Consumption. There is a maximum level of food consumption for each species that constitutes satiation or fullness. There is also a minimum consumption required to maintain standard metabolism. Consumption is generally expressed as a proportion of food mass to fish body mass consumed each day ( g gbw   1d 1), or alternatively, as a percent of body mass per day. Within the range of consumption between satiation and minimum maintenance, the growth rate varies with consumption as illustrated for sockeye salmon in Figure 2.4.

The consumption rate at satiation, ( Cs ), and by corollary, at lesser amounts of food, varies with temperature and changes systematically with the fish's weight ( Brett 1995). Salmonids respond physiologically to temperature by altering food consumption and the efficiency with which food is converted to growth ( Weatherly and Gill 1995). Cs is at a maximum at optimal temperature and declines at colder and warmer temperatures ( Brett 1971), yielding the characteristic shape of growth/ temperature response ( e. g., Figure 2.4). For example, coho consumption was 40% greater at 21oC than at 11oC in the laboratory experiments ( Everson 1973), but dropped off sharply above 22oC ( Brett et al. 1982). Consumption at satiation also declined with increasing fish weight ( Brett 1995). The rate of consumption of Everson's ( 1973) experimental fish at 3.6 grams was 45% of that of fish weighing 2.0 grams at the same temperature.

There is a maximum consumption rate for each species, ( Cmax), a key benchmark established in laboratory studies at optimum temperature, low weight, and unlimited food supply. Generally, the 5 6 consumption at 1 gram of weight and optimum temperature is the highest consumption likely to be observed for the species ( Cmax), and serves as an important reference point for growth computations ( Hanson 1997, Beauchamp et al. 1989, Hewett and Johnson 1987). The maximum consumption at satiation rations for other temperatures and weights ( Cs) is less than Cmax , and can be calculated as a proportion of maximum ( p) according to equation 5.6.

max C C p s = ( 5.6)

where:

( ) ) d g g , day t body weigh of gram food of ( gram re temperatu optimum and weight reference at ration satiation ess dimensionl n consumptio normalized 1  1  bw 1  1  max = = C p Cmax varies by species ( sockeye: Brett 1971; chinook: Brett et al. 1982; coho: Everson 1973, steelhead: Wurtsbaugh and Davis 1977).

To appropriately estimate growth rate ( g), it is necessary to establish the consumption at each weight ( Cw) and temperature ( CT). We follow the approach used in the Wisconsin bioenergetics model ( Hanson 1997) where consumption equations are of the basic form:

) ( max T f p C C T × × = ( 5.7)

CB w W CA C × = ( 5.8)

We develop equations for each independently, and then we will bring the two effects together by referencing consumption relative to Cmax and calculating the reduction from that benchmark due to each of the two factors. Hanson ( 1997) notes that developing a set of parameters for these relationships may be accomplished by deriving them from published reports, estimating them from specifically designed field or laboratory experiments, or borrowing parameters from closely related species.

The allometric relationship between consumption and weight ( eq. 5.8) generally has the form of a negative power function ( Ricker 1975) whose terms are the intercept, CA, and the coefficient, CB. The intercept value of CA is the consumption of a 1 gram fish and has units of grams per gram of body mass per day ( g g 1 bw d 1). These terms must be estimated from ad libitum feeding experiments conducted at the optimum temperature. There are relatively few such laboratory studies reporting values for salmonid species of interest in this report, although several other salmon species have been extensively studied. The laboratory studies of juvenile growth for coho ( Everson 1973) and steelhead ( Wurtsbaugh and Davis 1977) were not designed specifically to determine the allometric relationship of consumption to weight. However, in both studies, a series of one month long feeding trials were conducted on individuals drawn from a population of fish that was maintained in a natural stream between experiments over a year long period. Thus, the fish grew at natural rates between experiments and represented a range of weights from 1 to 4 grams, which is within the range of size expected for salmonids in natural streams in the first year. 5 7 We examined the laboratory data of Everson ( 1973) and Wurtsbaugh and Davis ( 1977) to estimate CA and CB for coho and steelhead looking only at trials where: 1) fish weights were reasonably near 1 gram; 2) rations were considered to be at satiation; and 3) temperatures were optimal, as suggested by high growth rates. Data matching these criteria were limited and some of our selected data values only marginally fit these criteria. Nevertheless, a relationship between consumption and weight at satiation ration followed an allometric relationship with CB equal to   0.254 for coho ( R2= 0.19) and   0.311 for steelhead ( R2= 0.41), albeit the R2 is relatively low. ( The poor fit in the relationship could be because some of the experimental data points did not match the criteria as closely as desirable). Stewart and Ibarra ( 1991) and the Wisconsin user's manual recommend values for CB of   0.275 for coho, based on the work of Beauchamp et al. ( 1989), and   0.30 for steelhead based on Rand et al. ( 1993). Although these studies were conducted with larger fish, the results are very similar. Calibrating predictions of weight gain of populations in natural streams, as described later, we found that selecting values for CB of   0.275 for both coho and steelhead, as suggested by other researchers, produced satisfactory modeling results. Setting CB at   0.3 for steelhead slightly but consistently underestimated growth, suggesting that consumption was not declining at quite this high of a rate in natural populations.

Maximum consumption observed in a few of the laboratory trials suggested values for the intercept, CA, of 0.11 and 0.16 g g 1d 1 for coho and steelhead, respectively. The allometric relationship developed from all trial data suggested CA equaled 0.083 and 0.16 for coho and steelhead, respectively. Calibrating the model predictions in natural streams suggested that CA was closer to 0.10 for coho ( 10% body weight per day). The User's Manual for the Wisconsin model ( Hanson 1997) recommends values of CA between 0.15 and 0.35, which are higher than those we derived using the laboratory data. We selected values for CA of 0.10 and 0.16 for coho and steelhead respectively. These are also the values of Cmax for each species.

Bartell et al. ( 1986), Beauchamp et al. ( 1989) and Hanson et al. ( 1997) have noted that the allometric parameters for the dependence of consumption on body mass ( CA and CB) are among the parameters that have the greatest influence on bioenergetics predictions. In subsequent model predictions, described later in this section, we found this to be true. Within the narrow range of values separating adult from juvenile studies, there was relatively little effect of CB on weight predictions, and we selected the widely cited values. However, the laboratory studies of juvenile fish did not support CA values greater than we selected. Growth predictions are sensitive to this parameter.

Food consumption in relation to weight as calculated with the allometric parameters is shown in Figure 5.1. There are significant differences in consumption levels between these two species.

The proportion of consumption at each weight ( Cw) relative to Cmax , is defined by the slope of the allometric equation ( CB) and can be calculated as:

CB w W p = ( 5.8) 5 8 To determine the effects of temperature on food consumption, a number of authors have used the Thornton and Lessem algorithm ( 1978) ( Beauchamp et al. 1989, Stewart and Ibarra 1991, Hanson et al. 1997). The algorithm estimates the maximum consumption at each temperature, expressed as the proportion of the maximum consumption at the optimal temperature ( pt). The algorithm fits two sigmoid curves to specified parameters, which include the optimal temperature and the upper and lower temperatures where consumption nears zero. Thus, the general shape of the relationship between temperature and consumption is assumed and key temperatures must be known or estimated to fit the proper shape of the curve for each species. The user's manual for the Wisconsin bioenergetics model ( Hanson 1997) suggests values for the key parameters of the Thornton and Lessem algorithm drawn from Stewart and Ibarra ( 1991) and Rand et al. ( 1993). We began with those parameters, but then calibrated them to fit the experimental data for coho ( Everson 1973) and steelhead ( Wurtsbaugh and Davis 1977) ( Figure 5.2). Small adjustments to the suggested parameters appeared to slightly improve the fit compared to the laboratory observations, although our final curve for coho is very similar to that presented by Stewart and Ibarra ( 1991). The parameter values we derived are provided in Table 5.1 and the fitted relationships are shown in Figure 5.2. The Thornton and Lessem equation is awkward to use in an EXCEL â spreadsheet format. We fit a polynomial regression to the Coho 0.0 0.2 0.4 0.6 0.8 1.0 0 5 10 15 20 25 30 Temperature ( oC) Polynomia Line Fit Steelhead 0 0.2 0.4 0.6 0.8 1 0 5 10 15 20 25 30 Temperature ( oC) Polynomial Line Fit Figure 5.2 Maximum consumption in relation to temperature computed with the Thornton and Lessem algorithm ( 1978). Consumption at Satiation 0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0 1 2 3 4 5 6 7 8 9 10 11 Weight ( gram) Consumption ( g g  1d 1)

Coho Steelhead Cmax Figure 5.1 Consumption at satiation in relation to weight as modeled by selected parameters for CA and CB described in text. 5 9 Thornton and Lessem algorithm results to provide an equation for calculating pt at each temperature ( Figure 5.2).

3 3 2 2 1 0 T T T p t × + × + × + = l l l l ( 5.9)

The fit was quite close ( Figure 5.2), especially in the range of temperatures likely to be observed during the summer, when the relationship was used for growth modeling. The polynomial terms describing the equations are provided in Table 5.1.

We now have mathematical expressions that account for the influence of weight ( pw), and temperature ( pt) on consumption at satiation where each is expressed in proportion to Cmax. The maximum consumption rate, ps, at each combination of weight and temperature expressed relative to Cmax, ( a constant value for each species) is calculated according to equation 5.10:

t w s p p p × = ( 5.10)

and ranges between 0 and 1. The actual amount of food consumed ( Cs), expressed in grams of prey mass in relation to grams of body mass per day, ( g gbw  1d 1), is equal to:

max C p C s s × = ( 5.11)

Table 5.1 Estimated physiological parameter values used in equations calculating consumption for coho and steelhead.

Relationship Parameter Coho Steelhead CA 0.10 0.16 Allometric CB  0.275  0.275 CQ 7 5 CK1 0.4 0.2 CTO 15.6 14.0 CTM 18 17.9 CTL 24 24 Thornton and Lessem function for temperature dependence ( 1978) f CK4 0.2 0.1 l 0  0.1419  0.1229 l 1 0.0544 0.0607 l 2 0.0061 0.0055 Regression fit to Thornton and Lessem algorithm generated by above parameters l 3  0.0003  0.0003 f Terminology as used by Hanson et al. ( 1995), see Appendix B The value of Cs computed over a range of temperature and weight is illustrated for coho in Figure 5.3. As expected from the formulation, the highest levels of consumption occur near optimal temperatures and at lowest weight. The deepest shades on the contour map represent starvation. Falling below the minimum maintenance consumption ( approximately 0.03 g gbw  1d 1 for coho juding from the laboratory trials) for extensive periods of time should result in no growth, or even 5 10 weight loss. The maximum potential consumption is relatively moderate for most of the temperatures and weights that juveniles are likely to encounter in the freshwater growth phase of their life history due to the interaction of the temperature and weight. However, consumption is severely limited only at high and low temperatures at all weights. Consumption approaches the maximum potential ( 0.10 g gbw  1d 1 for coho) only for a relatively few combinations of weight and temperature, and therefore for probably relatively little time during the life of a fish. Maximum consumption at all weights is achieved at optimal temperature ( Topt), where growth rate is greatest for each level of consumption ( approximately 17oC for coho).

The food consumption illustrated in Figure 5.3 represents the upper maximum controlled by the physiology of the fish at each combination of weight and temperature. We refer to this as the maximum potential consumption. In the natural stream environment, as well as in the laboratory, the amount of food available to consume may be less than the maximum that the fish can potentially consume and the fish may experience some degree of hunger. We distinguish consumption, which we use to refer to the physiological response controlling food intake, from ration, which we use to refer to the food supply. Ration, which we express as % satiation, is 100% satiation at the maximum consumption ( Cs) at each weight and temperature.

Consumption in natural streams depends on food supply, competition for food, and the size of the fish based on the past regime of water temperature. The Wisconsin model accounts for ecological constraints on the maximum consumption rate ( Cs) by imposing an additional proportionality ( Pvalue that can also range from 0 to 1 at rations less than the physiological maximum ( ps). Thus, when modeling consumption at less than satiation for each temperature and weight, Ci may be calculated as max C p P C s value i × × = ( 5.12) 2 4 6 8 10 12 14 16 18 20 22 24 26 Temperature ( oC) 1 2 3 4 5 6 7 8 9 10 Weight ( grams) Coho Food Consum ption, P roportion of Body W eight/ day Figure 5.3 Illustration of the relationship of the maximum food consumption at satiation with temperature and coho weight. Contours are the daily consumption rate, Cs, expressed in prey mass per fish body mass per day ( g gbw  1 d 1). This figure illustrates the maximum potential consumption where food supply is not limited and fish can eat to satiation. 5 11 It's important to note that, on any given day, field consumption may be limited by either the physiological limits imposed by temperature or weight or by the food supply. Consumption in streams must be determined from in situ observations of feeding, or inferred from weight gain.

Specific Growth Rate. The specific growth rate ( g) is the daily growth rate in relation to temperature and consumption, expressed in proportion of body weight per day. Specific growth rate functions (" growth curves") are defined with data from the laboratory studies. This relationship was established for sockeye by Brett ( 1971), and for chinook by Brett et al. ( 1982). Both Brett ( 1995 pp. 28 29) and Weatherly and Gill ( 1995) recently reaffirmed this relationship, some form of which appears to apply to all species of salmonids.

