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Scofield Reservoir predator-prey interactions:
investigating the roles of interspecific interactions and forage
availability on the performance of three predatory fishes
by
Phaedra Budy, Professor, Unit Leader
Lisa Winters, Graduate Research Assistant
Gary P. Thiede, Fisheries Biologist
Konrad Hafen, Undergraduate Researcher
Bryce Roholt, Undergraduate Researcher
US Geological Survey, Utah Cooperative Fish and Wildlife Research Unit,
Department of Watershed Sciences, and the Ecology Center
Utah State University, Logan, UT 84322-5210
2 April 2014
Project Completion Report
USU Control Number 130738
UDWR Contract Number 132579
Sport Fisheries Research (US)
Grant Number: F – 134 – R, Segment 1
Grant Period: 1 July 2011 to 30 June 2013
TABLE OF CONTENTS
Page
Acknowledgments………………………………………………………………………….……………….............…
ii
Preface....................…………………………………………………………………….………………….............…
iii
Chapter 1. Unwelcome invaders and trout predators: effective biological controls in a
western reservoir?................................................................................................................
1
Chapter 2. Quantifying the food web impacts of introduced piscivores in reservoir fish
assemblages..........................................................................................................................
50
Chapter 3. Management implications................................................................................
76
Appendix 1. Hydroacoustic assessment of fish density, abundance, and biomass in
Scofield Reservoir, Utah.......................................................................................................
89
Appendix 2. Agonistic behavior between three species of salmonids stocked into
Scofield Reservoir, Utah: an experimental evaluation.........................................................
100
Appendix 3. Evaluation of trout performance under various fish assemblages in
experimental ponds..............................................................................................................
110
Appendix 4. Demographics and diet of Utah chub in Scofield Reservoir, Utah..................
117
Suggested citation:
Budy, P., L. Winters, G.P. Thiede, K. Hafen, and B. Roholt. 2014. Scofield Reservoir predator-prey
interactions: investigating the roles of interspecific interactions and forage availability on the
performance of three predatory fishes. 2013 Project Completion Report to the Utah Division of
Wildlife Resources. UTCFWRU 2014(2):1-123.
USU Budy, Project Completion Report, Scofield Reservoir
i
ACKNOWLEDGMENTS
Funding and support was provided by Utah Department of Natural Resources, Division of
Wildlife Resources (UDWR; Federal Sport Fish Restoration, Grant number F – 134 – R, Project 1)
and US Geological Survey, Utah Cooperative Fish and Wildlife Research Unit, in-kind. We would
also like to acknowledge the support from the Ecology Center at Utah State University (USU).
We would like to thank Paul Birdsey, Justin Hart, Calvin Black, and Craig Walker from the UDWR
for their assistance and guidance. Thanks to our field and lab technicians: Michael Yarnall,
Konrad Hafen, Hannah Moore, Jared Baker, Bryce Roholt, Brie Estrada, and Dan Weber. Thanks
also to graduate students in the Fish Ecology Lab for assistance and advice: Stephen Klobucar,
Nick Heredia, and Christy Meredith. Thanks to Carl Saunders and Susan Durham for generous
assistance to Konrad Hafen during his trout behavior study.
For hydroacoustics work, we especially thank Chris Luecke at Utah State University and Paul
Birdsey and Justin Hart (UDWR). Calvin Black (UDWR) graciously provided the gill-net catch and
size data where needed.
We would like to sincerely thank the UDWR Fountain Green Fish Hatchery and personnel for
supplying three species of trout for the trout evaluation experiments in ponds and trout
behavioral experiments in tanks conducted at the USU Millville Aquatic Research Facility in
Millville, Utah. Also, thanks to Kesko Ranch (Ephriam, Utah) for selling us Utah chub. For
assistance with these experiments, we thank Carl Saunders, Stephen Klobucar, and Konrad
Hafen. Thanks to the Ecology Center at USU for support at the USU Millville Aquatic Research
Facility.
Permitting assistance was provided by Suzanne McMullin (UDWR) and Jenna Daniels and Aaron
Olsen of the Institutional Animal Care and Use Committee (IACUC) at Utah State University.
This research is conducted under State of Utah COR number 1COLL8712 and IACUC protocol
number 1544.
USU Budy. Project Completion Report. Scofield Reservoir
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PREFACE
In this completion report, we summarize our findings from an intensive two-year study of the fish
community of Scofield Reservoir, Utah. In 2011, a need was identified to gain a better understanding of
the ecological processes shaping the food web and fishery in Scofield Reservoir, historically the most
important trout fishery in Utah’s southeastern region and still one of the most heavily used fisheries in
the state. Rainbow trout (Oncorhynchus mykiss) was the primary species sought by anglers, and the
fishery is managed as a “put-grow-and-take” family fishery with rainbow trout stocked annually.
Historically, around 600,000 trout were stocked every year; however, stocking levels are now adjusted
nearly every year in response to the re-discovery of Utah chub (Gila atraria) in 2005. Tiger trout (Salmo
trutta X Salvelinus fontinalis) fingerlings were added in 2005 as a biological control for the Utah chub
and have since proven to be one of the more highly desirable sport fish in the state. Bear Lake-strain
cutthroat trout (O. clarkii utah) have been stocked annually since 2009, also as an additional Utah chub
control measure. Consequently, there are theoretically three top predators and conspecifics with the
potential for considerable competition for food and space. Further, despite its historic and
contemporary popularity, angler use of the reservoir has declined since 1986 by a substantial amount.
However, a public opinion survey revealed no consistent agreement of either (1) why the reservoir has
declined in popularity (e.g., stocked fish had low survival) or (2) what changes in regulation might
improve the fishing experience in the future (e.g., stricter or more relaxed regulations?).
Over the past two years, we have collected the necessary information to answer the following important
questions with important management implications for the management of the fishery of the reservoir:
1) How does the food web operate (i.e., basal flow of energy) and what are the abiotic and physical
constraints on fish performance?
2) What is the size and condition of the predator population in the reservoir, which species
contribute the greatest or least, and from which cohort?
3) What are the preferred prey species, degree of diet overlap, and consumption rates of the three
predators on Utah chub and other prey species?
4) Can the predator population limit the prey population through top-down control?
5) Are the three predators limited by interspecific competition for resources?
A quantitative assessment of the relative potential for biological control of an explosive prey fish by
three different trout predators in Scofield Reservoir is provided in Chapter 1. In Chapter 2, we quantify
the food web impacts of introduced piscivores in the Scofield Reservoir fish assemblage. Four
appendices (1) provide hydroacoustic fish density estimates of Scofield Reservoir, (2) summarize
controlled laboratory experiments describing agonistic interaction between the three trout species, (3)
describe the performance of the three trout species raised in experimental ponds, and (4) compile some
demographic information on Utah chub. Chapters 1 and 2 come directly from the MS Thesis (in Ecology)
of Lisa Winters, Department of Watershed Sciences, Utah State University completed in March 2014.
Management Implications and Projections are provided in Chapter 3, starting on page 76.
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CHAPTER 1
Unwelcome invaders and trout predators: effective
biological controls in a western reservoir?
Introduction
Biological agents are becoming increasingly common as a method of natural control to remove
invasive species and maintain native biodiversity worldwide (Freeman et al. 2010). Biological
control, an environmentally sound and effective means of reducing or mitigating nuisance
species and their impacts through the use of natural enemies, depends upon parasites,
pathogens, or predators to lower population densities of the nuisance species (Debach 1964;
Freeman et al. 2010; USDA 2013a). The United States alone has employed almost 200 biological
control agents towards nuisance weeds in agriculture (USDA 2013b). Since 2002, the Emerald
Ash Borer Agrilus planipennis, from northeastern Asia has been a poster-child of destructive
pest and threat to the United States economy (USDA 2013b). Nevertheless, despite seemingly
prolific use of biological control agents, vertebrate species represent a small portion of
biological control targets (Saunders et al. 2010). Only in the past few decades has the
biomanipulation of fishes become a common technique to prevent over-expanding prey bases
from negatively affecting sport fish (Stewart et al. 1981; Hartman and Margraf 1993; Irwin et al.
2003) or to improve water quality (Shapiro et al. 1975; Carpenter et al. 1985; Ireland 2010).
Many such techniques are employed within artificial systems, which commonly contain
intentionally introduced and intensively managed species (Anderson and Neumann 1996).
Reservoir systems exhibit characteristics intermediate to lotic and lentic habitat and thus
incorporate an atypical community of fish (Wetzel 1990; Anderson and Neumann 1996: Wetzel
2001). These artificial systems, manipulated through stocking, may have unpredictable food
webs with decoupled predator and prey dynamics (Kitchell and Crowder 1986; Ruzycki et al.
2001). In addition, reservoirs are usually relatively young, such that assemblage community
members have not co-evolved (Havel et al. 2005; Raborn et al. 2007). Finally, predominant uses
of reservoirs, such as for water storage and flood control, may dramatically change water
levels, leading to fluctuations in fish habitat and population dynamics (Gasinth and Gafny 1990;
Rose and Mesa 2013).
Similarly, the balance of interactions within reservoir food webs may be unstable due to their
simplicity, where the entire assemblage can be affected by random fluctuations in a single
species (Stein et al. 1995; Raborn et al. 2007). In the western USA, non-native Lake Trout
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Salvelinus namaycush, have had a substantial impact on native, and federally threatened, Bull
Trout S. confluentus, populations due to predation, competition for food, and varying life
history traits of these two top predators (Guy et al. 2011). Burbot lota lota, rapidly invading
Flaming Gorge Reservoir, Utah-Wyoming, threaten to be a detriment to native fishes through
predation or competitive interactions for shared prey resources (Gardunio et al. 2011). The
strength of interactions within reservoir food webs may change with the introduction of other
non-native fishes; however, establishment of a species is not always successful (Kohler et al.
1986; Williamson and Fitter 1996). Successful introductions to artificial assemblages may result
in a lengthened food chain (Walsworth et al. 2013), alter energy flow through the system
(Sousa et al. 2008), initiate novel predator-prey interactions (Kitchell et al. 1997; Romare and
Hansson 2003; Schoen et al. 2012), or alter trophic structure (Reissig et al. 2006; Skov et al.
2010; Ellis et al. 2011). The addition of species also increases potential for competition (Tyus
and Saunders 2000; Tronstad 2008). Consequently, unwelcome invaders may disrupt certain
linkages and alter the strength of interactions within a complex and not well understood
reservoir food web.
Our understanding of the use of top predators as a tool for biological control in aquatic systems
is of upmost importance, as there has been a surge of invasive fish species in recent years
(Sorenson and Stacey 2004). Piscivorous fishes represent a commonly introduced species,
intentionally stocked as a management tool in an attempt to control undesired species
(Courtenay and Kohler 1986), as well as to enhance angling opportunities (Martinez et al. 2009).
In the Laurentian Great Lakes, a salmonid stocking program was launched to trigger top down
control of an invasive and nuisance Alewife Alosa pseudoharengus, population, which resulted
in a valuable sport fishery and a reduction in Alewife abundance (Bunnell et al. 2006). After
Largemouth Bass Micropterus salmoides, were stocked in ponds to control Gizzard Shad D.
cepedianum, Shad populations plummeted, suggesting Largemouth Bass consumed enough
Shad to limit their overall abundance (Irwin et al. 2003). However, as with many introductions,
there can be adverse effects on non-target organisms as a result (Wittenberg and Cock 2001;
Simberloff 2009). A note-able example of a successful biological control with unintended
results is the Mosquitofish Gambusia affinis, introduced to prey on mosquito larvae, thus,
successfully controlling adult mosquito populations (Kumar and Hwang 2006); however, these
fish have decreased native fish populations through predation (Simberloff and Stiling 1996).
Nonetheless, when introduced as potential biological controls, top predators present a
potentially powerful tool for invasive species management, where a carefully-selected, uppertrophic level species theoretically uses the undesired organism as a primary food resource to
reduce the population size (Hoddle 2004).
USU Budy. Project Completion Report. Scofield Reservoir
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Predator growth and survival depends on the availability of prey resources. This dependence is
described by the classic supply-demand relationship; prey abundance and accessibility (i.e.,
supply) is directly related to biomass and production of predators in the system (i.e., demand;
Ney 1990). This link to prey availability, however, is not always apparent in manipulated,
artificial assemblages (Vatland et al. 2008). Predator size relative to prey size often limit (or
influence) predator ability to capture, handle, and consume prey (Hambright 1991; Magnhagen
and Heibo 2001; Juanes et al. 2002). Gape size limitations, where a larger gape increases the
potential size of prey captured, is exemplified by Yellow Perch Perca flavescens, where size
selection of Round Goby Neogobius melanostomus, prey increased with predator size
(Truemper and Lauer 2005). Further, young fish with rapid growth rates may quickly exceed
the gape of piscivores, thereby allowing them to become less vulnerable to predation and
attain high survival rates, such as Gizzard Shad throughout the southeast (Noble 1981;
Michaletz 2013). With few predators and abundant resources, the prey species may dominant
the assemblage (Stein et al. 1995). Additionally, based on optimal foraging theory, foraging
behavior should maximize fitness (the ultimate measure of performance), through choices
relating to foraging time, diet selection, and handling time (Werner and Hall 1974; Mittlebach
2002; Gill 2003). These behaviors and feeding choices are based on a theoretical goal of
expending the least amount of energy while still obtaining the most calories, and may result in
different interactions between predator and prey than those predicted by supply and demand
alone. Thus, the strength of complex food web interactions depends on prey life-history traits
and subsequent predator foraging decisions. Given these complexities, understanding the
mechanisms driving predator-prey relationships in cold water impoundments can be
challenging, but is also therefore critical for making informed management decisions (Johnson
and Goettle 1999).
Bioenergetics-based modeling, coupled with comprehensive field sampling, provides a
quantitative and predictive tool for managers to estimate current and future predator impacts
on prey populations (Rice and Cochran 1984; Hanson et al. 1997). This approach, based on a
balanced energy budget, uses physiological and allometric relationships driven by food,
temperature, and fish size to predict consumption, growth, or production of fish (Brandt and
Hartman 1993; Chipps and Wahl 2008). More specifically, the model can compute the
consumption, in terms of biomass and associated prey energy, necessary to satisfy the annual
growth of a fish, given the body mass, thermal experience, and diet of the modeled fish
(Beauchamp et al. 2007). The results can be used to effectively evaluate complex interactions
within reservoir food webs and the mechanisms that operate to structure these webs (Baldwin
et al. 2000; Beauchamp and VanTassell 2001; Irwin et al. 2003). In Bear Lake, Utah, Bonneville
Cutthroat Trout Oncorhynchus clarkii utah, and introduced Lake Trout prey heavily on endemic
prey fish; as a result, model simulations estimated strong predation impacts from Lake Trout
USU Budy. Project Completion Report. Scofield Reservoir
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with consumption exceeding prey fish production (Ruzycki et al. 2001). Similarly, Strawberry
Reservoir, Utah, stocked with a unique assemblage of salmonids, quantified monthly sport fish
consumption to highlight a bottleneck in Daphnia prey supply which was limiting to fish
production (Baldwin et al. 2000). In sum, this approach has been effectively used to 1) assess
water quality constraints on fish growth (Budy et al. 2011); 2) estimate management effects,
such as slot limits and angler harvest (Luecke et al. 1994); 3) investigate species invasion
success (Budy et al. 2013); and 4) predict predation pressure due to climate effects (Peterson
and Kitchell 2001; Mesa et al. 2013), among many other uses.
In Scofield Reservoir, Utah, the fast-reproducing non-game fish, Utah Chub Gila atraria, was
unintentionally introduced in the reservoir, and subsequently the population exploded in 2005.
Utah Chub are native to the nearby Snake River and Lake Bonneville basins, though not native
to the Colorado River drainage, where Scofield Reservoir is located. Within the past decade,
populations of sport fish, Bear Lake strain Bonneville Cutthroat Trout, Rainbow Trout O. mykiss,
and Tiger Trout Salmo trutta, female x Salvelinus fontinalis, male, have been stocked in
relatively high numbers as an effort to suppress the Utah Chub population. Since the majority
of the lake-wide fish abundance consisted of Utah Chub, there was concern the expanding Utah
Chub population would adversely affect the popular blue-ribbon sport fishery. Maintaining
balanced predator and prey populations can thus be an ongoing management challenge for
fisheries managers. Accordingly, the Scofield Reservoir food web poses a unique opportunity to
investigate the use of salmonids as biological control agents to control an unwelcome nongame fish population.
One project goal was to identify the best single, or combination of species for suppressing Utah
Chub abundance, as well as to assess the relative performance of three popular sport fish
(Cutthroat Trout, Rainbow Trout, and Tiger Trout). Specifically, our objectives were to:
1) estimate the abundance, biomass, and population growth trajectory of the principal prey
fish, Utah Chub;
2) quantify trout consumptive demand relative to production of Utah Chub; and
3) compare the relative abundance and condition of the three predator species.
To achieve these objectives, we estimated catch-per-unit-effort (CPUE) and collected fish for
measurements of growth and diet using a combination of common fisheries field techniques,
conducted hydroacoustics surveys of fish density, and lastly, assembled this information into
bioenergetic simulations of predator population consumption, compared with prey abundance
and production. This study, and the results presented herein, is also one of the first
documented rigorous studies of Tiger Trout ecology.
USU Budy. Project Completion Report. Scofield Reservoir
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Table 1.1. Summary of trout stocking by species by month from 2005-2012. The number
stocked and mean total length (TL) was estimated by state fish hatcheries.
Year
Month
2005
Cutthroat Trout
Number
Mean TL
stocked
(mm)
Rainbow Trout
Number
Mean TL
stocked
(mm)
Tiger Trout
Number
Mean TL
stocked
(mm)
July
----
----
478,484
83
----
----
September
----
----
----
----
103,716
122
October
----
----
100,003
169
----
----
June
----
----
399,214
78
----
----
October
----
----
134,880
151
46,800
135
May
----
----
467,365
74
----
----
October
----
----
100,960
135
129,941
150
----
----
----
----
139,375
152
86,052
207
24,320
217
----
----
October
----
----
58,533
201
122,500
148
November
----
----
162,544
170
----
----
90,132
193
----
----
----
----
October
----
----
----
----
108,560
160
November
----
----
80,100
183
----
----
80,143
203
----
----
----
----
----
----
74,523
183
119,635
149
81,152
199
----
----
----
----
----
----
91,702
187
116,681
182
2006
2007
2008
October
2009
May
2010
May
2011
May
October
2012
April
October
USU Budy. Project Completion Report. Scofield Reservoir
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Study Site
Scofield Reservoir is a high elevation (2,322 m) impoundment on the Price River, eventually
flowing to the Colorado River, located within the Manti-La Sal National Forest in Utah (Figure
1.1). The reservoir was created by Scofield Dam in 1926 and is predominantly used for irrigation
water storage, with recreation and flood control as additional benefits (Bureau of Reclamation
2011). The current reservoir has a capacity of 73,600 acre-ft (90,800,000 m³) at full pool, mean
surface area of 1,139 ha, and a mean depth of eight meters (Bureau of Reclamation 2009).
Scofield Reservoir is characterized as eutrophic, with ‘excessive’ total phosphorous enrichment
(Department of Environmental Quality 2010). Blue-green algae dominate the phytoplankton
community, indicative of poorer water quality, with blooms typically occurring in summer. The
reservoir stratifies thermally in summer, and hypolimnetic oxygen deficits historically lead to
fish kills of varying degrees (Hart and Birdsey 2008). Zooplankton composition is typically
dominated by the cladoceran, Daphnia, at densities of 0.09 per L and a biomass of 1.5 ug/L in
the summer.
Scofield Reservoir is managed as an extremely popular family fishery. Historically, around
600,000 age-1 (150-250 mm TL) Rainbow Trout were stocked every year. However, the fish
stocking program has been adjusted nearly every year since 2005 in response to the reappearance of Utah Chub in gill nets, with the goal to reduce the population before an
expansion of a magnitude similar to Utah’s Strawberry Reservoir (Hart and Birdsey 2008). Tiger
Trout and Bear Lake strain Bonneville Cutthroat Trout have been stocked in the fishery as
potential biological controls for Utah Chub, as well as an alternative sport fish. These
populations demonstrate little to no natural reproduction, and are artificially maintained with
approximately 80,000 of each species stocked yearly at 200 mm (Table 1.1). Other species
present in the reservoir include the Redside Shiner Richardsonius balteatus, and Mountain
Sucker Catostomus platyrhynchus.
Methods
Predator Abundance
We sampled fishes intensively from summer 2011 through autumn 2012 in Scofield Reservoir.
In this type of fixed-station sampling, we selected index sites to be representative of the
reservoirs’ longitudinal axis from the upper riverine zone to the lower lacustrine zone
(McMahon et al. 1996), while maintaining consistency with long-term Utah Division of Wildlife
Resources (UDWR) monitoring, in order to monopolize on previously collected data.
USU Budy. Project Completion Report. Scofield Reservoir
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We used gill netting to collect data to evaluate the size structure, growth rate, body condition,
and diet of trout in the reservoir. We set two horizontal sinking gill nets at each of eight index
sites within the reservoir (Figure 1.1). We set these experimental gill nets (1.8 m tall x 24 m
long with eight monofilament panels of 38-, 57-, 25-, 44-, 19-, 64-, 32-, and 51- mm bar mesh)
according to standard gill-net methods to capture a representative size distribution of all fish in
the reservoir (Beauchamp et al. 2009; Lester et al. 2009). We placed gill nets in littoral areas
offshore at depths fish were predicted to be most abundant; set before dusk and pulled after
dawn, spanning two crepuscular periods. We calculated catch-per-unit-effort (CPUE;
fish/net/hour) at each sample site for each trout species and Utah Chub. Within each season,
we summed catches from all gill nets and divided by total effort in order to estimate seasonal
reservoir-wide CPUE. We expressed relative abundance of each species as a percentage of
CPUE. We ran an analysis of variance (ANOVA) to compare the CPUE between each trout
species and by season. We completed all statistical analyses using SAS and a “proc glimmix”
statement, with an a priori α of 0.05 (SAS Institute Inc., Cary, NC, USA).
Predator Diet Composition
We collected Cutthroat Trout, Rainbow Trout, and Tiger Trout diets primarily from fish captured
in gill nets from July 2011 through October 2012. We placed all fish captured on ice and
removed and preserved their stomachs whole for later analysis. We identified all organisms
sampled from stomach contents to the lowest taxonomic level possible (Brooks 1957;
Edmonson 1959; Merritt and Cummins 1996). We grouped fish stomach contents by prey fish
(identified to species when possible), zooplankton, organic matter, aquatic invertebrates
(classified to order), and terrestrial invertebrates (classified to order). We counted and
weighed (blot-dry wet weight to nearest 0.001 g) prey fish, and weighed invertebrate prey en
masse by classification. We measured intact prey fish to nearest mm (backbone and standard
length). For model simulations, we determined diet composition as a proportion by wet
weight, and calculated seasonally aggregated percentages. We delimited seasons as follows:
spring (April – May), summer (June – August), and autumn (September – October). We applied
these seasonal diet data to the appropriate size-class bioenergetic simulations, which
interpolates changes in diet composition between seasonal inputs.
Predator Growth
We estimated annual mean size-at-age from a combination of otolith-aging data, analysis of
length-frequency modes, and mark-recapture data (from dye-marked fish). All otoliths were
aged whole, independently by at least two laboratory personnel who were experienced in
USU Budy. Project Completion Report. Scofield Reservoir
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otolith aging. We viewed otoliths under a microscope and aged them by counting the opaque
bands (annuli) from the center to the anterior edge. We then used size-at-age datasets to
estimate annual growth (g/year) from July 2011 to July 2012. We used the resulting growth
estimates for size-specific growth inputs in bioenergetics simulations.
