This document should be considered a 

“work in progress.” It has not gone through 

an external peer review process. It should 

not be cited in any reports until it has been 

completed and undergone a thorough peer 

review process. 
Effects of Fluctuating Flows from Glen Canyon Dam on the 

Early Life History Stages of Rainbow Trout in the 

Lee’s Ferry Reach of the Colorado River 

FY2003 Draft Report 

By: 

Josh Korman1

Matt Kaplinski2

Joseph E. Hazel, III2

Ted Melis3

Jeff Sneep1

Chris Magirl4

Prepared for: 

Grand Canyon Monitoring and Research Center 

Flagstaff, Arizona 

January 16, 2004 

1

Ecometric Research Inc. 

3560 W 22nd Ave., Vancouver, BC, V6S 1J3 

2

Namtek Inc., Northern Arizona University

Box 4099, Flagstaff, AZ 86011-4099 

3

Grand Canyon Monitoring and Research Center 

2255 N. Gemini Dr., Flagstaff, AZ, 86001 

4

USGS, 1675 W Anklam Road

Tucson, AZ, 85745 

(Text Only, Companion Document contains Tables and Figures) 
Executive Summary 

An experimental alteration of the hydrograph from Glen Canyon Dam, targeted at 

reducing the recruitment rate of rainbow trout through increased daily fluctuations in flow, 

was implemented from Jan.-Mar., 2003. This report describes the impact of the experimental 

flow regime on the early life stages of rainbow trout in the Lee’s Ferry reach. The study 

consisted of 3 components. First, we measured the timing of redd excavation and the 

distribution of redds across elevations (i.e., redd hypsometry) to estimate the potential egg and 

alevin mortality caused by the experimental flow regime. Second, we quantified the 

relationships between spawning habitat preference and depth, velocity, and substrate to 

evaluate the feasibility of controlling spawning elevations through changes in discharge. 

Finally, we estimated seasonal trends in rainbow trout fry recruitment and survival using 

length-frequency data and high-resolution ageing information integrated in a stock synthesis 

model. Estimates of recruitment and survival can be compared across years under different 

flow regimes form Glen Canyon Dam to evaluate the extent to which operations effect the 

production of young fish. Monitoring changes in fry density and distribution over the summer 

can potentially be used to evaluate the effects of operations on seasonal or shorter time-scales. 

Information from the redd survey, habitat use, and fry components of the study were used to 

provide a series of recommendations for future experimental flows targeted at reducing the 

production of young rainbow trout in the Lee’s Ferry reach. 

The overall effect of the Jan.-Mar. 2003 experimental flow on the mortality of eggs 

was derived by combining data on redd hypsometry, gravel temperatures, and the timing of 

redd excavation. Of the redds excavated before Apr. 1, 25% were located above 12 kcfs and 

would been stranded above the water surface after this date. The duration of exposure at 12 

kcfs prior to Apr. 1 was 11.5 hrs and exceeded the 10 hr. duration where total alevin mortality 

occurred based on field experiments in the Lee’s Ferry reach conducted by Montgomery and 

Tinning (1993). Thus, eggs and alevins at 12 kcfs and higher were very likely already dead 

before the change in flow on Apr. 1. The duration of exposure at 8 kcfs was 9 hrs. 

Considering the daily application of this exposure frequency and elevated temperatures at 10 

ii
kcfs in Mar., it is likely that very little of the egg deposition above 8 kcfs survived until Apr. 

1, corresponding to a 36-42% loss prior to the flow change. The loss of egg deposition due to 

enhanced fluctuating flows up to Apr. 1 therefore ranged from 25% - 40%. It is likely that the 

egg deposition after Apr. 1 above 8 kcfs (36%) was lost due to lethal temperatures associated 

with Sunday low-flow periods. In summary, the total egg deposition loss due to Glen Canyon 

Dam operations in 2003 ranged from 30 - 40% with about half of this mortality being a direct 

consequence of the enhanced fluctuating flows. 

The spawning habitat preference models we developed for rainbow trout in the Lee’s 

Ferry reach were useful for evaluating the extent to which increased discharge during the Jan. 

- Mar. experimental flow period altered the elevations where spawning occurred. Depths of 

0.5 – 1.5 m, velocities of 0.3 – 1 m/sec, and D85 values of 15-45 mm were preferred. 

Weighted useable area computations showed that higher discharges increased total spawning 

habitat availability at sites that had spawning habitat located at higher stages such as Four 

Mile and Powerline Bars, and reduced spawning habitat availability at deep-water redd sites 

such as Ferry Swale. The model also showed that the stages of preferred suitable spawning 

habitat at Four Mile and Powerline Bars were increased under higher discharges. Such 

changes in spawning habitat availability would increase the proportion of redds that would be 

dessicated and increase the duration of exposure. The redd hypsometry study showed that 

there was a significant proportion of redds excavated in deep-water that would not be 

dewatered at flows as low as 5 kcfs. The large decline in spawning habitat availability at Ferry 

Swale under high discharge suggests that spawning at deep-water sites could be suppressed 

through maintenance of high flows through the entire spawning period, however this 

conclusion needs to be validated by additional flow experiments. There was a large proportion 

of total redds formed at stages below 5 kcfs (40-50%) in the Lee’s Ferry reach, and we are 

uncertain about the efficacy of using high flows to reduce this proportion. 

As expected, Young-of-Year (YoY ) forklength increased over the summer and fall 

while densities generally declined. Talus shorelines had much higher densities compared to 

lower angle habitats (cobble bars and vegetated/sandy shorelines) and tended to attract or 

support slightly larger fish. The otoliths of 266 YoY rainbow trout captured between Apr. – 

iii
Oct. sample periods were extracted and successful age determinations were made for 237 of 

these fish. Hatch checks were clearly evident on all otoliths. Emergence checks tended to be 

subtler and could not always be identified, thus age was determined relative to the hatch date. 

There was little error in the estimation of days from hatch based on a blind-test using hatchery 

fish of known age. There was a very strong relationship between forklength and daily age 

from hatch. The best-fit von Bertalanffy relationship explained over 87% of the variation in 

forklength (n = 237). No seasonal trend in mean size-at-age was apparent. In 2003 the 

majority of hatching occurred between mid-Mar. and late-May with a peak in late-Apr. A 

weekly striping pattern was evident in at least 51% of the otoliths that were examined. The 

atypical increment formed every 7 days that caused the striping pattern tended to be 25% 

wider (3.12 microns) compared to the other increments (2.51 microns) when averaged across 

all striping cycles and fish, and the difference was statistically significant. It is very likely that

the larger width of atypical increments that occurred with a periodicity of exactly 7 days were 

caused by Sunday-steady low flows. Thus, steady flows likely increase the growth rates of 

young trout and in turn may improve their survival. 

The stock synthesis model was able to provide a good fits to the observed monthly 

length-frequency distributions. The most likely estimate of the weekly survival rate ranged 

from 0.88 to 0.90 across alternate recruitment models when the survival rate was assumed to 

be constant over the simulation period. The survival estimates were very precisely defined and 

precision was not dependent on the form of the recruitment model. 95% confidence limits 

were 0.88 +/- 0.015 for time-independent and best-fit Beta distribution recruitment models. 

All constant-survival models tended to over predict the number of fish present in the 45-75 

mm forklength range on the Sept. sample period. This suggests that the survival rate between 

the late-Jul. and early-Sep. trips was somewhat lower than the average rate for the rest of the 

summer, or that there was a displacement of fish from low angle habitats between these two 

periods. These effects could have been caused by the large change in the minimum flow from

10 to 5 kcfs that occurred after the labor-day weekend. 

Results from the redd surveys were used to define more effective flow regimes to 

reduce survival rates of rainbow trout eggs and alevins in the Lee’s Ferry reach. Increased 

iv
fluctuations in flow targeted at increasing egg mortality should be conducted over a fourmonth period between Feb. and May to coincide with the majority of spawning activity. We

estimated that 50 - 60% of the total amount of spawning occurred after Mar. 31. Terminating 

high flows after this date probably increased the number of redds excavated at lower stages 

where temperature and exposure impacts would be less severe. If increased fluctuating flows 

are to be implemented as part of a longer-term strategy, we do not recommend delaying the 

timing of the morning increase in discharge as was done in 2003. Significant temperatureinduced mortality can be achieved through implementation of low steady flows every Sunday. 

This strategy will result in lethal or sub-optimal temperatures occurring once per week above 

the Sunday minimum flow elevation by about mid-Mar. 

Three flow recommendations for Glen Canyon Dam were made based on results from

the 2003 YoY survey and analysis: 1) Fluctuating flows targeting YoY rainbow trout should 

be implemented from Apr. through July to coincide with the timing of hatch; 2) Summer 

steady flows very likely improve the growth of YoY rainbow trout and should be minimized if 

the objective is to reduce rainbow trout densities; and 3) Sudden reductions in the minimum 

daily flow have the potential to strand or displace many YoY rainbow trout in the Lee’s Ferry 

reach. 

v
Acknowledgments 

This project was supported through a cooperative agreement between the USGS and 

Ecometric Research Inc. as part of the science evaluation for the 2003 Glen Canyon Dam

experimental flow program. Dr. Steven Campana of the Canadian Department of Fisheries 

and Oceans performed the analysis of otolith microstructure and provided suggestions on the 

analysis and interpretation of otolith data. Steve Hall, Dave Foster, Scott Decker, Gene 

Tisdale, Elizabeth Fuller, Alexis Hall, Matt Morales, Mark Pillar, and Chris Franke 

participated in the field surveys. Scott Decker and Steve Hall provided useful comments on 

the survey design. Carol Fritzinger, Mike Yard, Dave Foster, Brian Derker, Lars Niemi, Steve 

Jones, and Rocky Levette provided logistical support. Nancy Hornewer of the USGS provided 

discharge data. 

vi
Table of Contents 

Executive Summary....................................................................................................................ii 

Acknowledgments..................................................................................................................... vi 

Table of Contents .....................................................................................................................vii 

1.0 Introduction .................................................................................................................... 1 

2.0 Redd Hypsometry and Timing ....................................................................................... 3 

2.1 Methods for Redd Hypsometry and Timing Study .................................................... 5 

2.1.1 Methods for Redd Surveys at Intensive Sites..................................................... 5 

2.1.2 Methods for Rapid Assessment Technique (RAT) Redd Surveys..................... 6 

2.1.3 Redd Count Model ............................................................................................. 7 

2.2 Results from Redd Hypsometry and Timing Study ................................................. 11 

2.3 Conclusions from Redd Hypsometry and Timing Study ......................................... 15 

3.0 Spawning Habitat Preference ......................................................................................... 0 

3.1 Methods for Spawning Habitat Preference Study ...................................................... 1 

3.1.1 Methods for Spawning Habitat Preference Data Collection .............................. 1 

3.1.2 Methods for Analysis of Spawning Habitat Preference Data............................. 3 

3.2 Results from Spawning Habitat Preference Study ..................................................... 7 

3.3 Conclusions from Spawning Habitat Preference Study ........................................... 11 

4.0 Fry Hatch Timing, Recruitment, and Mortality............................................................ 15 

4.1 Sampling and Analytical Methods for Young-of-Year Study.................................. 17 

4.1.1 Methods for Young-of-Year Field Surveys...................................................... 17 

4.1.2 Methods for Analysis of Otolith Microstructure for Ageing YoY Fish........... 18 

4.1.3 Methods to Analyze Catch and Age Data ........................................................ 21 

4.2 Results from Young-of-Year Sampling and Analysis.............................................. 25 

4.2.1 Results from Young-of Year Field Survey....................................................... 25 

4.2.2 Results from Analysis of Otolith Microstructure ............................................. 27 

4.2.3 Results from Stock Synthesis Model................................................................ 30 

4.3 Conclusions from Analysis of Young-of-Year Data ................................................ 32 

5.0 Preliminary Recommendations for the Design of Future Experimental Flow Regimes 

and Monitoring of their Effects................................................................................................ 35 

6.0 References .................................................................................................................... 42 

vii

1.0 Introduction 

Predation and competition by non-native salmonids has been identified as a potentially 

important factor contributing to the continued decline of humpback chub and other native 

fishes in Grand Canyon. To test this hypothesis, members of the Glen Canyon Dam Adaptive 

Management Workgroup elected to actively reduce the abundance of rainbow trout in Grand 

Canyon through a combination of mechanical removal and changes in discharge. A largescale mechanical removal program was initiated in Jan. 2003 to reduce non-native fish 

densities in a 16-mile section of the Colorado River that is heavily used by the largest 

humpback chub aggregation in Grand Canyon. An experimental alteration of the hydrograph 

from Glen Canyon Dam, targeted at reducing the recruitment rate of rainbow trout through 

increased daily fluctuations in flow, was implemented in Jan.-Mar., 2003 and 2004. 

The experimental hydrograph consisted of a increasing the maximum daily range in 

flows from a normal range of approximately 7–12 kcfs to 5-20 kcfs between Jan. and Mar. 

(Fig. 1.1). The flow regime exceeded both the maximum daily flow fluctuation and the 

ramping rates specified in the Glen Canyon Dam Record of Decision. High flows during the 

day were intended to increase the elevations where rainbow trout would spawn. Reduced 

flows at night were intended to increase the mortality rate on eggs and alevins on exposed 

portions of the gravel bars. The increased fluctuations in flows were also hypothesized to 

potentially reduce the survival of Young-of-Year (YoY) trout that had already emerged. A 

reduction in the maximum daily discharge on Apr. 1. to ca. 12 kcfs was intended to strand all 

surviving egg deposition above this elevation. 

This report presents the results from the first year of a two-year study designed to 

assess the effects of the 2003/2004 experimental flow regime on early life stages of rainbow 

trout in both the Lee’s Ferry reach and in Grand Canyon. We chose to work only in the Lee’s 

Ferry reach in 2003 for a number of reasons. The fishery for rainbow trout in the Lee’s Ferry 

reach is a valuable resource and understanding how operations from Glen Canyon Dam affect 

it is highly relevant to the overall GCD Adaptive Management Program. Reducing the 

1
production of young fish in the Lee’s Ferry reach would likely increase the size of catchable 

fish and is therefore a potential management tool for this resource. Many of the methods 

designed to meet the objectives of this project have never, or only rarely, been attempted in a 

river system as large as the Colorado River. We therefore elected to conduct the first year of 

the study in the Lee’s Ferry reach due to its logistical advantages. Finally, juvenile or adult 

rainbow trout emigrating from the Lee’s Ferry reach to Grand Canyon may be an important 

source of recruitment to the population in Grand Canyon. If this is the case, understanding the 

impacts of dam operations on the recruitment and survival young rainbow trout in the Lee’s 

Ferry reach is highly relevant to understanding how operations effect the population in Grand 

Canyon. We will attempt to address this issue as part of the FY 2004 study that will be

conducted in both the Lee’s Ferry reach and in Grand Canyon. 

The 2003 Lee’s Ferry study consisted of three components. First, we measured the 

timing of redd excavation and the distribution of redds across elevations (i.e., redd 

hypsometry) to estimate the potential egg and alevin mortality caused by the experimental 

flow regime. Second, we quantified the relationships between spawning habitat preference 

and depth, velocity, and substrate to evaluate the feasibility of controlling spawning elevations 

through changes in discharge. Finally, we estimated seasonal trends in rainbow trout fry 

recruitment and survival using length-frequency data and high-resolution ageing information 

integrated in a stock synthesis model. Estimates of recruitment and survival can be compared 

across years under different flow regimes form Glen Canyon Dam to evaluate the extent to 

which operations effect the production of young fish. The fry component of this study also has 

the potential to help evaluate the effects of dam operations over shorter time-scales. 

Information from the redd survey, habitat use, and fry components are used to provide a series 

of recommendations for future experimental flows targeted at reducing rainbow trout 

recruitment rates in the Lee’s Ferry reach. 

2
2.0 Redd Hypsometry and Timing 

Evaluation of the effects of the Jan.-Mar. 2003 enhanced fluctuating flows on the 

survival of rainbow trout eggs and alevins incubating in gravel substrates requires data on 

both seasonal timing and hypsometry of redd excavation. Timing of excavation determines the 

proportion of the total egg deposition potentially effected by the experimental flows. Within 

that proportion, redd hypsometry determines the extent of egg and alevin mortality caused by 

exposure- and temperature-induced impacts. 

There is a considerable body of literature documenting the effects of fluctuating flows

on the eggs and alevin stages of salmonids. Several studies have shown that salmonid eggs 

can tolerate long periods of dewatering. Reiser and White (1983) found that eggs dewatered 

for as long as four weeks (steelhead) and 1-5 weeks (chinook) showed essentially no effect on 

hatching success, or on the development and growth rate of alevins and juveniles, provided 

the sediment moisture content was maintained at 4% or higher. In a laboratory setting, Becker 

et al (1982) determined that the pre-hatch phases of chinook salmon development were 

tolerant to dewatering but that post hatch alevins were highly susceptible. Reiser and White 

(1983) cited the proximity of the redd to local ground water as one factor that might influence 

egg survival during dewatering. Chapman et al. (1986) found that flow fluctuations on the 

Columbia River did not prevent females from building redds and laying eggs above the 

minimum flow elevation. They found that 85% of the redds constructed above the minimum

flow elevation that were subjected to regular dewatering contained live embryos. Exposure of 

redds may result in elevated temperatures that induce lethal or sub-lethal effects such as 

developmental abnormalities. A maximum lethal temperature for rainbow trout eggs of 16 C 

has been well determined from numerous hatchery studies (Piper et al. 1986) and increased 

mortality and developmental abnormalities have been shown to occur at temperatures as low 

as 13 C (Raleigh et al. 1984, Crisp 1981, McEwan and Jackson 1996). 

Angradi et al. (1992) mapped the elevation of rainbow trout redds in Glen Canyon 

from Dec. 1990 to Apr. 1991 at four locations (4, 6, 8.9, and 14 Mile Bars). They found that 

3
the peak of spawning occurred between late Mar. to early May with 29%, 59%, and 83% of 

the redds located above 11, 8, and 5 kcfs, respectively. Montgomery and Tinning (1993) 

conducted laboratory and experimental field trials to measure the survival of rainbow trout 

eggs and alevins in Glen Canyon under fluctuating flows. They found that the moisture 

content of exposed sediments (9%) was well above the 4% minimum threshold for egg 

hatching estimated by Reiser and White (1983). In laboratory studies, they found that periods 

of exposure of up to 12 hrs. had no influence on hatching success, but that total mortality 

occurred when exposure was 15 hrs. (see Table 2.1 for a summary of results). Alevin survival 

rates were much more sensitive to exposure, with almost no survival at exposures of 12 hrs. or 

more. Both field and laboratory studies showed that an exposure period of 6 hrs. reduced 

alevin survival rates by 50% and that morality rates from exposure periods as low as 3 hrs. 

could be as high as 60% if temperatures exceeded 11 C. In the field, an exposure of 10 hrs. 

resulted in 100%l mortality of alevins, while 15 hrs. of exposure was required to produce the 

complete mortality rate in the laboratory. The authors suggest that differences in sediment 

moisture content and temperature regimes were the likely factors causing differences between 

laboratory and field results. 

In this study, we counted redds and surveyed their locations in the Lee’s Ferry reach 

from Feb. to May, 2003. The survey information allowed us to quantify redd hypsometry, 

which in conjunction with discharge data, was used to determine the durations of exposure. 

We also collected data on intergravel temperatures at a range of stages to improve our 

assessment of the extent of mortality resulting from exposure. Monthly redd count data were 

used as input to a model that estimated the timing of redd excavation and the total number of 

redds excavated over the spawning season. This information was used in conjunction with 

redd loss estimates to determine the overall impact of the fluctuating flow experiment on eggto-alevin survival in 2003. 

4
2.1 Methods for Redd Hypsometry and Timing Study 

 

Methods used for intensive redd surveys at four sites in the Lee’s Ferry reach are 

described in Section 2.2.1. Methods for a system-wide redd survey of the Lee’s Ferry reach 

are described in Section 2.1.2. Modelling and analytical methods used to interpret the redd 

count data are described in Section 2.1.3. 

2.1.1 Methods for Redd Surveys at Intensive Sites 

Intensive redd surveys in the Lee’s Ferry reach were conducted monthly from Feb. – 

May, 2003 (Table 2.2) at Four Mile Bar (FM), Ferry Swale (FS), Powerline Bar (PL), and 

Pumphouse Bar (PH) (Fig. 2.1). Criteria used to define redds included the presence of a pit 

which was usually composed of a coarse deposit, a sorted finer deposit downstream of the pit 

(tail spill), appropriate grain sizes (5 – 50 mm), and a lack of algae or macrophytes on the 

sediment in the vicinity of the redds. During the early surveys, a small fraction of redds were 

excavated with a shovel to determine egg presence and ensure that our criteria were sound. 

Identification of redds was likely more accurate when they were exposed or in shallow water 

(< 1m). In deeper water, the presence of fish exhibiting spawning behaviours was used to help 

identify redds. Redd surveys were conducted by systematically traversing each site by foot 

and boat at the minimum daily discharge. At sites where redd densities were high, spraypainted rocks were used to mark redds so they were not double-counted. These rocks were 

removed at the end of each survey. All redds were counted at every site on each survey and 

we made no attempt to determine the month of origin for individual redds in the field. 

Redd locations were surveyed with an electronic total station equipped with a digital data 

collector. A survey rod was placed over the central pit of each redd to obtain its position. For 

deeper redds, elevations were computed by subtracting the total depth of the redd from the 

surveyed elevation of the water surface. Total depth was measured using a Lowrance depthsounder mounted on a 7 m aluminum hulled motorized boat. The boat was spatially 

referenced with the total station by targeting a prism cluster mounted on a mast directly above 

the transducer. Point data were referenced to the Arizona State Plane NAD83, Arizona 

5
Central (FIPS 202) coordinate system, in meters, using benchmarks within a previously 

established control network. Survey accuracy in the field was maintained by horizontal and 

vertical checks of positional error between known reference points. Upon completion of each 

survey, field data were edited for spurious rod heights and miscodes. Ground point data had a 

positioning accuracy of better than 0.05 m and vertical accuracy that varied from 0.03 to 0.05 

m. Bathymetric point data had a positioning accuracy of better than 0.25 m and a vertical 

accuracy of 0.1 m or less. 

