Effects of Hydropeaking on Nearshore Habitat Use and Growth

of Age-0 Rainbow Trout in a Large Regulated River

JOSH KORMAN*

Ecometric Research, Inc., and Department of Zoology, University of British Columbia,

3560 West 22nd Avenue, Vancouver, British Columbia V6S 1J3, Canada

STEVEN E. CAMPANA

Population Ecology Division, Department of Fisheries and Oceans, Bedford Institute of Oceanography,

Post Office Box 1006, Dartmouth, Nova Scotia B2Y 4A2, Canada

Abstract.—We evaluated the effects of hourly variation in flow caused by power load following at Glen

Canyon Dam (‘‘hydropeaking’’) on the nearshore habitat use and growth of age-0 rainbow trout

Oncorhynchus mykiss downstream from the dam in the Colorado River, Arizona. Reduction in the extent

of hydropeaking is a common element of restoration efforts in regulated rivers, but empirical support for such

a practice is limited. Our assessment was based on a comparison of abundance in shoreline areas determined

by electrofishing at different flows as well as analysis of otolith microstructure. The catch rates of age-0

rainbow trout in nearshore areas were at least two- to fourfold higher at the daily minimum flow than at the

daily maximum, indicating that most age-0 rainbow trout do not maintain their position within immediate

shoreline areas when flows are high. A striping pattern, identified by the presence of atypical daily

increments formed every 7 d, was evident in over 50% of the 259 otoliths examined in 2003 but in only 6%

of the 334 examined in 2004. The weekly pattern corresponded to a reduction in the extent of hourly flow

fluctuations on Sundays during the growing season, which occurred in 2003 but not in 2004. The atypical

increments were 25% wider than the adjacent increments and were indicative of significant (F15, 2351⁄4 19.2, P

, 0.0001) short-term increases in otolith growth. The somatic growth rate among fish with otoliths where

striping was present (11.2 mm/month) was slightly greater than that among fish with otoliths without striping

(10.8 mm/month), but the difference was not significant. We provide evidence suggesting that otolith growth

improved on Sundays in 2003 because it was the only day of the week when most age-0 fish were found in

immediate shoreline areas, where higher water temperatures and lower velocities provided better growing

conditions.

Hydroelectric dams alter the magnitude and frequency of flows and can have negative effects on

downstream fish communities (Cushman 1985). In

some regulated rivers, power load following operations, often referred to as hydropeaking, result in

considerable hourly and diel variation in flow, depth,

and water velocity, creating a very unnatural and

potentially harsh physical environment. This effect is

of special concern for juvenile fish, which in large

rivers rear almost exclusively in shallow shoreline

habitats (Gaudin 2001) that are destabilized by hydropeaking operations (Freeman et al. 2001). Diel

variation in flow can result in stranding and mortality

of juvenile fish (Bradford 1997; Saltveit et al. 2001;

Halleraker et al. 2003), and is hypothesized to limit

survival rates via indirect effects from displacement out

of preferred habitats, food depletion, and increases in

stress, energetic costs, and predation risk (Scruton et al.

2003).

Most studies of the indirect effects of short-term

flow fluctuations have focused on the behavioral and

energetic responses of juvenile salmonids. Short-term

variation in flow results in both negligible (Robertson

et al. 2004) and extensive (Berland et al. 2004)

movement in Atlantic salmon Salmo salar parr.

Flow-dependent movement responses can be highly

variable among individuals, with some parr showing

strong site fidelity across a wide range of flows (Kemp

et al. 2003; Scruton et al. 2003). Juvenile salmonids

have been shown to use higher nose velocities with

increasing water flow and not to fully compensate for

increased velocity by changing microposition (Vehanen et al. 2000; Girard et al. 2004). Shirvell (1994)

reported that juvenile salmonids initially responded to

increased flow by moving closer to the streambed and

then, if necessary, moving laterally to seek out

appropriate velocity conditions. Surprisingly, there is

little evidence that such responses increase stress levels

or have negative bioenergetic consequences. Cardiac

activity in adult brook trout Salvelinus fontinalis and

* Corresponding author: jkorman@ecometric.com

Received February 7, 2008; accepted September 25, 2008

Published online February 5, 2009

76

Transactions of the American Fisheries Society 138:76–87, 2009

Copyright by the American Fisheries Society 2009

DOI: 10.1577/T08-026.1

[Article]
walleye Sander vitreus (Murchie and Smokorowksi

2004), and oxygen consumption in juvenile white

sturgeon Acipenser transmontanus (Geist et al. 2005),

did not increase under fluctuating flows. Stress

responses in juvenile brown trout Salmo trutta caused

by moderate flow fluctuations in experimental flumes

subsided within a few days after the fish became

habituated to the flow regime (Flodmark et al. 2002).

Hourly variation in flow in experimental channels had

no negative effects on the growth or survival of

rainbow trout Oncorhynchus mykiss fry (Irvine 1987).

Overall, experimental studies document relatively

little effect of hourly variation in river flows on

juvenile fish, yet stabilization of regulated flows can

increase the abundance of downstream fish populations

(Travnichek et al. 1995; McKinney et al. 2001; Connor

and Pflug 2004). However, in all these cases flow was

stabilized by increasing the minimum flow and by

reducing the extent of within-day variation, so the

effects of the two changes are confounded. Further, the

stabilized flow regimes may have benefited the

spawning and incubating life stages as well as the

survival of juvenile fish. Reduction in the extent of

hourly variation in flow is commonly recommended as

a way to improve the status of fish populations

(Dejalon and Sanchez 1994; Poff et al. 1997), but

empirical support for such recommendations is limited.

This discrepancy leads to considerable debate, especially in large-river settings, where lost hydropower

revenues resulting from flow stabilization can be

substantial.

