk-based approaches have not yet been used to establish 

temperature criteria in recent state agency reviews of water quality standards. 

In this paper we develop a risk-based approach to analyze summertime temperature effects on juvenile 

salmon species. We use available research findings to quantitatively evaluate the biological effects of 

temperature in combination with measured stream temperature ranging from very cold to very warm. 

Many currently exceed Washington’s temperature standard. Acute risk to high temperatures was assessed 

using laboratory-derived values of mortality in relation to duration of exposure. Despite warm 

temperatures, the risk analysis found that direct mortality from temperature is unlikely in the range of 

temperature in study streams because temperatures high enough to cause mortality are either never 

observed, or occur over too short of periods of time to cause death. The analysis suggested that there is 

little or no risk of mortality if annual maximum temperature is less than 26o

C, although site-specific 

analyses are suggested when annual maximum temperature exceeds 24o

C to affirm this result in local river 

conditions. Short-term occurrence of temperatures sufficient in duration and magnitude to cause mortality 

is feasible, within parts of the Pacific Northwest region, and therefore streams in other geographic areas or 

streams with known temperature extremes should be individually evaluated with the method. Chronic 

exposure to temperature was based on the growth potential of fish as assessed using a simplified 

bioenergetics approach developed in the report. This analysis found that growth predicted from ambient 

temperatures is somewhat less than the maximum potential growth in all streams regardless of temperature 

regime, because no stream experienced temperatures that fully optimized growth all of the time during the 

summer rearing period. Generally the effect of temperature regime on growth was small in the range of 

streams studied, but growth effects were evident at higher temperatures. The results suggest that 

quantitative analysis of growth effects can be determined with reasonably simple methods that can be 

applied at specific sites or at a region scale to identify appropriate temperature thresholds. Assuming a 10% 

growth loss represents an appropriate risk level, an upper threshold for the 7-day maximum temperature of 

16.5o

C is appropriate for coho and 20.5oC is appropriate for steelhead. Criteria derived in this manner are 

somewhat lower than those developed in a U.S.E.P.A. paper in 1977 and close to, but not identical, to those 

currently specified in Washington and Oregon criteria. 
 vi 

ACKNOWLEDGMENTS 

The Oregon Forest Industries Council, Washington Forest Protection Association and 

Weyerhaeuser Company provided funding for this project. We thank John Heffner, Brian 

Fransen, and Jason Walters who collected the temperature and fish data that made the risk 

assessment analysis presented in this report possible. We acknowledge the significant 

contributions of our many colleagues cited in this report who conducted the laboratory 

experiments over the past four decades on which the lethal and growth effects analyses are based. 

We remain especially inspired by the lifelong work of J.R. Brett who provided so much 

understanding of the underlying physiology of salmon that translates into their ecologic behavior. 

We thank Margaret Spence and Lucinda Tear for their assistance with the re-analysis of 

laboratory growth/temperature data borrowed from our co-workers’ previously published work. 

Peter Farnum contributed significantly with his careful review of our work. Finally, we would 

like to acknowledge the contributions of the scores of volunteers who participated in the 

Timber/Fish/Wildlife statewide temperature study in 1990, and the hundreds of volunteers who 

participated in the study of stream temperatures in the Puget Sound region under the sponsorship 

of the University of Washington Center for Urban Studies and Derek Booth. Their data help to 

establish the extent to which results from the analyses presented in this paper, based on a 

relatively few streams, may be applicable to the rest of the streams of Washington. 

Preferred Citation: 

Sullivan, K., D.J. Martin, R.D. Cardwell, J. E. Toll, and S. Duke. 2000. An analysis of the 

effects of temperature on salmonids of the Pacific Northwest with implications for selecting 

temperature criteria. Sustainable Ecosystems Institute, Portland Oregon. 

Copies of this report and Appendix C (under separate cover) may be obtained from: 

Librarian, Sustainable Ecosystems Institute, 0605 SW Taylors Ferry Rd, Portland, Oregon 97219 

(503.246.5008). The report can also be downloaded in PDF format from SEI’s website: 

www.sei.org. 
 1-1 

SECTION 1 INTRODUCTION AND OBJECTIVES

Maintaining the quality of aquatic environments that allow fish and other organisms to 

grow and prosper is a primary objective of the Clean Water Act adopted by Congress in 

1972. To administer the Act, the U.S. Environmental Protection Agency (EPA) and state 

water quality authorities throughout the nation have adopted numeric and qualitative 

criteria that establish environmental conditions known to protect aquatic life from adverse 

effects. Historically, physical environmental characteristics have been used to indicate the 

minimum requirements for biological health. The criteria address naturally occurring 

conditions that may be affected by human activities (e.g., temperature, sediment, dissolved 

oxygen, pH, and nutrients) and numerous exogenous pollutants introduced by 

manufacturing and agricultural activities. Water quality criteria are often a single value 

defining thresholds of favorable or adverse conditions (Suter et al. 1993). The public and 

the regulatory system have accepted simple physical criteria as indicators of biological 

health, although natural systems are dynamic and often exhibit a range of water quality 

conditions over time in response to many non-anthropogenic factors. Thus, even though 

criteria are often an over-simplification of real biological response, they have generally 

been accepted as necessary to effectively administer the regulatory system. 

The Environmental Protection Agency and other agencies have conducted water quality 

research over the years to accomplish two major objectives: 1) develop sound cause-andeffect relationships between water quality conditions and biological response, and 2) 

develop repeatable methodologies that use research findings to craft regulatory water 

quality criteria grounded in sound science. A primary technique used by researchers is to 

subject fish and other aquatic organisms to pollutants in a controlled laboratory setting to 

determine the relationships between dosage, length of exposure and biological responses 

such as growth loss, stress, altered behavior, disease, or death. Such laboratory-based 

research has been a cornerstone of fisheries science during this century and its validity has 

been confirmed in field-based studies (Brett 1971, Shuter et al. 1980, Baker et. al. 1995, 

Filbert and Hawkins 1995). Conversely, field observations alone are often not reliable for 

deriving water quality criteria because of variability in the natural environment and the 

complexity of factors controlling natural systems and habitat response. Brett (1971) 

observed that “it is inherently difficult to examine existing conditions and deduce the 

important biological factors which have occurred in the past to explain the present.” 

Laboratory studies were the basis for EPA recommended temperature criteria (U.S. EPA 

1977), and field studies have been used mainly for validating the appropriateness of water 

quality criteria (Hansen 1989, Mount et al. 1984). 

Most water quality criteria were originally adopted in the 1970s (e.g., U.S. EPA 1980) 

with relatively little revision since implementation (Hansen 1989). In recent years, water 

quality agencies in the Pacific Northwest have conducted scheduled reviews of criteria to 

reassure their effectiveness or change them if necessary (ODEQ 1995, WDOE 1999). 

Interest in the validity of temperature criteria has been particularly keen because of 

concern that temperature is one of the habitat elements that has contributed to the decline 

in certain runs of salmon and trout in the region (NAS Committee on Protection and 

Management of Pacific Northwest Anadromous Salmonids 1996). Within the home range 

of salmon in Washington, Oregon, and Idaho, over 2500 streams are currently listed on 

Clean Water Act section 303(d) lists, many for exceeding summer temperature criteria. 

High summertime temperatures in these streams are due in part to a variety of land use 
 1-2 

and manufacturing activities that have historically impacted temperature regimes (Sullivan 

et al. 1990), as well as natural phenomenon that affect stream temperature. 

The risk to salmon and trout populations associated with temperature is perceived to be 

high because: 1) the potential for biological effects exists according to laboratory-derived 

results; and, 2) many populations are already exposed to temperatures exceeding those 

believed to induce negative biological consequences. Water temperature plays a role in 

virtually every aspect of salmon life (Brett 1995; Weatherly and Gill 1995), and adverse 

levels of temperature can affect behavior (e.g. migration delays and timing), disease 

resistance, growth, and mortality (Brett 1956). Such concerns have led agencies to 

reconsider temperature requirements and tolerances of these species, with emphasis on 

those listed as threatened or endangered: chinook and coho salmon, steelhead trout, and 

bull trout. Recent reviews have called for lowering of temperature criteria to levels 

thought to be more desirable (less stressful) for these species (e.g. ODEQ 1995; WDOE 

1999, U.S. EPA in preparation). 

The scientific justification for these recommendations relies largely on review of the 

scientific literature and application of a number of implicit assumptions concerning the 

temperatures that occur and those that cause adverse effects. They also include safety 

factors to ensure that adverse effects and exposures are not underestimated. These 

assumptions and safety factors are usually developed using best professional judgment. A 

more objective risk assessment technique, where adverse effects are placed in an exposure 

context to identify population risk, has not yet been applied in these temperature criteria 

reviews, despite its accepted value for establishing criteria (Suter and Mabrey 1994) and 

risk for other pollutants (U.S. EPA 1995). 

Risk assessment involves comparing effects and exposure periods to achieve probability 

of adverse effects for the defined exposure. This process includes: 1) biological effects 

characterization, 2) exposure characterization, and 3) a risk characterization that combines 

the two. The effects characterization requires a quantitative measure of the biological 

effects of temperature, and the exposure characterization requires a quantitative 

measurement of the temperatures occurring in the fish’s environment. These quantitative 

measures are expressed as probabilities for the risk characterization, where the in situ 

temperature regime is related to the temperature biological effects relationships to 

estimate the likelihood of adverse biological impacts. 

A risk-based approach seems ideally suited to developing criteria for and assessing 

temperature risk to aquatic life. Fish are constantly exposed to temperatures that vary by 

minutes, hours, days, weeks, and months depending on celestial forces that guide the earth 

around the sun. Fish are thermoconformers; that is, they cannot maintain body 

temperatures much different from the water in which they occur. Thus their exposure is 

variable, ranging over the full array of optimal and suboptimal temperatures. 

Considerable laboratory study has been conducted on a variety of salmon and trout species 

to characterize their responses to temperature; these data may be sufficient to characterize 

the responses of some species and life stages. Finally, temperature is easy to measure and 

there is an abundance of data available in Washington and elsewhere to characterize 

temperature regimes and to evaluate exposure with considerable precision. 

The objectives of this paper are (1) to review relevant temperature research and (2) to 

evaluate the biological risks associated with ambient temperature regimens on populations 

of two species of juvenile salmonids using a probabilistic risk assessment. This 
 1-3 

assessment is based on laboratory data concerning the effects of temperature on growth 

and mortality. The analyses concentrate on the summer rearing life history phase of 

species within the Salmonidae family that dwell in stream environments, namely juvenile 

coho salmon and steelhead trout. There has also been considerable study of the thermal 

requirements of chinook, sockeye, pink, and chum salmon, but since these species are not 

typically found in western Washington streams during the summer months, they will not 

be directly considered here. This analysis illustrates the use of risk analysis for 

objectively deriving temperature criteria; similar techniques could be applied to other 

species, stocks, and life history phases (Hokanson 1977). We also use results to evaluate 

the biological effects of existing and proposed temperature criteria in the context of risk 

assessment techniques. 

This report contains: 

• A review of the scientific literature regarding the effects of water temperature on 

direct acute mortality and growth of fish, with emphasis on salmonids during 

fresh water rearing (Section 2) 

• A description of the temperature data collected from a variety of stream 

conditions in Washington used in the quantitative analyses, with a discussion of 

temperature indices (Section 3). 

• A synthesis of available scientific information into a quantitative, risk-based 

approach to evaluating biological response to acute or lethal temperatures in 

natural streams (Section 4), 

• A comprehensive development of a new, quantitative approach to assessing fish 

growth in response to long-term exposure to temperatures in natural streams 

(Section 5), 

• A synthesis of the risk-based approaches to suggest temperature criteria for coho 

and steelhead (Section 6), 

• A discussion of the use of scientific information, including methods developed in 

this report, to identify temperature standards in federal and state regulatory 

approaches (Section 7).

• A brief summary of key findings and a synthesis of information for policy-makers 

and scientists (Section 8). 
 1-4 
2-1 

SECTION 2 REVIEW OF THE PHYSIOLOGIC RESPONSE OF 

FISH TO ENVIRONMENTAL TEMPERATURE 

ABSTRACT 

In this section, the biological effects of temperature on fish are briefly reviewed, with 

emphasis on the fresh water rearing phases of salmonids. Lethal and non-lethal effects are 

discussed. 

Key findings: 

• Many of the lethal and non-lethal effects of temperature on salmonids are well 

understood and in many cases have been quantitatively established. 

• Both lethal and sub-lethal effects of temperature depend on its magnitude in relation 

to duration of exposure. 

• Fish have behavioral and physiological mechanisms to tolerate temporary excursions 

into stressful temperature levels. If exposure and magnitude limits are exceeded, 

mortality can occur. 

• Growth has been widely used to evaluate the sub-lethal response of fish to 

temperature.

INTRODUCTION 

Temperature is a dominant factor affecting aquatic life within the stream environment 

(Hynes 1970). Temperature influences all aspects of fish life, as well as those of the 

macroinvertebrates (Sweeney and Vannote 1986) and primary producers (algae, bacteria 

etc.) that dwell within the stream and serve as food for fish (Hynes 1970). As summarized 

by Brett (1956 pg. 76):

“Because of the all-pervading nature of environmental temperature, the fundamental 

thermal requirement of fishes is an external environmental temperature most suitable to 

their internal tissues... Temperature sets lethal limits to life; it conditions the animal 

through acclimation to meet levels of temperature that would otherwise be intolerable; it 

governs the rate of development; it sets the limits of metabolic rate within which the 

animal is free to perform; and it acts as a directive factor resulting in the congregation of 

fish within given thermal ranges, or movements to new environmental conditions.” 

Quantitatively defining the effects of temperature on key biological functions is essential 

for understanding how temperature contributes to fish success as well as how it places 

species at risk. Temperature effects have been extensively studies for all aspects of fish 

life. Although review of all temperature effects are beyond the scope of this report, we 

note that there are excellent references where temperature effects are discussed more fully 

(e.g., Groot et al. 1995), or where specific species are reviewed in detail (e.g. McCullough 
2-2 

1999 on chinook salmon). We narrow our review to aspects of temperature affecting the 

rearing of salmonid species in the fresh water environment. 

Two important elements of temperature affect the growth and survival of fish: 1) the 

relationship between temperature, metabolism, and food conversion efficiency over long 

periods, and 2) the thermal tolerance of fish to lethal temperatures over relatively short 

periods. Both aspects are important because ambient stream temperature may vary from 

very low levels in winter to occasionally high peaks in summer (e.g., Beschta et al. 1987; 

Sullivan et al. 1990). 

The thermal tolerance to temperature has been the focus of considerable laboratory testing 

for many fish species, including salmonids, beginning early in this century and continuing 

today (see reviews by Fry 1967, NAS/NAE 1973, Coutant 1977). Much of the available 

laboratory research on temperature tolerances was performed prior to 1980 and was 

stimulated principally by the need to assess the impact of heated effluent from power 

plants, dams and other facilities (Hokanson 1977). Since that time, temperature research 

has focused on studying additional species and refining the understanding of contributing 

factors such as the effect of acclimation temperatures, daily diurnal temperature 

fluctuations, food rations, and the interaction of temperature with other pollutants (e.g., 

Elliott 1976, Wurtsbaugh and Davis 1977, Brett et al. 1982, Thomas et al 1986, Coutant 

and Talmage 1977). 

These and other studies show that fish respond to temperature through physiological and 

behavioral adjustments that depend on the magnitude and duration of temperature 

exposure. Upper and lower temperature extremes that cause death after exposures ranging 

from minutes to 96 hours are termed acute temperature effects. Temperatures causing 

thermal stress after longer exposures, ranging from weeks to months, are termed chronic 

temperature effects. Endpoints of exposure to temperature over longer periods (chronic 

Effects of Temperature on Salmonids

10

15

20

25

30

35

Minutes Hours Days Weeks

Duration

Temperature ( oC)

Upper Critical Lethal Lim it

Zone of Resistance Mortality can occur in proportion to length of exposure

Behavioral adjustment (no grow th, no mortality) Zone of Tolerance

Zone of Preference

Grow th response depends entirely on 

food availability

Optimal grow th at all 

but starvation ration

Reduced grow th

Rapid death 

Figure 2.1 General biological effects of temperature on salmonids in relation to duration and magnitude 

of temperature. 
2-3 

effects) are sublethal and may include growth, competitive interactions, change in 

behavior, or disease. Temperature ranges defined by acute and chronic temperature 

effects are referred to as the zones of thermal resistance and tolerance ( Elliott 1981, 

Jobling 1981). The range of physiological response relative to temperature is summarized 

in Figure 2.1. The range of temperature over which feeding occurs without signs of 

abnormal behavior is referred to as the optimum temperature range (Elliott 1981). 

ACUTE TEMPERATURE EFFECTS 

The acute effects of temperature are frequently expressed as effects on survival that result 

from exposure to elevated temperatures for specified time periods. Mortality, expressed 

as the median lethal time (LT50), and the ultimate upper incipient lethal threshold (Brett 

1952) have been the most common endpoints measured. The median lethal time is the 

duration eliciting 50% mortality at a specific temperature. The ultimate upper incipient 

lethal limit is the temperature at which acute mortality does not increase with any further 

increase in the temperature. 

Laboratory studies repeatedly 

show that salmon have the ability 

to extend their temperature 

tolerance through acclimation. 

Brett (1956) reports that the rate of 

increase in ability to tolerate higher 

temperatures among fish is 

relatively rapid, requiring less than 

24 hours at temperatures above 

20o

C (e.g., Figure 2.2). 

Acclimation to low temperatures 

(less than 5o

C) is considerably 

slower (Brett 1956). Studies of the 

acute temperature effects on 

salmonids have yielded remarkably 

consistent results between studies 

and among salmon species (Brett 

1956; Lee and Rinne 1980), 

indicating temperature’s influence 

on fish with similar biochemistry 

and physiology. The upper lethal 

limit, that is the temperature at 

which death occurs within minutes, 

ranges from 27o

 to 30oC for 

salmonids (Jobling 1981). Fish 

acclimated at cold temperatures can have upper lethal limits 3o

 to 4oC lower. Many 

species of fish have considerably higher upper thermal levels than members of the 

Salmonidae family, which are classified in cold water temperature guilds (Magnuson et 

al. 1979). At temperatures below the upper lethal limit, fish can tolerate each successively 

lower temperature for exponentially increasing intervals of time. 

Behavioral mechanisms may allow fish in situ to resist short-term extreme temperature, 

and acclimation will promote resistance to high temperature, although there is an upper 

Figure 2.2 Example of a relationship between the time (min) 

for 50% mortality of brown trout (Salmo trutta, and the lethal 

temperature (oC) at different acclimation temperatures. (From 

Elliott 1981).
2-4 

limit to the temperature to which fish can acclimate (Jobling 1981). This resistance to the 

lethal effects of thermal stress enables fish to make excursions for limited times into 

temperatures that would eventually be lethal (Brett 1956; Elliott 1981). The period of 

tolerance prior to death is known as the “resistance time” and the duration-temperature is 

termed the “zone of resistance” (Figure 2.1) (Hokanson 1977, Jobling 1981). Laboratory 

studies have repeatedly found that 

salmon can spend very lengthy 

periods in streams of 24o

C or less 

without suffering mortality. Thus 

temperature as a direct cause of 

death generally ceases at 

temperatures less than 24° C 

(Brett 1956). Acute effects are 

not generally considered below 

this level, because continuous 

long duration exposure to 

temperature of this magnitude is 

not likely to occur in natural 

environments within the species’ 

normal geographical range. 

Laboratory studies testing daily 

fluctuations in temperature as 

large as 13.5°C did not shown 

effects on growth or mortality of 

salmonids, although lethal levels 

were never exceeded (Thomas et. 

al. 1986). 

SUBLETHAL TEMPERATURE EFFECTS 

Chronic exposure to sublethal 

temperatures can have a broad 

range of effects on the various 

functions of fish. Brett (1971) 

described 25 physiological 

responses for sockeye salmon and, similarly, Elliott (1981) identified 19 similar 

characteristics for brown trout. The relationship between these responses and temperature 

follow two general patterns: either the response (e.g., standard metabolic rate, active heart 

rate, gastric evacuation) increases continuously with rise in temperature, or the response 

(e.g., growth rate, swimming speed, feeding rate) increases with temperature to maximum 

values at optimum temperatures and then decreases as temperature rises (Brett 1971, 

Elliott 1981). In the latter case, the form of the responses to temperature and the optimum 

temperatures are not always the same for different functions, and the optimum 

temperature for a response may change if there is an alteration in another factor such as 

energy intake (Elliott 1981). 

Based on this theory, fish rarely occur within a temperature regime that is optimal for all 

functions given the natural diel and seasonal variability in water temperature. 

Consequently, fish have developed mechanisms to survive various levels of thermal stress 

both above and below optimal ranges to maintain the health and survival of a population. 

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

0 5 10 15 20 25 30 35 40

Temperature (oC)

Specific Growth Rate (% weight/day)

Yellow Walleye

Lake Trout

Sockeye Salmon

Northern Pike

Figure 2.3 Example of a multi-family specific growth rate curves 

(after Christie and Regier 1988) 
2-5 

Several studies have indicated that growth under fluctuating temperatures is essentially the 

same as that under constant temperature if the fluctuating temperature is expressed as the 

time-weighted mean1

 (Thomas et al. 1986; Brett 1971; Everson 1973; Iverson 1972; 

Wurtsbaugh and Davis 1977). 

Fish are poikilothermic and temperature plays a key role in regulating their metabolic 

functions. Fish tolerate suboptimal temperatures by metabolic adjustment and behavioral 

thermoregulation (Elliott 1981). For example, as temperatures increase above the 

optimum for feeding, the feeding rate declines and is completely inhibited at temperatures 

several degrees below the incipient lethal level (e.g, at 22o

C for brown trout, Elliott 1981; 

and 24o

C for sockeye and chinook salmon, Brett 1971). Similarly, the metabolic rate of 

and scope for activity declines, reducing the overall energy expenditure, which helps to 

conserve energy and reduce thermal stress. Behavioral adjustments, such as movements 

to cooler refuge sites, also enable fish to avoid thermal stress. Numerous observers have 

reported significant changes in salmonid activity at or near 22°C (Donaldson and Foster 

1941; Griffiths and Alderdice 1972; Wurtsbaugh and Davis 1977; Lee and Rinne 1980; 

Bisson et. al. 1988; Nielsen et al. 1994, Tang and Boisclair 1995; Linton et al. 1997; Biro 

1998). This temperature is consistent with a sharp drop in food consumption and 

conversion efficiency observed in laboratory studies (Brett et al. 1982). At very low 

temperatures, salmonids have been observed to cease feeding and seek cover under banks 

or within stream gravels (Everest and Chapman 1972). 

How large fish grow is fundamentally determined by environmental and population 

factors that determine the availability of food. Temperature, however, regulates how 

much growth can occur with the food that is available. Growth is dependent on the energy 

consumed by the fish balanced by its energy expenditures to meet basic demands such as 

metabolism and swimming. What is left over can be used to grow body mass and 

reproductive capability. The long-term exposure of salmonids to environmental 

temperature during their freshwater rearing phase has an important influence on the size 

fish achieve and potentially the timing at which they reach readiness for smolting 

(Weatherly and Gill 1995). 

The size of salmonids during juvenile and adult life stages influences survival and 

reproductive success. Although the large majority of anadromous salmonid growth occurs 

in the ocean environment, growth of juveniles in natal streams is especially important for 

anadromous salmonids that must reach minimum sizes before they can smolt (Weatherly 

and Gill 1995). Holtby and Scrivener (1989) and Quinn and Peterson (1996) 

demonstrated that the size achieved by juvenile coho at the end of their first summer 

growing period was a strong determinant of their later success in overwintering and 

smolting. Larger size generally conveys competitive advantage for feeding in the 

freshwater environment (Puckett and Dill 1985, Nielsen 1994) for both resident and 

anadromous species. Mason (1976) and Keith et al. (1998) found that the smaller fish 

tend to be those that are lost from rearing populations. Brett et al. (1971) described the 

freshwater rearing phase of juvenile sockeye as one of restricted environmental conditions 

and generally retarded growth. This synopsis is also generally true for salmonid species 

that dwell in stream and river environments for lengthy periods of time. 

To explore the effects of prolonged exposure to temperature, numerous investigators have 

found growth to be a reliable and measurable integrator of a variety of physiological 

 

1

 The maximum temperature studied cannot be high enough to elicit mortality. 
2-6 

responses (Brett 1971, 1995; Iverson 1972; Brungs and Jones 1977; Wurtsbaugh 1973). 

Growth rate is the most frequently reported measure of fish health from laboratory studies 

and occasionally from field studies. Growth can be viewed as the net effect of the 

environment on the 

relation between food 

consumption, metabolism, 

and activities of an 

organism (Warren 1971). 

Growth integrates a host 

of specific physiological 

responses to temperature, 

including metabolic rate 

(basal and active), feeding 

and digestion, and 

swimming performance or 

the ability to hold position 

with the current (Brett 

1995; Weatherly and Gill 

1995). 

Laboratory studies 

demonstrate that virtually 

all fish, including 

salmonids, grow best 

within a range of 

temperatures. Optimal 

growth generally occurs at 

a midpoint of 

temperatures where the 

fish live, and it declines in 

waters that are warmer or 

cooler. The range of 

temperature at which 

growth occurs is generally wide, and usually reflects the ambient temperatures likely to be 

found within the natural range of the specie’s habitats (Hokanson 1977). Significant 

differences in growth curves exist among fish families (Figure 2.3), from Christie and 

Regier 1988), but growth curves are often similar for species within the same genera and 

family. Because all salmonids have a similar biokinetic range of tolerance, performance, 

and activity, they are classified as temperate stenotherms (Hokanson 1977) and are 

grouped in the cold water guild (Magnuson et al. 1979). 

The effect of temperature on growth varies significantly with the ration of available food 

(Figure 2.4). For example, in Figure 2.4, sockeye salmon held at optimum temperature 

and fed satiation rations achieved 600% more growth than fish held at optimum 

temperature with starvation rations. As ration increases from maintenance level (no net 

growth) to satiation or excess level (more than is needed for growth, metabolism, and all 

physiological functions), the optimum temperature for growth shifts progressively to 

higher temperatures. This response is consistent for all salmonids where laboratory 

studies are available (Brett 1971; Everson 1973; Iverson 1972; Wurtsbaugh and Davis 

1977). 

Figure 2.4 Basic relationship between temperature, ration and growth of 

7-12 month-old sockeye salmon (from Brett et. al. 1969). 
2-7 

The relationship between food and temperature must be taken into account when 

considering the productivity of fish populations (Filbert and Hawkins 1995). Many studies 

have observed an increase in the growth and productivity of fish populations in streams 

when temperature (and correspondingly) food is increased. This tends to occur even in 

the cases where temperatures exceed preferred and sometimes lethal levels (Murphy et al. 

1981, Hawkins et. al., 1983, Martin 1985, Wilzbach 1985). This situation indicates that 

starved fish require somewhat lower temperature, although the low environmental 

temperature tends to create conditions of low food supply (Weatherly and Ormerod 1990). 

The forgoing discussion indicates that the optimum temperature for fish extends over a 

broad range depending on the function and the presence of other interacting factors. This 

optimum (preferred) range is defined by Elliott (1981) as the range over which feeding 

occurs and there are no external signs of abnormal behavior, i.e., thermal stress is not 

obvious. This delineates a wider range than the peak optimal temperatures where growth 

is maximized. 

Within the optimum temperature range, research has identified a preferred temperature 

range, which is defined as the temperature around which all individuals will ultimately 

congregate regardless of their prior temperature exposure history (Fry 1947). Some 

investigators specifically define the optimal temperature as the temperature at which 

maximum growth occurs, and refer to the range of temperature where growth occurs as 

“preferred” temperatures. Determining this range, however has resulted in considerable 

variability within the same species due to different experimental test procedures and the 

multiplicity of environmental factors that affect fish preference (Elliott 1981, Jobling 

1981). This uncertainty has led Elliott (1981) to conclude that the optimum temperature 

range defined based on physiologic response is a more realistic concept for studies on 

thermal stress then definitions based on concepts of “preference”. Different uses of the 

terminology can create confusion. 

Elliott’s optimum temperature definition fits well with the behavioral response of fish to 

natural temperature regimes. For example, Brett (1971) showed how behavioral 

thermoregulation by juvenile sockeye resulted in energy conservation. Vertical 

movements in a thermally stratified lake over the course of a day enabled the juveniles to 

maximize the efficiency of food conversion into growth by controlling energy intake and 

metabolism as temperature followed the solar cycle. The sockeye salmon exhibited varied 

behavior in selecting temperatures that did not solely reflect the preferred temperature 

available to them within the lake. Other field studies have also documented salmonid 

utilization of temperatures outside of the preferred range when those within or near the 

preferred level were readily accessible (e.g. Matthews et al. 1994, Brett 1971, Biro 1998). 

Metabolic characteristics are not the only response, but they are the most important and 

most easily quantifiable. Less quantifiable in a dose-response context are relationships 

involving temperature and disease resistance, and temperature effects on sensitivity to 

toxic chemicals and other stressors. It is well recognized that temperature can decrease 

disease resistance in the most sensitive individuals within each species’ population and 

influence their sensitivity to certain toxic chemicals (e.g., Cairns et al. 1978). However, 

the study of Cairns et al. (1978) concluded that “temperature-toxicity interactions are far 

more complex than earlier literature has indicated,” and increased temperature does not 

necessarily lead to increased sensitivity to toxic chemicals. For example, Linton et al. 

(1997) found that sublethal levels of ammonia enhanced growth at higher temperatures 

and Dockray et al. (1996) found better performance at high temperature when pH was 
2-8 

low. For temperature to affect the occurrence of disease, disease-causing organisms must 

be present, and either those organisms must be affected by temperature or fish must be in 

a weakened state due to the effect of temperature. In addition, some diseases may be more 

prevalent at high temperature, others are more prevalent at low temperature, and some are 

not apparently related to temperature. Therefore, for disease and pollutants, the specific 

nature and local presence of the disease-causing organism or pollutant influences its 

interaction with temperature. 

The response of fish to temperature in natural streams is not only based on physiological 

functions but also on the overall interaction with other ecological factors (e.g., predators, 

prey abundance, and competitors). Differences among species can confer competitive 

advantages in relation to environmental variables that are reflected by the species’ 

distribution (Brett 1971, Baltz et. al. 1982, Reeves et al. 1987, DeStaso and Rahel 1994). 

Natural stream environments nearly universally have increasing temperature from 

headwaters to their mouths (Hynes 1970), largely reflecting systematic changes in a 

variety of critical environmental factors that control heat transfer processes (Sullivan et al. 

1990). Systematic changes in the occurrence or dominance of species within river 

systems in part reflects these temperature patterns. 

ASSOCIATING BIOLOGICAL EFFECTS AND STREAM TEMPERATURE 

Identification of appropriate temperature criteria to protect fish is complicated by the 

highly variable nature of temperature at stream sites, coupled with the differing 

temperature requirements of fish species. Water temperature at individual sites varies 

0

5

10

15

20

25

0

0

0

2

0

4

0

6

0

8

10121416182

0

2

2

Temperature (oC)

Deschutes

Hard Cr.

 Incubation Sum m er R earing Overw intering, 

 Sm olting (1 yr) Em ergence (fry) Migration (adults )

 Jan Feb Mar Apr May June July Aug Sept Oct N ov D ec

Figure 2.5 Annual temperature regime of the Deschutes River (148 km2) and Hard Creek (2.3 km2), a headwater 

tributary, near Vail, Washington. Data are hourly measurements. 
2-9 

significantly with time, ranging from lows in winter to highs in summer, with daily 

fluctuations depending on stream and climatic characteristics (Figure 2.5). The life 

history phases of salmonids are generally adapted to the prevalent temperature as 

illustrated for two stream sites in Figure 2.5, although species and stocks have specific life 

history timing (Weatherly and Gill 1995). Growth of alevins within the gravel bed and as 

fry and resident adults within the stream is a function of temperature; the timing of 

movement of alevins, fry and adults also depends, in part, on temperature. 

The intent of temperature criteria is to index or describe key characteristics of the 

temperature regimen that have important measurable impacts on individuals or 

populations. Many authors favor identification of criteria appropriate for each life history 

phase (Bell 1973, Reiser and Bjornn 1979, Armour 1991) that reflect the temporal 

variability of temperature through the year. Hokannson (1977) described a quantitative 

means of establishing criteria for each life history phase of percids (perch, walleye) in a 

procedure he termed the “Envelope Method.” Quantitative estimates of fish response to 

temperature for maturation cycles, spawning times, migrations, activity and spatial 

distribution are compared to seasonal changes in temperature. In Hokannson’s example, 

natural history observations supplement experimental data where available. Temperature 

ranges that indicate optimal, sub-optimal, and lethal temperatures are plotted or tabulated 

for each life stage period (e.g., migration, spawning, incubation, rearing) to show the 

range and temporal distribution of temperature preference/tolerance regimes (temperature 

envelopes) during the hydrologic year. 

Recent development of temperature standards in Oregon and Washington have also 

endorsed life history-based criteria for salmonid and char species, and promoted reachand watershed-based approaches for determining criteria (ODEQ 1995; WDOE 1999). 

The intent is to identify 

a series of criteria that 

can be applied to limit 

impact on all species 

and life stages that may 

exist in a stream reach 

at that time of year. 

There has been some 

interest in “tailoring” 

criteria to specific time 

of the year, species, and 

even individual stream 

reaches. However, 

reach-, species-, or 

temporally-specific 

criteria can create 

enormous data 

collection and 

management issues. 

Criteria that can be applied on a regional basis require indices of the key characteristics of 

temperature regimes that are biologically meaningful, measurable without extraordinary 

means, and sensitive to human-caused effects. Factors to consider when reducing the 

variable summer temperature regime to simpler indices include: 1) the temperature 

threshold that reflects biological effects (e.g., usually a maximum but can be a minimum); 

Table 2.1 Characteristics of temperature regimes relevant to

temperature criteria.

Regime Characteristic Variables

Temperature Threshold

â Acute

â Sub-lethal

Temperature Fluctuation

Characteristic

â Maximum

â Mean

â Minimum

â Fluctuation

(maximum-minimum)

Averaging Period

â Instantaneous maximum

â 7-Day average

â Monthly or seasonal average
2-10 

2) the temperature statistic within the amplitude of fluctuation (e.g., maximum, mean or 

minimum); and 3) the averaging period that characterizes temperature exposure (e.g., 

hourly, daily, or weekly) (Table 2.1). 

ESTABLISHING WATER QUALITY CRITERIA 

In recent years, the EPA and National Academies of Science and Engineering have 

promoted risk assessment techniques to develop water quality criteria, including formal 

protocols that have been peer-reviewed nationally (Parkhurst et al. 1996, U.S. EPA 1995). 

Risk assessment is designed to enhance understanding of the potential adverse effects of a 

pollutant on species by combining the information from biological studies with an analysis 

of each population’s potential exposure to those effects. Risk occurs when the stress’ 

magnitude, frequency, and duration exceed the species’ ability to deal with that stress. 

Risk has little to do with the organisms’ or species’ sensitivity to a stress or to the 

concentration or level of environmental stress; risk depends entirely on whether the 

combination of exposure and sensitivity exceeds the organism’s ability to withstand or 

adapt to the stress (Suter et al. 1993; U.S. EPA 1992). 

Recent risk assessment techniques use more available data and disclose more uncertainties 

than assessments based on comparing a number denoting an effect, criterion or an 

exposure (Parkhurst et al. 1996; Solomon et al. 1996). Exposures and potential effects 

may be represented as probabilities of occurrence. Uncertainties about exposures and 

effects can also be expressed (e.g., as 95% confidence limits) and used in decisionmaking. Risk assessment techniques have been used to derive water quality criteria for 

aquatic life (U.S. EPA 1993), wildlife, and human health, and are being considered as one 

of the site-specific water quality criteria tools (Spehar and Adams, 1998). They can be 

used to evaluate broad effects if general patterns of exposure are known or to develop 

stream-specific criteria. 

To derive a meaningful biological measure for specific life phase requirements, careful 

consideration must be given to both magnitude and duration of temperature, since these 

factors together have great effect on the risk that temperature poses to fish. In the 

remainder of this report, we will use a risk assessment approach to quantitatively estimate 

acute and chronic effects of temperature on salmonids. Risk assessment requires a 

quantitative analysis of fish response to temperature, and a quantitative assessment of the 

exposure to temperature that a fish may experience during the period of interest. The 

overlap between effects and exposure determines the risks associated with temperatures 

experienced in the aquatic environment. 

CONCLUSIONS 

The implications of this research to the question of establishing temperature criteria are: 

• The effects of temperature on physiologic functions during the freshwater phase 

of salmonid life history are reasonably well understood, and in many cases have 

been quantitatively established in a laboratory setting. 

• Salmon and trout have physiological and behavioral mechanisms that resist death 

at high and low temperatures unless extreme maximums are achieved. 
2-11 

Establishing temperature criteria for water quality standards is benefited by 

consideration of both duration and magnitude of temperature within these 

extremes. 

• The effects of temperature on other factors, such as resistance to disease or 

pollutants, are more variable, depending on site conditions, and are less well 

characterized. 
2-12 
 3-1 

SECTION 3 TEMPERATURE CHARACTERISTICS OF STREAMS 

USED IN ANALYSIS

Abstract 

Temperature data collected during the summer months from a number of streams and 

rivers in Washington are used for biological analysis in following sections of this report. 

In this section, we introduce these data and summarize the site and temperature statistics 

for each of the monitoring stations, and compare them among sites and with previous 

temperature studies. Temperature data span a range of temperatures from cold to warm, 

and many stream sites exceed current temperature criteria. Sites are shown to be broadly 

representative of temperatures observed in many Washington streams. Temperature 

indices, including annual maximum, 7-day maximum, and 7-day mean temperature are 

closely related at each site, and any can be used to index stream temperature measured 

over longer periods. 

KEY FINDINGS INCLUDE: 

• The data used in this report are broadly representative of stream temperatures in fishbearing streams found in forested, rural, and urban streams in Washington. 

Temperature patterns are also probably representative of many streams throughout the 

Pacific Northwest. 

• Temperatures span a range of temperatures, from 12o

 to 26oC in the annual maximum 

water temperature. This temperature range is within the range that salmonids may 

experience growth and lethal effects from short and long-term exposure. 

• There is year-to-year variation in temperatures at sites, which affects short-duration 

temperature indices. 

• Various temperature indices such as annual maximum, 7-day maximum, and 7-day 

mean are closely related to one another. 

TEMPERATURE DATA 

Analysis of the biological effects of temperature that follows in Sections 4, 5 and 6 is 

based on temperature recorded at 19 stream sites in the Chehalis, Deschutes, and Toutle 

river watersheds, located in the Coast Range and the west slopes of the Cascade 

Mountains in Washington. Temperature has been monitored over the years for various 

monitoring and research projects. All sites are located on portions of the river systems 

where forestry is the dominant land use. Sites with hourly temperature records varying 

from very cool headwater streams that support cutthroat steelhead, and coho populations 

to warm river mainstems with more diverse fish communities were selected. 

Three sites are located in the mainstem of the headwaters of the Chehalis River near the 

town of PeEll and represent the largest river in our analysis. Bankfull stream widths 
 3-2 

average 30 to 60 meters. The amount of shade varies with stream width, ranging from low 

to high. The river and its tributaries flow through second growth forests, and riparian 

areas are in various stages of regrowth following past logging-related disturbance. Eight 

tributaries to the headwaters of the Chehalis River were monitored. Each is approximately 

5-10 meters in width at their confluence with the mainstem. Tributary streams were 

logged to the stream banks, and in some cases cleaned of woody debris, in the 1970’s. 

Most are now well shaded with second-growth alder and Douglas-fir plantations. The 

mainsteam of the Chehalis River supports fall chinook nearly as far upstream as site 3. 

The lower portions of the tributaries support steelhead and coho spawning, incubation and 

rearing. Several of these streams are the location of marine nutrient and fish carcass 

supplementation research previously reported in the literature (Bilby et al., 1996, 1998). 

Porter Creek is a tributary to the lower Chehalis River flowing from the Capitol Forest 

near Olympia. It is well shaded with a predominantly alder overstory. This stream was 

the site of a woody debris addition study (Cederholm et al. 1997). 

Four sites in the Deschutes River basin were monitored, including the mainstem, near the 

town of Vail and at the downstream end of the forest land use zone. A 2000-ha tributary, 

Thurston Creek, and two small streams (<300 ha) in the headwaters (Hard and Ware 

Creeks) have been monitored since 1974. Previous monitoring information is available in 

Sullivan et al. (1987). The smallest tributaries support cutthroat trout populations, while 

coho use the lower tributaries and mainstem. Anadromous fish are excluded from the 

upper tributaries by a barrier falls. 

Two sites are located in the Mt. St. Helens blast zone. These streams have experienced 

vegetative recovery since the eruption in 1980, and currently support populations of 

steelhead and coho, that at times are supplemented by hatchery fish. Previous research on 

the interaction of temperature and fish production has been reported by Bisson et al. 

(1988). 

Sites represent a range of small to large streams with shade varying from 0 to 100%. 

Maximum potential shade naturally varies among the sites with stream width. However, 

current shade is lower than potential at many sites due to past forest practices or natural 

disturbance. 

Water temperature was sampled to the nearest 0oC each hour by an electronic temperature 

recording device (HoboTemp® or Omnidata®) calibrated at the time of deployment and 

field-checked at least once each month. The temperature recorded by the instrument was 

the average temperature for the hour. Water temperature probes were placed in the stream 

near the bank and out of direct exposure to sunlight. 

Temperature Characteristics 

Temperatures span a range from predominantly cold to predominantly warm as indexed 

by the annual maximum temperature (the single highest hourly temperature during the 

year) (Table 3.1, Figure 3.1). Multiple years of data were available at some sites. The 

selection of years to include in the analysis was arbitrary, largely reflecting the ready 

access to data in the archives. Although additional years or sites could have been 

included, data would fall entirely within the range of data observed at the example sites. 

We did not feel that is was as important to include a large number of sites in this 

temperature analysis, as it was to select sites that span the range of temperatures likely to 

occur within Washington to the extent possible with the data available to us. 
 3-3 

Table 3.1 Basin and temperature characteristics of 18 stream sites used in acute (Section 4) and growth 

risk analysis (Section 5). These sites are referenced as temperature study sites in the text. 