Stage-discharge relationships were empirically developed for each site and used to 

translate surveyed redd elevations into their discharge equivalents. Water elevations were 

surveyed at 5, 8, 12, 15, and 20 kcfs at each site. Water elevations surveys were almost always 

conducted at steady discharges, which allowed us to assume that discharge at the site was 

equivalent to the discharge from Glen Canyon Dam at the time of the survey. In the few cases 

when this assumption did not hold, we routed the relevant portions of the Glen Canyon Dam

discharge record to the location of the study sites using a one-dimensional unsteady flow 

model (Wiele and Griffin 1997). Under unsteady flow, only data within 2 kcfs of the targeted 

discharges (5, 8, 12, 15, and 20 kcfs) were used in the estimation of the stage-discharge 

relationships. The stage-discharge data were plotted and a 2nd order polynomial was fit to the 

data (Fig. 2.2). The morphology and large size of Four Mile Bar required the development of 

two independent stage-discharge relationships. 

Continuously recording temperature loggers were buried in the gravel at an assumed 

egg pocket depth of 15 cm (Kondolf 2000) at a range of elevations at Four Mile and 

Powerline Bars during the first sampling period in Feb. The loggers were retrieved by stakingout their locations on the final survey in May. Temperature loggers recorded instantaneous 

temperature at a one-hr. interval. 

2.1.2 Methods for Rapid Assessment Technique (RAT) Redd Surveys 

 

Intensive sites were chosen because they were historically important spawning sites 

that likely made a significant contribution to the total number of redds excavated in the Lee’s 

6
Ferry reach. These sites were not necessarily representative in terms of the timing of redd 

excavation or hypsometry. We therefore used a “rapid assessment technique” (RAT) survey 

during the Mar., Apr., and May sampling sessions to derive a rough but system-wide estimate 

of redd numbers and hypsometry. Surveys were conducted by foot and by boat typically 

during steady flows. Surveys were initiated early in the morning working in a downstream

direction from Glen Canyon Dam to maximize the length of time at steady-low discharge. 

On each survey we visited all historical spawning locations (M. Yard, GCMRC, 

unpublished data presented in Foster 2002) and examined additional locations that had 

potential spawning habitat. The height of each redd above the water surface was estimated 

using an Abney level mounted to a survey rod. The depth of submerged redds was measured 

with either a survey rod or a depth sounder. If discharge from Glen Canyon Dam was not 

steady at the time of the survey, we used an unsteady flow model (Wiele and Griffin 1997) to 

predict discharge at each site at the time of the survey. The elevation of the water surface was 

estimated using the discharge at the time of the survey and the nearest stage-discharge 

relationship from the STARS model (Randle and Pemberton 1987). Redd elevations were then 

translated into their discharge-equivalent using the appropriate stage-discharge relationship 

and the heights of the redds above or below the water surface at the time of each survey. 

2.1.3 Redd Count Model 

The total numbers of redds and the timing of excavation over the spawning season can

be estimated from repeated redd counts. The Area-Under-the-Curve (AUC) method is the 

traditional method for analyzing such data (Irvine and Nelson 1995). The number of redds 

counted per survey is plotted as a function of time (e.g., weeks from start of spawning run) 

and linear interpolation is used to generate a curve. The integral, or total area-under-the-curve, 

determines the number of redd-weeks, which is in turn divided by an estimate of redd survey 

life (SLR in units of weeks) to determine the total number of redds excavated (E) over the 

season, 

7
(2.1) SLR

AUC E =

Redd survey life is the time required for a redd to loose the characteristics that make it 

discernable from other irregularities in the substrate. Survey life will be determined by a 

number of factors including substrate characteristics, slope, beach traffic, and inundation 

frequency. If survey life is longer or equal to the length of time between the start and end of 

spawning and there is no redd superimposition, the peak redd count will be equivalent to the 

total number of redds excavated over the season. However, if survey life is shorter than the 

duration of the spawning period, the total number of redds will be higher than the peak count. 

This latter situation is certainly the case for rainbow trout in the Lee’s Ferry reach, where a 

substantial amount of spawning occurs over a period of 3-5 months. 

 

The AUC method has a few significant limitations: 

1. it is difficult to characterize uncertainty in the estimate of the total number of redds; 

2. an arbitrary assignment of the beginning and ending spawning dates is required if the 

first and last surveys have non-zero counts; and 

3. the method does not explicitly take into account arrival timing, and is therefore not 

useful for determining the proportion of redds excavated before a certain date. 

All issues, especially 3), are highly relevant in the case of evaluating the impacts of the 

Jan.-Mar. 2003 experimental flows. More recently, maximum likelihood methods have been 

used to fit migration- or spawn-timing models to periodic count data in order to estimate 

escapement (Hilborn et al. 1999, Korman et al. 2002). These models overcome all three 

weaknesses of the AUC-method described above and are highly applicable to estimating the 

timing of rainbow trout spawning in Glen Canyon. In this analysis, we modified the modeling 

approach to include the effects of redd superimposition. Increased rates of redd 

superimposition will increase the total redd-to-observed redd ratio and therefore will have the 

same general effect as reducing redd survey life. While redd superimposition can be thought 

of as a component of redd survey life, the extent of superimposition will likely vary over the 

8
duration of the run, with increased superimposition occurring later in the spawning season as 

spawning habitat saturates. Such a dynamic would tend to underestimate the magnitude of the 

late component of the spawning run. 

We developed a model that estimated the magnitude and timing of redd excavation 

from periodic redd counts and assumptions about redd survey life and the rate of redd 

superimposition. The total number of redds observed on any week (RPt) was modeled based 

on the equation, 

(2.2) RPt = At − Ft − St 

where, At is the cumulative number of redds excavated up to and including week t, Ft is the 

cumulative number of redds that have been lost due to fading as they exceed their survey life, 

and St is the cumulative number of redds that are obscured from superimposition. 

The timing and magnitude of redd excavation was modeled using a Beta distribution, 

(2.3) ( ) ( )( ) A E dt

t

t ∫ t t

− − ∝ −

0

1 1 1 α β θ θ

where E is the total number of redds excavated over the spawning season, and α and β are 

parameters of the beta distribution that define the timing of spawning. θt represents the 

proportional date of the run and ranges from 1/52 on the first week to 1 on the last week (T), 

that is θt = t/T. Note that the form of the Beta distribution in eqn. 2.3 returns the cumulative 

frequency so the number of redds excavated on week t (At) is At - At-1. We modeled the 

number of redds that exceeded their survey life on week t as,

(2.4) Ft = Act−SLR − Sct−SLR

where SLR is the survey life in weeks. In other words, the number of redds lost due to fading 

does not include those already lost due to superimposition. 

9
Assuming a limited area of spawning habitat, the number of new redds that are 

superimposed on top of old redds on any week will be a function of the numbers of redds 

being excavated on that week and the number of redds present at a site relative to the total 

number of redds the site can support. We modeled the number of redds superimposed on any 

week (St-St-1) as, 

(2.5) SPsl SPsl

t

t

t t t RP SPhalf

RP S S A SP

SPsl

+ − =

−

−

−

1

1

1 * max

where, SPmax is the maximum proportion of new redds that can be superimposed on existing 

redds, SPhalf is the total number of redds present where the superimposition rate is reduced to 

1⁄2 of its maximum value, and SPsl is the slope of the relationship. 

The total number of redds excavated over the spawning season (E) and arrival model 

parameters (α, β) were fit to the observations of total redd counts across the four survey 

periods (RAT + Intensive sites) under different assumptions concerning survey life and the 

rate of superimposition. A spatial analysis of the redd count data at intensive sites (described 

below) was used to estimate the combined effect of redd survey life and superimposition on 

redd longevity. The predicted redd timing curve was used to estimate the total number of 

redds excavated over the entire season and the proportion excavated prior to Apr. 1. The latter 

estimate determined the total population of redds potentially affected by the Jan. – Mar. 2003 

enhanced fluctuating flows. Observation error was assumed to be normally-distributed. Mostlikely parameter estimates (MLEs) were computed by minimizing the sums of squares 

between the observed and predicted total redd counts over the four survey periods. As we did 

not conduct a RAT survey in Feb., we used the ratio of the intensive-to-RAT survey counts in 

Mar. (1.08) to expand the Intensive-site count in Feb. to a total (RAT + Intensive) count. 

We did not attempt to measure survey life or the rate of redd superimposition rate 

directly in the field, however an estimate of their combined effect was derived from a spatial 

analysis of the redd survey data. From the AUC method, the total number of redds that are 

10
excavated over a defined period is the ratio of the area-under-the-curve between two survey 

dates that span this period and the survey life (eqn. 2.1). We rearranged the AUC equation to 

estimate survey life based on the ratio of AUC to the actual number of redds excavated. Each 

intensive survey site was divided up into a grid of 1 m2

 cells using a Geographic Information 

System (GIS) and the presence or absence redds in each cell on each survey period was 

determined. The number of cells which contained a redd on survey x but not on survey x-1

provided a minimum estimate of the actual number of new redds deposited between the two 

surveys (the denominator). The number of cells which contained a redd on survey x-1 but not 

on survey x provided an estimate of the number of redds lost due to fading as they exceeded 

their survey life. Under the assumption of no redd superimposition, the number of cells with a 

redd present in two consecutive sampling periods provided an estimate of the number redds 

which were present on the first survey that did not fade by the second. The total number of 

cells that contained a red, whether new or counted on a previous survey, was equivalent to the 

number of redds present at-a-site. We computed the AUC between the first and last surveys 

and divided by the estimated number of “New” redds that were excavated on over this period 

to estimate survey life. These calculations were done for all four intensive survey sites. 

2.2 Results from Redd Hypsometry and Timing Study 

Redd counts at intensive sites increased steadily between the Feb. and Apr. sampling 

trips (Fig. 2.3a). Four Mile Bar comprised 65% of the total redds among the four intensively 

monitored sites. 30% of the redds were located below 5 kcfs and 46% were below 8 kcfs on 

the Mar. survey (Fig. 2.3b). 33% of the redds were located above 12 kcfs and would have 

been dewatered after the flow change on Apr. 1 (Fig. 1.1, Table 2.2). Redd hypsometry varied 

considerably between sites (Fig.’s 2.4 and 2.5). Four Mile and Powerline Bars had the highest 

percentage of redds above 12 kcfs. All redds at Ferry Swale were below 5 kcfs. There was 

considerable variation among sites in terms of the apparent timing of redd excavation. 

Spawning was earliest at the two most upstream sites (Powerline and Pumphouse Bars) with 

the majority completed by the Mar. survey. In contrast, the most downstream site (Four Mile 

11
Bar) showed an increase in redds between the Apr. and May sampling periods. Ferry Swale, 

located between the upper two sites and Four Mile Bar, showed an intermediate timing. 

The RAT survey documented 21 additional spawning locations in the Lee’s Ferry 

reach that were dominantly deep-water redd sites (Table 2.3, Fig. 2.1). The peak count of 

redds (427) occurred during the Apr. sampling trip (Fig. 2.6) and was less than the peak count 

across the four intensive sites (510). There was a considerably higher proportion of redds 

below 5 and 8 kcfs based on the RAT surveys relative to the distribution at intensive sites. 

The peak redd count summed across RAT and Intensive surveys was 936 and occurred during 

the Apr. survey (Table 2.4). The total percentage of redds from the combined surveys above 5, 

8, and 12 kcfs prior to the Apr. 1 flow change was 62, 42, and 25%, respectively. Redd 

hypsometry was fairly stable from Mar. through May. 

Estimates of redd survey life based on a spatial analysis of redd count data were 

reasonably consistent among intensive survey sites. The number of 1 m2

 grid cells that 

contained redds was very similar to the total number of redds counted, indicating that there 

were only rare cases when more than one redd was present in a 1 m2

 cell (Table 2.5). 

Estimates of survey life based on the ratio of the Area-under-the-Curve of the number of cells 

with redds present to the number of cells with new redds (e.g., Fig. 2.7) ranged from 4.6-6.8 

wks. with an average of 6.2 wks. (Table 2.6). Survey life was very consistent among all sites 

that had a reasonably high proportion of redds that were exposed for part of the day (PH, PL, 

FM). The lower survey life at Ferry Swale is not surprising. Redds were located in relatively 

mobile pea gravel that and were exposed to high velocities. Redds at Ferry Swale were also 

permanently submerged which would have increased the rate of algal recolonization following 

excavation (Angradi 1992). Given these characteristics, redds at Ferry Swale likely faded 

quite quickly if they were not actively maintained. Our spatial analysis cannot be used to 

determine if a cell classified as an “Old” redd (Table 2.5) was actually a new redd excavated 

on top of an existing redd. When we assumed that 100% of “Old” cells were superimposed by 

new redds to provide a maximum estimate of the number of new redds excavated between 

months, survey life dropped to an average of 4.7 weeks (Table 2.6). This is likely a lower 

bound on survey life, although it is possible that more than one instance of superimposition in 

12
a cell could occur between sample periods. We used a survey life estimate of five weeks for 

calculations of the total redds excavated in 2003 and the proportion excavated before Apr. 1. 

The majority of spawning sites in the Lee’s Ferry reach were permanently submerged (Table 

2.3) and therefore likely had redd survey lives closer to the estimate for Ferry Swale. 

The redd count model was able to fit the observed redd count data almost perfectly. 

(Fig. 2.8a). This is not surprising as the model contained as many parameters (4 assuming no 

redd superimposition) as there were data points. The model predicted that there was a 

substantial amount of spawning in Jun. and Jul (Fig. 2.8b). While we counted many redds in 

late May, we observed very few spawning fish during this survey. We suspect that if we had 

conducted a survey in late Jun. very few redds would have been observed. Unfortunately, 

because we did not conduct a Jun. survey there was no data to force the spawn-timing curve to 

end by late May and this resulted in the substantial amount of spawning estimated for Jun. and 

Jul. When we assumed that few redds would have been present by late June, which 

corresponds to an end in spawning activity by late May given a survey life of 5 wks. (yellow 

point in Fig.. 2.8a), the model provides a plausible fit to the Feb.-May data. The revised 

timing curve peaks at a similar date relative to the “Feb-May data only”-curve, but has a much 

steeper descending right-hand limb. The revised model predicted a run size of 3,200 redds 

with 52% excavated prior to Apr. 1. The “Feb.-May data only” fit predicted a run size of 

4,200 redds with 42% excavated prior to Apr. 1. The peak of spawning derived from either of 

these curves was about two months later than the average peak estimated for the 1990’s that 

occurred in late Jan. (Fig. 2.8b). This shift in spawn timing is consistent with anecdotal 

observations by the Arizona Game and Fish Department and fishing guides (D. Foster, Marble 

Canyon Fishing Guides, Marble Canyon, AZ, pers. comm..) that the peak of spawning has 

moved from early-winter to spring. 

Alternate assumptions concerning redd survey life and superimposition provided 

equally plausible fits to the observed change in the total number of redds over survey periods 

(Fig. 2.9). For example, an assumed survey life of 5 weeks resulted in an estimate of 4,200 

redds with 42% being excavated before Apr. 1. In contrast, increasing survey life to 10 weeks 

produced 2,100 redds with 53% excavated prior to Apr. 1. The effect of increasing the redd 

13
superimposition rate produced a similar type of response to reducing survey life, which was to 

generate a larger and later run. With an assumed survey life of 5 weeks, increasing the 

maximum rate of superimposition from SPmax=0 to SPmax=0.8 (with SPhalf=1000 and 

SPsl=3) produced 5,600 redds with only 35% excavated before Apr. 1 (Fig. 2.10). Increasing 

the sensitivity of the superimposition rate to the number of redds present (SPhalf=500) 

increased the total number of redds to 10,000 and reduced the percentage of redds excavated 

before Apr. 1 to 31%. 

A sensitivity analysis of the effects of survey life and the maximum rate of

superimposition on parameters of interest was conducted (Fig. 2.11). Assuming no 

superimposition, the percentage of redds excavated before Apr. 1 was as low as 42% with a 

survey life of 5 weeks and as high as 66% with a survey life of 16 weeks. The pre. Apr. 1 

percentage ranged from 35% at a maximum superimposition rate of 0.8 to 42% with no 

superimposition (assuming SLR=5 and SPhalf=1000). Our 5-6 week estimate of survey life 

(Table 2.6) implicitly accounts for the effects of redd superimposition, so the sensitivity 

analysis on survey life alone is of most relevance. A survey life of 4-8 weeks very likely 

brackets the total range possible, which corresponds to 40-50% of total redds being excavated 

before Apr. 1. 

Intergravel temperatures at Four Mile and Powerline Bars were highly variable and 

depended principally on seasonal changes in air temperature and the timing of inundation. 

Maximum daily temperatures at elevations >=10 kcfs at Four Mile Bar in Feb. were higher 

than those at Powerline Bar and approached the maximum recommended temperature limit 

for rainbow trout eggs (Fig. 2.12). As the season progressed, differences between sites became 

more pronounced. Lethal (16 C) temperatures occurred at all elevations >=10 kcfs by Mar. at 

Four Mile Bar. At Powerline Bar lethal temperatures did not occur until Apr. and then only at 

elevations >=15 kcfs. Differences in temperature regimes among sites were likely controlled 

by the ca. 3 hr. delay in the morning rise in discharge at Four Mile Bar (11.6 miles 

downstream of dam) relative to Powerline Bar (1.3 miles downstream). Under normal 

operations (e.g., post Apr. 1), flows began to rise by ca. 6:00 am at Powerline Bar and by 9:00 

am at Four Mile Bar. This lag provided sufficient time for increased ambient air temperatures 

14
and solar radiation to increase intergravel temperatures at exposed elevations at Four Mile Bar 

prior to their inundation, but not at Powerline Bar (Fig. 2.13). During the Jan. – Mar. 

experimental flow period, the morning increase in discharge from Glen Canyon Dam was 

delayed until 9:00. This delay resulted in lethal temperatures at Four Mile Bar at elevations 

>=10 kcfs during a month when this would likely not have occurred under normal weekday 

operations. Intergravel temperatures on Sunday were noticeably different when normal 

operations resumed on Apr. 1 (Table 2.7). Sunday flows during Jan-Mar. 2003 followed the 

experimental flow range of 5-20 kcfs, but were steady at 8 kcfs after Mar. 31. The effects of 

this change were very apparent in the gravel temperatures at both Four Mile and Powerline 

Bars. Lethal or sub-optimal temperatures at elevations >=10 kcfs were reached at both sites 

every Sunday after the flow change. 

2.3 Conclusions from Redd Hypsometry and Timing Study 

The overall effect of the Jan.-Mar. 2003 experimental flow on the mortality of eggs 

and alevins can be derived by combining data on redd hypsometry, gravel temperatures, and 

the estimates on the timing of redd excavation. Of the redds excavated before Apr. 1, 25% 

were above 12 kcfs (Table 2.4) and would been stranded above the water surface when 

normal operations resumed. The duration of exposure at 12 kcfs prior to Apr. 1 was 11.5 hrs 

(Table 2.8) and exceeded the 10 hr. duration where total alevin mortality occurred based on 

field experiments conducted in the Lee’s Ferry reach by Montgomery and Tinning (1993) 

(Table 2.2). Thus, eggs and alevins at 12 kcfs and higher were very likely already dead before 

the change in flow on Apr. 1. The duration of exposure at 8 kcfs was 9 hrs. Considering the 

daily application of this exposure frequency and the high intergravel temperatures observed at 

10 kcfs in Mar. (Fig. 2.12), it is likely that very little of the egg deposition above 8 kcfs 

survived until Apr. 1, corresponding to a 36-42% loss (Table 2.4). The loss of egg deposition 

due to enhanced fluctuating flows up to Apr. 1 therefore ranged from about 25% - 40%. As 50 

- 60% of the egg deposition occurred after Apr. 1, these mortality estimates reduce to 10-16% 

when the entire 2003 spawning run is considered. It should be noted that the post-Apr. 1 egg 

deposition above 8 kcfs (36%) was likely lost due to lethal temperature effects associated with 

15
Sunday low-flow periods (Table 2.7). The total egg deposition loss due to Glen Canyon Dam

operations in 2003 therefore ranged from 30 - 40% and about half of this mortality was a 

direct consequence of the enhanced fluctuating flows. Strictly speaking, the pre.-Apr 1 

mortality at 8-12 kcfs and the mortality after Apr. 1 cannot be attributed to the Jan.-Mar. 

experimental flow as it would have occurred under normal operations anyways. 

Lethal or sub-optimal egg incubation temperatures were reached at all elevations of 10 

kcfs and higher at Four Mile Bar as early as Mar. (Fig. 2.12). At Powerline Bar, average 

maximum temperatures at 10 kcfs and high generally did not become a problem until May. 

The delay in the morning rise in discharge in Mar. 2003 therefore resulted in increased 

temperature-related mortality during a period when it would not likely have occurred, except 

perhaps during Sunday-low flows. However, lethal or sub-optimal temperatures occurring 

prior to inundation were more severe and regular during the later part of the spawning period 

(Apr.-May) because elevated air temperatures and solar radiation resulted in higher 

intergravel temperatures, counteracting the effect of the earlier rise in discharge. Maximum

daily intergravel temperatures at elevations of 10 kcfs and higher above 15 C were recorded 

by Montgomery and Tinning (1993) at 9 Mile Bar. Flows over their study period (Mar. 16- 

Apr. 7, 1991) generally ranged from 3-20 kcfs on a daily basis, with no restriction on ramping 

rates. Consequently, the temperature loggers at their study site were usually inundated quite 

early in the morning and showed little daily variation in maximum temperatures during the 

weekday. On weekends however, flows generally did not exceed 10 kcfs and resulted in 

elevated temperatures at the 10kcfs stage and higher. We also observed a ‘Sunday-only’ 

temperature effect (Table 2.7) when normal operations resumed on Apr. 1. 