We evaluate the effects of hourly variation in flow

caused by hydropeaking at Glen Canyon Dam on the

nearshore habitat use and growth of age-0 rainbow

trout in the Lee’s Ferry reach of the Colorado River,

Arizona. Maximum flows over a 24-h (diel) cycle at

Glen Canyon Dam occur during the day and early

evening when power demand is high, and are reduced

at night when demand subsides (Figure 1a). We

propose two alternate hypotheses of how age-0 fish

will respond to this diel regime (Figure 2). The

shoreline-tracking hypothesis assumes that trout move

freely in response to hourly changes in flow so that

they remain in shallow and low-velocity habitat

typically used by age-0 fish (Chapman and Bjornn

1969; Everest and Chapman 1972). If this hypothesis

holds in the Lee’s Ferry reach, age-0 trout will need to

make two lateral movements per day with the rise and

fall in flow to remain within immediate nearshore areas

where these depth and velocity conditions occur. The

restricted-movement hypothesis assumes that age-0

trout restrict the extent to which they move in response

to hourly flow variation. If this hypothesis holds in the

Lee’s Ferry reach, only a limited proportion of trout

will be found within immediate nearshore areas at the

daily maximum flow. The remainder will be found

further from the bank in deeper and faster water, with

some perhaps remaining in the permanently submerged

zone. During the day in the Lee’s Ferry reach in

summer months, when daily maximum air temperatures on the surface of exposed gravel and sand bars

can reach 50–608C, a horizontal gradient in water

temperature is created where temperatures in the

immediate shoreline areas within 1–2 m from the

waters edge are 3–58C warmer than in the main flow

further offshore (Figure 2; Korman et al. 2006). Under

the shoreline-tracking hypothesis, age-0 trout will

experience warmer water temperatures during the day

because they maintain their position in immediate

nearshore areas. In contrast, trout that behave according to the restricted-movement hypothesis would

mostly be located further offshore during the day and

therefore in colder water.

The shoreline-tracking hypothesis assumes that

habitat use by age-0 fish in regulated rivers follows

the pattern observed in natural systems (e.g., Chapman

and Bjornn 1969; Everest and Chapman 1972; Bustard

and Narver 1975; Sheppard and Johnson 1985), where

fish limit themselves to immediate shoreline areas in

which the water depths and velocities are optimal for

feeding (Nislow et al. 2000, 2004), resting, and

avoiding piscivorous predators (Schlosser 1987; Walters and Juanes 1993; Rosenfeld and Boss 2001). The

restricted-movement hypothesis assumes that the

benefits of tracking optimal depths and velocities on

an hourly basis by remaining in immediate nearshore

areas do not outweigh the costs associated with

moving, such as increased exposure to predation while

moving (Biro et al. 2003), or the competitive

disadvantage of abandoning territories (Elliott 1986;

Ward et al. 2007). The density of benthic invertebrates

in the Lee’s Ferry reach in shoreline areas exposed to

air owing to hourly variation in flow, sometimes

referred to as varial zones, are considerably lower than

in permanently submerged zones (Blinn et al. 1995;

Benenati et al. 1998). Thus, reduced food density

within the varial zone could also limit the use of these

areas at the daily maximum flow and, therefore,

contribute to restricted movement.

We evaluate the shoreline-tracking and restrictedmovement hypotheses by comparing the catch rates of

age-0 rainbow trout in immediate shoreline areas

sampled at daily minimum and maximum flows. If

the shoreline-tracking hypothesis is correct, catch rates

should be similar under both flows (Figure 2). If the

restricted-movement hypothesis is correct, catch rates

should be much lower at the daily maximum flow

because fish will be more dispersed, with the majority

EFFECTS OF HYDROPEAKING ON RAINBOW TROUT 77
residing outside of the immediate shorelines areas that

are sampled. Both shoreline-tracking and restrictedmovement hypotheses predict potential negative bioenergetic consequences associated with hourly fluctuations in flow, and that growth should improve under a

more stable regime. Under the shoreline-tracking

hypothesis, food availability in the varial zone

occupied by fish during the day would be lower and

there would be higher energetic costs associated with

the additional movement required to remain within

immediate nearshore areas as flows fluctuate. Under

the restricted-movement hypothesis, fish would occupy

areas further offshore during the day, where water

velocity is higher and water temperature is colder. This

would lead to higher energetic costs and reduced

feeding efficiency associated with holding position at

higher velocities as well as reduced growth owing to

lower water temperature. In one year of our study

FIGURE 1.—(a) Hydrograph of releases from Glen Canyon Dam during a typical 5-d period in 2003 (July 10–14; solid line)

and 2004 (July 8–12; dashed line) and (b) the average daily minimum and maximum flows during the week (lines) and on

Sundays (bars) from January through September. The monthly averages of the daily minimum and maximum flows are based on

15-min automated flow measurements taken at Glen Canyon Dam.

78 KORMAN AND CAMPANA
(2003), flow remained low and steady over a 24-h

period on Sundays during summer months (Figure 1).

We evaluate whether growth improved under this

situation by comparing otolith microstructure from

samples taken in 2003, with microstructure from 2004,

when flows on Sunday underwent normal hourly

fluctuations. Results from the habitat use and growth

analysis are interpreted in relation to existing information and current hypotheses on the effects of hourly

variation in flow on the behavior and growth of

juvenile fish.

Methods

Study area.—The Lee’s Ferry reach of the Colorado

River, Arizona, begins at Glen Canyon Dam below

Lake Powell and extends 26 km downstream to the

confluence with the Paria River. The average flow in

2003 and 2004, when the study was conducted, was

328 m3

/s (U.S. Geological Survey gauge 09380000).

The reach is wide and shallow, with an average wetted

width and depth at this flow of 144 m and 5.2 m,

respectively (Randle and Pemberton 1987). Main-stem

water temperatures recorded at the downstream end of

the reach since 2003 have ranged from 98C to 158C but

typically range from 98C to 118C (Voichick and Wright

2007). The fish fauna in the Lee’s Ferry reach is almost

exclusively comprised of nonnative rainbow trout that

reproduce naturally (McKinney et al. 2001). Flow from

Glen Canyon Dam normally fluctuates on a diel cycle

driven by power demand, but controlled through

regulations on the maximum daily flow range,

minimum and maximum flows, and maximum downramp and upramp rates. There is little variation in flow

during low- and high-flow periods within a day (e.g.,

Figure 1a), and because upramp and downramp periods

are relatively short (total of 6 h per day) high flows

occur over the majority (13 h) of the day. Hourly

variation in flow during weekdays was very similar in

2003 and 2004, but variation in flow on Sundays

during the summer was much lower in 2003 (Figure 1).

Effects of hourly flow variation on nearshore habitat

use.—The catch rates of age-0 rainbow trout at 24

shoreline locations sampled by electrofishing at both

daily minimum and maximum flows between June 30

and July 6, 2004, were compared to evaluate the effects

of diel variation in flow on nearshore habitat use.