Thermal History
We estimated the thermal history of the modeled cohorts of trout from a combination of
monthly vertical temperature profiles and remote temperature data loggers placed at depths of
3-, 6-, and 9-m attached to a stationary buoy on the reservoir for a full year. Since the depth
distribution of catches in gill nets varied throughout the water column, and the reservoir is
shallow, we used the average of the three temperature loggers as the representative thermal
history. We modeled all sizes and species of trout with the same temperature regime. To
identify temperature sensitivity of model estimates of consumption, we also ran a set of
simulations using the species consumption thermal optimum (CTO) assigned for each day the
temperature was available in the reservoir. This scenario assumes trout will behaviorally
thermoregulate when possible (Budy et al. 2013).
Prey Abundance, Biomass, and Production
We conducted hydroacoustic surveys to provide density and abundance information of fishes >
100 mm TL (age-2 and older) in Scofield Reservoir (also see Appendix 1). We conducted surveys
during the new moon event when fish are most likely dispersed and to reduce the likelihood of
fish associating with the lake bottom, where they could not be detected by the acoustic
transducer. We conducted night-time cross-reservoir transects on Scofield Reservoir covering a
representative area of the reservoir. In August 2011, twelve acoustic transect distances ranged
from 436 – 2,279 m with mean depths ranging from 4.0 – 10.5 m. In June 2013, due to lower
water levels, eleven acoustic transect distances ranged from 417 – 2,250 m with mean depths
ranging from 4.4 – 7.6 m. We collected data using a Biosonics Model DE6000 scientific
echosounder with 420 kHz dual-beam transducer (6 X 15o) and towed the transducer on a fin at
1-m depth while recording data using Biosonics Visual Acquisition processing software. We
sampled at a rate of two pings per second traveling at a boat speed of 1 – 2 m/s (2 – 5 kph).
Pulse width of the signal was 0.4 ms. We processed acoustic target and density data using
Biosonics Visual Analyzer software, using single fish targets with dual-beam target strengths
ranging from -48 to -32 decibels (dB), representing fish 100 mm and larger (Dahm et al. 1985).
We selected only echoes that met the single-target shape criteria used by the analysis software
to calculate target strengths and densities. We treated transects as replicates in the analysis to
USU Budy. Project Completion Report. Scofield Reservoir
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produce mean fish per cubic meter with 1 standard error (SE), and then extrapolated density of
fish (fish/m3) to lake-wide abundance using lake volume.
To verify and apportion acoustic targets, we used gill-net catch information collected near the
time of acoustic surveys (August 2011 and May 2013; trawling is not possible in this shallow
reservoir). We summarized gill-net catch by species and size classes (100 – 150 mm, 151 – 250
mm, 251 – 350 mm, and fish larger than 350 mm) and determined percentage of species by size
class to delineate the acoustic-derived abundance estimates by species by size class.
Our hydroacoustic analysis could not adequately discern individual targets < 100 mm (i.e., age-1
and younger fish) and these small fish were rarely captured in gill nets in the reservoir;
however, these sizes of fish are likely important prey for trout (see Appendix 4). Therefore, we
estimated abundance of age-1 Utah chub by (1) estimating number of eggs deposited by adults
(age-2 and older), (2) using a survival rate of 0.1% (egg stage to age-0) to estimate abundance
of age-0 chub, then finally (3) using a survival rate of 30% (age-0 to age-1) to estimate
abundance of age-1 chub in the reservoir. We used sex ratio information from Graham (1961),
eggs-per-female information from Olson (1959; see Appendix 4) and conservative survival rates
based on Utah Chub (Olson 1959) and a surrogate species, Tui Chub (Gila bicolor; Jackson et al.
2004).
We estimated the production of Utah Chub for the 2011-2012 period for four size classes (< 100
mm, 100 - 150 mm, 151 - 250 mm, and 250 - 350 mm) using mean body size from each size
class and biomass from June (spring) 2013 hydroacoustic estimates. We estimated production
(P, kg/year) as:
P =G×B
where, G is the instantaneous rate of growth (natural log of the ratio of final to initial weight),
and B is the mean biomass (kg; Ney 1993). We extrapolated abundance estimates for < 100 mm
Utah Chub from literature values of survival and fecundity (Olsen et al. 1959; Jackson et al.
2004).
Bioenergetics Modeling
We used the Wisconsin bioenergetics program (Hanson et al. 1997) to estimate individual
predator consumption of Utah Chub prey (g/g/year) and developed models for each species of
predator trout in the reservoir. For Rainbow Trout, we used physiological parameters for
Steelhead Oncorhynchus mykiss (Rand et al. 1993). For Cutthroat Trout, we used their closest
published taxonomic surrogates (Steelhead) for most parameters. We based lower
USU Budy. Project Completion Report. Scofield Reservoir
9
consumption thermal optimum (CTO) and upper consumption thermal maximum (CTM)
temperature values on models of Dwyer and Kramer (1975; Beauchamp 1995; Ruzycki et al.
2001). For Tiger Trout, the closest published taxonomic surrogate was the Brown Trout Salmo
trutta, which we modeled similarly with parameters from Dieterman et al. (2004; see also
Whitledge et al. 2010).
We ran models over a time period of 1 year, initiated on July 26 and continued through July 25
of the following year. We accounted for an ontogenetic shift in diet preferences based on fish
size in model simulations; at approximately 350 mm trout switch from a predominantly
invertebrate-fueled diet to becoming increasingly piscivorous. Limited diet information by ages
also necessitated the use of size-classes; we used age-3 and age-5 cohort growth of each
species to be representative of small and large size classes, respectively, so annual growth
could be most accurately estimated given available data. Additionally, we accounted for
seasonal variation in feeding habits, incorporating diet proportions for six sampling periods
throughout the year: 26 July 2011, 5 October 2011, 22 April 2012, 6 May 2012, 6 June 2012, and
25 July 2012. We derived Predator and prey energy densities from the literature, and/or
taxonomically-close surrogates (Table 1.2). We set the percentage of indigestible prey biomass
at 10% for all invertebrate prey, 3.3% for prey fish (Stewart et al. 1983), and 25% for crayfish
(Stein and Murphy 1976). We scaled individual consumption estimates to the population level
using size class-based abundance for each trout species (g of prey fish/year) derived from
hydroacoustics surveys.
Additionally, we used bioenergetic efficiency (BioEff) as a scalar representation of the realized
percentage of maximum possible consumption (g/g/day; 0 - 100%) for each of the size-class
models based on fish growth observed in the field (Budy et al. 2013). This BioEff value is a proxy
for overall fish performance, where a BioEff near 100% indicates a fish feeding near their
maximum possible consumption rate (based on temperature, diet, and body size), whereas a
BioEff value near 0% indicates a fish performing poorly, feeding at a rate much lower than
theoretically possible.
USU Budy. Project Completion Report. Scofield Reservoir
10
Table 1.2. Wet weight energy density estimates of individual predators and prey items used for
bioenergetics simulations. All estimates are from literature sources; where noted, a similar
prey surrogate was substituted.
Energy density
(Joules/g)
4429
Source
Cummins and Wuycheck 1971
Chironomidae
3304
Cummins and Wuycheck 1971
Coleoptera
2448
Cummins and Wuycheck 1971
Decapoda
4507
Cummins and Wuycheck 1971
Ephemeroptera
3715
Cummins and Wuycheck 1971
Hemiptera
2621
Ciancio et al. 2007; Penczak et al. 1999
Isopoda
2624
Cummins and Wuycheck 1971
Mollusca
2007
Cauffope and Heymans 2005
Zooplankton
2445
Cladoceran, Cummins and Wuycheck 1971
Trichoptera
3342
Cummins and Wuycheck 1971
Fish
5230
Tui Chub (Gila bicolor), Raymond and Sobel 1990
Other aquatic invertebrates
3351
Cummins and Wuycheck 1971
Terrestrial invertebrates
2742
Cummins and Wuycheck 1971
Organic matter
2116
Penczak et al. 1999
Cutthroat Trout
5764
Steelhead, Hanson et al. 1997
Rainbow Trout
5921
Cummins and Wuycheck 1971; Hanson et al. 1997
Tiger Trout
5591
Brown Trout (S. trutta), Dieterman et al. 2004
Prey
Amphipoda
Predator Performance
We calculated condition of all predators using two indices, Fulton’s K (KTL ) and relative weight
(Wr ), since body condition is related to the availability of prey:
Fulton' s KTL =
W
*100,000
L3
Relative Weight Wr =100*(
W
)
WS
where W is the weight of the fish (mass, g), L is the total length (mm) of the fish, and WS is the
standard weight of a fish of the same length. The KTL index assumes larger ratios reflect a
USU Budy. Project Completion Report. Scofield Reservoir
11
healthier physiological state (Pope and Kruse 2007). We obtained equations and values for WS
from the literature for lentic Cutthroat Trout and lentic Rainbow Trout (Kruse and Hubert 1997;
Simpkins and Hubert 1996). We estimated parameters for lentic Tiger Trout using a lengthweight regression of summer 2011 Tiger Trout data (r2 = 0.97, a = -6.2159 and b = 3.4608). A
Wr of 100 is generally accepted as the national standard (Anderson and Neumann 1996). We
ran an analysis of variance (ANOVA) to compare Fulton’s KTL between each trout species and by
season.
Additionally, the Utah Division of Wildlife Resources has marked all stocked Cutthroat Trout and
Rainbow Trout since 2009 with unique fluorescent dyes (red, green, and orange) or adipose fin
clips, where each year corresponds to a different color dye or mark. We examined all Rainbow
Trout and Cutthroat Trout caught for fluorescent dye marks by using an ultraviolet lamp set up
in a dark room. We then calculated the relative return rate as the number of marked fish
recaptured, divided by the initial number stocked by Utah Division of Wildlife Resources.
We determined the proportional stock density (PSD) for each trout species following
procedures outlined in Anderson and Neumann (1996):
PSD=
(number of fish ≥ minimum quality length)
*100
(number of fish ≥ minimum stock length)
Values of PSD range from 1 to 100, and are a descriptor value of length-frequency data which
may identify potential for “imbalances” in predator-prey population dynamics. For most fish,
40-70 is a typical objective range for “balanced” populations. Values less than the objective
range indicate a population dominated by small fish, whereas values greater than the objective
range indicate a population comprised mainly of large fish. Stock and quality lengths vary by
species, and are based on percentages of world-record lengths (Gabelhouse 1984; Simpkins and
Hubert 1996; Kruse and Hubert 1997; Hyatt and Hubert 2001). Stock length (20-26% of worldrecord length) refers to the minimum size fish with recreation value, while “quality” length (3641% of world-record length) refers to the minimum size fish most anglers want to catch.
We also evaluated the relative stock density (RSD) using values reported in Table 1.3.
“Preferred” length (45-55% of world-record length) refers to the minimum size fish anglers
prefer to catch when given a choice. “Memorable” length (59-64% of world-record length)
refers to the minimum size fish most anglers remember catching, whereas “trophy” length (7480% of world-record length) refers to the minimum size fish considered worthy of
acknowledgement. Like PSD, RSD can provide useful information regarding population
dynamics, but is more sensitive to changes in year-class strength. We calculated RSD as
USU Budy. Project Completion Report. Scofield Reservoir
12
RSD=
(number of fish ≥specified length)
*100
(number of fish ≥minimum stock length)
For example, RSD-Preferred (RSD-P) was the percentage of stock length fish that were also
longer than the preferred length.
Table 1.3. Length categories (mm TL) proposed for trout species found in Scofield Reservoir
based on literature values. No Tiger Trout values were found in literature, so lotic Brown Trout
values were substituted for reference. Tiger Trout values were also calculated based on upper
percent of world record length, using the 2012 Utah Tiger Trout state record (820 mm).
Stock
(S)
Quality
(Q)
Preferred
(P)
Memorable
(M)
Trophy
(T)
Source
Cutthroat Trout
200
350
450
600
750
Kruse & Hubert 1997
Rainbow Trout
250
400
500
650
800
Simpkins & Hubert 1996
Brown Trout (lotic)
150
230
300
380
460
Hyatt & Hubert 2001
Tiger Trout
213
336
451
525
656
Scofield Reservoir
Species
Results
Predator Population Estimation
From July 2011 to October 2012 we captured and processed 699 Cutthroat Trout, 111 Rainbow
Trout, 398 Tiger Trout, and 8,489 Utah Chub. Fish were captured at a variety of locations
throughout the lake, with equal effort applied at each site. Utah Chub dominated the Scofield
Reservoir fish community and were the most abundant, regardless of season. We collected
approximately 5 Utah Chub per gill-net-hour in 2012 (CPUE = 3.3 ± 0.5 in spring, 6.8 ± 0.6 in
summer, and 5.2 ± 0.3 in autumn [mean ± 1 SE]; Figure 1.2), lower catch rates than previously
recorded from Utah Division of Wildlife Resource sampling in 2008 and 2009.
Trout community catch rates varied by season and by species; by season in 2012, trout were
caught more frequently in autumn (CPUE = 0.737 ± 0.053 fish per hour [mean ± 1.96 SE]), than
USU Budy. Project Completion Report. Scofield Reservoir
13
either the summer (CPUE = 0.242 ± 0.053 fish per hour) or spring (CPUE = 0.216 ± 0.053 fish per
hour; ANOVA; F=34.39; df=2, 14; P < 0.001). By species, Cutthroat Trout were numerically
dominant from Rainbow Trout, which were significantly less than Tiger Trout. There were no
significant differences in Cutthroat Trout and Tiger Trout catch.
Table 1.4. Seasonal catch rate (CPUE), an index of relative abundance, calculated as the number
of fish, per net, per hour, for Cutthroat Trout, Rainbow Trout, Tiger Trout, and Utah Chub in
Scofield Reservoir, Utah. One standard error is shown in parentheses. A “---“ indicates no data
was available.
Species
Season
CPUE
2011
2012
Spring
Summer
Autumn
--0.18 (0.022)
0.20 (0.048)
0.45 (0.1)
0.45 (0.08)
0.68 (0.03)
Spring
Summer
Autumn
--0.060 (0.015)
0.014 (0.010)
0.03 (0.01)
0.068 (0.02)
0.037 (0.005)
Spring
Summer
Autumn
-0.19 (0.03)
0.16 (0.04)
0.18 (0.08)
0.20 (0.03)
0.32 (0.01)
Spring
Summer
Autumn
--3.7 (0.4)
4.5 (1.4)
3.3 (0.5)
6.8 (0.6)
5.2 (0.3)
Cutthroat Trout
Rainbow Trout
Tiger Trout
Utah Chub
Estimates of abundance for Cutthroat Trout, Rainbow Trout, and Tiger Trout fluctuated based
on season. Notably, Cutthroat Trout abundance ranged from low abundance in August 2011 to
high abundance in June 2013 (Table 1.5; Figure 1.3), and made up 7% of the total catch in 2013.
Tiger Trout were caught less frequently in gill nets than Cutthroat Trout, but still increased in
abundance in early 2013, making up 4% of the total catch. Regardless of season, Rainbow Trout
had extremely low catch rates (< 1%), with the population appearing to consist of only a small
fraction of individuals compared to Cutthroat Trout and Tiger Trout. Proportionally, the
Cutthroat Trout population was dominated by large fish (81%) and the Tiger Trout population
USU Budy. Project Completion Report. Scofield Reservoir
14
varied by year (47% and 80% large, respectively); however, the population of all sizes of
Rainbow Trout remained low. Notably, species relative catch rates have shifted considerably
with Utah Chub establishment, as Rainbow Trout exhibited peak returns in 2005 when Utah
Chub were first discovered and have since declined to make up < 1% of the total catch (Figure
1.4).
Table 1.5. Predator abundance and density (trout/ha) estimates based on hydroacoustics
surveys in August 2011 and June 2013. The ‘small’ size class refers to trout < 350 mm TL and the
‘large’ size class refers to trout ≥ 350 mm TL.
2011
Species
Small
Cutthroat Trout
Large
2013
Density
Small
Large
Density
39,800 174,200
188
81,400
337,700
368
Rainbow Trout
39,800
15,100
48
0
9,650
8
Tiger Trout
48,600 189,500
209
153,800 135,100
254
Predator Diet Composition, Growth, and Thermal History
Utah Chub were the predominant prey item for large Cutthroat Trout and large Tiger Trout
throughout the year (Figure 1.5). The percentage of piscivory for large Cutthroat Trout ranged
from 35% in autumn 2011 to 69% in summer 2012. For large Tiger Trout, prey fish represented
19-79% of their total diet depending on season. We found only one Redside Shiner and no
trout in diets, therefore, for simplicity we assumed all prey fish were Utah Chub. The relative
importance of secondary food sources varied; large Cutthroat Trout relied heavily on
chironomids and terrestrial invertebrates, whereas large Tiger Trout consumed significant
portions of crayfish. Aquatic invertebrates, supplemented by lesser proportions of terrestrial
invertebrates and organic matter, represented the largest proportion of prey in diets of small
and large Rainbow Trout, during most of the year. Similarly, small Cutthroat Trout and small
Tiger Trout relied heavily on aquatic invertebrates, terrestrial invertebrates, and crayfish.
We determined annual growth estimates for a representative small and large size classes for
each trout species. For Cutthroat Trout, starting and ending weight used in bioenergetics
simulations were conservative estimates, determined from cohort length and weight
information (mean size) for two size classes (small: 274 - 333 g, large: 420 – 617 g; Figures 1.6
USU Budy. Project Completion Report. Scofield Reservoir
15
and 1.9). Rainbow Trout collected during both 2011 and 2012 ranged from age-2 to age-7
according to otoliths, with weight estimates based on otolith aging (small: 114 – 186 g, large:
495 – 614 g; Figures 1.7 and 1.9). There were no marked cohorts of Tiger Trout in Scofield
Reservoir, therefore mean weight at size was determined from otolith size-at-age data (small:
134 – 327 g, large: 761 – 1901 g; Figures 1.8-1.9).
Temperature was recorded at 1- hour intervals from 19 July 2011 – 7 September 2011 and 23
April 2012 to 7 June 2013, for depths of 3-, 6-, and 9-m from the surface (Figure 1.10). We
combined these data with monthly temperature profiles taken by hand using a data logger to
obtain a representative simulation year. Maximum average daily temperature during the
simulation period was 18.5°C. The consumption thermal optimum (CTO) for Rainbow Trout,
20°C, was higher than Rainbow Trout typically experienced in the reservoir, whereas CTO for
Tiger Trout, 17.5°C, was available in Scofield Reservoir during the summer months, and
temperatures near the CTO of Cutthroat Trout, 14°C, were available through much of the time
series. The consumption thermal optimum of a fish represents the optimum water
temperature of a fish needed for maximum achieved consumption, based on laboratory studies
(Hartman and Hayward 2007).
Prey Abundance, Biomass, and Production
We estimated biomass and production of Utah Chub to quantify the amount of prey available in
Scofield Reservoir. The biomass of Utah Chub varied from a low of 318,900 kg of Utah Chub in
August 2011 to a high of 627,600 kg of Utah Chub in June 2013 (Table 1.6). Age -1 chub, or
those < 100 mm TL, were estimated as 9,027,000 fish in the population in 2011 and 17,970,000
fish in 2013 (Table 1.6). Using biomass and mass gain by size class, Utah Chub production was
estimated at 433,960 kg/year (8,272,600 Chub/year) during the end of the summer growth
season in 2011. In comparison, Utah Chub production at the start of the season in 2013 was
649,630 kg/year (9,989,700 Chub/year; Figure 1.11).
Bioenergetics Simulations of Consumption
Of all predator species, an average, individual large Tiger Trout consumed the largest
proportion of prey fish, primarily Utah Chub. The average large individual Tiger Trout consumed
over 2,660 g of prey fish in a given year (63 Chub per year; Table 1.6). The average individual
large Cutthroat Trout consumed 1,820 g Utah Chub annually (49 chub per year). The mean
total length of Utah Chub eaten by Cutthroat Trout was 131 mm (37 g), while the mean total
length of Utah Chub eaten by Tiger Trout was 139 mm (42 g); these sizes were used to scale
consumption estimates (g) to number of Chub. Rainbow Trout did not display an affinity for
USU Budy. Project Completion Report. Scofield Reservoir
16
prey fish; an individual large Rainbow Trout consumed only 400 g of Utah Chub in a year. The
smaller sizes of trout (< 350 mm TL) had only a minor contribution towards the overall
consumption of Utah Chub prey.
Table 1.6. Estimated abundance (number) and biomass (kg) of Utah chub in Scofield Reservoir
in August 2011 and July 2013.
Estimated
abundance
Estimated
biomass (kg)
Age-1
9,027,189
73,120
≥ Age-2
2,651,017
245,805
Total ≥ age-1
11,678,206
318,925
Production
8,272,559
433,960
Age-1
17,968,822
145,547
≥ Age-2
5,312,360
482,056
Total ≥ age-1
23,281,182
627,603
Production
9,989,658
649,627
Age class by date
August 2011
June 2013
When we scaled the estimated consumption of an individual predator of each species up to the
overall reservoir-wide population of each species, proportional contributions to total piscivory
changed accordingly. The population of large Cutthroat Trout in the reservoir consumed over
615,000 kg of Utah Chub in a year (16.6 million Chub), about 65% of the overall reservoir-wide
consumption of Utah Chub. The smaller population of Tiger Trout contributed 33% of the
overall consumption, consuming about 359,000 kg (8.5 million Chub) yearly. The Rainbow
Trout population contributed less than 1% of the total consumption in Scofield Reservoir. Using
consumption thermal optimum (CTO) temperatures for each species when available in the
reservoir did not strongly influence consumption estimates of Utah Chub; Cutthroat Trout
consumption (kg) at optimal temperature was < 1% lower, and Tiger Trout consumption was <
2% lower. Therefore, although individual consumption of prey fish was less for Cutthroat Trout,
due to their higher lake-wide abundance, Cutthroat Trout currently exert the strongest
predation pressure on the Utah Chub population.
USU Budy. Project Completion Report. Scofield Reservoir
17
Table 1.6. Annual Utah Chub consumption (g) estimates per individual predator for two
representative size classes of Cutthroat Trout, Rainbow Trout, and Tiger Trout for the 2011 –
2012 simulation year. Abundance was estimated from hydroacoustics surveys in June 2013.
Population-level estimates are annual consumption (kg).
Individual
Consumption
(g)
Cutthroat Trout
Small
110
Large
1,820
Rainbow Trout
Small
Large
Tiger Trout
Small
Large
90
400
60
2,660
Population
Number of Chub
consumed
Abundance
Consumption
(kg)
Number of Chub
consumed
Total:
81,400
337,700
419,100
8,900
615,000
625,000
252,000
16,619,000
16,870,000
Total:
0
9,700
9,700
0
3,900
3,900
0
105,000
105,000
Total:
153,800
135,100
288,900
9,200
359,000
368,000
231,000
8,565,000
8,796,000
3
49
3
11
2
63
The combined consumption by Cutthroat Trout and Tiger Trout exceeds the annual production
estimate of Utah Chub. Large Cutthroat Trout and large Tiger Trout alone consumed over 22
million Chub scaling with end of summer hydroacoustic abundance estimates. When
consumption was calculated based on early summer predator abundance, large Cutthroat Trout
and large Tiger Trout consumed over 26 million chub in a year, approaching the combined
abundance estimate of 33 million Utah Chub including annual production (Figure 1.11).
Salmonid Performance
Bioenergetics simulations indicated that BioEff values for all predator species were much lower
than 100%, suggesting fish were not feeding near their maximum possible consumption rate
(Table 1.7). Cutthroat Trout fed at approximately 39% of maximum consumption rate, whereas
Rainbow Trout fed more efficiently at 47% of their maximum, and Tiger Trout were the most
efficient, feeding at 56% of their maximum rate during the 2011-2012 simulation year.
Additionally, Cutthroat Trout and Tiger Trout both exhibit lower bioenergetic efficiency (BioEff)
with larger size.
USU Budy. Project Completion Report. Scofield Reservoir
18
Table 1.7. Bioenergetic efficiency expressed as a percentage of the realized maximum possible
consumption rate, determined with the Fish Bioenergetics 3.0 model (Hanson et al. 1997).