 The delay in the morning rise in discharge in 2003, while effective at producing lethal 

or sub-optimal incubation temperatures at downstream spawning locations, was expensive 

because power was purchased at a higher rate relative to the cost of producing it from Glen 

Canyon Dam (C. Palmer, Western Area Power Administration, Salt Lake City, UT, pers. 

comm.). Achieving lethal or sub-optimal gravel temperatures one day per week would likely 

produce the same overall mortality as achieving such temperatures everyday. It is quite likely 

that the maximum temperatures we observed in Jan-Mar due to the delay in the timing of the 

16
discharge increase would have occurred on Sunday under normal operations of steady 8 kcfs 

(Table 2.7). Steady-low flows on Sunday would have the advantages of effecting upstream

spawning sites and reducing lost power revenues. As there are a substantial percentage of 

redds between 5 and 8 kcfs (Table 2.4) a Sunday daytime low flow of 5 kcfs would result in a 

larger increase in egg and alevin mortality due to temperature impacts. 

17
3.0 Spawning Habitat Preference 

Increased flow fluctuations from Glen Canyon Dam implemented in Jan. – Mar. 2003 

were in part designed to increase the elevations where rainbow trout would spawn. In theory, 

higher maximum discharges would lead to increased depth and velocities and shift preferred 

spawning habitat to higher elevations where daily periods of exposure would be more severe. 

A proportion of redds deposited at the highest elevations would be completely dewatered 

following a reduction in the maximum discharge after Mar. 31. Partial or permanent exposure 

of redds would lead to increased or complete mortality of incubating life stages and 

potentially reduce the population size of rainbow trout in Grand Canyon. 

Physical characteristics of salmonid spawning habitat have been well documented. In 

Montgomery and Tinning’s (1993) review of rainbow trout spawning habitat characteristics, 

average water velocities and depths ranged from 0.3-1.0 m/sec and 0.3-0.8 m, respectively. 

There are no estimates of velocity and depth preferences for rainbow trout spawning in the 

Lee’s Ferry reach or Grand Canyon, however a number of studies have examined gravel 

characteristics in spawning habitat. Kondolf et al. (1989) reported a median grain size (D50) 

for redds located below Glen Canyon Dam (Four- and Eight-Mile Bars) of 10 mm. The 

average D50 from four spawning areas in Glen Canyon measured by Angradi et al. (1992) 

was 46 mm, with sites closest to the dam having coarser material relative to spawning sites 

located downstream. Site morphology has also been shown to play an important role in 

determining spawning habitat preference. Many salmonid species have been observed to 

preferentially spawn where stream water down-wells into the gravel bed. Spawning is often 

observed at pool tail-outs where the lower water surface elevation of the downstream riffle 

creates a hydraulic gradient that induces down-welling (Kondolf 2000). 

Habitat preference is usually computed as the ratio of the relative utilization of a 

particular habitat characteristic (e.g. proportion of total redds at depths 0.2-0.4m) to the 

relative total availability of that condition (Bovee 1982). Preference values for particular 

conditions that are larger than one indicate preferential use of that condition while values less 
than one indicate avoidance. Preference curves are often used in conjunction with dischargedriven predictions of depth and velocity to compute Weighted Useable Area (WUA). WUA is 

simply the product of the preference for a particular location and the area of that location. In a 

two-dimensional application, an area is divided into a series of grid cells, and the sum of the 

product of the area of these cells and their preferences determines the total WUA at-a-site. 

Predictions of changes in depth and velocity as a function of discharge are used to evaluate 

how habitat availability changes in response to flow. 

In this section of the report we present results that document rainbow trout spawning 

habitat preference for depth, velocity, and grain size of the bed for in the Lee’s Ferry reach. 

We conducted intensive surveys of substrate characteristics and of depths and velocities 

across a range of discharges. Interpolated spatial fields of habitat characteristics developed 

from these data were used in conjunction with site-specific spawning preference relationships 

to predict how spawning habitat availability would change as a function of discharge from

Glen Canyon Dam.

3.1 Methods for Spawning Habitat Preference Study 

 

3.1.1 Methods for Spawning Habitat Preference Data Collection 

We measured depth, water velocity, and grain size at Four Mile Bar, Ferry Swale, and 

Powerline and Pumphouse Bars (fig. 2.1) across an evenly-spaced grid, and over all redds that 

were identified during the redd surveys. The locations of all habitat measurements were 

surveyed with an electronic total station equipped with a digital data collector (see Section 

2.1.1 for additional details concerning survey methods and accuracy). Measurements of depth 

and velocity were taken at steady flows of 5, 8, 12, and 20 kcfs. Measurements in shallow 

areas were made by wading with a Swoffer current meter attached to a topset wading rod. 

Velocities were taken at 6/10th of the total depth and 8 cm off the bottom. For areas that could 

not be waded, velocities were measured by lowering the impeller of the current meter to 2/10th

and 8/10th of the total depth using a 3⁄4” steel pipe mounted to a sliding attachment at the bow 

1
of a 7m aluminum-hulled motorized boat. It was not always possible to lower the impeller to 

8/10th of the total depth when water velocities or depths were high. In these cases, the depth of 

the measurement was usually 1.5 m. The depth of each velocity measurement was always 

recorded. Total depth was measured with a Lowrance depth-sounder. 

Grain size of the bed was characterized using a modified Wolman pebble count. On 

exposed areas of the bars, a 40-m transect was equally divided into eight 5-m sections. The baxis for each stone located immediately below the tape at 0.5 m increments was used to 

classify the particle into one of 18 categories of a modified Wentworth scale (Table 3.1). The 

starting and ending points of the transect were surveyed and interpolation was used to 

compute the spatial coordinates at the center of each 5-m section. A 1-m2

 wooden square, 

equally divided into 81 0.1-m increments, was used to select stones in areas that were not 

sampled by the transect method. An underwater video camera was used to measure grain size 

for areas that were submerged at the time of the survey. The video camera was equipped with 

a topside daylight viewing screen and digital video recording device. Two parallel lasers, 

located 10 cm apart on top of the camera, provided a horizontal scale for images of the 

bottom. A short video segment (5-10 sec.) was recorded at each location when the laser points 

became visible on the substrate. A microphone was used to record a location identifier on the 

audio track of the videotape. Following the field survey, digital video was downloaded onto a 

computer in an AVI-format using commercially available digital video editing software. 

These files were then loaded into a custom-software application (BVIS) to capture a single 

still image from the video segment for each measurement location (BVIS can be downloaded 

at http://www.mountainsoft.net). The user identified the laser points on the still image so that 

the scale of the image could be determined. A transect that intersected the laser points was 

then automatically drawn across the entire width of the image and divided into 15 equal 

widths. The b-axis of each stone located at the intersection of the transect and width 

boundaries was measured by the user and automatically recorded by the software. By 

measuring stones along an axis where the scale is known, the width of the stones in pixels 

could be translated into an absolute unit of measurement (mm). 

2
3.1.2 Methods for Analysis of Spawning Habitat Preference Data 

Six statistical descriptors were used to characterize particle size on the bed. D15, D50, 

and D85 refer to the sizes for which 15%, 50%, and 85% of the sample is finer. The geometric 

mean (DG), the geometric sorting index (SG), and skewness (SK) were computed using the 

formulas summarized by Kondolf et al. (1989) where, 

(3.1) DG = D15* D85

(3.2) 15

85

D

D SG =

(3.3) log( )

) 50 log(

SG

D

DG

SK =

Computation of grain size statistics at each habitat measurement location were based on a 

minimum of 10 measurements for terrestrial surveys and 15 measurements for video surveys 

of submerged substrate. 

Water velocity at specific depths at each measurement location was estimated by 

assuming a logarithmic vertical velocity profile (Gordon et al. 1994). The slope of the log 

depth-velocity relationship for each location was computed based on velocity measurements 

taken at two depths. The average water column velocity at 60% of the total depth, and the 

velocity 10 cm off the bottom were then computed. The latter estimate provided an index of 

the velocity that a fish would encounter when excavating a redd. The slope (b) of log depthvelocity profile was also used to compute shear stress (τ, in units of N*m-1*s-2) from, 

(3.4) τ = ρ × (b / 5.75)2

 

3
where, ρ is the density of water (998 kg/cm3 at 20 C). With this formulation, critical shear 

stress is directly proportional to substrate size. For example, a shear stress of 23 Nm-1s

-2 would 

be expected to move a substrate particle 23 cm in diameter. The vulnerability of bed 

movement could therefore be assessed by comparing shear stress with statistics quantifying 

particle size on the bed (Leopold 1994). 

At each survey site, spatial grids of water depth and bottom velocity at specific 

discharges, and substrate characteristics (e.g., D50), were interpolated from point values using 

a Universal (linear drift) Kriging algorithm (M. Boeringa, Amsterdam Water Supply, 

Amsterdan, the Netherlands, unpublished data). A summary of the input data used for these 

interpolations is provided in Table 3.2. Grid size for the modeled areas at each site was 

determined based on computational constraints and was 2 m2

, 1 m2, and 1.5 m2 for Four Mile 

Bar, Ferry Swale, and Powerline Bar, respectively. Pumphouse Bar was excluded from the 

analysis because the there were not enough redds to define habitat preference with any degree 

of certainty (Fig. 2.3a). 

A variety of spatial interpolation routines, including alternate Kriging algorithms

(quadratic drift, linear and gaussian) and regularized and tensioned splines were assessed 

using a cross-validation procedure. Surfaces were interpolated using a random selection of 

90% of the data for each of the algorithms being assessed. The remaining 10% of the sample 

points were then compared to the predicted values to estimate accuracy. Ten random draws 

were performed from topographic and velocity data at Four Mile Bar. Universal Kriging 

(linear drift) was the most accurate interpolation algorithm in all cases. The accuracy of 

interpolated topography and velocity measurements was +/- 5 cm and +/- 4 cm/sec, 

respectively. Portions of interpolated surfaces of depth and velocity located above the water 

elevation at the time of the survey, an artifact of the interpolation procedure, were removed by 

overlaying polygons that defined the waters edge. These polygons were digitized from 

surveyed points along the waters edge at each site at the four discharge levels. All interpolated 

surfaces above the waters edge for each discharge were removed. 

4
We needed to determine which cells of the interpolated surfaces of depth, velocity, and 

substrate contained redds, as preference for any habitat condition depended on the proportion 

of habitat that contained redds relative to the amount of that habitat condition over the entire 

site. As depth and velocity interpolations depended on discharge, it was important to select a 

redd survey where the discharge at which the redds were formed was known. We overlaid the 

locations of redds from the Mar. survey on to the interpolated surfaces. The Mar. survey was 

selected because the redd-forming discharge could be better determined relative to later 

surveys. The vast majority of redds present during the Mar. survey would have been created at 

either 5 or 20 kcfs as increased ramping rates during the Jan. – Mar. 2003 experimental flow 

period limited the amount of time at intermediate discharges. Less than 25% of the redds at 

Four Mile Bar were located at or below 5 kcfs in Mar. (Fig. 2.5) so the other 75% of the redds 

must have been created at 20 kcfs. At 5 kcfs, submerged redds tended to be in water less than 

10 cm deep and we observed no spawning activity at this flow during any of our surveys. 

Thus, redds located at or below 5 kcfs were very likely created at 20 kcfs as well. At 

Powerline Bar only a small fraction of redds were located at or below 5 kcfs so it is 

reasonable to assume that the dominant redd-forming discharge was also 20 kcfs. At Ferry 

Swale we only observed spawning activity at 5 kcfs during Feb. and Mar. surveys. Fish were 

unable to hold over most redds at 20 kcfs, very likely due to excessive velocities. Redds 

created at Ferry Swale prior to Apr. 1 were therefore likely formed at 5 kcfs. The total number 

of wetted cells at each site at the redd-forming discharge and the number of cells with redds 

was 10,763/213, 18,028/56, and 4,980/99 for Four Mile Bar, Ferry Swale, and Powerline Bar, 

respectively. 

A linear discriminant function analysis (DFA) was used to determine which habitat 

variables best classified grid cells into “redd” or “non-redd” groups (Systat 1997). All “redd” 

cells, and an approximately equal number of “non-redd” cells, selected at random across the 

entire site area, were used as input to the DFA. Utilization, availability, and preference were 

computed for the three most important variables identified in the DFA. The number of “redd” 

and “non-redd” cells among 16 depth (0 – 3 m in 0.2 increments), 10 bottom velocity (0 – 1.8 

m/sec in 0.2 increments), and 15 D85 categories (0 – 70 mm in 10 mm increments) were 

computed for each site at its “redd-forming” discharge. Spawning utilization (Uc,i) for each 

5
variable type ‘c’ and increment ‘i’ was computed as the ratio of the number of cells with redds 

at that increment relative to the total number of cells with redds. Total availability (Ac,i) was 

computed as the ratio of the total number of cells at that increment relative to the total number 

of wetted cells in the site. Plots of utilization and availability as a function of the increment 

value showed continuous relationships with some scatter likely due to small sample sizes for 

some increments. Utilization and availability relationships were therefore smoothed by fitting 

a Beta distribution to the data, 

(3.5) ( ) ( )( ) X YMax di

i

c i ∫ i i

− − = −

0

1 1

, 1 α β θ θ

where Xc,i refers to utilization or availability, α and β are parameters of the Beta distribution 

that define its shape, θi represents the proportional value of the habitat variable that ranges 

from 0 to 1, and Ymax is a scalar. Note that eqn. 3.5 returns the cumulative frequency, thus 

point densities for utilization and availability for increment ‘i’ were calculated as the 

difference between adjacent increments (e.g., Xc,i- Xc,i-1). The model was fit to the data by 

minimizing the sum of squared differences between predictions and observed ratios using a 

non-linear iterative search procedure. 

Spawning habitat preference (Pc,i) was computed as the ratio of smoothed values of 

utilization and availability, 

(3.6) 

c i

c i

c i A

U

P

,

,

, =

Spawning preference values larger than one indicated preferential use of that condition while 

values less than one indicated avoidance. Linear interpolation was used to predict preference 

for any continuous depth (D), velocity (V), or D85 value associated with a particular grid cell. 

Habitat values for each grid cells at specific discharges were derived from the interpolated 

surfaces of depth, velocity, and D85. The overall spawning preference for each grid cell at a 

specific discharge was computed as the product of the three variable-specific preferences, 

6
(3.7) P = PD

* PV* PD85

Weighted useable area at specific discharges was computed as the product of the sum of the 

overall preference values of all grid cells and the cell area. Note that the overall preference 

model assumes that each habitat component acts independently on the overall preference and 

that there are no interactions among variables. However, the model does not assume that all 

habitat variables are equally important as variables that show little preference would have 

values close to one. 

3.2 Results from Spawning Habitat Preference Study 

Grain size statistics for the stream bed varied across the three sampling sites (Table 

3.3). The median grain size (D50) and presence of larger particles (D85) was lower at Ferry 

Swale compared to the other sites. Powerline Bar had a higher fraction of smaller particles 

(D15). Grain sizes at Four Mile Bar and Ferry Swale were generally well sorted (low values 

of SG), but this was not the case at Powerline Bar. All sites showed a negative skew to the 

particle size distributions indicative of a longer tail extending into finer sediment sizes. 

Negative skew is a common characteristic of stream gravels used by spawning salmonids 

(Kondolf et al. 1989). D16, D50, and D86 values reported by Kondolf et al. (1989) for Four 

Mile and Twelve Mile Bars were 1, 10, and 30 mm, respectively and were considerably finer 

than the statistics at Four Mile Bar in 2003. This difference could reflect a coarsening of the 

bed over time or differences in sampling methodology. Kondolf et al.’s statistics were based 

on substrates collected from the surface to 10-15 cm depth, while the statistics presented in 

the upper portion of Table 3.3 were based on surface samples only. A limited sample of 

sediment collected in 2003 using Kondolf et al’s methodology (Table 3.3 lower portion) 

showed that grain size of sediments from 0-15 cm depth were generally lower than the grain 

size from sediments collected on the surface. Differences in our statistics from previous 

estimates are therefore partially the result of differences in methods. Surficial grain size 

statistics defining particle size (D15, D50, D85, DG) were reasonably well correlated with 

7
each other (Table 3.4). Statistics reflecting the shape of the grain size distribution (SG and 

SK) were not correlated with the other metrics or each other. Correlations among particle size 

statistics were highest at Four Mile Bar and Ferry Swale where variance in particle size (SG) 

was low relative to Powerline Bar. 

Discriminant functions predicted the presence of redds in grid cells based on habitat 

characteristics with an accuracy of 76-84% (Table 3.5). The functions explained 59%, 62%, 

46%, and 45% of the variance between “redd” and “non-redd” cells for Powerline Bar, Ferry 

Swale, Four Mile Bar, and all sites combined, respectively. Accuracy was highest at 

Powerline Bar (84%) and Ferry Swale (79%) and lowest at Four Mile Bar (76%) and for all 

sites combined (73%). More than half the sample size of the “all sites combined” analysis was 

made up of cells from Four Mile Bar. As a result, the pattern at Four Mile Bar dominated the 

pattern for “all sites combined”. The models tended to predict more false-positives (cells 

without a redd incorrectly predicted as having one) than false-negatives (cells with a redd 

incorrectly predicted as not having one). This pattern could reflect error in model formulation 

or structure, such as not accounting for interactions among variables or failure to include 

variables that are important components of spawning habitat preference. An alternate 

explanation for the large number of false-positives is that some of the quality spawning 

habitat is under-utilized because there are fewer spawners than there is habitat. Depth, bottom

velocity, and D85 were the most important variables for discriminating among “redd” and 

“non-redd” cells at Powerline Bar and Ferry Swale. Depth and D85 were the most important 

variables at Four Mile Bar and when all sites were combined. Water depth, bottom velocity, 

and D85 were selected as the variables to use for habitat preference and WUA computations. 

 

Patterns in total habitat availability varied considerably by site, however, utilization 

patterns were reasonably consistent. At Four Mile Bar, depths ranging from 0.5-1.25 m were 

utilized at a rate considerably higher than their overall availability (Fig. 3.1a). Differences 

between utilization and availability were much subtler with respect to near-bottom velocity 

and D85. Depths of 0.75-1.25 m were utilized disproportionately relative to their availability 

at Ferry Swale and there was also strong preferential use of near-bottom velocities ranging 

from 0.6-0.8 m/sec (Fig. 3.1b). There also appeared to be preferential use of substrates with 

8
D85 values of 20-30 mm. Increased utilization at depths of 0.6 m and 1.2 m was apparent at 

Powerline Bar (Fig. 3.1c). This is the only case where a bi-modal response in habitat 

preference was observed and where the Beta distribution failed to capture the habitat 

relationship. We had to manually alter the Beta distribution for the Powerline Bar depthutilization curve to provide the fit presented in Fig. 3.1c. At Powerline Bar, we also observed 

increased utilization at near-bottom velocities of 0.4-0.6 m/sec and for D85’s of 20-35 mm. 

Spawning habitat preference was somewhat consistent across sites but differences 

were apparent (Fig. 3.2). In general, depths of 0.5-1.25 m were preferentially utilized 

(preference > 1). A wider range in preferred near-bottom velocities was observed at Four Mile 

Bar (0.2 – 0.75 m/sec) relative to Powerline Bar (0.3 – 0.7 m/sec) and there was a preference 

for higher velocities at Ferry Swale (0.5-1.2 m/sec). Preferential use of particle sizes ranged 

from 20-30 mm at Ferry Swale and 10-40 mm at Powerline Bar. There was little evidence of 

preferential use of substrate at Four Mile Bar with the exception that areas with very fine 

(D85 < 5 mm) or large particles (D85 > 45 mm) were avoided. Differences in preference 

among sites and among variables within-a-site generally reflected the patterns seen in the 

discriminant function analysis; Particle size tended to be a relatively unimportant variable at 

most sites; Depth was an important variable at Powerline and Four Mile Bars; Velocity was a 

very important variable at Ferry Swale and moderately important at Powerline Bar. 

Habitat preference relationships were determined from the ratio of utilization to total 

habitat availability. The site boundaries of our study sites were delineated based on 

geomorphic features (gravel bars, pool tail-out, etc.). When comparing preference 

relationships among sites it is important to realize that site boundaries, which control the 

distribution of total habitat availability, will affect the preference relationships. It is therefore 

important to examinee utilization (Fig. 3.3) and availability (Fig. 3.4) relationships when 

commenting on differences in habitat preference among sites. For example, we saw a 

noticeable difference in preference for D85 between Four Mile and Powerline Bars (Fig. 3.2c) 

even though utilization was almost identical (Fig. 3.3c). Differences in the availability of D85 

among these sites (Fig. 3.4c) were therefore the cause for differences in preference. It would 

be wrong to conclude that the preference curve for D85 at Four Mile Bar suggests that 

9
substrate is not an important component of spawning habitat. The correct inference is that D85 

is not an important determinant for spawning at Four Mile Bar because the distribution of the 

material at this site matches the pattern of utilization. The same reasoning explains differences 

in near-bottom velocity preference between Ferry Swale and Four Mile Bar. The lack of 

strong preference for near bottom velocity at Four Mile Bar is driven by the fact that the 

distribution of velocities (Fig. 3.4b) nearly matches the utilization pattern. The availability of 

highly utilized velocity and substrate conditions at Four Mile Bar is probably a good part of 

the reason why it is the largest spawning site in the Lee’s Ferry reach (Table 2.3). 

Spatial patterns in predicted habitat preference at the redd-forming discharges for Four 

Mile Bare, Ferry Swale, and Powerline Bars were compared to redd locations (Fig. 3.5). The 

good correspondence between high preference and redd density is not an independent test of 

the models predictive ability since these same redd locations were used in the calculation of

the preference models in the first place. However, one interesting characteristic of the maps 

are the areas that showed relative high preference coupled with low redd density. This pattern 

was statistically reflected in the discriminant function analysis through the large number of 

false-positives (incorrectly determination that a cell contained a redd when it did not). As 

previously mentioned, this could indicate either underutilization of spawning habitat or 

incomplete model specification. Highly preferred habitat with low redd densities tended to 

occur at the downstream ends of the gravel bars of all three sites. Many salmonid species have 

been observed to preferentially spawn where stream water down-wells into the gravel bed 

(Kondolf 2000). Down-welling will often be greater at the upstream side of gravel bars where 

the hydraulic gradient is largest. Upstream portions of the gravel bars may have been 

preferentially used relative to downstream areas with similar habitat conditions due to the this 

factor. The spatial pattern in model error may in part reflect failure to include a variable which 

accounts for variation in down-welling across the bar surface.