Shoreline habitat was stratified into low- (cobble and

vegetated sand bars and debris fans) and high-angle

(talus slopes) types that could be sampled by backpack

and boat electrofishing, respectively. Twelve units

were randomly selected for sampling from both lowand high-angle habitat strata, and in the field, divided

into four, noncontiguous, 30-m and 50-m sections,

respectively. Each section was then electrofished under

one of the four following light and flow conditions: (1)

light (daytime) with daily maximum flow; (2) light

with daily minimum flow; (3) dark (nighttime) with

daily maximum flow; and (4) dark with daily minimum

flow. Thus, 12 replicate samples for each habitat type

(low or high angle) were obtained for each of the four

FIGURE 2.—Cross section of low-angle shoreline habitat at the maximum daily discharge showing the daytime horizontal water

temperature gradient and the daily variation in water surface elevation during summer months. The shaded circles represent the

hypothesized distribution of age-0 rainbow trout during the daily maximum flow on weekdays under the shoreline-tracking

hypothesis. The open circles represent the distribution under the restricted-movement hypothesis, where only a small proportion

of individuals remain in the immediate nearshore area close to the water’s edge. The width of shoreline habitat sampled by

electrofishing, which is referred to in the text as the immediate nearshore area, is also shown. The same sample width is applied

when sampling at the minimum flow, and the same water temperature gradient occurs at the minimum flow elevation when flows

remain low during the day (Sundays in 2003; see Figure 1).

EFFECTS OF HYDROPEAKING ON RAINBOW TROUT 79
alternate light level–daily flow combinations. The

electrofishing sites were 3–4 m wide, not enclosed by

block-nets, and fished methodically in an upstream

(backpack electrofishing) or downstream (boat electrofishing) direction. After collection, fish were anesthetized and fork lengths (FL) were measured to the

nearest millimeter (see Korman et al. 2009 [this issue],

for additional details on sampling). We assume that the

catch in nearshore zones (C) always represents a

constant proportion of the total abundance in these

areas (N) because capture probability (p) does not vary

with abundance or flow (i.e., C 1⁄4 pN). This assumption

is supported by a large number of depletion (n 1⁄4 66)

and mark–recapture (n 1⁄4 42) experiments conducted in

the Lee’s Ferry reach in 2006 and 2007, respectively

(Korman et al. 2009).

The statistical significance of the differences in logtransformed catch rates measured at daily minimum

and maximum flows and during the day and night was

determined via two-way nested analysis of variance

(ANOVA), where the effects of flow and light were

nested within sites. Log-transformed catch data met

ANOVA assumptions of normality based on the

Kolmogorov–Smirnov test (P-values 1⁄4 0.397 and

0.950 for the low- and high-angle habitats, respectively), and homoscedastic variance among treatments

based on Bartlett’s test (P-values 1⁄40.617 and 0.315 for

the low- and high-angle habitats, respectively).

The physical characteristics at diel sampling sites

were measured to evaluate the change in habitat

conditions in nearshore areas at daily minimum and

maximum flows. Depth and average water column

velocity were measured at 10 equally spaced locations

1.5 m from shore along an axis parallel to the direction

of flow at each site using a Swoffer current meter with

topset wading rod (Model 2100). Measurements were

taken at both the daily minimum and maximum flows.

Measurements at the daily maximum flow were also

taken further offshore at the edge of the permanently

submerged zone (Figure 2). The cross-sectional slope

at each site was measured using a laser level and survey

rod to estimate the vertical and horizontal distances

between elevations inundated by the daily minimum

and maximum flows. Statistical differences in depth

and velocity at daily maximum and minimum flows

within sites were evaluated using paired t-tests.

Effects of hourly flow variation on growth.—

Otoliths were extracted from a subsample of fish

captured by electrofishing during multiple surveys

conducted in 2003 and 2004 as part of a longer-term

study of the early life history dynamics of rainbow

trout in the Lee’s Ferry reach (Korman 2009).

Following the random-stratified design described

previously, 20 units were selected from both lowand high-angle habitat strata to sample by backpack

and boat electrofishing, respectively. On each sampling

trip, we returned to these same 40 units, but randomly

selected different 30-m and 50-m sections to sample in

low- and high-angle habitat, respectively. In 2003, six

sampling trips were conducted between April and

October, and only low-angle shorelines were sampled

(934 age-0 trout fish captured). In 2004, both shoreline

types were sampled and eight trips were conducted

between April and December (4,459 age-0 trout

captured). On each trip, 2.5% of low-angle shorelines

(600 m) were sampled in 2003 and 2004, and 4.5% of

high-angle shorelines (1,000 m) were sampled in 2004.

Electrofishing protocols were identical to those described for the nearshore habitat study component with

two exceptions: (1) sampling was only conducted

during darkness between midnight and dawn at the

daily minimum flow and (2) on each trip, a subsample

of five fish from each habitat type within 10-mm length

categories between 20 and 100 mm were sacrificed and

preserved in 95% ethanol for later analysis of otolith

microstructure.

Both sagittal otoliths were removed from a sample of

preserved fish to determine the daily age from hatch

and otolith growth using methods described in Stevenson and Campana (1992) and Campana (1992). A total

of 259 and 334 otoliths were successfully extracted and

aged from fish collected in 2003 and 2004, respectively. A striped pattern in daily increments was observed

on the otoliths of many individuals (see Results). This

visual pattern was identified by atypical increments

(different appearance, usually light in color under

transmitted light) formed at regular intervals. The

number of otoliths for which the striping pattern was

present, absent, or ambiguous was recorded. To

determine whether the striping pattern was associated

with periodic growth, a random sample of otoliths (n 1⁄4

15) with a clear striping pattern was examined and the

width of individual daily increments was measured.

The significance of differences in the width of atypical

and typical increments was determined using a twolevel nested ANOVA, where the effect of increment

type on width was nested within fish.

To determine whether the presence of otolith striping

was related to somatic growth, we compared length-atage relationships based on data from fish with and

without otolith striping. The constant, slope, and

standard deviation (SD) of linear length-at-age models

were estimated assuming observation error was lognormally distributed from the equation,

Lx;i 1⁄4 ðax þ bxAx;iÞemx;i

where L is FL (mm), A is age (days from hatch), a and

b are the constant (size at hatch) and slope (growth rate

80 KORMAN AND CAMPANA
in mm/d), respectively, m is a random deviate from a

normal distribution with a mean of 0 and a SD of rx

, x

is a classification term that denotes whether the data

and parameters are based on fish where striping was

present (x1⁄4p) or absent (x1⁄4a), and i denotes the index

number for individual fish. Although length at age is

commonly described using nonlinear models because

growth rates typically decline in older fish, there was

no indication of this pattern in our data (see Results).