Species
Cutthroat Trout
Size class
BioEff (%)
Small
Large
41.6
36.0
Small
Large
43.4
50.2
Small
Large
57.7
54.0
Rainbow Trout
Tiger Trout
The condition metric, KTL , additionally supported BioEff results. There were no significant
differences in KTL by season (ANOVA; F = 0.89; df = 2, 14; P > 0.05); however, there were strong
condition (i.e., performance) differences by species (ANOVA; F = 14.27; df = 2, 14; P < 0.001).
Cutthroat Trout exhibited the significantly lowest overall performance, with decreasing values
as seasons progressed (KTL = 0.89 – 0.82; Table 1.8). Rainbow Trout performance was
substantially higher than that of Cutthroat Trout, but also decreased seasonally (0.97 – 0.90).
Tiger Trout performed best in the summer (0.96), with lower condition in the spring and
autumn.
Table 1.8. Condition (KTL ) of Cutthroat Trout, Rainbow Trout, and Tiger Trout captured in
Scofield Reservoir during 2012. One standard error shown in parentheses. A “---“ indicates no
data was available.
Cutthroat Trout
Rainbow Trout
Tiger Trout
Season
Small
Large
Small
Large
Small
Large
Spring
0.83 (0.02)
0.93 (0.01)
0.97 (0.05)
---
0.84 (0.04)
0.89 (0.02)
Summer
0.79 (0.01)
0.88 (0.01)
0.91 (0.02)
1.03 (0.06)
0.84 (0.02)
0.98 (0.02)
Autumn
0.76 (0.01)
0.88 (0.02)
0.87 (0.04)
0.96 (0.2)
0.87 (0.02)
0.94 (0.03)
USU Budy. Project Completion Report. Scofield Reservoir
19
All three species of trout exhibited Wr values significantly lower than standard performance in
the summer of 2012 (all t-test P < 0.001; Figure 1.12). The average relative weight values for
Cutthroat Trout and Rainbow Trout were both 79, where a relative weight less than 80 is
considered severely thin. The average relative weight of Tiger Trout was much higher (96),
describing a fish in relatively good condition. When standard values for lotic Brown Trout
(Milewski and Brown 1994) were substituted in Tiger Trout calculations, the average relative
weight decreased to 90, indicating feeding conditions may be lacking or fish are competing.
While this assessment of fish condition was highlighted for the summer 2011 season, studies
show seasonal trends of Wr may be present in some systems, with the highest values occurring
October- May (Quist et al 2002); therefore, these estimates are conservative.
Based on limited mark-recapture data, Cutthroat Trout and Rainbow Trout both exhibited
relative return rates of < 0.01% (Table 1.9). Notably, we caught marked Rainbow Trout very
infrequently (Table 1.10), and as return rates are extremely low for both species of fish, there
are potential issues related to mark retention of the fluorescent dye utilized in marking. Thus
these data could not be incorporated into management recommendations.
USU Budy. Project Completion Report. Scofield Reservoir
20
Table 1.9. Relative return rates of Cutthroat Trout stocked in Scofield Reservoir and recaptured
from summer 2011- autumn 2012. “Ad clip” refers to adipose fin clip.
Season and
Year
Summer 2011
Fall 2011
Spring 2012
Summer 2012
Autumn 2012
Total
caught
Number
marked
Percent
marked
Mark type
81
7
8.6
3
42
197
179
109
Cohort
Total length
(mm)
Weight
(g)
Ad clip
2009
358.0
420.2
3.7
Red dye
2010
309.0
273.7
2
2.5
Green dye
2011
290.0
209.4
69
85.2
Unmarked
406.1
655.6
12
28.6
Ad Clip
2009
362.3
466.6
1
2.4
Red dye
2010
323.0
291.5
8
19.0
Green dye
2011
289.1
202.5
21
50.0
Unmarked
320.1
329.1
101
51.3
Ad clip
2009
399.7
661.3
10
5.1
Red dye
2010
339.4
324.4
11
5.6
Green dye
2011
308.7
237.5
10
5.1
Orange dye
2012
214.7
93.4
65
33.0
Unmarked
349.4
421.6
55
30.7
Ad clip
2009
409.4
658.4
1
0.6
Red dye
2010
346.0
311.4
26
14.5
Green dye
2011
318.1
248.4
8
4.5
Orange dye
2012
247.3
127.7
89
49.7
Unmarked
361.6
484.2
47
43.1
Ad Clip
2009
447.1
827.8
8
7.3
Red dye
2010
343.3
309.4
5
4.6
Green dye
2011
326.4
252.0
22
20.2
Orange dye
2012
275.9
164.0
27
24.8
Unmarked
353.9
510.5
USU Budy. Project Completion Report. Scofield Reservoir
21
Table 1.10. Relative return rates of Rainbow Trout stocked in Scofield Reservoir and recaptured
from summer 2011 – autumn 2012. “Ad clip” refers to adipose fin clip. A “---“ indicates data
was not available.
Season and
Year
Summer 2011
Fall 2011
Spring 2012
Summer 2012
Autumn 2012
Total
caught
Number
marked
Percent
marked
Mark type
27
2
7.4
4
3
13
27
6
Cohort
Total length
(mm)
Weight
(g)
Ad clip
2009
334.0
357.9
14.8
Red dye
2010
287.0
449.1
21
77.78
Unmarked
342.81
459.82
1
33.3
Ad clip
2009
320
334.5
1
33.3
Red dye
2010
316
333.9
0
0.0
Green dye
2011
---
---
1
33.3
Unmarked
340
426.3
1
7.7
Ad Clip
2009
344
374.1
2
15.4
Red
2010
330.5
443.0
4
30.8
Green
2011
297.0
250.5
6
46.2
Unmarked
273.2
224.5
2
7.4
Ad clip
2009
357.5
457.15
1
3.7
Red dye
2010
332.0
382.1
2
7.4
Green dye
2011
307.0
295.1
22
81.5
Unmarked
291.2
264.3
1
16.7
Ad clip
2009
251.0
134.9
1
16.7
Red clip
2010
326.0
285.4
0
0.0
Green clip
2011
---
---
1
16.7
Orange clip
2012
256.0
140.5
3
50.0
Unmarked
323.7
363.4
Stock density values of trout in Scofield Reservoir fluctuated widely, despite initial stockings at
similar numbers and sizes. In Scofield Reservoir, Rainbow Trout exhibited extremely low PSD
and RSD-P values, with few fish above the “preferred” length of 500 mm and no proportion of
fish above the “memorable” length of 650 mm (Table 1.11). Cutthroat Trout exhibited
relatively high values of proportional stock density, with a substantial proportion of fish above
the “preferred” length of 450 mm. Nearly all Tiger Trout in Scofield Reservoir were of
USU Budy. Project Completion Report. Scofield Reservoir
22
“preferred” length (450 mm), and there was a significant portion of fish of “memorable” length
(525 mm), indicating the population was dominated by large fish.
Table 1.11. Stock density index ranges for Scofield Reservoir trout species. Proportional stock
density (PSD), relative stock density of preferred length fish (RSD-P) and relative stock density
of memorable length fish (RSD-M) given. Values were calculated based on summer (1 June – 31
August) data for both 2011 and 2012.
Species
PSD
2011
RSD-P
RSD-M
PSD
2012
RSD-P
RSD-M
Cutthroat Trout
78.5
22.8
0.0
52.0
14.1
0.6
Rainbow Trout
8.0
4.0
0.0
5.0
0.0
0.0
Tiger Trout (Brown trout)
99.0
90.0
60.0
98.8
91.3
81.3
Tiger Trout (state record)
86.9
33.3
13.1
91.1
49.4
24.1
Discussion
In response to the exponential increase of the unwelcome Utah Chub in Scofield Reservoir in
recent years, we used a combination of field sampling, hydroacoustic surveys, and
bioenergetics simulations for three top predators in the system to quantitatively assess the
relative potential of these species to act as biological control agents. Comparison of trout
consumption of Utah Chub revealed striking differences among the species with important
management implications. We found that the population of large (≥ 350 mm) Cutthroat Trout
consumed the largest proportion of Utah Chub in Scofield Reservoir. These trout are voracious
predators, consuming 615,000 kg of Utah Chub in a single year. Furthermore, based on model
simulations, the average large Tiger Trout rely heavily on Utah Chub as well, which as a
population, equals almost 359,000 kg more Utah Chub consumed annually. However, Rainbow
Trout, known to exhibit variable rates of piscivory, do not consume a significant number of Utah
Chub in Scofield Reservoir. Not only does the average large Rainbow Trout consume few chub
in a year, but the population of Rainbow Trout is so small, their collective impact on Utah Chub
is insignificant. In sum, comparisons between bioenergetic estimates of predator consumption
versus Chub production demonstrated that Tiger Trout and Cutthroat Trout both appear to
have significant potential to act as effective biological control agents on Utah Chub, whereas
Rainbow Trout currently contribute little to Utah Chub control.
USU Budy. Project Completion Report. Scofield Reservoir
23
Utah Chub were caught in extremely high densities throughout the time period of this study,
confirming that the population of Utah Chub has increased to very high levels. Abundance
estimates from June 2013 suggest there are over 23 million chub (1,277,000 kg) in the
reservoir. This estimate is substantially lower when using acoustic data from late August,
perhaps because trout consumed large quantities of chub throughout the summer growing
season. Nonetheless, we estimated production of Utah Chub in 2013 at a rate of 2.5 million
chub per year. Utah Chub are a non-game fish that are not targeted by harvest and that exhibit
high reproductive potential (Neuhold 1957). As such, the high production rates of Utah Chub in
Scofield Reservoir are not surprising, and are most likely due to favorable littoral habitat and
abundant food supply (Olsen 1959). In Scofield Reservoir, summer Daphnia densities (0.1 per L)
were low compared to a nearby Utah reservoir (7.4 per L), possibly indicating Utah Chub feed
heavily on these available aquatic zooplankton (Baldwin et al. 2000).
Abundance and production estimates for Utah Chub are based on a few important
assumptions. Our acoustic surveys were only able to clearly account for fish 100 mm and larger,
as such, the age-0 and age-1 Utah Chub fell outside acoustic-target acceptance guidelines; and
abundance of age-1 Chub were necessarily back-calculated based on literature estimates of
fecundity (Olson 1959); sex ratios (Graham 1961), and survival values for Utah chub (held in
captivity; Olson 1959) and Tui Chub Gila bicolor (Jackson et al. 2004). Nonetheless, our results
are logical and consistent with expected values from similar prey-dominated systems. Eilers et
al. (2011) estimated up to 23 million Tui Chub present in Diamond Lake, Oregon before a
prescribed rotenone treatment, with a density of ‘catchable’ Tui Chub equal to 0.09 fish/m³.
Comparatively, we estimated densities of 0.04 fish /m³ - 0.13 fish /m³ of Utah Chub at Scofield
Reservoir. Additionally, historical evidence demonstrates that Utah Chub coexisting with
Cutthroat Trout display higher juvenile growth rates, delayed age at maturity, and larger size at
maturity, as chub that reach adult sizes are less vulnerable to predation (Johnson and Belk
1999). Utah Chub in Scofield Reservoir display similar life-history traits; there are a substantial
proportion of Utah Chub larger than gape-limited predators are able to consume.
Consequently, the reproductively-mature portion of this population of Chub must senesce
before trout predators will be fully capable of controlling the population (see Chapter 3).
The full impacts of Cutthroat Trout and Tiger Trout on Utah Chub in Scofield reservoir are likely
even greater, as this study did not consider non-consumptive effects of predators on Chub.
Predators may affect Utah Chub survival and production through behavioral changes, such as
predator avoidance. Understanding density dependent responses of the Utah Chub population,
stunting and potentially restraining Utah Chub within the size range at which predators are not
gape limited, is important for predicting long term effects of trout predation on chub
USU Budy. Project Completion Report. Scofield Reservoir
24
(Freckleton et al. 2003; Irwin et al. 2003). Effective biological control will be more difficult if the
Chub population growth rate increases at low densities (Hein et al. 2006).
Our bioenergetics model predictions demonstrated the large Cutthroat Trout population
annually could consume more than half the standing prey biomass in Scofield Reservoir,
highlighting their potential as an efficient biological control agent. The disparity between
consumption and production suggests strong predation pressure from Cutthroat Trout on Utah
Chub, but could also indicate that we are unsurprisingly underestimating one or more
components of Chub production (Jackson et al. 2004). Regardless, Bear Lake Cutthroat Trout,
the strain stocked in Scofield Reservoir, are known to exhibit traits of top-level predators and
attain large sizes using fishes as forage (Neilson and Lentsch 1988; Hepworth et al. 1999;
Hepworth et al. 2009). In Lake Chelan, Washington, Lake Trout consumption on Kokanee
Salmon O. nerka, exceeded Kokanee Salmon production rates, leading to an almost 90% decline
in the Kokanee Salmon population over 5 years (Schoen et al. 2012). Additionally, at Flaming
Gorge Reservoir, Utah-Wyoming, researchers postulated the slower-growing Utah Chub were
more vulnerable to predation by Lake Trout than fast-growing Kokanee Salmon (Yule and
Luecke 1993). Prey size is an important factor determining consumption (Scharf et al. 2000),
and Cutthroat Trout in Scofield Reservoir are consuming prey at and above their theoretical
gape limit, demonstrating consumption of chub up to 60% of their own total length.
Collectively, these observations and those of others support the argument that these
piscivorous trout may effectively control Utah Chub in Scofield Reservoir.
Similar to Cutthroat Trout, Tiger Trout appear to have potential as an effective biological
control agent. Despite a paucity of literature on Tiger Trout ecology, Tiger Trout in Scofield
Reservoir demonstrate the aggressive and piscivorous nature suggested of this new hybrid
species. Tiger Trout displayed strong predation impacts on Utah Chub prey, relatively high catch
rates, high condition factors, and modest BioEff values in Scofield Reservoir. Furthermore, Tiger
Trout have high proportional stock density (and relative stock density) values, indicating the
importance of this species to the Scofield Reservoir fishery. Tiger Trout are currently being
stocked in over 30 bodies of water throughout the state of Utah to potentially enhance
fisheries and consume undesired prey species. In addition to their contribution to the fishery
and ability to control undesired prey species, Tiger Trout have the added benefit that they are a
sterile hybrid (Brown Trout x Brook Trout) and unable to permanently expand beyond where
they are stocked, an important conservation consideration for native species and ecosystems
downstream (Zelasko et al. 2010).
Our study indicated Rainbow Trout, a ubiquitous species stocked throughout the western U.S.
and potential top predator, does not contribute substantially to predation pressure on Utah
USU Budy. Project Completion Report. Scofield Reservoir
25
Chub in Scofield Reservoir. There are at least a dozen strains of Rainbow Trout stocked
worldwide, which display a range of piscivory (Hudy and Berry 1983; Swales 2006). Rainbow
Trout have demonstrated piscivory at sizes greater than 250 mm TL (Beauchamp 1990), sizes as
small as 100 mm TL for age-1 fish (Juncos et al. 2013), and broadly feed on this more
energetically-favorable prey at least seasonally (Galbraith 1967; Taylor and Gerking 1980;
Juncos et al. 2011). However, Rainbow Trout in Flaming Gorge, UT-WY, consume primarily
aquatic invertebrates and zooplankton and rarely demonstrate an ontogenetic shift to piscivory
at larger sizes (Haddix and Budy 2005). The Utah Division of Wildlife Resources stocks Fish
Lake/DeSmet or Erwin/Sand Creek Rainbow Trout into Scofield Reservoir, likely originating from
the Eagle Creek, California strain, where their diet has been documented to not contain fish
prey (Hubert et al. 1994; Wagner 1996).
Rainbow Trout recreational fisheries and subsequent stocking programs are common
throughout North America (Baird et al. 2006; Swales 2006; Josephson et al. 2012); however,
performance varies widely across lentic systems (Hepworth et al. 1999). The success of
stocking programs may be attributed to strain (Babey and Berry 1989), age and size at stocking
(Baird et al. 2006), lack of predation (Matkowski 1989), abundant food supplies (Hubert and
Chamberlain 1996; Haddix and Budy 2005), or productivity of waters (Gipson and Hubert 1991;
Budy et al. 2011; Blair et al. 2013). Specifically, Rainbow Trout in Scofield Reservoir exhibit poor
returns and low survival, despite seemingly high prey availability, contributing to a declining
fishery. However, the few Rainbow Trout survivors display relatively high body condition
indices. It is possible that Fulton’s K and relative weight are not sufficient indices to detect a
decline in energy reserves, as water content will increase with food limitation (Josephson et al.
2012). Nonetheless, the few Rainbow Trout that persist are of size that parallel Rainbow Trout
in other systems. Mean total lengths of Rainbow Trout populations stocked in Utah reservoirs
at similar sizes to those of Scofield Reservoir were around 300 mm one year after stocking
(Hepworth et al. 1999), similar to the 290 – 310 mm TL range of Rainbow Trout in Scofield
Reservoir a year after being stocked. Low proportions of fish in the diet of Rainbow Trout may
limit maximum growth potential (Hubert and Gibson 1994; Luecke et al. 1999; Haddix and Budy
2005).
Furthermore, reduced Rainbow Trout performance may be due to competition for resources or
predator avoidance behaviors with other sport or non-game species. Rainbow Trout in Fish
Lake, Utah, have exhibited substantially lower catch and harvest rates in recent years, as the
community composed of Yellow Perch, Splake, Lake Trout, and Utah Chub led to a shift to
predation on Rainbow Trout by Lake Trout, as well as other potential food limitations
(Hepworth et al. 2011). Thus, while Rainbow Trout in Scofield Reservoir are stocked at
relatively large sizes and in large quantities, presence of other top predators in the system may
USU Budy. Project Completion Report. Scofield Reservoir
26
influence feeding and behavior of Rainbow Trout, leading to low apparent survival and also
possibly realized growth potential.
In contrast, Cutthroat Trout in Scofield Reservoir switch to a more piscivorous diet at smaller
sizes than observed elsewhere. In Scofield Reservoir, Cutthroat Trout switch to a Utah Chubbased diet around 300 mm TL, whereas Cutthroat Trout in Bear Lake, Utah-Wyoming, a very
unproductive reservoir, did not become more piscivorous until 380 mm TL with very high
mortality of these adult age-classes (Ruzycki et al. 2001). In Strawberry Reservoir, Utah, the
diet of stocked Bear Lake Cutthroat Trout is dominated by Daphnia prey, and fish only
represent a small fraction of their diet (Baldwin et al. 2000). Bioenergetic efficiency values for
Cutthroat Trout in Scofield Reservoir indicate they are feeding at less than half of their
maximum possible consumption rate, suggesting this species may be food-limited and/or
experiencing inter or intraspecific competitive exclusion effects (Budy et al. 2013).
The superior performance of the Tiger Trout stock in Scofield Reservoir contradicts
observations in other Utah reservoirs. Tiger trout stocked in Panguitch Reservoir after a
rotenone treatment in 2006 demonstrated poor survival and were found in limited numbers
during annual gill-net surveys (Hepworth et al. 2009). However, Tiger Trout in Panguitch
reservoir were stocked in lower quantities and at smaller sizes, with 20,000 fish at 75 mm
yearly, as opposed to 120,000 fish at 150 mm TL in Scofield Reservoir. Predation risk due to a
smaller size at stocking and lack of fish prey-base due to chemical treatment may have
contributed to the poor performance of Tiger Trout elsewhere. Additionally, piscivores may
interact strongly with prey populations in one system, but interact very weakly if at all with the
same prey in other systems (Rudolf 2012). Tiger Trout in lakes of eastern Washington rarely
became piscivorous, even in the presence of a dense Redside Shiner population (Miller 2010).
In contrast, Tiger Trout within our study exhibited strong preferences for fish prey (Utah Chub).
The high performance of Scofield Reservoir Tiger Trout is similar to High Savery Reservoir,
Wyoming, where state record Tiger Trout has become an annual occurrence (Carrico 2012).
This proven strong performance of Tiger Trout reflects on their potential to support a valuable
sport fishery.
Although we observed several different lines of evidence that consistently demonstrated trout
exert a strong predatory effect on Utah Chub, there are some notable uncertainties associated
with our bioenergetics estimates. Borrowing parameters from other species could produce
unreliable or biased results, especially when the physiology of the species varies. These
discrepancies may explain low BioEff values (Ney 1993; Chipps and Wahl 2008; Hartman and
Kitchell 2008) and lead to under- or over-estimates of consumption. While it is not uncommon
to borrow parameters (e.g., Beauchamp 1995; Beauchamp and VanTassel 2001; Ruzycki et al.
USU Budy. Project Completion Report. Scofield Reservoir
27
2001) improved parameter estimates may be needed, specifically for Tiger Trout, a new
species. Nevertheless, we borrowed metabolic costs of activity, respiration, and thermal habit
parameters for Tiger Trout from studies of Brown Trout (Ney 1990; Dieterman et al. 2004;
Whitledge et al. 2010). Nonetheless, Tiger Trout have been generally accepted to behave
similarly to Brown Trout, their close relative, and no studies to-date has quantified
consumption potential of this unique species.
Predator control of nuisance prey has been variable with other organisms. Despite citing
typical failure to control target species (Williamson and Fitter 1996), Simberloff (2009) argues
many species have been successfully eradicated and other species maintained at low densities
for long periods of time. Consequently, pessimism surrounding the potential to eradicate
invasive species or manage at very low densities may be unwarranted. The species-specific
models presented herein were based on wild fish, natural prey, and ecologically-realistic
temperature ranges, which typically results in models with reasonable predictive capability
(Mesa et al 2013). Others have used a similar approach to predict consumption of Brook Trout
S. fontinalis (Hartman and Cox 2008), Bull Trout S. confluentus (Mesa et al. 2013), Chinook
Salmon O. tshawytscha (Madenjian et al. 2004), hybrid Sunfish Lepomis cyanellus x L.
macrochirus (Whitledge et al. 1998), and Burbot (Paakkonen et al. 2003). Thus, we believe
these results represent a plausible reflection of the consumption occurring in Scofield Reservoir
and indicate strong potential for Cutthroat Trout and Tiger Trout to act as effective biological
control agents of undesired prey fish.
Management Implications
The findings of this study contribute to our understanding of the potential for stocked trout to
act as biological control agents on unwelcome forage fishes. Our results suggest that the high
rates of piscivory of Cutthroat Trout and Tiger Trout in artificial lentic ecosystems are likely
sufficient to effectively reduce the overall abundance of Utah Chub and control their ability to
dominate fish assemblages. Further, since 2009, there has been a dramatic reduction in Utah
Chub catch rates and the population no longer appears to be increasing. Additionally, Tiger
Trout caught in the reservoir have been of state record status, an exciting aspect of this new
fishery for anglers. Management regulations that protect large Cutthroat Trout and Tiger Trout
and increase predator densities may provide the predation pressure necessary to suppress
overabundant Utah Chub populations, whereas Rainbow Trout have little potential for
responsive management manipulations. A stocking shift from Rainbow Trout to Tiger Trout may
be beneficial, if chub reduction is the overall management goal. Careful scrutiny of the current
system as well as management goals and objectives, must continue to be taken into
consideration when determining management actions.
USU Budy. Project Completion Report. Scofield Reservoir
28
References
Anderson, R.O., & R.M. Neumann. 1996. Length, weight, and associated structural indices. Pages 447-482 in B.R.
nd
Murphy & D.W. Willis, editors. Fisheries Techniques, 2 edition. American Fisheries Society, Bethesda,
Maryland.
Babey, G.J., & C.R. Berry. 1989. Post-stocking performance of three strains of Rainbow Trout in a reservoir. North
American Journal of Fisheries Management 9:309-315.
Baird, O.E., C.C. Krueger, & D.C. Josephson. 2006. Growth, movement, and catch of Brook, Rainbow, and Brown
Trout after stocking into a large, marginally suitable Adirondack river. North American Journal of Fisheries
Management 26:180-189.