Increased discharge resulted in higher total spawning habitat availability at Four Mile 

and Powerline Bars where suitable spawning habitat was located at higher stages (Fig. 3.5). 

Increased discharge reduced spawning habitat availability at Ferry Swale where the majority 

of habitat was located in deeper water. At Four Mile Bar, the total wetted area increased by a 

10
factor of 2 as discharge was increased from 5 – 20 kcfs (Table 3.2) while weighted useable 

area increased by almost five-fold. At Powerline Bar, total wetted area across this same

discharge range increased by 1.4-fold while WUA increased over 9-fold. Increasing discharge 

at these high elevation gravel bars resulted in a disproportionate increase in spawning habitat 

availability. More importantly, as discharge increased, the proportion of spawning habitat at 

higher elevations also increased (Fig. 3.5a and c). Predictions of WUA by stage at the reddforming discharge of 20 kcfs provided closely matched the observed vertical distribution of 

redds during the Mar. survey. While this is not an independent test of the model, it is 

comforting that the vertical distribution of WUA predicted by the models matches the pattern 

in redd hypsometry. Ferry Swale represented a deep-water redd site where all spawning 

habitat was below the 5 kcfs stage. At this site we saw little increase in total wetted area with 

increasing discharge (1.2-fold) and a very large decrease in WUA (4.6-fold). Higher 

discharges may therefore suppress spawning at deep-water redd sites. In support of this 

hypothesis, we saw over a 2-fold increase in the number of new redds at Ferry Swale between 

the Mar. and Apr. surveys (Table 2.5) when maximum daily flows were reduced from 20 to 

12 kcfs. Increases in the number of new redds at the other sites over this same time period 

were considerably lower and ranged from 0.9 to 1.3-fold. 

3.3 Conclusions from Spawning Habitat Preference Study 

The rainbow trout spawning habitat preference component of this study represents a 

significant effort relative to the magnitude of habitat use studies conducted in most other river 

systems. We estimated habitat preference at 3 large sites and characterized depth and velocity 

fields at four levels of discharge. In other studies, depth and velocity fields are usually 

estimated using a multi-dimensional calibrated to a single level of discharge. The use of an 

underwater video camera to quantify particle size on the bed represents a significant 

improvement relative to other habitat studies where particle size is almost always roughly 

determined based on uncalibrated visual categorization. 

11
Depths of 0.5 – 1.5 m, velocities of 0.3 – 1 m/sec, and D85 values of 15-45 mm are 

preferred by rainbow trout spawning in the Lee’s Ferry reach. These statistics are reasonably 

close to the depth (0.3 – 0.8 m) and average velocity (0.3 – 1.0 m/sec) ranges reviewed in 

Montgomery and Tinning (1993). The D50 estimates of spawning areas in the Lee’s Ferry 

reach reported by Kondolf et al. (1989) and Angradi et al. (1992) of 10 mm and 46 mm, 

respectively, bound the range of D85 values of sediments that were utilized for spawning in 

our study. It is important to note that the range of spawning depths we observed in the habitat 

component of our study underestimates the maximum depth where spawning was observed. In 

the system-wide redd survey we found many deep-water redd sites (Table 2.3) where the 

depth of redd locations was 1-2 m and we observed some redds in water greater than 3 m

deep. Two inferences can be made from these observations: 1) the depth preference curves we 

developed from a limited number of sites are not representative of all spawning sites in the 

Lee’s Ferry reach; or b) depth is not the physical variable that trout are using to select 

locations for spawning. It is possible that locations on gravel bars with shallow depth often 

have appropriate velocities or grain sizes and/or tend to be in areas where down-welling is 

greatest. 

 

The spawning habitat preference models developed for the Lee’s Ferry reach were 

useful for evaluating the extent to which increased discharge during the Jan. - Mar. 

experimental flow period altered the elevations where spawning occurred. Weighted useable 

area computations showed that higher discharges increased total spawning habitat availability 

at sites that had available spawning habitat at higher stages such as Four Mile and Powerline 

Bars, and reduced spawning habitat availability at deep-water redd sites such as Ferry Swale. 

The model also showed that the stage of spawning at Four Mile and Powerline Bars was 

increased under higher discharge. Such changes in spawning habitat availability would likely 

increase the proportion of redds that would be dessicated and the duration of exposure 

resulting from flow fluctuations. The redd hypsometry study showed that there was a high 

proportion of redds excavated in deep-water that would not be dewatered at flows as low as 5 

kcfs. The large decline in spawning habitat availability at Ferry Swale under high discharge 

suggests that spawning at deep-water sites could be suppressed through maintenance of high 

flows. In support of this, we noted a large increase in the number of redds at Ferry Swale after 

12
the maximum daily flows were reduced from 20 to 12 kcfs on Apr. 1 (Fig. 2.5 for Ferry 

Swale). We are uncertain whether this increase in spawning activity was the result of the flow 

change effecting spawning habitat availability, or was a normal occurrence driven by seasonal 

trends in spawn timing. Maintaining high fluctuating flows until the end of Apr. following the 

peak of spawning would help resolve this uncertainty. In short, there is a large proportion of 

redds that are formed at stages below 5 kcfs (40-50%) in the Lee’s Ferry reach, and we are 

uncertain about the effectiveness of using high flows to reduce this proportion. 

 

Results from our spawning habitat preference study must be interpreted with caution. 

We developed preference curves for each site based on a single discharge. When we predicted 

WUA as a function of discharge we assumed that the preference relationships were stationary 

with respect to discharge. Other studies have shown that habitat availability-discharge

relationships do not always meet this assumption (e.g., Pert and Erman 1994). In many WUA 

studies there is often an implicit assumption that changes in habitat availability have some

type of population-level effect. We have not evaluated whether reductions in spawning habitat 

availability reduce the total egg deposition in the Lee’s Ferry reach or the survival of eggs and 

alevins. We have avoided this issue by using WUA to determine how discharge potentially 

influences the stage where spawning occurs. This has relevance for designing flow regimes 

that are more effective at increasing the percentage of redds which are potentially exposed due 

to fluctuating flows. We make no claims regarding the linkage between WUA predictions and 

impacts on recruitment of young trout. These impacts must be assessed through direct 

measurements such as the redd hypsometry and fry survival components of this study. 

The extent of rainbow trout spawning in the mainstem below Lee’s Ferry is currently 

unknown, but is highly relevant for evaluating alternate methods for reducing trout 

populations in Grand Canyon. Extensive mainstem spawning in Grand Canyon would imply 

that the source of rainbow trout in Grand Canyon is mostly local and can therefore be 

controlled by mechanical removal. On the other hand, if the amount of mainstem spawning is 

very limited, rainbow trout in Grand Canyon must originate from juveniles or adults 

emigrating from the Lee’s Ferry reach. In this case, reducing rainbow trout abundance in 

Grand Canyon will require continuous mechanical removal efforts or a reduction in the 

13
population above Lee’s Ferry. A redd and spawning habitat survey in Grand Canyon will be 

conducted early-Apr., 2004. Documentation of extensive mainstem spawning in Grand 

Canyon usurps the need to apply the preference model to downstream locations. However, the 

model will be useful if water clarity is poor and limits our ability to conduct a thorough redd 

survey. In this situation, a failure to observe redds may simply reflect poor sampling 

efficiency. Predictions of habitat availability at downstream locations will then be useful in 

deciding whether the absence of redds is a due to sampling limitations or lack of suitable 

habitat. In addition, the utilization curves will be helpful for focusing downstream redd survey 

efforts. 

14
4.0 Fry Hatch Timing, Recruitment, and Mortality 

The increased daily flow fluctuations of the Jan.-Mar., 2003 experimental hydrograph 

were in part designed to increase the mortality of young-of-year (YoY) rainbow trout. YoY 

stream-dwelling salmonids prefer near-shore habitats that are typically low-velocity and 

shallow areas with abundant cover (Montgomery and Tinning 1993). Daily variation in 

discharge will result in lateral shoreline movement that can cause stranding of juvenile fish, or 

lead to sub-lethal impacts related to increased stress levels, predation risk, energy expenditure, 

or reduced feeding opportunities (Cushman 1985). Published data on the impacts of sub-lethal 

effects of fluctuating flows are limited and must be inferred from studies on habitat use and 

physiological stress. Vehenan et al. (2002) observed that juvenile brown trout used higher 

nose velocities with increasing water flow and did not fully compensate for increased energy 

expenditures by changing microposition. Shirvell (1994) reported that fish initially responded 

to increased flow by moving closer to the streambed and then, if necessary, by moving 

laterally to seek out appropriate velocity conditions. Flodmark (2002) showed that daily 

variation in discharge did not seem to affect fish stress levels when peaking was done 

frequently. 

In contrast to the lack of data on sub-lethal effects of daily variation in discharge on 

juvenile fish, there are many observations of direct impacts caused by stranding. Factors that 

control the extent of stranding at a given site include riverbank profile, substrate type, fish size 

and age, species, time of day, exposure frequency, season, and temperature. The extent of 

stranding appears to be highest in low-angle habitats with abundant cover (Halleraker et al. 

2003). Small brown trout YoY (ca. 50 mm) have been shown to be more vulnerable to 

stranding than larger juveniles (75-90 mm). Stranding rates tended to increase at lower water 

temperatures and were highest if the flow reductions occurred during daylight hours (Bradford 

et al. 1995, Saltveit et al. 2003). A decrease in the down-ramping rate from 60 cm/hr. to 10 

cm/hr was shown to reduce stranding of brown trout YoY by 50% (Halleraker et al. 2003). 

For reference, the down-ramp rate in the Lee’s Ferry reach during the Jan.-Mar., 2003 

experimental flow period was approximately 15 cm/hr. Stranding rates have also been shown 

15
to increase following a long habituation time at steady flows (Halleraker et al. 2003). 

Stranding of small juveniles (ca. 50 mm) may be difficult to observe in the field. A 1-hr. 

search of a 75 m

2

 area by two technicians found less than 40% of the total fish known to be 

stranded in an enclosed area (Saltveit et al. 2003). 

Measuring the effect of fluctuating flows on factors that have direct impacts on 

regulating population size, such as survival and recruitment, is challenging. In this study, we 

measured changes in length-frequency and relative abundance of YoY rainbow trout over the 

summer and fall through monthly sampling. We also analyzed the microstructure of otoliths 

from a subsample of fish to establish a length-age relationship. The combined data were used 

to define the seasonal timing and magnitude of hatch that can be used to better-define the 

window for future flow regimes targeted at reducing YoY survival. Estimates of seasonal 

trends in hatching inferred from fry surveys were compared to estimates of the timing of egg 

deposition from redd surveys to evaluate the effectiveness of flows targeted at reducing egg 

survival. For example, the failure of eggs deposited early in the spawning season, when 

fluctuating flows were increased, to show-up as fry in subsequent months, would indicate that 

the fluctuating flows reduced egg or alevin survival relative to rates that occurred under 

normal operations later in the year. Finally, length-frequency data and information on size-atage was used to estimate the apparent survival rate of YoY in their first 1⁄2 year of life when 

they are likely most vulnerable to flow effects. The term ‘apparent’ is used because the 

survival rate that is estimated is not the true survival rate, but an index of the rate that depends 

on a number of factors including: size-dependent migration of fish out of the sampling area; 

gear-dependent size-vulnerability relationships; size-based differences in the distribution of 

fish among habitat types; and the distribution of effort across habitat and gear types. As we 

have no reason to believe that any of these factors would change over the sampling period or 

among years, differences in the apparent survival rate through time provide an index of the 

change in the true survival rate. Changes in the apparent survival rate within-season and 

across years can be compared to operational changes from Glen Canyon Dam to improve our 

understanding of how fluctuating flows can be used to regulate rainbow trout abundance. 

16
4.1 Sampling and Analytical Methods for Young-of-Year Study 

The YoY study in 2003 consisted of three major components. Field survey methods 

are described in Section 4.1.1. Analysis of otolith microstructure was used to estimate YoY 

age and make inferences on effects of fluctuating flows on growth. Methodologies for this 

analysis are described in Section 4.1.2. Estimates of recruitment and survival rate were based 

on a model combining ageing and field survey data. The model and related analytical methods 

are described in Section 4.1.3. 

 

4.1.1 Methods for Young-of-Year Field Surveys 

Rainbow trout YoY were sampled from twenty randomly selected 30 m sites on a 

near-monthly basis from Jun.-Oct., 2003 (Table 2.2). Limited sampling efforts conducted in 

Apr. and May were used to refine capture techniques and provide fish for development of 

daily ageing methods. Site selection was limited to potential fry habitat and included low 

angle cobble bars and vegetated or sandy shorelines as well as one low angle talus shoreline 

site. We initially avoided sites comprised of steep talus shorelines as we felt they would not be 

used by young fry and were challenging to sample by backpack electrofishing at night. We

added 6 steeper talus shoreline sites in the Sep. and Oct. sample periods to account for some

very noticeable changes in fish distribution and density. We performed most analyses on both 

sets of sites and refer to them as the “Original 20”and “Original 20 + 6” sets. 

Upon arrival at a site, a 30-meter length was measured with a survey tape and the 

upstream and downstream limits were marked with fluorescent glow sticks. Starting at the 

downstream end of the site, two fisheries technicians systematically worked their way

upstream catching all fish within 2.5 meters from the shoreline using a backpack electrofisher. 

Electrofisher settings were standardized at 100 Volts, a frequency of 80 Hz, and a pulse width 

of 8 msec. All sampling was conducted at night at the minimum discharge, which usually 

occurred between 23:00 – 6:00. A Q-beam spotlight (ca. 1 million candlepower) was used to 

illuminate the area in the vicinity of the anode. Every effort was taken to ensure that 

thoroughness and technique were consistent among sites and sample periods. On average, 10 

17
minutes of electrofishing effort over a 15 minute period was required to fish a 30 m site. 

Upon completion of sampling at each site, the forklengths of all fish were measured to the 

nearest mm and the majority of fish were released at the downstream end of the site. A 

subsample of fish were retained for ageing. 

The use of a boat electrofisher to sample YoY fish potentially has a number of 

advantages over backpack electrofishing. First, the increased power of a boat electrofisher 

(max. of 5000 Watts) relative to a backpack unit (max. of 600 Watts) increases the size and 

strength of the electric field and would therefore capture more fish per unit time. This in turn 

would increase the precision of length-frequency determinations. Second, a boat electrofisher 

is a more efficient, easier, and safer method for capturing YoY in steep shoreline habitats. 

This is particularly important for the 2004 study below Lee’s Ferry where such habitat types 

are quite common. We therefore performed a small comparison of catch rates and size 

distributions of fish captured by boat and backpack electrofishers. On Oct. 26, 2003 we 

sampled 4 talus shorelines sites and two vegetated/sandy shorelines sites in the Lee’s Ferry 

reach using both backpack and boat (Achilles) electrofishers. Each site was 75 m long with 

the lower 30 m and upper 45 m sections being sampled by backpack and boat electrofishers, 

respectively. All sampling was conducted at night (21:00 to 2:01) at near minimum flows. 

4.1.2 Methods for Analysis of Otolith Microstructure for Ageing YoY Fish 

A subsample of YoY fish captured on each trip were preserved in 95% ethanol and 

sent to a laboratory (www.mar.dfo-mpo.gc.ca/science/mfd/otolith/) for examination of otolith microstructure. 

The otoliths of teleost fish contain growth increments that are deposited with a daily 

periodicity (hence the term "daily ring"), thus providing precise age and growth information 

through much of the first year of life (Campana and Nielson 1985). Fish selected for ageing 

were randomly subsampled from the total catch on each survey using a length-stratified 

design: within each sample period 10 fish were selected at random from those available in 

each 10-mm length category. We sampled at non-random locations to meet the target sample 

size for each 10mm length category if we failed to meet the target based on the 20 randomly 

18
selected sites. Approximately 25% of the total number of fish captured across sampling trips 

were aged. 

The forklengths of preserved fish were measured in the laboratory prior to dissection. 

Both sagittal otoliths were removed from each fish and mounted individually on microscope 

slides in cyanoacrylate glue. Otoliths were polished close to the mid-plane, flipped and reglued, then polished to the growth plane with 30 μm and 3 μm lapping film, as per established 

procedures (Stevenson and Campana 1992). All otoliths were examined at a magnification of 

400-1250x under a compound microscope. Using the well-defined hatch check as a reference 

point, daily increments between the hatch check and edge were counted 2-4 times in at least 

one otolith of each pair (Campana 1992). The resulting count was recorded as the age from

hatch to capture. 

To determine otolith growth since the time of hatch, the otolith growth axis from the 

hatch check to the edge of the otolith was measured along the longitudinal axis. 

Measurements (± 1 μm) were made with an image analysis system working at a resolution of 

1280 x 1024. A check and that may have corresponded to emergence was occasionally

observed; however, the interpretation of this check was often ambiguous, so increment counts 

and otolith measurements between hatch and emergence, and between emergence and the 

edge, were only performed for a random subset of 50 otoliths. 

A von Bertalanffy model was fit to the length - age data using a non-linear iterative 

search procedure. The form of the von Bertalanffy model was, 

(4.1) = − +ν − −

∞

*( ) 0 *1 K iage t

iage L L e

where Liage is the predicted forklength (of preserved fish measured in the laboratory) at age 

iage (days from hatch), K is the Brody growth coefficient (a measure of the rate at which 

growth rate declines with age), L∞ is the asymptotic length, t0 is the predicted age at which 

fish length is zero, and v is a normally distributed error term with a standard deviation σfl. The 

seasonal trend in egg hatch was estimated by predicting the age of each fish that was captured 

19
from Apr. – Oct. sample periods (n=991) based on their forklengths measured in the field. 

Age was subtracted from the date of capture to estimate the date of hatch. A random

subsample of 90 fish were measured in the field, preserved individually in 95% ethanol for 1 

month, the ca. time between preservation and otolith extraction, and then re-measured in the 

laboratory. The correlation between preserved and live forklengths was extremely high (r2

 = 

0.997) and shrinkage of preserved fish averaged only 1%. We therefore did not bother to 

account for the effects of shrinkage when back-calculating age based on forklengths measured 

in the field. 

A striped pattern in increments was observed on the otoliths of many individuals. This 

visual pattern was identified from the presence of atypical daily increments (different

appearance, usually light in color) formed every 7 days. For all otoliths, the presence or 

absence of the striping pattern was recorded. The number of otoliths where the presence of a 

striping was ambiguous was also recorded. To determine if the 7-day striping pattern was 

associated with periodic growth, a random sample of otoliths (n=15) containing clear striping 

patterns were examined and digitally photographed at a microscopic magnification of 600x 

under oil immersion. Given the 1280 x 1024 resolution of the image analysis system, 

measurement accuracy was ~ 0.1 μm. The width of individual daily increments was measured 

as the distance between the midpoints of adjacent D zones (terminology of Kalish et al. 1995). 

Measurements were made perpendicular to the local growth axis, beginning at the D zone just 

medial to the white stripe, and proceeding distally. Increment sequences were measured only 

if every increment of the 7-day striping cycle was clearly defined. 

The validity of daily increment counts as accurate age indicators in young fish is well 

established (Campana and Neilson 1985). However, to insure accuracy in this application, we 

compared estimates of age derived from otoliths for a small sample of hatchery YoY where 

age was precisely known. As part of a normal hatchery operation, wild spawning rainbow 

trout were removed from their natal stream (Blackwater River, BC) and spawned in a B.C. 

hatchery on May 13, 2003. Eggs were reared under constant temperature (7 C) and moved to a 

feeding trough (10 C) at swim-up. Fish were sampled from the trough at 31 and 84 days after 

hatching and preserved in 95% ethanol. Otoliths from these fish were removed and the known 

20
number of days from hatch to sampling was compared to the estimates derived from the daily 

ring counts between the hatch check and the otolith edge. This was a blind-analysis as the age 

of fish was unknown to the technicians at the time they examined the otoliths. 

4.1.3 Methods to Analyze Catch and Age Data 

A stock synthesis model was used to estimate seasonal trends in the number of fry-athatch (hereafter referred to as fry recruitment) and the apparent mortality rate from the lengthfrequency and ageing data. The model predicted the number of fish alive for 45 weekly 

cohorts (Niage,t) from Jan. through Oct. based on the equation, 

(4.2) M

iage t iage t N N e−

, = −1, −1

*

where, t is the number of weeks from start of the simulation, iage is the age of a fish in weeks 

from hatch, and M is the instantaneous mortality rate. The weekly survival rate, S is equal to 

e

-M. Mortality was represented by a single parameter under the assumption that it was constant 

over weeks and across ages, or was represented by a vector of parameter values for more 

complex models that accounted for changing mortality over time or with age (e.g., Miage,t). 

To model age-specific differences in mortality, fish size (predicted from eqn. 4.1) was 

used in the allometric relationship between natural mortality and body length (Lorenzen 

2000), 

(4.3) c

r

l r l

l M = M *( )

where Ml is the instantaneous mortality rate at forklength l, Mr is the mortality rate at

reference forklength lr, and c is the allometric exponent of the mortality-length relationship. 

We assumed that c = -1 following Lorenzen’s (2000) analysis based on stocking experiments 

of salmonids of various sizes, Note that c = 0 can be used to simulate the case where mortality 

21
does not change with size (Mr = Ml). The reference forklength was assumed to be 50 mm and 

Mr (or Mr,t for the variable survival-over-time model ) was estimated from the data. 

The recruitment of fish to each weekly cohort of the YoY population, which is 

equivalent to the number of eggs that hatch per week (N1,t from eqn. 4.2), was estimated using 

three alternate methods: 

1) Time-independent recruitment estimates for all 45 weeks of the simulation period; 

2) time-dependent estimates of weekly recruitment based on the assumption that the 

seasonal trend in weekly recruitment can be approximated using a beta distribution; 

and 

3) back-calculation of the shape of the seasonal trend in recruitment from the combined 

catch and length-at-age data (see Section 4.1.2) with the overall magnitude of 

recruitment estimated by the stock synthesis model. To derive the shape of the 

seasonal trend, the back-calculated egg hatch trend over time was fit using a Beta 

distribution. 