The parameters for the length-at-age models were

estimated by maximizing the lognormal likelihood

using a nonlinear iterative search procedure. Four

models were evaluated: (1) pooled (ap1⁄4aa

, bp1⁄4ba, rp

1⁄4 ra); (2) individual (ap, aa, bp, ba, rp, ra); (3)

individual slopes (ap 1⁄4 aa

, rp 1⁄4 ra, bp, ba); and (4)

expected striping slope (ap 1⁄4 aa

, rp 1⁄4 ra, ba, bp 1⁄4 baz,

where z 1⁄4 og 3 f). The last model assumes that the

proportional increase in the somatic growth rate of fish

with otoliths where striping was present (z) can be

computed based on the measured average proportional

increase in the width of atypical increments (og), and

the expected frequency at which such increments form

(f). We evaluate the hypothesis that short-term

increases in growth rate occurred only on Sundays

when flows were low and steady by setting f1⁄41/7. The

models were compared by means of the Akaike

information criterion (AIC), where the model with

the smallest AIC value is considered to have the best

out-of-sample predictive power if the difference in AIC

values is 2 or more (Burnham and Anderson 2002).

The models were also compared by means of

likelihood ratio tests to determine the probability that

the improved fit of more complex models that

accounted for the effects of otolith striping (models

2–4), relative to the simplest model that did not (model

1), could be due to chance alone.

Results

Effects of Hourly Flow Variation on Nearshore

Habitat Use

During summer months, the difference between the

daily minimum and maximum flow rates of approximately 225 m3

/s (Figure 1) resulted in an average

horizontal shift in the water’s edge of 6.5 m and 2.2 m

in low- and high-angle shorelines, respectively. At a

typical cross section, the increase in stage from the

daily minimum to maximum flow was 0.75 m. In lowangle habitat at the daily maximum flow, average water

velocity was over fivefold higher at the edge of the

permanently submerged zone (6.5 m from shore)

compared with 1.5 m from shore (Table 1). In highangle habitat, the difference in the distance 1.5 m from

shore and the edge of the permanently submerged zone

(2.2 m from shore) was small owing to the higher

gradient of the shoreline. As a result, average velocities

at these two locations were similar (Table 1). Although

daily variation in flow during the summer was

substantial (Figure 1b), there were relatively minor

absolute changes in depth and velocity within the

immediate shoreline habitats sampled (Table 1).

Average depth 1.5 m from shore was 10–15 cm greater

at the daily maximum flow compared with that at the

daily minimum, but the difference was only statistically

significant (P , 0.05) in high-angle habitat (paired ttest: P 1⁄4 0.028). Average velocity was marginally

higher at the daily minimum flow compared with the

daily maximum in low-angle habitats and marginally

lower in high-angle habitats, but neither difference was

significantly different (paired t-test: P 1⁄4 0.100 and

0.107, respectively).

There was a very strong effect of flow on the catch

rates of rainbow trout. In low-angle habitats, catches at

the daily minimum flow were higher than at the daily

maximum at 11 of 12 sites sampled at night, and 9 of

TABLE 1.—Average depth (cm) and velocity (cm/s at 0.6.total depth) at 12 sites in both low- and high-angle shoreline habitats

based on 10 measurements per site taken 1.5 m from shore (immediate nearshore zone) sampled at the daily minimum (Min.) and

maximum (Max.) flows, June 30–July 6, 2004. Also shown are the average gradients between the elevations inundated by the

daily minimum and maximum flows and the average depths and velocities taken at the edge of the permanently submerged zone

at the daily maximum flow, which occurred 6.5 and 2.2 m from shore in low- and high-angle habitats, respectively (see Figure 2).

Standard errors are given in parentheses and show the variation in mean conditions across sample sites.

Variable

Low-angle habitat High-angle habitat

Min. flow Max. flow Min. flow Max. flow

Gradient (%) 12 37

Immediate nearshore zone

Depth 29 (7) 39 (13) 60 (19) 75 (16)

Velocity 7 (6) 3 (6) 6 (7) 12 (13)

Edge of permanently submerged zone

Depth 63 (8) 85 (14)

Velocity 17 (16) 12 (17)

EFFECTS OF HYDROPEAKING ON RAINBOW TROUT 81
11 sites sampled during the day (Table 2). In highangle shorelines, catches at the daily minimum flow

were higher than at the daily maximum in 9 of 10 and

10 of 11 sites sampled during night and day,

respectively. On average, catch rates in low-angle

habitats at the daily minimum flow were 4.5- and 5.1-

fold higher than those at the daily maximum flow

during day and night, respectively (Table 2). At highangle sites, catch rates at minimum flows were 3.5- and

2.2-fold higher than at maximum flows during day and

night, respectively. The increase in catch rates at the

daily minimum flow compared with those at the

maximum flow within sites was statistically significant

in both low-angle (F12, 12 1⁄4 4.09, P 1⁄4 0.011) and highangle (F11, 12 1⁄4 6.41, P 1⁄4 0.003) habitats (Figure 3).

Effects of Hourly Flow Variation on Otolith and

Somatic Growth

A striping pattern was evident in many of the

otoliths sampled from rainbow trout in 2003 but in only

a fraction of those sampled in 2004. The visual pattern

was identified by the presence of atypical daily

increments formed at a frequency of exactly 7 d

(Figure 4). The weekly pattern was evident in at least

51% (131) of the 259 otoliths examined in 2003, but in

only 6% (20) of the 334 otoliths examined in 2004

(Table 3). In general, striping was most evident in the

middle and outer sections of the otolith, and in larger

individuals. The dates on which atypical increments

were formed were determined for 12 randomly selected

otoliths where striping was present and where the edge

of the otolith was clearly defined. In all cases, atypical

increments were formed on Sundays, the only day of

the week in 2003 when flows were low and steady

during the day (Figure 1a). The frequency of otolith

striping varied over time (Table 3). In 2003, striping

was common in samples from all months except April,

while in 2004, striping was only common in April and

May and declined steadily in later months. The atypical

increments tended to be 25% wider (3.12 lm) than the

other increments (2.51 lm) when averaged across all

striping cycles from 15 fish. Within fish, the average

increment width of the atypical bands was larger than

the average width of the other increments in between

the atypical bands in 14 of 15 cases. The atypical

increment widths were significantly wider than the

TABLE 2.—Catch (n) of age-0 rainbow trout in low- and high-angle shoreline habitats sampled at night and day at the daily

minimum (Min.) and maximum (Max.) flows. The site lengths in the low- and high-angle habitats were 30 and 50 m,

respectively. Cells with missing values denote cases in which no sampling was conducted.