Growth rate curves for coho and steelhead have not been previously published, although the requisite laboratory studies were available to develop them. Experiments on growth of juvenile coho reported by Everson ( 1973) were conducted at temperatures between 11.1o and 22.5oC and rations between satiation and starvation. Experiments on steelhead growth reported by Wurtsbaugh and Davis ( 1977) were conducted at temperatures between 6.9o and 22.5oC and the full range of rations. Growth rate of the population during the experimental period, represented by the average population weights, was calculated as:

) ) ( 5 . 0 ( ) (

0 0 t w w w w g e e × + × - = ( 5.13)

where we and w0 are the weights at the end and beginning of the experiment, respectively, and t is the number of days ( generally 25) in each trial. Dry weights were used to calculate the growth rates because the moisture content of fish is similar to that of their prey in natural streams ( Winberg, 1971) and thus the dry weight relationships would appropriately match growth curves of fish living in natural environments to their natural food supply.

We used standard linear regression to build mathematical expressions for the growth rate g ( g gbw

1d 1) from food consumption, C, ( g gbw  1d 1), temperature, T, ( oC), and initial weight, wo, for coho and steelhead. To reduce multicollinearity problems during the model building process, the independent variables were centered by subtracting each sample value from the population mean. The models were built with the Reg procedure in SAS Ò Version 6.12 ( SAS Institute Inc., 1989). The general form of the model is:

W T C C C T T g × + × × + × + × + × + × + = 6 5 2 4 3 2 2 1 0 c c c c c c c ( 5.14)

The relationship for coho includes squared and linear terms for temperature and consumption and has high R2 ( 0.93) and low root mean square error ( 0.0023). Similarly, the relationship for steelhead includes squared and linear terms for temperature and consumption as well as a linear term accounting for weight of the fish. This relationship also has high R2 ( 0.97), and low root mean square error ( 0.0021). Statistically determined coefficients for these relationships are provided in Table 5.3. It is essential to note that the growth rate equation ( eq 5.14) will compute erroneous growth rates if the consumption term is not appropriately constrained for each weight and temperature as described earlier. Thus, the use of this model always requires a two step process where consumption level is estimated and then growth rate is selected. 5 12 The growth curves for coho and steelhead resulting from the full growth model formulation calculated at a weight of 1 gram are shown in Figure 5.4. They show the familiar form of sockeye and chinook that have been previously published by Brett and others ( Brett 1969, Brett et al. 1982, Weatherly and Gill 1995), although the curves in Figure 5.4 are derived mathematically. Brett et al. ( 1982) discussed how optimum temperature declines with decreasing ration, thus skewing the growth rate curves towards cooler temperature with less food. The mathematical formulation of equation 5.14 also produces skew in optimal temperature with declining consumption. The crossproduct ( c 5) between temperature and consumption determines this shape.

The growth curves for each species are similar, but differ in details such as optimal temperatures, growth rate, and consumption levels at which growth rates are achieved. One can see that the growth rate varies widely with the temperatures and food availability that salmon are likely to encounter in the natural environment of Pacific Northwest streams and rivers, suggesting that fish size should be strongly influenced by these two parameters.

In natural streams, temperature varies over the course of the day and some temperature value must be chosen to represent the daily temperature. Laboratory tests have noted that the daily average temperature approximates the constant exposure test conditions. Experiments where temperatures were fluctuated to mimic daily temperature regimes have found either no effect from variable temperature ( Thomas et al. 1986, Dickerson and Vinyard 1999) or an improvement in growth ( Spigarelli et al. 1982, Weatherly and Gill 1995). Water temperature fluctuated with natural stream temperatures in the experiments of Everson ( 1973) and Wurtsbaugh and Davis ( 1977). We select the daily mean temperature as appropriate to represent the temperature related to daily growth rate.

Table 5.2 Coefficients for specific growth rate relationship to temperature ( T) and consumption ( C). Coho data are from Everson ( 1973), Steelhead data are from Wurtsbaugh and Davis ( 1977).

Variable Coho Steelhead c 0  0.010649 0.00631 c 1 0.00096624  0.0007403 c 2  0.00008312  0.00003909 c 3 0.450620 0.4302104 c 4  3.02056  1.43765 c 5 0.01677 0.00735 c 6 NA  0.00517 c 7 NA NA Regression R square 0.93 0.97 5 13 Figure 5.4. Specific growth rate curves for coho salmon, steelhead trout at 1 gram weight. Coho growth curves based on Everson ( 1973), steelhead curves based on Wurtsbaugh and Davis ( 1977). Each line is the ration is expressed in % satiation, ps.

Coho Salmon  0.010  0.005 0.000 0.005 0.010 0.015 0.020 0.025 0.030 0 10 20 30 Tempe rature ( oC) Grow th Rate ( g g  1d 1 ) 100%

80%

60%

40%

30% Steelhead  0.010  0.005 0.000 0.005 0.010 0.015 0.020 0.025 0.030 0.035 0 10 20 30 Temperature ( oC) Growth Rate ( g g 1d 1) 100 80%

60%

40%

30% 5 14 Growth Model Substituting equations 5.13 and 5.11 back into equation 5.3 gives a model for computing weight as a function of initial weight and the time series of daily water temperatures and food supply to which a fish is exposed:

) 1 (

1 6 5 2 4 1 0 3 2 2 1 0 0 - - = × + × × + × + × + × + × + + × = Õ i i i i t i i i t w T C C C T T w w c c c c c c c ( 5.15)

The model simulates the change in mass of an individual fish or a cohort of specified size. We define a cohort as a group of similar aged fish of the same species experiencing identical environmental conditions ( Hanson et al. 1997). Cohort weights may be represented by the average population weight. It is important to recognize that the estimates of weight gain do not consider population interaction effects ( Walters and Post 1993). Thus, they do not account for changes in population density that can also affect the average weight and biomass of populations. Measuring growth as the difference in a cohort's weight between two dates is subject to biases from any size dependent movement and mortality ( Railsback and Rose 1999). Our estimates only address temperature and food effects with the assumption that changes in number or weight are unbiased by size.

The growth formulations ( equations 5.7 5.12) were easily programmed into an EXCEL Ò spreadsheet to perform the calculations of growth over time. To estimate weight gain of populations in natural streams, three parameters must be determined: initial weight ( w0), temperature ( T) and the food consumption ( Ci) that must reflect the food supply.

APPLICATION OF THE METHOD FOR PREDICTING GROWTH IN NATURAL STREAMS In the remainder of this section, we apply the growth model to fish populations observed in natural streams to demonstrate model behavior and corroborate its predictions. We show a number of simulations where the three input parameters were known with varying degrees of certainty. No new experiments to determine food consumption or to validate the growth model's ability to accurately predict weight gain were conducted. Instead, model performance is examined using data from a number of previously reported studies.

The parameters required by the model are rather modest. Daily mean temperature is known with certainty at any site where temperature is continuously recorded, and we restrict growth simulations to sites where this condition was met. Initial weight and food consumption must be determined from fish population characteristics observed in streams or from known food consumption amounts. Of these two variables, the consumption characteristics of juvenile salmonids in natural streams are by far the least well quantified ( Filbert and Hawkins 1995, Railsback and Rose 1999) and difficult to obtain ( e. g., Martin 1985). For this reason, we begin simulations with data from a field experiment where food was well known and the assumptions about the 3 input parameters were limited.

Mason's Feeding Experiment. Mason ( 1976) reported an experiment where juvenile coho in a natural stream on Vancouver Island, British Columbia were fed to satiation for a 2 month summer period. Data from this experiment were ideal for comparing observed with predicted growth since environmental limitations on food supply were reduced, if not 5 - 15 eliminated, for most of the individuals in the population. Thus errors associated with estimating consumption were minimized and the other two input parameters were well known.

Mason ( 1976) reported that temperatures were between 12o and 13oC during the study. We used a constant 12.5oC temperature for the simulation. Mason ( 1976) provided information on individual fish size within the population. We modeled three cases where the initial weight was set to the weight of the largest, average, and smallest fish at the start of the experiment. ( Figure 5.5). Fish were feed daily at what Mason calculated was a satiation ration for the population. For this simulation, growth was predicted at the maximum potential consumption determined by the weight and temperature ( eq. 5.11),

assuming that the available rations supplied 100% of the potential consumption and there was no food limitation. We also modeled the full range of rations ( ps) expressed as percentage of satiation of shown in increments of 80, 60, 40, and 30% in Figure 5.5.

After simulating growth for the two month period, the estimated weights of the largest and average fish, computed at high food ration, were very similar to their observed weights ( Figure 5.5). The largest fish in the population increased its weight by 286% ( 2 g to 5.9 g) during this period. The predicted weight for that fish, assuming 100% satiation, was within 2% of its observed weight. These results appear to corroborate growth predictions and confirm Mason's conclusion that some of the fish obtained satiation rations. Progressively smaller individuals within the population apparently ate at lesser rations than the largest individual. ( It should be noted that it is not known whether the fish that held these ranks at the end of the study were the same individuals as those at the start.) The weight of the average size fish was consistent with growth predictions at 80% ration. The smallest fish did not fare nearly as well, achieving little weight gain consistent with Coho Growth Mason, 1976 0 1 2 3 4 5 6 7 Initial Observed Observed Smallest Predicted Smallest Observed Average Predicted Average Observed Largest Predicted Largest Weight ( grams)

Ration 100%

Ration 80%

Ration 60%

Ration 40%

Ration 30% July 28 Sept 28 Largest Coho, Mason ( 1976)

0 1 2 3 4 5 6 7 Figure 5.5. Observed and predicted growth of coho in an experimental feeding study in a natural channel in British Columbia ( Mason 1976). The growth of the smallest, average and largest fish at the start of the feeding trial were modeled for the full range of satiation ( shown in bars). Weight of the smallest, average and largest fish at the end of the experiment are shown relative to growth simulations. The predicted daily weight of the largest size fish is shown at right. 5 - 16 subsistence at minimum maintenance rations. Mason ( 1976) observed that the smaller fish tended to be those dying or migrating downstream, emphasizing the importance of larger size providing survival benefit through more effective feeding strategies ( Puckett and Dill 1985, Nielsen 1994).

Overall, Mason ( 1976) observed many large fish remaining after the feeding experiment; moreover, the average population weight increased significantly. This has been taken as evidence that coho growth in streams tends to be limited by food supply. Other field experiments have showed improved growth resulting from activities that increased light ( and temperature) in streams, presumably improving the food resources ( Hawkins et al. 1983, Martin 1985, Bisson et al. 1988).

Fish Populations Living in Natural Stream Conditions. As they grow during the summer, juvenile fish living in streams in the Pacific Northwest experience temperatures that are relatively low in spring after emergence from the gravel, warm during the summer, and cool again in the fall after mid September. Food resources may be limited and the manner in which natural populations regulate their numbers to match individual and population growth needs with available food supply is complex and largely unknown ( Walters and Post 1993).

In the next set of simulations we estimate the average growth of a cohort of age 0 coho and steelhead in natural streams having natural and unknown food supplies. Fish population data were available from sites representing a number of treatments and controls in experiments previously reported in the literature ( Table 5.3). Physical habitat characteristics varied among sites. Like many streams within the region, the stream segments where fish were sampled generally contained low amounts of large woody debris ( LWD) and varying amounts of shade. The examples include sites with water temperature spanning very warm to cool ( but not cold, e. g., less than 12oC). Porter Creek was the site of a habitat improvement experiment where LWD was added to increase pool habitat ( Cederholm et al. 1997). Several sites were severely impacted by the Mount St. Helens eruption in 1980, but have since experienced recovery of vegetation ( Bisson et. al 1988). A dam break flood scoured portions of Huckleberry Creek and its floodplain in 1990 after several years of population monitoring during a feeding experiment ( Fransen et al. 1993). Bear Creek is a tributary to the Bogachiel River, above a barrier falls, that flowed through undisturbed old growth forest ( Martin 1985).

Although the experimental objectives and study designs differed, fish populations in these stream segments were sampled routinely in a similar way providing comparable data sets to draw from. Populations were sampled by electrofishing using the three pass removal method ( Young and Robson 1978, Bisson et al. 1988). The lengths of all captured fish were measured, while weights were sub sampled. We selected a number of cases from the available data where fish populations were sampled at the beginning and end of the summer. A few sites were sampled mid season. Early season sampling was conducted between March and July while end of season sampling was conducted between September and November. Coho occurred at all six sites and steelhead were found at four. The time periods encompassed by the temperature and fish population data did not always overlap at all sites, and, in a few cases, daily temperatures were not available. 5 - 17 Table 5.3. Characteristics of fish populations as determined by field surveys.