Site Watershed Basin 

Area 

(km2

) 

7-Day 

Maximuma

o

C 

7-Day 

Meanb

o

C 

Annual 

Maximumc

o

C 

Season 

Mediand

o

C 

Year 

Measured 

Deschutes River mainstem Deschutes 145.0 21.0 18.4 22.5 15.0 1994 

Thurston Creek Deschutes 9.1 14.9 14.1 15.5 12 1994 

Hard Creek Deschutes 3.0 14.0 13.0 14.0 11.0 1994 

Ware Creek Deschutes 2.8 17.5 16.1 18.3 12.9 1994 

Huckleberry Creek Deschutes 5.3 18.4 17.6 18.5 15.5 1991 

Chehalis River mainstem (Site 1) Chehalis 181.8 21.1 18.9 22.1 15.6 1997 

Chehalis River mainstem (Site 2) Chehalis 57.5 22.1 18.2 23.2 14.5 1997 

Chehalis River mainstem (Site 3) Chehalis 29.5 20.6 18.6 21.4 14.3 1997 

Crim Creek Chehalis 22.0 18.8 16.9 19.4 14.3 1997 

Lester Creek Chehalis 10.4 18.4 16.3 19.0 14.2 1997 

Thrash Creek Chehalis 16.7 15.3 14.3 15.8 12.3 1997 

Rogers Creek Chehalis 13.1 15.7 14.1 16.1 12.6 1997 

Big Creek Chehalis 9.0 16.5 14.6 16.9 12.5 1997 

Sage Creek Chehalis 5.3 16.5 14.6 16.9 12.5 1997 

Salmon Creek Chehalis 8.9 15.8 14.2 16.2 12.3 1997 

Mack Creek Chehalis 2.8 12.9 12.5 13.1 11.7 1997 

Porter Creek Chehalis 25 17.5 16.3 18.6 14.4 1990 

Hoffstadt Creek Toutle 25.6 24.5 18.4 26.0 14.0 1988 

Harrington Creek Toutle 8 19.1 16.7 20.5 13.3 1988 

a

 maximum value of the 7-day moving average of the daily maximum temperature 

 b maximum value of the 7-day moving average of the daily mean temperature 

c

 instantaneous maximum 

d

 median of daily mean temperature from June 1 to September 15

The coolest measured stream was Mack Creek. The temperature never exceeded 13o

C at 

any time during the summer (Figure 3.1). The warmest temperatures recorded in the 

temperature study occurred in Hoffstadt Creek located within the Mt. St. Helens blast 

zone, although this stream continues to cool with vegetation regrowth since previous 

studies (Bisson et al. 1988). The mainstem of the Chehalis River (sites 1, 2 and 3) 

experienced the longest duration of high temperature at or above 20o

C. The Chehalis 

River is among the warmest rivers in Washington and well exceeds existing state 

temperature standards. The contrast in seasonal temperature regime between a 

consistently warm and a consistently cool site within the same time period and watershed 

is shown in Figure 3.2. Except for Hoffstadt Creek, the temperatures of the other sixteen 

sites fell somewhere between these two. All streams that exceed 16o

C annual maximum 

temperature exceed current Washington water quality temperature standards. 

The minimum temperatures observed during the period between June 1 and September 15 

were between 7 and 9 o

C in all streams (Figure 3.3). This temperature is close to 

groundwater temperature and was typically experienced early in June. The maximum 

temperature observed reflects site characteristics such as openness to the sky, stream 

depth, and the extent of groundwater inflow (Sullivan et al. 1990). Despite large 

differences in the annual maximum temperatures among sites (Figure 3.1), most streams 

also spent a considerable amount of time at the same temperatures, most notably in the 

range between 12 and 17 o

C. This temperature is coincident with the optimal temperature 

range of many salmonids. 
 3-4 

6

8

1 0

1 2

1 4

1 6

1 8

2 0

2 2

2 4

1- Jun- 97 1- Jul- 97 31- Jul- 97 30- Aug- 97 29- Sep- 9

Cheha li s R. ( 2 ) Mack Cr.

Figure 3.2 Daily maximum temperature at one of the warmest sites (Chehalis 

River site 2), and one of the coolest sites (Mack Creek). 

Temperature Study Sites

10 12 14 16 18 20 22 24 26 28

M ack C r.

H ard C r

Thurston C r

Thrash C r.

R ogers C r.

Salmo n C r.

B ig C r.

Sage C r.

Ware C r

H uckleberry C r.

P orter Cr. 

Lester C r.

C rim C r.

H arringto n C r

C hehalis R . (3)

D eschutes R .

C hehalis R . (1)

C hehalis R . (2)

Hoffstadt Cr

Annual Maximum Temperature (oC)

Figure 3.1 Annual warmest temperature at the 19 temperature study sites 

included in the temperature risk assessment. 
 3-5 

Comparison of Study Sites to Other Temperature Studies in Washington, 

Oregon and Idaho 

Stream temperatures at the nineteen sites are representative of most other streams and 

rivers in Washington, and probably elsewhere in the Pacific Northwest as well. As an 

indication of how well the example data span the range of temperatures, we compare the 

relative occurrence of annual maximum temperature at sites included in other large-scale 

temperature studies that have been conducted in Washington. The 19 risk analysis sites 

represent temperature patterns in the same range as the 89 streams included in the 1990 

Timber/Fish/Wildlife statewide temperature study; the earlier statewide, multi-agency 

study represented a broad range of stream conditions found primarily in forests located 

throughout Washington (Sullivan et al. 1990). Using the annual maximum temperature to 

index long-term temperature, the distribution of temperature at sites were similar between 

the two studies, although this analysis includes a few more warm sites and a few less cold 

sites (Figure 3.4). Temperatures from seven sites included in both studies tended to be 

warmer than expected with mature riparian vegetation, due in part to past land use or 

recent natural disturbance. The 1990 study contained a number of undisturbed sites. 

Hourly Temperature Distribution

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

0.45

5 10 15 20 25

Temperature (oC)

Proportion of Hours

Deschutes R.

Hard Creek

Thurston Cr

Ware Cr

Chehalis Site 1

Chehalis Site 2 

Chehalis Site 3

Crim Cr

Lester Cr

Mack Cr

Rogers Cr

Sage Cr

Thrash Cr

Salmon Cr

Big Cr

Hoffstadt Cr

Huckleberry Cr

Harrington Cr

Porter Cr

Figure 3.3 Proportion of 2567 hourly temperature observations by temperature at 19 temperature 

study sites measured between June 1 and September 15.
 3-6 

Figure 3.4 also includes the cumulative distribution of maximum temperatures measured 

in a synoptic study of 570 urban/rural streams throughout the Puget Sound region 

coordinated by the University of Washington Center for Urban Studies. Temperature data 

were collected by volunteers within a 2-hour interval in the afternoon of August 19, 1998. 

Data from this study do not necessarily represent the hourly maximum temperature since 

temperatures did not 

necessarily coincide with 

the hottest hour of the 

year. However, the data 

are representative of the 

long-term average daily 

maximum for August 

(Derek Booth, pers. com.) 

and are likely to be within 

a few degrees of the 

annual maximum 

(Sullivan et al., 1990). 

Streams tended to be small 

tributary streams. Stream 

temperatures at these sites 

tended to be slightly 

cooler than risk analysis 

sites, and no streams were 

as warm as those included 

in this analysis. 

In further consideration of 

how the risk sites 

represent the range of 

high temperatures 

observed throughout the 

Pacific Northwest region, 

we examined published 

U.S. Geological Survey 

temperature records from 

Washington, Oregon and Idaho. We selected the year 1978-79 for several reasons: there 

were more sites recording temperature in the 1970’s and 1980’s than are operative today, 

and this was a period of rather high temperature throughout the region due to the 1977-78 

drought. The annual maximum temperatures are shown by state in Figure 3.5. (Note 

differences in the number of sites in each state.) The U.S.G.S. sites are primarily on larger 

rivers, although some smaller streams are also included. For example, the Columbia River 

mainstem is represented 6 times in the Washington data and 3 times in the Oregon data. 

Rivers such as the Columbia, Skagit, Yakima, Snake, Deschutes (OR), Willamette, Rogue, 

and Umpqua, to name a few, are included in this data set. (See Appendix A for a listing of 

U.S.G.S. sites.) None of the sites in the Timber/Fish/Wildlife study, University of 

Washington survey, or the risk sites (Table 3.1) were located within the zone of influence 

of dams. Dams often cause local heating or cooling depending on the release depth from 

the upstream reservoir. A few of the U.S. Geological Survey sites were located below 

dams. These sites were generally colder than expected given the size of the river at these 

locations. 

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

10 15 20 25 30

Annual MaximumTemperature (o

C)

Cumulative Proportion of Sites

Risk Analysis (n=19)

TFW 1990 (n=89)

U.Wash. Center Urban Studies (n=570)

Figure 3.4 Cumulative distribution of the annual maximum temperature 

at temperature study sites included in this study in comparison to the 

89 sites included in the statewide study of temperature conducted by 

Sullivan et al. (1990) and the 570 sites included in the University of 

Washington Center for Urban Studies synoptic survey of urban and 

rural streams in the Puget Sound area. The 1990 TFW study included 

sites located throughout Washington. 
 3-7 

Of the 129 U.S.G.S. sites, 11 (8.5%) had an annual maximum temperature greater than 

26o

C, the highest temperature observed at the risk analysis sites. One of those was in 

Washington (Yakima River), 1 was observed in Idaho (Snake River) and 9 were observed 

in Oregon. The John Day River in eastern Oregon reached temperatures as high as 31o

C. 

The remainder of the Oregon sites exceeding 26o

C were concentrated in the southwestern 

corner of the state, including the Applegate, Siuslaw, N. and S. Fork Umpqua, Calapooia 

River, and Elk Creek. 

The four sources of data cited in this report provide perspective on the temperatures of 

small forested and urban streams and moderate to large size rivers in both the dry interior 

and wet coastal zones. The sites included in the risk analysis are broadly representative of 

temperatures of moderate to small size streams (all sites had basin area less than 200 km2

). 

Larger rivers tend to fall within the temperature ranges observed in the smaller rivers. 

However, it is appropriate to recognize that the largest rivers, and those in some 

geographic areas, have different temperature regimes than most Pacific Northwest 

streams, and if temperature is of concern, these should be specifically evaluated to 

determine whether the duration of specific temperatures exceeds adverse levels. 

The data from the University of Washington website, the statewide Timber/Fish/Wildlife 

study, and the U.S. Geological Survey are presented merely to establish how well the 19 

risk analysis sites used in this report (Table 3) represent the streams found in a variety of 

geographic and land use settings that occur in the Pacific Northwest region. Only the 

records from the 19 sites are used for the analyses of acute and chronic temperature effects 

that follow in Sections 4,5, and 6 of this report. 

Regional Temperature, U.S.G.S. 1979

0

5

10

15

20

25

30

35

1 10 100 1000 10000 100000 1000000

Basin Area (km2)

Annual Maximum Temperature ( oC)

Washington (n=34) Oregon (n=78) Idaho (n=17)

Range of temperature 

at risk analysis sites

Figure 3.5. Annual maximum temperature for all stream and river sites listed in the 

U.S.Geological Survey Water resources data for Washington, Oregon, and Idaho for the 

year 1978-79. 
 3-8 

Deschutes River (Km 60.2)

15

16

17

18

19

20

21

22

23

74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97

Year

7-Day Maximum Temperature ( oC)

Figure 3.6 Annual maximum temperature of the Deschutes River near Vail, Washington from 1975-1995.

Deschutes River, WA (Km 60.2)

0.00

0.05

0.10

0.15

0.20

0.25

5 10 15 20 25

Temperature (oC)

Proportion of Hours

1988

1989

1990

1991

1992

1993

1994

1995

Figure 3.7 Frequency distribution of hourly temperature for 8 years in the Deschutes River near Vail, WA.

We had no hourly temperature records for comparable time intervals from the coldest 

streams (e.g., <12o

C). These are most likely to occur in well-shaded, small headwaters 

streams (Black, 2000). When well-shaded, these streams tend to hover near groundwater 

temperature (typically 6o

-10oC during the summer, depending on geographic location) 

with little, if any, daily fluctuation. Such patterns were evident in Norwegian Creek 

(western Washington) and Cee Cee Ah Creek (eastern Washington) as examples taken 

from the TFW statewide study in 1990 (Sullivan et al. 1990). To represent this type of 

stream, we assigned constant temperatures through the summer. All temperature indices, 

including daily mean and daily maximum were set to 10o

C for “Ten Site” and 8oC for 

“Eight Site”. 
 3-9 

Temporal Variation at a Site 

Temperature regimes at sites often vary somewhat from year to year due to climatic 

factors. Annual variation is illustrated in the 7-day maximum temperature at the 

Deschutes River mainstem (Figure 3.6). The long-term mean at this site was 20.3o

C 

averaged over 20 years but the upper temperature ranged ±1.25o

C the average. The shape 

of the frequency distribution, available in hourly increments for eight years since 1988, 

was similar from year to year, but tended to shift up or down the temperature scale (Figure 

3.7). Therefore, the time-averaged characteristics of temperature are likely to vary on an 

annual basis at a site. However, the relationship between the indexing characteristics such 

as maximum 7-day temperature and the overall distribution of temperature during the 

same summer period should remain consistent. 

Temperature Indices 

Later in this report (Sections 6 and 7), we will discuss the temperature indices that have 

been used to characterize the complex long-term temperature regime experienced by the 

biological community inhabiting streams (e.g., Figures 2.5 and 3.2) in ways that are 

meaningful ecologically. These indices or metrics are considered necessary as per Clean 

Water Act requirements to establish temperature criteria that are protective of salmonids 

or other designated uses. When water quality criteria are exceeded in water bodies, 

activities that contribute to or cause the exceedances to those water bodies may be 

restricted. Many streams and rivers are currently identified as exceeding water quality 

criteria according to the 305b reports from the states in the Pacific Northwest region, with 

a large number of them listed for temperature impairment. Therefore, the temperature 

criteria take on significant legal and economic meaning, and their appropriateness is of 

great concern to the public, scientists, and regulators. 

Temperature criteria generally specify a temperature threshold calculated over an 

averaging period (Table 2.1). For example, Washington’s current criteria, sometimes also 

referred to as standards, specifies the annual maximum temperature, expressed as the 

maximum hourly temperature that occurs each year. Oregon specifies the average of the 

daily maximum temperature of the 7 warmest consecutive days (ODEQ 1995). The U.S. 

EPA (1977) recommends the average of the daily mean temperature of the 7 warmest 

consecutive days (MWAT). Each of these measures for each temperature site is listed in 

Table 3.1. 

There is an implicit assumption with these indices that they are representative of 

temperatures that is biologically meaningful in some way. The relationship between 

short-term indices and acute temperatures, which are typically expressed for short 

intervals, may be direct as discussed in Section 4. However, the threshold values 

associated with state water quality criteria appear to be selected to prevent long-term 

chronic effects as discussed in Sections 5 and 6. Therefore, there is an implicit 

assumption that short-term indices, based on temperature measured for only a few hours 

each year, represent the effects of long-term exposure. This assumption is worthy of 

evaluation. Furthermore, there is no consensus on how to report stream temperature with 

meaningful but simplified measures: laboratory and field studies use a wide variety of 

methods, and seemingly, no two are alike. Lack of standardized methods for reporting 

temperature among both the physical and biological sciences makes comparison among 
 3-10 

studies difficult, and the selection of temperature criteria based on field ecological and 

laboratory studies tenuous. 

Our analyses of acute and chronic biological effects associated with natural stream 

temperatures that follow in Sections 5 and 6 of this report rely on hourly records 

summarized only to average daily temperature. These analyses are therefore not limited 

by the lack of consensus on methodology to compress long-term temperature regimes into 

very short duration indices. We will, however, use results based on detailed temperature 

records in Section 6 to evaluate whether short-term indices are reliable indictors of at least 

some long-term biological responses. 

Nevertheless, although there is some debate as to whether short-term indices are 

appropriate to represent long-term exposure, it appears that all of the short-term indices 

are closely related to one another (Figure 3.8). This makes selection among them a matter 

of procedural and logistical questions, rather than a biological question, since all similarly 

index the characteristics of the upper tail of the distribution of the temperatures sampled. 

Perhaps a more important question is how well the short-term measures correlate with 

temperature characteristics occurring over longer periods. The median temperature for the 

period from June 1 to September 15, a long-term measure, is shown in relation to the three 

short-duration indices in Figure 3.9. Although more variable, the short-term indices are 

well correlated with the season median, indicating that short-duration measures can 

meaningfully characterize seasonal temperature patterns, albeit with some loss of 

precision. Not surprisingly, the 7-day mean temperature (MWAT) is best correlated with 

the season median, probably because each is respectively characterizing the central 

tendency of the temperature within the daily and seasonal period. 

y = 1.117x - 1.427

R2 = 0.996

10

12

14

16

18

20

22

24

26

10 12 14 16 18 20 22 24 26

7-D ay M aximum T emperature ( o C )

Line of 1:1 Correspond ence

y = 1.5524x - 6.2644

R2 = 0.87

1 0

1 2

1 4

1 6

1 8

2 0

2 2

2 4

2 6

10 12 14 16 18 20 22 24 26

7-D ay M ean T emperature ( o C )

Line of 1:1 Correspondence

y = 0.6353x + 4.5983

R2 = 0.89

1 0

1 2

1 4

1 6

1 8

2 0

2 2

10 12 14 16 18 20 22 24 26

7-D ay M aximum T em perature ( o C )

Line o f 1 :1 Co rresp o nd ence

Figure 3.8 Relationships between temperature indices including annual maximum, 7-day mean (MWAT), and 7-day maximum. 
 3-11 

CONCLUSIONS 

• The data used in this report are broadly representative of stream temperatures in fishbearing streams found in forested, rural, and urban streams in Washington. Their 

temperatures also appear to be representative of many streams throughout the Pacific 

Northwest, based on comparisons of data from other sources in the region. 

• Observed temperatures at study sites span a range of temperatures, from 13o

 to 26oC 

in the annual maximum water temperature. This range encompasses most of the 

temperatures where salmonids may experience acute and chronic effects from short 

and long-term exposure. 

• Various temperature indices such as annual maximum, 7-day maximum, and 7-day 

mean that are often used in temperature criteria are closely related to one another and 

can be compared or used interchangeably with the appropriate correlation 

relationships. 

• Measures representing long duration exposure, such as the median temperature 

observed over the summer period are related to short-term measures. 

• There is year-to-year variation in temperatures at stream sites, which is reflected in 

short-duration temperature indices. 

7-Day Mean = 1.409x - 3.01

R2 = 0.83

7-Day Maximum = 1.80x - 6.39

R2 = 0.62

Annual Max = 1.976x - 8.11

R2 = 0.59

10

12

14

16

18

20

22

24

26

28

10 12 14 16 18 20

Season Median (oC)

Temperature (oC)

7-Day Maximum 7-Day Mean Annual Maximum

Figure 3.9 Relationship between season median temperature (June 1-Sept 15) with short 

duration indices. 
 3-12 
4-1 

SECTION 4 ASSESSMENT OF RISK OF SALMON SPECIES TO 

ACUTE TEMPERATURE IN STREAMS AND RIVERS

Abstract 

In this section, we examine temperature records from streams and rivers in Washington 

spanning a range of summertime maximum temperatures to determine whether acute 

lethal temperature conditions exist, and if they could be associated with water quality 

temperature criteria. A relationship between temperature and duration of exposure 

sufficient to cause mortality was established based on previously published research. 

Hourly temperature records were scanned for occurrences of sufficient continuous 

duration, defined for each level of temperature to cause mortality within salmonid 

populations. Although at least one stream had temperature as high as 26o

C, a temperature 

that can be lethal to salmonids, the length of exposure was not sufficient to cause 

mortality. We found no occurrence of acute lethal temperature conditions in any of the 

stream sites, which are broadly representative of streams and rivers in the Pacific 

Northwest. 

Key findings of this chapter: 

â There is sufficient information to quantitatively define the lethal effects of 

temperature on salmonids. 

â No occurrences of acute lethal temperatures were observed at stream sites with a 

wide range of temperatures including many with annual maximum temperatures 

that well exceed current water quality standards. 

â Nevertheless, lethal level temperatures of sufficient duration to cause mortality 

have been reported in the Pacific Northwest. Therefore, although not a common 

occurrence, attention should be paid to local site conditions that can lead to acute 

mortality. 

â A temperature threshold of 26o

C is suggested to prevent mortality of salmon and 

trout species in natural rivers and streams. Further analysis of temperature to 

determine exposure is suggested for streams where annual maximum temperature 

is between 24o

 and 26oC. 

INTRODUCTION 

Temperature duration and lethality relationships have been established through laboratory 

study for most salmon species. Acute effects of temperature typically have been assessed 

as effects on survival that result from continuous exposure to elevated temperatures for 

specified periods of time (usually from 1 to 96 hours). Mortality has been commonly 

expressed as the duration eliciting mortality of some specified portion of the population at 

a specific temperature (Brett 1952). This is a measure of mortality from temperatures 

occurring within the zone of resistance (Figure 2.1) (Fagerlund et al. 1995); that is, where 

the temperature must be experienced for some duration greater than 1 hour before 
4-2 

mortality occurs. These temperatures are more likely to occur than the ultimate lethal 

thresholds where mortality of most or all of the population occurs within a very brief time. 

Risk analysis is performed by quantitatively relating key temperature characteristics with 

specific measures of probable population response to those temperatures. We examined 

the likelihood that exposure to temperature is of sufficient magnitude and duration that it 

causes direct mortality within the fish population using conventional probabilistic risk 

assessment procedures (Parkhurst et al. 1996). Laboratory mortality data available from 

the literature were used to develop temperature effects relationships. Temperature data 

from streams monitored continuously during the summer months were used to assess 

exposure. Acute effects would most likely be associated with the occasional spikes of 

warm temperature that may induce mortality. 

ACUTE THERMAL EFFECTS CURVES ASSOCIATED WITH 50% MORTALITY 

Past research has emphasized the exposure duration causing 50% mortality in the 

population at a given temperature, as the most common lethality measure. Data from 

several sources were used to generate curves showing the relationship between temperature 

and duration to 50% mortality (EPA 1977, Brett 1952, and Golden 1978). Each curve 

estimates the length of time that 50% of a population can survive at some temperature above 

its upper incipient lethal temperature. This temperature is referred to as the LT50. At each 

successively lower temperature, the duration of exposure must be longer to achieve the same 

amount of mortality (Figure 2.2). 

EPA (1977, page 11 of text and page 38 of Appendix C) provides a regression equation 

relating exposure time (in minutes) to the LT50 (in °C): 

log10 t = a + b ⋅ LT50 (4.1) 

where t is the exposure time, and a and b are coefficients of the relationships. Equation 4.1 

can be arranged to 

LT50 = (log10 t − a)/ b (4.2) 

The regression coefficients, a and b, are provided in EPA (1977) for many fish species, 

including all those identified above, except cutthroat trout (pages 55-58 of Appendix B). 

From the coefficients provided, curves can be generated for selected species of salmon 

and trout: pink salmon, chum salmon, coho salmon, sockeye salmon, and chinook salmon. 

The coefficients in EPA (1977) were gathered from many different sources, including 

Brett's 1952 paper summarizing his study of lethal temperatures for the five salmon 

species. It is a necessary assumption of this analysis that the data from these laboratory 

studies conducted on a small number of fish and a few stocks are representative of the 

species, and that these relationships correctly characterize the mortality/temperature 

relationships. Golden (1978, Figure 4 on page 14) provides regression coefficients for 

cutthroat trout. Steelhead LT50 curves were generated using data from Alabaster and 
4-3 

Table 4.1 Regression coefficients for the relationship between duration and percent mortality of the sample population for laboand trout species. Most of the data are taken from a summary U.S.E.P.A document (1977). Studies report LT50, unless otherwis

Species Acclimation 

Temperature (oC) 

Source Age/Size a b N R Notes 

Coho salmon 15 Brett 1952 Juvenile 20.4066 -0.6858 6 -0.9681  20 “ “ 20.4022 -0.6713 4 -0.9985  23 “ “ 18.9736 -0.6013 5 -0.9956  17 Coutant 1970 Adult 5.9068 -0.1630 5 -0.9767 Reported acclimation temp. was the Co

(at Priest Rapids) during fall migration. 

Rainbow trout 15 Alabaster and Downing 1966 Juvenile 15.650 -0.5000 2 - (Steelhead) 18 Alabaster and Welcomme 1962 “ 18.4654 -0.5801 5 -0.9787 D.O. at 7.4 mg/l  18 “ “ 13.6531 -0.464 5 -0.9742 D.O at 3.8 mg/l  20 Alabaster and Downing 1966 “ 19.6250 -0.6250 2 -  20 Craigie 1963 Yearling 14.6405 -0.4470 3 -0.9787 Raised in soft water, tested in soft wate 20 “ “ 15.0392 -0.4561 3 -0.9917 Raised in soft water, tested in hard wat 20 “ “ 15.1473 -0.4683 3 -0.9781 Raised in hard water, tested in soft wat 20 “ “ 12.8718 -0.3837 3 -0.9841 Raised in hard water, tested in hard waCutthroat trout 23 Golden 1978 Juvenile 18.092 -0.56523 ? -0.996 Hatchery fish only  13-25 (fluctuating) “ “ 22.543 -0.71999 ? -0.999 Hatchery fish only  23 “ “ 18.3166 -0.5723 ? -0.999 Hatchery and wild fish pooled  13-25 (fluctuating) “ “ 18.1515 -0.5723 ? -0.992 Hatchery and wild fish pooled Chinook salmon 20 Blahm and McConnell Juv (spring run) 21.3981 -0.7253 3 -0.9579 50% mortality  20 Unpub.data “ 22.6664 -0.7797 4 -0.9747 10% mortality  20 “ “ 20.9294 -0.7024 3 -0.9463 90% mortality  20 “ Juv (fall run) 22.2124 -0.7526 4 -0.9738 50% mortality  20 “ “ 21.6756 -0.7438 4 -0.9550 10% mortality  20 “ “ 20.5162 -0.6860 3 -0.9475 90% mortality Sockeye salmon 15 Brett 1952 Juvenile 15.8799 -0.5210 7 -0.9126  20 “ “ 19.3821 -0.6378 5 -0.9602  23 “ “ 20.0020 -0.6496 4 -0.9981  20 McConnell and Blahn 1970 Juv/underyearling 16.7328 -0.5473 6 -0.9552  20 Unpublished data “ 17.5227 -0.5861 6 -0.9739 10% mortality  20 “ “ 15.7823 -0.5061 6 -0.9539 90% morality 
4-4 

Downing (1966) and Alabaster and Welcomme (1962), as cited in the EPA document. 

Data were combined where studies were reasonably comparable. Coefficients for each 

species are provided in Table 4.1 for experiments that tested a range of acclimation 

temperatures. Generally, the higher the acclimation temperature, the higher the LT50 

temperature. 

The acute thermal effects curves were generated in Excel using regression coefficients 

provided in EPA (1977) and Golden (1978) for a range of exposure times. While the 

equations provided in EPA (1977) were based on exposure times measured in minutes, we 

converted them to hours in order to be consistent with temperature measurements at field 

sites. To generate the hourly curves, equation 4.2 was modified to 

LT50 = ((log10 t ⋅ 60) − a)/ b (4.3) 

Although it was assumed that the regression coefficients in Appendix B of EPA (1977) 

were correct, one appeared to be in error. The value for a was given as 16.2444 for pink 

salmon at an acclimation temperature of 20°C from Brett's study (1952). The resulting 

curve did not match the one presented in Figure 5 of Brett (1952). To generate a curve 

more representative of Brett's (1952) figure, a value of 13.2444 was used for a instead. 

A few of the studies included in EPA (1977) were excluded from the analysis. These 

were studies in which the fish being tested showed signs of gas bubble disease or other 

effects of gas supersaturation. 

COMPARISON OF LT50 and LT10 MORTALITY RELATIONSHIPS 

Most of the available information on thermal effects is based on 50% survival. We felt 

that it was appropriate to use a more conservative population measure for risk assessment. 

Therefore, we also expressed acute effects as the duration of time needed to elicit 10% 

mortality (LT10) for each temperature and species studied. LT10 was selected because it 

is the amount of mortality considered acceptable in the control groups for acute toxicity 

tests (ASTM 1997), and 90% (100% - 10%) is a recommended protection level for species 

populations (SETAC 1994; Solomon et al. 1996). 

In the EPA (1977) document, two unpublished studies provided regression coefficients for 

both 50% and 10% (LT10) mortality curves at acclimation temperatures of 15o

C or 

higher. McConnell and Blahm (1970) calculated regression coefficients for sockeye 

salmon; and Blahm and McConnell (1970) calculated regression coefficients for both 

spring and fall runs of chinook salmon. Using the regression coefficients generated from 

these studies, LT50 and LT10 values for sockeye (Table 4.2) and chinook (Table 4.3) 

were calculated for a range of time periods, along with their ratio. 

For the range of exposure times, the LT10 values were 98.0 to 99.7% of the LT50 values. 

This is consistent with Brett (1958, page 76 and Figure 4), who indicated that differences 

between temperatures for 50% mortality and those for <50% mortality are relatively 

small. This "implies that temperatures of this order have only to increase slightly to cause 

a large difference in mortality." Based on visual inspection of the LT50 and LT10 curves, 

the slopes were similar. That is, on the log-time scale, the differences between the LT50 

and LT10 curves were approximately constant. (There was insufficient information 
4-5 

Table 4.2 Relationship between LT50 and LT10 for sockeye salmon. Acclimation temperature is 20oC. 

Data from McConnel and Blahm (1970), unpublished data (cited in U.S. EPA 1977). 

Time 

(hrs) 

LT50 (o

C)& LT10 (oC)# LT10/LT50 

Ratio (%) 

Delta T (o

C) 

.1 29.2 28.6 98 0.6 

.25 28.4 27.9 98.12 0.5 

.5 27.9 27.4 98.21 .0.5 

1 27.3 26.9 98.31 0.5 

2 26.8 26.3 98.41 0.4 

3 26.5 26.0 98.47 0.4 

4 26.2 25.8 98.52 0.4 

6 25.9 25.5 98.58 0.4 

8 25.7 25.3 98.63 0.4 

12 25.4 25.0 98.7 0.3 

16 25.1 24.8 98.74 0.3 

20 24.9 24.6 98.78 0.3 

24 24.8 24.5 98.81 0.3 

32 24.6 24.3 98.86 0.3 

40 24.4 24.1 98.9 0.3 

60 24.1 23.8 98.98 0.2 

80 23.8 23.6 99.03 0.2 

& regression coefficients a=16.7328, b=-0.5473 

# regression coefficients a=17.5227, b=-0.5861 

Table 4.3 Relationship between LT50 and LT10 for chinook salmon. Acclimation temperature is 20o

C. 

Data from McConnel and Blahm (1970), unpublished data (cited in U.S. EPA 1977). 

Type Time 

(hrs) 

LT50 (o

C)& LT10 (oC)# LT10/LT50 

Ratio (%) 

Delta T (o

C) 

Spring runa 0.1 28.4 28.1 98.74 0.4 

 0.25 27.9 27.6 98.86 0.3 

 0.5 27.5 27.2 98.95 0.3 

 2 26.6 26.4 99.13 0.2 

 4 26.2 26.0 99.23 0.2 

 6 26.0 25.8 99.28 0.2 

 8 25.8 25.6 99.33 0.2 

 10 25.7 25.5 99.36 0.2 

 16 25.4 25.2 99.43 0.1 

 24 25.1 25.0 99.49 0.1 

 40 24.8 24.7 99.57 0.1 

 60 24.6 24.5 99.64 0.1 

 80 24.4 24.3 99.68 0.1 

 100 24.3 24.2 99.72 0.1 

Fall runb

 0.1 28.5 28.1 98.65 0.4 

 0.25 28 27.6 98.6 0.4 

 0.5 27.6 27.2 98.56 0.4 

 2 26.8 26.3 98.49 0.4 

 4 26.4 25.9 98.44 0.4 

 6 26.1 25.7 98.42 0.4 

 8 26 25.5 98.4 0.4 

 10 25.8 25.4 98.39 0.4 

 16 25.6 25.1 98.36 0.4 

 24 25.3 24.9 98.33 0.4 

 40 25 24.6 98.3 0.4 

 60 24.8 24.4 98.27 0.4 

 80 24.6 24.2 98.25 0.4 

 100 24.5 24.1 98.24 0.4 

®ression coefficients LT50:a=21.3981, b=-0.7253 lT10: a=22.6664, b=-0.7797 

# regression coefficients LT50:s a=22.2121, b=-0.7526 LT10 a=21.6756, b=-0.7438 
4-6 

presented in Appendix B of the EPA (1977) document to statistically compare the slopes.) 

Had the differences not appeared constant, the application of a singe adjustment factor 

would not have been appropriate. 

ACUTE THERMAL EFFECTS CURVES AT 10% MORTALITY 

The adjustment factor estimated from the McConnell and Blahm (1970) and Blahm and 

McConnell (1970) data for sockeye and chinook salmon was assumed to be appropriate to 

use in estimating LT10 curves for the other salmon and trout species. For the other 

studies from which LT50 curves were generated, LT10 curves were estimated by applying 

a factor of 0.98 to each LT50 curve. The equation used to calculate the estimated LT10 

values is 

0.98 (log ( * 60) ) 10 10 ⋅ − = b

t a LT (4.4) 

The estimated LT10 curves for 15o

C acclimation for four salmon species are provided in 

Figure 4.1. (Relationships for all species and acclimation temperatures are graphically 

depicted in Appendix C, under separate cover.) 

The resulting LT10 lethal curves are 

very similar among salmon species 

although cutthroat trout have higher 

tolerance to high temperature than 

the other species (Figure 4.1). We 

note that using the relationship based 

on fluctuating acclimation 

temperature (Golden 1978) produced 

higher LT50’s than when tested at 

constant temperature. Continuous 

exposures of 3 to 30 hours are 

necessary to cause mortality at 

temperatures between 24o

 to 26oC, 

varying by species. The duration of 

time necessary to cause mortality 

decreases sharply with small 

increments of temperature above 

approximately 26o

C. Short duration 

excursions (less than two hours) 

above 27o

C are very likely to cause 

mortality of some individuals in the 

population because only one hour 

duration is necessary 

0.01

0.1

1

10

100

1000

24 25 26 27 28 29 30

Temperature (oC)

Time to 10% Mortality (Hours)

Cutthroat

R ainbo w 

(Steelhead) Chinook

Coho

Figure 4.1 Duration curve for the LT10 acute effects of 

temperature for coho and chinook salmon and cutthroat and 

steelhead trout, acclimated at 15o

C. (Data from Brett 1952, 

Alabaster and Downing 1966, Golden 1978; see Appendix C). 
4-7 

ACUTE EXPOSURE CHARACTERIZATION 

The maximum temperatures juvenile salmonids experienced in situ were determined 

through exposure characterization, (U.S. EPA 1992). Summertime temperatures for sites 

described in Section 3 (Table 3.1) were assembled for this study. The occurrence of 

potentially lethal temperatures was determined by examining the hourly temperature 

record at each of the 19 temperature sites for exposure periods defined as the number of 

continuous hours at or above the exposure temperature (temperature was rounded to the 

nearest o

C). We used a lower level of 16oC since it approximates the optimum

temperature for several salmon species (Weatherly and Gill 1995) although it is important 

to note that acute mortality does not commence until 24oC under naturally fluctuating 

conditions. Exposure was based on hourly temperatures so that we could capture 

relatively short duration effects and the LT10 data were expressed in hours. 

An exposure period is the number of consecutive hours each temperature (measured to the 

nearest 1.0°C) occurred within a period when the temperature was at or above 16°. For 

example, if the temperature increased from below 16° to 16.6°, a count of one was added 

to 16o

C. If the next hourly temperature was 18.5oC, then a count of one was added to16o, 

17o

 and 18oC. If the following hourly temperature decreased back to 17oC, then a count of 

one was added to 16o

, and 17o, and so forth. As soon as the next temperature retreated 

below each temperature degree category, the counting (i.e., duration of exposure) for that 

temperature ceased. When temperatures dropped below 16oC, the entire exposure period 

was concluded. In the example above, there was one exposure period where temperature 

reached 16° for 3 hours, 17° for two hours, and 18° for one hour. 

Exposure to temperatures above 16o

C varied significantly among sites. Table 4.2 lists the 

number of exposure periods for temperatures greater than 16o

C and the number of hours 

by temperature category for the warmest continuous exposure period at each site. At 

some sites there were no exposure periods at any time during the summer months, while at 

others both the magnitude and duration of exposure were relatively large. Hoffstadt Creek 

in the Mt. St. Helens Blast Zone experienced the warmest temperature (26.0o

C) which 

lasted one hour (Figure 4.3A and 4.4). This stream is shallow, and it heats and cools 

rapidly over the course of the day. The deeper Chehalis River sites (Figure 4.3B) did not 

reach quite as high a temperature (22o

C), but experienced higher temperatures over much 

longer continuous periods. 
4-8 

Table 4. 4 Number of exposure sequences where temperature was continuously greater than or equal to 16o C for one or more 

and the duration of continuous exposure (hours) at each temperature for the warmest period. 

Number of Hours by Temperature for the Maximum Sequence at the Site 

Site Total Number 

of Sequences 

_16_ _17_ _18_ _19_ _20_ _21_ _22_ _23_ _24_ 

Big Creek 6 4 0 0 0 0 0 0 0 0 Chehalis River Main 1 49 455 70 21 16 12 8 2 0 0 

Chehalis River Main 2 69 44 33 26 21 16 1 6 1 0 

Chehalis River Main 3 62 44 32 23 17 11 0 0 0 0 Crim Creek 54 22 14 7 3 0 0 0 0 0 Deschutes River Main 70 139 65 17 14 10 7 5 0 0 

Hard Creek 0 0 0 0 0 0 0 0 0 0 Harrington Creek 36 25 17 12 9 5 0 0 0 0 

Hoffstadt Creek 83 19 16 15 13 12 7 8 7 5 Huckleberry Creek 34 405 65 12 0 0 0 0 0 0 

Lester Creek 47 14 8 5 0 0 0 0 0 0 

Mack Creek 0 0 0 0 0 0 0 0 0 0 

Porter Creek 30 21 15 9 0 0 0 0 0 0 

Rogers Cr. 1 2 0 0 0 0 0 0 0 0 

Sage Cr. 17 7 0 0 0 0 0 0 0 0 

Salmon Cr. 2 3 0 0 0 0 0 0 0 0 

Thrash Cr. 0 0 0 0 0 0 0 0 0 0 Thurston Cr. 0 0 0 0 0 0 0 0 0 0 Ware Cr. 24 34 23 13 0 0 0 0 0 0 
4-9 

ACUTE RISK CHARACTERIZATION 

To assess acute risk to lethal temperature, the periods when duration of temperature 

equaled or exceeded the LT10 curve (e.g. Figure 4.1) were examined to determine if the 

duration of exceedance was of sufficient length to cause mortality. If the time spent at any 

temperature equaled or exceeded the time necessary to elicit mortality at the LT10 level, 

then an exceedance occurred. If an exceedance occurred, we assumed that 10% of the 

population died. The number of exceedances that occurred during the summer interval was 

counted. It follows that if 10% of the population died at each exposure, then the 

cumulative risk to the population (Lethal Risk) could be calculated using the number of 

exceedances (n) experienced by the population: 

[ ] n Lethal Risk = 1.0 − (1.0 − 0.10) (4.5) 

where lethal risk is defined as the proportion of the population that dies due to the 

temperature exposure. For example, if a stream’s temperature exceeded an LT10 once, the 

cumulative total mortality risk would be [1.0-(0.90)1

] or 10%; if it exceeded an LT10 four 

times, the total mortality risk would be [1.0-(0.90)4

] or 34%. 

There were no indicated periods of acute exposure for chinook, coho, steelhead, or 

cutthroat at any of the stream locations despite higher temperatures at some sites. The 

warmest period of exposure at each of the 19 temperature study sites in relation to species’ 

LT10 exposure curves is 

shown in Figure 4.2. 

Some sites did not 

exceed optimal 

temperature 

(approximately 16o

C) at 

any time during the 

summer and do not 

appear on the figure. For 

those that exceeded acute 

lethal temperatures (24o

C 

or greater), the exposure 

was generally much less 

than required to cause 

mortality. 

The only site that 

exceeded 24o

C (the 

temperature zone where 

mortality depends on 

duration of exposure) 

was Hoffstadt Creek, 

which peaked at 26.0o

C 

for one hour (Figure 4.2 and 4.3A). Hoffstadt Creek had an additional two exposure 

periods reaching 25o

C (three-hour duration) and twelve reaching 24oC (five-hour duration). 

Although the one-hour maximum temperature approached the magnitude and duration that 

could elicit mortality at this site, the duration of exposure at 24°, 25°, and 26o

C for 

Hoffstadt Creek was too short to directly cause mortality (Figure 4.3A). The Chehalis 

0.01

0.1

1

1 0

100

1000

10000

15 20 25 30

Te m p e rature ( o C )

H o u rs o f C o n tin u o u s Ex p o

C utthro at

Ste e lh e ad

C h in o o k

Coho

Figure 4.2 Hours of continuous exposure at temperature sites (see Table 

4.1) and LT10 lethality relationships for fish species. 
4-10 

A) Hoffs tadt Cre e k

0.1

1

10

100

1000

15 20 25 30

Tem perature (oC)

Hours of Continuous 

Exposure

LT10 Chino o k LT10 Cutthro at

LT10 Steelhead

LT10 Coho

B) Che halis Rive rM ains te m Site 1

0.1

1

10

100

1000

15 20 25 30

Tem perature ( oC)

Hours of Continuous 

Exposure

Figure 4.3 Example of how acute exposures were summarized: maximum number of 

sequential hours at each temperature for A) Chehalis River mainstem (site 1) and B) 

Hoffstadt Creek sites. Diamonds are the exposure for each temperature for the warmest 

period at each site. 

River mainstem experienced much longer duration at higher temperatures (Figure 4.3B), 

but never came close to lethal temperatures. 

No acute risks were identified since none of the water temperatures persisted long enough 

to exceed the lethal thresholds (LT10s) of salmonid species likely to occur in these 

streams. Since n equaled 0 at all sites, the probability of acute effects was 0. 
4-11 

Small to moderate size streams and rivers have sufficient fluctuation in the daily 

temperature that water does not spend long continuous duration at lethal temperature. The 

daily fluctuation is strongly dependent on stream depth (Adams and Sullivan, 1990, 

Sullivan and Adams, 1990). Within much of the Pacific Northwest, relatively shallow 

streams and rivers may be capable of achieving temperatures that can cause mortality 

within a few hours. Their shallowness, however, will generally preclude them from 

maintaining high lethal temperatures for more than a few hours during the day, and 

temperatures can fluctuate widely over the solar cycle (Brown 1969). For example, Figure 

4.4 shows Hoffstadt Creek during its warmest 7-day period in 1990. The duration of lethal 

temperature is short. 

Conversely, large deep rivers may maintain higher average temperature, but the daily 

fluctuation is smaller. For example, for much of the summer and its length, the Columbia 

River maintains an average temperature of 22o

C with a daily fluctuation of 0.5 oC. In the 

U.S.G.S. water resource records discussed in Section 3, the S. Umpqua River in southern 

Oregon reached 30o

C annual maximum temperature. Nevertheless, the daily minimum 

temperature on the hottest days did not exceed 21o

 to 23.5oC, indicating that duration of 

extreme temperature was short, and probably within lethal thresholds. Thus, duration of 

exposure at higher temperatures can be significant, but lethal temperatures that cause 

mortality in a few hours are not likely to occur in most cases. The dependence of daily 

fluctuation on stream depth makes the case that very large, deep rivers are different than 

most of the streams and rivers in the region (Section 3), and those rivers with extreme 

temperatures should probably be analyzed individually to fully understand their local site 

conditions. 

Table 4.5 shows the calculated temperature for 1-hour and 6-hour exposure that would 

cause mortality of 10% of the population based on relationships provided in Table 4.1, and 

the calculation methods described in this section. 

