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%.

Coho Steelhead Temperature Index 10% RMG 20% RMG 10% RMG 20% RMG MWAT ( Updated EPA method ( Table 6.1)

7 day Maximuma ( oC) 13.0 16.5 9.0 20.5 14.5 20.5 10.0 24 7 day Meanb ( oC) 12.8 14.8 9.0 19.0 13.0 17.0 10.0 19.0 19.7 coho 19.6 steelhead Annual Maximumc ( oC) 13.5 17.5 9.5 23.0 15.5 21.0 10.5 26.0 amaximum value of the 7 day moving average of the daily maximum temperature;

bmaximum 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 oC.)

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 aAll 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 ( oC) Metric Temperature ( oC) 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 ( 64oF) 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 ( oC) Metric Temperature ( oC) 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 ( 64oF) 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 ( oC) 7 Day Maximum ( oC) 7 Day Mean ( MWAT) ( oC) Annual Maximum Temperature ( oC)

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 18oC for coho and 19oC for steelhead. The growth analysis suggests that an upper threshold for the 7 day mean temperature of 14.8oC for coho and 17.0oC 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 17.8oC. The 7 day maximum criteria derived for coho are 16.5oC for 10% growth reduction and approximately 19oC for 20%. However, if just steelhead were considered, the threshold would be 20.5oC for 10% growth reduction.

Current WDOE criteria specifying an annual maximum of 16oC for Class AA streams are lower than that derived from the risk assessment approach. Risk assessment suggests the annual maximum should be between 13.5oC and 17.5oC for coho, or between 15.5 oC and 21.0oC for steelhead, to maintain no more than 10% growth loss. The current criteria for Class A (< 18oC) 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; 16.5oC 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 ‰ 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.

‰ 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.

‰ 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 Limit 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 30oC. 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. 26oC 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 Rainbow ( 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 17oC ( optimal temperature for growth). Growth rate declines with temperatures either warmer or colder than the optimal. Growth effects are significant at temperatures greater than 22oC and less than 9oC. 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 r a t u r e ( o C ) G r o w t h R a t e (

 1d 1) 1 0 0 %

8 0 %

6 0 %

4 0 %

3 0 % 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 Jul 18 Jul 26 Jul 3 Aug 11 Aug 19 Aug 27 Aug 4 Sep 12 Sep 20 Sep 28 Sep Weight ( g) Coho Predicted Coho Observed Steelhead P redicted 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 oC ( 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 1 0 1 2 1 4 1 6 1 8 2 0 2 2 2 4 2 6 7 Day M aximum Temperature ( oC) Line o f 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 1 0 1 2 1 4 1 6 1 8 2 0 2 2 2 4 2 6 7 Day M aximum T emperature ( oC ) Line o f 1 : 1 Correspondence 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 WDOECur rent WDOEProposed ( 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 26oC 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. SECTION 9 LITERATURE CITED Adams, S. M. and J. E. Breck. 1990. Bioenergetics. In: Shreck, C. B. and P. B. Moyle, eds. Methods for fish biology. American Fisheries Society. Bethesda, MD.

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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 Wet Daily Growth Rate ( g/ g/ d) Dry Daily Growth Rate ( g/ g/ d)

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 0.0019 0.0066 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 0.0135 0.0187 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 0.0197 0.0251 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 0.0200 0.0256 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 0.0003 0.0020 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 0.0068 0.0139 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 0.0079 0.0165 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 0.0084 0.0172 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  0.0032  0.0016 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 0.0105 0.0164 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 0.0147 0.0223 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 0.0174 0.0252 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 0.0086 0.0093 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 0.0142 0.0169 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 0.0151 0.0183 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 0.0201 0.0233 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 0.0051 0.0063 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 0.0108 0.0144 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 0.0209 0.0237 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 0.0186 0.0217 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 0.0048 0.0054 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 0.0108 0.0133 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 0.0160 0.0196 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 0.0164 0.0210 Section 10 6 Table A. 1 Continued. 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 Consump tion g/ g/ day Mid Weight Dry Consump tion g/ g/ day % Fish Moisture Dry/ Wet Wet Daily Growth Rate ( g/ g/ d) Dry Daily Growth Rate ( g/ g/ d)

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 0.0027 0.0031 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 0.0045 0.0051 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 0.0072 0.0078 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 0.0091 0.0103 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  0.0004  0.0024 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 0.0034 0.0031 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 0.0067 0.0068 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 0.0079 0.0085 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  0.0012  0.0036 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 0.0011  0.0003 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 0.0048 0.0047 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 0.0086 0.0094 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 0.0002  0.0011 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 0.0057 0.0065 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 0.0081 0.0085 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 0.0126 0.0141 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  0.0056  0.0083 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  0.0024  0.0034 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 0.0023 0.0011 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 0.0056 0.0061 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  0.0029  0.0029 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  0.0059  0.0094 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 0.0030 0.0040 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 0.0057 0.0068 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 0.0037 0.0049 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 0.0084 0.0131 Section 10 7 Table A. 1 Continued. 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 Consump tion g/ g/ day Mid Weight Dry Consump tion g/ g/ day % Fish Moisture Dry/ Wet Wet Daily Growth Rate ( g/ g/ d) Dry Daily Growth Rate ( g/ g/ d)

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 0.0126 0.0174 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 0.0160 0.0221 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 0.0005 0.0012 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 0.0062 0.0102 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 0.0108 0.0153 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 0.0152 0.0226 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  0.0007 0.0007 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 0.0032 0.0037 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 0.0075 0.0124 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 0.0084 0.0126 Section 10 8 Table A. 2 Experimental data for growth of steelhead trout in relation to temperature and food consumption. Data is from Wurtsbaugh and Davis, 1977.

Trial Temperat ure o C 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 Daily Growth Rate ( g/ g/ d)

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 Wurtsbaugh and Davis,

1977. Trial Temperat ure o C 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 Daily Growth Rate ( g/ g/ d)

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 ( oC) Extreme Maximum ( oC)

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 ( oC) Extreme Maximum ( oC)

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 ( oC) Extreme Maximum ( oC)

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 ( oC) Extreme Maximum ( oC)

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."