Data Sources Temperature Characteristics Fish Population Surveys Model Predictions Species Site Experiment Reference Year Regime Season median ( oC) Annual maximum ( oC) Dates sampled Days ( t) Initial weight ( g) End weight ( g) Increase in weight (%) Ave. Daily Growth Rate ( g g 1d 1) Estimated weight ( g) Difference from observed (%)

Coho Huckleberry Artificial feeding Fransen et al. 1993 1987 Cool 12.5 15.5 7/ 13 10/ 1 80 3.0 3.6 16 .0064 5.2 + 44 1988 Cool 11.6 15.0 5/ 12 10/ 24 163 0.6 4.2 600 .0114 3.8  10 1989 Cool 12.0 14.5 7/ 16 7/ 28 101 2.0 4.1 105 .0063 3.8  7 Dam break flood 1991 Warm 15.5 18.5 5/ 15 10/ 9 147 0.9 3.2 240 .0082 3.1  3 altered valley 1991 Warm 15.5 18.5 6/ 20 10/ 9 113 4.1 6.3 54 .0082 5.8  8 1998 Warm 14.0 18.0 6/ 23 9/ 15 84 2.7 4.0 57 .0072 5.0 + 16 Porter Cederholm et al. 1997 1988 Warm 13.5 18.0 6/ 28 9/ 28 92 3.0 6.1 103 .0069 5.7  7 Placement of large woody debris 1989 Warm 14.0 17.0 6/ 19 9/ 18 91 3.4 6.0 76 .0069 6.4 + 7 1990 Warm 14.6 18.6 6/ 18 8/ 27 70 3.7 6.5 76 .0085 6.6 + 2 1991 Warm 12.8 17.3 6/ 4 9/ 30 118 2.5 5.4 116 .0080 6.4 + 19 Harrington Volcanic eruption Bisson et al. 1988 1984 f Very warm 16.5 d 29.0 6/ 24 10/ 9 108 1.3 6.3 380 NA NA NA Vegetation regrowth 1990 Very warm 13.3 20.5 6/ 22 9/ 20 88 3.3 6.0 82 .0066 5.8  2 Hoffstadt Volcanic eruption Bisson et al. 1988 1984 f Very warm 16.7 d 29.5 6/ 26 10/ 3 100 2.4 5.6 133 NA NA NA Vegetation regrowth 1990 Very warm 15.0 26.0 6/ 21 9/ 20 88 3.6 6.1 69 .0054 5.9  5 Big Salmon carcass enhancement Bilby et al. 1996, 1998 1994 Cool 12.9 16.1 7/ 13 9/ 8 57 4.6 6.0 30 .0093 6.9 + 15 Salmon Salmon carcass enhancement Bilby et al. 1996, 1998 1994 Cool 12.6 16.2 7/ 20 9/ 8 50 3.6 5.5 53 .0069 5  9 Steelhead Porter Cederholm et al. 1997 1988 Warm 13.5 18.0 6/ 28 9/ 28 92 0.8 3.5 338 .0173 3.9 + 11 Placement of large woody debris 1989 Warm 14.0 17.0 6/ 19 9/ 18 91 0.6 3.3 450 .0210 3.6 + 9 1990 Warm 14.6 18.6 6/ 18 8/ 27 70 0.6 3.2 433 .0235 3.1  3 1991 Warm 12.8 17.3 6/ 4 9/ 30 118 0.4 3.4 750 .0191 3.8 + 12 Harrington Volcanic eruption Bisson et al. 1988 1984 Very warm 16.5 d 29.0 6/ 24 10/ 3 102 0.7 2.9 .0228 NA Vegetation regrowth 1990 Very warm 13.3 20.5 6/ 22 9/ 14 84 0.5 3.7 640 .0076 4.2 + 14 Hoffstadt Vegetation regrowth Bisson et al. 1988 1990 Very warm 15.0 26.0 6/ 21 9/ 20 91 2.1 5.5 162 .0180 4.2  24 Salmon Salmon carcass enhancement Bilby et al. 1996, 1998 1994 Cool 12.6 16.2 7/ 20 9/ 8 50 1.4 3.2 129 3.4 + 6 Cutthroat Bear Control for riparian buffer experiment Martin, 1985 1978 Cool 12.2 q 14 d 7/ 6 11/ 11 128 2.2 3.4 54 NA NA f Continuously recorded temperature data not available; population weights not simulated with model.

d Estimated from annual maximum temperature by relationship found in Section 3. q Through September 5 18 Consumption Rates in Natural Streams. Ideally, consumption rates in natural populations are determined by observation of food intake or availability ( e. g., Martin 1985, Filbert and Hawkins, 1995). Such data are rare. In lieu of direct measurement, consumption can be approximated by examining the pattern of growth over the growing period ( Beauchamp et al. 1989, Hanson et al. 1997). Consumption may be inferred by estimating how much food must have been consumed, given the prevailing temperature regime, to have maintained growth at the observed rate ( Hanson et al. 1997). To estimate consumption indirectly, populations should be sampled several times over the summer duration, because consumption rates should decline through time as fish grow, based on the expected allometric effect. Such data are also rare.

A naturally spawned coho population was sampled multiple times during the summer of 1991 at Huckleberry Creek. We selected 1991 data because fish were sampled a number of times over a long period from March to October, and we focused on the lower of two sites on the same stream because the population was naturally spawned fish with no augmentation. Coastal cutthroat trout were sampled multiple times at Bear Creek in 1978 ( Martin 1985). We assume that the cutthroat trout sampled at this site are representative of steelhead trout, for the purposes of establishing feeding patterns only. We use these two sites to evaluate food consumption patterns and to establish the relationships used to estimate ration for growth simulation of the other populations in our study.

The consumption of food by cutthroat trout at Bear Creek was measured using stomach evacuation techniques ( Martin 1985). In situ measurements of food consumption were computed by the modified Bajkov method ( 1935).

At Huckleberry Creek, neither food consumption nor food supply was measured. Apparent food consumption by coho between each sampling was derived using the growth rate curve. First, growth rate was computed for the full range of consumption from maintenance to satiation at the mean temperature and population weight for each interval. Consumption was then inferred by selecting the value from the computed Weight, Growth, and Food Consumption for Age 0 Coho 0 0.02 0.04 0.06 0.08 3/ 15 5/ 4 6/ 23 8/ 12 10/ 1 Date ( 1991) Rate ( g g 1d 1)

0 1 2 3 4 Mean Weight ( g) Growth rate Consump rate Mean Wt Weight, Growth and Food Consumption for Age 0 Cutthroat 0 0.04 0.08 0.12 0.16 5/ 20 7/ 9 8/ 28 10/ 17 12/ 6 Date ( 1978) Rate ( g g  1d 1)

0 0.5 1 1.5 2 2.5 3 3.5 4 Mean Weight ( g) Growth rate Consump rate Mean Wt Figure 5.6. Mean weight and estimated consumption and growth rates for age 0 coho at Huckleberry Cr. , lower site, and age 0 cutthroat trout at Bear Creek. 5 - 19 range that corresponded most closely to the observed growth rate. Important assumptions accompany this analysis; only food and temperature influence the observed data, our growth equations capture these factors correctly, there are no effects from population dynamics and there are no sampling errors. Therefore, this approach is only an approximation and it is preferable to establish consumption estimates by direct measurement of food supply or intake, such as was done for cutthroat.

Average population weight and estimated ( coho) and measured ( cutthroat) growth and consumption rates for the two populations are shown in Figure 5.6. Growth rate was calculated using equation 5.13 assuming a linear growth rate between each sampling interval. Weight gain slowed late in the season and growth rate and consumption declined from initially higher rates for both species ( Figure 5.6). This pattern is consistent with what would be expected for an allometric consumption relationship, where consumption declines with increasing weight. However, it can also suggest diminishing food supply that may accompany increasing size in an environment where food supply is fixed.

The pattern of consumption observed at the sites is of particular interest with regard to its implications, vis à vis the physiological limits of temperature and weight versus food supply. Maximum potential consumption ( Cs) was computed using the mean temperature and weight for each interval. It is plotted with apparent consumption in Figure 5.7. The apparent consumption was greatest during the early half of the summer and declined through the later half for both species. ( Note that the time intervals differ for the two sites).

An important implication of the observations in Figure 5.7 is that the observed consumption of both species was remarkably similar to each species's physiologicallydetermined potential consumption. This was true for coho from soon after emergence from the gravels until the end of June or early July and into September for cutthroat trout. Cutthroat trout actually showed greater consumption than estimated during this period, but values were probably within the error range due to estimates of Consumption for Age 0 Coho 0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 15 Mar 14 Apr 14 May 13 Jun 13 Jul 12 Aug 11 Sep 11 Oct Consumption ( g g 1d 1) Observed Maximum Potential Consumption for Age 0 Cutthroat 0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 13 Jun 13 Jul 12 Aug 11 Sep 11 Oct 10 Nov 10 Dec Consumption ( g g 1d 1) Observed Maximum Potential Figure 5.7. Maximum potential consumption based on temperature and weight for the period in relation to observed consumption. 5 - 20 average temperature and because the maximum potential is based on steelhead specific growth curves. During these periods, the data suggest that the physiological factors were controlling consumption and the average size fish was eating at satiation. That is, even if there were more food, the individual fish represented by the average population weight would not eat it. Later in summer, the apparent consumption was less than maximum potential for both species. The difference between the apparent and maximum potential consumption estimates suggests the degree of food limitation in the streams. The closer the observed lines are to potential, the closer available ration is to satiation.

It is interesting to note that it is during the early summper period that the number of individuals in a population following emergence is determined, suggesting that population density adjusts to match the food supply ( Chapman 1966). Carl ( 1983) observed that the number of coho in a population adjusted in response to population density, but that their daily growth rates were not dependent on density during the period when population adjustment after emergence occurred ( May to July). Similarly, rainbow trout growth rate was not correlated with their population density. Carl's ( 1983) observations of population dynamics and growth are consistent with results from our analysis of consumption.

For coho, the difference between apparent and potential consumption was much larger than for cutthroat in the later half of the summer. Apparent consumption declined to near starvation ration by the end of the summer, while estimated potential consumption remained relatively high because both temperature and weight were moderate. Potential consumption actually increased during part of this period reflecting favorable ( closer to optimum) temperatures. Declining food supply for coho during this period could be explained by increasing size of individuals in the population and limited food supply. Although the effects of size on consumption are not great, the absolute volume consumed by each fish must increase since the consumption is expressed as a proportion of body weight each day ( Hanson 1997). The same food supplies that may have been adequate for the population at small size may represent a much smaller proportion as the fish grow. The decline could also reflect a change in the food supply. Studies generally show fairly steady or increased food availability during the summer, depending on habitat conditions ( e. g., under forested vs. open riparian canopies, Hetrick et al. 1998).

Observed and maximum potential consumption were similar during the entire period for cutthroat trout ( Figure 5.7). During the time period coincident with coho, cutthroat were apparently consuming at or near maximum potential ( satiation) and food supply did not appear to be a major factor limiting the food consumption by this species. The decline in potential consumption in the fall can largely be attributed to lower temperature. One reason that consumption is nearer the maximum for much of the summer period may be that the cutthroat fry emerge later and are smaller than the coho in late summer. Thus, satisfying juvenile food demands is less challenging.

The apparent consumption patterns observed in populations inhabiting natural streams have important implications for the growth simulations. When consumption can be assumed to equal maximum potential ( eq 5.11) there is no need to calibrate the consumption estimates to account for the ecological constraints on food supply ( eq 5.12). This appears to be a reasonable assumption for age 0 cutthroat throughout the summer. We assume that age 0 steelhead have similar feeding patterns as cutthroat, and therefore, we assume that we can use the maximum potential consumption to represent age 0 steelhead. This also appears to be a reasonable assumption for coho until the end of June. 5 - 21 After June, apparent consumption by coho declined significantly relative to maximum potential and the consumption estimate must be modified by a factor reflecting food limitation ( P value in the Wisconsin model terminology). To do so for coho, we fit a linear regression to the apparent coho consumption from July to October ( Figure 5.7) Maximum Potential Consumption, Cs 0.00 0.02 0.04 0.06 0.08 0.10 0.12 0.14 0.16 23 May 2 Jun 12 Jun 22 Jun 2 Jul 12 Jul 22 Jul 1 Aug 11 Aug 21 Aug 31 Aug 10 Sep 20 Sep 30 Sep Consumption ( g g 1d 1) Steelhead Coho P value adjustment to coho A)

Relative Consumption, ps 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00 23 May 2 Jun 12 Jun 22 Jun 2 Jul 12 Jul 22 Jul 1 Aug 11 Aug 21 Aug 31 Aug 10 Sep 20 Sep 30 Sep Relative Consumption, P Coho Steelhead B)

Figure 5.8 Estimates of consumption at Huckleberry Creek. Data points are estimates of maximum potential consumption based on temperature and weight: A) Consumption as a proportion of body mass; B) Relative consumption, calculated as daily consumption divided by Cmax generated by the consumption equations. The line is the P value for coho that adjusts consumption based on field observations suggesting ecological constraints on food supply. 5 - 22 using a dated time step. Maximum consumption values and the ecological adjusted relationship are shown in Figure 5.8A. For readers familiar with the Wisconsin model, the relative consumption for each day, ( pi), is also shown in Figure 5.8B. During growth simulation, each consumption value is calculated in the defined time frame ( site adjusted P Value and maximum potential) and the value that is lower is selected. ( During the spring and early part of the summer, the P value is not calculated since the continuation of the calculation outside the time interval on which it is based may estimate unrealistic values, although it does appear to fit the data during this interval as well.) Ideally, population weight data would be available at each site where the growth simulations are conducted, at least for coho. However, this information was not available at other sites ( Table 5.4) since it is common to measure populations only at the beginning and end of the summer. Therefore, all population growth simulations for coho and steelhead that follow use the consumption estimates developed at Lower Huckleberry Creek, depicted in Figure 5.8A, to estimate consumption.