The beta-distribution used to model seasonal recruitment trends for methods 2) and 3) was, 

(4.4) , ( )( )( ) R R dt

t

t

r

t

r

t ∫ t

−

− − = −

1

1 1

max 1 α β θ θ

where Rmax is the maximum number of eggs hatching per week over the entire simulation 

period, and αr and βr are parameters of the Beta distribution that define the timing of hatch. θt

represents the proportional date of hatch and ranges from 1/45 on the first possible week of 

hatch (beginning on Jan. 1) to 1 on the last week T (beginning on Oct. 29), that is θt = t/T. 

The age-frequency computed by the model for each week based on recruitment and 

survival estimates was translated into a corresponding set of weekly length-frequencies based 

on the von Bertalanffy growth model parameterized from the otolith microstructure analysis 

(eqn. 4.1). The error term in the von Bertalanffy (σfl) model was used to predict the length22
frequency distribution for each weekly age. Within a cohort, the number of fish in a particular 

length bin was comprised of fish from a range of weekly ages. 

 We assumed that vulnerability to capture was a sigmoid saturating function of fish 

size, 

(4.5) s

h

s

fl FL fl

fl

+

V = 

where, fl specifies the 5 mm increment forklength category, Vfl is the forklength-dependent 

relative vulnerability (0-1), Flh is the size at which the fish are half as vulnerable as larger, 

fully vulnerable fish, and ‘s’ is a “steepness” parameter for the function (higher values of ‘s’ 

imply a “knife edge” change in vulnerability near size Flh). Parameters for this function (Flh = 

30 and s=10) were estimated based on observations of the numbers of fish that evaded 

capture. The model was parameterized so that fish below 20 mm were completely 

invulnerable, 30 mm fish had a vulnerability of 50%, and 40 mm fish were completely 

vulnerable. Vulnerability parameters were assumed to be constant across sampling periods. 

The simulated length-frequency was translated into an observed length-frequency by 

multiplying the predicted numbers of fish in each 5 mm forklength category by their assumed 

vulnerabilities. 

The model was fit to the Jun. – Oct. 2003 length-frequency data by estimating the 

apparent mortality rate(s) and weekly recruitment parameters. We assumed the error in the 

model followed a Poisson distribution (Hilborn and Mangel 1997), 

 

(4.6) ( ) !

( ) ,

,

,

,

,

ifl t

k

ifl t t

p N

ifl t iwk k

e p N

P k R M

t − ifl t t

=

Eqn. 4.6 computes the probability of the recruitment (Riwk) and mortality rate(s) (M) parameter 

estimates based on the observation of catching k fish in the iflth 5-mm length category on week 

t. The total catch for week t (Nt) and the proportion of the total catch in length category ifl

(pifl,t) in that week were predicted by the model as a function of the parameter estimates. A 

23
non-linear iterative search procedure (Solver as implemented in EXCEL) was used to 

minimize the sum of the negative log-likelihoods of eqn. 4.6 across all length categories and 

sample periods. The log-likelihood, Ln(L), for a single length category and period is, 

(4.7) ln( ) (ln( ) ln( )) ifl,t t ifl,t pifl,t Nt L = − p N + k +

Six alternate models were fit to the observed length-frequency data based on different 

assumptions regarding recruitment and survival dynamics. Three different models were used 

to simulate the weekly recruitment of hatching fry (time independent weekly values, timedependent weekly values modeled using a Beta distribution, and the back-calculated fry 

recruitment pattern where only the overall magnitude of recruitment is estimated by the stock 

synthesis model). The weekly mortality rate was either held constant over the simulation 

period or could vary by month. The simplest model consisted of 1 parameters defining 

recruitment and one parameter defining the overall weekly survival rate. The most complex 

model consisted of 45 weekly recruitment estimates and 10 individual monthly survival 

estimates (n=55). The stock synthesis model was fit to the length-frequency data collected 

from the 20 original sample sites only, which were predominantly low angle cobble bars and 

vegetated or sandy shorelines. We did not include data from the additional six talus shoreline 

sites sampled during the last two sample periods. The data suggest an ontogenetic habitat shift 

from low angle habitats to talus shorelines (see Section 4.2.1) as YoY grow. Including data 

from these additional six sites would introduce bias into estimates of recruitment and survival

due to changes in sampling. 

We used a likelihood ratio test to evaluate the statistical reliability of increasingly 

complex models. Twice the difference between the log-likelihood values from two different 

models has a Chi-square distribution with the degrees of freedom equal to the difference in the 

number of parameters between models (Hilborn and Mangel 1997). A more complex model 

would be considered to be significantly better than a simpler model if twice the difference in 

log-likelihoods among models exceeded the threshold Chi-square value at the specified Type I 

error level (e.g., α = 0.05). 

24
As we did not attempt to estimate an overall catchability for the Lee’s Ferry reach 

when fitting the model, the sum of the predicted recruitment rates over the simulation period 

represents the number of hatched eggs required to fit the sample catch. A reach-wide YoY 

population estimate could be derived by scaling-up the abundance of fish at any age by a 

factor that accounts for both the proportion of the total habitat that was sampled as well as the 

proportion of fish in the sampled areas that were not caught. As we did not use a multi-pass 

depletion approach in enclosed areas to capture fish, we have no way of estimating the latter 

proportion and therefore do not present estimates of reach-wide YoY population size. 

4.2 Results from Young-of-Year Sampling and Analysis 

4.2.1 Results from Young-of Year Field Survey 

Our initial efforts to capture fry by backpack electrofishing during the Apr. and May 

sample periods were not very successful. We fished during night and day, but always at 

relatively high flows (e.g. 8:00 – 22:00). In Apr., the few fry we were able to capture were 

very small (< 30 mm) and always located in the immediate vicinity of submerged redds. At 

this time we assumed that our lack of success at capturing fry was because very few fry had 

emerged. Based on our observation that very few redds were present during our pilot redd 

survey conducted in late Jan., this was a reasonable assumption, as there would be a minimum 

two month lag between the time of spawning and the time when we could capture emerging 

fry assuming 30 days spawn-to-hatch and an additional 30 days for emergence. Our May redd 

survey was conducted over the Memorial Day long-weekend when flows were held steady at 

8 kcfs for aerial photography. Test sampling for YoY during steady flows revealed that many 

small fry were present, and we obtained a sufficient sample for otolith analysis. We decided to 

perform representative fry sampling at 20 random locations immediately following the longweekend when flows returned to normal operations for that month (7.5-13.5 kcfs) as we 

suspected that flow and daily flow variation could effect catchability, and we wanted 

catchability in May to be comparable with subsequent sample periods. In spite of considerable 

effort, we were unable to catch fry on May 27 when discharge was increased to ca. 13 kcfs. At 

25
this point we realized that fry must not be following the waters edge on a diurnal basis as 

discharge was increased, but instead must have been holding near the minimum daily stage 

(7.4 kcfs in May). All future sampling was therefore conducted at night at the daily minimum

flow. Fish collected in Apr. and May were useful for ageing purposes, but mean size and 

abundance estimates derived from these fish cannot be compared with those in subsequent 

months because of differences in sampling methodology. 

As expected, fish size increased over the summer and fall while density generally 

declined (Table 4.1). Talus shorelines had much higher densities compared to lower angle 

habitats (cobble bars and vegetated/sandy shorelines) and tended to attract or support slightly 

larger fish. A considerable reduction in density and the percentage of sites with fish present 

was evident between the late-Jul. and early-Sep. sampling periods in low angle habitats. 

The longitudinal distribution of fry across sites showed a strong seasonal pattern (Fig. 

4.1). Highest densities were observed at the most upstream sites during the Jun. sample 

period. In Jul., densities in the upper portion of the Lee’s Ferry reach were somewhat reduced 

and higher densities were observed at the lower end of the reach. This could be indicative of 

either earlier spawning at upstream locations (Fig. 2.3) or downstream dispersal. By the Sep. 

sample period fry distribution based on the original 20 sites (i.e., low angle habitats) was 

essentially limited to the lower 2.5 miles of the Lee’s Ferry reach. We added an additional 6 

talus shoreline sites to our sampling program beginning in Sep. when we observed the 

dramatic decline in fish numbers in low angle habitats. When we include these additional sites 

in the analysis (Fig. 4.1b), fry were present in the upper river in Sept., but only in steeper talus 

shoreline habitats. The Oct. spatial distribution of YoY was very similar to that observed in 

Sept. The monthly length-frequency samples from the 20 original sites for Jun., Jul., Sept., 

and Oct. had modes of 25-30 mm, 40-45 mm, 55-60 mm, and 65-70 mm, respectively (Fig. 

4.2). 

The boat electrofisher was generally more efficient at capturing YoY relative to the 

backpack electrofisher, but differences depended on habitat type and fish size (Table 4.2). A 

45 m site, on average, took ca. 14 minutes to sample using 700 seconds of backpack 

26
electrofishing effort, In contrast, the same site sampled by a boat electrofisher took about half 

as long to sample in terms of both time (6 minutes) and electrofishing effort (350 seconds). At 

sites that were fully accessible to the boat (3.5 Mile, 4 Mile, 7 Mile, near Waterholes Canyon), 

the boat electrofisher caught 2- to 6-fold more fish compared to the backpack electrofisher. At 

sites that were very shallow where only ca. 50-75% of the site was accessible to the boat (6 

and 9 Mile), catches with the backpack electrofisher were equal or higher compared to the 

boat electrofisher. Catches were also higher with the backpack electrofisher at a site with a 

sandy/vegetated bottom as the hand-held anode was very effective at ‘straining’ stunned fish 

from submerged vegetation and algae. Average fish size captured with the boat electrofisher 

was consistently larger by ca. 10 mm at all locations except one site (9 Mile) that was only 

partially accessible to the boat. 

4.2.2 Results from Analysis of Otolith Microstructure

The otoliths of 266 YoY rainbow trout captured between Apr. – Oct. sample periods 

were extracted and successful age determinations were made for 237 of these fish. Age 

determinations were not possible for 29 fish due to difficulties encountered during preparation 

(otolith cracking or chipping) or for larger fish where counting increments became difficult to 

distinguish. Age estimation in fish > 50 mm of length was noticeably more difficult than for 

smaller fish, and fish >80 mm were very difficult to age reliably. For that reason, some ages 

for larger fish were not obtained, and replicate age estimates for larger fish were more 

variable than those for smaller fish. In contrast, the precision of otolith radius measurements 

was unaffected by fish size. 

Hatch checks were clearly evident on all otoliths (Fig. 4.3). Emergence checks tended 

to be subtler and could not always be identified, thus age was determined relative to the hatch 

date. A major white band was observed in more than half (54%) of the total number of 

otoliths examined. The white band was often characterized by narrowing growth increments 

of low visual contrast, which increased in width and contrast immediately distal to the band. 

A prominent check was often, but not always, associated with the band. The band was present 

in all collection months, but was usually seen only in fish above 30 mm in length. Bands near 

27
the edge of the otolith were difficult to identify because of their position. The position and 

age of formation of the band was measured in 52 fish from various sample periods and size 

categories. The age from hatch at which the white band was formed averaged 39 days and was 

highly variable (95% confidence limits of +/- 20 days). The average size of fish at the time the 

major white-band was formed, determined from length back-calculation using the best-fit von 

Bertalanffy relationship (eqn. 4.1), was 25 mm (+/- 7 mm). The width of the band averaged 3-

10 days. In the Lee’s Ferry reach, fish between 25 and 28 mm often showed remnants of a 

yolk sac. Rainbow trout require two to six weeks to emerge following hatch (Moyle 2002, 

McEwan and Jackson 1996). The average number of days between hatch and the formation of 

the major white band corresponds to the upper-end of emergence time requirements from the 

literature. The white band could therefore represent the transition period between yolk-sac 

absorption and first-feeding. 

There was little error in the estimation of days from hatch based on a blind-test using 

hatchery fish of known age (Fig. 4.4). The estimated age of hatchery fish averaging 28 mm

(n=11) in length sampled 31 days after hatch on Jul. 24, 2003 ranged from 30-36 days with an 

average of 32 days. The estimated age from hatch for fish averaging 52 mm in length (n=10) 

sampled 84 days after hatch on Sep. 15, 2003 ranged from 73-89 days with an average of 82 

days. The precision of the daily age from hatch was ca. +/- 7 days for larger fish and +/- 2 

days for smaller fish. 

There was a very strong relationship between forklength and daily age from hatch 

(Fig. 4.5). The best-fit von Bertalanffy relationship had parameters K=0.0035 (per day), L∞= 

150 (mm), and t0 = -14.06 (days) and explained over 87% of the variation in forklength (n = 

237). No seasonal trend in mean size-at-age was apparent. The absolute amount of variation in 

size-at-age increased with age but the relative variation remained constant (CV = 11%). YoY 

less than 80 mm grew on average 2.2 mm/week, equivalent to 10.3 mm/month or 0.3 mm/day. 

Very young fish 5-10 weeks from hatch grew ca. 3 mm/week while older fish 30-35 weeks 

from hatch grew at ca. 1⁄2 this rate. 

28
In 2003 the majority of hatching occurred between mid-Mar. and late-May with a peak 

in late-Apr (Fig. 4.6). A spawn-timing curve was derived from the hatch-timing curve by 

assuming a 30-day egg incubation time. Incubation time was estimated based on intergravel 

temperature measurements from the Lee’s Ferry reach in 2003 and literature values on the 

required number of thermal units from fertilization to hatch (Table 4.3). The peak of this fryderived spawn-timing curve was close to the peak spawn of the curve derived from the redd 

count data (Fig. 4.7). The redd-based spawn-timing curve fit to the Feb. – May redd survey 

data suggests that there was considerably more spawning in Jun. and Jul. than implied by the 

fry-derived curve. This could indicate either: a) considerably higher egg or fry mortality for 

fish spawned later in the year, or b) overestimation of the late-spawning component based the 

redd data. While we observed many redds during our late-May survey, little spawning activity 

was seen. When we fit the spawn-timing curve to the observed redd count data with the 

assumption that spawning was complete by the end of May (Fig. 2.8a), the fry- and reddderived spawn-timing curves are in closer agreement. However, because the modified spawntiming curve is based on anecdotal information, we cannot determine which of the two 

hypotheses is more likely. 

A weekly striping pattern was evident in at least 51% (131) of the 255 otoliths that 

were examined (Fig. 4.8). The striping pattern was ambiguous in 14% of the otoliths (36). In 

general, striping was most evident in the middle and outer sections of the otolith, and in larger 

individuals. However, since the striping pattern was only obvious when several consecutive 

cycles were present, it is probable that additional otoliths from smaller fish had the pattern, 

but could not be identified. Of the 15 daily increment sequences measured in striped otoliths, 

all but 3 spanned more than 1 week. The atypical increment formed every 7 days tended to be 

25% wider (3.12 microns) compared to the other increments (2.51 microns) when averaged 

across all striping cycles and fish, and the difference was statistically significant (Table 4.4). 

Within fish, the average increment width for the atypical bands was larger than the average 

width of the other increments in 14 of 15 cases, and the differences were often statistically 

significant when sample size for atypical increments was adequate. 

29
4.2.3 Results from Stock Synthesis Model 

The stock synthesis model was able to provide good fits to the observed monthly 

length-frequency distributions (Fig. 4.9). Increasingly complex models that allowed timeindependent weekly recruitment and/or variable survival rates across months explained the 

most variation in the data. The most likely estimate of the weekly survival rate ranged from

0.88 to 0.90 across alternate recruitment models when the survival rate was assumed to be 

constant over the simulation period (Table 4.5). All constant-survival models tended to over 

predict the number of fish present in the 45-75 mm length categories on the Sept. sample 

period (Fig. 4.9). The extent of this discrepancy decreased with increasingly complex 

recruitment models because a more dynamic recruitment pattern could help compensate for an 

apparently anomalously low survival rate that occurred between the late Jul. and early Sep. 

samples. All recruitment models showed a large decrease in the survival rate between the Jul. 

and Sep. sample periods when survival rate was allowed to vary by month (Table 4.5). We

cannot determine if the seasonal trend in survival rate, or the discrepancy between 

observations and predictions in Sep. under the constant-survival model, are the result of 

increased mortality between late-Jul. and early-Sep., or alternately, displacement from low 

angle habitats in Aug./earl-Sep. followed by recolonization of these habitats in Oct. 

The time-independent recruitment model did not result in a statistically significant (α

= 0.05) improvement in fit relative to the model where recruitment was simulated using the 

best-fit Beta distribution (Table 4.5) when survival rate was held constant. However, for the 

constant-survival model, the weekly recruitment model produced a significantly better fit 

compared to the back-calculated recruitment model (p<0.001). There were no statistically 

significant improvements among increasingly complex recruitment models when survival rate 

was allowed to vary by month. Inflexibility in recruitment dynamics associated with simpler 

models could be compensated by the variable survival rate, resulting in little difference in 

likelihoods (Table 4.5). Thus, recruitment rates and survival are confounded when survival 

rates are allowed to vary over time. Within recruitment models, allowing survival rate to vary 

by month always resulted in a statistically significant improvement in fit (p < 0.005) relative 

to the models where survival rate was held constant. 

30
The constant weekly survival estimate was very precisely defined and its precision 

was not dependent on the form of the recruitment model. 95% confidence limits were 0.88 +/- 

0.015 for time-independent and best-fit Beta distribution recruitment models (Fig. 4.10). The 

shape of the likelihood profiles for these two models was virtually identical even though the 

absolute value of the likelihoods for the more complex model was lower. As confidence limits 

are based on the difference between the minimum negative log likelihood and other values, 

identical shapes of the likelihood profiles for the two recruitment models meant that 

confidence limits were also identical. Survival rate and recruitment would have been 

completely confounded if only one length-frequency sample was collected. With multiple 

sampling episodes, survival rate had to explain differences in the same weekly cohorts 

through time and was therefore somewhat independent of the estimated recruitment rates. For 

example, estimates of very large recruitment values would force the model to estimate a low 

survival rate to fit the length-frequency data from the first sample period. The low survival 

rate would then under predict the abundance of these fish as they recruited to larger size 

classes in the next sample period and would therefore be considered improbable. 

 

Predictions of seasonal recruitment trends were dependent on the flexibility in the 

recruitment model. When survival rate was held constant, the time-independent weekly 

recruitment model predicted a strong recruitment peak in early May (Fig. 4.11) to produce the 

strong cohort of 25-35 mm fish captured during the late Jun. sample period (Fig. 4.2). The 

length-age model predicted that it took 8 weeks to reach 30 mm from hatch, which was the 

length of time between the early May recruitment peak and the late-Jun. sample period. The 

time-dependent best-fit Beta distribution recruitment model produced a recruitment trend that 

was essentially a smoother version of the time-independent one with a slightly earlier date of

peak hatch. The back-calculated recruitment trend produced a peak hatch near the same date 

as the time-independent weekly model. The fixed shape of the back-calculated model 

produced lower recruitment rates in Mar. and Apr. relative to other recruitment models that in 

turn resulted in a slightly higher estimate for the constant survival rate (Table 4.5). When 

survival rate was allowed to vary, recruitment trends could not be estimated reliably as they 

were completely confounded with survival rates. 

31
4.3 Conclusions from Analysis of Young-of-Year Data 

The stock synthesis model applied to length-frequency data from monthly sampling 

provided a very accurate estimate of the apparent weekly survival rate for YoY rainbow trout 

in the Lee’s Ferry reach. Increasing variation in size-at-age for older age groups tends to 

‘smear’ year-to-year variation in recruitment and survival estimates developed from annual 

length-frequency datasets. In this application, the relationship between length and age derived 

from analysis of otolith microstructure was very precise and the model was applied over a 

relatively short time period (10 months). This minimized the typical ‘smearing’ problems 

associated with many length-frequency analyses and led to a precise estimate of the weekly 

survival rate. The sampling and analytical approach can therefore be used to estimate apparent

survival rates in subsequent years under different GCD operations as a means for evaluating 

the effectiveness of flows targeted at reducing survival rates of rainbow trout YoY. The 

methodology will likely be able to detect smaller effects induced by dam operations than the 

current adult monitoring program that is subject to greater smearing effects. The YoY 

methodology also produces a signal in the same year as the experimental treatment. In 

contrast, 2-3 years are required before recruitment and survival effects on YoY manifest 

themselves in the adult population. 

The constant-survival rate model over-predicted the abundance of most size classes of

YoY during the early-Sep. sampling period. This suggests that the survival rate between the 

late-Jul. and early-Sep. trips was somewhat lower than the average rate for the rest of the 

summer, or that there was a displacement of fish from low angle habitats between these two 

periods. A key flaw in our sampling design was poor representation of steeper shoreline 

habitats during early sampling periods. We did not anticipate that YoY trout would utilize 

these habitats in their first 6 months of life. Without a consistent representation of these 

habitats across all sampling periods, we were not able to determine if the dramatic change in 

density in low angle cobble bars observed in early-Sep. was the result of mortality or 

migration to steeper shorelines. We plan on remedying this flaw in the 2004 YoY sampling 

32
program by using a combination of backpack and boat electrofishing in low angle and steeper 

habitats, respectively. We have shown that backpack shocking was most effective in very 

shallow habitats utilized by the smallest component of the YoY population. Boat 

electrofishing was more efficient in deeper habitats such as steeper talus shorelines, but 

tended to select for larger fish. As both gear types must be used to efficiently sample all 

habitat types, it is critical that they are consistently applied over sample periods in terms of the 

proportion of sites sampled by each method and the habitat types where they are utilized. 

The dramatic change in YoY density in low angle cobble bar habitats between the lateJul. to early-Sep. sample periods could have been caused by the large change in the minimum 

flow from 10 to 5 kcfs that occurred after the labor-day weekend (Table 2.2). There is a 

considerable body of literature documenting the effects of short-term changes in flow on 

stranding of juvenile salmonids. Stranding rates are highest for very young fish and in low 

angle complex habitats such as the cobble bars and vegetated shorelines. Low angle habitats 

(vegetated shorelines associated with sand bars, debris fans, and cobble bars) constitute over 

55% of the useable shoreline length in the Lee’s Ferry reach (cliff areas excluded), with 

steeper talus shorelines making up the remaining 45% (Steve Mietz, GCMRC GIS database, 

Flagstaff, AZ., unpublished data). Imposing a large mortality event in low angle habitats by 

sudden changes in the minimum flow could therefore cause a considerable impact to the 

population as a whole. Stranding rates have also been shown to be highest following a long 

habituation period at one flow regime (Halleraker et al. 2003). It is possible that YoY in low 

angle habitats were habituated to a 10 kcfs minimum over the summer and therefore very 

susceptible to stranding when the minimum flow was changed to 5 kcfs after the labor-day 

weekend. 

