although multiple factors have contributed to the decline of pacific salmon ( oncorhynchus spp . ) across portions of their natural range , dams have arguably played a major role in many locations ( nrc 1996 ; lichatowich 1999 ; ruckelshaus et al .
large dams ( > 15 m tall)designed to generate hydropower , control floods , or facilitate navigation  have had unintended side effects on the quantity and quality of habitat used by salmon . specifically , these dams have decreased the area for spawning and rearing , and have altered flows , sediments , and temperatures downstream ( pfmc 1979 ; ward and stanford 1979 ; ligon et al . 1995 ) .
the effects of large dams on thermal conditions have been documented worldwide , and include changes in the mean and variance of temperature at several temporal scales ( petts 1984 ; crisp 1987 ; usace 2000a ; preece and jones 2002 ; steel and lange 2007 ) .
these changes have significant biologic consequences because temperature influences the growth and reproduction of aquatic organisms ( ward and stanford 1979 ; schlosser et al .
the spawning of adults , the emergence of fry , and the migration of smolts coincide with thermal cues in natal lakes and streams ( e.g. , brett 1971 ; brannon 1987 ; roper and scarnecchia 1999 ) .
although changes in thermal regimes might have contributed to the declines of salmon ( nrc 1996 ) , we have much to learn about the underlying mechanisms ( see reviews in mccullough 1999 ; and richter and kolmes 2005 ) .
2006 ) , increase mortality ( smith et al . 2003 ; crozier and zabel 2006 ; gilhousen 1990 ) , perturb energetics ( brett 1995 ; rand and hinch 1998 ; macdonald et al .
2000 ) , slow growth ( bisson and davis 1976 ; pearcy 1992 ) , and create morbidity ( bartholomew 1998 ; materna 2001 ) .
indirect effects can also result from shifts in the timing of or phenotypes at life - history transitions ( quinn and adams 1996 ) .
for example , high temperatures during embryonic development can lead to premature emergence , which could expose fry to peak flows , scarce resources , or greater predation ( brannon 1987 ; murray and mcphail 1988 ; jensen and johnsen 1999 ; usace 2000a ) . since the first large dams in the pacific northwest were built in the early 1900s , many generations of salmon have spawned , developed , emigrated , and returned to spawn in the waters downstream .
possibly , these fish have begun to adapt to the new thermal regimes caused by dams ( waples et al .
although we can not infer adaptation from the available data , we can estimate the potential evolutionary impacts of dams and discuss the types of data that would be necessary to make such inferences . under this premise , we present an exploratory analysis of chinook salmon ( oncorhynchus tshawytscha ) in four parts .
second , we review the effects of water temperature on physiological performance at specific life stages .
third , we estimate the degree to which changes in physiological performance would influence the fitness of genotypes .
finally , we assess the possibility of an evolutionary response to selective pressures imposed by dams .
quantifying changes in water temperature downstream of a dam requires several types of data , including the location of the dam , the date of construction , and records of temperature .
linking thermal changes to the performance of chinook salmon , in particular , requires additional knowledge of the distribution of populations .
we compiled these data from a variety of sources ( willingham 1983 ; myers et al . 1998 ; usace 2000b ; streamnet 2005 ; city of tacoma 2005 ; kimbrough et al .
2006 ; usgs 2007 ) , and then imposed five criteria to identify several dams for our analysis .
first , the dam had to reside upstream of a gauge that recorded water temperatures .
second , the gauge s period of record had to span multiple years before and after the construction of the dam .
third , the dam had to occur on a river or large creek that supported chinook salmon in washington , oregon , california or idaho ( usa ) .
fourth , the distance between the dam and the gauge had to be < 30 km , to minimize the attenuation of thermal effects downstream ( usace 2000a ) .
finally , no other large dam could exist upstream of the focal dam prior to its construction . using these criteria
four dams selected for analysis based , in part , on the existence of water temperature gauges downstream .
names , types and locations of these dams are provided , along with the approximate dates when the dams became operational ( i.e. , the reservoirs upstream were created ) .
gauge identification numbers , distances between gauges and dams , and periods of temperature records are also provided .