Site

Low-angle habitat High-angle habitat

Night Day Night Day

Min. Max. Min. Max. Min. Max. Min. Max.

1 123 23 70 25 9 11 9 2

2 55 27 12 2 64 45 56 25

3 25 0 30 1 74 33 22 4

4 21 1 5 22 31 9

5 20 2 19 2 44 14 15 13

6 5 3 6 0 47 6 65 9

7 15 0 17 0 32 14 22 8

8 7 0 4 0 15 2

9 2 0 0 1 4 0 48 5

10 29 1 11 2 18 7 10 5

11 15 3 11 2 31 10 18 19

12 3 3 5 5 9 7 49 7

Average 27 5 17 4 33 15 31 9

Average/100 m 89 18 56 13 66 31 63 18

Min./Max. 5.1 4.5 2.2 3.5

FIGURE 3.—Average differences in the catch rates of age-0

rainbow trout based on sampling at the daily minimum and

maximum flow between June 30 and July 6, 2004, by time of

day and habitat. The error bars denote the 95% confidence

intervals of the mean differences. The raw data are presented

in Table 2.

82 KORMAN AND CAMPANA
typical ones based on the nested ANOVA (F15, 235 1⁄4

19.2, P , 0.0001).

Length-at-age comparisons indicated that somatic

growth was similar in fish with and without otolith

striping. There was a strong linear relationship between

age from hatch and FL (Figure 5), with age from hatch

explaining 85% (n 1⁄4123), 92% (n 1⁄476), and 88% (n 1⁄4

199) of the variation in fork lengths measured in 2003

based on otoliths where striping was present, absent,

and when both datasets were combined, respectively.

The growth rates estimated from separate regressions

for fish without striping and with striping were 0.360

(ba

) and 0.375 (bp) mm/d, respectively (Table 4). The

expected growth rate for fish with striping present,

computed based on the average increase in otolith

increment widths formed on Sundays (1.25-fold wider)

and the expected flow-dependent frequency of such

improved growth events (1 out of 7 d per week), was

0.374 mm/d (bp 1⁄4 ba 3 z, where z 1⁄4 1.25 3 (1/7)). The

expected growth rate was almost identical to the

FIGURE 4.—Cross section of an otolith from a 43-mm age-0 rainbow trout that was 81 d old (from hatch) when sampled on

July 30, 2003. The images show the weekly striping pattern, identified by white arrows, at magnifications of (a) 163 and (b)

4003. The stripes are indicative of increased otolith growth on Sundays in 2003, when flow was low and steady relative to

normal weekday operations (see Figure 1).

TABLE 3.—The number of rainbow trout otoliths sampled

and the percentage with a weekly striping pattern, by sampling

month in 2003 and 2004. The last two rows show the total

number of otoliths sampled across all months and the average

percentage that were striped.

Month

2003 2004

Number

sampled

%

Striped

Number

sampled

%

Striped

Apr 15 13 23 17

May 37 59 58 22

Jun 58 48 66 3

Jul 56 63 68 1

Aug 41 0

Sep 60 43 37 0

Oct 33 55

Nov 20 0

Dec 21 0

Total Average 259 51 334 6

FIGURE 5.—Relationships between age from hatch and fork

length in 2003 based on otoliths with (open circles; dashed

line) and without (shaded circles; solid line) a weekly striping

pattern. The relationships derive from the individual slope

model (Table 4), in which the growth rate estimates differ for

fish with and without striping but the intercept and variance

estimates are common to both data sets.

EFFECTS OF HYDROPEAKING ON RAINBOW TROUT 83
estimates from the individual (bp 1⁄4 0.375) and

individual slopes models (bp 1⁄4 0.373) for fish with

otolith striping present.

The AIC and likelihood-ratio tests both indicated

that the differences in length at age between age-0

rainbow trout with and without otolith striping were not

statistically discernable. The expected striping slope

model had the best out-of-sample predictive power

(lowest AIC) of all the length-at-age models considered. However, there was substantial support (Burnham

and Anderson 2002) for the pooled and individual

slopes models as well, indicating that there was

insufficient power to determine whether the growth

rates for fish with and without otolith striping were

different. Likelihood ratio tests indicated that there was

a high probability that the improved fit of the

individual (P 1⁄4 0.409), individual slope (P 1⁄4 0.175),

and expected striping slope (P 1⁄40.177) models relative

to that of the pooled model could be due to chance

alone.

Discussion

Our catch data provide support for the restrictedmovement hypothesis of juvenile salmonids response

to hourly variation in flow. The catch rates of age-0

rainbow trout at the daily minimum flow were at least

four- and twofold higher than those at the daily

maximum flow in low- and high-angle habitats,

respectively. The differences in catch rates reflect true

differences in density in nearshore areas, and hence,

habitat use, since capture probabilities at the daily

minimum and maximum flows were similar (Korman

et al. 2009). Limited use of immediate shoreline areas

by age-0 rainbow trout may seem unusual; however,

short-term variation in flow is an unnatural characteristic, and observations made under more stable

conditions (e.g., Chapman and Bjornn 1969; Everest

and Chapman 1972) are probably not applicable under

all conditions. Our results are consistent with findings

from experimental manipulations, which show that

juvenile fish are reluctant to shift their lateral position

in response to sudden increases in flow (Shirvell 1994;

Vehenan et al. 2000; Kemp et al. 2003, Vilizzi and

Copp 2005).

The incidence of weekly striping pattern in otoliths

varied with the frequency of steady flows from Glen

Canyon Dam. In 2003, when striping was common in

samples collected after April, flow was much more

stable on Sundays during the summer (daily flow

range, 24–73 m3

/s; Figure 1) than on weekdays and

Saturdays (167–262 m3

/s). In 2004, when there was

little evidence for weekly striping, the hourly variation

in flow on Sunday during the summer was similar to

that on other days of the week and that of weekday

operations in 2003. Of the 20 fish with a striping

pattern in 2004, 85% were caught in April and May,

while in 2003 the incidence of otolith striping was

evenly distributed among monthly samples collected

after April. Fish with striping in April and May 2004

were, on average, 95 and 128 d old from hatch,

respectively. These fish, therefore, came from the

cohort that hatched in January, which would have been

exposed to Sunday flows in February, March, and

April that were relatively stable compared with the

variation seen on other days in these months. Thus, the

conditions that created a weekly otolith striping pattern

in 2004 only affected the cohort that hatched in

January, and the frequency of striping for rainbow trout

captured after May declined progressively as the

abundance of this cohort declined over the growing

season owing to mortality.