Baldwin, C. M., D.A. Beauchamp, & J.J. Van Tassell. 2000. Bioenergetic assessment of temporal food supply and
consumption demand by salmonids in the Strawberry Reservoir food web. Transactions of the American
Fisheries Society 129(2):429–450.
Beauchamp, D. A. 1990. Seasonal and diel food habits of Rainbow Trout stocked as juveniles in Lake Washington.
Transactions of the American Fisheries Society 119:475–482.
Beauchamp, D. A., A.D. Cross, J.L. Armstrong, K.W. Myers, J.H. Moss, J.L. Boldt, & L.J. Haldorson. 2007. Bioenergetic
responses by Pacific Salmon to climate and ecosystem variation. North Pacific Anadromous Fish Commission
Bulletin No. 4: 257–269.
Beauchamp, D. A., M.G. LaRiviere, & G.L. Thomas. 1995. Evaluation of competition and predation as limits to
juvenile Kokanee and Sockeye Salmon production in Lake Ozette, Washington. North American Journal of
Fisheries Management 15:193–207.
Beauchamp, D. A., & J.J. Van Tassell. 2001. Modeling trophic interactions of Bull Trout in Lake Billy Chinook,
Oregon. Transactions of the American Fisheries Society 130:204–216.
Beauchamp, D. A., D.L. Parrish, & R.A. Whaley. 2009. Coldwater fish in large standing waters. Pages 97-118 in S.A.
Bonar, W.A. Hubert, & D.W. Willis, editors. Standard methods for sampling North American freshwater
fishes. American Fisheries Society, Bethesda, Maryland.
Blair, J.M., I. Ostrovsky, B.J. Hicks, R.J. Pitkethley, & P. Scholes. 2013. Growth of Rainbow Trout (Oncorhynchus
mykiss) in warm-temperate lakes: implications for environmental change. Canadian Journal of Fisheries and
Aquatic Sciences 70:815-823.
Brandt, S. B., & K.J. Hartman. 1993. Innovative approaches with bioenergetics models: future applications to fish
ecology and management. Transactions of the American Fisheries Society 122:731-735.
Brooks, J.L. 1957. Identifications and nomenclature of Cladocera, Calanoida, and Cyclopoida. Memoirs of the
Connecticut Academy of the Arts and Science 13(1).
Budy, P., M. Baker, & S. K. Dahle. 2011. Predicting fish growth potential and identifying water quality constraints: a
spatially-explicit bioenergetics approach. Environmental Management 48(4):691-709.
Budy, P., G.P. Thiede, J. Lobon-Cervia, G. Gonzalez Fernandez, P. McHugh, A. McIntosh, L. Asbjorn Vollestad, E.
Becares, & P. Jellyman. 2013. Limitation and facilitation of one of the world ’s most invasive fish: an
intercontinental comparison. Ecology 94(2):356-367.
USU Budy. Project Completion Report. Scofield Reservoir
29
Bunnell, D. B., C.P. Madenjian, & R.M. Claramunt. 2006. Long-term changes of the Lake Michigan fish community
following the reduction of exotic Alewife (Alosa pseudoharengus). Canadian Journal of Fisheries and Aquatic
Sciences 63:2434-2446.
Bureau of Reclamation. 2009. Scofield Dam. U.S. Department of the Interior.
Bureau of Reclamation. 2011. Scofield Project. U.S. Department of the Interior.
Carpenter, S.R., J.F. Kitchell, & J.R. Hodgson. 1985. Cascading trophic interactions and lake productivity. BioScience
35(10):634-639.
Carrico, M. 2012. High Savery Reservoir, Wyoming. Available: www.highsavery.com. (November 2013).
Cauffope, G., & S.J.J. Heymans. 2005. Energy contents and conversion factors for sea lion’s prey. Pages 225-237 in
S. Guenette & V. Christensen, editors. Food web models and data for studying fisheries and environmental
impacts on Eastern Pacific Ecosystems. Fisheries. Centre Research Reports 13(1).
st
Chipps, S.R., & D.H. Wahl. 2008. Bioenergetics modeling in the 21 century: reviewing new insights and revisiting
old constraints. Transactions of the American Fisheries Society 137:298-313.
Ciancio, J. E., M.A. Pascual, & D.A. Beauchamp. 2007. Energy density of Patagonian aquatic organisms and
empirical predictions based on water content. Transactions of the American Fisheries Society 136:1415-1422.
Courtenay, W. R., & C.C. Kohler. 1986. Regulating introduced aquatic species- a review of past initiatives. Fisheries
11(2):34-38.
Cummins, K. W., & J.C. Wuycheck. 1971. Caloric equivalents for investigations in ecological energetics.
International Association for Theoretical and Applied Limnology Proceedings No. 18.
Dahm, E., J. Hartmann, T. Lindem, & H. Loffler. 1985. ELFAC experiments on pelagic fish stock assessment by
acoustic methods in Lake Constance. European Inland Fisheries Advisory Commission Occassional Paper No.
15. FAO, Rome, Italy. 14 pp.
Debach, P.D. 1964. Biological control of insect pests and weeds. Chapman & Hall Ltd., London.
Department of Environmental Quality. 2010. Scofield Reservoir TMDL. Division of Water Quality, Salt Lake City, UT
84103.
Dieterman, D. J., W.C. Thorn, & C.S. Anderson. 2004. Application of a bioenergetics model for Brown Trout to
evaluate growth in Southeast Minnesota streams. Minnesota Department of Natural Resources, St. Paul, MN,
1-27.
Dwyer, W. P., & R.H. Kramer. 1975. The influence of temperature on scope for activity in Cutthroat Trout, Salmo
clarki. Transactions of the American Fisheries Society 104(3):552-554.
Edmonson, W.T. 1959.The rotifera in G.C. Whipple & H.B. Ward, editors. Freshwater Biology, 2nd edition. Wiley,
New York.
Eilers, J. M., H.A. Truemper, L.S. Jackson, B.J. Eilers, & D.W. Loomis. 2011. Eradication of an invasive cyprinid (Gila
bicolor) to achieve water quality goals in Diamond Lake, Oregon (USA). Lake and Reservoir Management
27(3):194-204.
Ellis, B.K., J.A. Stanford, D. Goodman, C.P. Stafford, D.L. Gustafson, D.A. Beauchamp, D.W. Chess, J.A. Craft, M.A.
Deleray, & B.S. Hansen. 2011. Long-term effects of a trophic cascade in a large lake ecosystem. Proceedings
of the National Academy of Sciences 108:1070-1075.
USU Budy. Project Completion Report. Scofield Reservoir
30
Freckleton, R.P., D.M. Silva Matos, M.L.A. Bovi, & A.R. Watkinson. 2003. Predicting the impacts of harvesting using
structured population models: the importance of density-dependence and timing of harvest from a tropical
palm tree. Journal of Applied Ecology 40:846-858.
Freeman, M.A., J.F. Turnbull, W.E. Yeomans, & C.W. Bean. 2010. Prospects for management strategies of invasive
crayfish populations with an emphasis on biological control. Aquatic Conservation: Marine and Freshwater
Ecosystems 20(2):211-223.
Gabelhouse, D.W. 1984. A length-categorization system to assess fish stocks. North American Journal of Fisheries
Management 4:273-285.
Galbraith, M. G. 1967. Size-selective predation on Daphnia by Rainbow Trout and Yellow Perch. Transactions of the
American Fisheries Society 96:1-10.
Gardunio, E.I., C.A. Myrick, R.A. Ridenour, R.M. Keith, & C.J. Amadio. 2011. Invasion of illegally introduced Burbot
to the upper Colorado River Basin, USA. Journal of Applied Icthyology 27:36-42.
Gasith, A., & S. Gafny. 1990. Effects of water level fluctuation on the structure and function of the littoral zone.
Pages 156-171 in M.M. Tilzer & C. Serruya, editors. Large Lakes. Springer Berlin Heidelberg.
Gill, A.B. 2003. The dynamics of prey choice in fish: the importance of prey size and satiation. Journal of Fish
Biology 63(1):105-116.
Gipson, R.D., & W.A. Hubert. 1991. Factors influencing the body condition of Rainbow Trout in small Wyoming
reservoirs. Journal of Freshwater Ecology 6:327-334.
Graham, R.J. 1961. Biology of the Utah chub in Hebgen Lake, Montana. Transactions of the American Fisheries
Society 90: 269-276.
Guy, C.S., T.E. McMahon, C.J. Smith, B.S. Cox, W.A. Fedenberg, & D.W. Garfield. 2011. Diet overlap of top-level
predators in recent sympatry: Bull Trout and nonnative Lake Trout. Journal of Fish and Wildlife Management
2(2):183-189.
Haddix, T., & P. Budy. 2005. Factors that limit growth and abundance of Rainbow Trout across ecologically distinct
areas of Flaming Gorge Reservoir, Utah–Wyoming. North American Journal of Fisheries Management
25(3):1082-1094.
Hambright, K. D. 1991. Experimental analysis of prey selection by Largemouth Bass: role of predator mouth width
and prey body depth. Transactions of the American Fisheries Society 120:500–508.
Hanson, M. J., D. Boisclair, S.B. Brandt, & S.W. Hewett. 1993. Applications of bioenergetics models to fish ecology
and management -where do we go from here. Transactions of the American Fisheries Society 122:1019-1030.
Hanson, P.C., T.B. Johnson, D.E. Schindler, & J.F. Kitchell. 1997. Bioenergetics model 3.0 for Windows. University of
Wisconsin, Sea Grant Institute, Technical Report WISCU-T-97-001. Madison, Wisconsin.
Hart, J., & P.W. Birdsey. 2008. Scofield Research Proposal. Price, UT: Utah Division of Wildlife Resources,
Southeastern Regional Office. 5 pp.
Hartman, K.J., & M.K. Cox. 2008. Refinement and testing of a Brook Trout bioenergetics model. Transactions of the
American Fisheries Society 137:357-363.
Hartman, K. J., and R. S. Hayward. 2007. Bioenergetics. Pages 515–560 in C. S. Guy and M. L. Brown, editors.
Analysis and interpretation of freshwater fisheries data. American Fisheries Society, Bethesda, Maryland.
USU Budy. Project Completion Report. Scofield Reservoir
31
Hartman, K.J., & J.F. Kitchell. 2008. Bioenergetics modeling: progress since the 1992 symposium. Transactions of
the American Fisheries Society 137:216-223.
Hartman, K.J., & F.J. Margraf. 1993. Evidence of predatory control of Yellow Perch (Perca flavescens) recruitment in
Lake Erie, USA. Journal of Fish Biology 43(1):109-119.
Havel, J.E., C.E. Lee, & J. VanderZanden. 2005. Do reservoirs facilitate invasions into landscapes? BioScience
55(6):518-525.
Hein, C.L., B.M. Roth, A.R. Ives, & J.M. Vander Zanden. 2006. Fish predation and trapping for Rusty Crayfish
(Orconectes rusticus) control: a whole-lake experiment. Canadian Journal of Fisheries and Aquatic Sciences
63:383-393.
Hepworth, D. K., C.B. Chamberlain, & M.J. Ottenbacher. 1999. Comparative sport fish performance of Bonneville
Cutthroat Trout in three small put-grow-and-take reservoirs. North American Journal of Fisheries
Management 19(1):774-785.
Hepworth, R.D., J. Warner, M.J. Ottenbacher, & M.J. Hadley. 2009. Panguitch Lake: an evaluation of the sport
fishery management plan. Utah Department of Natural Resources, Division of Wildlife Resources, Salt Lake
City.
Hepworth, R.D., M. Ottenbacher, & M.J. Hadley. 2011. Angler survey and monitoring results, Fish Lake Utah 2010.
Utah Department of Natural Resources, Division of Wildlife Resources, Salt Lake City.
Hoddle, M.S. 2004. Restoring balance: using exotic species to control invasive exotic species. Conservation Biology
18(1):38-49.
Hubert, W.A., & C.B. Chamberlain. 1996. Environmental gradients affect Rainbow Trout populations among lakes
and reservoirs in Wyoming. Transactions of the American Fisheries Society 125:925-932.
Hubert, W.A., D. Gipson, R.A. McDowell, & A.C. Stewart. 1994. Diet of Eagle Lake Rainbow Trout in Lake DeSmet,
Wyoming. North American Journal of Fisheries Management 14:457-459.
Hudy, M., & C.R. Berry Jr. 1983. Performance of three strains of Rainbow Trout in a Utah reservoir. North American
Journal of Fisheries Management 3(2):136-141.
Hyatt, M.W., & W.A. Hubert. 2001. Proposed standard-weight (Ws) equation and length-categorization standards
for Brown Trout (Salmo trutta) in lentic habitats. Journal of Freshwater Ecology 16(1):53-56.
Ireland, P.A. 2010. Changes in native aquatic vegetation, associated fish assemblages, and food habits of
Largemouth Bass (Micropterus salmoides) following the addition of triploid Grass Carp to manage Hydrilla
(Hydrilla verticillata) in Lake Conroe, TX. Master’s Thesis, Texas A&M University.
Irwin, B. J., D.R. Devries, & R.A. Wright. 2003. Evaluating the potential for predatory control of gizzard shad by
Bargemouth bass in small impoundments: a bioenergetics approach. Transactions of the American Fisheries
Society 132(5):913-924.
Jackson, L.S., D.H. Jackson, & D.W. Loomis. 2004. Analysis of Tui Chub data collected at Diamond Lake by the
Oregon Department of Fish and Wildlife, 1992-2003. Oregon Department of Fish and Wildlife, Roseburg,
Oregon.
Johnson, B.M., & J.P. Goettle. 1999. Food web changes over fourteen years following introduction of Rainbow
Smelt into a Colorado reservoir. North American Journal of Fisheries Management 19:629-642.
USU Budy. Project Completion Report. Scofield Reservoir
32
Johnson, J.B., & M.C. Belk. 1999. Effects of predation on life-history evolution in Utah Chub (Gila atraria). Copeia
4:948–957.
Josephson, D.C., J.M. Robinson, J.M. Lepak, & C.E. Kraft. 2012. Rainbow Trout performance in food-limited
environments: implications for future assessment and management. Journal of Freshwater Ecology
27(2):159-170.
Juanes, F., J.A. Buckel, & F.S. Scharf. 2002. Feeding ecology of piscivorous fishes. Pages 267-283 in P. J. B. Hart & J.
st
D. Reynolds, editors. Handbook of Fish Biology and Fisheries, 1 edition. Oxford, UK: Blackwell Publishing Ltd.
Juncos, R., D.A. Beauchamp, & P.H. Vigliano. 2013. Modeling prey consumption by native and nonnative
piscivorous fishes: implications for competition and impacts on shared prey in an ultraoligotrophic lake in
Patagonia. Transactions of the American Fisheries Society 142:268-281.
Juncos, R., D. Milano, P.J. Macchi, M.F. Alonso, & P.H. Vigliano. 2011. Response of Rainbow Trout to different food
web structures in northern Patagonia: implications for growth, bioenergetics, and invasiveness. Transactions
of the American Fisheries Society 140:415-428.
Kitchell, J.F., & L.B. Crowder. 1986. Predator-prey interactions in Lake Michigan: model predictions and recent
dynamics. Environmental Biology of Fishes 16:205-211.
Kitchell, J. F., D.E. Schindler, R. Ogutu-Ohwayo, & P.N. Reinthal. 1997. The Nile Perch in Lake Victoria: interactions
between predation and fisheries. Ecological Applications 7(2):653-664.
Kohler, C.C., J.J. Ney, & W.E. Kelso. 1986. Filling the void: development of a pelagic fishery and its consequences to
littoral fishes in a Virginia mainstem reservoir. Pages 166-177 in G.E. Hall & M. J. Van Den Avyle, editors.
Reservoir fisheries management: strategies for the 80’s. Southern Division, American Fisheries Society,
Bethesda, Maryland.
Kruse, C.G., & W.A. Hubert. 1997. Proposed standard weight (Ws) equations for Interior Cutthroat Trout. North
American Journal of Fisheries Management, 17(3):784-790.
Kumar, R., & J.S. Hwang. 2006. Larvicidal efficiency of aquatic predators: a perspective for mosquito biocontrol.
Zoological Studies 45(4):447-466.
Lester, N.P., P.E. Bailey, & W.A. Hubert. 2009. Coldwater fish in small standing waters. Pages 85-96 in S.A. Bonar,
W.A. Hubert, & D.W. Willis, editors. Standard methods for sampling North American freshwater fishes.
American Fisheries Society, Bethesda, Maryland.
Luecke, C., T.C. Edwards, M.W. Wengert, S. Brayton, & R. Schneidervin. 1994. Simulated changes in Lake Trout
yield, trophies, and forage consumption under various slot limits. North American Journal of Fisheries
Management 14(1):14-21.
Luecke, C.M., W. Wengert, & R.W. Schneidervin. 1999. Comparing results of a spatially explicit growth model with
changes in the length-weight relationship of Lake Trout (Salvelinus namaycush) in Flaming Gorge Reservoir.
Canadian Journal of Fisheries and Aquatic Sciences 56:162-169.
Madenjian, C.P., D.V. O'Connor, S.M. Chernyak, R.R. Rediske, & J.P. O'Keefe. 2004. Evaluation of a Chinook Salmon
(Oncorhynchus tshawytscha) bioenergetics model. Canadian Journal of Fisheries and Aquatic Sciences
61:627-635.
Magnhagen, C., & E. Heibo. 2001. Gape size allometry in Pike reflects variation between lakes in prey availability
and relative body depth. Functional Ecology 15:754-762.
USU Budy. Project Completion Report. Scofield Reservoir
33
Martinez, P.J., P.E. Bigelow, M.A. Deleray, W.A. Fredenberg, B.S. Hansen, N.J. Horner, S.K. Lehr, R.W. Schneidervin,
S.A. Tolentino, & A.E. Viola. 2009. Western Lake Trout woes. Fisheries 34:424-442.
Matkowski, S.M.D. 1989. Differential susceptibility of three species of stocked trout to bird predation. North
American Journal of Fisheries Management 9:184-187.
McMahon, T. E., A.V. Zale, & D.J. Orth. 1996. Aquatic habitat measurements. Pages 83 -120 in B.R. Murphy & D.W.
Willis, editors. Fisheries techniques. American Fisheries Society, Bethesda, Maryland.
Merritt, R.W., & K.W. Cummins, editors. 1996. An introduction to the aquatic insects of North America. Dubuque,
Iowa: Kendall & Hunt. 441 p.
Mesa, M.G., L.K. Weiland, H.E. Christiansen, S.T. Sauter, & D.A. Beauchamp. 2013. Development and evaluation of
a bioenergetics model for bull trout. Transactions of the American Fisheries Society 142(1):41-49.
Michaletz, P.H. 2013. Temperature, plankton and conspecific density influence dynamics of age-0 Gizzard Shad:
implications for a gape-limited piscivore. Ecology of Freshwater Fish. DOI: 10.1111/eff.12083.
Milewski, C.L., & M.L. Brown. 1994. Proposed standard weight (Ws) equation and length-categorization standards
for stream-dwelling Brown Trout (Salmo trutta). Journal of Freshwater Ecology 9(2):111-116.
Miller, A.L. 2010. Diet, growth, and age analysis of Tiger Trout from ten lakes in Eastern Washington. Master's
Thesis. Eastern Washington University.
Mittlebach, G.G. 2002. Fish foraging and habitat choice: a theoretical perspective. Handbook of Fish Biology and
Fisheries 1:251-266.
Neilson, B.R. & L. Lentsch. 1988. Bonneville Cutthroat Trout in Bear Lake: status and management. American
Fisheries Society Symposium 4:128-133.
Neuhold, J. M. 1957. Age and growth of the Utah Chub, Gila atraria (Girard), in Panguitch Lake and Navajo Lake ,
Utah, from scales and opercular bones. Transactions of the American Fisheries Society 85(1):217-233.
Ney, J. J. 1990. Trophic economics in fisheries: assessment of demand-supply relationships between predators and
prey. Reviews in Aquatic Sciences 2(1):55–81.
Ney, J. J. 1993. Bioenergetics modeling today: growing pains on the cutting edge. Transactions of the American
Fisheries Society 122:736-748.
Noble, R. L. 1981. Management of forage fishes in impoundments of the Southern United States. Transactions of
the American Fisheries Society 110(6):738-750.
Olson, H. F. 1959. Biology of the Utah Chub, gila atraria, of Scofield Reservoir, Utah. MS thesis. Utah State
University. Logan, Utah.
Paakkonen, J.P.J., O. Tikkanen, & J. Karjalainen. 2003. Development and validation of a bioenergetics model for
juvenile and adult Burbot. Journal of Fish Biology 63:956-969.
Penczak, T., A.A. Agostinho, N.S. Hahn, R. Fugi, & L.C. Gomes. 1999. Energy budgets of fish populations in two
tributaries of the Parana River, Parana, Brazil. Journal of Tropical Ecology 15:159-177.
Peterson, J. H., & J.F. Kitchell. 2001. Climate regimes and water temperature changes in the Columbia River:
bioenergetic implictions for predators of juvenile sSlmon. Canadian Journal of Fisheries and Aquatic Sciences
58:1831-1841.
USU Budy. Project Completion Report. Scofield Reservoir
34
Pope, K.L., & C.G. Kruse. 2007. Condition. Pages 423-472 in C.S. Guy & M.L. Brown, editors. Analysis and
interpretation of freshwater fisheries data. American Fisheries Society, Bethesda, Maryland.
Quist, M.C., C.S. Guy, R.J. Bernot, & J.L. Stephen. 2002. Seasonal variation in condition, growthand food habits of
walleye in a Great Plains reservoir and simulated effects of an altered thermal regime. Journal of Fish Biology
61(6):1329-1344.
Raborn, S. W., L.E. Miranda, & M.T. Driscoll. 2007. Prey supply and predator demand in a reservoir of the
Southeastern United States. Transactions of the American Fisheries Society 136(1):12-23.
Rand, P.S., D.J. Stewart, P.W. Seelbach, M.L. Jones, & L.R. Wedge. 1993. Modeling steelhead population energetics
in lakes Michigan and Ontario. Transactions of the American Fisheries Society 122:977-1001.
Raymond, A.W., & E. Sobel. 1990. The use of Tui Chub as food by Indians of the Western Great Basin. Journal of
California and Great Basin anthropology 12(1):2-18.
Reissig, M., C. Trochine, C. Quelimalinos, E. Balseiro, & B. Modenutti. 2006. Impact of fish introduction on
planktonic food webs in lakes of the Patagonian Plateau. Biological Conservation 132:437-447.
Rice, J.A., & P.A. Cochran. 1984. Independent evaluation of a bioenergetics model for Largemouth Bass. Ecology
65(3):732-739.
Romare, P., & L.A. Hansson. 2003. A behavioral cascade: top-predator induced behavioral shifts in planktivorous
fish and zooplankton. Limnology and Oceanography 48(5):1956-1964.
Rose, B.P. & M.G. Mesa. 2013. Effects of summer drawdown on the fishes and larval chironomids in Beulah
Reservoir, Oregon. Northwest Scientific Association 87(3):207-218.
Rudolf, V.H.W. 2012. Seasonal shifts in predator body size diversity and trophic interactions in size-structured
predator-prey systems. Journal of Animal Ecology 81:524-532.
Ruzycki, J. R., W.A. Wurtsbaugh, & C. Luecke. 2001. Salmonine consumption and competition for endemic prey
fishes in Bear Lake, Utah–Idaho. Transactions of the American Fisheries Society 130(6):1175-1189.
SAS Institute Inc. 2009. SAS Web Report Studio 4.2: Users guide, Cary, North Carolina: SAS Institute Inc.
Saunders, G., B. Cooke, K. McColl, R. Shine, & T. Peacock. 2010. Modern approaches for the biological control of
vertebrate pests: an Australian perspective. Biological Control 52(3):288-295.
Scharf, F.S., F.J. Juanes, & R.A. Rountree. 2000. Predator size - prey size relationships of marine fish predators:
interspecific variation and effects of ontogeny and body size on trophic-niche breadth. Marine Ecology
Progress Series 208:229-248.