er , epilimnetic release ; mr , multiple depth release ; hr , hypolimnetic release . for details , see usgs ( 2007 ) .
if only the year was known , the month was assumed to be january . in all cases , the first day of the month was assumed to be the starting point of the dam s operation .
a map of washington and oregon , usa , depicting the locations of four dams selected for analysis : ( a ) mayfield , ( b ) fall creek , ( c ) hill creek , and ( d ) lost creek .
the gauges downstream of these dams contained two types of gaps in their records of temperature .
the first type comprised days when the minimal and maximal temperatures were recorded but the mean temperature was not recorded . for these days , we estimated the mean temperature by averaging the minimal and maximal temperatures .
the second type comprised days when both the mean temperatures and extreme temperatures were missing . in these cases , we estimated the means by regression or interpolation ( see appendix a ) .
we assessed the changes in temperature associated with each dam by comparing daily mean temperatures , averaged among the years before and after construction of the dam . based on our findings
, the effect of a dam on water temperature depends on the time of year ( fig .
conversely , during the fall and early winter , water was generally warmer in the presence of a dam .
this reversal  from summer cooling to fall warming  was evident as early as august on the rogue river and as late as october on the cowlitz river ( fig .
2 ) . mean water temperatures before and after dam construction on the cowlitz river , fall creek , the middle fork of the willamette river , and the rogue river .
bold lines depicting the mean water temperatures are surrounded by thinner lines depicting the upper and lower bounds of the 95% confidence intervals . in theory , the effect of a dam on water temperature should depend not only on the time of year , but also on thermal stratification upstream and the depth of water released at the dam ( ward 1982 ; crisp 1987 ; poff and hart 2002 ) .
if the reservoir upstream is thermally stratified and the dam draws water from below the thermocline ( i.e. , the hypolimnetic layer ) , the water downstream of the dam should be unseasonably cold during summer and unseasonably warm during fall and winter .
if the dam draws water from above the thermocline ( i.e. , the epilimnetic layer ) , the water downstream should be unusually warm during the summer .
other dams draw water from multiple depths , enabling finer control over temperatures downstream . given that our sample contained three types of dams ( table 1 ) , we were surprised to discover these dams affected water temperatures similarly ( fig .
a rich literature describes the influence of water temperature on the development , growth , and survival of salmon at various life stages ( reviewed by mccullough 1999 ) . generally , embryonic development proceeds more rapidly at higher temperatures . in chinook salmon ,
a doubling of water temperature within tolerable limits will halve the time between the fertilization of an egg and the emergence of a fry ( beacham and murray 1990 ) .
embryonic survivorship remains high throughout a wide range of temperatures , but decreases precipitously outside that range .
for example , the survivorship of chinook embryos exceeds 90% between 5 and 15c ; however , below 2c and above 18c the rate drops to < 5% ( fig .
once salmon emerge from the gravel , their growth and survival continue to depend on temperature . for example , the growth rate of fry increases between 10 and 15c ( banks et al . 1971 ) , but survivorship decreases at higher temperatures ( fig .
because these data pertain to salmon raised at constant temperatures , we must assume variation in temperature does not alter these conclusions  a dubious assumption .
some salmon tolerate acute exposure to temperatures that would prove lethal during prolonged exposure ( murray and beacham 1987 ; tang et al .
1998 ) , but we need more research to define the general limits of tolerance in variable environments . embryos and fry of chinook salmon survive well over a range of approximately 10c
this conclusion was based on models that best fit published data for embryos ( velsen 1987 ; beacham and murray 1989 ) and fry ( olson et al . 1970 ) .
model selection was performed according to angilletta ( 2006 ) ; details are provided in appendix b. the thermal sensitivities of physiological performances can mediate the direct and indirect effects of dams on the fitness of salmon . because temperatures in the fall can approach those that stress chinook salmon ( e.g. , see fall creek in fig .