Hoffstadt Creek, Aug 7-14, 1990

10

12

14

16

18

20

22

24

26

28

0 24 48 72 96 120 144 168 192

Hours

Temperature ( oC)

Figure 4.4 Hourly temperature during the warmest 7-day period at Hoffstadt 

Creek in 1990. 
4-12 

The temperatures at which mortality occurs at exposures of 1 hour and 6 hours if very 

similar among species. This temperature is between 25.4o

C and 27.4oC, averaging 26.4oC. 

There is a low probability that the 6-hour duration temperature occurring in most natural 

streams in Washington, as suggested by Table 4.1. Therefore, an acute temperature for 

salmon of approximately 26o

C in the annual maximum temperature is suggested to prevent 

mortality with a margin of safety. We also suggest that a site-specific analysis of 

temperature be performed when annual maximum temperature is between 24o

 and 26oC, to 

determine whether duration is sufficient to cause mortality. It would appear that in most 

cases it will not, but until more analyses are completed over a broader geographic range 

this will not be known with certainty. Site specific analyses would also be warranted at 

sites of thermal pollutant sources that alter the normal daily temperature fluctuation. 

Table 4.5. Temperature of 1-hour and 6-hour duration that will cause mortality of 10% of the population 

for salmon and trout species. 

Species Age/size Acclimation Temp 

(

o

C) 

1-Hour 

Temperature (oC) 

6-Hour 

Temperature 

(

o

C) 

Coho salmon juvenile 15 26.6 25.5 

 20 27.2 26.1 

 23 28.0 26.8 

Rainbow trout juvenile 15 27.2 25.7 

 18 27.5 26.9 

 20 28.0 26.8 

Rainbow trout yearling 20 28.2 26.5 

 20 28.5 26.8 

 20 28.0 26.3 

 20 28.3 26.3 

Cutthroat trout juvenile 23 28.3 27.1 

 13-25 (fluctuating) 28.5 27.9 

 23 28.3 26.3 

 13-23 (fluctuating) 28.0 26.7 

Chinook salmon juvenile 15 26.8 25.4 

 20 27.5 26.8 

 jacks 19 30.1 27.1 

 juvnile spring 20 26.5 25.5 

 20 26.8 25.8 

 Juvenile fall 20 26.6 25.6 

 20 26.8 25.7 

Sockeye salmon juvenile 15 26.5 25.1 

 20 26.0 25.9 

 23 27.5 26.3 

 underyearling 20 26.8 25.4 

Chum salmon juvenile 15 26.3 24.9 

 20 27.8 25.9 

 23 28.2 26.6 

Discussion 

We conclude that direct mortality from temperature is unlikely to affect populations living 

in streams with temperatures similar to those evaluated in this paper. 

Mortality associated with acute lethal temperatures has been previously reported from 

streams similar to those in this study. In both cases, natural and logging-related 

disturbance caused complete denudation of the watershed, including all riparian vegetation 

and in-channel cover. In the years immediately following the1980 eruption of Mt. St. 
4-13 

Helens, temperatures of streams within the blast zone were extreme relative to those 

typically recorded in the Pacific Northwest. Bisson et al. (1988) and Martin et al. (1986) 

reported temperatures in Hoffstadt Creek and other moderate size streams in the blast zone 

that exceeded 29°C for short daily intervals several times during the summer. The lethality 

curve suggests that even short duration exposure to such warm temperatures could cause 

mortality of individual fish and repeated exposures could have a measurable effect on 

salmon populations. In this case, the acute effects analysis would have predicted mortality, 

although we do not have temperature data from these earlier studies to calculate the 

cumulative mortality. Populations of juvenile coho salmon successfully survived the 

temperature episodes exceeding lethal levels (Bisson et al. 1988). However, Martin et al. 

(1986) also reported significant mortality of juvenile coho within populations in the same 

streams as Bisson et al. (1988) that was proportional to the magnitude of daily temperature 

fluctuations (maximum fluctuation of 17°C) when temperatures exceeded 26°C. 

Temperatures have since sufficiently declined with vegetation re-growth that mortality is 

not expected at temperatures existing eight years after the eruption and beyond. Hall and 

Lantz (1969) reported no reduction in numbers of coho, and a 25% reduction in cutthroat 

trout, when summertime temperatures reached 30o

C in a small Oregon stream (Needle 

Branch) after clearcut logging and severe burning. 

Elsewhere in the Pacific Northwest, water temperatures greater than 24o

C are measured 

relatively rarely in streams and rivers (see Section 3). Based on the foregoing analysis, one 

can conclude there is low risk of acute lethality to salmonid species juveniles from 

temperatures observed at the nineteen sites in this risk assessment. The temperatures at 

these sites are representative of the potentially lethal temperature of most of the natural 

streams up to 5th order found in the Pacific Northwest, including many with disturbed 

riparian forest cover (Sullivan et al. 1990). Streams in this analysis include many sites 

affected by land use and catastrophic natural disturbance. Nevertheless, if temperatures 

should reach as high as 28o

C for as little as an hour, mortality of some portion of the 

population can be expected. 

CONCLUSIONS 

• There is sufficient information to quantitatively define the lethal effects of temperature 

on salmonids. 

• No occurrences of acute lethal temperatures were observed at stream sites with a wide 

range of temperatures including many with annual maximum temperatures that well 

exceed current water quality standards. 

• Nevertheless, lethal level temperatures of sufficient duration to cause mortality have 

been reported in the Pacific Northwest. Therefore, although not a common occurrence, 

attention should be paid to local site conditions that can lead to acute mortality. 

• A temperature threshold of 26o

C is suggested to prevent mortality of salmon and trout 

species in natural rivers and streams. Further analysis of temperature to determine 

exposure is suggested for streams where annual maximum temperature is between 24o 

and 26oC.
4-14 
5-1 

SECTION 5 DEVELOPMENT AND CORROBATION OF A BIOENERGETICS –BASED APPROACH TO EVALUATING SALMON 

GROWTH IN RELATION TO ENVIRONMENTAL TEMPERATURE 

Abstract 

Growth is an important biologic function for juvenile salmonids rearing in streams and rivers. In 

this section, we develop a quantitative method to evaluate the effects of the long-term temperature 

on the growth of salmonids. The mathematical model considers the effects of temperature and 

food consumption on daily growth rate. When applied over time to measured stream temperature 

regimes, the model simulates the weight gain of salmon species, and can be used to assess the 

importance of the cumulative or chronic effects of temperature on growth. At present, the model is 

formulated to assess the growth of juvenile salmonid species during the summer months with 

variable temperatures. Previous researchers have used similar approaches, although this specific 

method has not been explicitly described previously. We use well-established bioenergetics 

principles and models available in the scientific literature to help develop it. 

The relatively simple formulation appears to predict weights well according to a number of 

comparisons of observed and predicted growth at stream sites where fish populations had been 

sampled. The method is sensitive to temperature, making it a useful tool for evaluating salmon 

response to temperature in natural streams, and it allows direct comparisons among sites and 

species if desired. Application of bioenergetics principles to field observations using this tool 

suggests ecological adaptation to environmental temperature and food supply. The model may 

prove useful for helping field investigators sort out the complex relationships between population 

dynamics, environmental temperature, and food supply that control growth in natural streams. 

Because the model is central to determining the effects of temperature on fish growth in relation to 

temperature, the mathematical approach is developed in detail in this section. If the scientific basis 

for the growth analysis is not of interest, the reader is urged to move directly to Section 6, where 

the method is applied to develop temperature criteria. 

Key findings include: 

â Methods of predicting growth based on quantitative bioenergetics principles can be applied to 

streams to assess the effects of temperature on juvenile salmonid growth, with results that are 

consistent with observed wild fish population growth patterns. 

â The method is sensitive to temperature and can be applied to the daily temperature regime 

making it a useful tool for assessing the biological impacts of temperature in natural streams. 
5-2 

 INTRODUCTION 

Of primary interest in selecting temperature criteria is the prevention of adverse effects of longterm exposure to temperatures detrimental to fish. To explore the effects of prolonged exposure to 

temperature, numerous investigators have found growth to be a reliable and measurable integrator 

of a variety of physiological responses (Brett 1971, 1995; Iverson 1972; Brungs and Jones 1977; 

Wurtsbaugh 1973). Growth rate is the most frequently reported measure of fish health from 

laboratory studies and occasionally from field studies. By corollary, the weight a fish gains over a 

time interval is, in part, determined by the ambient temperature of the water in which it lives. 

The size of salmonids during juvenile and adult life stages influences survival and reproductive 

success. Although the large majority of anadromous salmonid growth occurs in the ocean 

environment, growth of juveniles in natal streams is especially important for anadromous 

salmonids that must reach minimum sizes before they can smolt (Weatherly and Gill 1995). 

Holtby and Scrivener (1989) and Quinn and Peterson (1996) demonstrated that the size achieved 

by juvenile coho at the end of their first summer growing period was a strong determinant of their 

later success in overwintering and smolting. Larger size also conveys competitive advantage for 

feeding in the freshwater environment (Puckett and Dill 1985, Nielsen 1994) for both resident and 

anadromous species. Mason (1976) and Keith et al. (1998) found that the smaller fish tend to be 

those that are lost from rearing populations. Brett et al. (1971) described the freshwater rearing 

phase of juvenile sockeye as one of restricted environmental conditions and generally retarded 

growth. This synopsis is generally true for salmonid species that dwell in stream and river 

environments for lengthy periods of time. 

Growth can be viewed as the net effect of the environment on the relation between food 

consumption, metabolism, and activities of an organism (Warren 1971). The bioenergetics of 

many fish species, including salmonids, has been widely studied and quantified (reviewed by 

Adams and Breck 1990, Brett 1995, and others). The food fish consume is allocated to maintaining 

basic metabolic functions, growth and waste. The most generalized representation of bioenergetics 

is of the form: 

Consumption = Metabolism + Growth + Wastes 

Researchers have developed mathematical relationships for the rate functions for various energetic 

processes (summarized in Brett 1995). We propose that a bioenergetics approach can be used to 

assess the effects of temperature exposure because many of the energetic relationships are based on 

temperature and the energy balance determines growth. 

Bioenergetics models vary in their formulations of specific functions and in the complexity with 

which they attempt to characterize the details of each (Ney 1993). The most comprehensive 

bioenergetics models consider all modes of energy intake and expenditures including consumption, 

respiration, active metabolism, specific dynamic action (swimming), wastes (including egestion 

and excretion), and growth (including body tissue and gonads) (Kitchell et al. 1974, Hewett and 

Johnson 1992). Bioenergetics models combine these relationships into an energy balance where 

the energy consumed and the energy expended to all functions must sum to one. Elliott’s (1976) 

relationships for brown trout are also widely cited. Bioenergetics models have been developed for 

a number of applications in fisheries management and their uses are many and varied (Hansen et al. 

1993, Ney 1993). They have been used routinely to manage the rearing of fish in hatcheries 

(McLean et al. 1993) and populations in natural environments (Hanson et al. 1997). Increasingly, 
5-3 

they have been used to explore ecological responses to environmental conditions (Filbert and 

Hawkins 1995, Preall and Ringler 1989, Railsback and Rose 1999). 

J.F. Kitchell and colleagues at the University of Wisconsin have made bioenergetics analysis 

available to a wide variety of scientists and managers. They have summarized research for many 

fish species, including salmonids, and packaged the energy functions in software for easier use of 

the multiple mathematical statements required for the energy balance (Kitchell et al. 1974, Hewett 

and Johnson 1992, Hansen et. al. 1997). In practice, model users are urged to supply data to 

calibrate model parameters and to validate population growth. There are a variety of data needs in 

conducting full bioenergetics analysis. The Wisconsin models require a relatively large number of 

parameters (15-30), some of which are measured from the population and environment of interest 

(Hanson 1997). Default values are supplied if the user is unable to develop local data, often times 

borrowed from similar species that have received greater laboratory and field study. The 

proliferation of parameters, each with its own estimation error, has led some to criticize 

bioenergetic models for being insensitive statistically and difficult to apply (Hansen et al. 1993). 

For practical applications, it is often desirable to construct the simplest model possible that can 

capture the key environmental or biological effects of interest (Kitchell et al. 1974). Moreover, Ney 

(1993) has suggested that elaborate energetic characterizations may not be necessary to provide 

satisfactory answers to some bioenergetics questions. Although grwoth is only one of the 

bioenergetic functions, many authors argue that it integrates a host of specific physiological 

responses to temperature, including metabolic rate (basal and active), feeding and digestion, and 

swimming performance or the ability to hold position with the current (Brett 1995; Weatherly and 

Gill 1995). For example, Brett et al. estimated weight gain of sockeye (1971) and chinook (1982) 

in relation to environmental temperature assuming that the relationship between temperature, food 

consumption and growth rate, such as illustrated in Figure 2.4, adequately integrates the organism’s 

response to temperature. Mallet et al. (1999) applied a temperature-modified form of a von 

Bertalanffy growth model, which has no explicit of energetics, to estimate the growth of grayling in 

a European River. Up to 25 to 30% of the energy consumed by salmonids is allocated to growth 

and the remainder is allocated to the other energy demands (Brett et al. 1982, Brett 1995). With the 

exception of respiration, the pattern of response of all of the energetic functions to temperature is 

similar to that of growth rate, with maximums and minimums of activity peaking and declining at 

similar optimal temperatures (e.g. Brett et al. 1971, Hansen et al. 1997). Energy consumed by 

respiration continually increases reaching maximums at temperatures coincident with shut down of 

other metabolic functions, including growth (e.g., 24o

C for salmonids, Brett 1995). 

Growth, or more precisely, gain in weight for our purpose, is one of the few energetic functions 

that can be readily measured in natural environments, and it perhaps is the quality of greatest 

interest for juvenile salmonids. Because bioenergetic functions respond similarly to growth, the 

approach of Brett et al. (1982) assumes that the other components of the energy equation can be 

ignored yielding a modeling approach that requires only a few parameters. 

In this section, we develop a mathematical approach to predict growth from temperature and food 

consumption, using realistic estimates of food supply. It follows the approach of Brett et al. (1971, 

1982) in that only growth rate as mediated by temperature and food consumption are accounted for 

in the energy balance. However, we use the bioenergetics approach to estimate the interaction of 

food consumption with temperature. We develop the rationale for each of the components of the 

growth model, and parameterize them based on laboratory and field studies of fish populations. 

We then compare model simulation results with fish population weight gain at a number of stream 
5-4 

sites to evaluate model performance. This analysis considers growth during summer rearing 

because high water temperatures are of a particular concern in how they may restrict growth. The 

analysis focuses primarily on coho and steelhead species because 1) they occur widely in Pacific 

Northwest streams, penetrating well into the headwaters, and 2) necessary biological data were 

available for constructing the model and evaluating its performance. The growth model is then 

used in Section 6 to explore methods for quantifying growth response to identify biologically-based 

water quality criteria. 

GROWTH MODEL 

The basis for the mathematical formulation for daily weight gain in juvenile salmonids is its 

relationship to temperature and food consumption. This relationship has been graphically depicted 

for sockeye and chinook salmon by Brett and colleagues (summarized in Brett 1995 and Weatherly 

and Gill 1995). The relationship for sockeye salmon is shown in Figure 2.4 of this report. 

The change in weight is calculated for defined scenarios of temperature, food availability, and 

species size characteristics on a daily basis, and summed through the growth period. Weight gain 

is determined by multiplying the daily specific growth rate by the body weight: 

i i wi ∆ w = g ⋅ (5.1) 

where: 

body weigh t, day (gram )

specific growth rate, day (gram /gram day)

current day' s growth, day (gram /day)

body weigh t

growth body weigh t

growth

w i

g i

w i

i

i

i

=

= ⋅

∆ =

 

Next day’s weight is computed by adding the daily growth to the current day’s weight: 

( )i i

i i i

g w

w w w

= + ⋅

+ = + ∆

1

1 (5.2) 

Weight at some time t can be computed as a function of an initial weight w0 and daily growth rate 

coefficients: 

∏( )

−

=

= ⋅ +

1

0

0 1

t

i

t i w w g (5.3) 

The daily specific growth rate is a function of the water temperature to which the fish are exposed 

that day and daily food consumption: 

f ( , ) i g Ti Ci g = (5.4) 

where: 
5-5 

daily consumptio n day (gram /gram day)

temperatur e, day ( C)

food body weigh t = ⋅

= °

C i

T i

i

i 

In turn, the consumption is a function of the water temperature to which the fish are exposed that 

day, the weight of the fish, and the food supply (Ri): 

f( , , ) Ci = Ti Wi Ri (5.5) 

Looking at equations 5.1, 5.4 and 5.5, one can see that growth is being modeled as a function of 

water temperature, consumption, and size of the fish. The interaction of these terms is discussed in 

detail as the mathematical relationships are developed. The fact that consumption is both a 

dependent and an independent variable of temperature introduces some complexity into the 

growthcalculation that must be addressed. 

The growth model is developed in two parts. First, the relationship between consumption, 

temperature and weight (equation 5.5) is estimated following the approach discussed by Stewart 

and Ibarra (1991), and used, for example, in the Wisconsin bioenergetics model (Hewett and 

Johnson 1992, Hanson 1997). Second, the relationships between specific growth rate, temperature 

and consumption (equation 5.4) are developed for several salmon species. We draw from 

previously published laboratory experiments to establish these relationships. These studies 

exposed juvenile salmonids for periods sufficiently long to determine the rate of growth (e.g., 

change in weight from one interval to the next) of the sample population at a given temperature and 

food consumption. Experimental data for coho salmon (Oncorhynchus kisutch) were obtained from 

Everson (1973) and for steelhead trout (Oncorhynchus mykiss) from Wurtsbaugh and Davis (1977). 

We assumed that these experimental data are representative of the species’ response to temperature 

and consumption in the natural environment. (Data from these experiments is provided in 

Appendix A.) 

Consumption. There is a maximum level of food consumption for each species that constitutes 

satiation or fullness. There is also a minimum consumption required to maintain standard 

metabolism. Consumption is generally expressed as a proportion of food mass to fish body mass 

consumed each day (g gbw

–1d-1), or alternatively, as a percent of body mass per day. Within the 

range of consumption between satiation and minimum maintenance, the growth rate varies with 

consumption as illustrated for sockeye salmon in Figure 2.4.

The consumption rate at satiation, (Cs ), and by corollary, at lesser amounts of food, varies with 

temperature and changes systematically with the fish’s weight (Brett 1995). Salmonids respond 

physiologically to temperature by altering food consumption and the efficiency with which food is 

converted to growth (Weatherly and Gill 1995). Cs is at a maximum at optimal temperature and 

declines at colder and warmer temperatures (Brett 1971), yielding the characteristic shape of 

growth/temperature response (e.g., Figure 2.4). For example, coho consumption was 40% greater 

at 21o

C than at 11oC in the laboratory experiments (Everson 1973), but dropped off sharply above 

22o

C (Brett et al. 1982). Consumption at satiation also declined with increasing fish weight (Brett 

1995). The rate of consumption of Everson’s (1973) experimental fish at 3.6 grams was 45% of 

that of fish weighing 2.0 grams at the same temperature. 

There is a maximum consumption rate for each species, (Cmax), a key benchmark established in 

laboratory studies at optimum temperature, low weight, and unlimited food supply. Generally, the 
5-6 

consumption at 1 gram of weight and optimum temperature is the highest consumption likely to be 

observed for the species (Cmax), and serves as an important reference point for growth computations 

(Hanson 1997, Beauchamp et al. 1989, Hewett and Johnson 1987). The maximum consumption at 

satiation rations for other temperatures and weights (Cs) isless than Cmax , and can be calculated as a 

proportion of maximum (p) according to equation 5.6. 

Cmax

C

p s = (5.6) 

where: 

( )

 (gram of food gram of body weigh t day , g g d )

satiation ration at reference weight and optimum temperatu re

normalized consumptio n dimensionl ess

-1 -1

bw

-1 -1

max =

=

C

p

Cmax varies by species (sockeye: Brett 1971; chinook: Brett et al. 1982; coho: Everson 1973, 

steelhead: Wurtsbaugh and Davis 1977). 

To appropriately estimate growth rate (g), it is necessary to establish the consumption at each 

weight (Cw) and temperature (CT). We follow the approach used in the Wisconsin bioenergetics 

model (Hanson 1997) where consumption equations are of the basic form: 

( ) CT = Cmax ⋅ p ⋅ f T (5.7) 

CB Cw = CA⋅W (5.8) 

We develop equations for each independently, and then we will bring the two effects together by 

referencing consumption relative to Cmax and calculating the reduction from that benchmark due to 

each of the two factors. Hanson (1997) notes that developing a set of parameters for these 

relationships may be accomplished by deriving them from published reports, estimating them from 

specifically designed field or laboratory experiments, or borrowing parameters from closely related 

species. 

The allometric relationship between consumption and weight (eq. 5.8) generally has the form of a 

negative power function (Ricker 1975) whose terms are the intercept, CA, and the coefficient, CB. 

The intercept value of CA is the consumption of a 1-gram fish and has units of grams per gram of 

body mass per day (g g-1

bw d-1). These terms must be estimated from ad libitum feeding 

experiments conducted at the optimum temperature. There are relatively few such laboratory 

studies reporting values for salmonid species of interest in this report, although several other 

salmon species have been extensively studied. The laboratory studies of juvenile growth for coho 

(Everson 1973) and steelhead (Wurtsbaugh and Davis 1977) were not designed specifically to 

determine the allometric relationship of consumption to weight. However, in both studies, a series 

of one-month long feeding trials were conducted on individuals drawn from a population of fish 

that was maintained in a natural stream between experiments over a year-long period. Thus, the 

fish grew at natural rates between experiments and represented a range of weights from 1 to 4 

grams, which is within the range of size expected for salmonids in natural streams in the first year. 
5-7 

We examined the laboratory data of Everson (1973) and Wurtsbaugh and Davis (1977) to estimate 

CA and CB for coho and steelhead looking only at trials where: 1) fish weights were reasonably 

near 1 gram; 2) rations were considered to be at satiation; and 3) temperatures were optimal, as 

suggested by high growth rates. Data matching these criteria were limited and some of our 

selected data values only marginally fit these criteria. Nevertheless, a relationship between 

consumption and weight at satiation ration followed an allometric relationship with CB equal to 

–0.254 for coho (R2

=0.19) and –0.311 for steelhead (R2=0.41), albeit the R2 is relatively low. (The 

poor fit in the relationship could be because some of the experimental data points did not match the 

criteria as closely as desirable). Stewart and Ibarra (1991) and the Wisconsin user’s manual 

recommend values for CB of –0.275 for coho, based on the work of Beauchamp et al. (1989), and 

–0.30 for steelhead based on Rand et al. (1993). Although these studies were conducted with larger 

fish, the results are very similar. Calibrating predictions of weight gain of populations in natural 

streams, as described later, we found that selecting values for CB of –0.275 for both coho and 

steelhead, as suggested by other researchers, produced satisfactory modeling results. Setting CB at 

–0.3 for steelhead slightly but consistently underestimated growth, suggesting that consumption 

was not declining at quite this high of a rate in natural populations. 

Maximum consumption observed in a few of the laboratory trials suggested values for the 

intercept, CA, of 0.11 and 0.16 g g-1d-1 for coho and steelhead, respectively. The allometric 

relationship developed from all trial data suggested CA equaled 0.083 and 0.16 for coho and 

steelhead, respectively. Calibrating the model predictions in natural streams suggested that CA was 

closer to 0.10 for coho (10% body weight per day). The User’s Manual for the Wisconsin model 

(Hanson 1997) recommends values of CA between 0.15 and 0.35, which are higher than those we 

derived using the laboratory data. We selected values for CA of 0.10 and 0.16 for coho and 

steelhead respectively. These are also the values of Cmax for each species. 

Bartell et al. (1986), Beauchamp et al. (1989) and Hanson et al. (1997) have noted that the 

allometric parameters for the dependence of consumption on body mass (CA and CB) are among 

the parameters that have the greatest influence on bioenergetics predictions. In subsequent model 

predictions, described later in this section, we found this to be true. Within the narrow range of 

values separating adult from juvenile studies, there was relatively little effect of CB on weight 

predictions, and we selected the widely cited values. However, the laboratory studies of juvenile 

fish did not support CA values greater than we selected. Growth predictions are sensitive to this 

parameter. 

Food consumption in relation to weight as calculated with the allometric parameters is shown in 

Figure 5.1. There are significant differences in consumption levels between these two species. 

The proportion of consumption at each weight (Cw) relative to Cmax , is defined by the slope of the 

allometric equation (CB) and can be calculated as: 

CB pw = W (5.8) 
5-8 

To determine the effects of 

temperature on food consumption, 

a number of authors have used the 

Thornton and Lessem algorithm 

(1978) (Beauchamp et al. 1989, 

Stewart and Ibarra 1991, Hanson 

et al. 1997). The algorithm 

estimates the maximum 

consumption at each temperature, 

expressed as the proportion of the 

maximum consumption at the 

optimal temperature (pt). The 

algorithm fits two sigmoid curves 

to specified parameters, which 

include the optimal temperature 

and the upper and lower 

temperatures where consumption 

nears zero. Thus, the general 

shape of the relationship between 

temperature and consumption is 

assumed and key temperatures must be known or estimated to fit the proper shape of the curve for 

each species. The user’s manual for the Wisconsin bioenergetics model (Hanson 1997) suggests 

values for the key parameters of the Thornton and Lessem algorithm drawn from Stewart and 

Ibarra (1991) and Rand et al. (1993). We began with those parameters, but then calibrated them to 

fit the experimental data for coho (Everson 1973) and steelhead (Wurtsbaugh and Davis 1977) 

(Figure 5.2). Small adjustments to the suggested parameters appeared to slightly improve the fit 

compared to the laboratory observations, although our final curve for coho is very similar to that 

presented by Stewart and Ibarra (1991). The parameter values we derived are provided in Table 5.1 

and the fitted relationships are shown in Figure 5.2. The Thornton and Lessem equation is 

awkward to use in an EXCEL spreadsheet format. We fit a polynomial regression to the 

Coho

0.0

0.2

0.4

0.6

0.8

1.0

0 5 10 15 20 25 30

Temperature (oC)

Polynomia Line Fit 

Steelhead

0

0.2

0.4

0.6

0.8

1

0 5 10 15 20 25 30

Temperature (oC)

Polynomial Line Fit

Figure 5.2 Maximum consumption in relation to temperature computed with the Thornton and Lessem 

algorithm (1978). 

Consum ption at Satiation

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

0.18

0 1 2 3 4 5 6 7 8 9 10 11

Weight (gram )

Consumption (g g -1d-1)

Coho Steelhead

Cmax

Figure 5.1 Consumption at satiation in relation to weight as 

modeled by selected parameters for CA and CB described in 

text. 
5-9 

Thornton and Lessem algorithm results to provide an equation for calculating pt at each 

temperature (Figure 5.2). 

3

3

2

pt = λ0 + λ1 ⋅T + λ2 ⋅T + λ ⋅T (5.9) 

The fit was quite close (Figure 5.2), especially in the range of temperatures likely to be observed 

during the summer, when the relationship was used for growth modeling. The polynomial terms 

describing the equations are provided in Table 5.1. 

We now have mathematical expressions that account for the influence of weight (pw), and 

temperature (pt) on consumption at satiation where each is expressed in proportion to Cmax. The 

maximum consumption rate, ps, at each combination of weight and temperature expressed relative 

to Cmax, (a constant value for each species) is calculated according to equation 5.10: 

s w t p = p ⋅ p (5.10) 

and ranges between 0 and 1. The actual amount of food consumed (Cs), expressed in grams of prey 

mass in relation to grams of body mass per day, (g gbw

-1d-1), is equal to: 

Cs ps Cmax = ⋅ (5.11) 

Table 5.1 Estimated physiological parameter values used in equations calculating consumption for coho 

and steelhead. 

Relationship Parameter Coho Steelhead 

Allometric CA 0.10 0.16 

CB -0.275 -0.275 

CQ 7 5 

CK1 0.4 0.2 

CTO 15.6 14.0 

CTM 18 17.9 

CTL 24 24 

Thornton and Lessem 

function for temperature 

dependence (1978)φ

CK4 0.2 0.1 

λ0 -0.1419 -0.1229 

λ1 0.0544 0.0607 

λ2 0.0061 0.0055 

Regression fit to Thornton 

and Lessem algorithm 

generated by above 

parameters 

λ3 -0.0003 -0.0003 

φ Terminology as used by Hanson et al. (1995), see Appendix B

The value of Cs computed over a range of temperature and weight is illustrated for coho in Figure 

5.3. As expected from the formulation, the highest levels of consumption occur near optimal 

temperatures and at lowest weight. The deepest shades on the contour map represent starvation. 

Falling below the minimum maintenance consumption (approximately 0.03 g gbw

-1d-1 for coho 

juding from the laboratory trials) for extensive periods of time should result in no growth, or even 
5-10 

weight loss. The maximum potential consumption is relatively moderate for most of the 

temperatures and weights that juveniles are likely to encounter in the freshwater growth phase of 

their life history due to the interaction of the temperature and weight. However, consumption is 

severely limited only at high and low temperatures at all weights. Consumption approaches the 

maximum potential (0.10 g gbw

-1d-1 for coho) only for a relatively few combinations of weight and 

temperature, and therefore for probably relatively little time during the life of a fish. Maximum 

consumption at all weights is achieved at optimal temperature (Topt), where growth rate is greatest 

for each level of consumption (approximately 17o

C for coho). 

The food consumption illustrated in Figure 5.3 represents the upper maximum controlled by the 

physiology of the fish at each combination of weight and temperature. We refer to this as the 

maximum potential consumption. In the natural stream environment, as well as in the laboratory, 

the amount of food available to consume may be less than the maximum that the fish can 

potentially consume and the fish may experience some degree of hunger. We distinguish 

consumption, which we use to refer to the physiological response controlling food intake, from 

ration, which we use to refer to the food supply. Ration, which we express as % satiation, is 100% 

satiation at the maximum consumption (Cs) at each weight and temperature. 

Consumption in natural streams depends on food supply, competition for food, and the size of the 

fish based on the past regime of water temperature. The Wisconsin model accounts for ecological 

constraints on the maximum consumption rate (Cs) by imposing an additional proportionality (Pvalue) that can also range from 0 to 1 at rations less than the physiological maximum (ps). Thus, 

when modeling consumption at less than satiation for each temperature and weight, Ci may be 

calculated as 

Ci Pvalue ps Cmax = ⋅ ⋅ (5.12) 

2 4 6 8 10 12 14 16 18 20 22 24 26

Temperature (oC)

1

2

3

4

5

6

7

8

9

1 0

Weight (grams)

Coho Food Consum ption, Proportion of Body W eight/day

Figure 5.3 Illustration of the relationship of the maximum food consumption at satiation with 

temperature and coho weight. Contours are the daily consumption rate, Cs, expressed in prey 

mass per fish body mass per day (g gbw-1 d-1). This figure illustrates the maximum potential 

consumption where food supply is not limited and fish can eat to satiation. 
5-11 

It’s important to note that, on any given day, field consumption may be limited by either the 

physiological limits imposed by temperature or weight or by the food supply. Consumption in 

streams must be determined from in situ observations of feeding, or inferred from weight gain. 

Specific Growth Rate. The specific growth rate (g) is the daily growth rate in relation to 

temperature and consumption, expressed in proportion of body weight per day. Specific growth 

rate functions (“growth curves”) are defined with data from the laboratory studies. This 

relationship was established for sockeye by Brett (1971), and for chinook by Brett et al. (1982). 

Both Brett (1995 pp. 28-29) and Weatherly and Gill (1995) recently reaffirmed this relationship, 

some form of which appears to apply to all species of salmonids. 

Growth rate curves for coho and steelhead have not been previously published, although the 

requisite laboratory studies were available to develop them. Experiments on growth of juvenile 

coho reported by Everson (1973) were conducted at temperatures between 11.1o

 and 22.5oC and 

rations between satiation and starvation. Experiments on steelhead growth reported by Wurtsbaugh 

and Davis (1977) were conducted at temperatures between 6.9o

 and 22.5oC and the full range of 

rations. Growth rate of the population during the experimental period, represented by the average 

population weights, was calculated as: 

(0.5 ( ) )

( )

0

0

w w t

w w

g

e

e

⋅ + ⋅

− = (5.13) 

where we and w0 are the weights at the end and beginning of the experiment, respectively, and t is 

the number of days (generally 25) in each trial. Dry weights were used to calculate the growth 

rates because the moisture content of fish is similar to that of their prey in natural streams 

(Winberg, 1971) and thus the dry weight relationships would appropriately match growth curves of 

fish living in natural environments to their natural food supply.

We used standard linear regression to build mathematical expressions for the growth rate g (g gbw1

d-1) from food consumption, C, (g gbw

-1d-1), temperature, T, (o

C), and initial weight, wo, for coho 

and steelhead. To reduce multicollinearity problems during the model building process, the 

independent variables were centered by subtracting each sample value from the population mean. 

The models were built with the Reg procedure in SAS Version 6.12 (SAS Institute Inc., 1989). 

The general form of the model is: 

g = + ⋅T + ⋅T + ⋅C + ⋅C + 5 ⋅C ⋅T + 6 ⋅W 2

3 4

2 χ 0 χ1 χ 2 χ χ χ χ (5.14) 

The relationship for coho includes squared and linear terms for temperature and consumption and 

has high R2

 (0.93) and low root mean square error (0.0023). Similarly, the relationship for 

steelhead includes squared and linear terms for temperature and consumption as well as a linear 

term accounting for weight of the fish. This relationship also has high R2

 (0.97), and low root mean 

square error (0.0021). Statistically determined coefficients for these relationships are provided in 

Table 5.3. It is essential to note that the growth rate equation (eq 5.14) will compute erroneous 

growth rates if the consumption term is not appropriately constrained for each weight and 

temperature as described earlier. Thus, the use of this model always requires a two step process 

where consumption level is estimated and then growth rate is selected. 
5-12 

The growth curves for coho and steelhead resulting from the full growth model formulation 

calculated at a weight of 1 gram are shown in Figure 5.4. They show the familiar form of sockeye 

and chinook that have been previously published by Brett and others (Brett 1969, Brett et al. 1982, 

Weatherly and Gill 1995), although the curves in Figure 5.4 are derived mathematically. Brett et 

al. (1982) discussed how optimum temperature declines with decreasing ration, thus skewing the 

growth rate curves towards cooler temperature with less food. The mathematical formulation of 

equation 5.14 also produces skew in optimal temperature with declining consumption. The crossproduct (χ5) between temperature and consumption determines this shape. 

The growth curves for each species are similar, but differ in details such as optimal temperatures, 

growth rate, and consumption levels at which growth rates are achieved. One can see that the 

growth rate varies widely with the temperatures and food availability that salmon are likely to 

encounter in the natural environment of Pacific Northwest streams and rivers, suggesting that fish 

size should be strongly influenced by these two parameters. 

In natural streams, temperature varies over the course of the day and some temperature value must 

be chosen to represent the daily temperature. Laboratory tests have noted that the daily average 

temperature approximates the constant exposure test conditions. Experiments where temperatures 

were fluctuated to mimic daily temperature regimes have found either no effect from variable 

temperature (Thomas et al. 1986, Dickerson and Vinyard 1999) or an improvement in growth 

(Spigarelli et al. 1982, Weatherly and Gill 1995). Water temperature fluctuated with natural stream 

temperatures in the experiments of Everson (1973) and Wurtsbaugh and Davis (1977). We select 

the daily mean temperature as appropriate to represent the temperature related to daily growth rate. 

 

Table 5.2 Coefficients for specific growth rate relationship to temperature (T) and consumption (C). Coho 

data are from Everson (1973), Steelhead data are from Wurtsbaugh and Davis (1977). 

Variable Coho Steelhead 

χ0 -0.010649 0.00631 

χ1 0.00096624 -0.0007403 

χ2 -0.00008312 -0.00003909 

χ3 0.450620 0.4302104 

χ4 -3.02056 -1.43765 

χ5 0.01677 0.00735 

χ6 NA -0.00517 

χ7 NA NA 

Regression R-square 0.93 0.97 
5-13 

Figure 5.4. Specific growth rate curves for coho salmon, steelhead trout at 1 gram weight. Coho growth 

curves based on Everson (1973), steelhead curves based on Wurtsbaugh and Davis (1977). Each line is the 

ration is expressed in % satiation, ps. 

Coho Salm on

-0.010

-0.005

0.000

0.005

0.010

0.015

0.020

0.025

0.030

0 10 20 30

Tem perature (oC)

Grow th Rate (g g -1d-1)

100%

80%

60%

40%

30%

Steelhead

-0.010

-0.005

0.000

0.005

0.010

0.015

0.020

0.025

0.030

0.035

0 10 20 30

Temperature (oC)

Growth Rate (g g-1d-1)

100

80%

60%

40%

30%
5-14 

Growth Model 

Substituting equations 5.13 and 5.11 back into equation 5.3 gives a model for computing 

weight as a function of initial weight and the time series of daily water temperatures and 

food supply to which a fish is exposed: 

)

(1

5 6 1

2

4

1

0

3

2

0 0 1 2

−

−

=

⋅ + ⋅ ⋅ + ⋅

= ⋅∏ + + ⋅ + ⋅ + ⋅ +

i i i i

t

i

t i i

C C T w

w w T T C

χ χ χ

χ χ χ χ (5.15) 

The model simulates the change in mass of an individual fish or a cohort of specified size. 

We define a cohort as a group of similar aged fish of the same species experiencing 

identical environmental conditions (Hanson et al. 1997). Cohort weights may be 

represented by the average population weight. It is important to recognize that the 

estimates of weight gain do not consider population interaction effects (Walters and Post 

1993). Thus, they do not account for changes in population density that can also affect the 

average weight and biomass of populations. Measuring growth as the difference in a 

cohort’s weight between two dates is subject to biases from any size-dependent movement 

and mortality (Railsback and Rose 1999). Our estimates only address temperature and 

food effects with the assumption that changes in number or weight are unbiased by size. 

The growth formulations (equations 5.7-5.12) were easily programmed into an EXCEL

spreadsheet to perform the calculations of growth over time. To estimate weight gain of 

populations in natural streams, three parameters must be determined: initial weight (w0), 

temperature (T) and the food consumption (Ci) that must reflect the food supply. 

APPLICATION OF THE METHOD FOR PREDICTING GROWTH IN NATURAL STREAMS 

In the remainder of this section, we apply the growth model to fish populations observed 

in natural streams to demonstrate model behavior and corroborate its predictions. We 

show a number of simulations where the three input parameters were known with varying 

degrees of certainty. No new experiments to determine food consumption or to validate 

the growth model’s ability to accurately predict weight gain were conducted. Instead, 

model performance is examined using data from a number of previously reported studies. 

The parameters required by the model are rather modest. Daily mean temperature is 

known with certainty at any site where temperature is continuously recorded, and we 

restrict growth simulations to sites where this condition was met. Initial weight and food 

consumption must be determined from fish population characteristics observed in streams 

or from known food consumption amounts. Of these two variables, the consumption 

characteristics of juvenile salmonids in natural streams are by far the least well quantified 

(Filbert and Hawkins 1995, Railsback and Rose 1999) and difficult to obtain (e.g., Martin 

1985). For this reason, we begin simulations with data from a field experiment where 

food was well known and the assumptions about the 3 input parameters were limited. 

Mason’s Feeding Experiment. Mason (1976) reported an experiment where juvenile coho 

in a natural stream on Vancouver Island, British Columbia were fed to satiation for a 2-

month summer period. Data from this experiment were ideal for comparing observed with 

predicted growth since environmental limitations on food supply were reduced, if not 
5−15 

eliminated, for most of the individuals in the population. Thus errors associated with 

estimating consumption were minimized and the other two input parameters were well 

known. 

Mason (1976) reported that temperatures were between 12o

 and 13oC during the study. 

We used a constant 12.5o

C temperature for the simulation. Mason (1976) provided 

information on individual fish size within the population. We modeled three cases where 

the initial weight was set to the weight of the largest, average, and smallest fish at the start 

of the experiment. (Figure 5.5). Fish were feed daily at what Mason calculated was a 

satiation ration for the population. For this simulation, growth was predicted at the 

maximum potential consumption determined by the weight and temperature (eq. 5.11), 

assuming that the available rations supplied 100% of the potential consumption and there 

was no food limitation. We also modeled the full range of rations ( ps) expressed as 

percentage of satiation of shown in increments of 80, 60, 40, and 30% in Figure 5.5. 

After simulating growth for the two-month period, the estimated weights of the largest and 

average fish, computed at high food ration, were very similar to their observed weights 

(Figure 5.5). The largest fish in the population increased its weight by 286% (2 g to 5.9 g) 

during this period. The predicted weight for that fish, assuming 100% satiation, was 

within 2% of its observed weight. These results appear to corroborate growth predictions 

and confirm Mason’s conclusion that some of the fish obtained satiation rations. 

Progressively smaller individuals within the population apparently ate at lesser rations 

than the largest individual. (It should be noted that it is not known whether the fish that 

held these ranks at the end of the study were the same individuals as those at the start.) 

The weight of the average size fish was consistent with growth predictions at 80% ration. 

The smallest fish did not fare nearly as well, achieving little weight gain consistent with 

Coho Growth

Mason, 1976

0

1

2

3

4

5

6

7

Initial Observed

Observed Smallest 

Predicted Smallest

Observed Average

Predicted Average

Observed Largest

Predicted Largest

Weight (grams)

Ration 100%

Ration 80%

Ration 60%

Ration 40%

Ration 30%

July 28

Sept 28 Largest Coho, M ason (1976)

0

1

2

3

4

5

6

7

Figure 5.5. Observed and predicted growth of coho in an experimental feeding study in a natural channel in 

British Columbia (Mason 1976). The growth of the smallest, average and largest fish at the start of the 

feeding trial were modeled for the full range of satiation (shown in bars). Weight of the smallest, average and 

largest fish at the end of the experiment are shown relative to growth simulations. The predicted daily weight 

of the largest size fish is shown at right. 
5−16 

subsistence at minimum maintenance rations. Mason (1976) observed that the smaller 

fish tended to be those dying or migrating downstream, emphasizing the importance of 

larger size providing survival benefit through more effective feeding strategies (Puckett 

and Dill 1985, Nielsen 1994). 

Overall, Mason (1976) observed many large fish remaining after the feeding experiment; 

moreover, the average population weight increased significantly. This has been taken as 

evidence that coho growth in streams tends to be limited by food supply. Other field 

experiments have showed improved growth resulting from activities that increased light 

(and temperature) in streams, presumably improving the food resources (Hawkins et al. 

1983, Martin 1985, Bisson et al. 1988). 

Fish Populations Living in Natural Stream Conditions. As they grow during the summer, 

juvenile fish living in streams in the Pacific Northwest experience temperatures that are 

relatively low in spring after emergence from the gravel, warm during the summer, and 

cool again in the fall after mid-September. Food resources may be limited and the manner 

in which natural populations regulate their numbers to match individual and population 

growth needs with available food supply is complex and largely unknown (Walters and 

Post 1993). 

In the next set of simulations we estimate the average growth of a cohort of age 0 coho 

and steelhead in natural streams having natural and unknown food supplies. Fish 

population data were available from sites representing a number of treatments and controls 

in experiments previously reported in the literature (Table 5.3). Physical habitat 

characteristics varied among sites. Like many streams within the region, the stream 

segments where fish were sampled generally contained low amounts of large woody 

debris (LWD) and varying amounts of shade. The examples include sites with water 

temperature spanning very warm to cool (but not cold, e.g., less than 12o

C). Porter Creek 

was the site of a habitat improvement experiment where LWD was added to increase pool 

habitat (Cederholm et al. 1997). Several sites were severely impacted by the Mount St. 