Comparison of Model Predictions to Observed Fish Growth The apparent consumption estimates were used to simulate growth of the fish populations listed in Table 5.3, using initial weights and temperature recorded over the period between the initial and final sampling of populations. We first simulated coho population growth at the two sites at Huckleberry Creek where the coho consumption estimates were developed ( Figure 5.8). Presumably, errors would be less here than at sites where consumption was not calibrated site specifically. The simulation results and observed population weights at the two sites are shown in Figure 5.9 Between the first and last sampling time, the naturallyspawned coho at the lower site increased in weight more than threefold from 0.9 to 3.2 grams. The predicted weight was within 3% ( 0.1 g) of the observed weight at the end of the summer. The predicted and observed values are so close that the simulation line appears to connect the observations. The model correctly predicted the ending population weight and those observed at each mid season sampling. This is perhaps not a surprising result given that the food consumption estimates ( Figure 5.8) were developed at this site. However, the simulation shows that, when food intake is calibrated at a given location, the model can accurately predict weight gain. Huckleberry Creek, Coho 0 1 2 3 4 5 6 7 28 May 11 Jun 25 Jun 9 Jul 23 Jul 6 Aug 20 Aug 3 Sep 17 Sep 1 Oct Weight ( g) Low er Predicted Low er Observed Upper Predicted Upper Observed Figure 5.9 Observed and simulated weight gain at the lower and upper sites at Huckleberry Creek. 5 - 23 Importantly, the model simulations are sensitive to the consumption estimates. For example, increasing daily consumption by 10, 20 and 30% above the values shown in Figure 5.8 through the simulation period yields final weights of coho at the lower Huckleberry site of 4.0, 4.9, and 6.0 grams respectively (+ 25, + 53 and + 88% greater than observed). Thus, errors have a cumulative effect since they are applied over time. Interestingly, this relatively moderate increase in consumption results in increased population weights that are proportionately consistent with those observed in field studies that found improved production in streams where shade had been removed, and presumably, food had increased. ( Hawkins et al. 1983, Hetrick et al. 1998). These results suggest that food supply doesn't have to increase by a large amount to significantly affect the weight that fish achieve over the course of the summer.

Predicted weight at the upper Huckleberry site was also close to that observed, but predictions were not as accurate ( 8%) ( Figure 5.9). The upper site had a large influx of larger fish between the May and June sampling dates, so the simulation was initiated in June after the population was determined. The upper site had a slightly different P value at the end of the summer, and the value was used to model this site only ( not shown). Growth of this population was low during the latter part of the summer and the rate of decline of the P value was greater at this site than the lower site, possibly because of the larger size of fish in the population. Nevertheless, despite nearing minimum maintenance ration at the end of the season, the large size of the population was maintained into the fall and no weight loss was observed. The model prediction was actually more accurate at the end of the season than at some of the mid points, where it tended to underestimate the weight.

In the next set of simulations, we applied the model to the rest of the populations listed in Table 5.3, except those where continuously recorded temperature data were not available. The consumption estimates established for the population at the lower site at Huckleberry Creek in 1991 were used for all of the coho simulations. We would expect that applying these assumptions to other sites could be a significant source of error, since presumably food supply differs among sites and the growth estimates are sensitive to this parameter. Furthermore, errors accumulate due to the effect of weight on consumption. We show the simulation for coho and steelhead at Porter Creek ( 1988) in Figure 5.10. Porter Creek is exemplified because the prediction accuracy was average for both coho and steelhead ( Table 5.3), the site was far removed from Huckleberry Creek, the simulation period was long, and both species were present. Predicted weights closely approximated observed weights for both species ( Coho:  7%, Steelhead:+ 11%). Porter Creek, 1988 0 1 2 3 4 5 6 7 24 Jun 2 Jul 10 Jul 18 Jul 26 Jul 3 Aug 11 Aug 19 Aug 27 Aug 4 Sep 12 Sep 20 Sep 28 Sep Weight ( g) Coho P redicted Coho Observed Steelhead Predicted Steelhead Observed Figure 5.10 Weight prediction and observed fish weights for coho and steelhead populations at Porter Creek, 1988. 5 - 24 Predicted relative to observed weights at the end of the simulation period are shown for all of the site by year data sets in Figure 5.11 and listed in Table 5.3. The accuracy of the growth simulations was estimated by calculating the sum of unexplained residual variations between observed and simulated length for each cohort of coho ( n= 14) and steelhead ( n= 7) through the following equation:

2 1 ) ( 1 i i i predicted observed predicted n i W W W n SCE - = å = ( eq. 5.16)

SCE for both coho and steelhead was 0.08 of the observed for both, while the average gain Predicted Relative to Observed Weights: Coho Salmon  100%  80%  60%  40%  20% 0% 20% 40% 60% 80% 100%

Huckleberry 87 Huckleberry 88 Huckleberry 89 Huckleberry 91 Huckleberry 98 Huckleberry ( Up)

Big 94 Salmon 94 Hoffstadt 90 Harrington 90 Porter 88 Porter 89 Porter 90 Porter 91 Predicted Relative to Observed (%) A.)

Predicted Relative to Observed Weights: Steelhead Trout  100%  80%  60%  40%  20% 0% 20% 40% 60% 80% 100%

Salmon 94 Hoffstadt 90 Harrington 90 Porter 88 Porter 89 Porter 90 Porter 91 Predicted Relative to Observed (%) B.)

Figure 5.11. Differences between predicted and observed average weights of fish populations. 5 - 25 in weight during the period was 1.25 and 4.14 times initial weight, respectively. Some predictions were above and some were below the observed weights, with no apparent patterns. Model predictions averaged within 4% of observed for both species considering the sign of the difference between observed and predicted, and 11% when averaging its absolute value. The largest prediction error occurred at Huckleberry Creek in 1987 (+ 44%). There was very little weight gain at this site during this year and our consumption estimates were clearly too high. As another example, the steelhead population at Hoffstadt Creek was under predicted by 24%, perhaps because the population was augmented by hatchery fish. Since there are no assumptions about food supply used to model steelhead, this error suggests that one or more of the model parameters were probably incorrect for this population and year. Our unexplained error is consistent with, but somewhat less than achieved by Mallet et al. ( 1999) for grayling in a European river where they used a similar approach for accounting for temperature but did not consider consumption.

The close correspondence of observed and predicted weights was welcome, if not somewhat surprising, given potential errors associated with using consumption estimates extrapolated from other streams in the case of coho, or from other species ( cutthroat), albeit closely related, in the case of steelhead. While the physiological based estimates of consumption should be broadly applicable to the species as formulated in this paper, varying perhaps by genetics among races, the ecological constraints on food supply can vary significantly among sites. Many factors potentially influence food supply; population dynamics and competition for food, ( Walters and Post 1993), riparian, inchannel habitat characteristics that control primary and secondary production ( Hawkins et al. 1983, Bilby and Bisson 1988, Hetrick, Murphy 1998, Railsback and Rose 1999) and even the energy content of prey ( Stewart and Ibarra 1991). Information about these riparian, in channel habitat and population characteristics are not directly measured, but are integrated by growth rate in the time series of daily temperatures, consumption and body weights. The average growth rate as a function of annual maximum temperature is shown in Figure 5.12. Railsback and Rose ( 1999) observed that measured growth rates of trout in the Sierra Nevada, and those predicted by the Wisconsin model, varied from year Growth Rate of Fish Populations Coho =  0.0002T + 0.0117 R2 = 0.20 Sthd=  0.0011T + 0.0399 R2 = 0.50 0.000 0.005 0.010 0.015 0.020 0.025 10 15 20 25 30 Annual Maximum Temperature ( oC) Daily Growth Rate ( g g  1d 1) Coho Steelhead Figure 5.12 Growth rate calculated by the model averaged for the sampling period in relation to annual maximum temperature at each site. 5 - 26 to year and with site characteristics such as flow. We observed similar variation in the sites sampled in multiple years ( Table 5.3), although we did not attempt to account for differences other than those caused by differences in temperature.

For the final two simulations, we examined how modeling weight over the full range of rations and weights of individuals within the population would relate to estimates based on averages. Presumably, individuals are feeding over the range of possible rations, from minimum maintenance to satiation, with consumption limited by their hierarchical position within the population ( Puckett and Dill 1985, Nielsen 1994). In theory, some fish Coho Huckleberry C reek 0 1 2 3 4 5 6 7 8 9 10 30 40 50 60 70 80 90 Length ( mm) Weight ( grams) Coho Huckleberry Creek 0 1 2 3 4 5 6 7 8 9 10 30 40 60 80 100 % of Satiation Weight ( grams)

Steelhead Salmon C reek 0 1 2 3 4 5 6 7 8 9 10 40 50 60 70 80 90 Length ( mm) Steelhead Salmon C reek 0 1 2 3 4 5 6 7 8 9 10 30 40 60 80 100 % of Satiation Weight ( grams)

Figure 5.13. Examples of length weight relationships for coho salmon population at the end of summer growing season at Huckleberry Creek and the steelhead trout population at Salmon Creek. Also shown are the predicted weight for each ration for Huckleberry Creek and Salmon Creek, calculated with the site's temperature from the period between population sampling, and a starting weight as observed at the site at the initial sampling. 5 - 27 may be feeding at satiation ration, although population density controls could prevent fish from achieving this level of ration ( Brett 1995). Individuals feeding at less than minimum maintenance ration over extended periods of time are likely to be lost from the population due to starvation.

Weight gain was simulated for the coho population at lower Huckleberry Creek ( 1991) and the steelhead population at Salmon Creek ( 1994) for the intervals between fish population samplings, with weight initialized at the observed average population weight. The maximum potential consumption was computed each day based on temperature and fish weight, then categorized into 5 rations between 100% satiation and minimum maintenance ( 30, 40, 60, 80 and 100%). A minimum of 30% satiation was chosen because this is very near starvation but still allows some growth at some temperatures.

Predicted ending weight at each ration level is shown in comparison to the range of individual weights observed in the population on the final sampling date in Figure 5.13. The range of weights observed within the two populations near the end of the summer was wide, but typical of what is usually observed: the largest fish were 4 to 6 times larger than the smallest fish for both species. There was a range of weight among individuals within the population early in the season, although it was considerably narrower than it was at the end of the summer ( Figure 5.14). The growth model predicted a range of weights that was similar to observed. Model predictions suggested that coho as large as 9.5 grams could occur at 100% satiation ration and the largest fish observed was 7.4 grams, a weight consistent with high consumption, but less than satiation. For steelhead, the largest predicted individual was about 3.5 grams, considerably smaller than the largest individual in the population assigned to the 0 age class by the field biologist. Seventy five percent of the steelhead individuals were within the predicted range. The steelhead that are larger than the 100% ration prediction could actually be yearling fish since scale analysis was not performed, and it can be difficult to distinguish age of the fish from observation alone.

Exact correspondence in individual weights was not expected since the average population weight was used to initialize the simulation. If we apply the same analysis of the growth of the largest, average and smallest fish in the coho population at Huckleberry Creek as we did earlier for Mason's population, we estimate the average degree of satiation for each ranked fish ( Figure 5.14). During this period there was only a small change in the number of fish in the population, although we do not know if the fish that held these ranks at the end of the summer were the same one's that held them at the beginning. As in Mason's experiment, the largest fish ate at a higher satiation level than smaller fish in the population. The largest fish is estimated to have eaten at 73% satiation, the average fish at 62%, and the smallest fish at 45% satiation, averaged over the growth period. These results are consistent with Fausch ( 1983), who found that dominant fish obtained more energy during natural feeding. 5 - 28 Modeling individuals within the range of population sizes produced qualitatively similar results as modeling the average fish for the range of rations, in terms of the range of weights predicted and the average consumption. Perhaps this is because the average population ration was in the mid range of rations. Nevertheless, the growth model suggested a plausible range of weights at the individual scale using a range of rations, and the model closely approximated the average population weight and at the population scale using average weights and field calibrated ration ( Figure 5.11)

Finally, we conducted a brief sensitivity analysis to demonstrate model behavior over a range of temperatures and food consumption levels. Weight gain was simulated using the same time interval, starting weight, and range of rations from satiation to minimum maintenance, allowing only the temperature to vary for each site. June 1 to Sept 15 was selected to represent the primary growth period for coho and steelhead since both species should have emerged from the stream bed by this date, and our data set from temperature sites was complete during this interval. A representative initial weight was estimated by regressing weight of all the populations sampled early in the summer season with time. Steelhead tended to be very similar in weight early in the season, despite differences in sample dates, averaging 0.5 grams on June 1 ( julian day 152). Coho weight varied more, and in some cases, included some sites with hatchery raised fish. Coho averaged 1.4 grams on June 1. Temperature simulations included site temperature, which varied Coho Growth Huckleberrry 1991 0 2 4 6 8 10 12 14 16 Initial Observed Observed Smallest Predicted Smallest Observed Average Predicted Average Observed Largest Predicted Largest Weight ( grams)

Ration 10 Ration 80 Ration 60 Ration 40 Ration 30 May 15 Oct 9 Figure 5.14 Observed and predicted weight of coho in Huckleberry Creek ( 1991). The growth of the smallest, average, and largest fish at the start of the summer ( May 15) were modeled for the full range of satiation ( shown in bars). Weight of each class at the end of the period ( Oct 9) are shown at the end of the summer relative to growth simulations. 5 - 29 through the season, as well as constant temperature for the period ( Figure 5.15). Simulated weights based on each site's temperature regime are plotted as circles; constant temperature predictions are the solid parabolic lines at each ration, and the observed fish population weights are plotted as squares. The solid lines computed at constant temperature follow the growth rate curves on which they are based, but they reflect the effect of long term exposure while compensating for effects of increasing weight on Coho Salmon 0 1 2 3 4 5 6 7 8 9 10 11 0 5 10 15 20 25 30 Temperature ( oC) Weight ( grams) Predicted 30% Predicted 40%

Predicted 60% Predicted 80%

Predicted 100% Average Field Population Constant Tempature 100% Satiation 80%

60%

40%

Steelhead 0.0 1.0 2.0 3.0 4.0 5.0 0 5 10 15 20 25 30 Tempera ture ( oC) Weight ( grams) Predic ted 20% Predic ted 40%

Predic ted 60% Predic ted 80% Predic ted 100% A v erage Field Population C o ns t a n t T emp e r a t ur e 1 0 0 % S at ia t io n 8 0 %

6 0 % 4 0 % 3 0 %

Figure 5.15. Summary of observed and predicted weights for coho salmon and steelhead trout. Solid lines are the weight predicted at constant temperature for 106 day simulation ( June 1 Sept 15). Circles are the predicted weights at each site and ration based on the daily temperature regime for the period. Squares are the average weight of observed populations. Initial weight was 1.4 grams and 0.5 grams for coho and steelhead, respectively. 5 - 30 consumption.