2 ) , warming by a dam could directly increase the mortality of embryos . in the laboratory ,
brief exposure to 17c during embryonic development caused more than 98% mortality between the fertilization and emergence ( geist et al .
more likely , however , dams indirectly influence fitness by advancing the timing of emergence .
emergence describes a developmental transition in which fry leave the interstitial gravel and enter the water column , thereby initiating a sustained , free - swimming residence in a stream or river . at this point in development ,
the energy stored as yolk has been virtually exhausted and the juvenile must begin to feed exogenously . early or late emergence could lead to mortality from excessive flows , abundant predators , or insufficient resources ( jensen and johnsen 1999 ; einum and fleming 2000 ) .
the warming caused by a dam in fall and winter would accelerate the development of embryos , leading to early emergence .
thus , a dam could disrupt a match between the actual and the optimal dates of emergence .
if salmon below the dam spawned later in the year , the temperatures experienced by their offspring would be lower because of seasonal cooling ( see fig .
2 ) . consequently , development would proceed more slowly and the timing of emergence would correspond to that before the dam s influence . alternatively ,
an evolutionary decrease in developmental rate would enable salmon to delay emergence despite the warm water below the dam . whether spawning behavior or developmental physiology would experience greater selective pressure
depends on the cost of each strategy and the impact on other stages of the life cycle .
the response to selection depends on the heritabilities of spawning behavior and embryonic physiology , and their genetic covariance .
the relative timing of spawning seems highly heritable in salmon , with estimates of narrow - sense heritabilities ranging from 0.4 to 1.3 ( e.g. , see smoker et al .
we know less about the heritability of thermal physiology in salmon , but additive genetic variance for developmental rate exists under some thermal conditions ( hebert et al .
these data suggest behavior , physiology , or both could evolve in response to thermal change .
the genetic covariance between spawning date and developmental rate could enhance or constrain adaptation , depending on its sign and magnitude ( lande and arnold 1983 ) .
despite the ideas discussed above , current models of thermal adaptation can not generate deep insights about the responses of salmon to dams .
first , these models focus on physiological responses to environmental temperature ( reviewed by angilletta et al . 2002 ; kingsolver and gomulkiewicz 2003 ) , ignoring the potential for behavioral responses that seem relevant to salmon ( e.g. , timing of spawning , placement of nests ) .
second , these models assume that temperature influences either survivorship ( lynch and gabriel 1987 ) or fecundity ( gilchrist 1995 ) uniformly throughout the life of the organism . because both the thermal impacts of dams and the thermal tolerances of salmon vary throughout the life cycle , simple models will not accurately predict the strength of selection . in the next section , we use an age - structured model to explore the impact of physiological performance on the fitness of a genotype .
matrix models of population growth provide a useful way to quantify the ecological and evolutionary consequences of environmental changes ( caswell 2001 ) , such as the thermal changes caused by dams .
salmon and most other organisms have life cycles with distinct stages that occur in a characteristic seasonal cycle .
matrix projections enable us to explore how a change in performance at a specific stage impacts the mean fitness of a population .
matrix models have been applied to salmon by classifying the life cycle in terms of either stage or age ( e.g. , kareiva et al . 2000 ; greene and beechie 2004 ; scheuerell et al . 2006 ; zabel et al .
ratner et al . ( 1997 ) developed an age - classified model for the spring run of chinook salmon from the south fork of the umpqua river ( oregon , usa ) . in this population ,
90% of juveniles migrate to the ocean during their first spring ( ratner et al .
therefore , the survivorship from oviposition to age 1 equals the product of survivorships for the embryo , fry , and smolt .
thus , the matrix form of the model for a female is as follows : where sx equals the survivorship from age x-1 to age x , bx equals the propensity to breed at age x , mx equals the fecundity at age x , and  equals the survivorship of adults during their migration to the spawning area .
the leading eigenvalue of this matrix (  ) represents the long - term rate of population growth ( caswell 2001 ) . for the case represented by matrix
a , we can not solve explicitly for  , but we can calculate this rate numerically given a set of parameter values . using values for chinook salmon of the umpqua river ( see table 2 of ratner et al .