Helens eruption in 1980, but have since experienced recovery of vegetation (Bisson et. al 

1988). A dam-break flood scoured portions of Huckleberry Creek and its floodplain in 

1990 after several years of population monitoring during a feeding experiment (Fransen et 

al. 1993). Bear Creek is a tributary to the Bogachiel River, above a barrier falls, that 

flowed through undisturbed old growth forest (Martin 1985). 

Although the experimental objectives and study designs differed, fish populations in these 

stream segments were sampled routinely in a similar way providing comparable data sets 

to draw from. Populations were sampled by electrofishing using the three-pass removal 

method (Young and Robson 1978, Bisson et al. 1988). The lengths of all captured fish 

were measured, while weights were sub-sampled. We selected a number of cases from the 

available data where fish populations were sampled at the beginning and end of the 

summer. A few sites were sampled mid-season. Early season sampling was conducted 

between March and July while end of season sampling was conducted between September 

and November. Coho occurred at all six sites and steelhead were found at four. The time 

periods encompassed by the temperature and fish population data did not always overlap 

at all sites, and, in a few cases, daily temperatures were not available. 
5−17 

Table 5.3. Characteristics of fish populations as determined by field surveys. 

Data Sources Temperature Characteristics Fish Population Surveys 

Species Site Experiment Reference Year Regime Season 

median 

(oC) 

Annual 

maximum 

(oC) 

Dates 

sampled 

Days 

(t) 

Initial 

weight 

(g) 

End 

weight 

(g) 

Increase 

in weigh

(%) 

Coho Huckleberry Artificial feeding Fransen et al. 1993 1987 Cool 12.5 15.5 7/13-10/1 80 3.0 3.6 16  1988 Cool 11.6 15.0 5/12-10/24 163 0.6 4.2 600  1989 Cool 12.0 14.5 7/16-7/28 101 2.0 4.1 105  Dam-break flood 1991 Warm 15.5 18.5 5/15-10/9 147 0.9 3.2 240  altered valley 1991 Warm 15.5 18.5 6/20-10/9 113 4.1 6.3 54  1998 Warm 14.0 18.0 6/23-9/15 84 2.7 4.0 57  Porter Cederholm et al. 1997 1988 Warm 13.5 18.0 6/28-9/28 92 3.0 6.1 103 

Placement of large woody debris 1989 Warm 14.0 17.0 6/19-9/18 91 3.4 6.0 76 

 1990 Warm 14.6 18.6 6/18-8/27 70 3.7 6.5 76  1991 Warm 12.8 17.3 6/4-9/30 118 2.5 5.4 116  Harrington Volcanic eruption Bisson et al. 1988 1984φ Very warm 16.5δ 29.0 6/24-10/9 108 1.3 6.3 380 

 Vegetation regrowth 1990 Very warm 13.3 20.5 6/22-9/20 88 3.3 6.0 82  Hoffstadt Volcanic eruption Bisson et al. 1988 1984φ Very warm 16.7δ 29.5 6/26-10/3 100 2.4 5.6 133  Vegetation regrowth 1990 Very warm 15.0 26.0 6/21-9/20 88 3.6 6.1 69  Big Salmon carcass 

enhancement 

Bilby et al. 1996, 1998 1994 Cool 12.9 16.1 7/13-9/8 57 4.6 6.0 30 

 Salmon Salmon carcass 

enhancement 

Bilby et al. 1996, 1998 1994 Cool 12.6 16.2 7/20-9/8 50 3.6 5.5 53 

Steelhead Porter Cederholm et al. 1997 1988 Warm 13.5 18.0 6/28-9/28 92 0.8 3.5 338 

Placement of large woody debris 1989 Warm 14.0 17.0 6/19-9/18 91 0.6 3.3 450 

 1990 Warm 14.6 18.6 6/18-8/27 70 0.6 3.2 433  1991 Warm 12.8 17.3 6/4-9/30 118 0.4 3.4 750  Harrington Volcanic eruption Bisson et al. 1988 1984 Very warm 16.5δ 29.0 6/24-10/3 102 0.7 2.9 

 Vegetation regrowth 1990 Very warm 13.3 20.5 6/22-9/14 84 0.5 3.7 640  Hoffstadt Vegetation regrowth Bisson et al. 1988 1990 Very warm 15.0 26.0 6/21-9/20 91 2.1 5.5 162  Salmon Salmon carcass 

enhancement 

Bilby et al. 1996, 1998 1994 Cool 12.6 16.2 7/20-9/8 50 1.4 3.2 129 

Cutthroat Bear Control for riparian 

buffer experiment 

Martin, 1985 1978 Cool 12.2θ 14δ 7/6-11/11 128 2.2 3.4 54 

φContinuously recorded temperature data not available; population weights not simulated with model. δ Estimated from annual maximum temperature by relationship found in Section 3. θThrough September
5-18 

Consumption Rates in Natural Streams. Ideally, consumption rates in natural 

populations are determined by observation of food intake or availability (e.g., Martin 

1985, Filbert and Hawkins, 1995). Such data are rare. In lieu of direct measurement, 

consumption can be approximated by examining the pattern of growth over the growing 

period (Beauchamp et al. 1989, Hanson et al. 1997). Consumption may be inferred by 

estimating how much food must have been consumed, given the prevailing temperature 

regime, to have maintained growth at the observed rate (Hanson et al. 1997). To estimate 

consumption indirectly, populations should be sampled several times over the summer 

duration, because consumption rates should decline through time as fish grow, based on 

the expected allometric effect. Such data are also rare. 

A naturally-spawned coho population was sampled multiple times during the summer of 

1991 at Huckleberry Creek. We selected 1991 data because fish were sampled a number 

of times over a long period from March to October, and we focused on the lower of two 

sites on the same stream because the population was naturally spawned fish with no 

augmentation. Coastal cutthroat trout 

were sampled multiple times at Bear 

Creek in 1978 (Martin 1985). We 

assume that the cutthroat trout 

sampled at this site are representative 

of steelhead trout, for the purposes of 

establishing feeding patterns only. 

We use these two sites to evaluate 

food consumption patterns and to 

establish the relationships used to 

estimate ration for growth simulation 

of the other populations in our study. 

The consumption of food by cutthroat 

trout at Bear Creek was measured 

using stomach evacuation techniques 

(Martin 1985). In situ measurements 

of food consumption were computed 

by the modified Bajkov method 

(1935). 

At Huckleberry Creek, neither food 

consumption nor food supply was 

measured. Apparent food 

consumption by coho between each 

sampling was derived using the 

growth rate curve. First, growth rate 

was computed for the full range of 

consumption from maintenance to 

satiation at the mean temperature and 

population weight for each interval. 

Consumption was then inferred by 

selecting the value from the computed 

Weight, Growth, and Food Consumption for 

Age 0 Coho

0

0.02

0.04

0.06

0.08

3/15 5/4 6/23 8/12 10/1

Date (1991)

Rate (g g-1d-1

)

0

1

2

3

4

Mean Weight (g)

Growth rate Consump rate Mean Wt

Weight, Growth and Food Consumption for 

Age 0 Cutthroat 

0

0.04

0.08

0.12

0.16

5/20 7/9 8/28 10/17 12/6

Date (1978)

Rate (g g-1d-1)

0

0.5

1

1.5

2

2.5

3

3.5

4

Mean Weight (g)

Growth rate Consump rate Mean Wt

Figure 5.6. Mean weight and estimated consumption 

and growth rates for age 0 coho at Huckleberry Cr. , 

lower site, and age 0 cutthroat trout at Bear Creek. 
5−19 

range that corresponded most closely to the observed growth rate. Important assumptions 

accompany this analysis; only food and temperature influence the observed data, our 

growth equations capture these factors correctly, there are no effects from population 

dynamics and there are no sampling errors. Therefore, this approach is only an 

approximation and it is preferable to establish consumption estimates by direct 

measurement of food supply or intake, such as was done for cutthroat. 

Average population weight and estimated (coho) and measured (cutthroat) growth and 

consumption rates for the two populations are shown in Figure 5.6. Growth rate was 

calculated using equation 5.13 assuming a linear growth rate between each sampling 

interval. Weight gain slowed late in the season and growth rate and consumption declined 

from initially higher rates for both species (Figure 5.6). This pattern is consistent with 

what would be expected for an allometric consumption relationship, where consumption 

declines with increasing weight. However, it can also suggest diminishing food supply 

that may accompany increasing size in an environment where food supply is fixed. 

The pattern of consumption 

observed at the sites is of 

particular interest with regard to 

its implications, vis-à-vis the 

physiological limits of 

temperature and weight versus 

food supply. Maximum potential 

consumption (Cs) was computed 

using the mean temperature and 

weight for each interval. It is 

plotted with apparent 

consumption in Figure 5.7. The 

apparent consumption was 

greatest during the early half of 

the summer and declined through 

the later half for both species. 

(Note that the time intervals differ 

for the two sites). 

An important implication of the 

observations in Figure 5.7 is that 

the observed consumption of both 

species was remarkably similar to 

each species’s physiologicallydetermined potential 

consumption. This was true for 

coho from soon after emergence 

from the gravels until the end of 

June or early July and into 

September for cutthroat trout. 

Cutthroat trout actually showed 

greater consumption than 

estimated during this period, but 

values were probably within the 

error range due to estimates of 

Consumption for Age 0 Coho

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0.09

15-Mar14-Apr

14-May

13-Jun

13-Jul

12-Aug

11-Sep

11-Oct

Consumption (g g- 1d-1)

Observed Maximum Potential

Consumption for Age 0 Cutthroat

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

13-Jun

13-Jul

12-Aug

11-Sep

11-Oct

10-Nov10-Dec

Consumption (g g- 1d-1)

Observed Maximum Potential

Figure 5.7. Maximum potential consumption based on temperature 

and weight for the period in relation to observed consumption. 
5−20 

average temperature and because the maximum potential is based on steelhead specific 

growth curves. During these periods, the data suggest that the physiological factors were 

controlling consumption and the average size fish was eating at satiation. That is, even if 

there were more food, the individual fish represented by the average population weight 

would not eat it. Later in summer, the apparent consumption was less than maximum 

potential for both species. The difference between the apparent and maximum potential 

consumption estimates suggests the degree of food limitation in the streams. The closer 

the observed lines are to potential, the closer available ration is to satiation. 

It is interesting to note that it is during the early summper period that the number of 

individuals in a population following emergence is determined, suggesting that population 

density adjusts to match the food supply (Chapman 1966). Carl (1983) observed that the 

number of coho in a population adjusted in response to population density, but that their 

daily growth rates were not dependent on density during the period when population 

adjustment after emergence occurred (May to July). Similarly, rainbow trout growth rate 

was not correlated with their population density. Carl’s (1983) observations of population 

dynamics and growth are consistent with results from our analysis of consumption. 

For coho, the difference between apparent and potential consumption was much larger 

than for cutthroat in the later half of the summer. Apparent consumption declined to near 

starvation ration by the end of the summer, while estimated potential consumption 

remained relatively high because both temperature and weight were moderate. Potential 

consumption actually increased during part of this period reflecting favorable (closer to 

optimum) temperatures. Declining food supply for coho during this period could be 

explained by increasing size of individuals in the population and limited food supply. 

Although the effects of size on consumption are not great, the absolute volume consumed 

by each fish must increase since the consumption is expressed as a proportion of body 

weight each day (Hanson 1997). The same food supplies that may have been adequate for 

the population at small size may represent a much smaller proportion as the fish grow. The 

decline could also reflect a change in the food supply. Studies generally show fairly 

steady or increased food availability during the summer, depending on habitat conditions 

(e.g., under forested vs. open riparian canopies, Hetrick et al. 1998). 

Observed and maximum potential consumption were similar during the entire period for 

cutthroat trout (Figure 5.7). During the time period coincident with coho, cutthroat were 

apparently consuming at or near maximum potential (satiation) and food supply did not 

appear to be a major factor limiting the food consumption by this species. The decline in 

potential consumption in the fall can largely be attributed to lower temperature. One 

reason that consumption is nearer the maximum for much of the summer period may be 

that the cutthroat fry emerge later and are smaller than the coho in late summer. Thus, 

satisfying juvenile food demands is less challenging. 

The apparent consumption patterns observed in populations inhabiting natural streams 

have important implications for the growth simulations. When consumption can be 

assumed to equal maximum potential (eq 5.11) there is no need to calibrate the 

consumption estimates to account for the ecological constraints on food supply (eq 5.12). 

This appears to be a reasonable assumption for age 0 cutthroat throughout the summer. 

We assume that age 0 steelhead have similar feeding patterns as cutthroat, and therefore, 

we assume that we can use the maximum potential consumption to represent age 0 

steelhead. This also appears to be a reasonable assumption for coho until the end of June. 
5−21 

After June, apparent consumption by coho declined significantly relative to maximum 

potential and the consumption estimate must be modified by a factor reflecting food 

limitation (P-value in the Wisconsin model terminology). To do so for coho, we fit a 

linear regression to the apparent coho consumption from July to October (Figure 5.7) 

Maximum Potential Consumption, Cs

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

0.16

23-May

2-Jun

12-Jun22-Jun

2-Jul

12-Jul22-Jul

1-Aug

11-Aug21-Aug31-Aug

10-Sep20-Sep30-Sep

Consumption (g g-1

d-1)

Steelhead Coho

P-value adjustment to coho

A)

Relative Consumption, ps

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

0.90

1.00

23-May

2-Jun

12-Jun22-Jun

2-Jul

12-Jul22-Jul

1-Aug

11-Aug21-Aug31-Aug

10-Sep20-Sep30-Sep

Relative Consumption, 

P

Coho Steelhead B)

Figure 5.8 Estimates of consumption at Huckleberry Creek. Data points are 

estimates of maximum potential consumption based on temperature and weight: 

A) Consumption as a proportion of body mass; B) Relative consumption, 

calculated as daily consumption divided by Cmax generated by the consumption 

equations. The line is the P-value for coho that adjusts consumption based on 

field observations suggesting ecological constraints on food supply. 
5−22 

using a dated time step. Maximum consumption values and the ecological adjusted 

relationship are shown in Figure 5.8A. For readers familiar with the Wisconsin model, the 

relative consumption for each day, (pi), is also shown in Figure 5.8B. During growth 

simulation, each consumption value is calculated in the defined time frame (site adjusted 

P-Value and maximum potential) and the value that is lower is selected. (During the 

spring and early part of the summer, the P-value is not calculated since the continuation of 

the calculation outside the time interval on which it is based may estimate unrealistic 

values, although it does appear to fit the data during this interval as well.) Ideally, 

population weight data would be available at each site where the growth simulations are 

conducted, at least for coho. However, this information was not available at other sites 

(Table 5.4) since it is common to measure populations only at the beginning and end of 

the summer. Therefore, all population growth simulations for coho and steelhead that 

follow use the consumption estimates developed at Lower Huckleberry Creek, depicted in 

Figure 5.8A, to estimate consumption. 

Comparison of Model Predictions to Observed Fish Growth 

The apparent consumption estimates were used to simulate growth of the fish populations 

listed in Table 5.3, using initial weights and temperature recorded over the period between 

the initial and final sampling of populations. We first simulated coho population growth 

at the two sites at Huckleberry Creek where the coho consumption estimates were 

developed (Figure 5.8). Presumably, errors would be less here than at sites where 

consumption was not calibrated site-specifically. The simulation results and observed 

population weights at the two sites are shown in Figure 5.9 

Between the first and last 

sampling time, the naturallyspawned coho at the lower 

site increased in weight more 

than threefold from 0.9 to 3.2 

grams. The predicted weight 

was within 3% (0.1 g) of the 

observed weight at the end of 

the summer. The predicted 

and observed values are so 

close that the simulation line 

appears to connect the 

observations. The model 

correctly predicted the ending 

population weight and those 

observed at each mid-season 

sampling. This is perhaps not 

a surprising result given that 

the food consumption 

estimates (Figure 5.8) were 

developed at this site. 

However, the simulation 

shows that, when food intake is calibrated at a given location, the model can accurately 

predict weight gain. 

Huckleberry Creek, Coho

0

1

2

3

4

5

6

7

28-May

11-Jun

25-Jun

9-Jul

23-Jul

6-Aug

20-Aug

3-Sep

17-Sep

1-Oct

Weight (g)

Low er Predicted Low er Observed

Upper Predicted Upper Observed

Figure 5.9 Observed and simulated weight gain at the lower and 

upper sites at Huckleberry Creek.
5−23 

Importantly, the model simulations are sensitive to the consumption estimates. For 

example, increasing daily consumption by 10, 20 and 30% above the values shown in 

Figure 5.8 through the simulation period yields final weights of coho at the lower 

Huckleberry site of 4.0, 4.9, and 6.0 grams respectively (+25, +53 and +88% greater than 

observed). Thus, errors have a cumulative effect since they are applied over time. 

Interestingly, this relatively moderate increase in consumption results in increased 

population weights that are proportionately consistent with those observed in field studies 

that found improved production in streams where shade had been removed, and 

presumably, food had increased. (Hawkins et al. 1983, Hetrick et al. 1998). These results 

suggest that food supply doesn’t have to increase by a large amount to significantly affect 

the weight that fish achieve over the course of the summer. 

Predicted weight at the upper Huckleberry site was also close to that observed, but 

predictions were not as accurate (-8%) (Figure 5.9). The upper site had a large influx of 

larger fish between the May and June sampling dates, so the simulation was initiated in 

June after the population was determined. The upper site had a slightly different P-value at 

the end of the summer, and the value was used to model this site only (not shown). 

Growth of this population was low during the latter part of the summer and the rate of 

decline of the P-value was greater at this site than the lower site, possibly because of the 

larger size of fish in the 

population. Nevertheless, despite 

nearing minimum maintenance 

ration at the end of the season, 

the large size of the population 

was maintained into the fall and 

no weight loss was observed. 

The model prediction was 

actually more accurate at the end 

of the season than at some of the 

mid-points, where it tended to 

underestimate the weight. 

In the next set of simulations, we 

applied the model to the rest of 

the populations listed in Table 

5.3, except those where 

continuously recorded 

temperature data were not 

available. The consumption 

estimates established for the 

population at the lower site at 

Huckleberry Creek in 1991 were used for all of the coho simulations. We would expect 

that applying these assumptions to other sites could be a significant source of error, since 

presumably food supply differs among sites and the growth estimates are sensitive to this 

parameter. Furthermore, errors accumulate due to the effect of weight on consumption. 

We show the simulation for coho and steelhead at Porter Creek (1988) in Figure 5.10. 

Porter Creek is exemplified because the prediction accuracy was average for both coho 

and steelhead (Table 5.3), the site was far removed from Huckleberry Creek, the 

simulation period was long, and both species were present. Predicted weights closely 

approximated observed weights for both species (Coho: -7%, Steelhead:+11%). 

Porter Creek, 1988

0

1

2

3

4

5

6

7

24-Jun

2-Jul

10-Jul18-Jul26-Jul

3-Aug

11-Aug19-Aug27-Aug

4-Sep

12-Sep20-Sep28-Sep

Weight (g)

Coho Predicted Coho Observed

Steelhead Predicted Steelhead Observed

Figure 5.10 Weight prediction and observed fish weights for 

coho and steelhead populations at Porter Creek, 1988. 
5−24 

Predicted relative to observed weights at the end of the simulation period are shown for all 

of the site by year data sets in Figure 5.11 and listed in Table 5.3. The accuracy of the 

growth simulations was estimated by calculating the sum of unexplained residual 

variations between observed and simulated length for each cohort of coho (n=14) and 

steelhead (n=7) through the following equation: 

2

1

1 ( )

i

i i

predicted

predicted observed n

i W

W W

n

SCE − = ∑=

 (eq. 5.16) 

SCE for both coho and steelhead was 0.08 of the observed for both, while the average gain 

Predicted Relative to Observed Weights: 

Coho Salmon

-100%

-80%

-60%

-40%

-20%

0%

20%

40%

60%

80%

100%

Huckleberry 87Huckleberry 88Huckleberry 89Huckleberry 91Huckleberry 98

Huckleberry (Up)

Big 94

Salmon 94

Hoffstadt 90

Harrington 90

Porter 88Porter 89Porter 90Porter 91

Predicted Relative to Observed (%)

A.)

Predicted Relative to Observed Weights:

Steelhead Trout

-100%

-80%

-60%

-40%

-20%

0%

20%

40%

60%

80%

100%

Salmon 94

Hoffstadt

90

Harrington

90

Porter 88Porter 89Porter 90Porter 91

Predicted Relative to Observed (%)

B.)

Figure 5.11. Differences between predicted and observed average weights of fish populations. 
5−25 

in weight during the period was 1.25 and 4.14 times initial weight, respectively. Some 

predictions were above and some were below the observed weights, with no apparent 

patterns. Model predictions averaged within 4% of observed for both species considering 

the sign of the difference between observed and predicted, and 11% when averaging its 

absolute value. The largest prediction error occurred at Huckleberry Creek in 1987 

(+44%). There was very little weight gain at this site during this year and our 

consumption estimates were clearly too high. As another example, the steelhead 

population at Hoffstadt Creek was under-predicted by 24%, perhaps because the 

population was augmented by hatchery fish. Since there are no assumptions about food 

supply used to model steelhead, this error suggests that one or more of the model 

parameters were probably incorrect for this population and year. Our unexplained error is 

consistent with, but somewhat less than achieved by Mallet et al. (1999) for grayling in a 

European river where they used a similar approach for accounting for temperature but did 

not consider consumption. 

The close correspondence of observed and predicted weights was welcome, if not 

somewhat surprising, given potential errors associated with using consumption estimates 

extrapolated from other streams in the case of coho, or from other species (cutthroat), 

albeit closely related, in the case of steelhead. While the physiological-based estimates of 

consumption should be broadly applicable to the species as formulated in this paper, 

varying perhaps by genetics among races, the ecological constraints on food supply can 

vary significantly among sites. Many factors potentially influence food supply; 

population dynamics and competition for food, (Walters and Post 1993), riparian, inchannel habitat characteristics that control primary and secondary production (Hawkins et 

al. 1983, Bilby and Bisson 1988, Hetrick, Murphy 1998, Railsback and Rose 1999) and 

even the energy content of prey (Stewart and Ibarra 1991). Information about these 

riparian, in-channel habitat and population characteristics are not directly measured, but 

are integrated by growth rate in the time series of daily temperatures, consumption and 

body weights. The average growth rate as a function of annual maximum temperature is 

shown in Figure 5.12. Railsback and Rose (1999) observed that measured growth rates of 

trout in the Sierra Nevada, and those predicted by the Wisconsin model, varied from year 

Growth Rate of Fish Populations

Coho = -0.0002T + 0.0117

R2 = 0.20

Sthd= -0.0011T + 0.0399

R2 = 0.50

0.000

0.005

0.010

0.015

0.020

0.025

10 15 20 25 30

Annual Maximum Temperature (oC)

Daily Growth Rate (g g-1d-1)

Coho Steelhead

Figure 5.12 Growth rate calculated by the model averaged for the sampling 

period in relation to annual maximum temperature at each site. 
5−26 

to year and with site characteristics such as flow. We observed similar variation in the 

sites sampled in multiple years (Table 5.3), although we did not attempt to account for 

differences other than those caused by differences in temperature. 

For the final two simulations, we examined how modeling weight over the full range of 

rations and weights of individuals within the population would relate to estimates based 

on averages. Presumably, individuals are feeding over the range of possible rations, from 

minimum maintenance to satiation, with consumption limited by their hierarchical 

position within the population (Puckett and Dill 1985, Nielsen 1994). In theory, some fish 

Coho

H uckleberry C reek 

0

1

2

3

4

5

6

7

8

9

10

30 40 50 60 70 80 90

Length (mm)

Weight (grams)

Coho 

Huc kle be rry Cre e k

0

1

2

3

4

5

6

7

8

9

10

30 40 60 80 100

% of Satiation 

Weight (grams)

Steelhead

Salmon Creek 

0

1

2

3

4

5

6

7

8

9

10

40 50 60 70 80 90

Length (mm)

Steelhead

Salmon Creek

0

1

2

3

4

5

6

7

8

9

10

30 40 60 80 100

% of Satiation 

Weight (grams)

Figure 5.13. Examples of length-weight relationships for coho salmon population at the end of 

summer growing season at Huckleberry Creek and the steelhead trout population at Salmon Creek. 

Also shown are the predicted weight for each ration for Huckleberry Creek and Salmon Creek, 

calculated with the site’s temperature from the period between population sampling, and a starting 

weight as observed at the site at the initial sampling. 
5−27 

may be feeding at satiation ration, although population density controls could prevent fish 

from achieving this level of ration (Brett 1995). Individuals feeding at less than minimum 

maintenance ration over extended periods of time are likely to be lost from the population 

due to starvation. 

Weight gain was simulated for the coho population at lower Huckleberry Creek (1991) 

and the steelhead population at Salmon Creek (1994) for the intervals between fish 

population samplings, with weight initialized at the observed average population weight. 

The maximum potential consumption was computed each day based on temperature and 

fish weight, then categorized into 5 rations between 100% satiation and minimum 

maintenance (30, 40, 60, 80 and 100%). A minimum of 30% satiation was chosen 

because this is very near starvation but still allows some growth at some temperatures. 

Predicted ending weight at each ration level is shown in comparison to the range of 

individual weights observed in the population on the final sampling date in Figure 5.13. 

The range of weights observed within the two populations near the end of the summer was 

wide, but typical of what is usually observed: the largest fish were 4 to 6 times larger than 

the smallest fish for both species. There was a range of weight among individuals within 

the population early in the season, although it was considerably narrower than it was at the 

end of the summer (Figure 5.14). The growth model predicted a range of weights that was 

similar to observed. Model predictions suggested that coho as large as 9.5 grams could 

occur at 100% satiation ration and the largest fish observed was 7.4 grams, a weight 

consistent with high consumption, but less than satiation. For steelhead, the largest 

predicted individual was about 3.5 grams, considerably smaller than the largest individual 

in the population assigned to the 0 age class by the field biologist. Seventy-five percent of 

the steelhead individuals were within the predicted range. The steelhead that are larger 

than the 100% ration prediction could actually be yearling fish since scale analysis was 

not performed, and it can be difficult to distinguish age of the fish from observation alone. 

Exact correspondence in individual weights was not expected since the average population 

weight was used to initialize the simulation. If we apply the same analysis of the growth 

of the largest, average and smallest fish in the coho population at Huckleberry Creek as 

we did earlier for Mason’s population, we estimate the average degree of satiation for 

each ranked fish (Figure 5.14). During this period there was only a small change in the 

number of fish in the population, although we do not know if the fish that held these ranks 

at the end of the summer were the same one’s that held them at the beginning. As in 

Mason’s experiment, the largest fish ate at a higher satiation level than smaller fish in the 

population. The largest fish is estimated to have eaten at 73% satiation, the average fish at 

62%, and the smallest fish at 45% satiation, averaged over the growth period. These 

results are consistent with Fausch (1983), who found that dominant fish obtained more 

energy during natural feeding. 
5−28 

Modeling individuals within the range of population sizes produced qualitatively similar 

results as modeling the average fish for the range of rations, in terms of the range of 

weights predicted and the average consumption. Perhaps this is because the average 

population ration was in the mid-range of rations. Nevertheless, the growth model 

suggested a plausible range of weights at the individual scale using a range of rations, and 

the model closely approximated the average population weight and at the population scale 

using average weights and field-calibrated ration (Figure 5.11) 

Finally, we conducted a brief sensitivity analysis to demonstrate model behavior over a 

range of temperatures and food consumption levels. Weight gain was simulated using the 

same time interval, starting weight, and range of rations from satiation to minimum 

maintenance, allowing only the temperature to vary for each site. June 1 to Sept 15 was 

selected to represent the primary growth period for coho and steelhead since both species 

should have emerged from the stream bed by this date, and our data set from temperature 

sites was complete during this interval. A representative initial weight was estimated by 

regressing weight of all the populations sampled early in the summer season with time. 

Steelhead tended to be very similar in weight early in the season, despite differences in 

sample dates, averaging 0.5 grams on June 1 (julian day 152). Coho weight varied more, 

and in some cases, included some sites with hatchery-raised fish. Coho averaged 1.4 

grams on June 1. Temperature simulations included site temperature, which varied 

Coho Growth

Huckleberrry 1991

0

2

4

6

8

10

12

14

16

Initial Observed

Observed Smallest 

Predicted Smallest

Observed Average

Predicted Average

Observed Largest

Predicted Largest

Weight (grams)

Ration 10

Ration 80

Ration 60

Ration 40

Ration 30

May 15

Oct 9

Figure 5.14 Observed and predicted weight of coho in Huckleberry Creek 

(1991). The growth of the smallest, average, and largest fish at the start 

of the summer (May 15) were modeled for the full range of satiation 

(shown in bars). Weight of each class at the end of the period (Oct 9) are 

shown at the end of the summer relative to growth simulations. 
5−29 

through the season, as well as constant temperature for the period (Figure 5.15). 

Simulated weights based on each site’s temperature regime are plotted as circles; constant 

temperature predictions are the solid parabolic lines at each ration, and the observed fish 

population weights are plotted as squares. The solid lines computed at constant 

temperature follow the growth rate curves on which they are based, but they reflect the 

effect of long-term exposure while compensating for effects of increasing weight on 

Coho Salmon

0

1

2

3

4

5

6

7

8

9

10

11

0 5 10 15 20 25 30

Temperature (oC)

Weight (grams)

 Predicted 30% Predicted 40%

Predicted 60% Predicted 80%

Predicted 100% Average Field Population 

Constant 

Tempature

100% Satiation

80% 

60%

40%

Steelhead

0.0

1.0

2.0

3.0

4.0

5.0

0 5 10 15 20 25 30

Tem perature (oC)

Weight (grams)

 Predic ted 20% Predic ted 40%

Predic ted 60% Predic ted 80%

Predic ted 100% A v erage Field Population 

C o nstant 

T emp erature

1 0 0 % 

Satiation

80%

60%

40%

30%

Figure 5.15. Summary of observed and predicted weights for coho salmon and steelhead 

trout. Solid lines are the weight predicted at constant temperature for 106-day simulation 

(June 1-Sept 15). Circles are the predicted weights at each site and ration based on the daily 

temperature regime for the period. Squares are the average weight of observed populations. 

Initial weight was 1.4 grams and 0.5 grams for coho and steelhead, respectively. 
5−30 

consumption. 

Weight can be strongly affected by both the prevailing temperature and the amount of 

food available to fish. Either factor can have the same level of effect, although maximum 

weight can only occur when prevailing temperatures are near the species’ physiological 

optimum temperature and food supply is high. Low to moderate weight can result from 

many combinations of temperature and food supply, perhaps helping to explain why it is 

difficult to determine whether food or temperature is limiting growth in natural streams 

from empirical field observations alone. 

Weight is maximized when temperature over the course of the summer is closer to optimal 

temperature (Figure 5.15). Washington streams and rivers tended to be near optimal, 

despite significant differences in the temperature patterns at the sites when indexed by the 

annual extremes such as annual temperature (Figure 3.1). The effects of temperature are 

more pronounced at higher levels of food. Interestingly, observed weights of coho and 

steelhead populations show patterns consistent with those simulated across the range of 

temperatures, although food supply at sites was not well known. The biomass of fish 

populations measured at the study streams is moderate to high relative to those reported 

from the coastal areas of the Pacific Northwest and Alaska (Bisson and Bilby 1998). At 

very low levels of food, growth is very low, but is somewhat better at cooler temperatures. 

The weights predicted using observed temperatures do not deviate far from those 

calculated with a constant temperature (Figure 5.15). Thus, since there is little loss of 

information using the constant temperature, these simulations suggest that the weight gain 

at sites much warmer and colder than those available in our data sets are realistic. The 

constant temperature can be represented by the median temperature of the period. Figure 

5.13 suggests that the model is sensitive to temperature, and would estimate significantly 

different weights for significantly different temperature profiles. The similarity in the 

weights predicted with the growth simulation methods at many of the sites (Figure 5.15) 

does not appear to reflect lack of sensitivity of the model, but rather the relatively narrow 

range of average temperature at streams within the sample. Population weights should be 

less in streams with predominantly warmer or colder than occur in our data sets. 

DISCUSSION 

We have developed a bioenergetics-based approach to assessing the effects of temperature 

on growth of salmonids. At this time, it has only been applied to age 0 steelhead trout and 

coho salmon living in natural streams. Growth of other salmonids could easily be modeled 

with the same approach if the basic consumption and growth functions can be established. 

There are many examples of growth modeling based on bioenergetics principles available 

in the literature. Most use similar methods for estimating consumption terms but provide 

more comprehensive evaluation of energetic functions in addition to growth (Beauchamp 

et al. 1989, Kitchell et al. 1974, Hewlett and Johnson 1987, Beauchamp et al. 1989, 

Hanson et al. 1997.). Our formulation differs from these in that energy consumed by 

growth, as evidenced by observed growth rate, is the only energy function considered, and 

we make no attempt to close the energy balance between intake and expenditure. Thus, 

we view our approach as bioenergetics-based but not a true bioenergetics model. 

However, the growth model developed here shares many key elements with bioenergetic 
5−31 

models, and therefore its application enjoys many of the same challenges and criticisms 

(Ney 1993, Hansen et al. 1993). 

In a review of bioenergetics models, Ney (1993) noted that corroborative studies of 

bioenergetics models showed a number of deficiencies that appear to compromise their 

ability to estimate consumption and growth of non-captive fish accurately. Some critics 

feel the models are overly complex (Ney 1993) and prone to errors in parameter 

estimation (Boisclair and Leggett 1989). A comprehensive model of energy functions 

results in the proliferation of parameters, which may create difficulties in adequately 

informing a number of the input variables, and defining some of the energetic 

relationships. Hansen et al. (1993) comment that bioenergetics models having 20 or more 

input parameters, each with its own estimation error, can lead to estimates of consumption 

or growth that, in some circumstances may have to differ by 100% or more to be judged 

statistically different (Boiscalir and Leggett 1989.) This poses particular problems for 

trying to use these models to sort out complex ecological interactions. Beauchamp et al. 

(1989) noted that bioenergetics models are used more frequently to predict consumption 

than growth because of the additional errors associated with bringing in other equations. 

To the contrary, Hanson (1997) argues that forcing a balance of the energy budget acts to 

limit error propagation (Bartell et al. 1986). 

The growth model developed in this paper is relatively simple, especially in field 

application, but the errors are unconstrained. Our model shares some of the same 

relationships that are most sensitive to the errors that are found in full bioenergetics 

models. Specifically, the consumption terms that apply to the physiologic controls of 

temperature and allometry, as well as the food supply, are both important in estimating 

growth (Stewart et al. 1983, Beauchamp et al. 1989, Bartell et al. 1986, Hanson et al. 

1993). The physiologic relationships appear to be reasonably well established for 

salmonid species, given the similarity of parameter values developed from different 

studies (Hanson et al. 1997). We achieved good modelling results using these values. 

There have been questions as to whether such laboratory study results can be used for 

predicting response of fish living in natural environments. Laboratory studies have unique 

conditions of food, environment, and population pressures that themselves may create 

stresses that may not be observed in natural settings. In our analyses, the gain in weight of 

fish living in stream environments was closely approximated by relationships derived 

from laboratory studies. This suggests that laboratory studies can be used with some 

confidence to predict responses in more natural ecological settings. 

Achieving a quantitative understanding of the cause and effect linkage between food 

consumption and temperature in natural environments is a significant challenge, as 

suggested by Figure 5.14, where different conditions can produce similar population 

weight. Field studies are labor intensive and field estimates are laden with their own 

assumptions and subject to their own errors (Ney 1990). Trying to discern such 

relationships by empirical observation alone is problematic, given the multivariate and 

dynamic nature of the interaction and the difficulty of measuring some of the key 

fundamental relationships in natural environments (Brett 1971, Boisclair and Leggett 

1991, Railsback 1997). While the growth model can not solve these problems, it can help 

field investigators to develop physiologically-based hypotheses that may help them 

understand the responses they observe. 
5−32 

We found that our simplified application of bioenergetics principles provided some useful 

structure to the analysis of field observations of environmental temperature and food 

supply that appeared to address some of the complexity of the interactions. This could 

lead to greater insight into their inter-relationships when compared to empirical 

observation alone. For example, our results suggest that food limitation existed for some 

species and not for others (at least at this age), and that food limitation varies in time in 

ways that have important implications for population dynamics. Additional study of food 

consumption and supply in natural streams, where data are extremely limited, would be 

very useful, given the importance of these factors in determining fish productivity and 

response to temperature. It seems clear from this analysis that there is more opportunity to 

affect productivity (population weight and density) by changing food supply than by 

changing temperature within the range of temperature observed in the study streams, at 

least during the summer months. This interpretation is consistent with field studies that 

have shown increased productivity with canopy removal, despite increases in temperature 

that at times have appeared to be adverse (Hawkins et al. 1983, Bisson et al. 1988). 

Importantly, the hypotheses presented by the model can be explicitly tested in field 

experimentation, and rejected if model results fail to predict observed responses. In this 

first application, the model hypotheses were confirmed, but more carefully implemented 

field experiments would be beneficial. We caution that these results corroborate the utility 

of the model, but do not constitute a rigorous test of the model or its underlying 

assumptions (Hansen et al. 1993). Validation would best be achieved by field and 

laboratory experiments to confirm the growth curves and allometric functions and to 

independently determine food availability, rather than estimate consumption from 

observed change in average population weight through time (e.g. Filbert and Hawkins 

1995, Martin 1985). Such tests would help refine the input parameters, as well as reveal 

whether our assumption that energetic functions, other than those captured in the 

allometric and growth rate functions, can be ignored in estimating growth effects of 

environmental temperature at a necessary level of precision. 

It was somewhat surprising that we were able to achieve such good predictions of weight 

gain compared to observations of fish populations given the sparse amount of data used to 

develop consumption estimates. Weight estimates for populations of juvenile salmonids 

were generally within 11% of observed following growth over several months during a 

rapid growth phase. However, the model is always likely to perform best using sitespecific estimates for food consumption. Nevertheless, extrapolated estimates proved 

satisfactory for our purposes. 

We conclude that the methods developed in this section perform well for the purpose of 

assessing the effects of environmental temperature on juvenile salmonid growth. Ney 

(1993) concluded that, in their present state of development, bioenergetics models are best 

suited for making relative rather than absolute predictions such as comparing outcomes of 

different habitat and food availability scenarios. This is how we emphasize use of this 

method. The method is used in Section 6 to evaluate the effects of temperature regime on 

salmonid growth relative to temperature thresholds that could be used as water quality 

criteria. 
5−33 

Conclusions 

â Methods of predicting growth based on quantitative bioenergetics principles can be 

applied to streams to assess the effects of temperature on juvenile salmonid growth, 

with results that are consistent with observed fish population growth patterns. 

â The method is sensitive to temperature and can be applied to the daily temperature 

regime making it a useful tool for assessing the biological impacts of temperature in 

natural streams. 
5−34 
 6-1 

SECTION 6 QUANTIFYING GROWTH EFFECTS IN RELATION TO 

TEMPERATURE THRESHOLDS 

Abstract 

In Section 5, we developed a method to estimate the effects of temperature and food 

consumption on the gain or loss of weight of coho salmon and steelhead trout during the 

summer months. In this section, we use the methodology specifically to identify 

temperature indices, including the duration and magnitude of threshold temperatures that 

minimize the negative growth effects of the temperatures that occur over long periods of 

time. The analysis provides an objective method to establish temperature criteria based on 

protecting the opportunity for growth, rather than avoiding catastrophic impacts. 

Analyses demonstrated that some effects from the ambient temperature occurred at all 

sites, because no stream spends all of its time at a fish’s optimal temperature for growth. 

Sites with both chronically high and chronically low temperatures were estimated to 

experience significant growth loss. Using the 7-day maximum temperature, and allowing 

10% growth loss, the upper threshold for coho salmon was found to be 16.5o

C and the 

lower limit was 13.0o

C. The range for steelhead was wider at 14.5 to 21oC. Many sites 

included in the analyses exceed current temperature criteria for Washington (annual 

maximum of 16o

C). Streams that far exceed the criteria also were predicted to have high 

growth loss. Those near the threshold criteria (±1o

C) appeared to experience the best 

temperatures for growth. Streams with lower temperature (<13o

C) also had higher growth 

loss and temperature less than 10o

C were adverse to growth of both coho and steelhead. 

The method could be used to identify thresholds for other temperature indices such as 

annual maximum and 7-day mean, or other levels of growth loss. 

Key findings include: 

â It is feasible to apply a risk-based approach that uses data without safety factors 

and produces an estimate of cumulative risks. This technique is a true quantitative 

benchmark that is measurable and testable. 

â The majority of temperatures experienced by salmonids are generally suboptimal 

for growth, and these exert some cost on the maximum potential growth. 

â Colder water temperatures are not necessarily better for rearing salmonids, and 

warmer water temperatures are not necessarily worse. Concepts of safety factors 

in selecting thresholds need to be exercised with some caution. Thresholds that 

are too low can also negatively effect growth. 

â Despite what appear to be large differences in temperature among sites, especially 

with regard to the warmest temperatures that occur each summer, there was less of 

a difference in the predominance of temperatures that are important to growth. 

Sites with significantly different temperature regimes often have similar predicted 

growth risk. 
 6-2 

â An upper threshold for the 7-day maximum temperature of 16.5o

C minimizes growth 

losses for coho. A 7-day maximum temperature or 20.5o

C minimizes growth losses 

for steelhead, based on an analysis that does not consider population dynamics. 

Introduction 

One of the most important aspects of temperature in natural environments is its effect on 

growth. Growth is regulated by a complex interrelationship between food supply, 

population dynamics, and the physiologic responses of the fish to temperature (Weatherly 

1972). Trying to discern such relationships by empirical observation alone is problematic, 

given the multivariate and dynamic nature of the interaction and the difficulty of 

measuring some of the key fundamental relationships in natural environments (Brett 1971, 

Boisclair and Leggett 1991, Railsback 1997). The physiological responses, including 

energy consumption and expenditures are generally studied under laboratory conditions, 

and have been quantified for many species of salmonids (Weatherly and Gill 1995, Brett 

1995). Field studies are labor intensive and they are laden with their own assumptions and 

subject to their own errors (Ney 1990). Consequently, there are relatively few field 

studies that have successfully established the linkage (Martin 1985, Filbert and Hawkins 

1995). A combination of field and laboratory study offers the best hope for establishing a 

quantitative understanding of the cause and effect linkage between growth and 

temperature in natural environments (Hansen et al. 1993). 

Laboratory studies have produced quantitative relationships between energy consumption 

and expenditure mechanisms. The rate at which most energetic functions proceed is 

mediated by ambient environmental temperature. Bioenergetics models have been 

developed to help manage understanding of the multitude of physiologic responses to 

temperature (Kitchell et al.1974), accounting for energy consumption and expenditures. 