There was strong evidence of increased otolith

growth on Sundays in 2003, the only day of the week

when flows were low and stable. Many authors have

speculated that there may be an energetic cost

associated with short-term variation in flows due to

hydropeaking (e.g., Scruton et al. 2003, 2005; Geist et

al. 2005), and the weekly otolith striping pattern that we

TABLE 4.—Comparison of fit and predictive power of four linear models fit to the 2003 length-at-age data for rainbow trout

downstream from Glen Canyon Dam. Parameters ba and bp are the slopes of the regressions (daily growth rates) based on

otoliths without and with striping, respectively. In the case of the expected striping slope model, bp was not estimated but

computed as a function of ba and the expected increase in somatic growth based on the increase in otolith growth seen on

Sundays. The model with the lowest Akaike information criterion (AIC) has the best predictive power; DAIC is the difference

between each model’s AIC value and the lowest AIC values among the models.

Model

Number of

parameters

Growth ratea

b Log likelihood AIC DAIC a bp

Pooled 3 0.370 143.42 280.83 1.82

Individual 6 0.360 0.375 144.86 277.73 4.93

Individual slopes 4 0.361 0.373 144.34 280.67 1.99

Expected striping slope 3 0.360 0.374 144.33 282.66

a Estimated slope (mm/d).

84 KORMAN AND CAMPANA
documented in rainbow trout supports this hypothesis.

In the Introduction, we proposed that patterns of

nearshore habitat use driven by flow fluctuations could

influence growth. The catch data showed that most age0 rainbow trout in the Lee’s Ferry reach do not use the

immediate nearshore areas during the day at the daily

maximum flow and must therefore be holding further

offshore. In this situation, age-0 trout may spend more

time concealed in the substrate to avoid piscivorous

predators and higher velocities. This strategy would

probably minimize the energetic cost of holding

position in faster water, but ration would decline.

Alternatively, if age-0 trout do not increase the amount

of time when they are concealed and do not change their

foraging behavior, energetic costs would probably

increase and may not be offset if feeding efficiency is

reduced because of higher velocities (Nislow et al.

2000, 2004). Either way, there is probably an energetic

cost associated with a restricted-movement response to

fluctuating flows. We speculate that the formation of

atypical increments and the increase in otolith growth

on Sundays in 2003 occurred because this was the only

day of the week when flows were low and stable and

this cost was not incurred.

The formation of atypical increments and higher

otolith growth may also have been driven by the

differences in temperatures to which age-0 rainbow

trout were exposed on Sundays. During the day on

Sundays in 2003, age-0 trout would be holding in

immediate nearshore areas and would, therefore, have

experienced daytime water temperatures that were 3–

58C warmer than in the main stem and near optimal for

growth (Figure 2). Given the support for the restrictedmovement hypothesis, age-0 trout would be located

further offshore in colder water during other days of the

week. This temperature dynamic alone could have

caused the increased otolith growth on Sundays in

2003. Our data are not sufficient to determine the

dominant factor or combination of factors leading to

increased otolith growth on Sundays. However, both

energetic and temperature hypotheses describing the

mechanism behind the increased otolith growth are

consistent with results from Neilsen and Geen (1985),

who showed that mean otolith increment width and

somatic growth of Chinook salmon O. tshawytscha fry

increased with higher ration and warmer temperature,

and was reduced when fry were forced to become more

active.

Differences in somatic growth rates in 2003 for age0 rainbow trout with and without atypical increments

(based on a comparison of length-at-age relationships)

were not statistically discernable in this study. Effects

of hourly flow fluctuation on somatic growth must,

therefore, be inferred from the observed otolith growth

response and results from other studies that describe

the relationship between otolith and somatic growth.

Over time frames of multiple weeks to months, otolith

growth is strongly correlated with somatic growth

(Bradford and Geen 1987). For example, in this study

the length of the longitudinal axis of otoliths from age0 rainbow trout predicted 90% and 83% of the

variation in FL in 2003 (n 1⁄4 235) and 2004 (n 1⁄4

310), respectively. However, Bradford and Geen

(1987), who measured the somatic growth of Chinook

salmon fry based on changes in length and weight,

showed that otolith and somatic growth decouple over

time scales of days to a few weeks. In contrast, Mugiya

and Oka (1991), who used more sensitive methods to

measure both otolith (calcium uptake rates) and

somatic (RNA : DNA ratios) growth in rainbow trout,

showed that growth was coupled at a daily timescale.

We conclude that the wider otolith increments of age-0

trout that were associated with low and steady flows on

Sundays in 2003 was indicative of a short-term

increase in somatic growth, but that this increase was

not detectable because our measure of somatic growth

(length at age) was not sensitive enough.

This study has shown that hourly variation in flow

caused by hydropeaking alters patterns of nearshore

habitat use for age-0 rainbow trout and that reducing

hourly variation in flow can lead to increased otolith

growth. In the Introduction, we proposed that restricted

movement of age-0 trout under fluctuating flows could

be caused by factors that select for strong site

attachment (e.g., increased predation risk while moving

or competitive disadvantages from abandoning feeding

territories) or by reduced food availability in the varial

zone, which limits the energetic profitability of

immediate nearshore areas during the daily maximum

flow. The data from this study are not sufficient to

determine which of these mechanisms or combination

of mechanisms caused the habitat use pattern we

observed, and this would be a useful focus for future

research. The effects of limited use of nearshore

habitats during the day under fluctuating flows on

juvenile somatic growth and survival rate remain to be

determined, and will require field experiments where

contrasting levels of flow stability are maintained for

long intervals (e.g., months). Although the costs of

such an experiment would be high owing to lost power

revenues, there is no substitute for large-scale field

experiments that provide contrasting conditions at the

broad temporal and spatial scales needed to determine

population-level responses.