Schoen, E.R., D.A. Beauchamp, & N.C. Overman. 2012. Quantifying latent impacts of an introduced piscivore:
pulsed predatory inertia of Lake Trout and decline of Kokanee. Transactions of the American Fisheries Society
141(5):1191-1206.
Shapiro, J., V. Lamarra, & M. Lynch. 1975. Biomanipulation: an ecosystem approach to lake restoration. Pages 8596 in P.L. Brezonik & J.L. Fox, editors. Water Quality Management Through Biological Control. University of
Florida.
Simberloff, D. 2009. We can eliminate invasions or live with them. Successful management projects. Biological
Invasions 11:149-157.
Simberloff, D., & P. Stiling. 1996. Risks of species introduced for biological control. Biological Conservation 78:185192.
USU Budy. Project Completion Report. Scofield Reservoir
35
Simpkins, D.G. & W.A. Hubert. 1996. Proposed revision of the standard-weight equation for Rainbow Trout. Journal
of Freshwater Ecology 11(3):319-325.
Skov, T., T. Buchaca, S.L. Amsinck, F. Landkildehus, B.V. Odgaard, J. Azevedo, V. Goncalves, P.M. Raposeiro, T.J.
Anderson, & E. Jeppesen. 2010. Using invertebrate remains and pigments in the sediment to infer changes in
trophic structure after fish introduction in Lake Fogo: a crater lake in the Azores. Hydrobiologia 654(1):13-25.
Sorenson, P.W. & N.E. Stacey. 2004. Brief review of fish pheromones and discussion of their possible uses in the
control of non-indigenous teleost fishes. New Zealand Journal of Marine and Freshwater Research 38(3):399417.
Sousa, R., A.J.A. Noguiera, M.B. Gaspar, C. Antunes, & L. Guilhermino. 2008. Growth and extremely high
production of non-indigenous invasive species Corbicula fluminea: possible implications for ecosystem
functioning. Estuarine, Coastal, and Shelf Science 2:289-295.
Stein, R.A., D.R. Devries, & J.M. Dettmers. 1995. Food-web regulation by a planktivore: exploring the generality of
the trophic cascade hypothesis. Canadian Journal of Fisheries and Aquatic Sciences 52:2518-2526.
Stein, R.A., & M.L. Murphy. 1976. Changes in the proximate composition of crayfish with size, sex, and life stage.
Journal of Fisheries Research Board of Canada 33:2450-2458.
Stewart, D.J., D. Weininger, D.V. Rottiers, & T.A. Edsall. 1983. An energetics model for Lake Trout, Salvelinus
namaycush: application to the Lake Michigan population. Canadian Journal of Fisheries and Aquatic Sciences
40:681–698.
Stewart, D.J., J.F. Kitchell, & L.B. Crowder. 1981. Forage fishes and their salmonid predators in Lake Michigan.
Transactions of the American Fisheries Society 110:751-763.
Swales, S. 2006. A review of factors affecting the distribution and abundance of rainbow trout (Oncorhynchus
mykiss Walbaum) in lake and reservoir systems. Lake and Reservoir Management 22:167-178.
Taylor, W.W., & S.D. Gerking. 1980. Population dynamics of Daphnia pulex and utilization by the Rainbow Trout
(Salmo gairdneri). Hydrobiologia 71:277-287.
Tronstad, L. 2008. Ecosystem consequences of declining Yellowstone Cutthroat Trout in Yellowstone Lake and
spawning streams. Doctoral dissertation. Laramie, Wyoming: University of Wyoming.
Truemper, H.A., & Lauer, T.E. 2005. Gape limitation and piscine prey size-selection by Yellow Perch in the extreme
southern area of Lake Michigan , with emphasis on two exotic prey items. Journal of Fish Biology 66:135-149.
Tyus, H.M., & J.F. Saunders. 2000. Nonnative fish control and endangered fish recovery: lessons from the Colorado
River. Fisheries 25:17-24.
U.S. Department of Agriculture, Animal and Plant Health Inspection Service. 2013a. Biological Control, Center for
Plant Health Science and Technology. Last modified April 4, 2013.
http://www.aphis.usda.gov/plant_health/cphst/projects/biological-control.shtml
U.S. Department of Agriculture. 2013b. Weeds and biological control agents. Technical Advisory Group, Biological
Control Agents of Weeds. DRAFT. May 9, 2013. Available:
http://www.aphis.usda.gov/plant_health/permits/tag/downloads/Weeds%20and%20Biological%20Control%
20Agents%20Released%20in%20US%20and%20Canada.pdf
Vatland, S., P. Budy, & G.P. Thiede. 2008. A bioenergetics approach to modeling striped bass and threadfin shad
predator-prey dynamics in Lake Powell, Utah-Arizona. Transactions of the American Fisheries Society
137(1):262-277.
USU Budy. Project Completion Report. Scofield Reservoir
36
Wagner, E.J. 1996. History and fluctuating asymmetry of Utah salmonid broodstocks. The Progressive Fish-Culturist
58:92-103.
Walsworth, T.E., P. Budy, & G.P. Thiede. 2013. Longer food chains and crowded niche space: effects of multiple
invaders on desert stream food web structure. Ecology of Freshwater Fish 22(3):439-452.
Werner, E.E., & D.J. Hall. 1974. Optimal foraging and the size selection of prey by the Bluegill Sunfish (Lepomis
macrochirus). Ecology 55(5):1042-1052.
Wetzel, R.G. 1990. Reservoir ecosystems: conclusions and speculations. Pages 227-238 in K.W. Thorton, B.I.
Kimmel, & F.E. Payne, editors. Reservoir Limnology: Ecological Perspectives. John Wiley and Sons, New York.
Wetzel, R.G. 2001. Limnology: lake and river ecosystems, third edition. Academic Press, San Diego, California.
Whitledge, G.W., R.S. Hayward, D.B. Noltie, & N. Wang. 1998. Testing bioenergetics models under feeding regimes
that elicit compensatory growth. Transactions of the American Fisheries Society 127:740-746.
Whitledge, G.W., P.G. Bajer, & R.S. Hayward. 2010. Laboratory evaluation of two bioenergetics models for Brown
Trout. Transactions of the American Fisheries Society 139(4):929-936.
Williamson, M., & A. Fitter. 1996. The varying success of invaders. Ecology 77(6):1661-1668.
Wittenberg, R., & M.J.W. Cock, editors. 2001. Invasive alien species: a toolkit of best prevention and management
practices. Wallingford, Oxon, UK: CAB International.
Yule, D.L., & C. Luecke. 1993. Lake Trout consumption and recent changes in the fish assemblage of Flaming Gorge
Reservoir. Transactions of the American Fisheries Society 122(6):1058-1069.
Zelasko, K.A., K.R. Bestgen, & C. White. 2010. Survival rates and movement of hatchery-reared Razorback Suckers
in the Upper Colorado River Basin, Utah and Colorado. Transactions of the American Fisheries Society
139(5):1478-1499.
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Figure 1.1. Map of Scofield Reservoir, Utah showing the eight locations where we sampled fish
in 2011 and 2012.
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CPUE (Chub/net/hr)
16
14
12
10
8
6
4
2
0
04
20
05
20
06
20
07
20
08
20
09
20
10
20
11
20
12
20
Year
Figure 1.2. Utah Chub catch-per-unit-effort (CPUE) from 2004 to 2012. Catch data from 2004 –
2010 courtesy of Utah Division of Wildlife Resources.
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2400
June 2013
2200
2000
1800
Utah Chub
Tiger Trout
Cutthroat Trout
Rainbow Trout
1600
1400
1200
1000
Thousands of fish
800
600
400
200
0
2400
August 2011
2200
2000
1800
1600
1400
1200
1000
800
600
400
200
0
100 - 150
151 - 250
251 - 350
> 350
Size class (mm)
Figure 1.3. Abundance estimates by size class of Utah Chub and three trout species derived
from hydroacoustic surveys in Scofield Reservoir on 6 June 2013 (spring) and 31 August 2011
(summer).
USU Budy. Project Completion Report. Scofield Reservoir
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1.0
Rainbow Trout
0.8
Proportion of total catch (based on CPUE)
0.6
spring
summer
autumn
0.4
0.2
0.0
1.0
Cutthroat Trout
0.8
0.6
0.4
0.2
0.0
1.0
Tiger Trout
0.8
0.6
0.4
0.2
0.0
1.0
0.8
Utah Chub
0.6
0.4
0.2
0.0
2004
2005
2006
2007
2008
2009
2010
2011
2012
Year
Figure 1.4. Proportion of total catch, based on CPUE, for each of the trout species and Utah
Chub in Scofield Reservoir. Solid lines represent data from Utah Division of Wildlife Resources
yearly spring sampling, with additional summer and autumn data collected for this study
(metrics calculated similarly).
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41
Figure 1.5. Seasonal diet composition of small (< 350 mm TL) and large (≥ 350 mm TL)
Cutthroat Trout, Rainbow Trout, and Tiger Trout, as well as small (< 250 mm TL) and large (≥
250 mm TL) Utah Chub captured in Scofield Reservoir. Diet composition was calculated as the
proportion of diet by wet weight (g).
USU Budy. Project Completion Report. Scofield Reservoir
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12
Summer 2011
10
n = 79
8
6
4
2
0
October 2011
12
n = 42
Cutthroat trout catch (%)
8
4
0
Spring 2012
n = 293
8
4
0
Summer 2012
n = 82
8
4
0
October 2012
12
n = 109
8
4
0
Spring 2013
n = 88
4
0
10
0
15
0
20
0
25
0
30
0
35
0
40
0
45
0
50
0
55
0
60
0
65
0
Total length bins (mm)
Figure 1.6. Length-frequency (%) distributions of Bear Lake-strain Cutthroat Trout captured in
gill nets during seasonal sampling events from July 2011 – May 2013 in Scofield Reservoir.
Number (n) captured in nets is given.
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16
Summer 2011
12
n = 27
8
4
0
60
October 2011
n=3
40
Rainbow trout catch (%)
20
0
Spring 2012
18
n = 29
12
6
0
25
Summer 2012
20
n = 11
15
10
5
0
October 2012
30
n=6
20
10
0
Spring 2013
40
n=7
30
20
10
0
10
0
15
0
20
0
25
0
30
0
35
0
40
0
45
0
50
0
55
0
60
0
65
0
Total length bins (mm)
Figure 1.7. Length-frequency (%) distributions of Rainbow Trout captured in gill nets during
seasonal sampling events from July 2011 – May 2013 in Scofield Reservoir. Number (n)
captured in nets is given.
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9
Summer 2011
n = 100
6
3
0
October 2011
9
n = 35
6
Tiger trout catch (%)
3
0
Spring 2012
n = 115
6
3
0
Summer 2012
9
n = 45
6
3
0
October 2012
n = 51
6
3
0
Spring 2013
n = 62
6
3
0
10
0
15
0
20
0
25
0
30
0
35
0
40
0
45
0
50
0
55
0
60
0
65
0
70
0
75
0
80
0
85
0
Total length bins (mm)
Figure 1.8. Length-frequency (%) distributions of Tiger Trout captured in gill nets during
seasonal sampling events from July 2011 – May 2013 in Scofield Reservoir. Number (n)
captured in nets is given.
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800
Tiger Trout
Cutthroat Trout
Rainbow Trout
Total length (mm)
700
600
500
400
300
200
100
5000
Weight (g)
4000
3000
TigerAge vs TRT TL
CUTT age vs CTT TL
Rain Age vs RB TL
2000
1000
0
2
3
4
5
6
7
8
Trout age (annuli count)
Figure 1.9. Mean (± 2 SE) total length- (top panel) and weight- (bottom panel) at-age of each
trout species in Scofield Reservoir. Ages were determined as annuli counts from 30 of each
species captured in 2012.
USU Budy. Project Completion Report. Scofield Reservoir
46
22
20
16
o
Temperature ( C)
18
14
12
10
8
6
4
Rainbow Trout CTO
Tiger Trout CTO
Cutthroat Trout CTO
2
0
l
Ju
20
11
g2
Au
01
1
p2
Se
01
1
t
Oc
20
11
v2
No
01
1
c2
De
01
1
J
2
an
01
2
b2
Fe
01
2
r
Ma
20
2
2
2
12
12
12
01
01
01
20
20
l2
r2
y
n
g2
p
Ju
A
Ju
Au
Ma
Date
Figure 1.10. Daily mean temperatures experienced by fish in Scofield Reservoir during 2011
and 2012. Horizontal lines show consumption thermal optimum (CTO) for each species, which
varied around the average temperature encountered throughout the year; Cutthroat Trout CTO
is 14 °C, Rainbow Trout CTO is 20 °C, and Tiger Trout CTO (assumed similar to Brown Trout) is
17.5 °C.
USU Budy. Project Completion Report. Scofield Reservoir
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Biomass of Utah chub (tonnes)
1500
Production
Age-1 Chub
> Age-2 Chub
1200
Cons. by Cutthroat Trout
Cons. by Tiger Trout
900
600
300
0
In lake
Consumed by
Figure 1.11. Comparison of Utah Chub abundance and annual production (In lake) versus
bioenergetics estimates of annual Cutthroat Trout and Tiger Trout consumption of Chub
(Consumed by) in Scofield Reservoir. Age-2 and older (100 – 350 mm) Chub abundance (± 1 SE)
is based on June 2013 hydroacoustic estimates.
USU Budy. Project Completion Report. Scofield Reservoir
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140
120
100
80
60
40
20
Cutthroat Trout
0
Relative weight (Wr)
140
120
100
80
60
40
20
Rainbow Trout
0
140
120
100
80
60
40
20
Tiger Trout
0
150
200
250
300
350
400
450
500
550
600
650
700
750
Total length (mm)
Figure 1.12. Relationship between total length and relative weight (Wr ) of Cutthroat Trout
(top), Rainbow Trout (middle), and Tiger Trout (bottom) from Scofield Reservoir in summer
2012. Reference line at Wr = 100.
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CHAPTER 2
Quantifying the food web impacts of introduced piscivores
in reservoir fish assemblages
Introduction
Ecologists are continually challenged with describing and quantifying the interactions that
define food webs and community structure of aquatic systems (Paine 1980; Polis 1991; Polis
and Strong 1996). Food webs are dynamic, as linkages between organisms rely on the
movement of nutrients, prey, and consumers (Polis et al. 1997). Human manipulations, such as
the widespread construction of reservoirs, present uncertainties in our understanding of
aquatic food web structure. Reservoirs in particular differ from the ‘typical’ structure and
functioning of aquatic ecosystems, making them an interesting study system to inform
freshwater ecology (Miranda and DeVries 1996; Havel et al. 2005). Fish populations and
subsequent food web interactions in impounded riverine systems likely differ from those in
lakes, as the highly-manipulated and recently-stocked fish communities in reservoirs are often
unstable (Stein et al. 1995; Havel et al. 2005).
Reservoirs are thus novel, dynamic ecosystems that often demonstrate complex trophic
interactions among resident species. Not only do species roles change with ontogenetic shifts,
but species composition in reservoirs may vary from natural rivers and lakes in the region and
may consist of an assemblage of species that did not co-evolve. This assemblage may include
both native stream species that persist in the reservoir, and non-native species either
intentionally or accidentally introduced (Olsen et al. 1995; Matthews et al. 2004; Clavero et al.
2013). These artificial assemblages may have an increased number of trophic positions relative
to a prior state (Walsworth et al. 2013), decoupled or novel predator-prey interactions (Noble
1986), or may result in increased potential for competition of nutrients and resources amongst
species (Ney 1996; Wuellner et al. 2010). Reservoirs are often managed similarly to natural
lakes because they are assumed to be functionally comparable; however, direct comparisons of
fish assemblages has demonstrated dissimilarity between systems (Terra and Araujo 2011).
These unnatural lentic habitats may be unsuitable for stream fishes, but favorable for
introduced piscivorous fishes (Gido et al. 2009; Kashiwagi and Miranda 2009; Franssen and
Tobler 2013). Understanding the biotic interactions of artificial assemblages of fish will expand
our knowledge of novel food web interactions, as well as assist towards developing and
implementing suitable management strategies for these highly manipulated fisheries.
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Top predators in reservoir systems that are highly managed (i.e., stocked) for fishing may
exhibit strong competition between species. Competition amongst coexisting species can
structure community dynamics and limit predator performance, in addition to, or in
combination with predation (Kitchell and Crowder 1986; Ney 1990; Wuellner et al. 2010).
Additionally, exploitative competition may be a strong driver of decreasing angler success
(Marrin and Erman 1982; Wuellner et al. 2010), or the poor relative return rates of stocked
sport fish. In Patagonian lakes, invasive rainbow trout Oncorhynchus mykiss and brown trout
Salmo trutta cause a niche shift in a native galaxiid, leading to reduced growth and survival
(Correa et al. 2012). Overlapping food habits may also lead to limited supplies of prey resources
for the predator community (Bacheler et al. 2004). In Flaming Gorge, Utah-Wyoming, kokanee
Oncorhynchus nerka and Utah chub Gila atraria fed extensively and similarly on zooplankton;
subsequently, zooplankton biomass declined, and the decline in kokanee growth was correlated
with increased chub densities (Teuscher and Luecke 1996). Similarly, the introduction of lake
trout Salvelinus namaycush into Bear Lake, Utah reduced survival of Bear Lake strain Bonneville
cutthroat trout O. clarki utah due to shared competition for Bear Lake sculpin Cottus extensus
prey (Ruzycki et al. 2001). Competition among top predators may therefore result in reduced
growth and survival, or affect behavior such as changes in habitat use (Werner and Hall 1977)
or variable recruitment success (Marrin and Erman 1982).
Piscivores are commonly introduced as a management tool in an attempt to control undesired
species (Courtenay and Kohler 1986), as well as to enhance or provide new angling
opportunities (Wuellner et al. 2010). The growth and survival of these piscivores depends on
prey fish availability (Ney and Orth 1986), subsequently, the size of prey consumed and the
frequency of piscivory both generally increase with predator size (Juanes et al. 2002).
Accordingly, body morphology is a major factor affecting foraging performance of fish; for
example, mouth gape size affects the size of prey species that can be eaten by piscivores
(Mittelbach and Persson 1998). While the selection of prey fish of larger sizes increases
proportionally with predator size, sizes of prey consumed often appear to be smaller than what
is thought possible based on gape size measurements (Truemper and Lauer 2005). Further, the
range of prey sizes eaten typically increases in larger predators, as maximum prey size often
increases rapidly while minimum prey size may change only modestly over a broad range of
predator sizes (Juanes et al. 2002). The overall impact of these introduced piscivores on
complex food web interactions thus depends, in part, on prey life-history traits relating to size
and predator avoidance, and subsequent predator foraging decisions (Lundvall et al. 1999).
Predator feeding is not solely based on predator and prey size, but also on a suite of
characteristics related to the search, pursuit, attack, and handling of prey by predators (Werner
and Gilliam 1984; Bronmark and Hansson 1998). According to optimal foraging theory, fish will
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forage in such a way as to maximize fitness, typically taking into consideration food quality,
quantity, and spatial distribution (Werner and Hall 1974; Svanback and Bolnick 2007). The
amount of food consumed relative to the prey density is a functional response, where the
amount of predation generally increases with prey density, up until a saturation point (Abrams
and Fung 2010). High spatiotemporal overlap between the predator and prey is necessary for
encounter, and strategies vary from the sit-and-wait predation of pike Esox spp. to the sensing
of prey electrical activity by paddlefish Polyodon spathula (Dodds and Whiles 2010).
On the other hand, defenses against predation, such as habitat and behavior choices or
mechanical and chemical protections by prey, act in concert with predator behavior to
influence predator diet (Romare and Hansson 2003; Sih et al. 2010; Dodds and Whiles 2010). If
prey detect predators before they are detected by predators, they have the potential to
actively avoid them. Increased refuge efficiency by prey species was shown to cause a decrease
in piscivorous perch Perca spp. growth rates, likely due to a necessary shift in predator diet
(Persson and Eklov 1995). The use of these structurally-complex habitats may therefore reduce
predation rates because of a decrease in predator-prey encounters (Tabor and Wurtsbaugh
1991; Christensen and Persson 1993; Carey and Wahl 2010). Encounter rates also depend on
the spatiotemporal patterns of prey. Zooplankton exhibit diel vertical migrations, foraging at
night to avoid visual predators (Hays 2003). A high cost of handling time for low energy returns
is unfavorable for predators; prey may employ behaviors (e.g., schooling), protective spines
(e.g., spiny water flea Bythotrephes), armor (e.g., mollusks), or toxins (e.g., Coleoptera) to deter
predators (Dodds and Whiles 2010). Finally, abiotic factors such as water clarity may impact
food web structure, as many fish are visual predators.
Introductions of a novel predator or prey into a system may cause unintended or unanticipated
impacts. Trout introduced into alpine lakes have had unintended effects on their amphibian
and invertebrate prey, as naïve prey are poorly adapted to novel predators (Knapp 2005; Sih et
al. 2010). In contrast, spiny water fleas make up a large part of salmonid diets in their native
Norway but function as a serious threat in North America, as top predators in North America
may be incapable of recognizing or capturing the newly introduced species (Yan et al. 2011).
Additionally, galaxioid fishes have been identified as in serious conservation crisis partially due
to negative competitive interactions with introduced brown trout and rainbow trout from the
northern hemisphere (McDowall 2006).
Rainbow trout are one of the most widely stocked and highly valuable sport fish world-wide
(FAO Inland Water Resources and Aquaculture 2003 in Fausch 2008). Located in the western
United States, Scofield Reservoir is a high-elevation Utah reservoir with an historic blue-ribbon
rainbow trout fishery. This reservoir now contains an assemblage of top predators including
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rainbow trout, Bear Lake strain Bonneville cutthroat trout, and tiger trout Salmo trutta, female
x Salvelinus fontinalis, male. These additional species have been stocked into the reservoir in
an effort to suppress Utah chub, an unwelcome and rapid invader that re-appeared in 2005.
Though each species is known for their individual piscivorous nature, this combination of
multiple potential top predators is unique, with relatively little knowledge of the likely
interspecific interactions between cutthroat trout, rainbow trout, and tiger trout. Subsequent
to the timing of chub invasion, rainbow trout have experienced apparent reductions in
abundance and survival.
Scofield Reservoir is typical of many systems in the intermountain west, where a nuisance
species may threaten the existence of a popular and artificial sport fishery. Therefore, Scofield
Reservoir presents an opportunity to better understand how multiple top predators in a food
web may interact in reservoirs throughout the West. In this study, we examined the
interspecific interactions among Scofield Reservoir piscivores using stable isotope analysis and
gut content analysis. Specifically, the objectives of this study were to (1) explore the food
habits and use gut content to calculate indices of diet overlap between top predators or top
predators and the principal prey fish, to identify potential for competition, and (2) evaluate
niche overlap using stable isotope techniques, by collecting tissue samples of fish and aquatic
organisms throughout the food web, to further characterize potential predation and
competition linkages of the three top predators in the system.
Study Site
Scofield Reservoir is a high elevation (2,322 m) impoundment on the Price River, a tributary of
the Green River, located in southeast Utah. The reservoir was created by Scofield Dam in 1926
and is predominantly used for irrigation water storage, with recreation and flood control as
additional benefits (Bureau of Reclamation 2011). The current reservoir has a capacity of
73,600 acre-ft at full pool, mean surface area of 2,815 acres (1,139 ha), and a mean depth of 8
m (Bureau of Reclamation 2009). Scofield Reservoir is classified as eutrophic, with “excessive”
total phosphorous enrichment (Division of Environmental Quality 2010), typical of reservoirs, as
high loads of organic materials and nutrients correspond to a proportionally large watershed
area relative to volume (Wetzel 1990). Carlson Trophic State Index calculated for 1981-2007, a
general measure of eutrophication based on Secchi depth, chlorophyll a, and phosphorous
indicated a mesotrophic system (Bureau of Reclamation 2012). Blue-green algae dominate the
phytoplankton community, indicative of poorer water quality, with blooms typically occurring in
summer. The reservoir stratifies thermally in summer, and hypolimnetic oxygen deficits
historically lead to fish kills of varying degrees (Hart and Birdsey 2008).