The literature suggests that typical daily fluctuations in flow are unlikely to have a 

direct mortality impact on YoY rainbow trout in the Lee’s Ferry reach, but our data almost 

certainly suggest that there is an impact on growth rate. Sampling observations showed that 

YoY do not follow the diurnal rise and fall of the waters edge but instead reside near the stage 

of the daily minimum flow. It is very likely that the larger width of atypical increments on the 

otolith that occurred with a periodicity of exactly 7 days (+ 50%) were caused by steady low 

33
flows on Sunday. We know of no other physical event that occurred with a periodicity of 

exactly 7 days over the study period. Changes in increment width or visual contrast in otolith 

microstructure can be induced by short-term temperature fluctuations (Campana and Nielsen 

1985). It is possible that water temperature at the stages where the majority of YoY are 

residing was higher on Sundays than other days during the week as water depth would be 

much shallower and velocities would be slower. Shoreline-offshore temperature gradients 

have been documented during long periods of steady flow in Grand Canyon (B. Vernieu, 

Grand Canyon Monitoring and Research Center, unpublished data). It is also possible that 

increased growth of YoY during Sunday-steady low flows was the result of increased feeding 

opportunities or reduced energy expenditure. YoY must have been residing very close to the 

substrate to maintain their position at the daily minimum flow elevation during the day when 

flows were high. This positioning would likely reduce their ability to forage or increase their 

energetic costs. The fact that we observed no lag effect in the otolith microstructure, that is, 

only 1 increment per week was affected by steady low flows, could suggest that the effect was 

an instantaneous one associated with temperature, rather than a smoothed effect associated 

with increased feeding opportunities or reduced energy expenditure. We plan on deploying 

continuously recording temperature loggers at increasing distance from the shoreline in the 

Lee’s Ferry reach for brief periods over the spring and summer in 2004. The presence of 

increased temperatures on Sunday at elevations where YoY reside, relative to temperatures at 

these elevations on other days of the week, would support the temperature hypothesis. 

34
5.0 Preliminary Recommendations for the Design of Future 

Experimental Flow Regimes and Monitoring of their Effects 

The 2003 study of the effects of fluctuating flows on the early life stages of rainbow 

trout in the Lee’s Ferry reach provided some useful information with respect to designing and 

monitoring future flow regimes. As we conducted no work in Grand Canyon in 2003, our 

recommendations apply only to the Lee’s Ferry reach. However, since the objective of the 

increased fluctuating flow experiment was to reduce the population of rainbow trout in Grand 

Canyon, our recommendations may be of limited value. On the other hand, if the processes we 

observed in the Lee’s Ferry reach are a good model for those in Grand Canyon, or if the 

source of rainbow trout in Grand Canyon is from juveniles and adults emigrating from the 

Lee’s Ferry reach, the recommendations we provide are of interest. 

 Fluctuating flows targeted at increasing egg-to-alevin mortality should be conducted 

over a four-month period between Feb. and May to coincide with the majority of spawning 

activity. We estimated that 50 - 60% of redd excavation in 2003 occurred after Mar. 31. 

Terminating high flows after this date probably increased the number of redds excavated at 

lower stages where temperature and exposure impacts would be less severe. We do not 

recommend delaying the timing of the morning increase in discharge as was done in 2003 

when lost hydropower revenue costs are considered. Significant temperature-induced 

mortality can be achieved by implementing low steady flows every Sunday. This strategy will 

result in lethal or sub-optimal temperatures occurring once per week above the Sunday 

minimum flow elevation by about mid-Mar. The downside of this strategy is that the Sunday 

low flows will likely increase the extent of spawning at stages below the minimum flow. An 

earlier rise in discharge will also increase the error in redd surveys which should be conducted 

at minimum flows during daylight hours. To limit these effects, steady low flows could be 

conducted every second week. We therefore recommend a bi-weekly steady low flow of 5 

kcfs over one or two days, coupled with fluctuating flows up to 20 kcfs from Feb. through 

May, as the most effective means of reducing egg and alevin survival rates in the Lee’s Ferry 

reach. 

35
The spawning habitat preference models developed for the Lee’s Ferry reach were 

useful for evaluating the extent to which increased discharge during the Jan. - Mar. 

experimental flow period altered the stages where spawning occurred. Weighted useable area 

computations showed that higher discharges increased total spawning habitat availability at 

sites that had available spawning habitat at higher stages such as Four Mile and Powerline 

Bars, and reduced spawning habitat availability at deep-water redd sites such as Ferry Swale. 

The model also showed that the stage of spawning at Four Mile and Powerline Bars was 

increased under higher discharge. Such changes in spawning habitat availability would likely 

increase the proportion of redds that would be desiccated and the duration of exposure 

resulting from flow fluctuations. The redd hypsometry study showed that there was a high 

proportion of redds excavated in deep-water that would not be dewatered at flows as low as 5 

kcfs. The large decline in spawning habitat availability at Ferry Swale under high discharge 

suggests that spawning at deep-water sites could be suppressed through maintenance of high 

flows. We noted an increase in spawning at Ferry Swale when flows were reduced on Apr. 1, 

but we are uncertain whether this increase was the result of the flow change effecting 

spawning habitat availability, or was a normal occurrence driven by seasonal trends in spawn 

timing. In summary, the high discharges implemented in Jan.-Mar. 2003 were successful at 

increasing the stages of spawning at a limited of number sites (thus increasing mortality 

exposure), but the efficacy of these flows for reducing the large proportion of redds that are 

formed at stages below 5 kcfs (40-50%) is highly uncertain. The ultimate test would be to 

measure redd hypsometry in subsequent years under normal operations and compare the 

results with hypsometry measured under the experimental flow regimes. Maintaining higher 

flows for the duration of the spawning period (Jan. – May) would improve this comparison as 

well as the effectiveness of the flow regime. 

36
A number of preliminary flow recommendations can be made based on results from

the 2003 Young-of-Year survey results: 

1. Fluctuating Flows Targeting YoY Rainbow Trout Should Be Implemented from 

Apr. through July. In 2003, very few fry or alevins were present during the Jan.-

Mar. period when flows fluctuated from 5-20 kcfs. Literature and experimental studies 

in the Lee’s Ferry reach (Montgomery and Tinning 1993) demonstrate that post-hatch 

stages of rainbow trout are the most sensitive to fluctuating flows. The timing of 

increased fluctuations targeted at fry should therefore begin on the date when a 

significant proportion of the eggs begin to hatch, which in 2003, was approximately 

Apr. 1. The negative effects of fluctuating flows will likely attenuate as YoY grow and 

migrate to steeper shoreline habitats. Increased fluctuating flows could probably be 

terminated by the end of Jul. if necessary. The 2004 study will provide a clearer 

picture of the ontogenetic habitat shift from low angle habitats to steeper shoreline that 

occurs over the summer and therefore provide better resolution on when enhanced 

fluctuating flows can be terminated.

 

2. Summer Steady Flows are Beneficial to YoY Rainbow Trout. The weekly striping 

pattern in otolith microstructure that we observed suggests that steady low flows, such 

as those conducted during the summer of 2000, almost certainly enhance the growth of 

YoY rainbow trout. Increased growth could lead to increased survival and higher 

abundance. In support of this hypothesis, preliminary analysis of rainbow trout lengthfrequency data from Grand Canyon shows the presence of a very strong cohort from

the 2000 brood year when the low steady flow experiment was conducted (S. Rogers, 

AGF, Flagstaff, AZ., unpublished data). 

3. Sudden Reductions in the Minimum Daily Flow Have the Potential to Strand or 

Displace Many YoY Rainbow Trout. Fluctuating flows, when implemented on a 

very regular basis, likely do not displace or strand large numbers of YoY rainbow trout 

in the Lee’s Ferry reach. However, the dramatic decline in the density of fish in low 

angle habitats that we observed following the labor-day weekend in 2003, suggests 

37
that large and sudden reductions in the daily minimum flow (e.g., 10 to 5 kcfs) may 

achieve this objective. The stranding literature suggests that the most effective 

‘stranding flow’ would consist of high steady flows (e.g. 15-20 kcfs) for a few days to 

allow sufficient time for YoY to habituate by moving to the waters edge associated 

with the high flow. A sudden drop in flow (e.g. to 5-8 kcfs) using an unrestricted 

ramping rate would likely strand a large number of young fish. Such a flow should be 

conducted in Jun. when the majority of YoY have already hatched but are still small 

and have not migrated in large numbers to less vulnerable steeper habitats. Better 

documentation of the ontogenetic habitat shift from low angle to steeper shorelines in 

2004 will be useful for defining the best time to conduct stranding flows. Conducting 

fry surveys before and after a flow change could assess their effects.

If managers must choose between flows targeted at reducing the survival rate of 

incubating stages vs. those targeted at fry that have already emerged, our preliminary opinion 

is that the latter choice would be better. We base this opinion both on the effectiveness of 

using flow to reduce survival as well as our ability to monitor these effects: 

1. Uncertainty in Altering Spawning Stages. It will take at least a few more intensive 

years of study, coupled with experimental flows, to determine if deep-water spawning 

can be minimized by high discharge. If it can’t, it is likely that redd dewatering will 

only effect a small component of the overall spawn and therefore not be that effective 

in reducing rainbow trout recruitment. Even if the Jan.-Mar. 2003 flow regime did 

limit deep-water spawning, recall that our estimate of the total egg deposition loss due 

to Glen Canyon Dam operations in 2003 ranged from 30 - 40%, with only about half 

of this mortality being a direct consequence of the enhanced fluctuating flows in Jan.-

Mar. It seems unlikely that an additional mortality on eggs and alevins of 15-20% 

would have a significant impact on adult abundance when post-emergent densitydependent processes are considered.

2. Density Dependence. There are many opportunities for density dependent processes 

to compensate for the reduced production of emergent fry due to exposure of redds. In 

38
comparison, reducing the survival of fry over the summer through flow fluctuations 

would occur after some, if not the majority, of density dependence.

3. Fluctuating Flows Likely Have a Larger Impact on Fry that Have Already 

Emerged than on Incubating Stages. This statement is supported by many studies 

that show considerable tolerance for egg stages of rainbow trout to prolonged periods 

of exposure, including field experiments in the Lee’s Ferry reach (Montgomery and 

Tinning 1993). The stranding literature clearly demonstrates the impacts of certain 

types of fluctuating flows on young salmonids. The large change in density and 

distribution of fry in the Lee’s Ferry reach following the flow change in early-Sep. 

suggests that stranding could be an efficient method for reducing fry production. 

‘Stranding-flows’ would likely be effective even if implemented on a bi-weekly or 

monthly basis. Relative to increased fluctuating flows implemented on a continuous 

basis, periodic stranding-flows would likely have less of an impact on other resources 

such as recreation.

4. Measuring the Effects in the Lee’s Ferry Reach. Uncertainty in estimates of egg 

and alevin mortality derived from redd counts is higher than the uncertainty associated 

with estimating summer fry survival. Identification of redds is a somewhat subjective 

process and error increases at deep-water sites. Even if the resulting redd hypsometry 

derived from the redd counts is accurate, we can only infer mortality rates based on 

exposure periods and intergravel temperatures. This inference could be improved by 

in-situ incubations studies as performed by Montgomery and Tinning (1993), but only 

at higher stages. We will likely never be able to evaluate the survival of incubating 

stages at deep-water sites that constitute a large component of the total production. In 

contrast, the precision and inferences of apparent fry survival estimates seem much 

better. In addition, information on changes in density, habitat use, and growth 

provided from the fry sampling improves our understanding of dam operations on 

survival and growth of young trout. 

39
5. Measuring the Effects in Grand Canyon. Accurate redd counts in Grand Canyon 

will very likely be much more difficult to achieve than in the Lee’s Ferry reach. 

Reduced water clarity will limit our ability to detect deep-water redds. It is important 

to conduct redd surveys at the minimum flow to maximize the proportion of redds that 

are exposed or in shallow water where they can be more easily identified. In lower 

Marble Canyon, the minimum flows occur mostly at night so we will be forced to 

conduct surveys either during the day at high flows or during the night. Preliminary 

mainstem redd surveys conducted at night in Grand Canyon (L. Coggins, GCMRC, 

Pers. Comm..) did not observe any spawning activity at higher stages that were 

exposed during minimum flows. If most of the mainstem spawning in Grand Canyon 

is in deep water, redd count estimates will be more uncertain relative to the Lee’s 

Ferry reach. If water clarity is low at the time of the surveys, the data could be very 

inconclusive. In contrast, fry sampling will be no more difficult downstream,

excluding logistic costs, than in the Lee’s Ferry reach.

The objective of work in FY04 is to continue with the redd hypsometry and fry 

surveys in both the Lee’s Ferry reach and in Grand Canyon. While we did not conduct any 

work in Grand Canyon in 2003, our gut-feeling based on observations in the Lee’s Ferry reach 

is that increased fluctuations in flow will have a smaller effect on early life stages in Grand 

Canyon relative to the Lee’s Ferry reach. If there is considerable mainstem spawning 

downstream of Lee’s Ferry, it is likely at lower stages. Many fisheries biologists have traveled 

through Marble Canyon and it is unlikely they would have missed large spawning sites 

located at higher stages. Thus, if there are many redds in the mainstem of Grand Canyon, it is 

unlikely that many will be dewatered through fluctuating flows. With regards to fry that have 

emerged from the gravel, it is likely that the impacts of fluctuating flows on growth and 

survival are less severe in steeper shoreline habitats than in low angle cobble bars and 

vegetated shorelines. This is supported by the dramatic change in the density of fry in low 

angle habitats that we observed in the Lee’s Ferry reach following the early-Sep. flow. As 

there is a higher proportion of steeper shoreline habitat downstream of Lee’s Ferry, it follows 

that impacts from fluctuating flows will be less severe. These hypotheses are highly 

40
speculative and we hope our work in FY04 will provide data to help evaluate them more 

rigorously.

41
6.0 References 

Angradi, T.R., Clarkson, R.W., Kinsolving, D.A., Kubly, D.M., and S.A. Morgensen. 1992. 

Glen Canyon Dam and the Colorado River: Responses of the aquatic biota to dam

operations. 

Report prepared by Arizona Game and Fish Department for Glen Canyon Environmental 

Studies. 

Becker, C.D., D.A. Neitzel, and D.H. Fickeisen. 1982. Effects of dewatering on Chinook 

salmon redds: Tolerance of four developmental phases to daily dewaterings. 

Transactions of the American Fisheries Society 111:624-637. 

Bovee, K.D. 1982. A guide to stream habitat analysis using the Instream Flow Incremental 

Methodology. Instream Flow Inf. Pap. 12. U.S. Fish Wildl. Serv. FWS-OBS 82/26. 

Bradford, M.J. Taylor, G.C., Allan, A., and P.S. Higgins. 1995. An experimental study of the 

stranding of Coho Salmon and Rainbow Trout during rapid flow decreases under 

winder conditions. North Amer. J. Fish. Manag. 15: 473-479. 

Campana, S.E. 1992. Measurement and interpretation of the microstructure of fish otoliths. P. 

59-71. In: D.K. Stevenson and S.E. Campana [eds]. Otolith microstructure 

examination and analysis. Can. Spec. Publ. Fish. Aquat. Sci. 117. 

Campana, S. E. and J.D. Neilson. 1985. Microstructure of fish otoliths. Can. J. Fish. Aquat. 

Sci. 42:1014-1032. 

Chapman, D.W., Weitkamp, D. E., Welsh, T.L., Dell, M.B., and T.H. Schadt. 1986. Effects 

of river flow on the distribution of chinook salmon (Onchoynhus-thawytscha) redds. 

Transactions of the American Fisheries Society. 115(4):537-547. 

42
Crisp, D.T. 1981. A desk study of the relationship between temperature and hatching time for 

the eggs of five species of salmonid fishes. Freshwater Biology 11:361-368. 

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. 

Flodmark, L.E.W., Urke, H.A., Halleraker, J.H., Arnekleiv, J.V., Vøllestad, L.A. & Poléo, 

A.B.S. 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. 

Foster, D. 2002. Fly fishing Lee’s Ferry, Arizona. The complete guide to fishing and boating 

the Colorado River below Glen Canyon Dam. Nononsense Fly Fishing Guidebooks, 

Sisters, Oregon. 

Gordon, N.D., T.A. McMahon, and B.L. Finlayson. 1994. Stream hydrology: an introduction 

for ecologists. John Wiley and Sons, New York.

Halleraker, J.H., Saltveit, J.J., Harby, A., Arnekleiv, J.V., Fjesdstad, H.P., and B. Kohler. 

Factors influencing stranding of wild juvenile brown trout (Salmo Trutta) during rapid 

and frequent flow decreases in artificial stream. Riv. Res. Applic. 19: 589-603. 

Hilborn, R., Bue, B.G., and S. Sharr. 1999. Estimating spawning escapements from periodic 

counts: a comparison of methods. Can J. Fish. Aquat. Sci. 56: 888-896. 

Hilborn, R., and M. Mangel. 1997. The ecological detective: Confronting models with Data. 

Princeton University Press, Princeton, NJ. 

Irvine, J.R., and T.C. Nelson. 1995. Proceedings of the 1994 salmon escapement workshop 

plus an annotated bibliography on escapement estimation. Can. Manuscr. Rep. Fish. 

Aquat. Sci. No. 2305. 

43
Kalish, J. M., Beamish, R. J., Brothers, E. B., Casselman, J. M., Francis, R. I. C. C., 

Mosegaard, H., Panfili, J., Prince, E. D., Thresher, R. E., Wilson, C. A., and Wright, P. 

J. 1995. Glossary for otolith studies. P. 723-729. In: Secor, D. H., J.M. Dean, and S.E. 

Campana. Recent Developments in Fish Otolith Research. University of South 

Carolina Press. Columbia, S.C. 

Kondolf, G.M. 2000. Assessing salmonid spawning gravel quality. Trans. Am. Fish. Soc. 

129:262-281. 

Kondolf, G.M., and M.G. Wolman. 1993. The sizes of salmonid spawning gravels. Water 

Resources Research, 29:2275-2285. 

Kondolf. G.M., S.S. Cook, H.R. Maddux and W.R. Persons. 1989. Spawning gravels of 

rainbow trout in the Grand Canyon, Arizona. Journal of the Arizona Nevada Academy 

of Science, 23:19-28. 

Korman, J., Ahrens, R.N.M., Higgins, P.S., and Walters, C.J. 2002. Effects of observer 

efficiency, arrival timing, and survey life on estimates of escapement for steelhead 

trout (Oncorhynchus mykiss) derived from repeat mark-recapture experiments. Can. J. 

Fish. Aquat. Sci. 59: 1116-1131. 

Leopold, L.B. 1994. A view of the river. Harvard University Press, Cambridge, 

Massachusetts. 294 pp. 

McEwan, D. and T.A. Jackson. 1996. Steelhead restoration and management plan for 

California. Department of Fish and Game. 

McKinney, T., Speas, D.W., Rogers, R.S., and W.R. Persons. 1999. Rainbow trout in the 

Lee’s Ferry recreational fishery below Glen Canyon Dam, Arizona, following 

44
establishment of minimum flow requirements. Report prepared for Grand Canyon 

Monitoring and Research Center by Arizona Game and Fish Department. 

Montgomery, W. L. and K. Tinning. 1993. Impact of fluctuating water levels on early life 

history of rainbow trout (Draft). Report prepared for Glen Canyon Environmental 

Studies. 

Moyle, P. 2002. Inland fishes of California, 2nd Ed. University of California Press, Berkeley, 

CA. 

Pert. E.J., and D.C. Erman. 1994. Habitat use by adult rainbow trout under moderate artificial 

fluctuations in flow. Trans. Am. Fish. Soc. 123: 913-923. 

Piper, R.G., McElwain, I.B., Orme, L.E., McCraren, J.P., L.G. Fowler, and J.R. Leonard. 

1986. Fish Hatchery Management. American Fisheries Society. 517 pp. 

Raleigh, R. F., T. Hickman, R. C. Solomon, and P. C. Nelson. 1984. Habitat suitability 

information: rainbow trout. U.S. Fish and Wildlife Service, Division of Biological 

Services (FWS/OBS-82d/10.60). Washington, DC. 

Reiser, D.W., and R.G. White. 1983. Effects of complete redd dewatering on salmonid egghatching success and development of juveniles. Transactions of the American 

Fisheries Society. 112:532-540. 

Randle, T.J., and E.L. Pemberton. 1987. Results and analysis of STARS modeling efforts of 

the Colorado River in Grand Canyon. U.S. Department of Interior, Bureau of 

Reclamation. NTIS No. PB88-183421/AS. 

Saltveith, S.J., Halleraker, J.H., Arnekleiv, J.V., and A. Harby. 2001. Field experiments on 

stranding in juvenile Atlantic Salmon (Salmo Salar) and Brown Trout (Salmo Trutta) 

45
during rapid flow decreases caused by hydropeaking. Regu. Rivers: Res. Mgmt. 17: 

609-622. 

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. Can. J. 

Fish. Aquat. Sci. 51: 1644-1652. 

Stevenson, D.K. and S.E. Campana [ed]. 1992. Otolith microstructure examination and 

analysis. Can. Spec. Publ. Fish. Aquat. Sci. 117: 130 pp. 

Systat, 1997. Statistics. SPSS Inc. 

Vehanen, T., Bjerke, P.P. Heggenes, J., Huusko, A., and A. Maki-Petays. 2000. Effect of 

fluctuating flow and temperature on cover type selection and behaviour by juvenile 

brown trout in artificial flumes. J. Fish. Bio. 56: 923-937. 

Wiele S. M. and E.R. Griffin. 1997. Modifications to a one-dimensional model of unsteady 

flow in the Colorado River through the Grand Canyon. U.S. Geological Survey Water 

Resources Investigation Report 97-4046. 

46
Table 2.1. Summary of survival experiments conducted by Montgomery and Tinning (1993) 

in the laboratory and the Lee’s Ferry reach. All experimental results were conducted at water 

temperatures < 11 C except the one experiment that is noted. 