1997 ) , we computed a value of  = 1.17 , which indicates a growing population (  > 1 )
. predicted dates of emergence for a range of spawning dates at each of four locations impacted by dams .
emergence dates were predicted from water temperature recorded before and after the construction of dams .
these dates are reported on a relative scale ( days after august 1 ) , along with the standard deviation of inter - annual variation in parentheses .
analysis of variance was used to infer significant differences between predicted emergence dates before and after the construction of dams .
assessing the effects of environmental change on population dynamics requires two steps . in the first step
, we must relate changes in the environment to changes in life - history traits ( sx , mx , bx or  ) . in the second step
, we must relate changes in life - history traits to changes in mean fitness (  ) .
the latter relationship is referred to as the demographic sensitivity ( sp ) : where p equals a life - history trait .
for example , the demographic sensitivity for s1 , the survivorship from oviposition to age 1 , depends on da / ds1 : using estimates of these parameters for chinook salmon in the umpqua river , the demographic sensitivity of s1 ( 123.23 ) greatly exceeds the sensitivities of all other survivorships in the matrix ( 0.120.35 ) . in this framework
, the impact of a change in water temperature on fitness equals the product of the demographic and thermal sensitivities : because survivorship changes nonlinearly with temperature , the effect on  depends on the mean temperature , as well as the change in temperature .
although dams have elevated the temperatures experienced by embryos and fry , temperatures during fall and winter usually lie within the tolerable limits ( compare figs 2 and 3 ) .
therefore , the warming caused by dams likely imposes little direct thermal stress on embryos or fry .
nevertheless , the winter warming caused by dams could have indirect effects on s1 , such as those believed to stem from early emergence ( see previous section ) .
assume  approximates the fitness of a genotype with the life cycle and vital rates described by the matrix a. then , we need only two pieces of data to connect early emergence to a decrement in fitness .
first , we must know the degree to which a dam advances the date of emergence . as a first approximation
, we can calculate this advance from records of water temperature and studies of developmental rate .
second , we must know the relationship between the timing of emergence and the survivorship of juveniles ( s1 ) .
unfortunately , this relationship has not been resolved for any population of chinook salmon , let alone the populations that spawn near our four dams .
therefore , we conducted a sensitivity analysis by exploring a set of plausible relationships between the date of emergence and s1 .
as a first step in our analysis , we calculated emergence dates in the rogue river for a set of potential spawning dates .
we focused on the rogue river because it lies close to the umpqua river and the chinook salmon in these two rivers undergo similar life cycles ( myers et al . 1998 ; odfw 2003 ) ; thus , we felt comfortable using the parameter values reported by ratner et al .
for each year for which we had thermal data , we used a degree - day model to estimate the timing of emergence associated with each spawning date ( ranging from august 1 to january 1 ) .
we summed thermal units over successive days until we reached an empirically estimated threshold for emergence ( 889 units ; wedemeyer 2001 ) . using this procedure , we calculated emergence dates during the periods before and after the construction of a dam . although water temperatures recorded by the gauges ( fig .
2 ) probably differ from those experienced by embryos in the gravel , we assumed a similar relationship between recorded temperatures and actual temperatures existed before and after the construction of the dam .
as a second step , we used predicted dates of emergence to estimate the potential impact on fitness .
we used hypothetical functions describing the relationship between the date of emergence and s1 ; these functions differed in the optimal date of emergence and the strength of selection on the timing of emergence ( fig .
optimal emergence dates were chosen by assuming development under predam conditions for three spawning dates :
these hypothetical spawning dates span the temporal window of spawning within the rogue river ( myers et al .
three strengths of selection were chosen based on a recent study of the empirical relationship between emergence date and juvenile survivorship in atlantic salmon ( einum and fleming 2000 ) .
for all functions , the maximal value of s1 was set to 0.00267 ( ratner et al .