Some have been packaged into software programs (Hewlett and Johnson 1992, Hanson et 

al. 1997), and have been proven to be useful tools for a number of applications in fisheries 

management (Hansen et al. 1993). These include the rearing of fish in hatcheries 

(McLean et al. 1993), and populations in natural environments (Hanson et al. 1997). The 

downside to such models is that they often require many parameters to inform a number of 

energetic functions, most of which are difficult to quantify in natural environments. Thus 

their application to explore ecological responses to environmental temperature are more 

limited, although researchers have recently found them promising for this purpose (Filbert 

and Hawkins 1995, Preall and Ringler 1989, Railsback and Rose 1999). 

In Section 5, we developed and corroborated a bioenergetics-based approach that can be 

used to evaluate the effects on growth of the variable temperatures that occur over the 

juvenile rearing period in natural stream environments. The model treats the population as 

a cohort, and does not account for population density effects. Thus, the method allows us 

to focus on temperature differences among streams while minimizing biological data 

requirements. The mathematical model simulates weight gain over a specified duration, 

and requires only three input parameters (temperature, initial weight, and daily food 

consumption). The method is sufficiently simple that it can be applied in field 

experimental studies. Only the food consumption term is difficult to assess in streams 

(Filbert and Hawkins 1995). To our knowledge, no simple methodology for assessing 

food availability has been developed. 
 6-3 

The ecological constraints on food supply can vary significantly among sites, and they are 

potentially influenced by many factors: population dynamics and competition for food, 

(Walters and Post 1993), riparian, in-channel habitat characteristics as they control 

primary and secondary production (Hawkins et al. 1983, Bilby and Bisson 1988, Hetrick, 

Murphy 1998, Railsback and Rose 1999) and the energy content of food prey (Stewart and 

Ibarra 1991). Information about these riparian, in-channel habitat and population 

characteristics is embodied in the time series of daily temperatures, consumption and body 

weights of fish as they grow in natural environments over time. 

Despite difficulties in establishing in situ food consumption, we were able to generate 

estimates of consumption for juvenile coho and steelhead from observations of fish 

growth over time that produced close correspondence between simulated and observed 

weight gain, even when parameters were extrapolated among streams. Weight gain 

predicted for 16 populations of coho and 8 populations of steelhead were generally within 

11% of the observed, during a rapid growth period where weight gain ranged from 67 to 

415%. Based on corroboration with observed population growth, we concluded that the 

method is a useful tool for quantifying the effect of temperature regime on growth, though 

not biomass until population effects are accounted for. 

In this section, we use the model to perform a series of relative comparisons of growth 

effects from observed stream temperatures from a number of stream sites with widely 

varying temperature profiles. We pay particular attention to interpreting results relative to 

temperature thresholds that are often used for water quality criteria. 

Growth Simulation Method 

The growth model was used to simulate weight gain for coho and steelhead using standard 

timelines, initial weights, and consumption estimates for each species (Table 6.1). Only 

the temperature varied, according to measured daily temperature at the 19 sites. Thus, this 

assessment isolated the effects of the long-term temperature on growth. Temperature 

profiles at the sites varied from very warm to cold (Table 6.2, and further described in 

Section 3), and many exceed the current Washington Department of Ecology temperature 

criteria. The temperature data set did not include very cold streams (12o

C or less). These 

typically occur in the extreme headwaters, and are small and non-fish-bearing in this 

region (Black 2000). To 

represent these streams, 

we also conducted two 

simulations using a 

constant temperature of 

8o

 and 10oC. 

Growth of coho and 

steelhead populations 

was characterized by the 

average population 

weight according to the 

parameters identified in 

Table 6.1. Simulations 

were run from June 1 

through September 15 

Table 6.1 Input variables for growth simulations. See Section 5 for a 

full description of the growth model and the determination of 

parameters. 

Input Variables Coho Steelhead 

Simulation Period June 1-Sept 15 June 1-Sept 15 

Initial Weight (g) 1.4 0.5 

Food Consumption 

(g g-1d-1) 

Declines through time with 

ecological constraint (see 

Section 5, Figure 5.8) 

Assumed at maximum 

potential calculated by 

temperature and weight 

(see Section 5, Figure 

5.8) 

Temperature (o

C) Daily mean calculated from 

hourly temperature 

measurements 

Daily mean calculated 

from hourly temperature 

measurements 
 6-4 

because we had a complete temperature record for all of the sites and this period 

encompasses most, if not all, of the growth occurring during the summer rearing period. 

The consumption was varied according to observed or estimated rates inferred from 

growth of fish between time periods as described in Section 5. The analysis found that 

there are important differences between fish species in their consumption patterns. Age 0 

steelhead appear to have no environmental constraint on food supply, subject only to 

physiological constraints imposed by temperature and weight. Coho show ecological 

constraint on food supply beginning in late June that lasts through the summer season. 

Fausch (1984) observed similar patterns, concluding that these differences are likely to 

influence the species’ response to temperature. 

Coho

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

Weight (g)

Steelhead

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

Weight (g)

Figure 6.1 Estimated weight after 106-day growth period using the same initial weight and 

food consumption assumptions at each site. 
 6-5 

Temperature Effects on Weight Gain 

The predicted weights after 107 days of simulated growth are shown in Figure 6.1. 

Because the initiating assumptions are the same, the predicted weights are similar. Any 

differences in observed weight are due to the temperature at each of the sites. Coho vary 

up to 1.2 grams (31% of the mean), and cooler sites generally had greater weights. The 

predicted weights of steelhead vary by only 0.5 grams among sites (12% of the mean), 

despite large differences in the temperatures. 

Relative Temperature Effects on Growth 

Our approach to evaluating the effects of temperature on salmonid growth is to estimate 

the weight gain achieved during the summer according to the observed temperature 

regimes and estimates of food availability, then compare that growth to a reference point. 

Our reference point is the growth the fish would have achieved if temperatures had been at 

optimal for growth, and at the same level of food consumption. Brett (1971), Brett et al. 

(1982), Railsback and Rose (1997), Preall and Ringler (1989) used a similar reference 

approach, and growth models as a basis for prediction. 

Establishing Optimal Temperature and Growth. Previous studies have established a 

maximum potential growth as the point of reference. Many have used the size calculated 

at constant optimal temperature and maximum consumption (a reference only observed at 

optimal temperature and low weight). (See Section 5 for a full discussion of this point). 

This represents the maximum possible growth that fish could achieve, and it is probably 

only observed in the laboratory setting where both of those parameters can be maintained 

at required levels and for a short period in a fish’s life. 

It’s important to note that optimal temperature is not static but varies with weight and 

ration (Stewart and Ibarra 1991, Brett et al. 1971, Brett 1995). The change in optimal 

temperature with rations between the minimum required for basic metabolic functions and 

the maximum at satiation is shown for coho and steelhead in Figure 6.2. Note that 

consumption rates differ for the two species, as does the range of optimal temperatures. 

There are also changes in 

consumption rates as the fish 

gain weight, referred to as 

allometric relationships (Brett 

1995, Stewart and Ibarra 1991). 

In the optimal growth 

simulations, we reduce 

consumption according to fish 

weight and any ecological 

constraints on the food supply. 

Optimal temperature is then 

reduced accordingly. This 

yields realistic estimates of 

optimal growth reflecting welldocumented allometric effects. 

By adjusting optimal 

temperature with consumption, 

we believe the reference optimal 

growth simulation realistically 

Optimal Temperature in Relation to Food Consumption

10

11

12

13

14

15

16

17

18

0 0.05 0.1 0.15 0.2

Consumption (g g-1d-1)

Optimal Temperature ( oC)

Coho Steelhead

Figure 6.2 Optimal temperature in relation to food consumption. 

Consumption is expressed as grams of prey per gram of body mass 

per day. The range displayed is from satiation to minimum 

maintenance for each species. 
 6-6 

represents the potential effects of temperature. It is not clear if previous researchers who 

have used optimal growth benchmarks have performed this adjustment. 

In the second scenario, weight is calculated allowing the daily growth rate (gi) to vary 

with temperature according to the specific growth curves in Figure 5.4, and food 

consumption assumptions (as illustrated in Figure 5.8). We express growth in terms of the 

Reduction In Maximum Growth (RMG). RMG is defined as the percentage of reduction 

in growth for the site specific temperature compared to the maximum growth achieved at 

the optimum temperature for each ration. 

RMG (%) is calculated according to: 

(1 ) x100

W

W RMG

t optimal

t

=

= − (6.1) 

A sketch of the calculation is provided in Figure 6.3. Since no stream had optimal 

temperature all of the time, the gain in weight with varible temperature should be less than 

that for the optimal case. The RMG is expressed as a percentage of the maximum weight. 

The lower the value of RMG, the less the deviation from the optimal growth rate. A RMG 

value of 0% suggests there is no growth loss due to the temperature at the site. 

The reduction in maximum growth due to temperature varied among species (Table 6.2). 

It is clear that fish spent only a portion of their time in the optimal temperature range 

during their growth period, since RMG was greater than zero at all sites for both species. 

Consequently, there was some cost to growth for salmonids living in Pacific Northwest 

streams due to their temperatures during the rearing growth phase (Table 6.2). However, 

the RMG were generally within 20% for both species at all sites. 

0

0.5

1

1.5

2

2.5

3

3.5

4

26-Mar-97

6-Jun-97

16-Aug-97

27-Oct-97

W eight (grams)

RMG

 Grow th at 

optim al te m p

Grow th at 

observed 

tem p

Figure 6.3 Illustration of the reduction in maximum growth based 

on two temperature scenarios; growth at constant optimal 

temperature and with the variable growth rate based on the site’s 

temperature regime. 
 6-7 

Table 6.2 Location and temperature characteristics of temperature sites used in reduction in growth analysis. 

Site characteristics Temperature characteristics Reductio

Site Watershed Basin Area 

(km2) 

Year 7-Day Maximuma

oC 

7-Day Meanb

oC 

Annual Maximumc

oC 

Season Mediand oC Co

Deschutes River mainstem 

Deschutes 145.0 1994 21.0 18.4 22.5 15.0 13

Thurston Creek Deschutes 9.1 1994 14.9 14.1 15.5 12 10Hard Creek Deschutes 3.0 1994 14.0 13.0 14.0 11.0 19Ware Creek Deschutes 2.8 1994 17.5 16.1 18.3 12.9 14Huckleberry Creek Deschutes 5.3 1991 18.4 17.6 18.5 15.5 18Chehalis River mainstem (Site 1) 

Chehalis 181.8 1997 21.1 18.9 22.1 15.6 16

Chehalis River mainstem (Site 2) 

Chehalis 57.5 1997 22.1 18.2 23.2 14.5 15

Chehalis River mainstem (Site 3) 

Chehalis 29.5 1997 20.6 18.6 21.4 14.3 13

Crim Creek Chehalis 22.0 1997 18.8 16.9 19.4 14.3 11Lester Creek Chehalis 10.4 1997 18.4 16.3 19.0 14.2 9.Thrash Creek Chehalis 16.7 1997 15.3 14.3 15.8 12.3 8.Rogers Creek Chehalis 13.1 1997 15.7 14.1 16.1 12.6 6.Big Creek Chehalis 9.0 1997 16.5 14.6 16.9 12.5 6.Sage Creek Chehalis 5.3 1997 16.5 14.6 16.9 12.5 9.Salmon Creek Chehalis 8.9 1997 15.8 14.2 16.2 12.3 8.Mack Creek Chehalis 2.8 1997 12.9 12.5 13.1 11.7 6.Porter Creek Chehalis 25 1990 17.5 16.3 18.6 14.4 17Hoffstadt Creek Toutle 25.6 1990 24.5 18.4 26.0 14.0 24Harrington Creek Toutle 8 1990 19.1 16.7 20.5 13.3 16Eight (Constant) NA NA NA 8 8 8 8 28Ten (Constant) NA NA NA 10 10 10 10 7.a maximum value of the 7-day moving average of the daily maximum temperature  b maximum value of the 7-day moving average of the daily mean temperature c instantaneous maximum d median of daily mean temperature from June 1 to September 1 
 6-8 

Temperature criteria for water quality standards are generally applied for time-averaged 

characteristics of temperature such as the warmest 7-day average of daily maximum and 

mean temperature, or the annual maximum temperature (instantaneous measure) (see 

Section 3). Growth reduction (RMG) is shown relative to various time-averaged 

temperature indices in Figure 6.4. RMG was at a minimum when the stream’s 

temperature index most closed approximated the species’optimal temperature. All streams 

had some growth loss due to its long-term temperature, since no stream had RMG equal to 

0. RMG tended to increase for streams significantly warmer or colder than the optimum 

or lowest point of the growth curves. Generally, the sites with lowest growth loss had 

indexing mean temperatures within the range of optimal temperatures (e.g., Figure 6.2). 

These are 14o

 to 17oC for coho and 11.5 o to 14oC for steelhead using the 7-day maximum 

measure. Both species showed steep response in growth loss at higher and lower 

temperatures than their optimal range. There was significantly growth loss for both coho 

0

10

20

30

5 10 15 20 25

7-Day Maximum Temperature (oC)

Coho Steelhead 

A)

0

10

20

30

5 10 15 20 25 30

Annual Maximum Temperature (oC)

Coho Steelhead 

B)

0

10

20

30

5 10 15 20

7-Day Mean Temperature (oC)

Coho Steelhead 

C)

0

10

20

30

5 10 15 20

Season Median Temperature (oC)

Coho Steelhead 

D)

Figure 6.4 Estimated growth risk at temperature study sites in relation to several time-averaged temperature metrics: A) 7-

day maximum, B) annual maximum, C) 7-day mean, and D) median temperature for the simulation period.
 6-9 

and steelhead when the 7-day maximum temperature was less than 10oC and greater than 

24o

C. The patterns are similar for both species, although coho predictions are more 

variable with site temperature than steelhead. These two species often cohabit the same 

streams, and with the growth simulation method, they are predicted to have similar level 

of response at approximately the same temperatures, despite fundamental differences in 

their specific growth rate/temperature curves (Section 5). 

The general patterns in RMG described for the 7-day maximum temperature also held true 

for all of the time-averaging periods (Figure 6.4). Of course, the range of temperature 

where growth was optimized varies with each of the temperature indices. The relationship 

with the least scatter was the 7-day mean temperature, suggesting it may be the best for 

temperature criteria. 

While the species have similar patterns of response, there were also important, nonintuitive, differences in simulation results. Although the optimal temperatures for coho are 

higher than steelhead (Figure 6.2), their growth was maximized within a narrower and 

lower range of temperatures. Steelhead maximized growth at a wider and somewhat 

higher range of temperatures. We believe that these differences reflect the food supply 

with which each species is modeled. Because no food limitation was assumed for 

steelhead, their optimal temperatures were on the high end of the optimal range. This 

gave steelhead a broader temperature range where growth was not compromised by 

temperature. Coho had a narrower range of temperature where growth was optimized, and 

this range was consistent with the optimal growth range indicated by their specific growth 

curves. Assumptions of food supply restrictions helped ensure that the optimal growth for 

coho would include those temperatures associated with low consumption (13 to16.5 o

C). 

The short-duration indices appear to be useful for characterizing the long-term 

temperature pattern in a way that is meaningful to fish growth. In Figure 6.5, the daily 

mean temperature for the entire simulation period is shown for three sites. Some sites 

Coho

0

5

10

15

20

25

6-Jun

12-Jun18-Jun24-Jun30-Jun

6-Jul

12-Jul18-Jul24-Jul

30-Jul

5-Aug

11-Aug

17-Aug23-Aug29-Aug

4-Sep

10-Sep

Mean Daily Temperature ( oC)

Hoffstadt Cr Big Cr Hard Cr Optimal

Figure 6.5 Temperature regime at selected sites with estimated optimal temperature. 
 6-10 

spend a lot of time above optimal temperature (e.g., Hoffstadt Creek), and some spend all 

or most of the time below optimal (e.g. Hard Creek). Growth is impacted in both these 

cases by approximately 20%. The sites where temperature was close to optimal for the 

longest time (e.g., Big Creek), had the least effects on growth. Most site temperatures 

tended to be well below optimal early in the summer growing season when growth rates 

are maximized, partly because fish were smaller. Better growth early in the season 

appeared to compensate somewhat for very warm temperatures later in the season. Figure 

6.5 also illustrates how optimal temperature declines through time with decreasing food 

consumption due to weight gain. 

Since the sites span a range of temperature regimes, the site with the lowest growth loss 

represents the temperature profile that best encourages growth for that species. In a sense, 

this site fits the concept of an “index” stream, although in this case, the reference is 

defined by fish growth rather than its naturalness or lack of disturbance. Interestingly, Big 

Creek had the most optimal temperature for both coho and steelhead. This site has an 

annual maximum temperature of 16.9o

C, nearly 1 degree over the Washington water 

temperature criteria. 

Risk Associated with Growth Limitation 

Holtby and Scrivener (1989) and Quinn and Peterson (1996) found that coho size at the 

end of summer was a primary factor influencing overwintering survival and smolting. 

Holtby and Scrivener (1989) provided an equation relating probabilities of overwintering 

success to coho length. We translated this relationship to an equation based on weight 

using a population length/weight 

relationship (Ricker 1975). The 

relationship between weight and the 

probability of overwintering success from 

these two studies are shown in Figure 6.6. 

Holtby and Scrivener (1989) found a large 

increase in overwintering success with 

increased size, possibly because the coho 

were so small at Carnation Creek 

(generally less than 2 grams). According 

to their relationship, weight of about 6 

grams or more yields an 80% or better 

probability of successfully overwintering. 

Note that we have extended Holtby and 

Scrivener’s relationship beyond the limits 

of their data to cover the larger fish sizes 

at our sites. Quinn and Peterson (1996) 

found more modest improvement in 

overwintering success at Big Beef Creek 

with fish size, although the fish in this 

stream were significantly larger than 

Carnation Creek. These authors found 

that the probability of successfully overwintering was about 50% for fish >89 mm 

(approximately 8 grams) and only 17% for fish <60 mm (approximately 2.5 grams). 

These values agree more closely with fish sizes in our study streams. We connect the two 

lines to determine a relationship of overwintering success with weight. 

Pr = 5.8511xW+ 3.1915

0

20

40

60

80

100

0 2 4 6 8 10 12

Weight (grams)

Holtby and Scrivener 1989

Quinn and Peterson 1996

Figure 6.6 Probability of success for coho overwintering 

survival (from Holtby and Scrivener 1989) based on end of 

summer fish size. Data from Quinn and Peterson (1996) are 

also shown. The two points were connected with a linear 

regression. 
 6-11 

We use both of the relationships to illustrate the potential effect of growth reduction on 

overwintering success. The probability of success for each individual in the population at 

Salmon Creek was calculated 

based on its weight (shown as 

actual weight in Figure 6.7). 

Quinn and Peterson’s data are 

used in Figure 6.7A and Holtby 

and Scrivener’s relationship is 

used to calculate Figure 6.7B. 

Despite the large differences in 

estimated probabilities with the 

two relationships, both produce 

similar relative results. There is 

a reduction in overwintering 

success with lower weight; the 

magnitude of change is 

approximately equal to the 

percentage change in weight. 

That is, a 10% reduction in 

growth calculated for the 

population results in an average 

reduction in overwintering 

success of 9%. 

The important outcome of this 

analysis is the suggestion that 

the relatively small changes in 

weight that we calculate due to 

temperature (e.g. Figure 6.1) 

are sufficient to affect 

individual and population 

overwintering success to some 

extent. A 10% reduction in 

growth would be difficult to 

statistically detect given the 

typical range of sizes in natural 

populations. However, a 20% 

reduction should be detectable, 

especially when field 

experiments are guided by 

hypotheses generated from the growth model. Brett et al. (1982) suggested a 20% upper 

limit for change in weight due to temperature for chinook populations living in the 

Nechako River. This appears to be somewhat high for coho, based on implications for 

loss of overwintering success. However, it should be noted that many factors affect the 

survival of salmon in the marine environment. 

Use of either relationship extends them beyond the original data or application developed 

by the authors. Therefore, even though we use both relationships to estimate the effect of 

growth reduction from temperature on overwintering success, we acknowledge 

uncertainty in this analysis. Additional research quantifying the effect of size on success 

at later life history stages would increase confidence in the analysis of risk to growth loss 

Probability of Overwintering Success

Quinn and Peterson

0

5

10

15

20

25

30

35

40

0-10

11-20

21-30

31-40

41-50

51-60

61-70

71-80

81-90

91-100

Probability Category

Number

Actual Weight -10% -20%

A)

Probability of Overwintering Success

Holtby and Scrivener

0

5

10

15

20

25

0-10

11-20

21-30

31-40

41-50

51-60

61-70

71-80

81-90

91-100

Probability Category

Number

Actual Weight -10% -20%

B)

Figure 6.7 Histogram of probability of overwintering survival based on 

weight of individuals within the population and simulated effects with 

redcution in weight due to temperature. Probability calculated based 

on Quinn and Peterson results from Big Beef Creek, WA (A) and from 

Holtby and Scriverner’s from Carnation Creek (B). 
 6-12 

due to temperature. This, in turn, creates uncertainty regarding the choice of 10% as the 

growth reduction limit. It appears clear that at least 5% growth loss can be expected at all 

sites due to long duration exposure, even when the bulk of temperatures are near optimal. 

The difference in temperature thresholds selected at 10 and 20% RMG is quite significant. 

Increased understanding of the role of juvenile size in determining success at later life 

history stages would improve confidence in selecting an appropriate limit to growth loss. 

In the Carnation Creek study, improved growth of steelhead fry with increased 

temperature after logging did not translate to larger smolts after two to three years of 

rearing (Hartman and Scrivener 1990). Steelhead will usually spend at least one 

additional year in the stream regardless of size achieved in the first year. Thus the impacts 

on growth from temperature are shown to be small in this analysis, which is consistent 

with observations at Carnation Creek. The negative or positive effects on growth are not 

great enough to change weight sufficiently to change migratory patterns, that is, to speed 

up or delay them by one year. 

This is in contrast to coho at Carnation Creek, where temperature increased growth 

sufficiently to bring some fish to smolting size in one year. It should be noted that 

Southern British Columbia represents the most northerly locale where coho are typically 

able to reach smolting size in one year (Sandercock 1991). Coho at Carnation Creek 

typically migrate at 2-years rather than one, presumably due to lower temperatures. In this 

case, increasing the temperature accelerated growth to the point where the coho 

outmigrated after the first season. This was interpreted as negative for the species, 

because the timing of their migration made them more susceptible to predation in the 

estuary and ocean environment. Where fish typically migrate in one year (e.g., 

Washington and Oregon), growth improvement would probably benefit their success by 

producing fish of larger size, according to Quinn and Peterson (1995). 

Growth Loss and Temperature Criteria 

We translate the RMG data (Figure 6.4) to zones of reduction in maximum growth to 

facilitate identifying thresholds of growth response in Figure 6.8. The range of 

temperatures was determined by ordering the site temperature data and estimating the 

temperature where the 10 and 20% boundaries occurred. The range of 20% reduction 

encompasses most of the stream temperatures typically observed in the region. The range 

of temperature where there was relatively little effect less than 10%) was fairly narrow for 

both species. It should be noted that at temperatures above and below the ranges 

illustrated, there is high growth loss due to temperature (30% or more). 

Discussion 

The temperature assessment approach provides a method for indexing the relative effects 

of stream temperature regimes on salmonid growth. A value of the approach is that the 

relative effects of temperature and food consumption can be evaluated independently of 

other habitat or population characteristics, and each other. Results can be also be used to 

directly compare growth effects among species. Because these factors are assumed 

constant in this analysis, the growth estimates only account for the direct effects of 

temperature, and do not account for population dynamics. 
 6-13 

Species were similar in their range of response. However, coho were more temperature 

sensitive, and steelhead tended to grow somewhat better at warmer temperatures than 

coho. If bioenergetics relationships are correct, this can be explained by differences in 

food supply. This, in turn, may reflect the different foraging strategies that each species 

utilizes when coexisting in the same streams (Bisson et al. 1988b). Selection of 

temperature criteria in management situations may be most useful if they reflect the most 

sensitive species (coho) when both species are present. 

Fish were predicted to be growing near optimal within many streams, including a number 

that exceed current water quality standards. The temperature ranges observed at the sites 

included in this analysis are representative of current conditions in Washington streams 

and rivers. The vegetative overstory of many has been disturbed within the last 50 years, 

and therefore streams may be currently warmer than they have been at other times in their 

history. However, the range of temperature represented at these sites is likely to be 

representative of the range that has occurred historically, given the history and frequency 

of fire disturbance in the region (Agee 1993). 

The patterns of biological growth response in relation to all of the time-averaged 

temperature regime metrics demonstrates that they can be used to index the temperature 

regimes of sites in biologically meaningful ways. Short averaging periods such as 7-day 

and even the annual instantaneous maximum temperature are strongly indicative of the 

long-term temperature regime that partially controls fish growth during the summer. 

7-Day Maximum

0

5

10

15

20

25

30

Coho Steelhead

Temperature ( oC)

10%

20%

20%

Annual Maximum

0

5

10

15

20

25

30

Coho Stealhead

Temperature ( oC)

20%

20%

10%

7-Day Mean

0

5

10

15

20

25

30

Coho Steelhead

Temperature ( oC)

20%

10%

20%

Figure 6.8 Ranges of temperature where reduction from maximum growth is 0-10% and 11-20%. At temperatures above and 

below these ranges RMG exceeds 20%. 
 6-14 

CONCLUSIONS 

â It is feasible to apply a risk-based approach that uses data without undefined 

safety factors and produces an estimate of cumulative risks. This technique is a 

true quantitative benchmark that is measurable and testable. 

â Modeled growth using measured temperature suggest that the majority of 

temperatures experienced by salmonids are generally suboptimal for growth, and 

these exert some cost on the maximum potential growth. 

â Despite what appear to be large differences in temperature among sites, especially 

with regard to the warmest temperatures that occur each summer, there is 

considerable similarity in the predominance of temperatures that are important to 

growth. Sites with significantly different temperature regimes can have similar 

predicted effects on growth. 

â An upper threshold for the 7-day maximum temperature of 16.5o

C minimizes 

growth losses for coho. A 7-day maximum temperature or 20.5o

C minimizes 

growth losses for steelhead. 

â Concepts of safety factors in selecting temperature thresholds defined for 

salmonids need to be exercised with some caution. Thresholds that are both too 

low and too high can negatively affect growth. 

â The criteria above assume 10% growth loss as the acceptable level of risk. There 

is uncertainty associated with this number, since there are relatively few 

quantitative data to base it on. Further research could help confirm acceptable 

risk levels. 
7-1

SECTION 7 RELATIONSHIP BETWEEN EXISTING AND 

PROPOSED TEMPERATURE CRITERIA AND RISK 

ASSESSMENT FINDINGS

Abstract 

Understanding the biological effects of temperature on fish is essential for effectively 

managing stream temperature under the Clean Water Act. One of the key elements of 

water quality management is to establish temperature criteria (e.g., water quality 

standards) that will limit human-caused impacts to the beneficial uses of the stream (e.g., 

fish). Ideally, criteria are based on an understanding of the interaction of fish physiology 

and ecology (biological effects) and the physical watershed and climatic processes that 

control the temperature of streams (exposure). Scientific understanding of these factors 

should then lead to criteria that are realistic and appropriate in assigning a temperature 

threshold that appropriately reflects temperature. In this section, we review and compare 

methods of determining temperature criteria used to select criteria, including those 

developed in this report. 

Key findings of this chapter are: 

â Risk assessment allows the effects of magnitude, duration and frequency of 

temperature on fish growth and survival to be quantified in an objective and 

repeatable manner. 

â The U.S. EPA (1977) temperature criteria were found to be the most objectively 

defined and consistent with risk analysis results. They generally appear to allow 

up to 20% reduction in growth due to temperature. 

â Criteria derived from review of scientific literature without quantitative synthesis 

are generally consistent with risk assessment and U.S.EPA methods, although 

they tend to overestimate the benefits of cold temperatures and slightly 

underestimate the positive growth effects at temperatures somewhat higher than 

optimum.

Introduction 

From a scientific basis, methods for deriving temperature criteria should be explicitly 

defined and based on sound scientific data that pass data quality screening criteria (ASTM 

1997). A criteria derivation protocol needs to have: 

â clearly defined, transparent and repeatable methodology; 

â data quality objectives, attributed data sources, and quality control screens; 

â defined levels of protection for species populations, communities or ecosystems; 
7-2

â stated assumptions, safety factors, and data extrapolation factors; and 

â temperature criteria that incorporate magnitude, frequency, and duration as 

decision variables. 

In previous sections of this report we: reviewed the scientific literature elucidating the 

growth and acute lethal response of salmon to temperature during the rearing life history 

phase (Section 2); explored relationships between long-term and short-term temperature 

indices (Section 3); performed risk analysis on the effects of temperature on mortality of 

salmon (Section 4); developed a quantitative method to asses effects of long-term 

exposure to temperature on growth (Section 5); and established temperature threshold 

criteria (Section 6). In this section we evaluate water quality temperature criteria derived 

from several methods that have been adopted or proposed by various authors and agencies 

relative to their biological effects, including the analyses developed in this report. 

Criteria and Methods To Derive Them 

Review of scientific literature and agency policy documents identified a number of 

different approaches to derive water temperature criteria for the summer maximum 

temperature. Methods fall into three general categories: 

â criteria derived from experimental temperature tolerance studies; 

â criteria derived from field observations of fish occurrence under different 

temperature regimes; and 

â criteria derived from professional review of temperature information.

The degree of objectivity or subjectivity by which the information is synthesized into 

recommended criteria, the degree to which data forms the basis for the criteria, and the 

extent to which population effects can be probabilistically determined varies between 

methods.

Experimental Information-based Method (EPA) 

The EPA has published temperature criteria for a number of fish species based on a 

review of laboratory-based research on the thermal tolerance of fish (Brungs and Jones, 

1977, also cited as U.S.E.P.A. 1977). Brungs and Jone’s method includes identification of 

acute and chronic threshold values, definition of averaging time of specific daily 

temperature characteristics, and explicit treatment of safety factors to ensure the 

recommended criteria control population level effects. Temperature criteria are based on 

temperature tolerance studies that generally follow the protocols developed by the 

NAS/NAE (1973). These protocols include procedures to derive specific temperature 

criteria for both chronic and acute exposure. Criteria for chronic exposure are derived 

from incipient lethal temperature and physiological (bioenergetic) performance (e.g., 

growth optima) data. The temperature assessment methods described in Sections 4,5 and 
7-3

6 of this report are based on similar data such as the acute lethal and growth 

temperature/ration relationships. 

From the perspective of selecting temperature criteria, some very simple principles can be 

derived from the growth curves that are meaningful (Figure 5.4). Beginning with the 

coolest temperatures (0o C), growth increases with temperature up to the temperature up to 

optimal where the maximum growth rate is achieved without any increase in ration 

(approximately 16o C). This improved growth is due to increased food conversion 

efficiency and consumption. At temperatures above the maximum growth rate, growth 

rates cannot be maintained because consumption declines and metabolic energy costs 

increase. Further increases or maintenance of growth rate must come from increased 

food, if possible within satiation limits. Because the shape of growth curves is broad at 

the maximum, there is little or no negative effect of temperature several degrees above 

optimum. 

Brungs and Jones (1977) describe an objective method for developing threshold criteria 

based on optimal temperature and the range of preferred temperatures from laboratory 

derived growth curves available at the time. The criterion for chronic exposure is 

expressed as the maximum seasonal 7-day moving average of the daily mean temperature. 

Brungs and Jones (1977) refer to this metric as the “maximum weekly average 

temperature” (MWAT). This value may be derived for different seasons and life stages 

(e.g., summer rearing or fall/winter incubation). The scientific rationale for using the 

MWAT as a temperature limit is based on data showing that moderate temperature 

fluctuations can be tolerated as long as the incipient lethal temperature is not exceeded for 

long periods. The method also assumes that optimum temperatures are neither necessary 

nor realistic at all times to maintain viable fish populations (NAS/NAE 1973). 

Criteria for protection from exposure to extreme temperatures are based on thermal 

tolerance data. Regression equations of median survival times (LT50) (e.g., Figure 2.2) 

are used to predict the upper incipient lethal temperature for a 24-hour exposure, and a 

2°C safety factor is subtracted to derive a short-term criterion that will provide 100% 

survival (Brungs and Jones 1977). Since LT10’s and LT50’s are very close, the safety 

factor is sufficient to preclude effects on any fraction of the population. 

Protocols for deriving criteria based on experimental temperature tolerance studies have 

not changed since being proposed by the Environmental Protection Agency (Brungs and 

Jones 1977). Various authors continue to recommend this method as an option for 

protecting fish habitat (Armour 1991). 

The acute and chronic temperatures for the EPA protocol were computed for the rearing 

stage of seven native salmonids in Washington using the experimental temperature 

tolerance data that could be found in the literature (Table 6.1). This includes relevant data 

reported by the NAS/NAE (1973) and any newer data that could be found. Original 

criteria reported by Brungs and Jones (1977) are shown for comparison with the updated 

numbers. Note, the latter results are slightly different than criteria reported by Brungs and 

Jones (op cit) for the same species because our values are based on data from individual 

studies and not on the average of several studies. The results from multiple evaluations of 

the same species produced similar results (usually within 1o

C), and differences among 

salmonid species were relatively small. 
 7-4 

Table 7.1 Water temperature criteria (maximum weekly average temperature and acute exposure maximums during growt

based on the EPA methodology (Brungs and Jones 1977). Values reported by Brungs and Jones are shown f

Species tem p. (°C) lethal level (°C)a intercept (a) slope (b) optimum Source (°C) 

O ncorhynchu s cla rki 13-23 fluctuating 25.5 18.1515 -0.5723 -- (a) G olden 1978 18.50 

cutthroat trout 23 25.7 -- -- -- (b) G olden 1978 18.57 

16 22.6 -- -- -- (c) Vigg and Koch 1980 

constant -- -- -- 15 (d) Dwyer and Kram er 1975

Oncorhynchus gorbuscha 24 23.9 14.7111 -0.4459 -- (e) Brett 1952 17.97 

pink salm on 

Oncorhynchus keta 23 23.8 15.3825 -0.4721 -- (f) Brett 1952 17.93 

chum salm on 9 23.2 15.9272 -0.5575 -- (g) Blaham and Parente, unpublished in: NAS/NAE 1973 17.73 

O ncorhynchu s kisutch 23 25.0 18.9736 -0.6013 -- (h) Brett 1952 19.67 

coho salm on 23 25.0 (hh) Dehart 1975

10 23.5 18.4136 -0.641 -- (i) Blaham and McConnel, unpublished in: NAS/NAE 1973 19.17 

10-13 fluctuating 26.0 -- -- -- (j) Thom as et al. 1986 20.00 

constant -- -- -- 17 (k) Averett 1968 

Brungs and Jones (1977) estim ate 18.00 

O ncorhynchus m ykiss 18 26.5 18.4654 -0.5801 -- (l) Alabaster and W elcom m e 1962 20.30 

rainbow/steelhead 20 -- 19.625 -0.625 -- (m ) Alabaster and Downing 1966 19.17 

16 25.6 -- -- -- (n) Hokanson et al. 1977 19.83 

constant -- -- -- 17.2 (o) Hokanson et al. 1977 20.30 

fluctuating -- -- -- 15.5 (p) Hokanson et al. 1977 18.87 fluctuating -- -- -- 16.5 (q) W urtsbaugh and Davis 1977 19.53 

Brungs and Jones (1977) estim ate 19.00 

Oncorhynchus nerka 23 24.8 20.002 -0.6496 -- (r) Brett 1952 18.27

sockeye salm on 20 23.5 16.7328 -0.5473 -- (s) McConnel and Blahm , unpublished in: NAS/NAE 1973 17.83

constant 15 (t) Brett et al. 1969 

15 (u) Shelbourn et al. 1973 

Brungs and Jones (1977) estim ate 18.00 

Oncorhynchus tshawytscha 20 25.1 22.9065 -0.7611 -- (v) Brett 1952 21.03 

chinook salm on 20 24.7 21.3981 -0.7253 -- (w) Blaham and McConnel, unpublished in: NAS/NAE 1973 20.90 

-- -- -- -- 19 (x) Brett et al. 1982 

a Data represent the ultim ate upper incipient lethal tem perature where available or the upper incipient lethal tem perature when an estim ate of the ultim ate level was not available. 

M edian Survival Tim e Tem 
 7-5 

Differences between maximum and minimum MWAT and acute criteria are 3.3° and 3.5°

C, respectively (Table 6.1). This exercise demonstrates that the EPA method is highly 

objective and reproducible; there were no difficulties in updating the analysis with results 

of more recent studies. Brungs and Jones also provide criteria for other life history stages. 

Field Observation Methods 

Observation of temperature at which fish occurrence is verified is another method used for 

estimating fish temperature requirements. Bovee (1978) recommended the use of fish 

observations where temperature is simultaneously collected to determine a “probability of 

use” curve based on the distribution of observations. This technique of characterizing 

physical environmental conditions in conjunction with fish observations forms the basis 

for the U.S. Fish and Wildlife Service Instream Flow Incremental Methodologies (IFIM) 

Habitat Suitability Index model (HSI) (McMahon 1983). This method has been used to 

evaluate population effects from physical habitat alterations. Presumably, this method 

would reflect preference temperatures of the fish, but their quantitative relationship to 

optimal temperature, growth, or lethal temperatures cannot be known since results would 

also strongly reflect the ambient temperature occurring when observations were made and 

may not indicate true preference or long-term exposure. 

Eaton et al. (1995) proposed a technique for deriving the maximum thermal tolerance of 

fish matching stream temperature records with fish presence data. Their fish and 

temperature data matching system (FTDMS) provides a direct measure of the 

temperatures that are utilized by fish populations in nature. In this protocol, fish presence 

data are matched with weekly mean temperatures taken from the same location and time 

period to derive a fish presence by temperature frequency distribution. An estimate of the 

maximum temperature tolerance for a species is assumed to be equivalent to the 

temperature at which 95% of the fish observations occur for a large (n = 1000 matches) 

database that represents the geographic range limits of the species. The 95th percentile 

observation is proposed as a safe estimate of thermal tolerance to protect against 

inaccuracies in temperature records and biased observations of fish presence that may be 

contained in the database. 

The FTDMS is recommended as an 

approach for determining the 

maximum temperatures that limit the 

distribution of salmonids. With 

regard to temperature criteria, this 

method seems to relate best to the 

acute temperatures rather than the 

chronic temperatures. Table 7.2 lists 

the mean weekly temperature derived 

from over 1000 field observations 

(Eaton et. al 1995). The field 

observation method of establishing 

upper lethal limits suggested by Eaton 

et al. (1995) produced very similar 

results to those of Brungs and Jones 

Table 7.2 Water temperature criteria for salmonids based on 

the fish and temperature data matching system (FTDMS). 

Taken from Table 1 in Eaton et al. (1995). 

Species Mean weekly temperature 

(

o

C) for the 95th percentile 

observation 

Oncorhynchus clarki (Cutthroat trout) 23.2 

O. gorbushcha (pink salmon) 21.0 

O. keta (chum salmon) 16.8 

O. kisutch (coho salmon) 23.4 

O. mykiss (steelhead/rainbow trout) 24.0 

O. tschawytscha (chinook salmon) 24.0 
 7-6 

(1977) which were based on laboratory tests (Table 7.1): field observation estimates were 

within 1°C of EPA’s acute temperature criteria for all salmonids except chum salmon. 

These observations suggest that laboratory derived criteria can predict the thermal 

tolerance limits in nature reasonably well. 

RISK ASSESSMENT APPROACH 

The temperature assessment methods described in Sections 5 and 6 followed a risk 

assessment approach and provided a time-integrated and quantitative estimate of the 

influence of the temperature regime on the potential growth of salmon within selected 

stream reaches where temperature was known. This approach could be used to develop 

site-specific or regional temperature criteria for both acute and chronic effects on summer 

rearing. Here we use the results of the acute and growth analyses to evaluate generally 

applied temperature indices used as temperature criteria. The assessment of acute 

temperatures suggested that 26o

C is threshold temperature for salmonid species (Section 

4). 

The reduction from maximum potential growth due to temperature regime was calculated 

for a number of stream segments with widely varying temperature regimes in Section 6, 

based on a growth model developed in Section 5. The range of temperatures where growth 

was within 10% and 20% of optimum based on those analyses is shown in Figure 7.1. For 

each temperature indices, coho and steelhead reduction from maximum growth (RMG) are 

7-Day Maximum

0

5

10

15

20

25

30

Coho Steelhead

Temperature ( oC)

10%

20%

20%

Annual Maximum

0

5

10

15

20

25

30

Coho Stealhead

Temperature ( oC)

20%

20%

10%

7-Day Mean

0

5

10

15

20

25

30

Coho Steelhead

Temperature ( oC)

20%

10%

20%

Figure 7.1 Range of temperature where reduction from maximum potential growth (RMG, %) was 10 and 20% during the 

summer months for coho and steelhead, using three temperature indices. (See Section 6 for methods and Table 6.2 for 

results by site. RMG is greater than 20% at temperatures outside of the temperature ranges indicated. RMG is minimized 

near the optimal temperature for each species and increases at temperatures warmer or cooler than the optimal. 
 7-7 

plotted together to facilitate comparison among species. Growth is highest within the 

range of optimal temperatures, and declines at temperature higher and lower than optimal. 

There is a narrow range of temperature where growth is optimized for each species. 

Growth rate is highest at sites with 7-day maximum temperatures between 9o

C to 17oC. 

Patterns are similar for other temperature indices (7-day mean and annual maximum 

temperature), although the temperature range enveloping various levels of growth 

reduction vary with each temperature index. 

Steelhead and coho often occur together in stream environments, and their growth 

responses are similar, although there are important differences in threshold values. 

Selecting the criteria based on the 10% RMG for the more thermally sensitive coho would 

suggest an upper threshold of 16.5o

C for the 7-day maximum temperature and 14.8oC for 

the 7-day mean temperature. Selecting the criteria based on the 10% RMG for the 

steelhead would suggest an upper threshold of 20.5o

C for the 7-day maximum temperature 

and 17.0o

C for the 7-day mean temperature. The upper end of the temperature range is 

well below temperatures where behavioral avoidance has been observed (e.g. Bisson et al. 

1988, Nielsen et al. 1994), and interspecies competitive interactions have been noted (e.g., 

Reeves et al. 1987; Taniguchi et al. 1998). 

The growth effects predicted by the criteria will be the same wherever the fish live. Even 

though streams for resident trout may be naturally colder due to proximity to headwaters, 

the growth/food/and temperature effects should be the same. It should also be noted that 

these criteria are only appropriate for streams with normal seasonal and daily temperature 

fluctuations. Streams or other aquatic environments (e.g., thermal plumes at discharge 

sites) with significantly different temperature patterns would require site specific analysis, 

i.e., characterization of their temperature regimes to determine exposure. 

Table 7.3 Temperature criteria (oC) for growth of juvenile salmonids derived from temperature analysis at 

values of reduction from maximum growth (RMG) of 10% and 20%. 