Weight can be strongly affected by both the prevailing temperature and the amount of food available to fish. Either factor can have the same level of effect, although maximum weight can only occur when prevailing temperatures are near the species' physiological optimum temperature and food supply is high. Low to moderate weight can result from many combinations of temperature and food supply, perhaps helping to explain why it is difficult to determine whether food or temperature is limiting growth in natural streams from empirical field observations alone.

Weight is maximized when temperature over the course of the summer is closer to optimal temperature ( Figure 5.15). Washington streams and rivers tended to be near optimal, despite significant differences in the temperature patterns at the sites when indexed by the annual extremes such as annual temperature ( Figure 3.1). The effects of temperature are more pronounced at higher levels of food. Interestingly, observed weights of coho and steelhead populations show patterns consistent with those simulated across the range of temperatures, although food supply at sites was not well known. The biomass of fish populations measured at the study streams is moderate to high relative to those reported from the coastal areas of the Pacific Northwest and Alaska ( Bisson and Bilby 1998). At very low levels of food, growth is very low, but is somewhat better at cooler temperatures.

The weights predicted using observed temperatures do not deviate far from those calculated with a constant temperature ( Figure 5.15). Thus, since there is little loss of information using the constant temperature, these simulations suggest that the weight gain at sites much warmer and colder than those available in our data sets are realistic. The constant temperature can be represented by the median temperature of the period. Figure 5.13 suggests that the model is sensitive to temperature, and would estimate significantly different weights for significantly different temperature profiles. The similarity in the weights predicted with the growth simulation methods at many of the sites ( Figure 5.15) does not appear to reflect lack of sensitivity of the model, but rather the relatively narrow range of average temperature at streams within the sample. Population weights should be less in streams with predominantly warmer or colder than occur in our data sets.

DISCUSSION We have developed a bioenergetics based approach to assessing the effects of temperature on growth of salmonids. At this time, it has only been applied to age 0 steelhead trout and coho salmon living in natural streams. Growth of other salmonids could easily be modeled with the same approach if the basic consumption and growth functions can be established.

There are many examples of growth modeling based on bioenergetics principles available in the literature. Most use similar methods for estimating consumption terms but provide more comprehensive evaluation of energetic functions in addition to growth ( Beauchamp et al. 1989, Kitchell et al. 1974, Hewlett and Johnson 1987, Beauchamp et al. 1989, Hanson et al. 1997.). Our formulation differs from these in that energy consumed by growth, as evidenced by observed growth rate, is the only energy function considered, and we make no attempt to close the energy balance between intake and expenditure. Thus, we view our approach as bioenergetics based but not a true bioenergetics model. However, the growth model developed here shares many key elements with bioenergetic 5 - 31 models, and therefore its application enjoys many of the same challenges and criticisms ( Ney 1993, Hansen et al. 1993).

In a review of bioenergetics models, Ney ( 1993) noted that corroborative studies of bioenergetics models showed a number of deficiencies that appear to compromise their ability to estimate consumption and growth of non captive fish accurately. Some critics feel the models are overly complex ( Ney 1993) and prone to errors in parameter estimation ( Boisclair and Leggett 1989). A comprehensive model of energy functions results in the proliferation of parameters, which may create difficulties in adequately informing a number of the input variables, and defining some of the energetic relationships. Hansen et al. ( 1993) comment that bioenergetics models having 20 or more input parameters, each with its own estimation error, can lead to estimates of consumption or growth that, in some circumstances may have to differ by 100% or more to be judged statistically different ( Boiscalir and Leggett 1989.) This poses particular problems for trying to use these models to sort out complex ecological interactions. Beauchamp et al. ( 1989) noted that bioenergetics models are used more frequently to predict consumption than growth because of the additional errors associated with bringing in other equations. To the contrary, Hanson ( 1997) argues that forcing a balance of the energy budget acts to limit error propagation ( Bartell et al. 1986).

The growth model developed in this paper is relatively simple, especially in field application, but the errors are unconstrained. Our model shares some of the same relationships that are most sensitive to the errors that are found in full bioenergetics models. Specifically, the consumption terms that apply to the physiologic controls of temperature and allometry, as well as the food supply, are both important in estimating growth ( Stewart et al. 1983, Beauchamp et al. 1989, Bartell et al. 1986, Hanson et al. 1993). The physiologic relationships appear to be reasonably well established for salmonid species, given the similarity of parameter values developed from different studies ( Hanson et al. 1997). We achieved good modelling results using these values.

There have been questions as to whether such laboratory study results can be used for predicting response of fish living in natural environments. Laboratory studies have unique conditions of food, environment, and population pressures that themselves may create stresses that may not be observed in natural settings. In our analyses, the gain in weight of fish living in stream environments was closely approximated by relationships derived from laboratory studies. This suggests that laboratory studies can be used with some confidence to predict responses in more natural ecological settings.

Achieving a quantitative understanding of the cause and effect linkage between food consumption and temperature in natural environments is a significant challenge, as suggested by Figure 5.14, where different conditions can produce similar population weight. Field studies are labor intensive and field estimates are laden with their own assumptions and subject to their own errors ( Ney 1990). Trying to discern such relationships by empirical observation alone is problematic, given the multivariate and dynamic nature of the interaction and the difficulty of measuring some of the key fundamental relationships in natural environments ( Brett 1971, Boisclair and Leggett 1991, Railsback 1997). While the growth model can not solve these problems, it can help field investigators to develop physiologically based hypotheses that may help them understand the responses they observe. 5 - 32 We found that our simplified application of bioenergetics principles provided some useful structure to the analysis of field observations of environmental temperature and food supply that appeared to address some of the complexity of the interactions. This could lead to greater insight into their inter relationships when compared to empirical observation alone. For example, our results suggest that food limitation existed for some species and not for others ( at least at this age), and that food limitation varies in time in ways that have important implications for population dynamics. Additional study of food consumption and supply in natural streams, where data are extremely limited, would be very useful, given the importance of these factors in determining fish productivity and response to temperature. It seems clear from this analysis that there is more opportunity to affect productivity ( population weight and density) by changing food supply than by changing temperature within the range of temperature observed in the study streams, at least during the summer months. This interpretation is consistent with field studies that have shown increased productivity with canopy removal, despite increases in temperature that at times have appeared to be adverse ( Hawkins et al. 1983, Bisson et al. 1988).

Importantly, the hypotheses presented by the model can be explicitly tested in field experimentation, and rejected if model results fail to predict observed responses. In this first application, the model hypotheses were confirmed, but more carefully implemented field experiments would be beneficial. We caution that these results corroborate the utility of the model, but do not constitute a rigorous test of the model or its underlying assumptions ( Hansen et al. 1993). Validation would best be achieved by field and laboratory experiments to confirm the growth curves and allometric functions and to independently determine food availability, rather than estimate consumption from observed change in average population weight through time ( e. g. Filbert and Hawkins 1995, Martin 1985). Such tests would help refine the input parameters, as well as reveal whether our assumption that energetic functions, other than those captured in the allometric and growth rate functions, can be ignored in estimating growth effects of environmental temperature at a necessary level of precision.

It was somewhat surprising that we were able to achieve such good predictions of weight gain compared to observations of fish populations given the sparse amount of data used to develop consumption estimates. Weight estimates for populations of juvenile salmonids were generally within 11% of observed following growth over several months during a rapid growth phase. However, the model is always likely to perform best using sitespecific estimates for food consumption. Nevertheless, extrapolated estimates proved satisfactory for our purposes.

We conclude that the methods developed in this section perform well for the purpose of assessing the effects of environmental temperature on juvenile salmonid growth. Ney ( 1993) concluded that, in their present state of development, bioenergetics models are best suited for making relative rather than absolute predictions such as comparing outcomes of different habitat and food availability scenarios. This is how we emphasize use of this method. The method is used in Section 6 to evaluate the effects of temperature regime on salmonid growth relative to temperature thresholds that could be used as water quality criteria. 5 - 33 Conclusions ‰ Methods of predicting growth based on quantitative bioenergetics principles can be applied to streams to assess the effects of temperature on juvenile salmonid growth, with results that are consistent with observed fish population growth patterns.

‰ The method is sensitive to temperature and can be applied to the daily temperature regime making it a useful tool for assessing the biological impacts of temperature in natural streams. 5 - 34 6 1 SECTION 6 QUANTIFYING GROWTH EFFECTS IN RELATION TO TEMPERATURE THRESHOLDS Abstract In Section 5, we developed a method to estimate the effects of temperature and food consumption on the gain or loss of weight of coho salmon and steelhead trout during the summer months. In this section, we use the methodology specifically to identify temperature indices, including the duration and magnitude of threshold temperatures that minimize the negative growth effects of the temperatures that occur over long periods of time. The analysis provides an objective method to establish temperature criteria based on protecting the opportunity for growth, rather than avoiding catastrophic impacts.

Analyses demonstrated that some effects from the ambient temperature occurred at all sites, because no stream spends all of its time at a fish's optimal temperature for growth. Sites with both chronically high and chronically low temperatures were estimated to experience significant growth loss. Using the 7 day maximum temperature, and allowing 10% growth loss, the upper threshold for coho salmon was found to be 16.5oC and the lower limit was 13.0oC. The range for steelhead was wider at 14.5 to 21oC. Many sites included in the analyses exceed current temperature criteria for Washington ( annual maximum of 16oC). Streams that far exceed the criteria also were predicted to have high growth loss. Those near the threshold criteria ( ± 1oC) appeared to experience the best temperatures for growth. Streams with lower temperature (< 13oC) also had higher growth loss and temperature less than 10oC were adverse to growth of both coho and steelhead. The method could be used to identify thresholds for other temperature indices such as annual maximum and 7 day mean, or other levels of growth loss.

Key findings include:

‰ It is feasible to apply a risk based approach that uses data without safety factors and produces an estimate of cumulative risks. This technique is a true quantitative benchmark that is measurable and testable.

‰ The majority of temperatures experienced by salmonids are generally suboptimal for growth, and these exert some cost on the maximum potential growth.

‰ Colder water temperatures are not necessarily better for rearing salmonids, and warmer water temperatures are not necessarily worse. Concepts of safety factors in selecting thresholds need to be exercised with some caution. Thresholds that are too low can also negatively effect growth.

‰ Despite what appear to be large differences in temperature among sites, especially with regard to the warmest temperatures that occur each summer, there was less of a difference in the predominance of temperatures that are important to growth. Sites with significantly different temperature regimes often have similar predicted growth risk. 6 2 ‰ An upper threshold for the 7 day maximum temperature of 16.5oC minimizes growth losses for coho. A 7 day maximum temperature or 20.5oC minimizes growth losses for steelhead, based on an analysis that does not consider population dynamics.

Introduction One of the most important aspects of temperature in natural environments is its effect on growth. Growth is regulated by a complex interrelationship between food supply, population dynamics, and the physiologic responses of the fish to temperature ( Weatherly 1972). Trying to discern such relationships by empirical observation alone is problematic, given the multivariate and dynamic nature of the interaction and the difficulty of measuring some of the key fundamental relationships in natural environments ( Brett 1971, Boisclair and Leggett 1991, Railsback 1997). The physiological responses, including energy consumption and expenditures are generally studied under laboratory conditions, and have been quantified for many species of salmonids ( Weatherly and Gill 1995, Brett 1995). Field studies are labor intensive and they are laden with their own assumptions and subject to their own errors ( Ney 1990). Consequently, there are relatively few field studies that have successfully established the linkage ( Martin 1985, Filbert and Hawkins 1995). A combination of field and laboratory study offers the best hope for establishing a quantitative understanding of the cause and effect linkage between growth and temperature in natural environments ( Hansen et al. 1993).

Laboratory studies have produced quantitative relationships between energy consumption and expenditure mechanisms. The rate at which most energetic functions proceed is mediated by ambient environmental temperature. Bioenergetics models have been developed to help manage understanding of the multitude of physiologic responses to temperature ( Kitchell et al. 1974), accounting for energy consumption and expenditures. Some have been packaged into software programs ( Hewlett and Johnson 1992, Hanson et al. 1997), and have been proven to be useful tools for a number of applications in fisheries management ( Hansen et al. 1993). These include the rearing of fish in hatcheries ( McLean et al. 1993), and populations in natural environments ( Hanson et al. 1997). The downside to such models is that they often require many parameters to inform a number of energetic functions, most of which are difficult to quantify in natural environments. Thus their application to explore ecological responses to environmental temperature are more limited, although researchers have recently found them promising for this purpose ( Filbert and Hawkins 1995, Preall and Ringler 1989, Railsback and Rose 1999).