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Scofield Reservoir is managed as a put-grow-and take sport fishery. Historically, around
600,000 150-250 mm long rainbow trout were stocked every year. However, the fish stocking
program has been adjusted nearly every year since 2005 in response to the re-appearance of
Utah chub in gill nets and fear of a population expansion, with potential for negative impacts to
the trout fishery (Hart and Birdsey 2008). Tiger trout and Bear Lake-strain Bonneville cutthroat
trout have been stocked in the fishery as a potential biological control for the Utah chub as well
as alternative sport fishes. These populations demonstrate little to no natural reproduction,
and are artificially maintained with approximately 80,000 of each stocked yearly at 200 mm
long (see Table 2.1). Non-game species also present include the redside shiner Richardsonius
balteatus and mountain sucker Catostomus platyrhynchus.
Methods
Field sampling
We sampled fishes in spring, summer, and autumn of 2012 in Scofield Reservoir. In this type of
fixed-station sampling, we selected index sites to be representative of the reservoirs’
longitudinal axis from the upper riverine zone to the lower lacustrine zone (McMahon et al.
1996), while maintaining consistency with long-term Utah Division of Wildlife Resources
monitoring (Figure 2.1). We set horizontal sinking gill nets (24 m long x 1.8 m tall with eight
monofilament panels of 38-, 57-, 25-, 44-, 19-, 64-, 32-, and 51 mm bar mesh) according to
standard gill-net methods to capture a representative size distribution of all fish in the reservoir
(Beauchamp et al. 2009). We placed two nets at each of eight sample sites within the reservoir
in littoral areas offshore at depths that fish were predicted to be most abundant; set before
dusk and pulled at dawn, spanning two crepuscular periods.
Diet analysis
We collected cutthroat trout, rainbow trout, tiger trout, and Utah chub diets from each gillnetting survey. We immediately preserved trout and chub stomachs in 95% ethanol for later
analysis. We identified all organisms from stomach contents to the lowest taxonomic level
possible (Brooks 1957; Edmonson 1959; Merrit and Cummins 1996) to determine abundance of
species and composition. We grouped stomach contents by prey fish (identified to species
when possible), zooplankton, organic matter, aquatic invertebrates (classified to order), and
terrestrial invertebrates (classified to order). We counted and weighed (blot-dry wet weight to
nearest 0.001 g) individual prey fish, and weighed invertebrate prey en masse by classification.
We measured intact prey fish to the nearest mm (backbone and standard length), then
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calculated the contribution of each prey category to the diet of each predator species as the
mean proportion by weight (g) for each stomach individually, and then averaged across all
nonempty stomachs (Chipps and Garvey 2007):
Q
N
MWi = 1⁄N � ( Wij � � Wij )
j=1
i=1
where, 𝑁 is the total number of fish with nonempty stomachs, 𝑊𝑖𝑗 is the weight of prey 𝑖 in the
stomach of predator𝑗, and 𝑄 is the total number of prey categories.
We used Schoener’s index of diet overlap to calculate the percent diet overlap between
species. The index is determined using the formula:
n
∝ =1-0.5 �� |pxi -pyi |�
i=1
where, ∝ is the degree of overlap, 𝑛 is the number of food categories, 𝑝𝑥𝑖 is the proportion of
food category i in the diet of species x, and 𝑝𝑦𝑖 is the proportion of food category i in the diet of
species y (Schoener 1970). Index values range from 0 to 1, where a value approaching 0 means
the species share no prey resources and a value closer to 1 means the species have identical
prey utilizations. Values exceeding 0.6 are considered “biologically significant” in terms of
overlap in resource use (Wallace 1981). We pooled all of the fish from each species into
representative ‘small’ (fish < 350 mm TL) and ‘large’ (≥ 350 mm TL) size classes with diet overlap
values calculated for each season of the study period.
Trophic position analysis
From all gill-netted trout and chub, we removed a small dorsal muscle tissue sample for isotopic
diet analysis. We quantified longer-term dietary habits of a subset of cutthroat trout (n = 15),
rainbow trout (n = 11), tiger trout (n = 8), and Utah chub (n = 10) using stable isotope analysis.
Specifically, we assessed fish trophic position and dietary carbon source based on the
respective δ¹⁵N and δ¹³C signatures of dorsal muscle tissue (Post 2002). We dried tissue
samples in an oven for 48 hours at 70°C, ground them to a powder, encapsulated them in 8.5
mm tin capsules, and then samples were analyzed at the Washington State University Stable
Isotope CORE for a mass spectrometry-based determination of isotopic signatures. Signatures
are an expressed ratio (¹⁵N:¹⁴N and ¹³C:¹²C), per mille (‰) values relative to ratios of the
standard atmospheric N₂ and Pee Dee Belemnite, respectively.
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We then plotted isotopic signature values as coordinates in niche space to determine both
resource and habitat use of each species (Newsome et al. 2007). We used the program SIBER
(Stable Isotope Bayesian Ellipses in R) to fit standard ellipses based on multivariate normal
distributions and maximum likelihood estimators. We then calculated standard ellipse area and
overlap, corrected for small sample size (Jackson et al. 2011). Additionally, we used stable
isotope data to estimate the relative contribution of Utah chub prey to predator diets using the
program SIAR (Stable Isotope Analysis in R), a linear mass balance mixing model (Philips and
Gregg 2001; Clarke et al. 2005).
Gape limit
To determine the gape size of predators, which indicates the size of fish prey available, we
measured several morphometric features of each predator, including total body length (nearest
mm TL) and length of the lower (LLJ) and upper (LUJ) jaw, using a digital caliper (to nearest 0.1
mm). We then calculated gape size (G) trigonometrically assuming a maximum mouth opening
of 60o during food uptake, using the equation as per Jensen et al. (2004):
Gpred = [(LLJ sin60)2 + (LUJ – LLJ cos60)2]0.5
where, Gpred is the gape size of the predator, LLJ is the length of the predators lower jaw (mm),
and LUJ is the length of the predators upper jaw (mm). Second, we also measured the stretched
width (laterally left to right) of the predator’s mouth using a digital caliper (to nearest 0.1 mm;
Truemper and Lauer 2005).
To determine actual sizes of prey consumed by piscivores, we examined diet contents of
predators. From fish prey found in diets, when possible, we obtained prey: backbone lengths,
standard lengths, and total length measurements. To determine the size of prey “vulnerable”
to predators based on their gape, we also measured body depth and total length of prey (i.e.,
Utah chub) found in the reservoir, and created a relationship relating body depth to total
length.
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Results
Diet
Food habits varied substantially among trout species and Utah chub in Scofield Reservoir. Utah
chub were found in the diets of large (≥ 350 mm TL) cutthroat trout (n = 53) throughout every
season in 2012 (Figure 2.2). Large cutthroat trout stomachs contained from 50-100% Utah chub
prey. Aquatic invertebrates (primarily chironomids) and terrestrial invertebrates made up the
remainder of large cutthroat trout diets. However, considerable differences in individuals based
on size were evident. Aquatic invertebrates composed 25-85% of small (< 350 mm TL) cutthroat
trout diets (n = 65). Small cutthroat trout relied heavily on zooplankton as well (up to 53%).
Tiger trout displayed similar food habits to cutthroat trout. Large tiger trout (n = 57) were
distinguished by their piscivorous diet, containing 45-80% Utah chub by season. Small tiger
trout (n = 39) displayed a similar affinity for aquatic invertebrates, but additionally, tiger trout
consumed a substantial proportion of crayfish at all sizes throughout all seasons (upwards of
20%). Small tiger trout demonstrated a varied diet of not only aquatic invertebrates (primarily
Ephemeroptera, Isopoda, and Mollusca) and crayfish, but included prey fish and terrestrial
invertebrates.
Rainbow trout differed considerably from these trends. Diets of small (n = 67) and large
rainbow trout (n = 13) were similar, characterized by low proportions of prey fish (Figure 2.3).
Approximately half of the rainbow trout diet was composed of aquatic invertebrates (primarily
Chironomids, with some Isopoda). These rainbow trout also included crayfish, terrestrial
invertebrates, and organic matter as a substantial proportion of their diet.
Utah chub diets consisted substantially of aquatic invertebrates at both smaller (n = 30) and
larger (n = 20) sizes (ranging from 20-81%). Smaller Utah chub also relied more heavily on
zooplankton and organic matter, whereas large Utah chub were found to have a sizable
proportion of crayfish in their diet (also see Appendix 4).
Trout and chub also displayed substantial changes in food habits across seasons. The
proportion of Utah chub in large cutthroat trout diets in autumn was twice that in spring. Small
cutthroat trout relied heavily on aquatic invertebrates regardless of season, but consumed a
greater diversity of invertebrates in spring than in summer and autumn. Small cutthroat trout
exhibited a strong affinity for zooplankton in autumn, much more than the remainder of the
year.
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Large tiger trout consumed 30% more prey fish in summer than during spring or autumn 2012.
Small tiger trout consumed a hefty 80% of aquatic invertebrates in the spring, whereas only
34% in summer and 56% in autumn. Overall, tiger trout incorporated crayfish as 20% of their
diet annually, whereas small tiger trout relied more heavily on crayfish for about 42% of their
diet in summer. Furthermore, both small and large rainbow trout had a high proportion of
aquatic invertebrates, terrestrial invertebrates, and organic matter in their diet regardless of
season.
Finally, Utah chub consumed less aquatic invertebrates in summer than in the spring (20-30%
compared with 50-80%), but made up the difference with substantial zooplankton consumption
at both small (40%) and large (20%) size classes in the summer. Small and large Utah chub also
exhibited an exceptionally strong affinity for zooplankton (72% and 100%) in autumn 2012.
Diet overlap amongst Scofield Reservoir trout and Utah chub was lowest in spring 2012. Based
on Schoener’s index, there was significant diet overlap between small cutthroat trout (α = 0.63)
and small rainbow trout (α = 0.64) with Utah chub, as well as large cutthroat trout with large
tiger trout (α = 0.66) in spring 2012. There is also evidence of diet overlap between small and
large tiger trout (α = 0.68). Similarly, there is significant diet overlap between small cutthroat
trout with large rainbow trout (α = 0.68) and small tiger trout (α = 0.61), as well as overlap
between large cutthroat trout (α = 0.69) with large tiger trout during the summer season. In
autumn, there were many instances of significant diet overlap, the strongest involving: small
cutthroat trout with small Utah chub (α = 0.76), large cutthroat trout with large tiger trout (α =
0.77), and large rainbow trout with both small and large tiger trout (α = 0.66). Similar patterns
of extensive diet overlap were observed with summer and autumn 2011 food habits.
Trophic position
Organisms collected displayed carbon and nitrogen signatures indicative of their habitat use
and feeding position. Tiger trout (n = 10) had the highest carbon isotopic signature, indicative of
their use of more littoral primary carbon sources. Utah chub (n = 10), however, had the most
negative carbon isotopic signature, reflecting usage of pelagic habitat at all sizes. Large Utah
chub were significantly more depleted in carbon than tiger trout, as well as cutthroat trout
(both students t-tests P = 0.02). In comparison, there were no statistical differences between
carbon signatures of any large trout species, they all utilized similar carbon sources (Figure 2.4).
Based on δ¹⁵ N results, large cutthroat trout (n = 13) and large tiger trout share a similar
position as top predators in this system, feeding at trophic positions corresponding to high δ¹⁵ N
values (students t-test P > 0.05). The nitrogen signature of large rainbow trout was also
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statistically similar to tiger trout, although less similar to cutthroat trout (both students t-tests P
> 0.05). Large Utah chub occupy trophic positions similar to rainbow trout and tiger trout, but
have a trophic position lower than large cutthroat trout (students t-test P < 0.001).
Small Utah chub fed more littorally, and additionally fed at lower trophic positions, with ¹⁵δN
signatures similar to trout. Small cutthroat trout fed at nearly the same position as both small
and large Utah chub. All three trout species varied in niche space with respect to size class,
demonstrating an ontogenetic shift around 350 mm in length, to higher trophic positions.
These results suggest that for tiger trout and rainbow trout, small individuals fed in littoral
areas whereas the diet of larger individuals originated in more pelagic areas. Cutthroat trout
sampled for stable isotope analysis shifted to feed more littorally with an increase in size.
When the isotopic niche of these species was plotted in 2-dimensional niche space, large tiger
trout had a very broad niche (ellipse area, EA = 10.4); they consumed food at a wide range of
trophic positions and vary with respect to their basal resources (Figure 2.5). Large cutthroat
trout (EA = 2.2) and large rainbow trout (EA = 2.7) both had smaller more focused isotopic niche
areas which overlapped significantly with the tiger trout niche (78% and 64%, respectively), and
with each other (31% and 26%, respectively). The isotopic niche of tiger trout only overlapped
17% with both cutthroat trout and rainbow trout.
Small rainbow trout (EA = 3.2) and small tiger trout (EA = 2.8) overlap significantly with respect
to their isotopic niches (45% of the rainbow trout niche and 52% of the tiger trout niche),
sharing similar prey resources at an intermediate trophic position. Small cutthroat trout (EA =
6.6), closely share a feeding niche space with all sizes of Utah chub, as demonstrated by their
similar isotopic signatures, with 19% of their niche overlapping with the entirety of the small
chub niche. Small Utah chub may consume more pelagic prey than do small rainbow trout,
however, adult (i.e., large) rainbow trout may share a large proportion of niche space with
small Utah chub.
Gape limit
Utah chub found in cutthroat trout diets ranged from 80 to 272 mm in total length. Cutthroat
trout became piscivorous at approximately 320 mm TL, and consumed Utah chub near and well
above both their horizontal and vertical gape size (Figure 2.6). In several instances, cutthroat
trout consumed Utah chub that were greater than 50% of their body size. One 425 mm
cutthroat trout consumed a 272 mm Utah chub (64% of the trout’s body size). However, on
average, cutthroat trout consumed fish prey 30% of their body size. Gape-width limit was very
similar to gape-size limit calculated using vertical gape measurements.
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Utah chub found in tiger trout diets ranged from 37 to 234 mm in total length. Tiger trout
switched to piscivory at approximately 340 mm TL and consumed fish very close to or just
exceeding their horizontal and vertical gape sizes. On average, tiger trout consumed prey fish
approximately 28% of their body size. One 418 mm tiger trout consumed a 202 mm Utah chub
(48% of the trout’s body size). Comparable to cutthroat trout, gape-width limit was very similar
to gape-size limit calculated using vertical gape measurements. Rainbow trout, however,
demonstrated limited piscivory in Scofield Reservoir, and we found no measureable fish prey in
diets.
Discussion
In this study, we present new information on diet among a unique assemblage of trout in a
lentic environment, and quantified species interactions and diet overlap of three top trout
predators. We also described the feeding niches of three top predators and Utah chub using
stable isotope analyses. Overall, the fish composition in Scofield Reservoir was dominated by
Utah chub, a species which consumed large quantities of a suite of aquatic invertebrates and
zooplankton throughout the growing season. Isotopic signatures of Utah chub, indicative of its
feeding position and primary food source, compared similarly to small cutthroat trout. We
observed consistently low numbers of rainbow trout, which consumed few prey fish, and relied
substantially on aquatic and terrestrial invertebrates for food resources. High overlap was
demonstrated amongst rainbow trout with all other species, strength of overlap varying based
on season. Additionally, we found cutthroat trout and tiger trout share a top trophic position in
the food web, relying on an ontogenetic shift to piscivory to consume Utah chub as a
substantial proportion of their adult diet. Both cutthroat trout and tiger trout consumed Utah
chub at and above theoretical predictions of gape limit, demonstrating they were not foodlimited based on gape morphology or prey size.
Throughout the study period, all large trout relied extensively on shared resources. We found
evidence that large cutthroat trout and large tiger trout exhibited high diet overlap, with both
species feeding primarily on Utah chub. There was no evidence of predation by one species on
the other; Utah chub dominated the diets of both. The diets of cutthroat trout reported in this
study differed substantially from diets reported from other lentic systems. At least 50% of
cutthroat trout diet in Scofield Reservoir consisted of Utah chub throughout the year. Small
cutthroat trout displayed an expansive diet throughout all seasons and fed on many different
aquatic and terrestrial invertebrates, crayfish, and zooplankton. Within Strawberry Reservoir,
Utah, Daphnia were important prey for juvenile cutthroat trout and were seasonally important
to adult fish; additionally, fish was a minor contributor to adult diet (Baldwin et al. 2000).
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However, in Bear Lake, Utah-Idaho, Bonneville cutthroat trout are a dominant piscivore,
predominantly consuming cisco Prosopium gemmifer and sculpin Cottus extensus (Ruzycki et al.
2001). The similar piscivorous behavior of cutthroat trout in Scofield Reservoir is likely
influenced by the abundant fish prey.
The potential for competition between top predators is indicated by similar trophic habits
among cutthroat trout and tiger trout. Small tiger trout consumed a wide variety of aquatic
invertebrates, whereas large tiger trout switched to a diet primarily of prey fish and crayfish.
Tiger trout have been described as a ‘predator trout’ capable of utilizing nuisance prey fish, and
as such, added to stocking programs for this purpose (Hepworth et al. 2009; Hepworth et al.
2011). In contrast, tiger trout in eastern Washington appeared to rely substantially on Daphnia,
and were intermittently piscivorous (to pumpkinseed Lepomis gibbosus and redside shiners),
but only in the summer months (Miller 2010). The same study postulated that kokanee salmon
were outcompeting tiger trout for zooplankton, and thus tiger trout had adapted a more
benthic diet. While we observed tiger trout consuming a significant proportion of crayfish, a
benthic prey item, fish still were the favored prey of tiger trout in this study.
The shared chub diet of cutthroat trout and tiger trout alone does not necessarily indicate
competition for prey resources (Matthews et al. 1982). Resources must be limited and fitness
or performance-related factors such as growth, condition, and fecundity must be negatively
affected in order for competition to occur. Consequently, there is likely minimal potential for
competition between these trout species as Utah chub are abundant and as cutthroat trout and
tiger trout are caught at large sizes and in high numbers throughout the reservoir (see chapter
2). However, if the Utah chub population collapsed under high predation, or if the trout
population continued to expand via increased stocking rates, then con-specific competition and
predation rates may increase.
Both predator size and prey size strongly influence predation (Hambright 1991; Fritts and
Pearsons 2006). Cutthroat trout and tiger trout displayed an ontogenetic shift to piscivory
around 330 mm TL, and consumed prey fish at 30% of their own size. Additionally, the mouth
size and morphometry of these top piscivores does not appear to be limiting prey consumption,
as both species also consumed prey at and above their theoretical gape limits. Piscivorous
mouth gape size restricts the size of prey based on the body depth of that prey; however, food
can be attacked and then manipulated in such a way as to consume larger than expected sizes
(Nilsson and Bronmark 2000). In contrast, Hambright (1991) and Hill et al. (2004) both suggest
the vulnerability of prey larger than predator gape size is reduced to zero. Subsequently,
predation may also depend on prey morphology and predator behavior. Northern pike Esox
lucius prefer shallow bodied roach Rutilus rutilus before deep-bodied bream Abramis brama in
USU Budy. Project Completion Report. Scofield Reservoir
61
Swedish lakes, experiments that also highlighted pike may swallow larger prey than suggested
by gape measurements (Nilsson and Bronmark 2000). Convincingly, our study also
demonstrates the ability of cutthroat trout and tiger trout to consume prey up to 50% of their
own size, much larger than gape limit alone would predict.
Unlike the piscivorous cutthroat trout and tiger trout, rainbow trout behaved mostly as
invertivores in Scofield Reservoir and occupied a more pelagic habitat than the other trout
species. Large rainbow trout displayed a similar propensity for aquatic invertebrates as small
rainbow trout. Although our sample size of rainbow trout diets was low, the results agreed
with previous studies, demonstrating that rainbow trout relied heavily on aquatic invertebrates,
zooplankton, and a small percentage of prey fish (Tabor et al. 1996; Baldwin et al. 2000; Haddix
and Budy 2005). In contrast, however, there are many studies which depict rainbow trout as
aggressive piscivores (Beauchamp 1990; McDowell 2003; Yard et al. 2011; Juncos et al. 2011).
Consequently, the suite of top predators in Scofield Reservoir may have indirect effects on
rainbow trout through exploitative competition or behavioral interactions (Duffy et al. 2007; Sih
et al. 2010). For example, the presence of cutthroat trout and tiger trout could cause rainbow
trout to decrease activity, exhibit predator avoidance, or change feeding strategies (Romare
and Hansson 2003). Catch rates of rainbow trout throughout our study were low, and the BlueRibbon rainbow trout fishery has abated from this western reservoir.
We observed no biologically-significant diet overlap between large rainbow trout and Utah
chub, contradicting expectation that rainbow trout were performing poorly due to shared food
resources, and thereby limited food resources, with Utah chub. The small sample size of
rainbow trout, which inflated average contributions of prey towards diet composition, may
have substantially and unrealistically altered diet proportions. Marrin and Erman (1982) found
minimal diet overlap between brown trout and rainbow trout with tui chub Gila bicolor, and
Tahoe sucker Catostomus tahoensis, demonstrating these trout and nongame fish partition
resources sufficiently, and contradicting the common belief in that system that competition for
food resources was the cause for the decrease in trout performance. However, apparent
survival of rainbow trout, a relevant measure of interaction strength (Keeley 2001), is extremely
low in Scofield Reservoir, and we may not have captured changes in competitive abilities with
size and environmental conditions (Hayes 1989).
Our analyses of stable isotope data corroborate with overall diet contributions, and reaffirm
evidence of potential for competition amongst this unique assemblage of species. High
nitrogen signatures of large cutthroat trout and large tiger trout confirm these top predators
are piscivorous share a top trophic position in the food web, relying heavily on prey fish.
USU Budy. Project Completion Report. Scofield Reservoir
62
Carbon signatures from tiger trout suggest they utilize more littoral resources, a pattern
corresponding with the high percentage of crayfish in diets.
The trophic positions of small cutthroat trout, rainbow trout, and tiger trout all shifted to higher
nitrogen values at larger sizes, indicating a strong ontogenetic shift in diet preferences with
size. Rainbow trout also shifted to a more negative δ¹³C value as they became larger. Vander
Zanden et al. (1999) documented a similar carbon shift of lake trout in bass-invaded Canadian
lakes, reflecting a diet shift towards zooplankton and reduced dependence on littoral prey fish.
This diet shift from the littoral to the pelagia for rainbow trout in Scofield Reservoir may
indicate rainbow trout are altering their trophic niche in response to direct or indirect
competition with the new predators recently stocked into this system (Correa et al. 2012).
Despite low sample size, our results indicate rainbow trout are now performing poorly in
Scofield Reservoir as suggested, in part, by very low rates of apparent survival and return to the
creel, a pattern that could very likely be due to feeding constraints or other limitations
associated with the other predators.
Our dietary findings are particularly important given the recent interest in tiger trout and
managers’ desire to expand their use in stocking regimes. Several lines of evidence suggest that
tiger trout are performing very well and are not limited by strong food web interactions such as
competition or predation. Utah chub make up at least 30% of the diet of tiger trout seasonally,
suggesting encounter rates are high and prey fish are energetically favorable (Wallace 1981).
Large tiger trout also have a wide breadth of diet preferences, relying on a diversity of prey. In
addition, this hybrid species feeds more littorally than other trout, thus potentially allowing
them to minimize competition for food and space (Petchey 2000; Helland et al. 2011).
Accordingly, tiger trout hold a high trophic position in the ecosystem. Tiger trout are also ideal
predators to stock into reservoirs as sport fishes, because they are sterile hybrids, and energy
normally allocated for gamete production should be allocated into growth (Budy et al. 2012). In
addition, sterile trout are easier to control over the long-term (Scheerer and Thorgaard 1987), a
consideration that has important conservation implications.