Temperature Index Coho Steelhead 

10% RMG 20% 

RMG 

10% 

RMG 

20% 

RMG 

MWAT (Updated EPA 

method (Table 6.1) 

7-day Maximuma 

(

o

C) 

13.0-16.5 9.0-20.5 14.5-20.5 10.0-24 

7-day Meanb (

o

C) 12.8-14.8 9.0-19.0 13.0-17.0 10.0-19.0 19.7 coho 

19.6 steelhead 

Annual Maximumc 

(

o

C) 

13.5-17.5 9.5-23.0 15.5-21.0 10.5-26.0 

a

maximum value of the 7-day moving average of the daily maximum temperature; 

b

maximum value of the 7-day moving average of the daily mean temperature; 

c

 instantaneous maximum observed during the summer;
 7-8 

Review Approach 

Temperature criteria derived by the review approach are based on the professional 

interpretation of temperature requirement information organized by life stages and time 

periods for each species of interest. These temperature requirements are derived primarily 

from key review articles (e.g., Bjornn and Reiser 1991, Bell 1973, 1986). Measurements 

of performance optima from laboratory studies and field observations of temperature 

during different life stages may also be used. 

Criteria, for example, are derived by selecting a temperature regime low enough to protect 

the most sensitive life stage for the summer juvenile rearing period. Protection of this life 

stage is assumed to protect all other life stages that may occur at the same time (e.g., adult 

holding). The review method is not a defined protocol, but rather is a general approach 

for evaluating temperature information. Review-based approaches are inherently more 

subjective as analysts attempt to explicitly synthesize a number of factors and species into 

one recommended criteria. 

Table 7.4. Examples of four water temperature envelopes by life stage summaries for spring chinook 

salmon. (Temperature in o

C.) 

Life stage Bell (1973) Bjornn and Reiser 

(1991) 

Armour (1991)a

 ODEQ (1995) 

Adult migration 3.3-13.3 3.3-13.3 3.3-13.3 3.3-13.3 

Spawning 5.6-13.9 5.6-13.9 5.6-13.9 5.6-12.8 

Incubation 5.0-14.4 5.0-14.4 5.0-14.4 4.5-12.8 

Juvenile rearing Optimum 7.2-14.4 Preferred 12-14 7.9-13.8 Positive growth 4.5-19.1 

Optimum production 10.0-15.6 

a

All data are for the recommended temperature range

Data linking fish performance and temperature are evaluated by a professional, or group 

of professionals, who identify the temperature range that provides some level of 

protection. While the analysis may include laboratory or field derived data, the manner in 

which such data are used is not explicitly defined, as it is in the EPA and FTDMS 

methods (e.g. ODEQ 1995, WDOE 1998a). Also, the level of protection1

 generally is not 

explicitly defined and appears to vary depending on policy objectives and the amount of 

available information. 

Bell (1973) conducted one of the first reviews of temperature to establish criteria, 

compiling most of the information known at the time, and presented the data in the form 

of temperature ranges or envelopes by species and life stage. Bell (1973) synthesized a 

temperature range from the available information to provide a recommendation. 

However, he did not describe the method by which the recommendations were derived, 

including consideration of safety factors, and he did not attribute the recommendations 

with specific citations (i.e., only a list of references is given). Thus the scientific source 

for each recommendation cannot be verified and the primary data sources that may have 

been used are not directly tied to the final recommendations. 

 

1

 Level of protection refers to what percentage of the individuals representing a race, subspecies or species are protected 

(e.g., 90, 95 99, 100%). Similarly, it refers to the percentage of streams that would be protected, and what percentage of the

time. For example, a goal may be to protect 95% of the salmonid races and streams 95% of the time. 
 7-9 

A series of subsequent reviews have relied heavily on the work of Bell (1973) to develop 

and revise temperature criteria. For example both Armour (1991) and ODEQ (1995) cite 

Bjornn and Reiser (1991) as the source for some of their temperature recommendations. 

Bjornn and Reiser (op cit) reference Bell (1986), which is the second edition of Bell 

(1973), as source for their temperature information. The Bell (1973) report is also the 

basis for U.S. Fish and Wildlife Service recommended temperature criteria for coho 

salmon (Laufle et al. 1986), chinook salmon (Allen and Hassler 1986), and steelhead trout 

(Pauley et al. 1986). These references are also cited for temperature criteria in the 

“ManTech Report” sponsored by the National Marine Fisheries Service (Spence et al. 

1996). The interdependence of these review reports results in recommended temperature 

criteria that are remarkably similar. For example, chinook criteria shown in Table 7.4 are 

the same in each of four different papers. 

This review approach has formed the basis for temperature criteria in the Pacific 

Northwest in recent years, despite the lack of firmly documented primary data (ODEQ 

1995, WDOE 1999). Criteria developed in Oregon (ODEQ 1995) and proposed in 

Washington (WDOE 1999a) appear to be based on less quantitative approaches than 

advocated by Hokanson (1977), Brungs and Jones (1977), Armour (1991) and Eaton et al. 

(1995), although experimental biological effects data are available for this purpose. The 

primary weaknesses of review approaches are the absence of a clearly defined decision 

process for selecting and evaluating temperature information, synthesizing factors of 

safety and uncertainty into the criteria, and lack of clear linkages to field and laboratory 

data. In some cases, the numbers for juvenile rearing derived from literature reviews are 

consistent with those produced from risk analysis and other quantitative methods (e.g., 

Bjornn and Reiser 1991). However, in most cases, the recommended ranges assume 

greater growth at lower temperatures than is likely to occur and less growth at warmer 

temperatures. 

Temperature Criteria 

Existing and proposed temperature criteria, including objective criteria and those that were 

derived primarily by the review method described above, are presented in Tables 7.5 and 

7.6. Only criteria relevant to the growth period of juvenile salmon and trout, exclusive of 

bull trout, are shown for this example. The Oregon temperature criteria combine trout and 

salmon species together, with a different standard for bull trout. The proposed 

Washington criteria group salmon species and steelhead with one criteria, and cutthroat 

trout for another. Washington's proposed criteria also vary the maximum temperature by 

specified periods during the summer months, a detail we will not address during 

subsequent analyses. 
 7-10 

Each criterion/standard in Tables 7.5 and 7.6 typically is assumed to represent the noeffect level for the most sensitive life stage of the most sensitive species, plus a safety 

factor. The EPA’s goal is to protect 95% of the species 95% of the time. This level of 

protection is extended to include economically important species, ecological keystone 

species, and threatened and endangered species (Stephan et al. 1985). Other groups of 

scientists have suggested that protecting 90% of the species will protect aquatic 

communities (SETAC 1994). It is not always necessary to protect 95% of the individuals 

in a population when it is desired to protect the species, based on rationales presented by 

Ricker (1975). However, in the case of the ODEQ and WDOE criteria, the actual level of 

protection embodied in the criteria has not been defined. 

Temperature criteria from the various methods have the following similarities and 

differences (Tables 7.5 and 7.6): 

• Most index the juvenile growth phase, which lasts several months, with the 

warmest 7-day period occurring during that interval. 

• No criteria use an averaging period longer than a week. One specifies the annual 

instantaneous maximum (e.g., Washington’s existing temperature criteria). 

• Criteria vary in whether the daily maximum or mean is used to calculate 

temperature during the averaging period. 

• No criteria state an acceptable level of variation in the threshold temperature, 

indicating that are likely to result from natural factors and uncertainty. 

Table 7.5 Existing and proposed temperature criteria for anadromous salmon species and steelhead 

derived from various methodologies relevant to the summer growth period. Numbers are maximum 

allowable values. 

 ACUTE SUB-LETHAL 

Method Temperature 

(

o

C) 

Metric Temperature 

(

o

C) 

Metric 

EPA 

(Brungs and Jones 1977) 

24.0 Annual instantaneous 

maximum a

18.0 Maximum 7-day moving 

average of the daily mean 

Eaton et. al. (1995) 23.5 Maximum 7-day moving 

average of the daily mean 

-- -- 

Risk Assessment 

 (this report) 

25.5 Annual instantaneous 

maximum a

16.5 Maximum 7-day moving 

average of the daily maximum 

ODEQ (1995) None specified -- 17.8 (64o

F) Maximum 7-day moving 

average of the daily maximum 

WDOE proposed (1999) 21.0 

 (June-Sept) 

Annual instantaneous 

maximum 

16.5 Maximum 7-day moving 

average of the daily maximum 

WDOE (current) None specified -- 16.0 (AA)b

18.0 (A) 

21.0 (B) 

Annual instantaneous 

maximum 

a

 assumed at least a 1-hour interval 

b

 streams are classified as AA, B and C according to WAC 173-201-080 
 7-11 

• Some criteria do not specify upper acute temperature levels, relying instead on 

the temperature criteria derived for chronic effects to control maximum 

temperature. 

• For most of the criteria, the temperature range of 16o

 to 18oC is used as the 

upper maximum. 

• No criteria establish a minimum threshold temperature. 

The temperature criteria derived from the review approach are more variable, although 

much of the data used are similar among the evaluations (ODEQ, 1995 WDOE, 1999). 

The Oregon and proposed Washington criteria are reasonably similar to the EPA criteria 

for salmonids (Tables 7.5 and 7.6). 

The differences in the temperature indices used by different sources makes it difficult to 

compare them. In Table 7.7 we translate each recommended threshold to each of the 

short-term indices, using the relationships between indices shown in Figure 3.7 (Section 

3). When placed on a common footing, it is evident that there are differences among the 

recommended criteria. 

Table 7.6 Existing and proposed temperature criteria cutthroat trout derived from various methodologies 

relevant to the summer growth period. Numbers are maximum allowable values. 

 ACUTE SUB-LETHAL 

Method Temperature 

(

o

C) 

Metric Temperature 

(

o

C) 

Metric 

EPA 

(Brungs and Jones 1977) 

24.2 Annual instantaneous 

maximum a

18.5 Maximum 7-day moving 

average of the daily mean 

Eaton et. al. (1995) 23.2 Maximum 7-day moving 

average of the daily Mean 

-- -- 

Risk Assessment 

 (this report) 

26.3 Annual instantaneous 

maximum a

16.5 Maximum 7-day moving 

average of the daily 

maximum 

ODEQ (1995) None specified -- 17.8 (64o

F) Maximum 7-day moving 

average of the daily 

maximum 

WDOE proposed (1999) 14.5 Annual instantaneous 

maximuma

13.0 Maximum 7-day moving 

average of the daily 

maximum 

WDOE (current) None specified -- 16.0 (AA)b

18.0 (A) 

21.0 (B) 

Annual instantaneous 

maximum 

a assumed at least a 1-hour interval b

 streams are classified as AA, B and C according to WAC 173-201-080
 7-12 

Table 7.7 Threshold temperatures for short-term duration indices from various sources. 

Temperatures have been translated to common values using relationships 

among temperature indices developed in Section 3 (see Figure 3.7). Values 

in bold type are original reported numbers. Authors report 

recommendations in a variety of metrics. Each recommended value is also 

translated to the other metrics using the regression relationships presented 

in Section 3 to facilitate their comparison. 

Species Reference 

Sub-Lethal Thresholds Acute Threshold-- 

 

Annual 

Maximum 

Temperature (o

C) 

7-Day 

Maximum (o

C) 

7-Day Mean 

(MWAT) 

 (o

C) 

Annual Maximum 

Temperature (o

C)

Coho salmon EPA 1977 21.5 21.2 18.0 24.9 

 Risk assessment (this report) 17.5 16.5 14.8 25.5 

 ODEQ (1995) 19.0 17.8 16.0 -- 

 WDOE (existing) 16.0 15.5 14.3 -- 

 WDOE (proposed) 17.0 16.5 15.0 21.0 

 Eaton (1995) -- -- 23.4 30.5 

 

Steelhead trout EPA 1977 24.0 23.0 19.0 26.0 

 Risk assessment (this report) 21.0 20.5 17.0 26.0 

 ODEQ (1995) 19.0 17.8 16.0 -- 

 WDOE (existing) 16.0 15.5 14.3 -- 

 WDOE (proposed) 17.0 16.5 15.0 21.0 

Eaton (1995) -- -- 24.0 31.0 

 

The temperature analysis developed in this report produced similar though not identical 

criteria to those developed using a variety of other methods. The thresholds derived from 

the risk assessment methods are somewhat lower than the EPA recommendations (Brungs 

and Jones 1977), largely because of the restriction to 10% growth loss and the realistic 

accounting of food consumption. Brungs and Jones (1977) used the 7-day mean 

temperature (MWAT) of 18o

C for coho and 19oC for steelhead. The growth analysis 

suggests that an upper threshold for the 7-day mean temperature of 14.8o

C for coho and 

17.0o

C for steelhead will maintain growth within 10% of optimum, and 19oC will maintain 

growth within 20% of optimum. 

Eaton et al. (1995) primarily studied the upper temperature limiting salmonid distribution 

but not growth. Their numbers are several degrees higher than our recommendation for 

acute thresholds. Indeed, their thresholds are so high that it would suggest that coho and 

steelhead still live in natural streams until maximum temperatures reach upper critical 

lethal levels for a significant period of time (at least 1 week). This appears to confirm that 

the thresholds we have identified are conservative and not likely to result in population 

loss. No site included in the risk assessment had 7-day mean temperature anywhere close 

to the upper threshold that limits distribution, although this was not true for some rivers in 

the region found in U.S.G.S. water resources records (see Appendix B). Coho, steelhead 
 7-13 

and/or cutthroat trout were present in all of these streams. We estimate that growth loss 

due to temperature for Eaton’s (1995) upper limits for coho and steelhead would be 

approximately 50%. 

Temperature criteria derived with the risk-based methods have only moderate agreement 

with criteria derived through various reviews. It appears that the review approach tends to 

recommend similar temperatures for the lower end of the range but lower temperatures for 

the upper end of the range, than was found by risk analysis and other methods. While the 

example ranges we cite are for chinook salmon, the temperature response curves for coho 

(this report) are very similar to those of chinook (Brett et al. 1982). It is difficult to 

directly compare with recommendations from these reviews since no indexing temperature 

measure is provided. 

ODEQ (1995) criteria appear to match results of the growth assessment reasonably well, 

despite its reliance on reviews rather than laboratory data. ODEQ (1995) specifies the 

maximum 7-day temperature at 17.8o

C. The 7-day maximum criteria derived for coho 

are 16.5o

C for 10% growth reduction and approximately 19oC for 20%. However, if just 

steelhead were considered, the threshold would be 20.5o

C for 10% growth reduction. 

Current WDOE criteria specifying an annual maximum of 16o

C for Class AA streams 

are lower than that derived from the risk assessment approach. Risk assessment suggests 

the annual maximum should be between 13.5o

C and 17.5oC for coho, or between 15.5 oC 

and 21.0o

C for steelhead, to maintain no more than 10% growth loss. The current criteria 

for Class A (<18o

C) and Class B (21oC) streams is more comparable to risk assessment 

results. The proposed temperature criteria published as a discussion draft by WDOE 

(1999) are very similar to those derived with the growth assessment for 10% growth loss 

for coho; 16.5o

C 7-day maximum for all anadromous salmon rearing. This criteria is 

lower than needed for steelhead. 

Discussion 

The quantitative analysis confirmed that biologically meaningful temperature thresholds 

could be identified with and of the indices (annual maximum, 7-day maximum or 7-day 

mean). There is no consensus on what index to use for temperature criteria, introducing 

additional confusion in comparing among them. This study found that all of the most 

typical indices are closely related to one another, and that any could be used with 

satisfactory results. The 7-day mean temperature was most closely correlated with growth 

loss estimates and therefore may be the best indexing measure for this purpose. However, 

other measure are quite suitable. It is important that the selected temperature match the 

time-averaging period appropriately. It should be noted that the longer the averaging 

period, the lower the threshold value. 

The growth analysis developed in this paper can form a basis for selecting temperature 

criteria, but some other methods also were reproducible and produced similar though not 

identical results. The risk assessment results described in this report rely on similar 

laboratory data as used by Brungs and Jones in developing EPA recommendations (1977). 

Our results suggest lower criteria by a few degrees, primarily because we use observed 

temperature regimes to estimate the growth of fish over the long-term, and because we 

account for realistic estimates of food consumption. Analyzing temperature relative to 

duration did affect the choice of thresholds. In the case of coho, temperature thresholds 
 7-14 

were lowered, while in the case of steelhead the thresholds were similar, and could 

possibly be raised. 

CONCLUSIONS 

â Risk assessment-based approaches allows the effects of magnitude, duration and 

frequency of temperature on fish growth and survival to be quantified in an 

objective and repeatable manner. 

â Moderate temperatures are likely to be more biologically productive for salmonid 

species than very warm or cold temperatures at the level of food availability that 

appears to exist in streams. 

â The U.S. EPA (1977) temperature criteria were found to be the most objectively 

defined and consistent with risk assessment results. They generally appear to 

allow up to 18% reduction in coho growth due to temperature. 
 8-1 

SECTION 8 A DISCUSSION OF THE SCIENTIFIC AND MANAGEMENT 

IMPLICATIONS OF FINDINGS

Introduction 

In this section we review the information presented in the report and identify the scientific 

and management implications of the results. This section serves as both a summary of key 

findings and a synthesis of information for scientists and policy-makers. 

For ease of reading, we conduct this discussion without extensive referencing from within 

the report or from external documents or the scientific literature. Although these sources 

of information are critical for the context of this discussion, they have been described and 

referenced in detail in the main body of the report. We have included several key figures 

found in previous chapters. 

The Regulatory Context of Temperature Criteria 

The Clean Water Act requires states to protect the public’s values for water bodies. To 

administer the CWA, the state water quality agencies must: 

• assign beneficial uses to each water body (e.g., fishable, swimmable, aquatic life), 

• specify water quality criteria that are sufficient to protect the designated beneficial uses, 

• assess and report on the condition of water bodies relative to those criteria (305b), 

• identify the sources of pollutants, 

• develop various management steps to protect or restore water body conditions to meet criteria, 

• monitor the water quality on an ongoing basis. 

The type of regulatory activities and management restrictions that may be imposed 

depends on the current and projected condition of the water body relative to the criteria. 

Therefore, the water quality criteria have enormous legal and economic meaning, and their 

appropriateness is of great concern to the public, scientists, and regulators. 

States have specified fish species in the cold water guild (salmon and char) as the 

designated beneficial uses in many streams and rivers of the Pacific Northwest region. 

Water temperature plays a role in virtually every aspect of fish life, and adverse levels of 

temperature can affect behavior (e.g. feeding patterns or the timing of migration), growth, 

and vitality. Fish have ranges of temperature wherein all of these functions operate 

normally contributing to their health and reproductive success. Outside of the range, these 

functions may be partially or fully impaired, manifesting in a variety of internal and 

externally visible symptoms. Fish have a number of physiologic and behavioral 

mechanisms that enable them to resist adverse effects of temporary excursions into 

temperatures that are outside of their preferred or optimal range. However, high or low 

temperatures of sufficient magnitude, if exceeded for sufficient duration, can exceed their 

ability to physiologically adapt and can cause growth or weight loss, disease, competitive 
 8-2 

Effects of Temperature on Salmonids

10

15

20

25

30

35

Minutes Hours Days Weeks

Duration

Temperature ( oC)

Upper Critical Lethal Lim it

Zone of Resistance Mortality can occur in proportion to length of exposure

Behavioral adjustment (no grow th, no mortality) Zone of Tolerance

Zone of Preference

Grow th response depends entirely on 

food availability

Optimal grow th at all 

but starvation ration

Reduced grow th

Rapid death 

Figure 8.1 The general biological effects of 

temperature on salmonids. Effects depend on the 

level of temperature and its duration. Two important 

elements of temperature affect the growth and 

survival of fish: 1) the relationship between 

temperature, metabolism and food conversion 

efficiency as it controls growth over relatively long 

periods, and 2) the thermal tolerance of fish to lethal 

temperatures over relatively short periods. Fish have 

various physiological and behavioral mechanisms 

that enable them to resist adverse effects of temporary 

excursions into temperatures outside of their 

preferred or optimal growth range but within the 

upper critical lethal limit. 

displacement by species better adapted to the prevailing temperature, or even death. Fish 

are adapted over some evolutionary time frame to the prevailing water temperatures, and 

climatic gradient is among the primary factors that determine the extent of a species’ 

geographic distribution. 

States in the Pacific Northwest have 

authorized numeric temperature 

criteria to protect fish for nearly 

three decades. Many are currently 

reviewing their criteria in the 

triennial review process specified in 

the Clean Water Act, with the help 

of the U.S. EPA, who must also 

approve them. Many streams and 

rivers in the Pacific Northwest 

region have been identified by the 

states as exceeding their current 

water quality criteria according to 

their 305b reports to Congress, with 

a large number of them listed for 

temperature impairment (303d lists). 

To further add to public concern, 

specific species or stocks within the 

salmon and char genera have also 

been listed as threatened or 

endangered under the Endangered 

Species Act in a number of 

geographic locations. The ESA 

does not require identification of a 

specific “pollutant” causing 

population decline, but high levels 

of stream temperature during the 

summer months are widely viewed 

as one of the primary habitat 

conditions that contributes to the 

decline of cold water fish species in 

freshwater habitats. 

The Characteristics of Temperature Criteria 

Temperature criteria typically have two key elements—a threshold temperature that 

signals when adverse biological response is likely to occur, and an averaging period that 

indexes the duration of exposure likely to trigger that response. The combination of the 

threshold temperature and the duration of exposure to that temperature are an expression 

of the risk imposed by the environmental temperature to the targeted fish species. 

Because of the ability of fish to acclimate and adapt to temperatures outside of their 

optimal conditions, both exposure duration and magnitude of temperature are necessary to 

determine the degree of risk that this fluctuation may pose to each species. Environmental 

temperature can annually range over the entire spectrum from optimal to adverse in 

response to the cyclic movement of the sun. Exposure to temperature is mediated by 
 8-3 

stream, geographic, and riparian forest characteristics. A factor of safety is typically 

added when selecting numeric criteria to account for the uncertainties in knowledge 

associated with each of these elements and any factors that are not unaccounted for. 

The averaging period has typically been either the annual maximum temperature 

(observed for a period as short as an hour, but more probably occurring for several hours 

on sequential days), or a weekly average (generally focused on the warmest seven 

consecutive days) observed for the year. For example, Washington’s current criteria 

specify the annual maximum temperature, expressed as the maximum hourly temperature 

that occurs each year. Oregon specifies the average of the daily maximum temperature of 

the 7 warmest consecutive days. The U.S. EPA (1977) recommends the average of the 

daily mean temperature of the 7 warmest consecutive days (MWAT). Some have also 

argued that the daily temperature fluctuation should also be accounted for, but this 

characteristic has not been widely specified in states’ criteria. 

The Basis for Derivation of Temperature Criteria 

A number of different approaches have been used to develop and justify the temperature 

criteria that are currently widely used in the Pacific Northwest region. All draw upon a 

large body of scientific research focused on the thermal tolerance of fish. There has been 

considerable laboratory testing for many fish species, including salmonids, beginning 

early in the 1900’s and continuing today. Much of the available research on temperature 

tolerances was performed prior to 1980 and was stimulated principally by the need to 

assess the impact of heated effluent from power plants, dams and other facilities. Since 

that time, the research focus has been to add species and refine the understanding of 

contributing factors such as the effect of acclimation temperatures, daily diurnal 

temperature fluctuations, and food rations, and to enhance understanding of the interaction 

of temperature with other pollutants. A considerable amount of the available research has 

been performed in the laboratory setting. Ecological field studies have lagged behind 

laboratory work, although their application has increased in recent years. 

Various methods have been used to analyze temperature effects on fish to develop criteria. 

The methods vary in terms of degree of objectivity or subjectivity by which the 

information is synthesized into recommended criteria, the degree to which data forms the 

basis for the criteria, and the extent to which population effects can be probabilistically 

determined. The temperature criteria in use in Pacific Northwest states have largely been 

drawn from professionals’ review and interpretation of available scientific literature (e.g. 

ODEQ 1995). There also has been some effort to use the more well-established scientific 

relationships to synthesize objective analyses of threshold temperatures and the duration 

of exposure (e.g., U.S. EPA 1977). This approach has not been widely integrated into 

regulatory activity. 

In recent years, the EPA and the National Academies of Science and Engineering have 

promoted risk assessment techniques to develop water quality criteria, including protocols 

that have been peer-reviewed nationally. Risk assessment is designed to enhance 

understanding of the potential adverse effects of a pollutant on a species by combining the 

information from biological studies with an analysis of each population’s potential 

exposure to those effects. These methods are formal, objective, and analytical. They have 

been primarily applied to contaminant pollutants; guidance for other pollutants is still 

under development. Risk assessment can lead to site or season-specific criteria. 
 8-4 

To date, naturally occurring “pollutants”, such as water temperature, have not been 

addressed with risk assessment techniques to determine criteria. Instead, temperature 

criteria generally are simple indices that summarize the seasonal and diurnal range of 

temperature observed in natural streams into the averaging period, and that address the 

complex array of biological responses of all of the life functions with the temperature 

threshold value. Although there is a general interest in tailoring criteria to specific life 

functions at specific times of year, this has not been widely accomplished to date. In most 

states, simple numeric indices are applied over broad regions to primarily address the high 

temperatures that may occur during the warm summer months, targeting the most 

sensitive species that are likely to occur in the water body. The methods described earlier 

fall short of the objectively rigorous expectations of formal risk assessment. 

Criteria selection teams are faced with a challenge. It is difficult to match simple criteria 

to multi-species communities dwelling in streams and rivers whose temperatures naturally 

vary with position in watershed and climate. Specifying the wrong criteria could have 

negative, possibly catastrophic, biological consequences. At the same time, the need for 

management solutions that may accompany even small changes in criteria can have large 

economic and legal consequences. In addition, all approaches to developing biologically 

meaningful temperature criteria face significant technical challenges. Some of these stem 

from the selection process itself. Subjective reviews often lack a clearly defined decision 

process for selecting and evaluating temperature information, and they fail to establish a 

clear linkage between field and laboratory data. Furthermore, subjective evaluations often 

use unquantified safety and uncertainty factors. 

The more that scientific research can be used to quantitatively assess the extent that risk to 

fish is minimized, such as those promoted as risk assessment techniques, the more 

confident the public and regulators can be that temperature criteria are protective. Such 

confidence does not currently exist. Over the past 25 years since temperature criteria were 

first adopted, there has been considerable debate over them but little scientific 

experimentation to validate or improve them. While the subjective analyses that form the 

basis of current temperature criteria are apparently consistent with the scientific literature, 

they have failed to generate measurable hypotheses that can be scientifically tested and 

rejected. 

The objective of this report was to synthesize relevant temperature research and to 

develop quantitative risk assessment techniques that could be objectively applied to 

natural streams 1) to identify the risks posed by ambient temperature and to suggest 

temperature criteria, and 2) to formulate experimentally testable hypotheses. Analysis 

focused on the summer rearing phase of juvenile salmonids because most existing 

temperature criteria target annual maximum temperatures, salmonids are of primary 

interest in much of the Pacific Northwest region, and there is a rich history of laboratory 

experimentation available to draw from. 

Quantitative Analyses to Assess the Effects of Temperature on Fish 

in Natural Environments 

The conceptual approach that frames this report is that temperature is a fundamental 

component of fish habitat. Water temperature is the thermostat that controls energy intake 

and expenditure. The overall success of individual fish is partially a result of the 

cumulative effect of its environmental temperature on its ability to grow and survive over 
 8-5 

time. If energy intake is adequate to fuel the physiological energy consumption, mediated 

in large part by the environmental temperature, then the organism can live in a healthy 

state. The individual is not likely to be healthy if the water temperatures force energy 

consumption at a pace that cannot be sustained by food intake, dictated in part by appetite 

and in part by food supply, for long periods of time. If the duration of moderately 

negative temperatures is fairly short, cessation of feeding or refuge seeking by the 

individual fish may be sufficient to withstand short-term excursions into higher 

temperatures. If this continues for long, the fish loses growth opportunity, and may be 

displaced by competitors in the population. If temperatures reach a more severe level of 

impairment, it creates physiological stress, loss of appetite, and can leave the fish open to 

disease and competitive pressures from other species. Stress is exacerbated at high 

temperature because dissolved oxygen content of the water is inversely related to its 

temperature. If temperatures reach very high levels, it invokes significant stress that 

causes immediate death. For salmonids, this temperature occurs at approximately 30o

C. 

Low temperatures can also induce cessation of feeding, but unless water freezes, the fish 

also can withstand excursions into cold water temperature by limiting activity. 

Temperature is not the only ecological factor of importance to biologic productivity, but if 

its central effect on the individual can be accounted for, then the influence of other 

environmental factors, such as food supply and population dynamics, may become more 

apparent in the complex ecology of natural environments. 

We work from the assumption that there is a continuum of biologic response to 

temperature that ranges from healthy, as indicated by maximum growth, to unhealthy, 

culminating in direct mortality. Along this spectrum there are a variety of ways that 

temperature effects manifest in the organism’s physiologic condition or its behavior. 

Some of these characteristics can be readily observed in natural environments and are 

simultaneously amenable to quantification and prediction based on measured temperature. 

These include the growth (weight change), direct mortality, and embryo development (not 

addressed in this report). These functions lend themselves to mathematical expressions, 

many of which have already been established for many species, including salmonids. 

Laboratory studies have shown that activity rates are closely correlated with temperature 

and that they can be predicted with some precision with linear or non-linear equations. 

We were able to develop models for direct mortality and growth as a function of 

temperature for several species of salmonids using laboratory data and bioenergetic 

principles available in the scientific literature (Figure 8.2). An extensive portion of this 

report describes and corroborates these models. The relationships were formulated in a 

way that they can be applied in natural ecological settings. The growth model simulates 

weight gain over time in relation to daily temperature and food supply. Its formulation 

constitutes a new contribution to modeling fish biological response to habitat factors. 

Importantly, the models predict qualities that allow them to be corroborated against 

measurable population characteristics, and therefore they produce hypotheses that can be 

rejected by direct observation. The methods are objective and repeatable. Our 

comparisons of simulated growth (or more specifically weight gain) of 21 populations 

living in natural streams showed consistent and close agreement with observed weight 

characteristics (Figure 8.3). 

We were not able to fully corroborate the acute temperature model because, when 

temperature records available to us were scanned for occurrence of combinations of 

temperature and duration sufficient to cause mortality, none were found. The data 
 8-6 

Figure 8.2 The acute and chronic effects of temperature species have 

been quantified for a number of salmonid species. Direct mortality in 

relation to exposure time for 4 species is shown in A. If the 

combination of temperature and continuous duration depicted by the 

regression lines occurs, 10% of the population is likely to die in each 

incidence of exceedence. In natural stream environments, stream 

temperatures must generally exceed the highest short duration 

temperatures (e.g. 26o

C or higher) for there to be a risk of direct 

mortality because temperatures rarely remain at these temperatures 

continuously due to natural daily temperature fluctuation. 

A. 

0.01

0.1

1

10

100

1000

24 25 26 27 28 29 30

Temperature (oC)

Time to 10% Mortality (Hours)

Cutthroat

R ainbo w 

(Steelhead) Chinook

Coho

The relationship between daily temperature, food consumption, and 

growth rate for coho salmon is shown in B. Growth rate is strongly 

influenced by temperature, with optimal growth occurring when fish 

feed at satiation ration and temperature is approximately 17o

C 

(optimal temperature for growth). Growth rate declines with 

temperatures either warmer or colder than the optimal. Growth 

effects are significant at temperatures greater than 22o

C and less 

than 9o

C. Each line represents a level of food consumption. This 

relationship and one for steelhead (not shown) were used with daily 

temperature measured in a number of streams and rivers to assess the 

long-term effect of temperature on weight gain during the summer 

months. During this period, temperature in many of the study 

streams ranged over much of the spectrum of positive growth shown 

by the curves. 

B. 

C o h o S a lm o n

-0 .0 1 0

-0 .0 0 5

0 .0 0 0

0 .0 0 5

0 .0 1 0

0 .0 1 5

0 .0 2 0

0 .0 2 5

0 .0 3 0

0 10 20 30

T e m p e ra tu re ( o C )

G ro w th R ate ( - 1d - 1)

100%

80%

60%

40%

30%

represented a wide variety of 

streams, including many with 

high annual maximum 

temperature. Therefore, 

although no mortality was 

reported in the field studies, 

model predictions cannot be 

considered fully corroborated 

until direct mortality is observed 

at predicted exposures. Failure 

to detect mortality is consistent 

with the general perception that 

direct mortality from 

temperature rarely occurs within 

the natural range of a species 

distribution. Nevertheless, lethal 

temperatures can and have 

occurred in the region, and there 

are situations where further 

analysis for risk of direct 

mortality to salmonids is 

warranted. 

Some concern has been 

expressed that the use of 

information from laboratory 

studies to define temperature 

criteria for fish living in natural 

streams is inappropriate. Such 

concerns confuse the synthesis 

of scientific information into 

temperature criteria since so 

much of the most relevant 

information comes from 

laboratory experiments. 

Laboratory and field studies 

each have unique limitations. 

Laboratory studies are 

conducted in highly artificial 

environments that create stresses 

from the experimental 

procedures. Field studies are 

labor intensive, and discerning 

the effect of temperature by 

empirical observation in streams 

is problematic given the 

multivariate and dynamic nature 

of the interaction, and the 

difficulty of measuring some of 

the key fundamental 

relationships in natural 
 8-7 

Porter Creek, 1988

0

1

2

3

4

5

6

7

24-Jun

2-Jul

10-Jul18-Jul26-Jul

3-Aug

11-Aug19-Aug27-Aug

4-Sep

12-Sep20-Sep28-Sep

Weight (g)

Coho Predicted Coho Observed

Steelhead Predicted Steelhead Observed

Figure 8.3 Simulation of weight gain for coho and steelhead in 

relation to temperature and food supply using the growth model 

developed in this report in relation to observed weight of 

populations living in natural streams. Model simulations 

computed daily weight gain using the measured daily temperature 

and estimates of food consumption derived from observing 

population growth rates and back calculating how much food had

to have been consumed to account for the weight gain between 

two sampling intervals. Results shown for Porter Creek for both 

species are similar to those observed at most sites. Overall, 

predicted weight deviated only 8% from observed on average for 

both species in 21 population simulations. Such good modeling 

results establish confidence in use of the model to estimate the 

effects of temperature on growth for determining temperature 

criteria. It also confirms the strong signature of prevailing 

temperature on the size of fish in natural streams. 

environments. Many scientists have argued that best way to investigate ecological 

problems involves a combination of laboratory and field experiments. 

Our results suggest that laboratory results are of fundamental value in explaining observed 

fish growth in natural streams. We could not reject the hypotheses regarding growth in 

relation to environmental temperature using simulations based on the laboratory research. 

In fact, the simulations were remarkably representative of the observed weight gain of 

naturally spawned and 

hatchery-raised populations in 

streams. We also believe that 

the direct mortality from the 

lethal temperature model would 

predict mortality consistent 

with the temperatures where 

death has been observed, but 

we do not know if the 

proportion of population 

experiencing mortality would 

be as we predict. Confirming 

this may be important for 

assessing the environmental 

factors controlling species 

distribution, but less important 

to establishing criteria. 

Temperature criteria should 

primarily target sublethal 

effects to protect fish health. 

Some biological responses to 

temperature can be observed, 

but they are not amenable to 

mathematical expression or 

prediction. These include 

behavioral responses such as 

cessation of feeding and 

seeking refuge, and competitive 

interactions. There are internal 

physiological stress effects that 

stimulate such externally 

visible symptoms, and additional study associating stress measures with temperature 

characterization would be a useful augmentation of the analyses of growth. Although 

associated with environmental temperature, the occurrence of some responses depends on 

the presence of specific factors such as cold water refuges or disease organisms that 

respond consistently with the prevailing temperature. (The role of temperature in 

increasing incidence of disease is particularly problematic since some disease organisms 

are more virulent in cold temperatures while others are more virulent in warm 

temperatures.) There are also factors that interact directly, indirectly or independently of 

temperature, to affect the organism’s condition. These include, but are not limited to, 

ecological constraints on food supply, population interactions, and genetic adaptability. 

Increased understanding of these in the context of environmental temperature would 

enhance understanding of the effects of environmental temperature. 
 8-8 

The Scientific Basis for Translating Quantitative Biological Analyses 

to Temperature Criteria 

A synthesis of the scientific literature supports the premise that the temperatures 

associated with the spectrum of biologic responses fall between low level growth loss and 

direct mortality, escalating as temperatures move towards the extremes of the tolerance 

range. The criteria suggested by our analyses for growth and direct mortality envelop 

these responses. For coho (one of the more comprehensively studied species), the 

approximate temperatures associated with various biological effects that we could broadly 

interpret from our modeling and the scientific literature are listed in Table 8.1. 

On one end of the spectrum is direct mortality from short-term exposure to high 

temperature. Clearly, direct mortality is an unacceptable endpoint condition, and would 

not fully protect fish. However, it is important to be on the alert for these conditions, 

because there are some geographic, watershed, and climatic conditions where acute lethal 

temperatures have been documented in natural conditions or due to management activities. 

On the other end of the temperature spectrum, positive growth for juveniles, or weight 

maintenance for adults, is a measurable quality that is very responsive to temperature 

(among other factors). Therefore, it can be a sensitive and early indicator of the general 

health of individual fish. While a variety of ecological factors are known to influence 

population characteristics, the growth simulations showed that there is a very strong 

temperature signature in the size and condition of fish observed in natural streams. 

Table 8.1 The spectrum of coho salmon response at temperature thresholds synthesized from this report 

and the scientific literature. Threshold values are approximations, due to lack of consistency in reporting 

results among studies. Results were standardized to 7-day maximum temperature using regression 

relationships between various temperature indices described in Section 3. Assumptions regarding the 

relationship between reported measures and 7-day maximum temperatures were assigned to standardize 

results. 

Biologic Response 

Approximate 

Temperature Threshold 

o

C (Average of the 7-day 

daily maximum 

temperature) 

Upper Critical Lethal Limit (death within minutes) 31 

Geographic limit of species (Eaton 1995) 30 

Growth loss 40% (simulated at average food supply) 30 

Acute threshold (this report) 26 

Acute threshold selected by U.S. EPA 1977 25 

Complete cessation of feeding ( laboratory studies) 24 

Growth loss of 20% (simulated at average food supply) 22.5 

Increased incidence of disease (under specific situations) 22 

Temporary movements to thermal refuges 22 

Growth loss of 10% (simulated at average food supply) 16.5 

Optimal growth at range of food satiation (laboratory) 14-17 

Growth loss of 10% (simulated at average food supply) 9.5 

Cessation of feeding and movement to refuge 4 
 8-9 

There is no consensus among physical or biological scientists as to how to report 

temperature regimes represented in their studies. Therefore, we had to translate these 

reported temperature measures to a common standard (7-day average of the daily 

maximum temperature) using relationships between temperature indices developed in the 

report. The values in Table 8.1 should therefore be viewed as approximate1

. One can see 

that simulated growth loss values identify biologic effects closest to what appears to be the 

healthiest conditions. At the temperatures where avoidance behavior or competitive 

exclusion can be observed, the growth simulation would have predicted measurable and 

possibly significant growth loss. 

Importantly, the analysis leads to the conclusion that the cumulative effects on potential 

weight gain due to the temperature regime for the summer rearing period can be a bell 

weather of more visible, and possibly more serious, effects observed at higher 

temperatures. It appears that temperature criteria selected on the basis of growth can be 

protective without explicitly accounting for all biologic responses or causal mechanisms. 

Concentrating on those that can be quantified and simulated (growth, direct mortality, 

incubation) allows the interactions between biologic response to environmental 

characteristics to be quantified. Thus, it may be the most sensitive indicator of effects that 

can also be measured in populations in natural streams without sacrificing fish. This also 

allows multiple species living in a common stream to be assessed and compared on the 

same objective basis, and in relation to observed and potential stream temperature 

regimens. 

Temperature Thresholds Based on Risk Assessment 

The growth simulation method was very sensitive to temperature, predicting a range of 

average population weights that varied with temperature regime. The method is capable 

of assessing a specific biological response on a continuous temperature scale. However, 

the results also support the concept that useful thresholds can be assigned, experimentally 

tested, and justified with probabilistic risk assessment. When this approach is applied at a 

site, with interpretation assisted by the mathematical model, it appears that rather small 

changes in average population weight could be detected. According to typical size 

distributions in populations of juvenile salmon populations, a minimum detectable weight 

loss or gain due to any factor would be approximately 20%. The growth simulation can 

associate such small changes with a temperature threshold. Without an assist in 

hypothesis formulation by the growth simulation techniques, it would probably be 

difficult to have confidence in interpreting the influence of temperature on population 

weight differences as small as 20% . 

Risk level for establishing thresholds. A quantitative expression of the consequence of 

size completes the formal appraisal of risk. There is ample evidence to suggest that larger 

size conveys many competitive and survival benefits. We attempted to associate risk with 

growth loss to guide selection of threshold values. We did so for coho salmon based on 

scientific literature that suggests that size at the end of the juvenile growth phase 

contributes to the individual’s success at later life history stages. We found that weight 

loss as small as 20% of the average population weight at the end of the juvenile summer 

rearing phase may be important in this context. However, the research results supporting 

this conclusion are neither abundant nor sufficiently consistent to have full confidence in 

using them to select risk criteria. For example, later success in the marine environment 

 

1 The 7-day maximum and annual maximum temperature are closely related and are often within 1o

 to 2oC of each other. 
 8-10 

y = 1.117x - 1.427

R2

 = 0.996

10

12

14

16

18

20

22

24

26

10 12 14 16 18 20 22 24 26

7-D ay M aximum T emperature ( o C )

Line o f 1 :1 Co rrespo nd ence

y = 0.6353x + 4.5983

R2 = 0.89

1 0

1 2

1 4

1 6

1 8

2 0

2 2

10 12 14 16 18 20 22 24 26

7-D ay M aximum T emperature ( o C )

Line o f 1 :1 Co rresp o nd ence

Figure 8.4 The relationship between various temperature 

indices currently used as the averaging period for 

temperature criteria. The averaging period has typically 

been either the annual maximum temperature (observed for

a period as short as an hour, but more probably occurring 

for several hours on sequential days), or a weekly average 

(generally focused on the warmest seven consecutive days) 

observed for the year. For example, Washington’s current 

criteria specify the annual maximum temperature, 

expressed as the maximum hourly temperature that occurs 

each year. Oregon specifies the average of the daily 

maximum temperature of the 7 warmest consecutive days. 

The U.S. EPA (1977) recommends the average of the daily 

mean temperature of the 7 warmest consecutive days 

(MWAT). All of these indices represent the upper tail of 

the distribution of temperatures observed during the 

summer months, and are closely related to one another. We

conclude that any of the indices can be used for the purpose

of temperature criteria because they are closely related. 

Furthermore, the short-term measures appear to adequately

represent chronic exposure and long-term effects. 

also depends on timing and location factors. Nevertheless, within the juvenile rearing 

phase, studies have consistently reported that larger size generally conveys a number of 

advantages such as better feeding position and lower mortality. Such observations 

indicate that working with growth or weight maintenance is a useful approach. The 

threshold level of growth loss is an important policy choice if it is used to determine the 

thresholds in numeric temperature criteria. Knowledge of the implications of growth and 

size to organism success is not as well quantified as desired to guide that important 

decision. Establishing the effect of size on organism success merits greater research 

attention. 

Nevertheless, several lines of 

evidence, as well as precedent in the 

scientific literature, suggest growth 

loss values between 10 and 20% as an 

appropriate, risk-guided threshold. 