In Section 5, we developed and corroborated a bioenergetics based approach that can be used to evaluate the effects on growth of the variable temperatures that occur over the juvenile rearing period in natural stream environments. The model treats the population as a cohort, and does not account for population density effects. Thus, the method allows us to focus on temperature differences among streams while minimizing biological data requirements. The mathematical model simulates weight gain over a specified duration, and requires only three input parameters ( temperature, initial weight, and daily food consumption). The method is sufficiently simple that it can be applied in field experimental studies. Only the food consumption term is difficult to assess in streams ( Filbert and Hawkins 1995). To our knowledge, no simple methodology for assessing food availability has been developed. 6 3 The ecological constraints on food supply can vary significantly among sites, and they are potentially influenced by many factors: population dynamics and competition for food, ( Walters and Post 1993), riparian, in channel habitat characteristics as they control primary and secondary production ( Hawkins et al. 1983, Bilby and Bisson 1988, Hetrick, Murphy 1998, Railsback and Rose 1999) and the energy content of food prey ( Stewart and Ibarra 1991). Information about these riparian, in channel habitat and population characteristics is embodied in the time series of daily temperatures, consumption and body weights of fish as they grow in natural environments over time.

Despite difficulties in establishing in situ food consumption, we were able to generate estimates of consumption for juvenile coho and steelhead from observations of fish growth over time that produced close correspondence between simulated and observed weight gain, even when parameters were extrapolated among streams. Weight gain predicted for 16 populations of coho and 8 populations of steelhead were generally within 11% of the observed, during a rapid growth period where weight gain ranged from 67 to 415%. Based on corroboration with observed population growth, we concluded that the method is a useful tool for quantifying the effect of temperature regime on growth, though not biomass until population effects are accounted for.

In this section, we use the model to perform a series of relative comparisons of growth effects from observed stream temperatures from a number of stream sites with widely varying temperature profiles. We pay particular attention to interpreting results relative to temperature thresholds that are often used for water quality criteria.

Growth Simulation Method The growth model was used to simulate weight gain for coho and steelhead using standard timelines, initial weights, and consumption estimates for each species ( Table 6.1). Only the temperature varied, according to measured daily temperature at the 19 sites. Thus, this assessment isolated the effects of the long term temperature on growth. Temperature profiles at the sites varied from very warm to cold ( Table 6.2, and further described in Section 3), and many exceed the current Washington Department of Ecology temperature criteria. The temperature data set did not include very cold streams ( 12oC or less). These typically occur in the extreme headwaters, and are small and non fish bearing in this region ( Black 2000). To represent these streams, we also conducted two simulations using a constant temperature of 8o and 10oC.

Growth of coho and steelhead populations was characterized by the average population weight according to the parameters identified in Table 6.1. Simulations were run from June 1 through September 15 Table 6.1 Input variables for growth simulations. See Section 5 for a full description of the growth model and the determination of parameters.

Input Variables Coho Steelhead Simulation Period June 1 Sept 15 June 1 Sept 15 Initial Weight ( g) 1.4 0.5 Food Consumption ( g g 1d 1) Declines through time with ecological constraint ( see Section 5, Figure 5.8) Assumed at maximum potential calculated by temperature and weight ( see Section 5, Figure 5.8)

Temperature ( oC) Daily mean calculated from hourly temperature measurements Daily mean calculated from hourly temperature measurements 6 4 because we had a complete temperature record for all of the sites and this period encompasses most, if not all, of the growth occurring during the summer rearing period.

The consumption was varied according to observed or estimated rates inferred from growth of fish between time periods as described in Section 5. The analysis found that there are important differences between fish species in their consumption patterns. Age 0 steelhead appear to have no environmental constraint on food supply, subject only to physiological constraints imposed by temperature and weight. Coho show ecological constraint on food supply beginning in late June that lasts through the summer season. Fausch ( 1984) observed similar patterns, concluding that these differences are likely to influence the species' response to temperature.

Coho 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 Weight ( g)

Steelhead 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 Weight ( g)

Figure 6.1 Estimated weight after 106 day growth period using the same initial weight and food consumption assumptions at each site. 6 5 Temperature Effects on Weight Gain The predicted weights after 107 days of simulated growth are shown in Figure 6.1. Because the initiating assumptions are the same, the predicted weights are similar. Any differences in observed weight are due to the temperature at each of the sites. Coho vary up to 1.2 grams ( 31% of the mean), and cooler sites generally had greater weights. The predicted weights of steelhead vary by only 0.5 grams among sites ( 12% of the mean), despite large differences in the temperatures.

Relative Temperature Effects on Growth Our approach to evaluating the effects of temperature on salmonid growth is to estimate the weight gain achieved during the summer according to the observed temperature regimes and estimates of food availability, then compare that growth to a reference point. Our reference point is the growth the fish would have achieved if temperatures had been at optimal for growth, and at the same level of food consumption. Brett ( 1971), Brett et al. ( 1982), Railsback and Rose ( 1997), Preall and Ringler ( 1989) used a similar reference approach, and growth models as a basis for prediction.

Establishing Optimal Temperature and Growth. Previous studies have established a maximum potential growth as the point of reference. Many have used the size calculated at constant optimal temperature and maximum consumption ( a reference only observed at optimal temperature and low weight). ( See Section 5 for a full discussion of this point). This represents the maximum possible growth that fish could achieve, and it is probably only observed in the laboratory setting where both of those parameters can be maintained at required levels and for a short period in a fish's life.

It's important to note that optimal temperature is not static but varies with weight and ration ( Stewart and Ibarra 1991, Brett et al. 1971, Brett 1995). The change in optimal temperature with rations between the minimum required for basic metabolic functions and the maximum at satiation is shown for coho and steelhead in Figure 6.2. Note that consumption rates differ for the two species, as does the range of optimal temperatures. There are also changes in consumption rates as the fish gain weight, referred to as allometric relationships ( Brett 1995, Stewart and Ibarra 1991). In the optimal growth simulations, we reduce consumption according to fish weight and any ecological constraints on the food supply. Optimal temperature is then reduced accordingly. This yields realistic estimates of optimal growth reflecting well documented allometric effects. By adjusting optimal temperature with consumption, we believe the reference optimal growth simulation realistically Optimal Temperature in R elation to F o o d Consumption 10 11 12 13 14 15 16 17 18 0 0.05 0.1 0.15 0.2 Consumption ( g g 1d 1) Optimal Temperature ( oC) Coho Steelhead Figure 6.2 Optimal temperature in relation to food consumption. Consumption is expressed as grams of prey per gram of body mass per day. The range displayed is from satiation to minimum maintenance for each species. 6 6 represents the potential effects of temperature. It is not clear if previous researchers who have used optimal growth benchmarks have performed this adjustment.

In the second scenario, weight is calculated allowing the daily growth rate ( gi) to vary with temperature according to the specific growth curves in Figure 5.4, and food consumption assumptions ( as illustrated in Figure 5.8). We express growth in terms of the Reduction In Maximum Growth ( RMG). RMG is defined as the percentage of reduction in growth for the site specific temperature compared to the maximum growth achieved at the optimum temperature for each ration.

RMG (%) is calculated according to:

100 ) 1 ( x W W RMG optimal t t = - = ( 6.1)

A sketch of the calculation is provided in Figure 6.3. Since no stream had optimal temperature all of the time, the gain in weight with varible temperature should be less than that for the optimal case. The RMG is expressed as a percentage of the maximum weight. The lower the value of RMG, the less the deviation from the optimal growth rate. A RMG value of 0% suggests there is no growth loss due to the temperature at the site.

The reduction in maximum growth due to temperature varied among species ( Table 6.2). It is clear that fish spent only a portion of their time in the optimal temperature range during their growth period, since RMG was greater than zero at all sites for both species. Consequently, there was some cost to growth for salmonids living in Pacific Northwest streams due to their temperatures during the rearing growth phase ( Table 6.2). However, the RMG were generally within 20% for both species at all sites.

0 0.5 1 1.5 2 2.5 3 3.5 4 26 Mar 97 6 Jun 97 16 Aug 97 27 Oct 97 Weight ( grams)

RMG Grow th at optim al tem p Grow th at obs e rve d tem p Figure 6.3 Illustration of the reduction in maximum growth based on two temperature scenarios; growth at constant optimal temperature and with the variable growth rate based on the site's temperature regime. 6 7 Table 6.2 Location and temperature characteristics of temperature sites used in reduction in growth analysis.

Site characteristics Temperature characteristics Reduction from maximum growth (%)

Site Watershed Basin Area ( km2) Year 7 Day Maximuma oC 7 Day Meanb oC Annual Maximumc oC Season Mediand oC Coho Steelhead Deschutes River mainstem Deschutes 145.0 1994 21.0 18.4 22.5 15.0 13.5 11.4 Thurston Creek Deschutes 9.1 1994 14.9 14.1 15.5 12 10.0 9.5 Hard Creek Deschutes 3.0 1994 14.0 13.0 14.0 11.0 19.2 13.7 Ware Creek Deschutes 2.8 1994 17.5 16.1 18.3 12.9 14.7 8.7 Huckleberry Creek Deschutes 5.3 1991 18.4 17.6 18.5 15.5 18.1 12.7 Chehalis River mainstem ( Site 1) Chehalis 181.8 1997 21.1 18.9 22.1 15.6 16.8 16.0 Chehalis River mainstem ( Site 2) Chehalis 57.5 1997 22.1 18.2 23.2 14.5 15.3 13.1 Chehalis River mainstem ( Site 3) Chehalis 29.5 1997 20.6 18.6 21.4 14.3 13.8 11.2 Crim Creek Chehalis 22.0 1997 18.8 16.9 19.4 14.3 11.6 9.9 Lester Creek Chehalis 10.4 1997 18.4 16.3 19.0 14.2 9.3 8.5 Thrash Creek Chehalis 16.7 1997 15.3 14.3 15.8 12.3 8.8 8.1 Rogers Creek Chehalis 13.1 1997 15.7 14.1 16.1 12.6 6.4 7.0 Big Creek Chehalis 9.0 1997 16.5 14.6 16.9 12.5 6.2 6.9 Sage Creek Chehalis 5.3 1997 16.5 14.6 16.9 12.5 9.1 7.7 Salmon Creek Chehalis 8.9 1997 15.8 14.2 16.2 12.3 8.1 7.7 Mack Creek Chehalis 2.8 1997 12.9 12.5 13.1 11.7 6.2 9.0 Porter Creek Chehalis 25 1990 17.5 16.3 18.6 14.4 17.1 10.5 Hoffstadt Creek Toutle 25.6 1990 24.5 18.4 26.0 14.0 24.6 15.1 Harrington Creek Toutle 8 1990 19.1 16.7 20.5 13.3 16.5 9.5 Eight ( Constant) NA NA NA 8 8 8 8 28.1 35.4 Ten ( Constant) NA NA NA 10 10 10 10 7.1 18.3 a maximum value of the 7 day moving average of the daily maximum temperature b maximum value of the 7 day moving average of the daily mean temperature c instantaneous maximum d median of daily mean temperature from June 1 to September 1 6 8 Temperature criteria for water quality standards are generally applied for time averaged characteristics of temperature such as the warmest 7 day average of daily maximum and mean temperature, or the annual maximum temperature ( instantaneous measure) ( see Section 3). Growth reduction ( RMG) is shown relative to various time averaged temperature indices in Figure 6.4. RMG was at a minimum when the stream's temperature index most closed approximated the species'optimal temperature. All streams had some growth loss due to its long term temperature, since no stream had RMG equal to 0. RMG tended to increase for streams significantly warmer or colder than the optimum or lowest point of the growth curves. Generally, the sites with lowest growth loss had indexing mean temperatures within the range of optimal temperatures ( e. g., Figure 6.2). These are 14o to 17oC for coho and 11.5 o to 14oC for steelhead using the 7 day maximum measure. Both species showed steep response in growth loss at higher and lower temperatures than their optimal range. There was significantly growth loss for both coho 0 10 20 30 5 10 15 20 25 7 Day Maximum Temperature ( oC) Coho Steelhead A)

0 10 20 30 5 10 15 20 25 30 Annual Maximum Temperature ( oC) Coho Steelhead B)

0 10 20 30 5 10 15 20 7 Day Mean Temperature ( oC) Coho Steelhead C)

0 10 20 30 5 10 15 20 Season Median Temperature ( oC) Coho Steelhead D)

Figure 6.4 Estimated growth risk at temperature study sites in relation to several time averaged temperature metrics: A) 7 day maximum, B) annual maximum, C) 7 day mean, and D) median temperature for the simulation period. 6 9 and steelhead when the 7 day maximum temperature was less than 10oC and greater than 24oC. The patterns are similar for both species, although coho predictions are more variable with site temperature than steelhead. These two species often cohabit the same streams, and with the growth simulation method, they are predicted to have similar level of response at approximately the same temperatures, despite fundamental differences in their specific growth rate/ temperature curves ( Section 5).

The general patterns in RMG described for the 7 day maximum temperature also held true for all of the time averaging periods ( Figure 6.4). Of course, the range of temperature where growth was optimized varies with each of the temperature indices. The relationship with the least scatter was the 7 day mean temperature, suggesting it may be the best for temperature criteria.