The combination of gut content and stable isotope analysis performed in this study provided a
more complete understanding of potential limitations due to competitive or predatory
interactions among a suite of novel predators. Competition for food resources between sport
and nongame fishes is commonly cited as a reason for decreased salmonid angling success, and
competition is often assumed to exist between desired sport fish and undesired invasive prey
species (Marking 1992). Nevertheless, competition does not always occur, and, our data
suggests two of the top trout predators are not competing, likely because food is not limiting.
Both tiger trout and cutthroat trout are monopolizing upon the abundant prey fish, Utah chub,
USU Budy. Project Completion Report. Scofield Reservoir
63
and their growth and survival rates are high. However, prey availability may change spatially, as
well as on a seasonal or annual basis, and increased growth rates of prey may result in chub
outgrowing predator gape quickly, causing a shift in predator-prey dynamics. This novel fish
community should be monitored carefully, as large-scale recent changes in the food web as
well as annual changes in reservoir volume may result in an extremely dynamic predator-prey
system.
References
Abrams, P.A. & Fung, S.R. 2010. Prey persistence and abundance in systems with intraguild predation and type-2
functional responses. Journal of Theoretical Biology 264: 1033-1042.
Bacheler, N.M., Neal, J.W. & Noble, R.L. 2004. Diet overlap between native bigmouth sleepers (Gobiomorus
dormitor) and introduced predatory fishes in a Puerto Rico reservoir. Ecology of Freshwater Fish 13: 111–118.
Baldwin, C.M., Beauchamp, D.A. & Van Tassell, J.J. 2000. Bioenergetic assessment of temporal food supply and
consumption demand by salmonids in the Strawberry Reservoir food web. Transactions of the American
Fisheries Society 129: 429-450.
Beauchamp, D.A. 1990. Seasonal and diel food habits of rainbow trout stocked as juveniles in Lake Washington.
Transactions of the American Fisheries Society 119: 475-482.
Beauchamp, D.A., Parrish, D.L. & Whaley, R.A. 2009. Coldwater fish in large standing waters. In: Bonar, S.A.,
Hubert, W.A. & Willis, D.W., eds. Standard methods for sampling North American freshwater fishes. American
Fisheries Society: Bethesda, Maryland, pp 97-118
Brooks, J.L. 1957. Identifications and nomenclature of Cladocera, Calanoida, and Cyclopoida. Memoirs of the
Connecticut Academy of the Arts and Science 13(1).
Bronmark, C. & Hansson, L.A. 1998. Consumption patterns, complexity and enrichment in aquatic food chains.
Proceedings of the Royal Society 265: 901-906.
Budy, P., Thiede, G.P., Dean, A., Olsen, D. & Rowley, G. 2012. A comparative and experimental evaluation of
performance of stocked diploid and triploid brook trout. North American Journal of Fisheries Management 32:
1211-1224.
Bureau of Reclamation. 2009. Scofield Dam. U.S. Department of the Interior.
Bureau of Reclamation. 2011. Scofield Project. U.S. Department of the Interior.
Bureau of Reclamation. 2012. Scofield Reservoir. U.S. Department of the Interior.
Carey, M.P. & Wahl, D.H. 2010. Interactions of multiple predators with different foraging modes in an aquatic food
web. Oecologia 162: 443-452.
Chipps, S. R., & Garvey, J.E. 2007. Assessment of diets and feeding patterns. In: Guy, C.S. & Brown, M.L., eds.
Analysis and interpretation of freshwater fisheries data. American Fisheries Society: Bethesda, Maryland, pp
473-514
USU Budy. Project Completion Report. Scofield Reservoir
64
Christensen, B., & Persson, L. 1993. Species-specific antipredator behaviours: effects on prey choice in different
habitats. Behavioral Ecology and Sociobiology 32: 1-9.
Clarke, L.R., Vidergar, D.T., & Bennett, D.H. 2005. Stable isotopes and gut content show diet overlap among native
and introduced piscivores in a large oligotrophic lake. Ecology of Freshwater Fish 14: 267-277.
Clavero, M., Hermoso, V., Aparicio, E. & Godinho, F.N. 2013. Biodiversity in heavily modified waterbodies: native
and introduced fish in Iberian reservoirs. Freshwater Biology 58: 1190-1201.
Correa, C., Bravo, A.P. & Hendry, A.P. 2012. Reciprocal trophic niche shifts in native and invasive fish: salmonids
and galaxiids in Patagonian lakes. Freshwater Biology 57: 1769-1781.
Courtenay, W.R. & Kohler, C.C. 1986. Regulating introduced aquatic species- a review of past initiatives. Fisheries
11: 34-38.
Department of Environmental Quality. 2010. Scofield Reservoir TMDL. Division of Water Quality. Salt Lake City,
Utah.
Dodds, W.K. & Whiles, M.R. 2010. Freshwater ecology: concepts and environmental applications of limnology,
second edition. Elsevier Inc., United States.
Duffy, J.E., Cardinale, B.J., France, K.E., Mcintyre, P.B., Thebault, E. & Loreau, M. 2007. The functional role of
biodiversity in ecosystems: incorporating trophic complexity. Ecology Letters 10: 522-538.
Edmonson, W.T. 1959.The rotifera. In: Whipple, G.C. & Ward, H.B., eds. Freshwater Biology, 2nd edition. Wiley:
New York.
FAO Inland Water Resources and Aquaculture Service. 2003. Fishery Record Collections. FIGIS Data Collection. FAO
- Rome. Food and Agriculture Organization.
Fausch, K.D. 2008. A paradox of trout invasions in North America. Biological Invasions 10: 685-701.
Franssen, N.R. & Tobler, M. 2013. Upstream effects of a reservoir on fish assemblages 45 years following
impoundment. Journal of Fish Biology 82: 1659-1670.
Fritts, A.L. & Pearsons, T.N. 2006. Effects of predation by nonnative smallmouth bass on native salmonid prey: the
role of predator and prey size. Transactions of the American Fisheries Society 135: 853-860.
Gido, K.B., Schaefer, J. & Falke, J.A. 2009. Convergence of littoral zone fish communities in reservoirs. Freshwater
Biology 54: 1163-1177.
Haddix, T. & Budy, P. 2005. Factors that limit growth and abundance of rainbow trout across ecologically distinct
areas of Flaming Gorge Reservoir, Utah-Wyoming. North American Journal of Fisheries Management 25: 10821094.
Hambright, K.D. 1991. Experimental analysis of prey selection by largemouth bass: role of predator mouth width
and prey body depth. Transactions of the American Fisheries Society 120: 500-508.
Hart, J., & Birdsey, P.W. 2008. Scofield Research Proposal. Price, UT: Utah Division of Wildlife Resources,
Southeastern Regional Office. 5 pp.
Havel, J.E., Lee, C.E. & VanderZanden, J. 2005. Do reservoirs facilitate invasions into landscapes? BioScience 55:
518-525.
Hayes, J.W. 1989. Social interaction between 0+ brown and rainbow trout in an experimental stream design. New
Zealand Journal of Marine Freshwater Resources 23: 163-170.
USU Budy. Project Completion Report. Scofield Reservoir
65
Hays, G.C. 2003. A review of the adaptive significance and ecosystem consequences of zooplankton diel vertical
migrations. Hydrobiologia 503: 163-170.
Helland, I.P., Finstad, A.G., Forseth, T., Hesthagen, T. & Ugedal, O. 2011. Ice-cover effects on competitive
interactions between two fish species. Journal of Animal Ecology 80: 539-547.
Hepworth, R.D., Warner, J., Ottenbacher, M.J. & Hadley, M.J. 2009. Panguitch Lake: an evaluation of the sport
fishery management plan. Utah Department of Natural Resources, Division of Wildlife Resources, Salt Lake
City.
Hepworth, R.D., Ottenbacher, M. & Hadley, M.J. 2011. Angler survey and monitoring results, Fish Lake Utah 2010.
Utah Department of Natural Resources, Division of Wildlife Resources, Salt Lake City, Utah.
Hill, J.E., Nico, L.G., Cichra, C.E. & Gilbert, C.R. 2004. Prey vulnerability to peacock cichlids and largemouth bass
based on predator gape and prey body depth. Proceedings of the Annual Conference, Southeast Association
for Fish and Wildlife Agencies 58: 47-56.
Jackson, A.L., Inger, R., Parnell, A.C. & Bearhop, S. 2011. Comparing isotopic niche widths among and within
communities: SIBER - Stable Isotope Bayesian Ellipses in R. The Journal of Animal Ecology 80: 595–602.
Jensen, H., Bohn, T., Amundsen, P. & Aspholm, P.E. 2004. Feeding ecology of piscivorous brown trout (Salmo
trutta L.) in a subarctic watercourse. Annales Zoologici Fennici 41: 319-328.
Juanes, F., Buckel, J.A. & Scharf, F.S. 2002. Feeding ecology of piscivorous fishes. In: Hart, P.J.B & Reynolds, J.D.,
eds. Handbook of Fish Biology and Fisheries, first edition. Oxford, UK: Blackwell Publishing Ltd, pp 267-283.
Juncos, R., Milano, D., Macchi, P.J., Alonso, M.F. & Vigliano, P.H. 2011. Response of rainbow trout to different food
web structures in Northern Patagonia: implications for growth, bioenergetics, and invasiveness. Transactions
of the American Fisheries Society 140: 415-428.
Kashiwagi, M.T., & Miranda, L.E. 2009. Influence of small impoundments on habitat and fish communities in
headwater streams. Southeastern Naturalist 8: 23-36.
Keeley, E.R. 2001. Demographic responses to food and space competition by juvenile steelhead trout. Ecology 85:
1247-1259.
Kitchell, J.F., & Crowder, L.B. 1986. Predator-prey interactions in Lake Michigan: model predictions and recent
dynamics. Environmental Biology of Fishes 16: 205-211.
Knapp, R.A., Hawkins, C.P, Ladau, J. & McClory, J.G. 2005. Fauna of Yosemite National Park lakes has low resistance
but high resilience to fish introductions. Ecological Applications 15: 835-847.
Layman, C.A., Araujo, M.S., Boucek, R., Hammerschlag-Peyer, C.M., Harrison, E., Jud, Z.R., Matich, P., Rosenblatt,
A.E., Vaudo, J.J., Yeager, L.A., Post, D.M. & Bearhop, S. 2012. Applying stable isotopes to examine food-web
structure: an overview of analytical tools. Biological Reviews 87: 545-562.
Lundvall, D., Svanback, R., Persson, L. & Bystrom, P. 1999. Size-dependent predation in piscivores: interactions
between predator foraging and prey avoidance abilities. Canadian Journal of Fisheries and Aquatic Sciences
56: 1285-1292.
Marking, L.L. 1992. Evaulation of the toxicants for the control of carp and other nuisance species. Fisheries 17: 613.
Marrin, D.L. & Erman, D.C. 1982. Evidence against competition between trout and nongame fishes in Stampede
Reservoir, California. North American Journal of Fisheries Management 2: 262-269.
USU Budy. Project Completion Report. Scofield Reservoir
66
Matthews, W.J., Gido, K.B. & Gelwick, F.P. 2004. Fish assemblages of reservoirs, illustrated by Lake Texoma
(Oklahoma-Texas , USA) as a representative system. Lake and Reservoir Management 20: 219–239.
Matthews, W.J., Bek, J. & Surat, E. 1982. Comparative ecology of the darters Etheostoma podostemone, E.
flabellare, and Percina roanoka in the upper Roanoke River drainage, Virginia. Copeia 805-814.
McDowall, R.M. 2003. Impacts of introduced salmonids on native galaxiids in New Zealand upland streams: a new
look at an old problem. Transactions of the American Fisheries Society 132: 229-238.
McDowall, R.M. 2006. Crying wolf, crying foul, or crying shame: alien salmonids and a biodiversity crisis in the
southern cool-temperate galaxioid fishes? Review in Fish Biology and Fisheries 16: 233-422.
McMahon, T.E., Zale, A.V. & Orth, D.J. 1996. Aquatic habitat measurements. In: Murphy, B.R. & Willis, D.W., eds.
Fisheries Techniques. American Fisheries Society, Bethesda, Maryland, pp 83-120.
Merritt, R.W., & Cummins, K.W., eds. 1996. An introduction to the aquatic insects of North America. Dubuque,
Iowa: Kendall & Hunt.
Miller, A.L. 2010. Diet, growth, and age analysis of tiger trout from ten lakes in Eastern Washington. Masters
Thesis. Eastern Washington University.
Miranda, L.E. & DeVries, D.R., eds. 1996. Multidimensional approaches to reservoir fisheries management.
American Fisheries Society Symposium 16, Bethesda, Maryland.
Mittelbach, G.G., & Persson, L. 1998. The ontogeny of piscivory and its ecological consequences. Canadian Journal
of Fisheries and Aquatic Sciences 55: 1454–1465.
Newsome, S.D., del Rio, C.M. & Bearhop, S. 2007. A niche for isotopic ecology. Frontiers in Ecology and the
Environment 5: 429-436.
Ney, J.J. 1990. Trophic economics in fisheries: assessment of demand-supply relationships between predators and
prey. Reviews in Aquatic Sciences 2: 55-81.
Ney, J.J. 1996. Oligotrophication and its discontents: effects of reduced nutrient loading on reservoir fisheries. In:
Miranda, L.E. & DeVries, D.R., eds. Multidimensional approaches to reservoir fisheries management.
American Fisheries Society Symposium 16: Bethesda, Maryland, pp 285-295.
Ney, J.J., & Orth, D.J. 1986. Coping with future shock: the challenge of matching predator stocking programs to
prey abundance. In: Stoud, R.H., ed. Fish culture in fisheries management. American Fisheries Society:
Bethesda, Maryland, pp 81-92.
Nilsson, P.A. & Bronmark, C. 2000. Prey vulnerability to a gape-size limited predator: behavioural and
morphological impacts on northern pike piscivory. Oikos 88: 539-546.
Noble, R.L. 1986. Management of reservoir fish communities by influencing species interactions: predator-prey
interactions in reservoir communities. In: Hall, G.E. & Van Den Avyle, M.J., eds. Reservoir fisheries
management strategies for the 80's. American Fisheries Society: Bethesda, Maryland, pp 137-143.
Olson, M.H., Mittelbach, G.G. & Osenberg, C.W. 1995. Competition between predator and prey: resource-based
mechanisms and implications for stage-structured dynamics. Ecology 76: 1758–1771.
Paine, R.T. 1980. Food webs: linkage, interaction strength and community structure. Journal of Animal Ecology 49:
667–685.
Persson, L. & Eklov, P. 1995. Prey refuges affecting interactions between piscivorous perch and juvenile perch and
roach. Ecology 76: 70-81.
USU Budy. Project Completion Report. Scofield Reservoir
67
Petchey, O.L. 2000. Prey diversity, prey composition, and predator population dynamics in experimental
microcosms. Journal of Animal Ecology 69: 874-882.
Phillips, D.L., & Gregg, J.W. 2001. Uncertainty in source partitioning using stable isotopes. Oecologia 127: 171-179.
Polis, G.A. 1991. Complex trophic interactions in deserts: an empirical critique of food web theory. The American
Naturalist 138: 123–155.
Polis, G.A., & Strong, D.R. 1996. Food web complexity and community dynamics. The American Naturalist 147(5):
813–846.
Polis, G.A., Anderson, W.B. & Holt, R.D. 1997. Toward an integration of landscape and food web ecology: the
dynamics of spatially subsidized food webs. Annual Review of Ecological Systems 28: 289-316.
Post, D.M. 2002. Using stable isotopes to estimate trophic position: models, methods, and assumptions. Ecology
83: 703-718.
Romare, P. & Hansson, L. 2003. A behavioral cascade: top-predator induced behavioral shifts in planktivorous fish
and zooplankton. Limnology and Oceanography 48: 1956-1964.
Ruzycki, J.R., Wurtsbaugh, W.A. & Luecke, C. 2001. Salmonine consumption and competition for endemic prey
fishes in Bear Lake, Utah–Idaho. Transactions of the American Fisheries Society 130: 1175–1189.
Scheerer, P.D. & Thorgaard, G.H. 1987. Performance and developmental stability of triploid tiger trout (brown
trout x brook trout). Transactions of the American Fisheries Society 116: 92-97.
Schoener, T.W. 1970. Non-synchronous spatial overlap of lizards in patchy habitats. Ecology 51: 408–418.
Sih, A., Bolnick, D.I., Luttbeg, B., Orrock, J.L., Peacor, S.D., Pintor, L.M., Preisser, E., Rehage, J.S. & Vonesh, J.R.
2010. Predator-prey naiveté, antipredator behavior, and the ecology of predator invasions. Oikos 119: 610621.
Stein, R.A., DeVries, D.R. & Dettmers, J.M. 1995. Food-web regulation by a planktivore: exploring the generality of
the trophic cascade hypothesis. Canadian Journal of Fisheries and Aquatic Sciences 52: 2518-2526.
Svanback, R. & Bolnick, D.I. 2007. Intraspecific competition drives increased resource
natural population. Proceedings of the Royal Society 274: 839-844.
use diversity within a
Tabor, R.A. & Wurtsbaugh, W.A. 1991. Predation risk and the importance of cover for juvenile rainbow trout in
lentic systems. Transactions of the American Fisheries Society 120: 728-738.
Tabor, R.A., Luecke, C. & W. Wurtsbaugh. 1996. Effects of daphnia availability on growth and food consumption of
rainbow trout in two Utah reservoirs. North American Journal of Fisheries Management 16: 591-599.
Terra, B.F. & Araujo, F.G. 2011. A preliminary fish assemblage index for a transitional river-reservoir system in
southeastern Brazil. Ecological Indicators 11: 874-881.
Teuscher, D., & Luecke, C. 1996. Competition between kokanees and Utah chub in Flaming Gorge Reservoir, UtahWyoming. Transactions of the American Fisheries Society 125: 505-511.
Truemper, H.A. & Lauer, T.E. 2005. Gape limitation and piscine prey size-selection by Yellow Perch in the extreme
southern area of Lake Michigan, with emphasis on two exotic prey items. Journal of Fish Biology 66: 135–149.
Vander Zanden, M.J., Casselman, J.M. & Rasmussen, J.B. 1999. Stable isotope evidence for the
consequences of species invasions in lakes. Nature 401: 464–467.
USU Budy. Project Completion Report. Scofield Reservoir
food web
68
Wallace, R.K. 1981. An assessment of diet-overlap indexes. Transactions of the American Fisheries Society 110: 72–
76.
Walsworth, T., Budy, P. & Thiede, G.P. 2013. Longer food chains and crowded niche space: effects of multiple
invaders on desert stream food web structure. Ecology of Freshwater Fish 22: 439-452.
Werner, E.E., & Gilliam, J.F. 1984. The ontogenetic niche and species interactions in size-structured populations.
Annual Review of Ecology and Systematics 15: 393-425.
Werner, E.E. & Hall, D.J. 1974. Optimal foraging and size selection of prey by the bluegill sunfish (Lepomis
macrochirus). Ecology 55: 1042-1052.
Werner, E.E., & Hall, D.J. 1977. Competition and habitat shift in two sunfishes (Centrarchidae). Ecology 58: 869876.
Wetzel, R.G. 1990. Land-water interfaces: metabolic and limnological regulators. Verhandlungen des
Internationalen Verin Limnologie 24: 6–24.
Wuellner, M.R., Chipps, S.R, Willis, D.W. & Adams Jr, W.E. 2010. Interactions between walleyes and smallmouth
bass in a Missouri river reservoir with consideration of the influence of temperature and prey. North American
Journal of Fisheries Management 30: 445-463.
Yan, N.D., Lueng, B., Lewis, M.A. & Peacor, S.D. 2011. The spread, establishment and impacts of the spiny water
flea, Bythotrephes longimanus, in temperate North America: a synopsis of the special issue. Biological
Invasions 13: 2423-2432.
Yard, M.D., Coggins Jr., L.G., Baxter, C.V., Bennett, G.E. & Korman, J. 2011. Trout piscivory in the Colorado River,
Grand Canyon: effects of turbidity, temperature, and fish prey availability. Transactions of the American
Fisheries Society 140: 471-486.
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Figure 2.1. Map of Scofield Reservoir, Utah showing the eight locations (denoted as black
circles) where we sampled fish in 2011 and 2012.
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Figure 2.2. Seasonal diet composition of small (< 350 mm TL) and large (≥ 350 mm TL) cutthroat
trout, rainbow trout, and tiger trout captured in Scofield Reservoir. Diet composition was
calculated as the proportion of diet by wet weight (g).
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Figure 2.3. Proportion of Utah chub diet contribution for each predator species in Scofield
Reservoir. Proportions are displayed with 95% confidence intervals in dark gray, then 75%, 25%,
to 5% credibility intervals in light gray.
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14
13
12
Utah chub
11
tiger
trout
rainbow trout
9
14
15
δ Nitrogen
cutthroat trout
10
cutthroat trout
13
tiger
trout
12
rainbow trout
11
Utah chub
10
9
-29
-28
-27
-26
-25
-24
δ13Carbon
Figure 2.4. Stable isotope bi-plots for ¹³C and ¹⁵N of (top panel) small trout (< 350 mm TL) and
small chub (< 250 mm TL), and (bottom panel) large trout (≥ 350 mm TL) and large chub (≥ 250
mm TL). Error bars show 1 standard error.
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Figure 2.5. Two-dimensional isotopic (δ¹⁵N and δ¹³C) niche plots of (left panel) large (≥ 350 mm)
cutthroat trout, rainbow trout, tiger trout, and Utah chub (≥ 250 mm) from Scofield Reservoir,
and (right panel) small (< 350 mm) cutthroat trout, rainbow trout, tiger trout, and Utah chub (<
250 mm).
USU Budy. Project Completion Report. Scofield Reservoir
74
300
Cutthroat trout
250
200
150
100
Utah chub total length (mm)
50
0
300
Rainbow trout
250
200
vertical gape
gape width
Utah chub
150
100
50
0
300
Tiger trout
250
200
150
100
50
0
150
200
250
300
350
400
450
500
550
600
650
700
750
Predator total length (mm)
Figure 2.6. Relationship between cutthroat trout (top), rainbow trout (middle), and tiger trout
(bottom) length with Utah chub length in Scofield Reservoir, Utah. Gray circles represent actual
sizes of prey found in trout diets. Lines show the calculated gape limit for trout based on two
gape measurements. Although rainbow trout did consume fish prey, we found no measureable
fish prey in diets.
USU Budy. Project Completion Report. Scofield Reservoir
75
CHAPTER 3
Scofield Reservoir predator-prey interactions:
management implications
I.
Collectively, our results suggest that if a primary management goal for the fishery of
Scofield Reservoir is to maintain a popular sport fishery, rainbow trout currently provide
little return.
A. Across both years of the study, rainbow trout had extremely low catch rates, with the
population appearing to consist of only a small fraction of individuals compared to cutthroat
trout and tiger trout. Relative abundance of rainbow trout was less than 1% of the total
catch (i.e., CPUE) every year. In addition, their apparent survival is very low.
B. Rainbow trout exhibit extremely low PSD and RSD-P values, with few fish above the
“preferred” length of 500 mm and no proportion of fish above the “memorable” length of
650 mm. In contrast, cutthroat trout exhibit relatively high values of proportional stock
density, with a substantial proportion of fish above the “preferred” length of 450 mm.
Nearly all tiger trout in Scofield Reservoir are of “preferred” length (450 mm), and there is a
significant portion of fish of “memorable” length (525 mm), indicating the population is
dominated by large, healthy fish. Notably, multiple Utah state record tiger trout have been
caught at Scofield Reservoir recently.
C. Based on isotopic analyses, while tiger trout and cutthroat trout appear to show
considerable niche differentiation, with tiger trout being more littoral and cutthroat trout
being more pelagic, rainbow trout demonstrate considerable overlap with both small
cutthroat trout and large tiger trout. Further, because rainbow trout are largely
insectivorous in Scofield Reservoir, their diet overlaps with Utah chub. These results suggest
the potential for competition between rainbow trout and Utah chub, and rainbow trout and
both of the other trout, perhaps contributing to their poor performance in the reservoir.