We selected a growth loss of 10% as a 

threshold in our discussions and to 

compare temperature criteria with 

other existing criteria derived from 

other approaches. This level should 

prevent a statistically observable 

change in average population weight, 

assuming that population numbers 

remain consistent for the period. It is 

possible that somewhat higher growth 

loss would be acceptable, although we 

suspect that growth loss can’t be 

much higher since temperatures 

associated with higher growth loss 

begin to coincide with the outward 

manifestation of other adverse effects 

such as avoidance behavior (Table 

8.1). We do not attempt to quantify 

the response of resident adult fish to 

growth loss as an indicator of adverse 

temperature effects, although the 

same physiologic mechanisms 

manifest as weight loss in resident 

adults and undoubtedly have 

ecological ramifications. 

Averaging periods for criteria. 

Temperature criteria use short-term 

averaging periods as indices of the 

long-term response. Results provided 

in this report confirm that these 

indices can be used reliably to 

represent the long-term temperature 

regime. All of the indices (annual 

maximum temperature, 7 day 

averages of the daily maximum or 
 8-11 

daily mean temperature) characterize the upper tail of the sampled temperature 

distribution, and they are closely correlated with each other. This makes selection among 

them a matter of procedural and logistical concerns, rather than a biological question. 

Some standardization of reporting measures would be most helpful. We urge scientists to 

provide at least one of these indexing measures with their study results, thus enabling 

comparisons among them as well as their use in supporting the development of 

temperature criteria for regulatory purposes. We found that the average of the maximum 

7 consecutive days of the daily mean temperature (MWAT, U.S. EPA 1977) was best 

correlated with growth simulations, but the annual maximum and 7-day maximum were 

also quite suitable. Appropriate temperature thresholds vary with each index. 

Temperature Criteria Derived From Risk Assessment 

Thresholds generated from risk assessment are reasonably consistent with criteria 

developed previously, including those derived from subjective review methods and 

objective analysis, and those currently authorized by states. The upper temperature 

thresholds associated with 10% weight reduction are 16.5o

 and 20.5oC for coho and 

steelhead respectively (Figure 8.4). Sub-lethal thresholds suggested by the risk 

assessment method tend to be slightly lower than those derived from objective methods 

(e.g. EPA 1977), probably because we directly accounted for realistic estimates of food 

7-Day Maximum

0

5

10

15

20

25

30

Coho Steelhead

Temperature ( oC)

10%

20%

20%

Annual Maximum

0

5

10

15

20

25

30

Coho Stealhead

Temperature ( oC)

20%

20%

10%

7-Day Mean

0

5

10

15

20

25

30

Coho Steelhead

Temperature ( oC)

20%

10%

20%

Figure 8.4 Temperature range for increments of growth loss associated with long-term temperature regime as expressed for 3

temperature indices and 2 species of salmonids. The inner range represents up to 10% growth loss and the outer range represents

up to 20% growth loss. Above and below the ranges shown the growth loss exceeds 20% and was as high as 50% near the

extremes of the temperature range. Risk assessment associated with growth loss suggests that a 10% limit would prevent any

measurable effect on average coho population weight. A loss of 20% would be detectable and the temperature associated with this

level of growth loss coincides with temperatures associated with avoidance behavior. Therefore, thresholds selected at 10% may

be most appropriate for establishing temperature criteria. The threshold temperature varies with each index.
 8-12 

7-Day Maximum Temperature

0

5

10

15

20

25

Risk

Assess

Bell 1973 EPA 1977 ODEQ

1995

WDOECurrentWDOEProposed

(1999)

Temperature ( oC)

10%

20%

20%

Figure 8.5 A comparison of temperature criteria for coho and recommended ranges from a variety of 

sources standardized to the 7-day maximum temperature. The risk assessment is the range of values 

developed in this report. Bell (1973) is the original source of temperature range recommendations that 

have been widely used as the basis for subjective analyses. These were used in part to form criteria 

used by Oregon (ODEQ 1995) and the current and proposed criteria for Washington (WDOE). The 

U.S. EPA used an objective approach based on the growth curves to determine threshold criteria. The 

various methods vary in terms of degree of objectivity or subjectivity by which the information is 

synthesized into recommended criteria, the degree to which data forms the basis for the criteria, and 

the extent to which population effects can be probabilistically determined. The temperature criteria in 

use in Pacific Northwest states have largely been drawn from professionals’ review and interpretation 

of available scientific literature (e.g. ODEQ 1995). There has been some previous effort to use the 

more well-established scientific relationships to develop objective analyses of threshold temperatures 

and the duration of exposure (e.g., U.S. EPA 1977). This approach has not been widely integrated into 

regulatory activity. 

availability in the simulations. The risk-derived thresholds tend to be somewhat higher 

than those emerging from subjective evaluations (e.g. WDOE 1999), possibly because we 

did not add arbitrary safety factors (Figure 8.5). We believe that the choice of low 

thresholds of growth loss (e.g. 10%) provides an adequate margin of safety. Using the 

growth loss to set the risk level also prevents any unintended consequences of selecting 

values that are too low for a particular species. Our results demonstrated that 

temperatures that are low relative to a species’ optimum have growth loss effects that are 

comparable to those associated with those that are high relative to the optimum. 

When two or more species coexist, as is often the case, it may be appropriate to select the 

threshold for the more sensitive species. In the case of coho and steelhead, there would be 

no negative effect on steelhead by targeting lower temperatures appropriate for coho, the 

more sensitive species. If the margin between species is wider, the tradeoffs for species 

could be evaluated in selecting the temperature threshold if growth models for all species 

were available. 

The fundamental relationship quantifying growth and mortality were similar, though not 

identical, for the two salmon species we modeled. However threshold temperatures 
 8-13 

generated through growth simulation varied between them, reflecting the differences in 

food consumption estimated by observing population growth in natural streams that was 

used in the modeling. This result highlights the importance of food availability as an 

important factor determining fish growth, a conclusion consistent with observations from 

field ecological studies. The simulation results suggest that coho populations ate at 

approximately 50% of satiation rations and steelhead, using a different feeding strategy, 

ate at 100% satiation. Importantly, food availability influences the temperature threshold 

for adverse effects. There is very little documentation of how much food is available for 

fish dwelling in streams and rivers, and how management activities may alter it. Greater 

understanding of how site and watershed conditions determine how much food is available 

and how it is allocated within populations would allow understanding of how temperature 

affects total productivity in addition to its effect on weight gain of the individuals in the 

population. 

The analyses documented in this report addressed the juvenile rearing phase of coho 

salmon and steelhead trout. These species are widely distributed within the region, they 

are listed as threatened and endangered in a number of locations, and there was sufficient 

laboratory and population data to build models and corroborate them in natural streams. 

Nevertheless, this is a limited representation of the fish species that occur in the Pacific 

Northwest. Similar techniques could be applied to all fish species if the fundamental 

laboratory relationships used in the growth simulation method were available. Currently, 

there are a number of gaps in information for key functions used in modeling species of 

interest such as cutthroat trout and bull trout. 

Analysis of lethal temperatures suggested that a threshold of 26o

C for annual maximum 

temperature is a signal of imminent risk of direct mortality to salmonids. Although the 

occurrence of water temperature this high is rare, it has occasionally been observed in 

natural streams as well as in those impacted by anthropogenic activities in some situations. 

We also recommend site-specific analysis of duration of exposure when annual maximum 

temperature is between 24o

 and 26oC in order to assure that duration/magnitude thresholds 

are not exceeded. The relationship between thresholds for growth and mortality suggests 

that, if growth thresholds are met, lethal temperatures will not occur. However, there are 

situations where rivers and streams cannot be expected to meet these criteria, even under 

natural conditions. Acute criteria may be most helpful for triggering additional study in 

certain situations, and for prioritizing restoration activities. 

It may be useful to vary temperature criteria on a seasonal basis matching fish 

requirements, although differing criteria for too fine a resolution of time may be difficult 

to administer and may offer relatively little additional benefit if ambient temperatures are 

generally within exposure duration limits. The risk-based approach could be used to 

investigate the need for more finely tuned seasonal criteria. or to develop site-specific 

criteria. The concept of selecting criteria for particular species appears valid, and the risk 

assessment method can be employed to help guide the selection of appropriate criteria for 

target species or it can be used to address multiple species living in the same location. 

There was no indication in our analysis that criteria for daily fluctuating temperature 

would improve biological characterization. Also, some states have a maximum allowable 

increase in temperature as well as an upper threshold. The value of this provision is not 

immediately apparent in the context of either acute or chronic effects analysis discussed in 

this report. 
 8-14 

Uncertainties in Applying Criteria in Natural Environments 

There are natural factors contributing to uncertainty and variability when it comes to 

administering temperature criteria. There are systematic patterns in temperature dictated 

by watershed and geographic conditions. It is important to recognize that the attainment 

of biologically based criteria will vary with watershed characteristics. Temperature 

regime also varies annually by as much as several degrees due to climatic factors, so it 

may be appropriate to establish confidence limits around threshold values to determine 

whether water quality standards are attained. 

Conclusion 

In conclusion, the analytical approaches to assessing risk to salmon associated with 

ambient environmental temperature presented in this report appear to be promising 

techniques for objectively defining temperature criteria. They could also assist ecological 

field studies to segregate the effects of temperature from other habitat and population 

factors that influence productivity. The risk-based analyses support the approach and 

general range of numeric threshold values currently used as temperature criteria by Pacific 

Northwest states. However, the specific numbers generated by quantitative risk analysis 

techniques vary slightly from existing authorized criteria. Assuming the most sensitive 

salmon species is used to select the threshold, and a growth loss threshold of 10%, the 

levels suggested by risk assessment are slightly higher than used by Washington and 

slightly lower than used by Oregon. Additional research is needed to develop the 

biological basis for other species of interest. 
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 9-11
 Section 10-1 

SECTION 10 APPENDICES 

Appendix A. Data used to develop relationships between growth rate, temperature and 

consumption determined from laboratory studies...................................................10-2 

Appendix B. Regional temperature data for the Pacific Northewest Region from U.S. 

Geological Survey Water Resources Data. .........................................................10-11 

Appendix C. Acute effects of temperature on salmon and trout: data used, analyses and 

assumptions............................................................ ..........Under Separate Cover
 Section 10-2 

APPENDIX A 

DATA USED TO DEVELOP RELATIONSHIPS BETWEEN GROWTH 

RATE, TEMPERATURE AND RATION AND CONSUMPTION 

DETERMINED FROM LABORATORY STUDIES 

Coho salmon 

Steelhead trout 
 Section 10-3 

DESCRIPTION OF METHODOLOGY 

The temperature risk assessment relies on growth/ration/temperature relationships 

developed in laboratory experiments. In these experiments, the growth of individual or 

populations of fish are tracked over intervals of time under known temperature and food 

ration. Previously published studies were used to either obtain growth curves already 

developed by the authors (sockeye and chinook salmon), or to develop curves from 

original data provided by researchers (coho salmon and steelhead trout). In the studies 

used in this analysis, temperatures were maintained at constant levels for the duration of 

each trial, and various levels of food were provided as treatments. Trials were repeated at 

several levels of temperature. 

Data used for growth of coho salmon was taken from Everson (1973). Growth of juvenile 

coho was studied in 60 trials where temperature was varied from 11.1o

 to 22.4oC and food 

ration was varied from satiation to near starvation. Experiments were replicated in 1969 

and 1970. Everson reported growth rates of individual fish. Table A.1 shows the trial 

averages. The average for each trial at each temperature/treatment replication were used in 

to develop specific growth curves in the main body of the report. 

Data used for growth of steelhead analysis taken from Wurtsbaugh and Davis (1977). 

Growth of juvenile steelhead was studied in the laboratory. A total of 44 trials were 

completed over the course of a year, varying temperature from 6.9o

 to 22.5oC and ration 

over the range from satiation to near starvation. Table A.2 shows data for each trial. 

Both studies were conducted at Oregon State University, and local stocks were used. In 

both experiments, the fish were acclimated for approximated 14-16 days before trials, and 

fasted for 48 hours before tests began. The fish were fed for 23 days, and their weights 

were measured after 25 days. 
 Section 10-4 

Abbreviations used in the tables for coho: 

Variable Information 

Season Trials were run during 5 intervals of time over a 1 year time span (1969-70) 

Temperature Temperature was daily mean temperature averaged for the 30 day interval of the trial 

Food Level At each temperature, 4 food trials ranging from near starvation to satiation were run.. Each trial 

had 3-4 fish. 

Rep Rep appears on the Everson all sheet. It is the data for each of the 3-4 fish in the trial. 

Initial Wet Weight (g) Starting weight of the fish, measured. 

Final Wet Weight (g) Ending weight of the fish, measured. 

Initial Dry Weight (g) Starting dry weight of the fish, estimated based on dry to weight weight factor, Table 1 

Final Dry Weight (g) Ending dry weight of the fish, measured after sacrificing the fish. 

 Dry Food (mg) Measured dry weight of the food fed to the fish during the trial. 

Wet Food (mg) Estimated wet weight of the food, based on factors in Table 1 

Init Weight Wet Weight 

Consumption (g/g/day 

Estimated consumption per day for wet weight of fish relative to the initial wet weight at 

beginning of trial. 

MidWeight Wet Weight 

Consumption (g/g/day) 

Estimated consumption per day for wet weight of fish relative to the midpoint between start and 

end wet weight. 

Init Weight Dry 

Consumption g/g/day 

Estimated consumption per day for dry weight of fish relative to the initial dry weight at beginning 

of trial. 

Mid Weight Dry 

Consumption g/g/day 

Estimated consumption per day for dry weight of fish relative to the midpoint between start and 

end dry weight. 

% Fish Moisture Dry/Wet Fish moisture factor, from Table 1 

Daily Growth Rate—Wet 

(g/g/d) 

Growth rate calculated as (end weight-start weight)(.5*(start weight+endweight)/30 using wet 

weights 

Daily Growth Rate—Dry 

(g/g/d) 

Growth rate calculated as (end weight-start weight)(.5*(start weight+endweight)/30 using dry 

weights 

Note that rations and growth rates can be expressed as grams of body weight per day, referenced as 

g/g/d, g g-1 d-1, or g/g/day. These are also sometimes referenced as % body
 Section 10-5 

Table A.1 Experimental data for growth of coho salmon in relation to temperature and food consumption. Data is from Everson, 1973. 

Trial Fish Size Tempera

ture 

Food 

Level 

Initial 

Wet 

Weight 

(g) 

Final 

Wet 

Weight 

(g) 

Initial 

Dry 

Weight 

(g) 

Final 

Dry 

Weight 

(g) 

 Dry 

Food 

(mg) 

Wet Food 

(mg) 

Init 

Weight 

Wet 

Weight 

Consumpt

ion 

(g/g/day 

Mid Weight 

Wet Weight 

Consumption 

(g/g/day) 

Init 

Weight 

Dry 

Consum

ption 

g/g/day 

Mid 

Weight 

Dry 

Consump 

tion 

g/g/day 

% Fish 

Moisture 

Dry/Wet 

Trial 1 Med 14.5 1 1.68 1.78 0.30 0.36 344 480 0.0098 0.0095 0.0395 0.035 0.1780 

Trial 2 Med 14.5 2 1.88 2.82 0.34 0.60 924 1289 0.0235 0.0185 0.0946 0.067 0.1780 Trial 3 Med 14.5 3 1.69 2.91 0.30 0.63 1297 1809 0.0457 0.0310 0.1841 0.111 0.1780 Trial 4 Med 14.5 4 1.43 2.64 0.25 0.57 1344 1875 0.0443 0.0309 0.1783 0.109 0.1780 Trial 5 Med 18.6 1 1.58 1.60 0.28 0.30 345 481 0.0103 0.0102 0.0414 0.040 0.1780 Trial 6 Med 18.6 2 1.82 2.22 0.32 0.49 1023 1426 0.0269 0.0241 0.1083 0.086 0.1780 Trial 7 Med 18.6 3 3.09 3.91 0.55 0.91 1657 2311 0.0253 0.0222 0.1018 0.077 0.1780 Trial 8 Med 18.6 4 2.64 3.37 0.47 0.79 1912 2666 0.0350 0.0302 0.1410 0.103 0.1780 Trial 9 Med 20.8 1 1.33 1.21 0.24 0.23 342 477 0.0122 0.0128 0.0491 0.050 0.1780 

Trial 10 Med 20.8 2 1.62 2.21 0.29 0.47 957 1334 0.0278 0.0234 0.1121 0.084 0.1780 Trial 11 Med 20.8 3 1.68 2.66 0.30 0.62 1392 1942 0.0397 0.0307 0.1597 0.106 0.1780 Trial 12 Med 20.8 4 2.23 3.75 0.40 0.87 2008 2801 0.0433 0.0315 0.1745 0.107 0.1780 Trial 13 Med 11.1 1 1.77 2.29 0.34 0.45 439 608 0.0116 0.0101 0.0434 0.037 0.1940 Trial 14 Med 11.1 2 1.92 2.94 0.37 0.62 793 1096 0.0195 0.0153 0.0728 0.054 0.1940 Trial 15 Med 11.1 3 1.67 2.62 0.32 0.56 949 1312 0.0274 0.0208 0.1022 0.073 0.1940 Trial 16 Med 11.1 4 1.81 3.38 0.35 0.73 1262 1745 0.0329 0.0227 0.1226 0.078 0.1940 Trial 17 Med 15.2 1 2.03 2.35 0.39 0.47 445 615 0.0106 0.0098 0.0396 0.075 0.1940 Trial 18 Med 15.2 2 1.77 2.44 0.34 0.53 764 1057 0.0202 0.0168 0.0751 0.075 0.1940 Trial 19 Med 15.2 3 1.49 2.86 0.29 0.61 1088 1504 0.0339 0.0232 0.1262 0.075 0.1940 Trial 20 Med 15.2 4 1.71 3.04 0.33 0.66 1256 1737 0.0345 0.0247 0.1288 0.075 0.1940 Trial 21 Med 17.8 1 1.59 1.82 0.31 0.36 443 612 0.0132 0.0122 0.0491 0.075 0.1940 Trial 22 Med 17.8 2 1.85 2.52 0.36 0.53 796 1101 0.0218 0.0181 0.0811 0.075 0.1940 Trial 23 Med 17.8 3 1.74 2.88 0.34 0.63 1071 1481 0.0286 0.0216 0.1067 0.075 0.1940 Trial 24 Med 17.8 4 2.42 3.96 0.47 0.89 1413 1954 0.0283 0.0212 0.1054 0.075 0.1940 
 Section 10-6 

Table A.1 Continued. Experimental data for growth of coho salmon in relation to temperature and food consumption. Data is from Eve

Trial Fish Size Tempera

ture 

Food 

Level 

Initial 

Wet 

Weight 

(g) 

Final 

Wet 

Weight 

(g) 

Initial 

Dry 

Weight 

(g) 

Final 

Dry 

Weight 

(g) 

 Dry 

Food 

(mg) 

Wet Food 

(mg) 

Init 

Weight 

Wet 

Weight 

Consumpt

ion 

(g/g/day 

Mid Weight 

Wet Weight 

Consumption 

(g/g/day) 

Init 

Weight 

Dry 

Consump

tion 

g/g/day 

Mid 

Weight 

Dry 

Consump 

tion 

g/g/day 

% Fish 

Moisture 

Dry/Wet 

Trial 25 Large 9.4 1 4.11 4.46 0.86 0.95 472 633 0.0051 0.0049 0.0182 0.075 0.2100 

Trial 26 Large 9.4 2 4.08 4.67 0.86 1.00 711 955 0.0078 0.0073 0.0277 0.075 0.2100 Trial 27 Large 9.4 3 3.70 4.59 0.78 0.99 873 1172 0.0106 0.0094 0.0374 0.075 0.2100 Trial 28 Large 9.4 4 3.80 4.99 0.80 1.09 1122 1505 0.0132 0.0114 0.0469 0.075 0.2100 Trial 29 Large 13.1 1 2.85 2.81 0.60 0.56 313 419 0.0049 0.0049 0.0174 0.075 0.2100 Trial 30 Large 13.1 2 3.30 3.67 0.69 0.76 569 764 0.0077 0.0073 0.0273 0.075 0.2100 Trial 31 Large 13.1 3 3.83 4.69 0.80 0.99 902 1211 0.0105 0.0095 0.0374 0.075 0.2100 Trial 32 Large 13.1 4 3.57 4.55 0.75 0.97 1066 1430 0.0133 0.0117 0.0473 0.075 0.2100 Trial 33 Large 15.8 1 2.96 2.87 0.62 0.56 330 443 0.0050 0.0051 0.0177 0.075 0.2100 Trial 34 Large 15.8 2 3.10 3.20 0.65 0.64 467 627 0.0069 0.0068 0.0246 0.075 0.2100 Trial 35 Large 15.8 3 3.29 3.79 0.69 0.79 761 1022 0.0104 0.0096 0.0368 0.075 0.2100 Trial 36 Large 15.8 4 3.59 4.65 0.75 1.00 1099 1475 0.0137 0.0119 0.0487 0.075 0.2100 Trial 37 Small 11.4 1 0.70 0.73 0.12 0.12 142 208 0.0098 0.0098 0.0396 0.075 0.1700 Trial 38 Small 11.4 2 0.76 0.90 0.13 0.16 202 295 0.0130 0.0119 0.0524 0.075 0.1700 Trial 39 Small 11.4 3 0.72 0.93 0.12 0.16 235 343 0.0158 0.0139 0.0636 0.075 0.1700 Trial 40 Small 11.4 4 0.69 1.01 0.12 0.18 321 469 0.0228 0.0185 0.0916 0.075 0.1700 Trial 41 Small 14.8 1 0.77 0.66 0.13 0.11 144 211 0.0091 0.0099 0.0366 0.075 0.1700 Trial 42 Small 14.8 2 0.58 0.55 0.10 0.09 145 212 0.0121 0.0125 0.0487 0.075 0.1700 Trial 43 Small 14.8 3 0.56 0.60 0.09 0.10 170 249 0.0149 0.0143 0.0598 0.075 0.1700 Trial 44 Small 14.8 4 0.63 0.75 0.11 0.13 267 390 0.0206 0.0188 0.0827 0.075 0.1700 Trial 45 Small 17.2 1 0.82 0.75 0.14 0.13 161 235 0.0096 0.0100 0.0385 0.075 0.1700 Trial 46 Small 17.2 2 0.65 0.54 0.11 0.08 139 203 0.0104 0.0114 0.0419 0.075 0.1700 Trial 47 Small 17.2 3 0.69 0.76 0.12 0.13 210 307 0.0149 0.0142 0.0599 0.075 0.1700 Trial 48 Small 17.2 4 0.74 0.89 0.13 0.16 277 405 0.0181 0.0165 0.0729 0.075 0.1700 Trial 49 Med 15.8 1 1.65 1.86 0.31 0.37 352 452 0.0091 0.0086 0.0376 0.075 0.1890 Trial 50 Med 15.8 2 1.83 2.38 0.35 0.53 643 825 0.0150 0.0131 0.0618 0.075 0.1890 
 Section 10-7 

Table A.1 Continued. Experimental data for growth of coho salmon in relation to temperature and food consumption. Data is from Eve

Trial Fish Size Tempera

ture 

Food 

Level 

Initial 

Wet 

Weight 

(g) 

Final 

Wet 

Weight 

(g) 

Initial 

Dry 

Weight 

(g) 

Final 

Dry 

Weight 

(g) 

 Dry 

Food 

(mg) 

Wet Food 

(mg) 

Init 

Weight 

Wet 

Weight 

Consumpt

ion 

(g/g/day 

Mid Weight 

Wet Weight 

Consumption 

(g/g/day) 

Init 

Weight 

Dry 

Consump

tion 

g/g/day 

Mid 

Weight 

Dry 

Consump 

tion 

g/g/day 

% Fish 

Moisture 

Dry/Wet 

Trial 51 Med 15.8 3 1.70 2.49 0.32 0.55 823 1056 0.0207 0.0168 0.0854 0.075 0.1890 

Trial 52 Med 15.8 4 1.93 3.14 0.36 0.73 1202 1543 0.0267 0.0203 0.1101 0.075 0.1890 Trial 53 Med 19.2 1 1.53 1.56 0.29 0.30 316 406 0.0088 0.0088 0.0364 0.075 0.1890 Trial 54 Med 19.2 2 1.84 2.22 0.35 0.48 626 804 0.0145 0.0132 0.0600 0.075 0.1890 Trial 55 Med 19.2 3 1.60 2.22 0.30 0.49 759 975 0.0203 0.0170 0.0838 0.075 0.1890 Trial 56 Med 19.2 4 1.64 2.62 0.31 0.65 1029 1320 0.0268 0.0207 0.1106 0.075 0.1890 Trial 57 Med 22.4 1 1.97 1.93 0.37 0.38 404 518 0.0087 0.0088 0.0361 0.075 0.1890 Trial 58 Med 22.4 2 1.54 1.69 0.29 0.33 395 507 0.0109 0.0104 0.0451 0.075 0.1890 Trial 59 Med 22.4 3 1.62 2.03 0.31 0.45 742 952 0.0196 0.0174 0.0808 0.075 0.1890 Trial 60 Med 22.4 4 1.84 2.36 0.35 0.51 844 1084 0.0201 0.0173 0.0830 0.075 0.1890 
 Section 10-8 

Table A.2 Experimental data for growth of steelhead trout in relation to temperature and food consumption. Data is from Wurtsbaugh 

Trial Temperat

ure oC 

Season Initial Wet 

Weight 

(g) 

Final Wet 

Weight 

(g) 

Initial Dry 

Weight 

(g) 

Final Dry 

Weight 

(g) 

Dry Weight 

Consumption 

(g/g/d) 

Mid Weight 

Dry 

Consumption 

(g/g/d) 

% Fish 

Moisture 

Dry/Wet 

Final Dry 

Weight % 

Dry 

Daily 

Growth 

Rate 

(g/g/d) 

Wet Da

Grow

Rate (g/

Trial 1 6.9 Winter 2.03 1.82 0.443 0.337 0.5 0.04870 0.218 0.185 -0.011 -0.436 Trial 2 6.9 Winter 2.01 1.96 0.438 0.380 1.5 0.15345 0.218 0.194 -0.006 -0.101 Trial 3 6.9 Winter 1.95 2.1 0.425 0.456 2.5 0.27525 0.218 0.217 0.003 0.296 Trial 4 6.9 Winter 1.91 2.3 0.416 0.534 4.2 0.49874 0.218 0.232 0.010 0.741 Trial 5 9.4 Spring 2.35 2.35 0.470 0.430 2.5 0.28127 0.2 0.183 -0.004 0.000 Trial 6 9.4 Spring 2.25 2.71 0.450 0.526 4.9 0.59764 0.2 0.194 0.006 0.742 Trial 7 9.4 Spring 2.36 3.19 0.472 0.657 7 0.98800 0.2 0.206 0.013 1.196 Trial 8 9.4 Spring 2.23 3.38 0.446 0.690 10.2 1.44779 0.2 0.204 0.017 1.640 Trial 9 10 Autumn 0.98 0.96 0.190 0.177 2.2 0.10086 0.194 0.184 -0.003 -0.082 Trial 10 10 Autumn 1.04 1.31 0.202 0.261 4.3 0.24857 0.194 0.199 0.010 0.919 Trial 11 10 Autumn 1.02 1.64 0.198 0.341 7.9 0.53226 0.194 0.208 0.021 1.865 Trial 12 10 Autumn 1 1.89 0.194 0.412 14 1.06054 0.194 0.218 0.029 2.464 Trial 13 10.1 Winter 1.96 1.66 0.414 0.300 0.6 0.05355 0.211 0.181 -0.013 -0.663 Trial 14 10.1 Winter 1.96 1.82 0.414 0.351 1.5 0.14340 0.211 0.193 -0.007 -0.296 Trial 15 10.1 Winter 1.97 2.02 0.416 0.396 2.7 0.27391 0.211 0.196 -0.002 0.100 Trial 16 10.1 Winter 1.94 2.35 0.409 0.496 4.9 0.55443 0.211 0.211 0.008 0.765 Trial 17 12.6 Spring 2.29 2.29 0.463 0.428 3.8 0.42313 0.202 0.187 -0.003 0.000 Trial 18 12.6 Spring 2.33 2.82 0.471 0.541 6.1 0.77173 0.202 0.192 0.006 0.761 Trial 19 12.6 Spring 2.24 3.17 0.452 0.650 9.1 1.25390 0.202 0.205 0.014 1.375 Trial 20 12.6 Spring 2.28 3.75 0.461 0.810 12.7 2.01701 0.202 0.216 0.022 1.950 Trial 21 13 Winter 1.92 1.7 0.394 0.296 2 0.17235 0.205 0.174 -0.011 -0.486 Trial 22 13 Winter 1.84 1.72 0.377 0.316 3 0.26013 0.205 0.184 -0.007 -0.270 Trial 23 13 Winter 1.86 2.21 0.381 0.477 5.7 0.61180 0.205 0.216 0.009 0.688 Trial 24 13.3 Autumn 1 0.93 0.199 0.169 2.2 0.10127 0.199 0.182 -0.006 -0.290 
 Section 10-9 

Table A.2 Continued. Experimental data for growth of steelhead trout in relation to temperature and food consumption. Data is from W1977. 

Trial Temperat

ure oC 

Season Initial Wet 

Weight 

(g) 

Final Wet 

Weight 

(g) 

Initial Dry 

Weight 

(g) 

Final Dry 

Weight 

(g) 

Dry Weight 

Consumpti

on (g/g/d) 

Mid Weight 

Dry 

Consumptio

n (g/g/d) 

% Fish 

Moisture 

Dry/Wet 

Final Dry 

Weight % 

Dry 

Daily 

Growth 

Rate 

(g/g/d) 

Wet Da

Growt

Rate (g/g

Trial 25 13.3 Autumn 0.98 1.17 0.195 0.227 4.6 0.24265 0.199 0.194 0.006 0.707 Trial 26 13.3 Autumn 1.02 1.55 0.203 0.315 7.7 0.49822 0.199 0.203 0.017 1.650 Trial 27 13.3 Autumn 0.97 2.11 0.193 0.473 16.5 1.37294 0.199 0.224 0.034 2.961 Trial 28 15.2 Spring 2.28 2.33 0.456 0.419 5.5 0.60184 0.2 0.18 -0.003 0.087 Trial 29 15.2 Spring 2.25 3.17 0.450 0.669 10.2 1.42656 0.2 0.211 0.016 1.358 Trial 30 15.2 Spring 2.25 3.73 0.450 0.791 15.4 2.38846 0.2 0.212 0.022 1.980 Trial 31 16.2 Summer 1.14 1.21 0.239 0.241 4.9 0.29412 0.21 0.199 0.000 0.238 Trial 32 16.2 Summer 1.23 1.57 0.258 0.345 6.8 0.51315 0.21 0.22 0.012 0.971 Trial 33 16.2 Summer 1.18 1.64 0.248 0.364 9.5 0.72661 0.21 0.222 0.015 1.305 Trial 34 16.2 Summer 1.2 2.13 0.252 0.494 14.3 1.33376 0.21 0.232 0.026 2.234 Trial 35 16.4 Autumn 0.94 1.29 0.185 0.258 8.3 0.45980 0.197 0.2 0.013 1.256 Trial 36 16.4 Autumn 0.92 2.04 0.181 0.469 20.1 1.63423 0.197 0.23 0.035 3.027 Trial 37 19.5 Summer 1.19 1.28 0.258 0.274 6 0.39911 0.217 0.214 0.002 0.291 Trial 38 19.5 Summer 1.21 1.53 0.263 0.341 7.9 0.59621 0.217 0.223 0.010 0.934 Trial 39 19.5 Summer 1.16 1.63 0.252 0.380 10.3 0.81307 0.217 0.233 0.016 1.348 Trial 40 19.5 Summer 1.18 2.02 0.256 0.475 15.7 1.43412 0.217 0.235 0.024 2.100 Trial 41 22.5 Summer 1.1 1.21 0.243 0.258 7.4 0.46327 0.221 0.213 0.002 0.381 Trial 42 22.5 Summer 1.1 1.25 0.243 0.273 9.2 0.59294 0.221 0.218 0.005 0.511 Trial 43 22.5 Summer 1.11 1.41 0.245 0.326 11 0.78515 0.221 0.231 0.011 0.952 Trial 44 22.5 Summer 1.16 1.61 0.256 0.390 13.4 1.08202 0.221 0.242 0.017 1.300 
 Section 10-10 

LITERATURE CITED 

Brett, R.J., J. E. Shelbourn, and C.T. Shoop. 1969. Growth rate and body composition of 

fingerling sockeye salmon, Oncorhynchus nerka, in relation to temperature and ration 

size. J. Fish. Res. Bd. Canada 26:2363-2394. 

Brett, R.J., W.C. Clarke, and J.E. Shelbourn. 1982. Experiments on thermal requirements for 

growth and food conversion efficiency of juvenile chinook salmon Oncorhynchus 

tshawytscha. Can. Tech. Rep. Fish. Aquat. Sci. No. 1127. 

Everson, L.B. 1973. Growth and food consumption of juvenile coho salmon exposed to natural 

and elevated fluctuating temperatures. M.S. Thesis, Oregon State University, Corvallis, 

OR. 68 pp. 

Wurtsbaugh, W.A. and G. E. Davis. 1977. Effects of temperature and ration level on the growth 

and food conversion efficiency of Salmo gairdneri, Richardson. J. Fish. Biol. 11:87-98. 
 Section 10-11 

APPENDIX B 

REGIONAL TEMPERATURE DATA FOR THE PACIFIC NORTHWEST 

REGION, FROM U.S. GEOLOGICAL SURVEY WATER RESOURCES 

DATA 

Washington 

Oregon 

Idaho 

From 1978-79 Water Resources Data 

U.S. Geological Survey
 Section 10-12 

Table B.1 Annual maximum temperature for 1978-79 and most extreme temperature 

measured at all stream and river sites listed in the U.S.G.S. water resources inventory for 

Washington. 

State River USGS Station Number Basin 

Area 

 (km2

) 

1979 Annual 

Maximum 

Temperature 

(

o

C) 

Extreme 

Maximum (o

C) 

Washington Wynoochee River 12037400 401 22.5 24 

 N.F. Skokomish River 12056500 148 14 15 

 Skokomish River 12056500 588 16 20.5 

 Nisqually River near National 12082500 344 18 18.5 

 Nisqually River near LaGrande 12086500 756 18 18.5 

 Green River 12113000 1,033 22.5 24 

 Cedar River near Landsburg 12117500 313 15.5 19.5 

 Cedar River near Renton 12119000 477 23 24 

 M.F. Snoqualmie River 12141300 399 18.5 

 N. F. Snoqualmie River 1214200 166 21 

 S. F. Snoqualmie River 108 19 

 Skagit River 12179000 3,300 13 

 Tank Creek 12197040 6 17.5 

 Minkler Creek 12197110 13 21 21 

 Black Creek 12197680 1 16.5 17.5 

 Wiseman Creek 12197700 8 16 16.5 

 Skagit River near Sedro Woolley 12199000 7,809 18 18 

 Skagit River near Mount Vernon 12200500 8,011 17.5 17.8 

 Kalama River 14223600 325 20.5 21.5 

 Cowlitz River near Randall 14233400 2,668 18.5 19 

 Cowlitz River below Mossyrock Dam 14234810 2,989 11 15 

 Cowlitz River below Mayfield Dam 14238000 3,626 12.5 21 

 Tilton River 14236200 365 21.5 24.5 

 Columbia River at Warrendale 14128910 621,600 22 22.5 

 Columbia River at Bradwood, OR 14247400 665,900 21.5 22.5 

 Columbia River at Northport, WA 12433000 154,600 20 21 

 Columbia River at Grand Coulee Dam 12472900 193,500 19 19 

 Columbia River at Richland 12473520 251,000 21 21.5 

 Columbia River at Umatilla 14019250 554,300 22 22.5 

 Pend Oreille River 12398600 65,300 23 24.5 

 Spokane River 12433000 15,590 20.5 24.5 

 Yakima River at Kiona 12510500 14,543 28 29 

 Snake River at Anatone 13334300 241,000 23.5 25 

 Snake River at Burbank 13353200 281,800 23.5 24 
 Section 10-13 

Table B.2 Annual maximum temperature for 1978-79 and most extreme temperature measured at 

all stream and river sites listed in the U.S.G.S. water resources inventory for Oregon.

State River USGS Station Number Basin 

Area 

 (km2

) 

1979 Annual 

Maximum 

Temperature 

(

o

C) 

Extreme 

Maximum (o

C) 

Oregon Columbia River at Rainier 14245295 664,900 21.5 23.5 

 Columbia River at Wama 14247295 665,000 21.5 22.5 

 Columbia River at Vancouver 14144700 624,200 22 23.5 

 Owyhee River 13184000 29,300 25 25 

 Meadow Creek near Starkey 13318050 86 25 25 

 Meadow Creek above Bear Cr. 133`8060 125 26 26.5 

 Minam River 13331500 622 24.5 27 

 Umatilla River 14020000 339 24.5 25 

 John Day River 14048000 19,600 31 33 

 Deschutes River near Bend 14064500 4,556 17 17 

 Deschutes River near Madras 14092500 20,250 14 18 

 Deschutes River near Moody 14103000 27,200 23 23 

 DonnerundBlitzen River 10396000 518 25.5 28.5 

 Willamette at Portland 14211720 28,700 24 27.5 

 Bull Run River 14138850 124 17 17 

 Fir Creek 14138870 14 14.5 15 

 N. F. Bull Run River 14138900 22 14 

 S.F. Bull Run River 14139800 17 

 M. F. Willamette near Oak Ridge 14144800 668 20 23 

 Hills Creek 14144900 136 19 22.5 

 M.F. Willamette above Salt Cr. 14145500 1,015 17 25 

 M. F. Willamette below N. Fork 14148000 2,393 19 23.5 

 M.F. Willamette near Dexter 14150000 2,593 17.5 18.5 

 Fall Creek 14150300 306 25 25 

 Wineberry Creek 14150800 114 24 26.5 

 Fall Creek below Wineberry Cr. 14151000 482 20.5 26 

 M.F. Willamette at Jasper 14152000 3,471 17.5 21 

 Coast Fork Willamette River 14152500 187 24.5 25.5 

 McKenzie River below Trail Cr. Dam 14158850 477 11 12 

 McKenzie River at McKenzie Bridge 14159000 901 13 13.5 

 S. Fork McKenzie 14159200 414 17 17 

 S. Fork McKenzie near Rainbow 14159500 539 14 20 

 Blue River 14161100 119 23 23 

 McKenzie River near Vida 14162500 2,409 15 16 

 Willamette River at Harrisburg 14166000 8,860 20 24 

 Mary's River 14171000 412 22.5 23.5 

 Calapooia River at Holley 14172000 272 29 29.5 

 Calapooia River at Albany 14173500 963 28 28.5 

 N. Santiam River below Boulder 14178000 559 17.5 19.5 

 Breitenbush River 14179000 275 16.5 18 

 N. Santiam River at Niagara 14181500 1,173 13.5 16.5 

 S. Santiam River below Cascadia 14185000 451 24 25 

 M. Santiam River near Cascadia 14185800 269 22 22.5 

 Quartzville Creek 14185900 257 24 25.5 

 S. Santiam River near Foster 14187200 1,443 14.5 15.5 

 S. Santiam River near Waterloo 14187500 1,658 18 26 

 Santiam River 14189000 4,640 23 23.5 

 Willamette River at Salem 14191000 18,900 24 25.5 

 Tualatin River near Gaston 14202500 126 24 

 Tualatin River at West Linn 14207500 1,829 23.5 27.5 
 Section 10-14 

Table B.2 Continued Annual maximum temperature for 1978-79 and most extreme temperature 

measured at all stream and river sites listed in the U.S.G.S. water resources inventory for Oregon.

State River USGS Station Number Basin 

Area 

(km2

) 

1979 Annual 

Maximum 

Temperature 

(

o

C) 

Extreme 

Maximum (o

C) 

Oregon Nehalem River 14301000 1,728 24.5 24.5 

 Nestucca River 14303600 466 23.5 24 

 Big Rock Creek 14304850 18 19.5 

 Siletz River 14305500 523 24.5 

 Siuslaw River 14307620 1,523 31 31 

 S. Umpqua River at Days Cr. 14308600 1,660 29 30 

 S. Umpqua River near Roseburg 14312260 4,657 25 29 

 N. Umpqua River at Winchester 14319500 3,481 26 26.5 

 Umpqua River 14321000 9,539 27 30 

 Rogue River below Prospect 14330000 982 20.5 20.5 

 S. Fork Rogue River 14335075 637 20 20 

 Rogue River at McLeod 14335075 1.787 14.5 14.5 

 Big Bull Creek 14337500 635 24 24 

 Rogue River near McLeod 14337600 2,429 14.5 18 

 Elk Creek 14337800 204 27.5 28.5 

 W. Branch Elk Creek 14337870 37 24 25.5 

 Elk Creek near Trail 14338000 344 31.5 31.5 

 Rogue River at Dodge Bridge 14339000 3,147 18.5 20 

 Rogue River at Raygold 1435900 5,317 19 22 

 Rogue River at Grants Pass 14361500 6,369 17 23.5 

 Rogue River near Merlin 14370400 8,472 22.5 25.5 

 Rogue River at Marial 14372250 9,873 24.5 27.5 

 Rogue River near Agnes 14372300 10,202 24.5 26.5 

 Elliot Creek 14361600 134 22.5 23 

 Carberry Creek 14361700 178 23.5 23.5 

 Applegate River near Copper 14362000 583 25.5 26.5 

 Applegate River near Applegate 14366000 1,251 26 28 

 Applegate River near Wilderville 14369500 1,808 28 28 
 Section 10-15 

Table B.3 Annual maximum temperature for 1978-79 and most extreme temperature measured at 

all stream and river sites listed in the U.S.G.S. water resources inventory for Idaho.

State River USGS Station Number Basin 

 Area (km2

) 

1979 Annual 

Maximum 

Temperature 

(

o

C) 

Extreme 

Maximum (o

C) 

Idaho Kootenai River at Leonia 12305000 30,407 14 

 Kootenai River near Copeland 12318500 34,710 19 24 

 Kootenai River at Porthill 12322000 35 20 23.5 

 Salmon River at Whitebird 13269000 35,090 25 28 

 Yankee Fork Salmon River 13296000 505 16 17.5 

 N. Fork Clearwater near Canyon 13340600 3,520 21.5 23 

 Clearwater River near Peck 13341050 6,320 21 25 

 Clearwater River at Spalding 13342500 24,790 22 28 

 Bear River at Border, WY 10039500 6,439 23 23 

 Salt River above reservoir, Alpin WY 13023000 1,160 16.5 21 

 Snake River at Weiser 13269000 565 28.5 

 Snake River at Hells Canyon Dam 13269000 190,000 19.5 20 

 Snake River near Irwin 13032500 13,533 15.5 18.5 

 Snake River at Neeley 13077000 35,200 23.5 23.5 

 Snake River at Minidoka 13081500 40,700 23 23.5 

 Snake River at King Hill 13081500 92,700 20.5 23.5 

 Willow Creek near Ririe 13058000 1,620 25 
 Section 10-16 
Calculation of Acute Temperature Relationships Appendix C 

 

Appendix C - Page 1 

APPENDIX C 

ACUTE EFFECTS OF TEMPERATURE ON SALMON AND TROUT: DATA USED, 

ANALYSES AND ASSUMPTIONS 

Chinook salmon 

Chum salmon 

Coho salmon 

Cutthroat trout 

Pink salmon 

Rainbow trout 

Sockeye salmon 

DESCRIPTION OF METHODOLOGY 

This memorandum summarizes how estimated acute thermal effects curves were generated for 

selected species of salmon and trout: pink salmon, chum salmon, coho salmon, sockeye salmon, 

chinook salmon, rainbow trout (steelhead), and cutthroat trout. Effects of elevated temperatures on 

these fish species were of interest; thus, curves were generated from available data for acclimation 

temperatures of 15°C and higher. 