While the species have similar patterns of response, there were also important, nonintuitive differences in simulation results. Although the optimal temperatures for coho are higher than steelhead ( Figure 6.2), their growth was maximized within a narrower and lower range of temperatures. Steelhead maximized growth at a wider and somewhat higher range of temperatures. We believe that these differences reflect the food supply with which each species is modeled. Because no food limitation was assumed for steelhead, their optimal temperatures were on the high end of the optimal range. This gave steelhead a broader temperature range where growth was not compromised by temperature. Coho had a narrower range of temperature where growth was optimized, and this range was consistent with the optimal growth range indicated by their specific growth curves. Assumptions of food supply restrictions helped ensure that the optimal growth for coho would include those temperatures associated with low consumption ( 13 to16.5 oC).

The short duration indices appear to be useful for characterizing the long term temperature pattern in a way that is meaningful to fish growth. In Figure 6.5, the daily mean temperature for the entire simulation period is shown for three sites. Some sites Coho 0 5 10 15 20 25 6 Jun 12 Jun 18 Jun 24 Jun 30 Jun 6 Jul 12 Jul 18 Jul 24 Jul 30 Jul 5 Aug 11 Aug 17 Aug 23 Aug 29 Aug 4 Sep 10 Sep Mean Daily Temperature ( oC) Hoffstadt Cr Big Cr Hard Cr Optimal Figure 6.5 Temperature regime at selected sites with estimated optimal temperature. 6 10 spend a lot of time above optimal temperature ( e. g., Hoffstadt Creek), and some spend all or most of the time below optimal ( e. g. Hard Creek). Growth is impacted in both these cases by approximately 20%. The sites where temperature was close to optimal for the longest time ( e. g., Big Creek), had the least effects on growth. Most site temperatures tended to be well below optimal early in the summer growing season when growth rates are maximized, partly because fish were smaller. Better growth early in the season appeared to compensate somewhat for very warm temperatures later in the season. Figure 6.5 also illustrates how optimal temperature declines through time with decreasing food consumption due to weight gain.

Since the sites span a range of temperature regimes, the site with the lowest growth loss represents the temperature profile that best encourages growth for that species. In a sense, this site fits the concept of an " index" stream, although in this case, the reference is defined by fish growth rather than its naturalness or lack of disturbance. Interestingly, Big Creek had the most optimal temperature for both coho and steelhead. This site has an annual maximum temperature of 16.9oC, nearly 1 degree over the Washington water temperature criteria.

Risk Associated with Growth Limitation Holtby and Scrivener ( 1989) and Quinn and Peterson ( 1996) found that coho size at the end of summer was a primary factor influencing overwintering survival and smolting. Holtby and Scrivener ( 1989) provided an equation relating probabilities of overwintering success to coho length. We translated this relationship to an equation based on weight using a population length/ weight relationship ( Ricker 1975). The relationship between weight and the probability of overwintering success from these two studies are shown in Figure 6.6. Holtby and Scrivener ( 1989) found a large increase in overwintering success with increased size, possibly because the coho were so small at Carnation Creek ( generally less than 2 grams). According to their relationship, weight of about 6 grams or more yields an 80% or better probability of successfully overwintering. Note that we have extended Holtby and Scrivener's relationship beyond the limits of their data to cover the larger fish sizes at our sites. Quinn and Peterson ( 1996) found more modest improvement in overwintering success at Big Beef Creek with fish size, although the fish in this stream were significantly larger than Carnation Creek. These authors found that the probability of successfully overwintering was about 50% for fish > 89 mm ( approximately 8 grams) and only 17% for fish < 60 mm ( approximately 2.5 grams). These values agree more closely with fish sizes in our study streams. We connect the two lines to determine a relationship of overwintering success with weight. Pr = 5.8511xW+ 3.1915 0 20 40 60 80 100 0 2 4 6 8 10 12 Weight ( grams) Holtby and Scrivener 1989 Quinn and Peterson 1996 Figure 6.6 Probability of success for coho overwintering survival ( from Holtby and Scrivener 1989) based on end of summer fish size. Data from Quinn and Peterson ( 1996) are also shown. The two points were connected with a linear regression. 6 11 We use both of the relationships to illustrate the potential effect of growth reduction on overwintering success. The probability of success for each individual in the population at Salmon Creek was calculated based on its weight ( shown as actual weight in Figure 6.7). Quinn and Peterson's data are used in Figure 6.7A and Holtby and Scrivener's relationship is used to calculate Figure 6.7B. Despite the large differences in estimated probabilities with the two relationships, both produce similar relative results. There is a reduction in overwintering success with lower weight; the magnitude of change is approximately equal to the percentage change in weight. That is, a 10% reduction in growth calculated for the population results in an average reduction in overwintering success of 9%.

The important outcome of this analysis is the suggestion that the relatively small changes in weight that we calculate due to temperature ( e. g. Figure 6.1) are sufficient to affect individual and population overwintering success to some extent. A 10% reduction in growth would be difficult to statistically detect given the typical range of sizes in natural populations. However, a 20% reduction should be detectable, especially when field experiments are guided by hypotheses generated from the growth model. Brett et al. ( 1982) suggested a 20% upper limit for change in weight due to temperature for chinook populations living in the Nechako River. This appears to be somewhat high for coho, based on implications for loss of overwintering success. However, it should be noted that many factors affect the survival of salmon in the marine environment.

Use of either relationship extends them beyond the original data or application developed by the authors. Therefore, even though we use both relationships to estimate the effect of growth reduction from temperature on overwintering success, we acknowledge uncertainty in this analysis. Additional research quantifying the effect of size on success at later life history stages would increase confidence in the analysis of risk to growth loss Probability of Overwintering Success Quinn and Peterson 0 5 10 15 20 25 30 35 40 0 10 11 20 21 30 31 40 41 50 51 60 61 70 71 80 81 90 91 100 Probability Category Number Actual Weight  10%  20% A)

Probability of Overwintering Success Holtby and Scrivener 0 5 10 15 20 25 0 10 11 20 21 30 31 40 41 50 51 60 61 70 71 80 81 90 91 100 Probability Category Number Actual Weight  10%  20% B)

Figure 6.7 Histogram of probability of overwintering survival based on weight of individuals within the population and simulated effects with redcution in weight due to temperature. Probability calculated based on Quinn and Peterson results from Big Beef Creek, WA ( A) and from Holtby and Scriverner's from Carnation Creek ( B). 6 12 due to temperature. This, in turn, creates uncertainty regarding the choice of 10% as the growth reduction limit. It appears clear that at least 5% growth loss can be expected at all sites due to long duration exposure, even when the bulk of temperatures are near optimal. The difference in temperature thresholds selected at 10 and 20% RMG is quite significant. Increased understanding of the role of juvenile size in determining success at later life history stages would improve confidence in selecting an appropriate limit to growth loss.

In the Carnation Creek study, improved growth of steelhead fry with increased temperature after logging did not translate to larger smolts after two to three years of rearing ( Hartman and Scrivener 1990). Steelhead will usually spend at least one additional year in the stream regardless of size achieved in the first year. Thus the impacts on growth from temperature are shown to be small in this analysis, which is consistent with observations at Carnation Creek. The negative or positive effects on growth are not great enough to change weight sufficiently to change migratory patterns, that is, to speed up or delay them by one year.

This is in contrast to coho at Carnation Creek, where temperature increased growth sufficiently to bring some fish to smolting size in one year. It should be noted that Southern British Columbia represents the most northerly locale where coho are typically able to reach smolting size in one year ( Sandercock 1991). Coho at Carnation Creek typically migrate at 2 years rather than one, presumably due to lower temperatures. In this case, increasing the temperature accelerated growth to the point where the coho outmigrated after the first season. This was interpreted as negative for the species, because the timing of their migration made them more susceptible to predation in the estuary and ocean environment. Where fish typically migrate in one year ( e. g., Washington and Oregon), growth improvement would probably benefit their success by producing fish of larger size, according to Quinn and Peterson ( 1995).

Growth Loss and Temperature Criteria We translate the RMG data ( Figure 6.4) to zones of reduction in maximum growth to facilitate identifying thresholds of growth response in Figure 6.8. The range of temperatures was determined by ordering the site temperature data and estimating the temperature where the 10 and 20% boundaries occurred. The range of 20% reduction encompasses most of the stream temperatures typically observed in the region. The range of temperature where there was relatively little effect less than 10%) was fairly narrow for both species. It should be noted that at temperatures above and below the ranges illustrated, there is high growth loss due to temperature ( 30% or more).

Discussion The temperature assessment approach provides a method for indexing the relative effects of stream temperature regimes on salmonid growth. A value of the approach is that the relative effects of temperature and food consumption can be evaluated independently of other habitat or population characteristics, and each other. Results can be also be used to directly compare growth effects among species. Because these factors are assumed constant in this analysis, the growth estimates only account for the direct effects of temperature, and do not account for population dynamics. 6 13 Species were similar in their range of response. However, coho were more temperature sensitive, and steelhead tended to grow somewhat better at warmer temperatures than coho. If bioenergetics relationships are correct, this can be explained by differences in food supply. This, in turn, may reflect the different foraging strategies that each species utilizes when coexisting in the same streams ( Bisson et al. 1988b). Selection of temperature criteria in management situations may be most useful if they reflect the most sensitive species ( coho) when both species are present.

Fish were predicted to be growing near optimal within many streams, including a number that exceed current water quality standards. The temperature ranges observed at the sites included in this analysis are representative of current conditions in Washington streams and rivers. The vegetative overstory of many has been disturbed within the last 50 years, and therefore streams may be currently warmer than they have been at other times in their history. However, the range of temperature represented at these sites is likely to be representative of the range that has occurred historically, given the history and frequency of fire disturbance in the region ( Agee 1993).

The patterns of biological growth response in relation to all of the time averaged temperature regime metrics demonstrates that they can be used to index the temperature regimes of sites in biologically meaningful ways. Short averaging periods such as 7 day and even the annual instantaneous maximum temperature are strongly indicative of the long term temperature regime that partially controls fish growth during the summer. 7 Day Maximum 0 5 10 15 20 25 30 Coho Steelhead Temperature ( oC)

10% 20%

20% Annual Maximum 0 5 10 15 20 25 30 Coho Stealhead Temperature ( oC) 20%

20% 10% 7 Day Mean 0 5 10 15 20 25 30 Coho Steelhead Temperature ( oC)

20%

10%

20%

Figure 6.8 Ranges of temperature where reduction from maximum growth is 0 10% and 11 20%. At temperatures above and below these ranges RMG exceeds 20%. 6 14 CONCLUSIONS ‰ It is feasible to apply a risk based approach that uses data without undefined safety factors and produces an estimate of cumulative risks. This technique is a true quantitative benchmark that is measurable and testable.

‰ Modeled growth using measured temperature suggest that the majority of temperatures experienced by salmonids are generally suboptimal for growth, and these exert some cost on the maximum potential growth.

‰ Despite what appear to be large differences in temperature among sites, especially with regard to the warmest temperatures that occur each summer, there is considerable similarity in the predominance of temperatures that are important to growth. Sites with significantly different temperature regimes can have similar predicted effects on growth.

‰ An upper threshold for the 7 day maximum temperature of 16.5oC minimizes growth losses for coho. A 7 day maximum temperature or 20.5oC minimizes growth losses for steelhead.

‰ Concepts of safety factors in selecting temperature thresholds defined for salmonids need to be exercised with some caution. Thresholds that are both too low and too high can negatively affect growth.

‰ The criteria above assume 10% growth loss as the acceptable level of risk. There is uncertainty associated with this number, since there are relatively few quantitative data to base it on. Further research could help confirm acceptable risk levels. 7 1 SECTION 7 RELATIONSHIP BETWEEN EXISTING AND PROPOSED TEMPERATURE CRITERIA AND RISK ASSESSMENT FINDINGS Abstract Understanding the biological effects of temperature on fish is essential for effectively managing stream temperature under the Clean Water Act. One of the key elements of water quality management is to establish temperature criteria ( e. g., water quality standards) that will limit human caused impacts to the beneficial uses of the stream ( e. g., fish). Ideally, criteria are based on an understanding of the interaction of fish physiology and ecology ( biological effects) and the physical watershed and climatic processes that control the temperature of streams ( exposure). Scientific understanding of these factors should then lead to criteria that are realistic and appropriate in assigning a temperature threshold that appropriately reflects temperature. In this section, we review and compare methods of determining temperature criteria used to select criteria, including those developed in this report.

Key findings of this chapter are:

‰ Risk assessment allows the effects of magnitude, duration and frequency of temperature on fish growth and survival to be quantified in an objective and repeatable manner.

‰ The U. S. EPA ( 1977) temperature criteria were found to be the most objectively defined and consistent with risk analysis results. They generally appear to allow up to 20% reduction in growth due to temperature.

‰ Criteria derived from review of scientific literature without quantitative synthesis are generally consistent with risk assessment and U. S. EPA methods, although they tend to overestimate the benefits of cold temperatures and slightly underestimate the positive growth effects at temperatures somewhat higher than optimum.

Introduction From a scientific basis, methods for deriving temperature criteria should be explicitly defined and based on sound scientific data that pass data quality screening criteria ( ASTM 1997). A criteria derivation protocol needs to have:

‰ clearly defined, transparent and repeatable methodology;

‰ data quality objectives, attributed data sources, and quality control screens;

‰ defined levels of protection for species populations, communities or ecosystems; 7 2 ‰ stated assumptions, safety factors, and data extrapolation factors; and ‰ temperature criteria that incorporate magnitude, frequency, and duration as decision variables.