Experimental Results 

Survival Rate (%) 

Exposure Duration Eggs Alevins

(hrs) (days)

3 26 100 100

3 (>11 C) 26 100 40 

6 16 50

12 16 100 12.5

15 13 10 0

Field Results

Survival Rate (%) 

Stage Exposure Duration Alevins

(kcfs) (hrs) (days)

1 0 22 72

5 6 22 32

10 10 22 0

15 15 22 0

Table 2.2. Sampling Schedule for 2003 Lee’s Ferry redd and fry surveys. The discharge 

column shows the typical daily flow range for the month. 

Month Date

Daily Discharge 

Range (kcfs) Trip Type 

Jan. 23 5.0 - 20.1 Pilot Redd 

Feb. 10-15 5.0 - 20.2 Redd 

Mar. 8-12 5.0 - 20.1 Redd 

Apr. 8-11 6.5 - 12.1 Redd and Pilot Fry 

May 24-26 7.4 - 12.4 Redd and Pilot Fry 

Jun. 25-26 10.0 - 16.8 Fry 

Jul. 26-27 10.1 - 17.3 Fry 

Aug. 10.2 - 17.4 No trip 

Sept. 8-9 5.0 - 9.8 Fry 

Oct 24-26 4.9 - 9.8 Fry 

47
Table 2.3. Peak redd counts at intensive sites and those surveyed using the rapid assessment 

technique (RAT). The hypsometry column specifies the elevation of the majority of redds at 

each site (‘deep’ = redds mostly below 5 kcfs stage; ‘> 5 kcfs’ = redds mostly above 5 kcfs 

stage). Note that peak counts among sites could occur in different survey months. 

ID Site Name River Mile Site Type Hypsometry 

Peak 

Count 

1 Dam Island (top-center) -15.3 RAT >5 kcfs 4 

2 Dam Island (River-Right) -15.0 RAT >5 kcfs 13 

3 Dam Island (Deep) -14.9 RAT Deep 16 

4 Pumphouse Bar -14.5 Intensive >5 kcfs 26 

5 

D/S End of Pumphouse on 

River-Right -14.4 RAT Deep 14

6 Powerline -13.8 Intensive >5 kcfs 105

7 

Center of channel upstream of 

Honey Draw -13.4 RAT Deep 52

8 Tire Bar -13.2 RAT >5 kcfs 19 

9 Catchings Bar -12.7 RAT Deep 2

10 

Center of channel at U/S end 

of Slough -12.4 RAT Deep 68

11 Prop Bar @ Stranding Pool -11.8 RAT >5 kcfs 7 

12 Ferry Swale -11.1 Intensive Deep 96

13 Petroglpyh Bar on RL -10.2 RAT Deep 4 

14 Duck Island Main -10.0 RAT Deep 48

15 

Duck Island Inside Channel at 

D/S end -9.9 RAT >5 kcfs 62

16 

Downstream of Duck Island in 

Center Channel -9.3 RAT Deep 4

17 8 Mile Bar -9.0 RAT Deep 37 

18 

7.5 Mile Bar in center of 

channel -8.8 RAT Deep 4

19 7.5 Mile Bar on River-Left -8.2 RAT >5 kcfs 49 

20 6 Mile Bar on RL -5.8 RAT >5 kcfs 6 

21 Cliff on RR Upstream of FM -4.3 RAT Deep 34 

22 Four Mile Bar -4.0 Intensive >5 kcfs 372 

23 Water Holes -3.9 RAT >5 kcfs 12 

24 3.5 Mile Bar -3.0 RAT Deep 98 

25 Fall Ck. -2.5 RAT >5 kcfs 4 

Total 1156

48
Table 2.4. Combined redd counts from intensive and rapid assessment technique surveys by 

discharge elevation and survey period. The percentage of redds above 5, 8 and 12 kcfs is also 

shown. 

 

Discharge 

(kcfs) Mar. Apr. May

<=5 288 459 332

5-8 152 143 217

8-12 125 106 118

12-15 163 152 134

15-20 29 76 77

Total 757 936 878

% > 5 62 51 62 

% > 8 42 36 37 

% > 12 25 24 24 

49
Table 2.5. Summary of spatial analysis of redd count data to determine redd survey life. The 

number of 1m2

 grid cells containing redds were summed into the following 3 categories: New 

= a redd was present in a cell in the second month (e.g. Mar.) but not in the first (e.g., Feb.); 

Faded = a cell where a redd was present in the first month but not in the second month; Old =a 

cell where a redd was present in both the first and second months. The number of cells which 

contained redds (“Present”) is simply the sum of the cells in the “New” and “Old” columns. 

The column labeled Redd Count is the actual number of redds counted on each survey. PH, 

PL, FS, and FM refer to Pumphouse Bar, Powerline Bar, Ferry Swale, and Four Mile Bar, 

respectively.

# of 1m2

 Cells 

Site Period New Faded Old Present 

Redd 

Count 

PH Feb 15 15

Feb-Mar 19 9 6 25 26

Mar-Apr 17 19 6 23 23

Apr-May 3 22 1 4 4

PL Feb 42 43

Feb-Mar 72 16 26 98 101

Mar-Apr 88 81 17 105 105

Apr-May 18 84 21 39 41

FS Feb 20 20

Feb-Mar 34 20 0 34 36

Mar-Apr 76 34 0 76 88

Apr-May 59 73 3 62 96

FM Feb 171 174

Feb-Mar 162 109 62 224 230

Mar-Apr 214 150 74 288 293

Apr-May 212 150 138 350 371

50
Table 2.6. Statistics used to estimate survey life at four sites based on a spatial analysis of

redd data. Survey life (in units of weeks) was computed based on the ratio of the integral of 

the number of redds present over the entire survey period (redd-weeks) to the number of new 

redds excavated over the same period. Survey life calculations were made assuming no redd 

superimposition (top), or that 100% of cells classified as “Old” redds were actually new redds 

that were superimposed on top of existing redds. PH, PL, FS, and FM refer to Pumphouse 

Bar, Powerline Bar, Ferry Swale, and Four Mile Bar, respectively. 

. 

Site New AUC Survey

Redds (Redd-Wks.) Life (Wks.)

No Superimposition 

PH 39 265.1 6.8

PL 178 1161.2 6.5

FS 169 783.4 4.6

FM 588 3919.4 6.7

Avg. 6.2

100% Superimposition on Old Redds 

PH 52 265.1 5.1

PL 242 1161.2 4.8

FS 172 783.4 4.6

FM 862 3919.4 4.5

Avg. 4.7

51
Table 2.7. Maximum intergravel daily temperatures on Sunday at Four Mile and Powerline 

Bars. Values are means of two temperature loggers at each elevation per site. 

Four Mile Bar Powerline Bar 

Elevation (kcfs) Elevation (kcfs) 

Month Day 18 15 10 5 18 15 10 5

Feb 16 15 13 13 9 8 9 9 10

23 14 13 12 10 8 8 9 10

Mar 2 16 14 15 10 8 9 9 10

9 17 15 16 12 8 8 8 10

16 18 16 17 12 11 10 10 10

23 22 20 21 14 10 9 9 10

30 20 18 21 14 12 9 9 10

Apr 6 17 15 16 10 17 14 15 11

13 28 26 27 11 27 21 21 12

20 24 22 24 11 26 20 20 12

27 30 27 29 12 29 23 22 12

May 4 28 25 26 11 26 21 21 12

11 31 28 30 12 30 23 24 12

18 32 28 28 12 31 25 24 12

52
Table 2.8. Timing of inundation and exposure of various stages in the Lee’s Ferry reach, Jan.-

Apr, 2003. 

Jan. – Mar., 2003 

Stage Hour Hour Hours

(kcfs) Dried-Out Wetted Exposed

5 0

6 1:30 9:00 7.5

8 0:30 9:30 9

10 23:30 10:00 10.5

12 23:00 10:30 11.5

15 22:00 11:00 13

20 20:00 12:00 17

Apr., 2003 

Monday - Saturday 

Stage Hour Hour Hours

(kcfs) Dried-Out Wetted Exposed

5 0

6 0

8 0:00 5:30 5.5

10 22:00 7:00 9

12 21:00 11:00 14

15 24

20 24

Sunday

Stage Hour Hour Hours

(kcfs) Dried-Out Wetted Exposed

5 0

6 0

>=8 24

53
Table 3.1. Particle size categories (b-axis diameter) used to characterize grain size of gravel 

bars in the Lee’s Ferry reach, 2003. 

Size Category 

(mm) Substrate Type

<1 Sand and Finer 

1-2 

2-4 

4-6 

6-8 

8-10 

10-12 

12-14 

14-16 

16-20 

20-24 Small Gravel

24-28 

28-32 

32-40 

40-48 Medium Gravel 

48-64 Large Gravel

64-128 Small Cobble

>128 Larger Cobble and Boulder 

54
Table 3.2. Number of elevation, substrate, depth, and velocity observations (Points) collected 

at Four Mile Bar (FM), Ferry Swale (FS), Powerline Bar (PL), and Pumphouse Bar (PH). 

Depth and velocity observations were collected at 5, 8, 12, and 20 kcfs. The total area of each 

study site by discharge is also shown (Area m2

). 

Site Observation Elevation Substrate Depth and Velocity

Type 5 8 12 20

FM Points 1868 196 187 348 246 460

Area (m2) 12,966 16,864 21,948 28,352

FS Points 679 136 177 232 141 129

Area (m2) 17,818 20,160 21,241 21,328

PL Points 734 64 52 124 93 257

Area (m2) 7,831 8,790 9,705 11,510

PH Points 335 22 90 79 46 66

Area (m2) Not Computed

Grand Total Points 3616 418 506 783 526 912 

55
Table 3.3. Grain size characteristics of surficial substrate (top) at Four Mile Bar (FM), Ferry 

Swale (FS), Powerline Bar (PL), and Pumphouse Bar (PH). N refers to the number of habitat 

locations where statistics were computed, with each location consisting of 10-15 

measurements. D15, D50, and D85 refer to the grain size where 15%, 50%, and 85% of the 

sample is finer. The average statistical values across the number of location samples are 

shown. Grain size statistics for depth-integrated samples collected in the top 15 cm of gravel 

bars are shown in the lower table. 

Surface Samples 

Statistic FM FS PL

N 196 136 64

Average D15 7.45 6.13 4.96 

Average D50 18.67 12.53 19.10 

Average D85 36.83 21.05 46.56 

Average Geometric Mean (DG) 15.52 10.46 13.41 

Average Geometric Standard Deviation (SG) 2.55 2.58 4.73 

Average Skewness (SK) -0.16 -0.18 -0.23 

Integrated 0-15 cm Samples 

‘Data for lower table to be provided by GCMRC 

Statistic FM FS PL

N

Average D15 

Average D50 

Average D85 

Average Geometric Mean (DG) 

Average Geometric Standard Deviation (SG) 

Average Skewness (SK) 

56
Table 3.4. Correlations (r2

) among grain size statistics (surface only) at 3 locations in the 

Lee’s Ferry reach. D15, D50, and D85 refer to the grain size where 15%, 50%, and 85% of the 

sample is finer, respectively. DG, SG, and SK, refer to the geometric mean, geometric 

standard deviation (sorting index), and skeweness, respectively. 

All Sites 

D15 D50 D85 DG SG

D50 0.38

D85 0.18 0.61

DG 0.78 0.62 0.59

SG 0.25 0.01 0.06 0.09

SK 0.05 0.07 0.00 0.04 0.12

Four Mile Bar 

D15 D50 D85 DG SG

D50 0.35

D85 0.24 0.70

DG 0.81 0.61 0.64

SG 0.18 0.04 0.10 0.04

SK 0.08 0.08 0.00 0.05 0.12

F erry Swale 

D15 D50 D85 DG SG

D50 0.59

D85 0.32 0.74

DG 0.86 0.76 0.65

SG 0.28 0.00 0.01 0.15

SK 0.03 0.04 0.00 0.02 0.11

Powerline Bar 

D15 D50 D85 DG SG

D50 0.26

D85 0.13 0.33

DG 0.77 0.41 0.54

SG 0.52 0.01 0.00 0.30

SK 0.11 0.16 0.01 0.09 0.16

57
Table 3.5. Summary statistics and classification tables from the discriminant function analysis 

predicting the presence of redds within grid cells based on the habitat characteristics depth, 

near-bottom velocity, and particle size characteristics D50, D85, and SG (geometric standard 

deviation which provides a sorting index). The F-to-Remove statistic determines the relative 

importance of variables included in the disrciminant function. 

Powerline Bar 

Canonical correlation 0.59 Predicted 

F-to-Remove Non-Redd Redd

Depth 62 Non-Redd 72 23 95

Vel 6.0 Observed Redd 8 96 104

D50 0.43

D85 1.0 % Correct 84

SG 0.0

Ferry Swale 

Canonical correlation 0.62 Predicted 

F-to-Remove Non-Redd Redd

Depth 22.1 Non-Redd 39 18 57

Vel 29.9 Observed Redd 5 47 52

D50 0.8

D85 1.4 % Correct 79

SG 2.7

Four Mile Bar 

Canonical correlation 0.46 Predicted 

F-to-Remove Non-Redd Redd

Depth 79.9 Non-Redd 146 74 220

Vel 0.3 Observed Redd 29 184 213

D50 1.0

D85 6.4 % Correct 76

SG 0.3

All Sites Combined 

Canonical correlation 0.45 Predicted 

F-to-Remove Non-Redd Redd

Depth 155.5 Non-Redd 215 157 372

Vel 0.0 Observed Redd 43 326 369

D50 0.0

D85 6.4 % Correct 73

SG 0.3

58
Table 4.1. Sample size, density, frequency of fish presence, and forklength of young-of-year 

rainbow trout in the Lee’s Ferry reach in 2003 by habitat type and sample period. Statistics in 

parentheses for Sep. and Oct. are based on values from the original 20 sites sampled from Jun. 

– Sep. plus 6 additional talus shoreline sites sampled in Sep. and Oct only. Values not in 

parentheses are based on the original 20 sites only. 

Jun Jul Sep Oct

# of Sites 

Cobble Bar 11 11 11 11 

Veg./Sand 8 8 8 8

Talus 1 1 7 7

Total 20 20 26 26

Total Catch 294 240 49 (182) 83 (183) 

Density (#/100 m) 

Cobble Bar 48 38 8 18 

Veg./Sand 44 30 4 8

Talus 47 140 50 (69) 30 (52) 

Average 46 40 9 (24) 15 (24) 

% of Sites with Fish 

Cobble Bar 91 91 60 50 

Veg./Sand 100 91 38 63

Talus 100 100 100 (100) 100 (100)

Total 95 90 53 (64) 58 (68) 

Mean Forklength (mm) 

Cobble Bar 37 45 55 69 

Veg./Sand 33 44 54 69

Talus 56 49 54 (61) 76 (75) 

Average 37 45 55 (58) 70 (72) 

59
Table 4.2. Comparison of catch-per-effort and mean size of rainbow trout YoY based on boat 

and backpack electrofishing at 6 sites in the Lee’s Ferry reach, Oct. 2003. 

Fish/100 m

Mean Forklength 

(mm) 

Site Back Boat Back Boat

3.5 Mile 30 62 68 75 

4 Mile 7 13 64 84 

6 Mile 33 20 71 90 

7 Mile 7 42 67 85 

9 Mile 43 13 83 76 

Waterholes 63 31 69 79

Average Effort for a 45 m site 

Back Boat

Time (minutes) 14 6 

Seconds Shocked 700 350 

Seconds/meter 16 8

Table 4.3. Relationship between water temperature, accumulated thermal units (ATUs) and 

the number of days for rainbow trout eggs to hatch (top) from Piper et al. (1982). Prediction of 

hatch dates based on these relationships and specific spawn dates are also shown (lower). 

Temperature-driven statistics between spawn and hatch dates are based on intergravel 

measurements at Four Mile Bar at an elevation of 5 kcfs . 

Temperature ATUs (C) Days to 

F C to Hatch Hatch

45 7.2 347 48

50 10.0 310 31

55 12.8 307 24

Average Predicted

Spawn Temperature Hatch

Date over Incubation (C) Date 

Feb-01 9 Mar-10

Mar-01 9.5 Apr-02

Apr-01 10 May-02

May-01 10.5 May-30

60
Table 4.4. Summary statistics for the increment widths on otoliths from a sample of 15 fish 

where a weekly striping pattern was evident. 

 

# of Increments 

Measured 

Average Width of 

Increments (microns)

Width 

Comparison 

Fish Atypical Typical Atypical Typical 

Atypical > 

Typical 

Statistical 

Probability that 

Width of Atypical 

Increment > Width 

of Typical 

Increment 

 

All 15 38 227 3.12 2.51 Yes 0.000 

 

1 1 6 2.55 2.35 Yes

2 5 30 2.17 2.17 0.998

3 2 12 3.03 2.77 Yes

4 4 24 2.37 2.18 Yes 0.219

5 3 18 2.60 2.10 Yes 0.167

6 5 30 2.73 2.38 Yes 0.030

7 4 24 2.58 2.36 Yes 0.455

8 2 12 3.72 3.00 Yes 0.003

9 3 18 4.03 2.75 Yes 0.000

10 2 12 5.00 3.01 Yes 0.287

11 1 6 4.13 2.39 Yes

12 2 12 4.68 3.69 Yes 0.058

13 3 17 4.31 2.78 Yes 0.092

14 1 6 2.31 2.10 Yes

15 1 6 2.98 2.83 Yes

61
 

Table 4.5. Summary of stock synthesis model parameter estimates for alternate models that 

simulate weekly survival rate (constant over simulation or varying by month) and recruitment 

(independent weekly values, weekly values estimated from a 3-parameter Beta distribution, 

and weekly values estimated by fitting the magnitude parameter of the Beta distribution with 

shape parameters determined by back-calculation of hatch dates from catch data and lengthage relationship). The “Parameters” row shows the number of recruitment and survival 

parameters (Recruitment + Survival) estimated by each model. 

Weekly Beta Beta 

Best-fit Back-calculated

Constant Survival 

Parameters 45 + 1 3 + 1 1 + 1 

Weekly Survival Rate 0.88 0.88 0.90 

Negative Log Likelihood 145.28 167.37 194.1

Mar.-Jun. Recruitment 7,785 7,176 4,243 

Monthly Survival 

Parameters 45+10 3+10 1+10

Jun. Weekly Survival 0.75 0.75 0.68 

Jul. Weekly Survival 0.97 0.97 0.99 

Aug. Weekly Survival 0.75 0.70 0.71 

Sep. Weekly Survival 0.88 0.99 0.99 

Negative Log Likelihood 132.87 134.13 134.78

Mar.-Jun. Recruitment 1,211 6,852 16,145 

62
0

5

10152025

1-Nov

1-Dec

31-Dec

30-Jan

1-Mar

31-Mar

30-Apr

30-May

29-Jun

29-Jul

28-Aug

Discharge (kcfs)

Figure 1.1. Hourly hydrograph for the 2003 water year from Glen Canyon Dam. Red circles denote sampling pe
#

#

#

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# # #

# # #

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# #

N

Site Type

Intensive

# RAT

1000 0 1000 2000 Meters

#

#

#

#

#

Figure 2.1. Map of the Lee’s Ferry Reach showing locations of intensive (red) and rapid 

assessment technique (green) redd survey sites. Glen Canyon Dam can be seen near the top of 

the map. The intensive sampling sites Pumphouse Bar, Powerline Bar, Ferry Swale, and Four 

Mile Bar are located at increasing distances downstream from the dam.

64
0

5,000

10,000

15,000

20,000

25,000

926 927 928 929 930 931 932 933 934

Elevation (m.s.l.)

Dis

c

h

arg

e (cfs)

PH

PL

FS

FM-upper 

FM-lower

Data

Figure 2.2. Stage-discharge data (circles) and best-fit relationships at Pumphouse Bar (PH), 

Powerline Bar (PL), Ferry Swale (FS), and Four Mile Bar (FM-upper, FM-lower). 

65
0

100

200

300

400

500

600

Feb Mar Apr May

# of R

edds

FM FS

PL PH

a) 

0

100

200

300

400

500

600

Feb Mar Apr May

# of

R

edd

s

<=5 5-8

8-12 12-15

15-20

b) 

Figure 2.3. Distribution of redd counts across a) sample sites and b) elevation (in kcfs) at four 

intensively sampled sites.

66
a) Pumphouse Bar 

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# 15000 - 25000

N

909 18 Meters

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Figure 2.4. Redd location and elevation at a) Pumphouse Bar, b) Powerline Bar, c) Ferry 

Swale, and d) Four Mile Bar across all four survey periods. 

67
b) Powerline Bar 

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Figure 2.4. Con’t. 

68
c) Ferry Swale 

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# < 5000

20 0 20 40 Meters

N

Figure 2.4. Con’t 

69
d) Four Mile Bar 

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# <5000 # 5000 - 8000

# 8000 - 12000

12000 - 15000

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N

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Figure 2.4. Con’t 

70
Four Mile Bar

0

100

200

300

400

Feb Mar Apr May

# of Redds

<=5 5-8

8-12 12-15

15-20

Ferry Swale

0

20

40

60

80

100

120

Feb Mar Apr May

# of Redds

Powerline Bar

0

20

40

60

80

100

120

Feb Mar Apr May

# of 

Redds

Pumphouse Bar

0

5

10

15

20

25

30

Feb Mar Apr May

# of Redds

Figure 2.5. Summary of redd hyspometry (kcfs) at four intensive sites by survey period. 

0

100

200

300

400

500

Mar Apr May

# of R

edds

<5 5-8 8-12 12-15 15-20

Figure 2.6. Redd hypsometry from the rapid assessment surveys. 

71
N

10 0 10 20 Meters

Figure 2.7. Example of 1 m2 grid showing the change in the location of redds between Apr. 

and May survey periods at Ferry Swale. Green cells denote locations where a redd was 

present in May but not in Apr (“New” redds). Yellow cells denote locations where a redd was 

present in Apr. but not in May (“Faded” redds). Red cells denote locations where a redd was 

present in both Apr. and May(“Old” redds). The total number of cells with redds in May is the 

sum of green and redd cells. 