Most of the available thermal effects information is based on 50% survival; however, curves for 

90% survival (10% mortality) were desired. The process by which 10% mortality curves were 

estimated from the 50% mortality information is detailed here. Three attachments are provided to 

illustrate the data used and analyses: 

Attachment 1: Acute Effects of Temperature on Salmonids: Median Lethal Times 

(LT50) in Relation to Temperature

 Attachment 2: LT50 to LT10 Conversion Factors for Pacific Salmon: Sockeye and 

Chinook. 

 Attachment 3: Acute Effects of Temperature on Salmonids: Times to 10% Mortality 

(LT10) In Relation to Temperature. 
Calculation of Acute Temperature Relationships Appendix C 

 

Appendix C - Page 2 

ACUTE THERMAL EFFECTS CURVES ASSOCIATED WITH 50% MORTALITY 

Data from several sources were used to generate curves showing the relationship between 

temperature and duration to 50% mortality (EPA 1977, Brett 1952, and Golden 1978). Each curve 

estimates the length of time 50% of a species population can survive at some temperature above its 

upper incipient lethal temperature. For the remainder of this memorandum, this temperature will be 

referred to as the LT50, the temperature causing 50% mortality in a population of fish within a 

specified length of time. 

EPA (1977, page 11 of text and page 38 of Appendix A) provides a regression equation relating 

exposure time (in minutes) to the LT50 (in EC): 

 log[exposure time] = a + b*LT50, 

which can also be written as 

 LT50 = (log[exposure time] - a)/b. 

The regression coefficients, a and b, are provided in EPA (1977) for many fish species, including all 

those identified above, except cutthroat trout (pages 55-58 of Appendix B). Golden (1978, Figure 4 

on page 14) provides regression coefficients for cutthroat trout. The coefficients in EPA (1977) 

were gathered from many different sources, including Brett's 1952 paper summarizing his study of 

lethal temperatures for the five salmon species. In the attachments, the specific studies are cited 

rather than the EPA (1977) document. 

The acute thermal effects curves provided in Attachment 1 were generated in Excel using regression 

coefficients provided in EPA (1977) and Golden (1978) and the second form of the regression 

equation presented above for a range of times (durations). For the five salmon species, Brett (1952) 

provided ultimate upper incipient lethal temperatures, and the acute curves were discontinued at 

these values. For rainbow trout and cutthroat trout, the curves were discontinued at 25 °C. 

Although it was assumed that the regression coefficients in Appendix B of EPA (1977) were 

correct, one appeared to be in error. The value for a was given as 16.2444 for pink salmon at an 

acclimation temperature of 20 °C from Brett's study (1952). The resulting curve did not match the 

one presented in Figure 5 of Brett (1952). To generate a curve more representative of Brett's (1952) 

figure, a value of 13.2444 was used for a instead. 

A few of the studies included in EPA (1977) were excluded from Attachment 1. These were studies 

in which the fish being tested showed signs of gas bubble disease or other effects of gas 

supersaturation. 
Calculation of Acute Temperature Relationships Appendix C 

 

Appendix C - Page 3 

COMPARISON OF LT50 AND LT10 CURVES 

In the EPA (1977) document, two unpublished studies provided regression coefficients for both 

50% and 10% (LT10) mortality curves at acclimation temperatures of 15EC or higher. McConnell 

and Blahm (1970) calculated regression coefficients for sockeye salmon; and Blahm and 

McConnell (1970) calculated regression coefficients for both spring and fall runs of chinook 

salmon. Using the regression coefficients generated from these studies, LT50 and LT10 values 

were calculated for a range of durations. 

Attachment 2 contains two tables (one each for sockeye and chinook salmon) of the calculated 

values and their ratios (i.e, LT10/LT50). For the range of durations calculated, the LT10 values 

were 98.0 to 99.7% of the LT50 values. This is consistent with Brett (1958, page 76 and Figure 4), 

who indicated that differences between temperatures for 50% mortality and those for <50% 

mortality are relatively small, "implying that temperatures of this order have only to increase 

slightly to cause a large difference in mortality." 

ACUTE THERMAL EFFECTS CURVES @ 10% MORTALITY 

Based on the comparison of LT50 and LT10 curves generated from the McConnell and Blahm 

(1970) and Blahm and McConnell (1970) studies, and to be somewhat conservative, LT10 curves 

were estimated for the other studies by applying a factor of 0.98 to each curve. That is, each LT10 

value was estimated to be 98% of the LT50 value calculated from the regression equation. The 

estimated LT10 curves are presented in Attachment 3. 

Based on visual inspection of the LT50 and LT10 curves included in Attachment 1 from these two 

studies, the slopes were similar. That is, on the log-time scale, the differences between the LT50 

and LT10 curves were approximately constant. (There was insufficient information presented in 

Appendix B of the EPA (1977) document to statistically compare the slopes.) Had the differences 

not appeared constant, the application of a singe adjustment factor would not have been appropriate. 

KEY ASSUMPTIONS/ISSUES 

The following text summarizes key assumptions used when evaluating the available data and 

estimating the LT10 curves. Other issues relevant to the use of these data are also identified. The 

information is presented in bullet form and can be expanded upon at a later date if desired. 

• The most important assumption is that the data provided in EPA (1977) were 

representative of the same species from different locations (i.e., different stocks). There 

are many factors to consider with such an assumption: different environmental influences 

(water quality, temperature fluctuations), genetic differences, size, life stage, etc. 
Calculation of Acute Temperature Relationships Appendix C 

 

Appendix C - Page 4 

• As stated above, the regression coefficients reported in EPA (1977) were assumed to be 

correct. 

• The adjustment factor estimated from the McConnell and Blahm (1970) and Blahm and 

McConnell (1970) data for sockeye and chinook salmon was assumed to be appropriate 

to use in estimating LT10 curves for the other salmon and trout species. 

• Effects curves appear to differ for fish tested using a constant acclimation temperature 

versus a fluctuating one (see Attachment 1 for cutthroat trout). Can results based on 

constant acclimation temperatures be applied to fish living in streams with temperatures 

fluctuating on a daily and seasonal basis? 

• For many of the studies, the test fish were obtained from a hatchery, and sometimes from 

a limited number of females. While this limited variability in the biological responses to 

temperature because of the genetics, it also may have limited the representativeness of the 

results for a wider population of fish of same species. 

APPENDIX C REFERENCES 

Brett, J.R. 1952. Temperature tolerance in young Pacific salmon, genus Oncorhynchus. J. Fish. 

Res. Board Can. 9(6): 265-323. 

Brett, J.R. 1958. Implications and assessments of environmental stress. Pages 60-83 in P.A. 

Larkin, editor. The Investigation of Fish-Power Problems. H.R. MacMillan Lectures in 

Fisheries, Univ. of British Columbia, Vancouver. 

EPA. 1977. Temperature criteria for freshwater fish: protocol and procedures. U.S. 

Environmental Protection Agency Office of Research and Development, Environmental 

Research Laboratory, Duluth, Minnesota. EPA-600/3-77-061. 130p. 

Golden, J.T. 1978. The effects of fluctuating temperatures on the lethal tolerance limits of coastal 

cutthroat trout (Salmo clarki clarki). Thesis, Oregon State University, Corvallis, Oregon. 

29p. 
Calculation of Acute Temperature Relationships Appendix C 

 

Appendix C 

APPENDIX C 

ATTACHMENT 1 

Acute Effects of Temperature on Salmonids: 

Median Lethal Times (LT50) in Relation to Temperature 
CHINOOK SALMON

Acclim. Source Age/Size a b N r Time(hr) LT50(C)

15C Brett(1952) juv. frshwtr fry 16.4454 -0.5364 4 -0.9906 0.1 29.2

0.25 28.5

0.5 27.9

1 27.32 26.84 26.26 25.98 25.7

10 25.512 25.313 25.314 25.216 25.1

20C Brett(1952) juv. frshwtr fry 22.9065 -0.7611 7 -0.9850 0.1 29.1

0.25 28.6

0.5 28.2

2 27.44 27.06 26.78 26.6

10 26.416 26.224 25.940 25.760 25.480 25.3

100 25.1

24C Brett(1952) juv. frshwtr fry 18.9940 -0.5992 9 -0.9923 0.1 30.4

0.25 29.7

0.5 29.2

1 28.72 28.24 27.76 27.48 27.2

10 27.116 26.724 26.440 26.156 25.872 25.696 25.4

120 25.3140 25.1

17C Coutant (1970) "Jacks" (1-2 yrs) 13.2502 -0.4121 4 -0.8206 0.1 30.3

0.25 29.3

0.5 28.6

242526272829303132333435

0.1 10 

Duration (hr)

Temperature (C)

LTLTLTUULTLT

242526272829303132333435

0 40 80 

Duration (hr)

Temperature (C)

LTLTLTULTLT
CHINOOK SALMON

Acclim. Source Age/Size a b N r Time(hr) LT50(C)

1 27.82 27.1

The 17C and 19C temperatures were Columbia River 3 26.7temperatures (at Grand Rapids Dam) during fall 4 26.4migrations two different years. 6 25.9

8 25.6

10 25.412 25.214 25.1

19C Coutant (1970) "Jacks" (1-2 yrs) 9.4683 -0.2504 4 -0.9952 0.1 34.7

0.25 33.1

0.5 31.9

1 30.72 29.53 28.84 28.36 27.68 27.1

10 26.720 25.526 25.1
CHINOOK SALMON

Acclim. Source Age/Size a b N r Time(hr) LT50(C)

20C Blahm & juv. (spring run) 21.3981 -0.7253 3 -0.9579 0.1 28.4

McConnell (1970) 0.25 27.9unpublished data 0.5 27.5

1 27.12 26.64 26.28 25.8

10 25.716 25.420 25.324 25.126 25.1

20C Blahm & juv. (spring run) 22.6664 -0.7797 4 -0.9747 0.1 28.1

McConnell (1970) 0.25 27.6unpublished data 0.5 27.210% mortality 1 26.8

2 26.44 26.06 25.88 25.6

10 25.512 25.416 25.220 25.1

20C Blahm & juv. (spring run) 20.9294 -0.7024 3 -0.9463 0.1 28.7

McConnell (1970) 0.25 28.1unpublished data 0.5 27.790% mortality 2 26.8

4 26.46 26.28 26.0

10 25.816 25.620 25.424 25.332 25.1

24252627282930

0.1 1 10 

Duration (hr)

Temperature (C)

LT50 foUULT5LT50 (2LT10 (2LT90 (2

24252627282930

0 40 

Duration (hr)

Temperature (C)

LT50 fUULT5LT50 (LT10 (LT90 (
CHINOOK SALMON

Acclim. Source Age/Size a b N r Time(hr) LT50(C)

20C Blahm & juv. (fall run) 22.2124 -0.7526 4 -0.9738 0.1 28.5

McConnell (1970) 0.25 28.0unpublished data 0.5 27.6

1 27.22 26.84 26.48 26.0

10 25.816 25.620 25.424 25.334 25.1

20C Blahm & juv. (fall run) 21.6756 -0.7438 4 -0.9550 0.1 28.1

McConnell (1970) 0.25 27.6unpublished data 0.5 27.210% mortality 1 26.8

2 26.34 25.96 25.78 25.5

10 25.412 25.314 25.216 25.1

20C Blahm & juv. (fall run) 20.5162 -0.6860 3 -0.9475 0.1 28.8

McConnell (1970) 0.25 28.2unpublished data 0.5 27.890% mortality 2 26.9

4 26.46 26.28 26.0

10 25.916 25.620 25.424 25.332 25.1

24252627282930

0.1 1 10 

Duration (hr)

Temperature (C)

LT50 foEst. UULT50 (2LT10 (2LT90 (2

24252627282930

0 40 

Duration (hr)

Temperature (C)

LT50 fUULT5LT50 (LT10 (LT90 (
COHO SALMON

Acclim. Source Age/Size a b N r Time(hr) LT50(C)

15C Brett(1952) juv. frshwtr fry 20.4066 -0.6858 6 -0.9681 0.1 28.6

0.25 28.0

0.5 27.6

1 27.22 26.74 26.36 26.08 25.8

10 25.712 25.616 25.420 25.332 25.0

20C Brett(1952) juv. frshwtr fry 20.4022 -0.6713 4 -0.9985 0.1 29.2

0.25 28.6

0.5 28.2

2 27.34 26.86 26.68 26.4

10 26.316 25.924 25.740 25.448 25.256 25.170 25.0

23C Brett(1952) juv. frshwtr fry 18.9736 -0.6013 5 -0.9956 0.1 30.3

0.25 29.6

0.5 29.1

1 28.62 28.14 27.66 27.38 27.1

10 26.916 26.624 26.340 25.956 25.772 25.596 25.3

120 25.1140 25.0

17C Coutant (1970) adult 5.9068 -0.1630 5 -0.9767 0.1 31.5

Reported acclimation temp. was the Columbia River 0.2 29.6temp. (at Priest Rapids Dam) during fall migration. 0.3 28.5

242526272829303132

0.1 10 

Duration (hr)

Temperature (C)

LT5LT5LT5UULLT5

242526272829303132

0 40 80 

Duration (hr)

Temperature (C)

LT5LT5LT5UULT5
COHO SALMON

Acclim. Source Age/Size a b N r Time(hr) LT50(C)

0.4 27.80.5 27.20.6 26.70.7 26.30.8 25.90.9 25.6

1 25.3

1.1 25.1

1.15 25.0
RAINBOW TROUT

Acclim. Source Age/Size a b N r Time(hr) LT50(C)

15C Alabaster & juvenile 15.6500 -0.5000 2 --- 0.1 29.7

Downing (1966) 0.25 28.9

0.5 28.3

1 27.72 27.14 26.56 26.28 25.9

10 25.712 25.616 25.320 25.124 25.0

18C Alabaster & juvenile 18.4654 -0.5801 5 -0.9787 0.1 30.5

Welcomme (1962) 0.25 29.8Dissolved Oxygen at 7.4 mg/L 0.5 29.3

2 28.24 27.76 27.48 27.2

10 27.016 26.724 26.440 26.080 25.5

120 25.2160 25.0

18C Alabaster & juvenile 13.6531 -0.4264 5 -0.9742 0.1 30.2

Welcomme (1962) 0.25 29.3Dissolved Oxygen at 3.8 mg/L 0.5 28.6

1 27.82 27.14 26.46 26.07 25.98 25.79 25.6

10 25.512 25.314 25.216 25.0

242526272829303132

0.1 10 

Duration (hr)

Temperature (C)

LT50 for 15C Acclim. (Alabaster LT50 for 18C Acclim. (7.4 DO, ALT50 for 18C Acclim. (3.8 DO, ALT50 for 20C Acclim. (Alabaster 

242526272829303132

0 40 80 

Duration (hr)

Temperature (C)

LT50 for 15C Acclim. (AlabasteLT50 for 18C Acclim. (7.4 DO, ALT50 for 18C Acclim. (3.8 DO, ALT50 for 20C Acclim. (Alabaster 
RAINBOW TROUT

Acclim. Source Age/Size a b N r Time(hr) LT50(C)

20C Alabaster & juvenile 19.6250 -0.6250 2 --- 0.1 30.2

Downing (1966) 0.25 29.5

0.5 29.0

1 28.62 28.14 27.66 27.38 27.1

12 26.820 26.532 26.140 26.080 25.5

120 25.2160 25.0

20C Craigie (1963) yearling 14.6405 -0.4470 3 -0.9787 0.1 31.0

Raised in soft water, tested in soft water (SS) 0.25 30.1

0.5 29.4

1 28.82 28.14 27.46 27.08 26.8

10 26.516 26.120 25.924 25.732 25.440 25.248 25.0

20C Craigie (1963) yearling 15.0392 -0.4561 3 -0.9917 0.1 31.3

Raised in soft water, tested in hard water (SH) 0.25 30.4

0.5 29.7

1 29.12 28.4

242526272829303132

0.1 10 

Duration (hr)

Temperature (C)

LT50 for 20C AcclimLT50 for 20C AcclimLT50 for 20C AcclimLT50 for 20C AcclimLT50 for 20C Acclim

242526272829303132

0 40 80 

Duration (hr)

Temperature (C)

LT50 for 20C Acclim. (ALT50 for 20C Acclim. (SLT50 for 20C Acclim. (SLT50 for 20C Acclim. (HLT50 for 20C Acclim. (H
RAINBOW TROUT

Acclim. Source Age/Size a b N r Time(hr) LT50(C)

4 27.86 27.48 27.1

12 26.716 26.420 26.232 25.840 25.660 25.272 25.0

20C Craigie (1963) yearling 15.1473 -0.4683 3 -0.9781 0.1 30.7

Raised in hard water, tested in soft water (HS) 0.25 29.8

0.5 29.2

1 28.52 27.94 27.36 26.98 26.6

10 26.416 26.020 25.824 25.632 25.340 25.148 25.0

20C Craigie (1963) yearling 12.8718 -0.3837 3 -0.9841 0.1 31.5

Raised in hard water, tested in hard water (HH) 0.25 30.5

0.5 29.7

1 28.92 28.13 27.74 27.36 26.98 26.6

10 26.312 26.1
RAINBOW TROUT

Acclim. Source Age/Size a b N r Time(hr) LT50(C)

16 25.820 25.524 25.332 25.0
CUTTHROAT TROUT

Acclim. Source Age/Size a b N r Time(hr) LT50(C)

23C Golden (1978) juvenile 18.092 -0.56523 ? -0.996 0.1 30.6

1975 tests, hatchery only 0.25 29.9

0.5 29.4

1 28.98 27.3

16 26.724 26.432 26.240 26.060 25.780 25.5

120 25.2160 25.0

13-25C Golden (1978) juvenile 22.543 -0.71999 ? -0.999 0.1 30.2

1975 tests, hatchery only 0.25 29.7

0.5 29.3

2 28.44 28.08 27.6

16 27.224 26.940 26.680 26.2

160 25.8240 25.5300 25.4560 25.0

23C Golden (1978) juvenile 18.3166 -0.5723 ? -0.990 0.1 30.6

1976 tests, hatchery and wild trout data pooled 0.25 30.0

0.5 29.4

1 28.92 28.44 27.88 27.3

12 27.016 26.824 26.540 26.180 25.6

120 25.3160 25.0

24252627282930313233

0.1 10 

Duration (hr)

Temperature (C)

LT50, 23C Acclim. LT50, 13-25C AcclLT50, 23C Acclim. LT50, 13-23C Accl

24252627282930313233

0 120 240 36

Duration (hr)

Temperature (C)

LT50, 23C AcclimLT50, 13-25C AccLT50, 23C AcclimLT50, 13-23C Acc
CUTTHROAT TROUT

Acclim. Source Age/Size a b N r Time(hr) LT50(C)13-23C Golden (1978) juvenile 18.1515 -0.5723 ? -0.992 0.1 30.4

1976 tests, hatchery and wild trout data pooled 0.25 29.7

0.5 29.1

1 28.62 28.14 27.66 27.38 27.0

12 26.720 26.332 26.040 25.860 25.580 25.3

120 25.0
SOCKEYE SALMON

Acclim. Source Age/Size a b N r Time(hr) LT50(C)

15C Brett(1952) juv. frshwtr fry 15.8799 -0.5210 7 -0.9126 0.1 29.0

0.25 28.2

0.5 27.6

1 27.12 26.54 25.96 25.68 25.3

10 25.112 25.013 24.914 24.924 24.4

20C Brett(1952) juv. frshwtr fry 19.3821 -0.6378 5 -0.9602 0.1 29.2

0.25 28.5

0.5 28.1

2 27.14 26.76 26.48 26.2

10 26.016 25.724 25.440 25.148 25.056 24.9

116 24.4

23C Brett(1952) juv. frshwtr fry 20.0020 -0.6496 4 -0.9981 0.1 29.6

0.25 29.0

0.5 28.5

1 28.12 27.64 27.16 26.98 26.7

10 26.516 26.224 25.940 25.656 25.472 25.296 25.0

120 24.9220 24.4

23242526272829303132

0.1 10 

Duration (hr)

Temperature (C)

LT50 foLT50 foLT50 foUULT50 LT50 (2

23242526272829303132

0 40 80 120 

Duration (hr)

Temperature (C)

LT50 foLT50 foLT50 foUULT50 LT50 (2
SOCKEYE SALMON

Acclim. Source Age/Size a b N r Time(hr) LT50(C)

20C McConnell & juvenile 16.7328 -0.5473 6 -0.9552 0.1 29.2

Blahm (1970) (under yearling) 0.25 28.4unpublished data 0.5 27.9

1 27.32 26.84 26.26 25.98 25.7

10 25.516 25.120 24.940 24.4

20C McConnell & juvenile 17.5227 -0.5861 6 -0.9739 0.1 28.6

Blahm (1970) (under yearling) 0.25 27.9unpublished data 0.5 27.410% mortality 1 26.9

2 26.33 26.04 25.86 25.58 25.3

10 25.212 25.028 24.4

20C McConnell & juvenile 15.7823 -0.5061 6 -0.9539 0.1 29.6

Blahm (1970) (under yearling) 0.25 28.9unpublished data 0.5 28.390% mortality 2 27.1

4 26.56 26.18 25.9

10 25.716 25.320 25.124 24.944 24.4

24252627282930

0.1 1 10 

Duration (hr)

Temperature (C)

LT50 for UULT50 LT50 (20LT10 (20LT90 (20

24252627282930

0 40 

Duration (hr)

Temperature (C)

LT50 foUULT50 LT50 (2LT10 (2LT90 (2
CHUM SALMON

Acclim. Source Age/Size a b N r Time(hr) LT50(C)

15C Brett(1952) juv. frshwtr fry 15.8911 -0.5252 8 -0.9070 0.1 28.8

0.25 28.0

0.5 27.4

1 26.92 26.34 25.76 25.48 25.2

10 25.012 24.816 24.620 24.440 23.8

20C Brett(1952) juv. frshwtr fry 16.1894 -0.5168 9 -0.9750 0.1 29.8

0.25 29.1

0.5 28.5

2 27.34 26.76 26.48 26.1

10 26.016 25.624 25.240 24.860 24.480 24.2

132 23.8

23C Brett(1952) juv. frshwtr fry 15.3825 -0.4721 4 -0.9652 0.1 30.9

0.25 30.1

0.5 29.5

1 28.82 28.24 27.56 27.28 26.9

10 26.716 26.324 25.940 25.456 25.172 24.996 24.6

120 24.4240 23.8

23242526272829303132

0.1 10 

Duration (hr)

Temperature (C)

LT50 LT50 LT50 UUL

23242526272829303132

0 40 80 120 

Duration (hr)

Temperature (C)

LT5LT5LT5UUL
PINK SALMON

Acclim. Source Age/Size a b N r Time(hr) LT50(C)

15C Brett(1952) juv. frshwtr fry 12.8937 -0.4074 8 -0.9884 0.1 29.7

0.25 28.8

0.5 28.0

1 27.32 26.54 25.86 25.48 25.1

10 24.812 24.616 24.320 24.124 23.9

20C Brett(1952) juv. frshwtr fry 13.2444 -0.4074 7 -0.9681 0.1 30.6

16.2444 in EPA (1977), but 0.25 29.6

the resulting curve does not 0.5 28.9match the information 1 28.1presented in Brett (1952) 2 27.4<Fig. 5> 4 26.7

6 26.28 25.9

10 25.716 25.224 24.832 24.540 24.256 23.9

24C Brett(1952) juv. frshwtr fry 14.7111 -0.4459 6 -0.9690 0.1 31.2

0.25 30.4

0.5 29.7

1 29.02 28.34 27.76 27.38 27.0

10 26.816 26.324 25.940 25.456 25.172 24.896 24.6

120 24.3192 23.9

23242526272829303132

0.1 10 

Duration (hr)

Temperature (C)

LT50 LT50 LT50 UUL

23242526272829303132

0 40 80 12

Duration (hr)

Temperature (C)

LT5LT5LT5UUL
Calculation of Acute Temperature Relationships Appendix C 

 

Appendix C 

APPENDIX C 

ATTACHMENT 2 

LT50 to LT10 Conversion Factors for Pacific Salmon: Sockeye and Chinook.
Calculation of Acute Temperature Relationships Appendix C 

 

Appendix C 

Attacment 2: Sockeye Salmon (20C Acclimation Temperature) 

From McConnell & Blahm (1970) unpublished data 

16.7328 -0.5473 17.5227 -0.5861 LT10/LT50 Delta T 

Time(hr) LT50(C) Time(hr) LT10(C) Ratio (C) 

0.1 29.2 0.1 28.6 98.00% 0.6 

0.25 28.4 0.25 27.9 98.12% 0.5 

0.5 27.9 0.5 27.4 98.21% 0.5 

1 27.3 1 26.9 98.31% 0.5 

2 26.8 2 26.3 98.41% 0.4 

3 26.5 3 26.0 98.47% 0.4 

4 26.2 4 25.8 98.52% 0.4 

6 25.9 6 25.5 98.58% 0.4 

8 25.7 8 25.3 98.63% 0.4 

12 25.4 12 25.0 98.70% 0.3 

16 25.1 16 24.8 98.74% 0.3 

20 24.9 20 24.6 98.78% 0.3 

24 24.8 24 24.5 98.81% 0.3 

32 24.6 32 24.3 98.86% 0.3 

40 24.4 40 24.1 98.90% 0.3 

60 24.1 60 23.8 98.98% 0.2 

80 23.8 80 23.6 99.03% 0.2 

Calculation of Acute Temperature Relationships Appendix C 

 ___________ 

Appendix C 

Attachment 2: Chinook Salmon (20C Acclimation Temperature) From Blahm & McConnell (1970) unpublished data 

Spring Run Fall Run 

21.3981 -0.7253 22.6664 -0.7797 LT10/LT50 Delta T 22.2124 21.6756 -0.7526 21.6756 

Time(hr) LT50(C) Time(hr) LT10(C) Ratio (C) Time(hr) Time(hr) LT50(C) Time(hr) 

0.1 28.4 0.1 28.1 98.74% 0.4 0.1 0.1 28.5 0.1 

0.25 27.9 0.25 27.6 98.86% 0.3 0.25 0.25 28.0 0.25 

0.5 27.5 0.5 27.2 98.95% 0.3 0.5 0.5 27.6 0.5 

2 26.6 2 26.4 99.13% 0.2 2 2 26.8 2 4 26.2 4 26.0 99.23% 0.2 4 4 26.4 4 6 26.0 6 25.8 99.28% 0.2 6 6 26.1 6 8 25.8 8 25.6 99.33% 0.2 8 8 26.0 8 

10 25.7 10 25.5 99.36% 0.2 10 10 25.8 10 

16 25.4 16 25.2 99.43% 0.1 16 16 25.6 16 

24 25.1 24 25.0 99.49% 0.1 24 24 25.3 24 

40 24.8 40 24.7 99.57% 0.1 40 40 25.0 40 

60 24.6 60 24.5 99.64% 0.1 60 60 24.8 60 

80 24.4 80 24.3 99.68% 0.1 80 80 24.6 80 

100 24.3 100 24.2 99.72% 0.1 100 100 24.5 100 
Calculation of Acute Temperature Relationships Appendix C 

 

Appendix C 

APPENDIX C 

ATTACHMENT 3 

Acute Effects of Temperature on Salmonids: Times to 10% Mortality (LT10) 

In Relation to Temperature.
CHINOOK SALMON -- Estimated LT10s

Acclim. Source Age/Size a b N r Time(hr) LT10*(C)

15C Brett(1952) juv. frshwtr fry 16.4454 -0.5364 4 -0.9906 0.1 28.6

0.25 27.9

0.5 27.3

1 26.82 26.24 25.76 25.48 25.1

10 25.012 24.813 24.814 24.716 24.6

20C Brett(1952) juv. frshwtr fry 22.9065 -0.7611 7 -0.9850 0.1 28.5

0.25 28.0

0.5 27.6

2 26.84 26.46 26.28 26.0

10 25.916 25.724 25.440 25.160 24.980 24.8

100 24.6

24C Brett(1952) juv. frshwtr fry 18.9940 -0.5992 9 -0.9923 0.1 29.8

0.25 29.1

0.5 28.6

1 28.22 27.74 27.26 26.98 26.7

10 26.516 26.224 25.940 25.556 25.372 25.196 24.9

120 24.8140 24.6

242526272829303132333435

0.1 10 

Duration (hr)

Temperature (C)

LT1LT1LT1LT1LT1

242526272829303132333435

0 40 80 

Duration (hr)

Temperature (C)

LT1LT1LT1LT1LT1
CHINOOK SALMON -- Estimated LT10s

Acclim. Source Age/Size a b N r Time(hr) LT10*(C)

17C Coutant (1970) "Jacks" (1-2 yrs) 13.2502 -0.4121 4 -0.8206 0.1 29.7

0.25 28.7

0.5 28.0

1 27.32 26.6

The 17C and 19C temperatures were Columbia River 3 26.1temperatures (at Grand Rapids Dam) during fall 4 25.8migrations two different years. 6 25.4

8 25.1

10 24.912 24.714 24.6

19C Coutant (1970) "Jacks" (1-2 yrs) 9.4683 -0.2504 4 -0.9952 0.1 34.0

0.25 32.5

0.5 31.3

1 30.12 28.93 28.24 27.76 27.18 26.6

10 26.220 25.026 24.6

20C Blahm & juv. (spring run) 21.3981 -0.7253 3 -0.9579 0.1 27.9

McConnell (1970) 0.25 27.3unpublished data 0.5 26.9

1 26.52 26.14 25.78 25.3

10 25.216 24.920 24.824 24.626 24.6

24252627282930

0.1 1 10 

Duration (hr)

Temperature (C)

LT10* for 20C AccLT10* (20C, fall ruLT10 (20C, fall runLT10* (20C, spring LT10 (20C, spring 

24252627282930

0 40 

Duration (hr)

Temperature (C)

LT10* for 20C AccliLT10* (20C, fall runLT10 (20C, fall run, LT10* (20C, spring LT10 (20C, spring r
CHINOOK SALMON -- Estimated LT10s

Acclim. Source Age/Size a b N r Time(hr) LT10*(C)

20C Blahm & juv. (spring run) 22.6664 -0.7797 4 -0.9747 0.1 28.1

McConnell (1970) 0.25 27.6unpublished data 0.5 27.210% mortality NOT ADJUSTED 1 26.8

2 26.44 26.06 25.88 25.6

10 25.512 25.416 25.220 25.132 24.948 24.6

20C Blahm & juv. (fall run) 22.2124 -0.7526 4 -0.9738 0.1 27.9

McConnell (1970) 0.25 27.4unpublished data 0.5 27.0

1 26.62 26.24 25.88 25.4

10 25.316 25.020 24.924 24.834 24.6

20C Blahm & juv. (fall run) 21.6756 -0.7438 4 -0.9550 0.1 28.1

McConnell (1970) 0.25 27.6unpublished data 0.5 27.210% mortality NOT ADJUSTED 1 26.8

2 26.34 25.96 25.78 25.5

10 25.416 25.120 25.024 24.932 24.740 24.6
COHO SALMON -- Estimated LT10s

Acclim. Source Age/Size a b N r Time(hr) LT10*(C)

15C Brett(1952) juv. frshwtr fry 20.4066 -0.6858 6 -0.9681 0.1 28.0

0.25 27.5

0.5 27.0

1 26.62 26.24 25.86 25.58 25.3

10 25.212 25.116 24.920 24.832 24.5

20C Brett(1952) juv. frshwtr fry 20.4022 -0.6713 4 -0.9985 0.1 28.6

0.25 28.1

0.5 27.6

2 26.74 26.36 26.18 25.9

10 25.716 25.424 25.240 24.848 24.756 24.670 24.5

23C Brett(1952) juv. frshwtr fry 18.9736 -0.6013 5 -0.9956 0.1 29.7

0.25 29.0

0.5 28.5

1 28.02 27.54 27.06 26.88 26.6

10 26.416 26.124 25.840 25.456 25.272 25.096 24.8

120 24.6140 24.5

242526272829303132

0.1 10 

Duration (hr)

Temperature (C)

LT10LT10LT10LT10

242526272829303132

0 40 80 

Duration (hr)

Temperature (C)

LT10LT10LT10LT10
COHO SALMON -- Estimated LT10s

Acclim. Source Age/Size a b N r Time(hr) LT10*(C)

17C Coutant (1970) adult 5.9068 -0.1630 5 -0.9767 0.1 30.8

Reported acclimation temp. was the Columbia River 0.2 29.0temp. (at Priest Rapids Dam) during fall migration. 0.3 28.0

0.4 27.20.5 26.60.6 26.20.7 25.80.8 25.40.9 25.1

1 24.8

1.1 24.6

1.15 24.5
RAINBOW TROUT -- Estimated LT10s

Acclim. Source Age/Size a b N r Time(hr) LT10*(C)

15C Alabaster & juvenile 15.6500 -0.5000 2 --- 0.1 29.1

Downing (1966) 0.25 28.4

0.5 27.8

1 27.22 26.64 26.06 25.78 25.4

10 25.212 25.116 24.820 24.624 24.5

18C Alabaster & juvenile 18.4654 -0.5801 5 -0.9787 0.1 29.9

Welcomme (1962) 0.25 29.2Dissolved Oxygen at 7.4 mg/L 0.5 28.7

2 27.74 27.26 26.98 26.7

10 26.516 26.224 25.940 25.580 25.0

120 24.7160 24.5

18C Alabaster & juvenile 13.6531 -0.4264 5 -0.9742 0.1 29.6

Welcomme (1962) 0.25 28.7Dissolved Oxygen at 3.8 mg/L 0.5 28.0

1 27.32 26.64 25.96 25.57 25.48 25.29 25.1

10 25.012 24.814 24.716 24.5

242526272829303132

0.1 10 

Duration (hr)

Temperature (C)

LT10* for 15C Acclim. (AlabastLT10* for 18C Acclim. (7.4 DO, LT10* for 18C Acclim. (3.8 DO, LT10* for 20C Acclim. (Alabast

242526272829303132

0 40 80 

Duration (hr)

Temperature (C)

LT10* for 15C Acclim. (Alabaster & LT10* for 18C Acclim. (7.4 DO, AlLT10* for 18C Acclim. (3.8 DO, AlLT10* for 20C Acclim. (Alabaster & 
RAINBOW TROUT -- Estimated LT10s

Acclim. Source Age/Size a b N r Time(hr) LT10*(C)

20C Alabaster & juvenile 19.6250 -0.6250 2 --- 0.1 29.6

Downing (1966) 0.25 28.9

0.5 28.5

1 28.02 27.54 27.06 26.88 26.6

12 26.320 25.932 25.640 25.580 25.0

120 24.7160 24.5

20C Craigie (1963) yearling 14.6405 -0.4470 3 -0.9787 0.1 30.4

Raised in soft water, tested in soft water (SS) 0.25 29.5

0.5 28.9

1 28.22 27.54 26.96 26.58 26.2

10 26.016 25.620 25.324 25.232 24.940 24.748 24.5

242526272829303132

0.1 10 

Duration (hr)

Temperature (C)

LT10* for 20C AccLT10* for 20C AccLT10* for 20C AccLT10* for 20C AccLT10* for 20C Acc

242526272829303132

0 40 80 

Duration (hr)

Temperature (C)

LT10* for 20C AcclimLT10* for 20C AcclimLT10* for 20C AcclimLT10* for 20C AcclimLT10* for 20C Acclim
RAINBOW TROUT -- Estimated LT10s

Acclim. Source Age/Size a b N r Time(hr) LT10*(C)

20C Craigie (1963) yearling 15.0392 -0.4561 3 -0.9917 0.1 30.6

Raised in soft water, tested in hard water (SH) 0.25 29.8

0.5 29.1

1 28.52 27.84 27.26 26.88 26.6

12 26.216 25.920 25.732 25.340 25.160 24.772 24.5

20C Craigie (1963) yearling 15.1473 -0.4683 3 -0.9781 0.1 30.1

Raised in hard water, tested in soft water (HS) 0.25 29.2

0.5 28.6

1 28.02 27.34 26.76 26.38 26.1

10 25.916 25.520 25.324 25.132 24.840 24.648 24.5
RAINBOW TROUT -- Estimated LT10s

Acclim. Source Age/Size a b N r Time(hr) LT10*(C)

20C Craigie (1963) yearling 12.8718 -0.3837 3 -0.9841 0.1 30.9

Raised in hard water, tested in hard water (HH) 0.25 29.9

0.5 29.1

1 28.32 27.63 27.14 26.86 26.38 26.0

10 25.812 25.616 25.320 25.024 24.832 24.5
CUTTHROAT TROUT -- Estimated LT10s

Acclim. Source Age/Size a b N r Time(hr) LT10*(C)

23C Golden (1978) juvenile 18.092 -0.56523 ? -0.996 0.1 30.0

1975 tests, hatchery only 0.25 29.3

0.5 28.8

1 28.38 26.7

16 26.224 25.932 25.740 25.560 25.280 25.0

120 24.7160 24.5

13-25C Golden (1978) juvenile 22.543 -0.71999 ? -0.999 0.1 29.6

1975 tests, hatchery only 0.25 29.1

0.5 28.7

2 27.94 27.48 27.0

16 26.624 26.440 26.180 25.7

160 25.3240 25.0300 24.9560 24.5

23C Golden (1978) juvenile 18.3166 -0.5723 ? -0.990 0.1 30.0

1976 tests, hatchery and wild trout data pooled 0.25 29.4

0.5 28.8

1 28.32 27.84 27.38 26.8

12 26.516 26.324 26.040 25.680 25.1

120 24.8160 24.5

24252627282930313233

0.1 10 

Duration (hr)

Temperature (C)

LT10*, 23C Acclim. LT10*, 13-25C AccLT10*, 23C Acclim. LT10*, 13-23C Acc

24252627282930313233

0 120 240 36

Duration (hr)

Temperature (C)

LT10*, 23C AcclimLT10*, 13-25C AccLT10*, 23C AcclimLT10*, 13-23C Acc
CUTTHROAT TROUT -- Estimated LT10s

Acclim. Source Age/Size a b N r Time(hr) LT10*(C)13-23C Golden (1978) juvenile 18.1515 -0.5723 ? -0.992 0.1 29.7

1976 tests, hatchery and wild trout data pooled 0.25 29.1

0.5 28.6

1 28.02 27.54 27.06 26.78 26.5

12 26.220 25.832 25.540 25.360 25.080 24.8

120 24.5
SOCKEYE SALMON -- Estimated LT10s

Acclim. Source Age/Size a b N r Time(hr) LT10*(C)

15C Brett(1952) juv. frshwtr fry 15.8799 -0.5210 7 -0.9126 0.1 28.4

0.25 27.7

0.5 27.1

1 26.52 26.04 25.46 25.18 24.8

10 24.612 24.513 24.414 24.424 23.9

20C Brett(1952) juv. frshwtr fry 19.3821 -0.6378 5 -0.9602 0.1 28.6

0.25 28.0

0.5 27.5

2 26.64 26.16 25.98 25.7

10 25.516 25.224 24.940 24.648 24.556 24.4

116 23.9

23C Brett(1952) juv. frshwtr fry 20.0020 -0.6496 4 -0.9981 0.1 29.0

0.25 28.4

0.5 27.9

1 27.52 27.04 26.66 26.38 26.1

10 26.016 25.724 25.440 25.156 24.972 24.796 24.5

120 24.4220 24.0

23242526272829303132

0.1 10 

Duration (hr)

Temperature (C)

LT10* foLT10* foLT10* foLT10* (2LT10 (20

23242526272829303132

0 40 80 120 

Duration (hr)

Temperature (C)

LT10* foLT10* foLT10* foLT10* (2LT10 (20
SOCKEYE SALMON -- Estimated LT10s

Acclim. Source Age/Size a b N r Time(hr) LT10*(C)

20C McConnell & juvenile 16.7328 -0.5473 6 -0.9552 0.1 28.6

Blahm (1970) (under yearling) 0.25 27.9unpublished data 0.5 27.3

1 26.82 26.24 25.76 25.48 25.2

10 25.016 24.620 24.440 23.9

20C McConnell & juvenile 17.5227 -0.5861 6 -0.9739 0.1 28.6

Blahm (1970) (under yearling) 0.25 27.9unpublished data 0.5 27.410% mortality NOT ADJUSTED 1 26.9

2 26.33 26.04 25.86 25.58 25.3

10 25.212 25.020 24.632 24.352 23.9
CHUM SALMON -- Estimated LT10s

Acclim. Source Age/Size a b N r Time(hr) LT10*(C)

15C Brett(1952) juv. frshwtr fry 15.8911 -0.5252 8 -0.9070 0.1 28.2

0.25 27.5

0.5 26.9

1 26.32 25.84 25.26 24.98 24.6

10 24.512 24.316 24.120 23.940 23.3

20C Brett(1952) juv. frshwtr fry 16.1894 -0.5168 9 -0.9750 0.1 29.2

0.25 28.5

0.5 27.9

2 26.84 26.26 25.98 25.6

10 25.416 25.024 24.740 24.360 24.080 23.7

132 23.3

23C Brett(1952) juv. frshwtr fry 15.3825 -0.4721 4 -0.9652 0.1 30.3

0.25 29.5

0.5 28.9

1 28.22 27.64 27.06 26.68 26.4

10 26.216 25.724 25.440 24.956 24.672 24.496 24.1

120 23.9240 23.3

23242526272829303132

0.1 10 

Duration (hr)

Temperature (C)

LT10LT10LT10

23242526272829303132

0 40 80 120 

Duration (hr)

Temperature (C)

LT10LT10LT10
PINK SALMON -- Estimated LT10s

Acclim. Source Age/Size a b N r Time(hr) LT10*(C)

15C Brett(1952) juv. frshwtr fry 12.8937 -0.4074 8 -0.9884 0.1 29.1

0.25 28.2

0.5 27.5

1 26.72 26.04 25.36 24.98 24.6

10 24.312 24.116 23.820 23.624 23.4

20C Brett(1952) juv. frshwtr fry 13.2444 -0.4074 7 -0.9681 0.1 30.0

16.2444 in EPA (1977), but 0.25 29.0

the resulting curve does not 0.5 28.3match the information 1 27.6presented in Brett (1952) 2 26.9<Fig. 5> 4 26.1

6 25.78 25.4

10 25.216 24.724 24.332 24.040 23.756 23.4

24C Brett(1952) juv. frshwtr fry 14.7111 -0.4459 6 -0.9690 0.1 30.6

0.25 29.7

0.5 29.1

1 28.42 27.84 27.16 26.78 26.4

10 26.216 25.824 25.440 24.956 24.672 24.396 24.1

120 23.9192 23.4

23242526272829303132

0.1 10 

Duration (hr)

Temperature (C)

LT10LT10LT10

23242526272829303132

0 40 80 12

Duration (hr)

Temperature (C)

LT10LT10LT10