In previous sections of this report we: reviewed the scientific literature elucidating the growth and acute lethal response of salmon to temperature during the rearing life history phase ( Section 2); explored relationships between long term and short term temperature indices ( Section 3); performed risk analysis on the effects of temperature on mortality of salmon ( Section 4); developed a quantitative method to asses effects of long term exposure to temperature on growth ( Section 5); and established temperature threshold criteria ( Section 6). In this section we evaluate water quality temperature criteria derived from several methods that have been adopted or proposed by various authors and agencies relative to their biological effects, including the analyses developed in this report.

Criteria and Methods To Derive Them Review of scientific literature and agency policy documents identified a number of different approaches to derive water temperature criteria for the summer maximum temperature. Methods fall into three general categories:

‰ criteria derived from experimental temperature tolerance studies;

‰ criteria derived from field observations of fish occurrence under different temperature regimes; and ‰ criteria derived from professional review of temperature information.

The degree of objectivity or subjectivity by which the information is synthesized into recommended criteria, the degree to which data forms the basis for the criteria, and the extent to which population effects can be probabilistically determined varies between methods.

Experimental Information based Method ( EPA)

The EPA has published temperature criteria for a number of fish species based on a review of laboratory based research on the thermal tolerance of fish ( Brungs and Jones, 1977, also cited as U. S. E. P. A. 1977). Brungs and Jone's method includes identification of acute and chronic threshold values, definition of averaging time of specific daily temperature characteristics, and explicit treatment of safety factors to ensure the recommended criteria control population level effects. Temperature criteria are based on temperature tolerance studies that generally follow the protocols developed by the NAS/ NAE ( 1973). These protocols include procedures to derive specific temperature criteria for both chronic and acute exposure. Criteria for chronic exposure are derived from incipient lethal temperature and physiological ( bioenergetic) performance ( e. g., growth optima) data. The temperature assessment methods described in Sections 4,5 and 7 3 6 of this report are based on similar data such as the acute lethal and growth temperature/ ration relationships.

From the perspective of selecting temperature criteria, some very simple principles can be derived from the growth curves that are meaningful ( Figure 5.4). Beginning with the coolest temperatures ( 0o C), growth increases with temperature up to the temperature up to optimal where the maximum growth rate is achieved without any increase in ration ( approximately 16o C). This improved growth is due to increased food conversion efficiency and consumption. At temperatures above the maximum growth rate, growth rates cannot be maintained because consumption declines and metabolic energy costs increase. Further increases or maintenance of growth rate must come from increased food, if possible within satiation limits. Because the shape of growth curves is broad at the maximum, there is little or no negative effect of temperature several degrees above optimum.

Brungs and Jones ( 1977) describe an objective method for developing threshold criteria based on optimal temperature and the range of preferred temperatures from laboratory derived growth curves available at the time. The criterion for chronic exposure is expressed as the maximum seasonal 7 day moving average of the daily mean temperature. Brungs and Jones ( 1977) refer to this metric as the " maximum weekly average temperature" ( MWAT). This value may be derived for different seasons and life stages ( e. g., summer rearing or fall/ winter incubation). The scientific rationale for using the MWAT as a temperature limit is based on data showing that moderate temperature fluctuations can be tolerated as long as the incipient lethal temperature is not exceeded for long periods. The method also assumes that optimum temperatures are neither necessary nor realistic at all times to maintain viable fish populations ( NAS/ NAE 1973).

Criteria for protection from exposure to extreme temperatures are based on thermal tolerance data. Regression equations of median survival times ( LT50) ( e. g., Figure 2.2) are used to predict the upper incipient lethal temperature for a 24 hour exposure, and a 2 ° C safety factor is subtracted to derive a short term criterion that will provide 100% survival ( Brungs and Jones 1977). Since LT10' s and LT50' s are very close, the safety factor is sufficient to preclude effects on any fraction of the population.

Protocols for deriving criteria based on experimental temperature tolerance studies have not changed since being proposed by the Environmental Protection Agency ( Brungs and Jones 1977). Various authors continue to recommend this method as an option for protecting fish habitat ( Armour 1991).

The acute and chronic temperatures for the EPA protocol were computed for the rearing stage of seven native salmonids in Washington using the experimental temperature tolerance data that could be found in the literature ( Table 6.1). This includes relevant data reported by the NAS/ NAE ( 1973) and any newer data that could be found. Original criteria reported by Brungs and Jones ( 1977) are shown for comparison with the updated numbers. Note, the latter results are slightly different than criteria reported by Brungs and Jones ( op cit) for the same species because our values are based on data from individual studies and not on the average of several studies. The results from multiple evaluations of the same species produced similar results ( usually within 1oC), and differences among salmonid species were relatively small. 7 4 Table 7.1 Water temperature criteria ( maximum weekly average temperature and acute exposure maximums during growth season) for salmonids based on the EPA methodology ( Brungs and Jones 1977). Values reported by Brungs and Jones are shown for comparison.

Species temp. ( ° C) lethal level ( ° C) a intercept ( a) slope ( b) optimum Source ( ° C) maximum ( ° C) data Oncorhynchus c lark i 13 23 fluctuating 25.5 18.1515  0.5723  ( a) Golden 1978 18.50 a, d cutth roat trout 23 25.7    ( b) G olden 1978 18.57 b, d 16 22.6    ( c) V igg and Koch 1980 24.20 a constant    15 ( d) Dwyer and K ram er 1975 Oncorhynchus gorbuscha 24 23.9 14.7111  0.4459  ( e) B rett 1952 17.97 e, t pink salm on 23.91 e Oncorhynchus keta 23 23.8 15.3825  0.4721  ( f) B rett 1952 17.93 f, t chum salm on 9 23.2 15.9272  0.5575  ( g) Blaham and Parente, unpublished in: NAS/ NAE 1973 17.73 g, t 23.89 f 20.90 g Oncorhynchus k isutch 23 25.0 18.9736  0.6013  ( h) B rett 1952 19.67 h, k coho salm on 23 25.0 ( hh) Dehart 1975 10 23.5 18.4136  0.641  ( i) B laham and M cConnel, unpublished in: NAS/ NAE 1973 19.17 I, k 10 13 fluctuating 26.0    ( j) Thom as et al. 1986 20.00 j, k constant    17 ( k) A verett 1968 24.30 h 21.80 I Brungs a nd Jones ( 1977) estimate 18.00 24 .00 Oncorhynchus m ykiss 18 26.5 18.4654  0.5801  ( l) A labaster and W elcom m e 1962 20.30 l, o rainbow/ steelhead 20  19.625  0.625  ( m ) Alabaster and Downing 1966 19.17 l, p 16 25.6    ( n) Hokanson et a l. 1977 19.83 l, q constant    17.2 ( o) Hokanson et a l. 1977 20.30 n, o fluctuating    15.5 ( p) Hokanson et a l. 1977 18.87 n, p fluctuating    16.5 ( q) W urtsbaugh and D avis 1977 19.53 n, q 24.39 l 24.35 m Brungs a nd Jones ( 1977) estimate 19.00 24 .00 Oncorhynchus nerka 23 24.8 20.002  0.6496  ( r) Brett 1952 18.27 sockeye salm on 20 23.5 16.7328  0.5473  ( s) M cConnel a nd B lahm , u npublished in: NAS/ NAE 1973 17.83 constant 15 ( t) B rett et a l. 1969 23.93 15 ( u) Shelbourn et a l. 1973 22.80 Brungs a nd Jones ( 1977) estimate 18.00 22 .00 Oncorhynchus tshawytscha 20 25.1 22.9065  0.7611  ( v) B rett 1952 21.03 v, x chinook salm on 20 24.7 21.3981  0.7253  ( w) Blaham and M cConnel, unpublished in: NAS/ NAE 1973 20.90 w, x     19 ( x) B rett et a l. 1982 23.95 v 23.15 w a D ata represent the ultim ate upper incipient lethal tem perature w here available o r the upper incipient lethal tem perature when an estim ate o f the u ltim ate level was not available.

Median Survival Time Temperature criteria 7 5 Differences between maximum and minimum MWAT and acute criteria are 3.3 ° and 3.5 ° C, respectively ( Table 6.1). This exercise demonstrates that the EPA method is highly objective and reproducible; there were no difficulties in updating the analysis with results of more recent studies. Brungs and Jones also provide criteria for other life history stages.

Field Observation Methods Observation of temperature at which fish occurrence is verified is another method used for estimating fish temperature requirements. Bovee ( 1978) recommended the use of fish observations where temperature is simultaneously collected to determine a " probability of use" curve based on the distribution of observations. This technique of characterizing physical environmental conditions in conjunction with fish observations forms the basis for the U. S. Fish and Wildlife Service Instream Flow Incremental Methodologies ( IFIM) Habitat Suitability Index model ( HSI) ( McMahon 1983). This method has been used to evaluate population effects from physical habitat alterations. Presumably, this method would reflect preference temperatures of the fish, but their quantitative relationship to optimal temperature, growth, or lethal temperatures cannot be known since results would also strongly reflect the ambient temperature occurring when observations were made and may not indicate true preference or long term exposure.

Eaton et al. ( 1995) proposed a technique for deriving the maximum thermal tolerance of fish matching stream temperature records with fish presence data. Their fish and temperature data matching system ( FTDMS) provides a direct measure of the temperatures that are utilized by fish populations in nature. In this protocol, fish presence data are matched with weekly mean temperatures taken from the same location and time period to derive a fish presence by temperature frequency distribution. An estimate of the maximum temperature tolerance for a species is assumed to be equivalent to the temperature at which 95% of the fish observations occur for a large ( n = 1000 matches) database that represents the geographic range limits of the species. The 95th percentile observation is proposed as a safe estimate of thermal tolerance to protect against inaccuracies in temperature records and biased observations of fish presence that may be contained in the database.

The FTDMS is recommended as an approach for determining the maximum temperatures that limit the distribution of salmonids. With regard to temperature criteria, this method seems to relate best to the acute temperatures rather than the chronic temperatures. Table 7.2 lists the mean weekly temperature derived from over 1000 field observations ( Eaton et. al 1995). The field observation method of establishing upper lethal limits suggested by Eaton et al. ( 1995) produced very similar results to those of Brungs and Jones Table 7.2 Water temperature criteria for salmonids based on the fish and temperature data matching system ( FTDMS). Taken from Table 1 in Eaton et al. ( 1995).

Species Mean weekly temperature ( oC) for the 95th percentile observation Oncorhynchus clarki ( Cutthroat trout) 23.2 O. gorbushcha ( pink salmon) 21.0 O. keta ( chum salmon) 16.8 O. kisutch ( coho salmon) 23.4 O. mykiss ( steelhead/ rainbow trout) 24.0 O. tschawytscha ( chinook salmon) 24.0 7 6 ( 1977) which were based on laboratory tests ( Table 7.1): field observation estimates were within 1 ° C of EPA's acute temperature criteria for all salmonids except chum salmon. These observations suggest that laboratory derived criteria can predict the thermal tolerance limits in nature reasonably well.

RISK ASSESSMENT APPROACH The temperature assessment methods described in Sections 5 and 6 followed a risk assessment approach and provided a time integrated and quantitative estimate of the influence of the temperature regime on the potential growth of salmon within selected stream reaches where temperature was known. This approach could be used to develop site specific or regional temperature criteria for both acute and chronic effects on summer rearing. Here we use the results of the acute and growth analyses to evaluate generally applied temperature indices used as temperature criteria. The assessment of acute temperatures suggested that 26oC is threshold temperature for salmonid species ( Section 4).

The reduction from maximum potential growth due to temperature regime was calculated for a number of stream segments with widely varying temperature regimes in Section 6, based on a growth model developed in Section 5. The range of temperatures where growth was within 10% and 20% of optimum based on those analyses is shown in Figure 7.1. For each temperature indices, coho and steelhead reduction from maximum growth ( RMG) are 7 Day Maximum 0 5 10 15 20 25 30 Coho Steelhead Temperature ( oC)

10% 20%

20% Annual Maximum 0 5 10 15 20 25 30 Coho Stealhead Temperature ( oC) 20%

20% 10% 7 Day Mean 0 5 10 15 20 25 30 Coho Steelhead Temperature ( oC)

20%

10%

20%

Figure 7.1 Range of temperature where reduction from maximum potential growth ( RMG, %) was 10 and 20% during the summer months for coho and steelhead, using three temperature indices. ( See Section 6 for methods and Table 6.2 for results by site. RMG is greater than 20% at temperatures outside of the temperature ranges indicated. RMG is minimized near the optimal temperature for each species and increases at temperatures warmer or cooler than the optimal. 7 7 plotted together to facilitate comparison among species. Growth is highest within the range of optimal temperatures, and declines at temperature higher and lower than optimal.

There is a narrow range of temperature where growth is optimized for each species. Growth rate is highest at sites with 7 day maximum temperatures between 9oC to 17oC. Patterns are similar for other temperature indices ( 7 day mean and annual maximum temperature), although the temperature range enveloping various levels of growth reduction vary with each temperature index.

Steelhead and coho often occur together in stream environments, and their growth responses are similar, although there are important differences in threshold values. Selecting the criteria based on the 10% RMG for the more thermally sensitive coho would suggest an upper threshold of 16.5oC for the 7 day maximum temperature and 14.8oC for the 7 day mean temperature. Selecting the criteria based on the 10% RMG for the steelhead would suggest an upper threshold of 20.5oC for the 7 day maximum temperature and 17.0oC for the 7 day mean temperature. The upper end of the temperature range is well below temperatures where behavioral avoidance has been observed ( e. g. Bisson et al. 1988, Nielsen et al. 1994), and interspecies competitive interactions have been noted ( e. g., Reeves et al. 1987; Taniguchi et al. 1998).