, we computed  for a genotype spawning before and after the construction of the dam by inserting s1 in the matrix model of ratner et al .
nine hypothetical relationships between the timing of emergence and the survivorship of juveniles ( s1 ) used in our sensitivity analysis .
these relationships vary in the optimal date of emergence ( early , intermediate , and late ) and the strength of selection for the optimal date ( strong , intermediate , and weak ) .
first , the water temperatures imposed by dams appear to influence emergence dates in a complex manner ( table 2 ) .
if salmon spawn in august , their fry would emerge at approximately the same time or slightly later than they would have emerged before the dam existed .
however , if salmon spawn in september or later , their fry would emerge much earlier than they would have before the dam .
because the thermal effects of dams change seasonally , the results of our sensitivity analysis depend strongly on the optimal date of emergence .
in general , the presence of a dam shifts the optimal spawning date to a later time , such that offspring would emerge on the historical date despite their accelerated development .
however , the optimal spawning date shifts more for salmon that historically spawned in october or november than it does for salmon that historically spawned in september ( fig .
5 ) . predicted fitnesses (  ) of chinook salmon before ( filled symbols ) and after ( open symbols ) the construction of the lost creek dam on the rogue river .
each plot corresponds to one of the relationships between emergence date and juvenile survivorship depicted in fig .
4 . from left to right , plots show earlier to later optimal dates of emergence . from top to bottom , plots show decreasing strengths of selection for emergence on the optimal date . within the range of conditions we considered , the dam s influence on developmental rate should have caused the natural selection of spawning date .
to illustrate this point , consider a hypothetical population in the rogue river whose optimal spawning date was september 1 before the construction of the lost creek dam .
suppose that immediately after the dam began to influence water temperature , the mean spawning date was september 1 with a standard deviation of 12.4 days ( based on beechie et al .
figure 6 illustrates the predicted relationships between the spawning date and the relative fitness for the conditions of our sensitivity analysis .
the slope of each relationship represents a linear selection gradient (  ) , which quantifies the strength of directional selection ( range = 0.0240.036 day ) .
predicted relationships between spawning date and relative fitness (  , standardized to a mean of 1 ) following the construction of the lost creek dam on the rogue river .
each plot corresponds to one of the relationships between emergence date and juvenile survivorship depicted in fig .
to right , plots show earlier to later optimal dates of emergence . from top to bottom , plots show decreasing strengths of selection for emergence on the optimal date .
first , we can standardize the selection gradients by the standard deviation of spawning date ; a standardized selection gradient (  ) provides a useful metric to compare the strength of selection among traits and populations ( lande and arnold 1983 ; arnold and wade 1984 ) . in our analysis ,
standardized selection gradients ranged from 0.29 to 0.52 , indicating strong selection compared to values reported for other natural populations ( kingsolver et al .
second , with knowledge of the phenotypic variance and the narrow - sense heritability of spawning time , we can predict the evolutionary response to selection ( i.e. , the change in the mean phenotypic value per generation ) . using a conservative estimate of the heritability of spawning date ( 0.2 ; hard 2004 ) , we predict an evolutionary change in the mean spawning time of 0.71.1 days per generation , depending on the relationship between emergence date and juvenile survivorship ( see fig .
this calculation suggests the mean spawning date would approach the new optimum within 20 generations ( 85 years ) .
5 ) , evolution of the spawning date might have restored some of this loss during the last three decades and may continue to do so in the future .
although dams have been altering the environments of salmon for decades , we still have much to learn about their ecological and evolutionary impacts .
we explored some potential impacts by quantifying the thermal effects of dams and their consequences for the performance of chinook salmon .
this exercise indicated that dams impose substantial loss of fitness and strong selection of phenotypes .
2005 ; neira et al . 2006 ) , the evolutionary responses to this selection should be rapid .
furthermore , this framework for analyzing physiological , ecological , and evolutionary responses applies to other kinds of environmental change experienced by salmon .
although our analysis offers insights on the potential for adaptation , we do not know whether chinook salmon have actually adapted to the thermal changes caused by dams . our calculations involved many assumptions to simplify the population dynamics of chinook salmon .
for example , we assume juvenile survivorship depends only on emergence date ( i.e. , no density - dependence ) , spawning behavior does not influence other traits , and genetic variance remains constant during adaptation .
for example , we might also consider processes that would ameliorate a negative effect of early emergence on juvenile survivorship .
a phenological advance of the entire aquatic community could prevent a mismatch between emerging fry and their prey .
furthermore , poor survivorship of embryos could lead to better survivorship of fry , through known mechanisms of density dependence ( einum et al .