72
0

50

100

150

200

250

No

v-01

Dec01Dec31

Jan-30

Mar-01Mar-31

Apr-3

0

Ma

y-3

0

Jun-29

Jul2

9

Aug-28Sep-27

Oct-27

R

e

d

ds Excavated

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

Rip

e Fis

h C

P

E (

#/min)

Fit to Feb.-May

Fit to Feb.-May + Jun. Survey = 0

Ripe Fish (93-97)

0

200

400

600

800

1000

1200

Nov01

De

c-01

De

c-31

Jan-30

Mar-01Mar-31

Apr-3

0

Ma

y-3

0

Jun-29

Jul-2

9

Aug-28Sep-27

Oct2

7

R

e

d

ds Present

Fit to Feb.-May

Fit to Feb.-May = Jun. Survey = 0

Observed a) 

b) 

Figure 2.8. Observed and predicted numbers of redds present in 2003 assuming a survey life 

of 5 weeks and no redd superimposition (a). Predictions were made using the Feb-May redd 

count data only (redd dots and blue line), and using the same data but assuming no redds 

would be present by late Jun (redd + yellow dots). The corresponding spawn timing curves are 

shown in b) and are compared with the catch rate (CPE) of ripe fish from 1993-1997 

(McKinney et al. 1999). 

73
0

50

100

150

200

No

v-01

Dec01Dec31

Jan-30

Mar-01Mar-31

Apr-3

0

Ma

y-3

0

Jun-29

Jul-2

9

Aug-28Sep-27

Oct-27

Actu

al R

e

d

d

s

Arrivals - SLR=5 Faded - SLR=5

Arrivals - SLR=10 Faded - SLR=10

Apr. 1

0

250

500

750

1000

No

v-01

De

c-01

De

c-31

Jan-30

Mar-01Mar-31

Apr-3

0

Ma

y-3

0

Jun-29

Jul-2

9

Aug-28Sep-27

Oct2

7

Ob

s

erv

e

d R

e

d

d

s

Observed

SLR=5

SLR=10

a) 

b) 

Figure 2.9. Timing of a) excavation of redds (“arrivals”) and loss of redds due to fading 

(“faded”) under two assumptions of survey life (SLR=5 and 10 weeks), and b) observed and 

predicted number of redds under these two assumptions. Red dots in b) are the observed redd 

counts. The total number of redds excavated, and the percentage of excavation prior to Apr. 1 

for SLR = 5 and 10 was 4,175 (42%) and 2,143 (53%), respectively. 

74
a) 

0

100

200

300

400

500

600

700

Nov01

De

c-01

De

c-31

Jan-30

Mar-01Mar-31

Apr-3

0

Ma

y-3

0

Jun-29

Jul-2

9

Aug-28Sep-27

Oct2

7

Actu

al Re

d

d

s

Arrivals - SpHalf=500

Faded - SPHalf=500

Superimposed - SPHalf=500

Arrivals - SPHalf=1000

Faded - SPHalf=1000

Superimposed - SPHalf=1000

Apr. 1

0

250

500

750

1000

Nov01

De

c-01

De

c-31

Jan-30

Mar-01Mar-31

Apr-3

0

Ma

y-3

0

Jun-29

Jul-2

9

Aug-28Sep-27

Oct2

7

Observed R

e

d

d

s

Observed

SPHalf = 1000

SpHalf = 500

b) 

Figure 2.10. Timing of a) excavation of redds (“arrivals”), loss of redds due to fading 

(“faded”), and number of redds that are superimposed for two estimates of SPHalf, the 

number of redds present where the maximum superimposition rate is reduced to 1⁄2 of its 

maximum value (SPHalf=500 or 1000). The maximum superimposition rate (SPmax) and 

survey life (SLR) were set at 0.8 and 5 weeks, respectively. Predicted and observed redd 

numbers are shown in b).

75
a) 

0

4

8

12

16

20

30 40 50 60 70 80

Percent of Redds Excavated Before Apr. 1

Re

d

d

S

urv

e

y Life

(S

L

R in we

e

k

s)

0.0

0.2

0.4

0.6

0.8

1.0

M

a

ximum R

e

d

d

S

u

p

erim

p

o

sitio

n R

ate

(S

P

max)

Survey Life

Superimposition

0

4

8

12

16

20

0 2,000 4,000 6,000 8,000

Total # of Redds Excavated over Spawning Season

Re

d

d

S

urv

e

y Life

(S

L

R in we

e

k

s)

0.0

0.2

0.4

0.6

0.8

1.0

M

a

ximum R

e

d

d

S

u

p

erim

p

o

sitio

n R

ate

(S

P

max)

Survey Life

Superimposition

b) 

Figure 2.11. The effect of alternate assumptions about redd survey life (SLR) and the 

maximum rate of superimpostion (SPmax) on a) the percent of redds excavated prior to Apr. 

1, and b) the total number of redds excavated over the spawning season. The superimposition 

rate was assumed to be 0 over the range of survey life values that were explored. Survey life 

was assumed to be 5 weeks over the range of superimposition rates that were explored. For 

the superimposition rate analysis, the number of redds present required to reduce the 

superimposition rate to 1⁄2 of its maximum value (SPhalf) was set at 1000. 

76
a) Powerline Bar

0

5

10

15

20

25

30

Feb Mar Apr May

Intergravel T

e

m

per

ature (C)

18 kcfs - d/s

18 kcfs - u/s

15 kcfs - d/s

15 kcfs - u/s

10 kcfs - d/s

10 kcfs - u/s

5 kcfs - d/s

5 kcfs - u/s

Four Mile Bar

0

5

10

15

20

25

30

Feb Mar Apr May

Intergravel T

e

m

per

ature (

C)

18 kcfs

15 kcfs - d/s

15 kcfs - u/s

12 kcfs - u/s

10 kcfs - d/s

10 kcfs -u/s

5 kcfs - d/s

5 kcfs - u/s

b) 

Figure 2.12. Intergravel temperatures at a) Powerline and b) Four Mile Bars at different 

elevations. Bar height represents the mean monthly temperature (based on hourly 

observations) with error bars denoting the average daily minimum and maximum values. 

Green and red lines denote the maximum recommended (13 C) and lethal temperature (16 C) 

limits for rainbow trout egg incubation, respectively. 

77
Powerline Bar

0

5

10

15

20

25

30

06 00:0

0

06 06:0

0

06 12:0

0

06 18:0

0

07 00:0

0

07 06:0

0

07 12:0

0

07 18:0

0

08 00:0

0

08 06:0

0

08 12:0

0

08 18:0

0

09 00:0

0

09 06:0

0

09 12:0

0

09 18:0

0

10 00:0

0

Intergra

v

el T

emp

erature (C)

6

7

8

9

10

11

12

13

R

o

uted Disch

arge (

kcfs)

18 kcfs 15 kcfs

10 kcfs 5 kcfs

Discharge

a) 

b) Four Mile Bar

0

5

10

15

20

25

30

06 00:0

0

06 06:0

0

06 12:0

0

06 18:0

0

07 00:0

0

07 06:0

0

07 12:0

0

07 18:0

0

08 00:0

0

08 06:0

0

08 12:0

0

08 18:0

0

09 00:0

0

09 06:0

0

09 12:0

0

09 18:0

0

10 00:0

0

Intergra

v

el T

emp

erature (

C)

6

7

8

9

10

11

12

13

R

o

uted Disch

arge (

kcfs)

Figure 2.13. Comparison of intergravel thermographs and hydrographs (Apr. 6-9, 2003) at a) 

Powerline Bar and b) Four Mile Bar. The effect of differences in the timing of inundation on 

temperature at the 10 kcfs stage is clearly seen. 

78
0

0.05

0.1

0.15

0.2

0.25

0 0.5 1 1.522.5 3

Depth (m)

Proportio

n

Redds Only

All Cells

Best-Fit Redds Only

Best-Fit All Cells

0

0.1

0.2

0.3

0.4

0 0.25 0.5 0.75 1 1.25 1.5 1.75 2

Velocity (m/sec)

Proportion

0

0.05

0.1

0.15

0.2

0.25

0 15 30 45 60 75

D85 (mm)

Proportion

a) 

Figure 3.1. Proportion of all grid cells (magenta) across a range of depth, near-bottom

velocity, and D85 categories at Four Mile Bar (a), Ferry Swale (b), and Powerline Bar (c) and 

proportions for cells in these categories where a redd was present during the Mar. survey 

(blue). Depth and velocity statistics are based on measurements taken at 20 kcfs at Four Mile 

and Powerline Bars, and at 5 kcfs at Ferry Swale, the assumed discharges when the Mar. redds 

were excavated at these sites. 

79
0

0.1

0.2

0.3

0.4

0 0.5 1 1.5 2 2.5 3

Depth (m)

Proportion

Redds Only

All Cells

Best-Fit Redds Only

Best-Fit All Cells

0

0.1

0.2

0.3

0.4

0.5

0 0.25 0.5 0.75 1 1.25 1.5 1.75 2

Velocity (m/sec)

Proportio

n

0

0.1

0.2

0.3

0.4

0.5

0 15 30 45 60 75

D85 (mm)

Proportio

n

b) 

Figure 3.1. Con’t (Ferry Swale). 

80
0

0.1

0.2

0.3

0.4

0.5

0 0.5 1 1.5 2 2.5 3

Depth (m)

Proportion

Redds Only

All Cells

Best-Fit Redds Only

Best-Fit All Cells

0

0.1

0.2

0.3

0.4

0.5

0 0.25 0.5 0.75 1 1.25 1.5 1.75 2

Velocity (m/sec)

Proportion

0

0.05

0.1

0.15

0.2

0.25

0 15 30 45 60 75

D85 (mm)

Proportion

c) 

Figure 3.1. Con’t (Powerline Bar). 

81
0

1

2

3

4

5

6

0 0.511.5 2 2.5 3

Depth (m)

Preference

FM

FS

PL

0

1

2

3

4

5

6

0 0.25 0.5 0.75 1 1.25 1.5 1.75 2

Velocity (m/sec)

Preference

0

1

2

3

4

5

6

0 15 30 45 60 75

D85 (mm)

Preference

a) 

b) 

c) 

Figure 3.2. Comparisons of preference for a) depth, b) near-bottom velocity, and c) D85 at 

Four Mile Bar (FM), Ferry Swale (FS), and Powerline Bar (PL). Curves are the best-fit Beta 

distribution models fit to the ratio of “redd-only cells”- to “all cells”.

82
0

0.1

0.2

0.3

0.4

0.5

0 0.511.5 2 2.5 3

Depth (m)

Utilization

FM

FS

PL

0

0.1

0.2

0.3

0.4

0.5

0 0.25 0.5 0.75 1 1.25 1.5 1.75 2

Velocity (m/sec)

Utilization

0

0.1

0.2

0.3

0.4

0.5

0 15 30 45 60 75

D85 (mm)

Utilization

 

a) 

b) 

c) 

Figure 3.3. Comparisons of the spawning habitat utilization as a function of a) depth, b) nearbottom velocity, and c) D85 at Four Mile Bar (FM), Ferry Swale (FS), and Powerline Bar 

(PL). Curves are the best-fit Beta distribution models fit to data from “redd-only” grid cells at 

each site (blue lines in Fig. 3.1). 

83
0

0.02

0.04

0.06

0.08

0.1

0.12

0 0.5 1 1.5 2 2.5 3

Depth (m)

Proportion

FM

FS

PL

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

0 0.25 0.5 0.75 1 1.25 1.5 1.75 2

Velocity (m/sec)

Proportion

0

0.05

0.1

0.15

0.2

0.25

0 15 30 45 60 7

D85 (mm)

Proportion

5

a) 

b) 

c) 

Figure 3.4. Comparisons of the availability of a) depth, b) near-bottom velocity, and c) D85 at 

Four Mile Bar (FM), Ferry Swale (FS), and Powerline Bar (PL). Curves are the best-fit Beta 

distribution models fit to data from all grid cells at each site (magenta lines in Fig. 3.1). 

84
#

#

#

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# ###

#

#

#

#

#

# # #

#

# #

##

#

#

# #

#

#

# #

#

# #

##

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#

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#

#

#

#

# # # # # #

#

#

#

#

# #

#

#

#

#

#

#

#

#

# # #

#

#

#

#

#

#

#

# # # #

#

#

#

#

#

# # #

#

#

#

#

# #

#

##

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###

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# # #

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# #

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# #

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#

# # # #

#

#

#

#

# #

##

#

##

#

#

#

#

#### ##

#

##

Fm_20_d85

#

# #

#

#

#

#

#

#

#

#

#

#

0 - 0.5

0.5 - 1

1 - 2

2 - 3

3 - 4

4 - 5

5 - 6

>6

Redds in Mar.

20 0 20 40 Meters N

Figure 3.5. Maps of predicted habitat preference at Four Mile Bar at 20 kcfs (a) above), Ferry 

Swale at 5 kcfs (b), and Powerline Bar at 20 kcfs (c) based on the product of preferences for 

depth, bottom velocity, and D85. Location of redds during the Mar. survey, from which 

habitat preferences were developed, are also shown. The black arrow denotes the direction of 

water flow. 

85
Figure 3.4. Con’t (b) - Ferry Swale)

N

#

# #

#

# #

#

#

#

#

#

##

##

#

#

#

#

#

#

#

##

#

# ##

#

#

##

#

# #

# #

#

#

#

#

#

## #

#

#

#

#

#

#

#

# # #

Fs_5_d85

0 - 0.5

0.5 - 1

1 - 2

2 - 3

3 - 4

4 - 5

5 - 6

>6

# Redds

20 0 20 40 Meters

86
N

# # #

# # #

# #

# ## ##

##

#

#

#

# # # # # # #

# #

# # # ## # # # ##

#

#

#

##

## # # # # #

#

##

## # ##

#

## # ## # # # ##

#

#### ## ### # # # #

#

#

# # # # ## # # #### ## # ### ##

#

### ##

# ## ## #

#

# #

### ## # ## # #

# # #

# # # #

# #

#

#

##

Pl_20_d85a

0 - 0.5

0.5 - 1

1 - 2

2 - 3

3 - 4

4 - 5

5 - 6

>6

# Redds in Feb. and Mar.

20 0 20 40 Meters

Figure 3.4. (c) Powerline Bar). 

87
a) 

b) 

c) 

Figure 3.5. Predictions of Weighted Useable Area (WUA) at 5 stages (expressed in discharge 

units of kcfs) at four levels of discharge (x-axis) at a) Four Mile Bar, b) Ferry Swale, and c) 

Powerline Bar. The total WUA across stages is equivalent to the total bar height. Also shown 

is the distribution of redds over the same stages from the Mar. survey (from Fig. 2.5). The 

redd distributions should be compared to the distribution of WUA at 20 kcfs at Four Mile and 

Powerline Bars, and at 5 kcfs at Ferry Swale. These discharges are the assumed flows that the 

redds were created at.

Powerline Bar

0

2,000

4,000

6,000

8,000

10,000

5 8 12 20 Mar.

Redds

Discharge (kcfs)

W

U

A by Elevation (

m2

)

Ferry Swale

0

10,000

20,000

30,000

40,000

50,000

5 8 12 20 Mar.

Redds

WU

A

b

y Elevati

on (

m2)

Four Mile Bar

0

5,000

10,000

15,000

20,000

25,000

30,000

5 8 12 20 Mar.

Redds

WU

A b

y Elevation (m2)

15-20

12-15

8-12

5-8

<5

88
a) 

b) 

Figure 4.1. Density at the a) 20 original YoY sampling locations by river mile and b) the 

density at the original 20 sites plus an additional 6 talus shoreline sites sampled on the Sep. 

and Oct. sample trips. Glen Canyon Dam is located at river mile – 15.6 and Lee’s Ferry is 

located at river mile 0. 

Original 20 Sites

0

50

100

150

200

0 -4 -8 -12 -16

C

P

E (

#/100 m)

June July

Sep Oct

Original 20 Sites + 6 Additional Sites in Sep-Oct

0

50

100

150

200

0 -4 -8 -12 -16

River Mile

C

P

E (

#/100 m)

89
Figure 4.2. Observed length frequency of YoY rainbow trout at the original 20 sampling sites 

in the Lee’s Ferry reach in 2003 over four sampling periods. Sample sizes for each lengthfrequency distribution are shown. 

0

20

40

60

80

100

20-

2

5

30-

3

5

40-

4

5

50-

5

5

60-

6

5

70-

7

5

80-

8

5

90-

9

5

Forklength (mm)

# of Fry at 2

0 Site

s

Jun. 25-26

Jul. 26-27

Sep. 8-9

Oct. 24-26

49 83

240 

294 

90
Figure 4.3. Magnified image (16x) of a rainbow trout otolith from Lee’s Ferry captured on 

Sept., 2003 showing emergence (E) and hatch (H) checks. Each pair of dark and light rings 

represents one day of growth. 

Figure 4.4. Comparison of estimated and known young-of-year age from hatch of wild 

rainbow trout that were spawned in a hatchery, hatched on Jun. 23, 2003, and sampled on Jul., 

24, 2003 (at 31 days from hatch), and Sep. 15, 2003 (at 84 days from hatch). 

20

30

40

50

60

70

80

90

100

20 30 40 50 60 70 80 90 100

Known Total Days from Hatch

E

stimate

d T

otal D

a

y

s fro

m

H

atc

h

91
Figure 4.5. Relationship between forklength and the estimated age from hatch for YoY 

collected over 7 months in the Lee’s Ferry reach, 2003. The best-fit von Bertalanffy model to 

all the data is also shown.

Figure 4.6. Back-calculation of the week of hatch for 991 YoY fish captured in the Lee’s 

Ferry reach from Apr. – Oct. 2003. Diamonds represent the data and the solid line represents 

the best-fit beta distribution to the data that was used as one of the methods to drive fry 

recruitment rates in the stock synthesis model. For

k ngth (

m

m)

0

20

40

60

80

100

0 50 100 150 200 250

Age (days from Hatch)

Oct Sept

July June

May April

Best-fit von B

le

0

20

40

60

80

100

120

140

1-

J

a

n

31-

Jan

2-Mar

1-

A

pr

1-

M

a

y

31-

May

30-

Jun

30-

Jul

29-

A

u

g

Date of Hatch

# of Fi

s

h

92
Figure 4.7. Comparison of spawn-timing curves based on redd counts (assuming a redd 

survey life of 5 wks) and the back-calculated curve based on the estimated timing of hatch 

from the fry data (Fig. 4.6) and an assumed egg incubation of 30 days. The redd-derived 

spawn-timing curves are based on either the Feb.-May redd survey data only (blue line), or the 

same data and the assumption that spawning was completed by the end of May (orange line). 

0

20

40

60

80

100

120

1-Ja

n

31-Ja

n

2-

M

ar

1-

A

pr

1-

M

a

y

31-Ma

y

30-Ju

n

30-Jul

29-

A

u

g

Date of Spawn

# of H

atched 

E

g

g

s

0

50

100

150

200

250

# of R

e

d

ds E

x

cavated

Back-calculated from Fry Data

Redds (Feb. - May data only)

Redds (Feb.-May data + Jun. Survey = 0

93
a) 

b)

Figure 4.8. Images of a YoY rainbow trout otolith from the Lee’s Ferry reach sampled in 

Apr. showing a weekly striping pattern (identified by white arrows) at magnifications of a) 

16x and b) 400x. 

94
Figure 4.9. Best-fit stock synthesis model fits (lines) to observed (points) length-frequency 

data based on alternate models that simulate weekly survival rate (constant over simulation or 

varying by month) and recruitment (independent weekly values, weekly values estimated 

from a 3-parameter Beta distribution, and weekly values estimated by fitting the magnitude 

parameter of the Beta distribution with shape parameters determined by back-calculation of 

hatch dates from catch data and length-age relationship). 

2

20

B

Weekly Recruitment - Monthly Survival

0

20

40

60

80

100

20 30 40 50 60 70 80 90 100

Beta - Fit Recruitment - Monthly Survival

0

20

40

60

80

100

20 30 40 50 60 70 80 90 100

Back-calculated Recruitment - Monthly Survival

0

20

40

60

80

100

20 30 40 50 60 70 80 90 100

Forklength (mm)

Weekly Recruitment - Constant Survival

0

20

40

60

80

100

0 30 40 50 60 70 80 90 100

Catch

Pred-Jun. Pred-Jul.

Pred-Sep. Pred-Oct

Obs - Jun Obs- Jul.

Obs - Sep Obs - Oct

Beta - Fit Recruitment - Constant Survival

0

20

40

60

80

100

30 40 50 60 70 80 90 100

Catch

ack-calculated Recruitment - Constant Survival

0

20

40

60

80

100

20 30 40 50 60 70 80 90 100

Forklength (mm)

Catch

95
Figure 4.10. Likelihood profiles of weekly survival rate estimates (constant-survival model) 

for the time-independent and time-dependent best-fit Beta distribution recruitment models. 

Differences between the minimum negative log likelihood and other values are virtually the 

same within recruitment models, so the resulting probability profiles are indistinguishable. 

Confidence limits can be derived by finding the x-axis value below the intersection of the 

orange line and the one minus the desired confidence interval specified on the right-hand axis 

(e.g. a 95% limit would be found at Chi-Square probability of 0.05). 

Figure 4.11. Best-fit weekly fry recruitment patterns (number of eggs that hatch) for the 

constant-survival model based on alternate methods of simulating recruitment (independent 

weekly values, weekly values estimated from a 3-parameter Beta distribution, and weekly 

values estimated by fitting the magnitude parameter of the Beta distribution with shape

parameters determined by back-calculation of hatch dates from catch data and length-age 

relationship). 

100

125

150

175

200

225

250

275

300

0.8 0.82 0.84 0.86 0.88 0.9 0.92 0.94 0.96 0.98 1

Weekly Survival Rate

N

egativ

e Log Lik

eliho

o

d

0

0.2

0.4

0.6

0.8

1

C

hiS

quare Proba

bilit

y

Time-Dependent (Beta)

Time-Independent

Chi-Square Probability

0

250

500

750

1000

1250

30-Jan 1-Mar 1-Apr 1-May 1-Jun 1-Jul

Date of Hatch

Recr

uitment

Weekly

Beta

Back-calculated

96
97