Acknowledgments

This project was supported through a cooperative

agreement between the U.S. Geological Survey and

EFFECTS OF HYDROPEAKING ON RAINBOW TROUT 85
Ecometric Research, Inc. We thank Mike Yard, Steve

Hall, Jeff Sneep, and Dave Foster for help with

electrofishing. Linda Marks, Tania Davignon-Burton,

and Jenna Denyes provided the necessary expertise in

otolith preparation and interpretation. Thanks to Mike

Bradford, Carl Walters, and Eric Parkinson for helpful

comments on earlier versions of this manuscript and to

Ted Melis, Lew Coggins, Mike Yard, Barbara Ralston,

Carol Fritzinger, and Matthew Andersen of Grand

Canyon Monitoring and Research Center for their

support throughout this project. Two anonymous

reviewers and an associate editor from this journal

provided suggestions that greatly improved the quality

of this manuscript.

References

Benenati, P. L., J. P. Shannon, and D. W. Blinn. 1998.

Desiccation and recolonization of phytobenthos in a

regulated desert river: Colorado River at Lee’s Ferry,

Arizona, USA. Regulated Rivers 14:519–532.

Berland, G., T. Nickelsen, J. Heggenes, F. Okland, E. B.

Thorstad, and J. Halleraker. 2004. Movements of wild

Atlantic salmon parr in relation to peaking flows below a

hydropower station. River Research and Applications

20:957–966.

Blinn, D. W., J. P. Shannon, L. E. Stevens, and J. P. Carder.

1995. Consequences of fluctuating discharge for lotic

communities. Journal of the American Benthological

Society 108:215–228.

Biro, P. A., J. R. Post, and E. A. Parkinson. 2003. Population

consequences of a predation-induced habitat shift by

trout in whole-lake experiments. Ecology 84:691–700.

Bradford, M. J. 1997. An experimental study of stranding of

juvenile salmonids on gravel bars and in side channels

during rapid flow decreases. Regulated Rivers 13:395–

401.

Bradford, M. J., and G. H. Geen. 1987. Size and growth of

juvenile Chinook salmon back-calculated from otolith

growth increments. Pages 453–461 in R. C. Summerfelt

and G. E. Hall, editors. The age and growth of fish. Iowa

State University Press, Ames.

Burnham, K. P., and D. R. Anderson. 2002. Model selection

and multimodel inference, 2nd edition. Springer-Verlag,

New York.

Bustard, D. R., and D. W. Narver. 1975. Aspects of the winter

ecology of juvenile coho salmon (Oncorhynchus kisutch)

and steelhead trout (Salmo gairdneri). Journal of the

Fisheries Research Board of Canada 32:667–680.

Campana, S.E. 1992. Measurement and interpretation of the

microstructure of fish otoliths. Canadian Special Publication of Fisheries and Aquatic Sciences 117:59–71.

Chapman, D. W., and T. C. Bjornn. 1969. Distribution of

salmonids in streams, with special reference to effects of

food and feeding. Pages 153–176 in T. G. Northcote,

editor. Symposium on salmon and trout in streams.

University of British Columbia, Vancouver.

Connor, E. J., and D. E. Pflug. 2004. Changes in the

distribution and density of pink, chum, and Chinook

salmon spawning in the upper Skagit River in response to

flow management measures. North American Journal of

Fisheries Management 24:835–852.

Cushman, R. M. 1985. Review of ecological effects of rapidly

varying flows downstream from hydroelectric facilities.

North American Journal of Fisheries Management

5:330–339.

Dejalon, G. G., and P. Sanchez. 1994. Downstream effects of

a new hydropower impoundment on macrophyte,

macroinvertebrate, and fish communities. Regulated

Rivers 9:253–261.

Elliott, J. M. 1986. Spatial distribution and behavioural

movements of migratory trout Salmo trutta in a Lake

District stream. Journal of Animal Ecology 55:907–922.

Everest, F. H., and D. W. Chapman. 1972. Habitat selection

and spatial interaction by juvenile Chinook salmon and

steelhead trout in two Idaho streams. Journal of the

Fisheries Research Board of Canada 29:91–100.

Flodmark, L. E. W., H. A. Urke, J. H. Halleraker, J. V.

Arnekleiv, L. A. Vøllestad, and A. B. S. Pole ́o. 2002.

Cortisol and glucose response in juvenile brown trout

subjected to a fluctuating flow regime in an artificial

stream. Journal of Fish Biology 60:238–248.

Freeman, M. C., Z. H. Bowen, K. D. Bovee, and E. R. Irwin.

2001. Flow and habitat effects on juvenile fish

abundance in natural and altered flow regimes. Ecological Applications 11:179–180.

Gaudin, P., editor. 2001. Habitat shifts in juvenile riverine

fishes. Hydrobiologia 135(Supplement):1–393.

Geist, D. R., R. S. Brown, V. Cullina, S. R. Brink, K. Lepla,

P. Bates, and J. A. Chandler. 2005. Movement,

swimming speed, and oxygen consumption of juvenile

white sturgeon in response to changing flow, water

temperature, and light level in the Snake River, Idaho.

Transactions of the American Fisheries Society 134:803–

816.

Girard, I. L., J. W. A. Grand, and S. O. Steingrimsson. 2004.

Foraging, growth, and loss rate of young-of-the-year

Atlantic salmon (Salmo salar) in relation to habitat use in

Catamaran Brook, New Brunswick. Canadian Journal of

Fisheries and Aquatic Sciences 61:2339–2349.

Halleraker, J. H., J. J. Saltveit, A. Harby, J. V. Arnekleiv, H.

P. Fjesdstad, and B. Kohler. 2003. Factors influencing

stranding of wild juvenile brown trout (Salmo trutta)

during rapid and frequent flow decreases in artificial

stream. River Research and Applications 19:589–603.

Irvine, J. R. 1987. Effects of varying flows in man-made

streams on rainbow trout (Salmo gairdneri Richardson)

fry. Pages 83–97 in J. F. Craig and J. B. Kemper, editors.

Regulated streams. Plenum, New York.

Kemp, P. S., D. J. Gilvear, and J. D. Armstrong. 2003. Do

juvenile Atlantic salmon parr track local changes in water

velocity? River Research and Applications 19:569–575.

Korman, J. 2009. Early life history dynamics of rainbow trout

in a large regulated river. Doctoral dissertation. University of British Columbia, Vancouver.

Korman, J., M. Kaplinski, and J. Buszowski. 2006. Effects of

air and main-stem water temperatures, hydraulic isolation, and fluctuating flows from Glen Canyon Dam on

water temperatures in shoreline environments of the

Colorado River in Grand Canyon. Report prepared for

the Grand Canyon Monitoring and Research Center,

Flagstaff, Arizona.