furthermore , we could consider the interaction of spawning date and developmental rate during adaptation .
our focus on spawning behavior simply reflects the fact that heritabilities of phenological traits tend to exceed those of physiological traits ( reviewed by carlson and seamons 2008 ) .
nonetheless , a change in the thermal sensitivity of developmental rate could also help salmon populations recover the loss of fitness caused by dams .
indeed , physiological adaptation might proceed more rapidly than behavioral adaptation if genetic variance in spawning date erodes over time or if adults incur substantial costs of delayed spawning . for all of these reasons
, one should interpret our analysis as a means of generating testable hypotheses about the impacts of dams and as a guide for identifying crucial data to confirm these impacts . based on our analysis , we should improve predictions about the impacts of dams by resolving certain empirical relationships .
specifically , three factors contributed to the range of selective pressures reported in fig . 6 : ( i ) the thermal effects of dams ; ( ii ) the historical date of spawning ; and ( iii ) the relationship between emergence and survivorship .
to precisely define selective pressures on salmon , all three factors must be quantified empirically for the population of interest . moreover ,
a general understanding of these selective pressures requires that these analyses be carried out for multiple dams .
furthermore , additional data on the genetic variances of spawning date and developmental rate would provide better estimates of the response to selection .
finally , we can use experimental approaches to establish whether populations have already responded to the selective pressure predicted by our analysis .
common garden experiments , using eggs from populations affected by dams and populations unaffected by dams , could demonstrate whether developmental rate has adapted to the changes in thermal regimes
. studies of spawning date in a common garden would also be informative yet more challenging , because of the need to rear large numbers of salmon to adulthood .
still , only field studies can reveal how the thermal impacts of dams interact with predation risk , food availability , and hydrological conditions .
reservoirs upstream of dams impact the timing of migration by juveniles , and reduced flows downstream of dams prolong the migration of both juveniles and adults ( waples et al .
, dams prevent adults from reaching their historical spawning grounds , reducing the extent and variability of habitats that might favor one life - history type over another ( sheer and steel 2006 ) .
changes in physical habitats both upstream and downstream of dams also create new opportunities for predators and competitors .
dams are often mitigated by hatcheries , which present another source of competitors and an additional source of selection ( ford et al .
construction of large dams has also facilitated massive withdrawals of water for agricultural and municipal purposes .
runoff from these areas increases the concentrations of nutrients , contaminants , and salts in downstream reaches ( nrc 1996 ) .
these changes in water quality adversely affect the development , growth , health , and survival of salmon ( regetz 2003 ; loge et al .
given this combination of stressors , our analysis likely represents an overly optimistic outlook on the ability of salmon to adapt to the environmental changes imposed by dams .
 dams designed for hydropower and other purposes alter the environments of many economically important fishes , including chinook salmon ( oncorhynchus tshawytscha ) . 
 we estimated that dams on the rogue river , the willamette river , the cowlitz river , and fall creek decreased water temperatures during summer and increased water temperatures during fall and winter . 
 these thermal changes undoubtedly impact the behavior , physiology , and life histories of chinook salmon . 
 for example , relatively high temperatures during the fall and winter should speed growth and development , leading to early emergence of fry . 
 evolutionary theory provides tools to predict selective pressures and genetic responses caused by this environmental warming . here 
 , we illustrate this point by conducting a sensitivity analysis of the fitness consequences of thermal changes caused by dams , mediated by the thermal sensitivity of embryonic development . based on our model , 
 we predict chinook salmon likely suffered a decrease in mean fitness after the construction of a dam in the rogue river . 
 nevertheless , these demographic impacts might have resulted in strong selection for compensatory strategies , such as delayed spawning by adults or slowed development by embryos . because the thermal effects of dams vary throughout the year , we predict dams impacted late spawners more than early spawners . similar analyses could shed light on the evolutionary consequences of other environmental perturbations and their interactions . 
Introduction
Impacts of dams on water temperature
Consequences of dams for the performance of salmon
Modeling selective pressures imposed by dams
Conclusions and future directions
Supporting information