86 KORMAN AND CAMPANA
Korman, J., M. Yard, C. J. Walters, and L. G. Coggins. 2009.

Effects of fish size, habitat, flow, and density on capture

probabilities of age-0 rainbow trout estimated from

electrofishing at discrete sites in a large river. Transactions of the American Fisheries Society 138:58–75.

McKinney, T., D. W. Speas, R. S. Rogers, and W. R. Persons.

2001. Rainbow trout in a regulated river below Glen

Canyon Dam, Arizona, following increased minimum

flows and reduced discharge variability. North American

Journal of Fisheries Management 21:216–222.

Mugiya, Y., and H. Oka. 1991. Biochemical relationship

between otolith and somatic growth in the rainbow trout

Oncorhynchus mykiss: consequence of starvation, resumed feeding, and diel variations. U.S. National Marine

Fisheries Service Fishery Bulletin 89:239–245.

Murchie, J. J., and K. E. Smokorowski. 2004. Relative activity

of brook trout and walleyes in response to flow in a

regulated river. North American Journal of Fisheries

Management 24:1050–1057.

Neilsen, J. D., and G. H. Geen. 1985. Effects of feeding

regime and diel temperature cycles on otolith increment

formation in juvenile Chinook salmon, Oncorhynchus

tshawytscha. U.S. National Marine Fisheries Service

Fishery Bulletin 83:91–101.

Nislow, K. H., C. L. Folt, and D. L. Parrish. 2000. Spatially

explicit bioenergetic analysis of habitat quality for age-0

Atlantic salmon. Transactions of the American Fisheries

Society 129:1067–1081.

Nislow, K. H., A. J. Sepulveda, and C. L. Folt. 2004.

Mechanistic linkage of hydrologic regime to summer

growth of age-0 Atlantic salmon. Transactions of the

American Fisheries Society 133:79–88.

Poff, N. L., J. D. Allan, M. B. Bain, J. R. Karr, K. L.

Prestegaard, B. D. Richter, R. E. Sparks, and J. C.

Stromberg. 1997. The natural flow regime. Bioscience

47:769–784.

Randle, T. J., and E. L. Pemberton. 1987. Results and analysis

of STARS modeling efforts of the Colorado River in

Grand Canyon. U.S. Bureau of Reclamation, Glen

Canyon Environmental Studies, Report NTIS PB88-

183421/AS, Flagstaff, Arizona.

Robertson, M. J., C. J. Pennell, D. A. Scruton, G. J.

Robertson, and J. A. Brown. 2004. Effect of increased

flow on the behaviour of Atlantic salmon parr in winter.

Journal of Fish Biology 65:1070–1079.

Rosenfeld, J. S., and S. Boss. 2001. Fitness consequences of

habitat use for juvenile cutthroat trout: energetic costs

and benefits in pools and riffles. Canadian Journal of

Fisheries and Aquatic Sciences 58:585–593.

Saltveit, S. J., J. H. Halleraker, J. V. Arnekleiv, and A. Harby.

2001. Field experiments on stranding in juvenile Atlantic

salmon (Salmo salar) and brown trout (Salmo trutta)

during rapid flow decreases caused by hydropeaking.

Regulated Rivers 17:609–622.

Schlosser, I. J. 1987. The role of predation in age- and sizerelated habitat use by stream fishes. Ecology 68:651–

659.

Scruton, D. A., L. M. N. Ollerhead, K. D. Clarke, C. Pennel,

K. Alfredsen, A. Harby, and D. Kelley. 2003. The

behavioural response of juvenile Atlantic salmon (Salmo

salar) and brook trout (Salvelinus fontinalis) to experimental hydropeaking on a Newfoundland (Canada) river.

River Research and Applications 19:577–587.

Scruton, D. A., C. J. Pennell, M. J. Robertson, L. M. N.

Ollerhead, K. D. Clarke, K. Alfredsen, A. Harby, and

R. S. McKinley. 2005. Seasonal response of juvenile

Atlantic salmon to experimental hydropeaking power

generation in Newfoundland, Canada. North American

Journal of Fisheries Management 25:964–974.

Sheppard, J. D., and J. H. Johnson. 1985. Probability of use

for depth, velocity, and substrate by subyearling coho

salmon and steelhead in Lake Ontario tributary streams.

North American Journal of Fisheries Management

5:277–282.

Shirvell, C. S. 1994. Effect of changes in streamflow on the

microhabitat use and movements of sympatric juvenile

coho salmon and Chinook salmon in a natural stream.

Canadian Journal of Fisheries and Aquatic Sciences

51:1644–1652.

Stevenson, D. K., and S. E. Campana, editors. 1992. Otolith

microstructure examination and analysis. Canadian

Special Publication of Fisheries and Aquatic Sciences

117.

Travnichek, V. H., M. B. Bain, and M. J. Maceina. 1995.

Recovery of a warmwater fish assemblage after the

initiation of a minimum-flow release downstream from a

hydroelectric dam. Transactions of the American Fisheries Society 124:836–844.

Vehanen, T., P. P. Bjerke, J. Heggenes, A. Huusko, and A.

Maki-Petays. 2000. Effect of fluctuating flow and

temperature on cover type selection and behaviour by

juvenile brown trout in artificial flumes. Journal of Fish

Biology 56:923–937.

Vilizzi, L., and G. H. Copp. 2005. An analysis of 0þ barbell

(Barbus barbus) response to discharge fluctuations in a

flume. River Research and Applications 21:421–438.

Voichick, N., and S. A. Wright. 2007. Water temperature data

for the Colorado River and tributaries between Glen

Canyon Dam and Spencer Canyon, northern Arizona,

1988–2005. U.S. Geological Survey Data Series 251.

Walters, C. J., and F. Juanes. 1993. Recruitment limitation as a

consequence of natural selection for use of restricted

feeding habitats and predation risk taking by juvenile

fishes. Canadian Journal of Fisheries and Aquatic

Sciences 50:2058–2070.

Ward, D. M., K. H. Nislow, J. D. Armstrong, S. Einum, and

C. L. Folt. 2007. Is the shape of the density–growth

relationship for stream salmonids evidence for exploitative rather than interference competition? Journal of

Animal Resource Ecology 76:135–138.

EFFECTS OF HYDROPEAKING ON RAINBOW TROUT 87