D. In observational studies of aggression in the laboratory, rainbow trout were more
aggressive, demonstrating more chases, bites, and contact relative to both tiger trout and
cutthroat trout. In controlled pond experiments measuring growth of the three trout
species held in different combinations, rainbow trout grew bigger when in the presence of
tiger trout or cutthroat trout, whereas the other two trout species grew best when reared
with only conspecifics. Although it is difficult to extrapolate from the lab to the field, it is
possible that excessive energy expenditure could be inhibiting rainbow trout growth (i.e.,
they could be spending too much time chasing the other trout species) in the reservoir,
where the other trout strongly dominate the species composition.
USU Budy. Project Completion Report. Scofield Reservoir
76
E. In addition, although anecdotal, while working in the dock area at Scofield Reservoir, our
technicians noted that in several cases, anglers who were enthusiastic about the large
“rainbow trout” they had recently caught were actually holding a Bonneville cutthroat trout.
These anecdotal observations suggest a need for outreach efforts including signage at boat
launches and possibly informational brochures.
II. Similarly, if a primary management goal for the fishery of Scofield Reservoir is to
control the Utah chub population, collectively our results suggest that rainbow trout
are contributing little to the biological control of Utah chub, whereas tiger trout and
cutthroat trout are consuming millions of Utah chub each year and appear to be
keeping the Utah chub population somewhat in check.
A. The diets of large (> 350 mm) tiger trout and cutthroat trout contain as much as 79% Utah
chub, and on average they consume Utah chub of 30% of their body size. Although our
sample size of rainbow trout stomachs was low, our results agreed with previous studies
(e.g., in Flaming Gorge Reservoir), and demonstrated that rainbow trout rely heavily on
aquatic invertebrates, zooplankton, and consume only small percentage of prey fish (< 5%).
B. The optimum temperature for rainbow trout consumption (CTO, 20 oC) was available in the
epilimnion (i.e., near surface waters) during summer 2012 from late July to mid-August
(Figure 3.1). The CTO for tiger trout was often available in Scofield Reservoir during the
summer months and temperatures near the CTO of cutthroat trout were available through
much of the time series. Unsurprisingly, bioenergetic efficiency (i.e., proportion of
maximum consumption) was greatest for tiger trout. Temperatures available in the
reservoir during the summer growth season (June – September) for trout, although not
optimum at all times, were within acceptable ranges for good growth and consumption by
trout as demonstrated by bioenergetic efficiency rates of 36 – 58%.
C. On average, an individual large tiger trout was responsible for the majority of consumption
of prey fish, primarily Utah chub – the average large tiger trout consumes over 2,660 g of
prey fish in a given year (63 chub per year), contributing to over half (52%) of the total
annual piscivory at Scofield Reservoir. An average large individual cutthroat trout consumes
1,820 g chub annually (49 chub per year), accounting for 35% of the annual consumption of
Utah chub.
D. When scaled up to the overall reservoir-wide population of each species, proportional
contributions to total piscivory change accordingly. The population of large cutthroat trout
in the reservoir consumes over 16.6 million Utah chub in a year, about 65% of the overall
reservoir-wide consumption of chub. The smaller population of large tiger trout contributes
33% of the overall consumption, consuming about 8.5 million Utah chub yearly. The
USU Budy. Project Completion Report. Scofield Reservoir
77
rainbow trout population contributes less than 1% of the total piscivory in Scofield
Reservoir.
E. We estimate that the population of cutthroat trout and tiger trout (combined) consumed
76% of the Utah chub biomass (standing stock + production) in Scofield Reservoir. Given
the uncertainties associated with estimates of predator and prey abundance and predation
rates, if the predation (i.e., consumption) estimate approaches 60% of the prey biomass
(including production for fast growing, r-selected species like Utah chub), then predation is
likely capable of regulating the prey population (Dave Beauchamp, personal
communication). Since the goal is the suppression of Utah chub, rather than conservation
of Utah chub, it is noteworthy that populations of these two predators, tiger trout and
cutthroat trout, are likely consuming a very large proportion of available Utah chub biomass.
However, we note that our acoustic estimates likely underestimate the total Utah chub
abundance in Scofield Reservoir due to (1) an inability to accurately estimate small < 100
mm chub, (2) an inability to discern targets within shallow, dense-macrophyte areas (e.g., in
the southern end of reservoir), and (3) boundary effects along the slopes and surfaces of the
reservoir.
III.
Based on the sum of these results and some preliminary cohort modeling described
below, our results suggest that the reservoir population of trout (tiger trout and
cutthroat trout) must be maintained at a minimum of 320,000 trout, preferably with a
species composition of approximately 75% tiger trout and 25% cutthroat trout in order
control the Utah chub population.
A. If all else stays similar to the time of this study (e.g., environmental conditions in the
reservoir, survival rates of stocked fishes), we estimate that a goal of maintaining the
reservoir at a minimum of 320,000 trout with a species composition of 75% tiger trout and
25% cutthroat trout could be met with several different combinations of stocking densities
(Table 3.1). For example, for the next three years, stocking 250,000 tiger trout and 40,000,
30,000, and 30,000 cutthroat trout for 2014, 2015, and 2016, respectively should keep the
total trout population at a safe minimum density (350,000 – 400,000) and would bring the
ratio back in favor of tiger trout (closer to 75%). A simple spreadsheet could be provided
that managers can use to “game play” given production abilities.
B. However, for the near future (next 3 – 5 years) more is likely going to be better, and trout
densities should be maintained at very high densities if production allows, unless their
condition appears to change and begins to decline (see also Utah chub decline below). If
600,000 total tiger and cutthroat could be stocked for 3 more years, the Utah chub
population would likely be “under control”. See below.
USU Budy. Project Completion Report. Scofield Reservoir
78
C. Although the Utah chub population no longer appears to be increasing exponentially (Figure
3.2), the current density of Utah chub in the reservoir is extremely high and nearly 10-fold
greater than the density of the trout population. In addition, Utah chub > 250 mm are less
susceptible to predation (see Appendix Figure A4.4) and simultaneously the most fecund.
And, the large discrepancy between fish production and fish consumption reported here
suggests we may be underestimating the size of the chub population (young-of-year [age-0]
and age-1 in particular) and/or their growth rates.
1. Thus the situation should be considered still volatile, and it is imperative that the
reservoir be monitored with hydroacoustics and gill netting in the spring, but preferably
in both the spring and autumn for the next three to five years (i.e., the length of time it
will take for Utah chub > 200 mm to senesce [Figure 3.3], and for tiger trout to increase
in relative abundance).
2. We also recommend starting and ichthyoplankton-net tow regime to the spring
sampling in order to capture recruitment patterns of Utah chub
3. The greatest densities of trout that can be produced should be stocked, until the Utah
chub population appears to be consistently declining over a three-year time frame.
D. As tiger trout were not marked, we cannot make an assessment about the relative
performance (e.g., growth) of different cohorts of tiger trout stocked at different times or
different sizes. For cutthroat trout, the best overall performance resulted from fish adiposeclipped and stocked in May 2009 at 207 mm; likewise for rainbow trout adipose-clipped and
stocked in May 2009 at 200 mm (Figure 3.4).
E. Based on a limited time series analysis described below, before biological control became
effective in about 2009, the chub population was experiencing an extremely high annual
increase, more than 500% increase in chub abundance each year (λ = 6.5; 95% CI = 1.6 –
26.3). After biological control appeared to become effective in 2009, the annual population
growth rate declined to 1.52 (95% CI = 0.06 – 35.92) indicating the population is still
increasing, however this later estimate is highly uncertain due to the short time series and
large confidence intervals. Nonetheless, the population trajectory did change dramatically
in 2009 from an exponential increase prior to 2009 to a much flatter linear relationship after
2009, suggesting the chub population is under control.
We also estimated the future year when those unsusceptible large adults reach an
abundance no longer of management concern, in terms of the effectiveness of biological
control. Under those scenarios, we estimate that by 2021 there will be very few adult chub
remaining from the 2013 cohort (Figure 3.5). By 2019, there will be very few adult chub >
250 mm, those that had escaped gape limitation prior to reaching effective numbers of
stocked trout as described above. We anticipate that these are both conservative estimates
USU Budy. Project Completion Report. Scofield Reservoir
79
(the date for meeting effective control targets is likely earlier), as the stocked predators are
getting larger and likely more effective over time. Nonetheless, we note these projections
are somewhat limited, as they are based on one point in time, 2013, for reasons associated
with analytical and data restrictions. Therefore, annual hydroacoustic estimates of chub
abundance are critical for effective monitoring and management of this uncertain system.
IV. Anticipated effects of the Gooseberry Narrows Project
A.
The effects of a 10% decline in reservoir acres are difficult to anticipate. The reduction in
reservoir volume should be estimated and monitored (see Appendix Figure A1.1). If the
reservoir volume declines significantly, the carrying capacity for both Utah chub and trout will
also decline, while trout predation rates may actually increase.
B. The reservoir currently becomes slightly too warm for tiger trout and cutthroat trout during
some times of the year (mid to late summer). If the volume declines, water temperatures may
increase, which could be simultaneously bad for the performance of trout and good for the
population of Utah chub. Similarly, the likelihood of the reservoir going anoxic will increase,
which could be simultaneously bad for trout, which are more sensitive to low oxygen
concentrations, with lesser impacts on Utah chub.
C. Belatedly, as directly stated in the Gooseberry Narrows Findings report, “The overall probability
of eutrophication (algae bloom) in Scofield Reservoir for the period studied shows an increase
from 68.3 to 73.5% (about a 5.2% increase). This will increase the probability of fish kills.”…due
to anoxic conditions. See above.
V. Additional recommendations
A.
We strongly recommend that the reservoir be consistently monitored via gill netting,
hydroacoustics, and ichthyoplankton tows for the next 3 – 5 years (see above). A full field study
would not be required.
B. The various estimates and cohort modeling should be updated annually based on hydroacoustic
estimates and netting results.
C. Tiger trout should be dye marked to assess cohort strength, growth, and relative survival.
D. It might be useful to do some more explicit comparisons to Strawberry Reservoir, at the time
period leading up to the rotenone treatment.
E. Given the amazing performance of tiger trout and the good performance of cutthroat trout,
relative to the poor performance of rainbow trout and the need for biological control of Utah
USU Budy. Project Completion Report. Scofield Reservoir
80
chub, anglers should be made aware of the overall situation in Scofield Reservoir. This
awareness should include the trophic dynamics currently operating, the new opportunities
(state record tiger trout), and anglers should be provided with identification tips (so cutthroat
trout are not confused with rainbow trout). An outreach and education sign, similar to signage
at Strawberry Reservoir, should be posted at boat launches and docks and perhaps other areas.
Cohort Trend Analysis for Trout and Chub: Predictions for the Future
In order to try and start developing an estimate of the number of trout and of what species composition
will be required to try and control the Utah chub (Gila atraria) population of Scofield Reservoir, we
completed a simple cohort analysis of stocked trout over the time series available, and compared this to
the trend in chub. We applied a range of biologically-realistic survival rates, ranging from 30-70% to the
cohorts of stocked tiger trout (Salmo trutta, female X Salvelinus fontinalis, male), and Bear Lake strain
cutthroat trout (Oncorhynchus clarkii utah) each year, and removed fish from the cohort after they were
age-6 and age-7 for cutthroat trout and tiger trout, respectively. In comparison, Rozengranz et al,
(1999) reported survival rates of 37 – 51% for lake-dwelling cutthroat trout in Alaska, Hegge et al. (1991)
reported a survival rate of 49% for brown trout (Salmo trutta) in Norway, Glenn et al. (1989) reported
survival of 44% for brown trout stocked into small lakes, and Quinn and Kwak (2011) reported mean
annual survival of 22 – 36% for stream-dwelling brown trout.
We used the catch-per-unit-effort (CPUE) from the annual UDWR spring-time, gill-netting survey (the
most comparable data over time) to estimate the time trend in chub abundance. We also relied on the
spring gill netting, because it likely represents the most accurate index of the “adult” (i.e., large chub)
population trend. Further, we corroborated our results from this simple cohort analysis with estimates
of the abundance of tiger trout (238,130) and cutthroat trout (214,128) from the August 2011
hydroacoustic survey (Table 3.1).
Based on this simple analysis, in about 2007, Utah chub started increasing exponentially in the reservoir
(Figure 3.1). An exponential increase is indicative of a population growing without limitation. After
2009, based on the spring gill netting, the density of the Utah chub population appeared to be
substantially reduced, with little, if any, rate of increase. Thus it appears that when the combined
population of tiger trout and cutthroat trout in the reservoir reached about 320,000 trout, with a
species composition of about 77% tiger trout and 23% cutthroat trout, the trout population was
controlling the chub population (Table 3.1). These estimates also indicate that annual survival of tiger
trout and cutthroat trout are approximately 52% and 87%, respectively, based on corroboration with
August 2011 acoustics-based estimates of tiger trout and cutthroat trout abundance (Table 3.1).
Therefore, we estimate that in order to prevent an increasing trend in chub, the reservoir would need to
be maintained at a minimum of 320,000 trout, ideally with a species composition more skewed toward
tiger trout over cutthroat trout (Table 3.1). Note that while this abundance and composition of trout
appears to have reversed the increasing population trend of chub, the current estimates of Utah chub
USU Budy. Project Completion Report. Scofield Reservoir
81
are extremely high and appear to have increased (nearly doubled) from 2011 to 2013, based on
hydroacoustic estimates (see Appendix 1). As such, in order to further suppress the chub population,
the numbers of trout in the reservoir should be substantially increased. Because the survival of
cutthroat trout appears to be considerably higher (Table 3.1) than that of tiger trout, more tiger trout
will be needed. We suggest this ratio should be maintained at 75% tiger and 25% cutthroat. As of 2012,
we estimate that the proportion of tiger trout has declined to 47% (Table 3.1).
We estimated the population trend for Utah chub in Scofield Reservoir using 8 years of CPUE estimates
obtained from Utah Division of Wildlife Resources, 2005 – 2012, using linear regression of logtransformed annual changes in population growth rate as a function of time step (Morris and Doak
2002). We express trend as lambda (λ), the annual population growth rate with 95% confidence
intervals (CI). We calculated λ for the time period 2005 – 2012, before biological control appeared to be
effective, and the time period 2009 – 2012, when it appears that biological control became effective. A
λ > 1 indicates a positive population trend, a λ = 1 indicates no change in population growth rate, and a
λ < 1 indicates the population is declining; however, given the short, time series available, and when
95% confidence intervals overlap 1.0, we cannot completely rule out either a population increase or
decrease.
Based on this limited time series analysis, before biological control became effective in about 2009, the
chub population was experiencing an extremely high annual increase, more than 500% increase in chub
abundance each year (λ = 6.5; 95% CI = 1.6 – 26.3). After biological control appeared to become
effective in 2009, the annual population growth rate declined to 1.52 (95% CI = 0.06 – 35.92) indicating
the population is still increasing; however, because confidence intervals overlap 0 and we have only
three data points, we cannot say for certain whether the population is now increasing or decreasing.
Nonetheless, the population trajectory did change graphically from an exponential increase prior to
2009, to a much flatter linear relationship after 2009.
Utah chub > 250 mm are generally not susceptible to predation, and in Scofield Reservoir, these are the
fish that remain of concern for biological control. Therefore, we used: 1) the age-specific estimated
abundance of Utah chub as of 2013, 2) the cohort-specific annual survival rate, and 3) a maximum age of
10 (Johnson and Belk 1999) to project forward. We used this approach to estimate the future year
when those unsusceptible large adults reach an abundance no longer of management concern, in terms
of the effectiveness of biological control. Under those scenarios, we estimate that by 2021 there will be
very few adult chub remaining from the 2013 cohort (Figure 3.5). By 2019, there will be very few adult
chub > 250 mm, those that had escaped gape limitation prior to reaching effective numbers of stocked
trout as described above. We anticipate that these are both conservative estimates (the date for
meeting effective control targets is likely earlier), as the stocked predators are getting larger and likely
more effective over time. Nonetheless, we note these projections, uncertain as they are, are based on
one point in time, 2013, for reasons associated with analytical and data restrictions.
USU Budy. Project Completion Report. Scofield Reservoir
82
Table 3.1. Abundance of tiger trout and cutthroat trout in Scofield Reservoir based on number stocked and three cumulative, annual survivalrate scenarios since initiation of stocking in 2005 (tiger trout stocked each October) and 2009 (cutthroat trout stocked each May). Acousticbased abundance estimates, conducted on 31 August 2011 and 6 June 2013, are provided for reference. To reach that 2011 acoustic abundance
estimate, we determined the “best” survival-rate scenario to be 52% for tiger trout and 87% for cutthroat trout. In 2009, Utah chub abundance
began to decline, in that year we estimated there to be nearly 320,000 trout (77% tiger and 23% cutthroat) in the reservoir.
Annual survival rate (%)
Annual survival rate (%)
Acousticbased
tiger trout
abundance
estimate
Number
of
cutthroat
trout
stocked
Acousticbased
cutthroat
trout
abundance
estimate
Total
abundance
of tiger &
cutthroat
under
“best”
survival
scenarios
%
tiger
trout
%
cutthroat
trout
Year
Number
of tiger
trout
stocked
30%
52%
70%
2005
103,716
103,716
103,716
103,716
103,716
100%
0%
2006
46,800
77,915
100,525
150,516
100,525
100%
0%
2007
129,941
153,315
182,013
258,677
182,013
100%
0%
2008
139,375
185,370
233,658
366,443
233,658
100%
0%
2009
122,500
178,111
243,535
434,621
86,052
43,026
25,816
74,521
318,056
77%
23%
2010
108,560
161,993
234,711
466,228
90,132
111,645
97,877
154,667
389,378
60%
40%
2011
119,635
164,992
237,974 488,110
80,143
135,966
109,506
214,085
452,059
53%
47%
2012
116,681
164,716
238,489
81,152
149,135
114,004
266,549
505,039
47%
53%
2013
238,130
504,931
30%
50%
288,833
USU Budy, Project Completion Report, Scofield Reservoir
87%
214,128
419,079
83
Future
We have made some attempts to build a more elaborate population viability model for Utah chub;
however, the data are too limited and getting quite “circular”. We are estimating survival from
abundance estimates for one year, and then would be applying those same survival estimates to make
predictions into the future. This model could be developed with the caveat that the only utility would
be to further explore different configurations of species and sizes of spawners to stock in a given year.
Even then, it would be somewhat speculative. Annual hydroacoustic estimates of chub abundance are
critical for effective monitoring and management of this uncertain system.
References
Glenn, C.L., A.O. Bush, and R.C. Rounds. 1989. Survival and growth of rainbow trout and brown trout in
farm dugouts and winterkill lakes. The Progressive Fish-Culturist 51: 121-126.
Hegge, O., B.K. Dervo, and J. Skurdal. 1991. Age and size at sexual maturity of heavily exploited arctic
char and brown trout in Lake Atnsju, Southeastern Norway. Transactions of the American Fisheries
Society 120: 141-149.
Johnson, J.B., and M.C. Belk. 1999. Effects of predation on life-history evolution in Utah chub (Gila
atraria). Copeia 4: 948-957.
Morris, W.F., and D.F. Doak. 2002. Quantitative conservation biology: theory and practice of population
viability analysis. Sinauer Associates, Inc. Sunderland, Massachusetts.
Quinn, J.W., and T.J Kwak. 2011. Movement and survival of brown trout and rainbow trout in an Ozark
tailwater river. North American Journal of Fisheries Management 31: 299-304
Rozencrans, G, R.P. Marshall, R.D. Harding, and D.R. Bernard. 1999. Estimating natural mortality and
abundance of potamodromous lake dwelling cutthroat trout at Florence Lake, Alaska, Alaska
Department of Fish and Game, Fishery Manuscript Number 99-1, Anchorage, Alaska.
USU Budy, Project Completion Report, Scofield Reservoir
84
25
Epilimnion
maximum
average
minimum
20
15
10
0
25
Thermocline
o
Temperature ( C)
5
20
15
10
Rainbow CTO
Tiger CTO
Cutthroat CTO
5
0
25
Hypolimnion
20
15
10
5
0
Apr
Jun
Aug
Oct
Dec
Feb
Apr
Jun
Date (2012 - 2013)
Figure 3.1. Temperatures (maximum, mean, and minimum) measured at three depths (3, 6, and 9 m
from surface) in the “no wake” zone in the dam arm of Scofield Reservoir, April 2012 to June 2013.
Cutthroat trout CTO (optimum temperature for consumption) is 14 °C, tiger trout CTO (assumed similar
to brown trout) is 17.5 °C, and rainbow trout CTO is 20 °C.
USU Budy. Project Completion Report. Scofield Reservoir
85
16
Utah chub CPUE
14
f = e0.5331x
R2=0.85, p < 0.0001
12
10
8
6
4
2
0
2005
2006
2007
2008
2009
Year
Figure 3.2. Exponential rise of Utah chub catch-per-unit-effort (CPUE) in annual spring-time gill-net sets
by Utah Division of Wildlife Resources in Scofield Reservoir, Utah, 2005 to 2009. Equation for
exponential growth fit is given along with R2 value.
USU Budy. Project Completion Report. Scofield Reservoir
86
Utah chub abundance (X 1000)
600
Projections by age class
age-2
age-3
age-4
age-5
age-6
age-7
500
400
300
200
100
0
2014
2015
2016
2017
2018
2019
2020
2021
Date
Figure 3.3. Future projections of abundance for seven Utah chub age cohorts starting from 2013 based
on abundance by chub cohort from June 2013 hydroacoustic estimates.
USU Budy. Project Completion Report. Scofield Reservoir
87
Average total length (mm) by mark type
500
480
Cutthroat trout
Rainbow trout
460
440
2009 ad clip
2010 red
2011 green
2012 orange
420
400
380
360
340
320
300
280
260
240
220
200
180
Ap
1
1
0
0
3
3
2
2
3
3
0
2
2
9
0
1
1
9
0
0
9
9
00 00 01 01 01 01 01 01 01 01 01
00 00 01 01 01 01 01 01 01 01 01
r 2 ep 2 eb 2 Jul 2 ec 2 ay 2 ct 2 ar 2 ug 2 an 2 un 2
r 2 ep 2 eb 2 Jul 2 ec 2 ay 2 ct 2 ar 2 ug 2 an 2 un 2
p
O
O
A
J
J
J
J
M
M
F
F
D
D
A
A
S
S
M
M
Date
Figure 3.4. Growth of cutthroat trout (left panel) and rainbow trout (right panel) that were batch
marked in the hatchery (2009 – 2012) with adipose clips or fluorescent dyes and subsequently
recaptured in Scofield Reservoir, 2011 – 2012. Numbers of recaptures were very low for rainbow trout
(0 – 27 recaptures per year; see Chapter 1, Table 1.9) and higher for cutthroat trout (41 – 197 recaptures
per year; see Chapter 1, Table 1.10).
USU Budy. Project Completion Report. Scofield Reservoir
88
APPENDIX 1
Hydroacoustic assessment of fish density, abundance,
and biomass in Scofield Reservoir, Utah
Objective
On 31 August 2011 and 6 June 2013, we conducted hydroacoustic surveys on Scofield Reservoir, Utah.
Coupled with gill netting near the time of acoustic surveys to verify acoustic targets, we determined
density, abundance, and biomass for Utah chub Gila atraria and three species of trout.
Study lakes
At full pool, Scofield Reservoir has a volume of 73,600 acre-feet or 90,784,263 m3 covering an area of
2,815 acres or 1,139 ha; however, water levels are controlled by a dam and vary widely (Figure A1.1).
Scofield Reservoir is managed as a “put-grow-and-take” fishery with rainbow trout Oncorhynchus mykiss
stocked annually. Historically, around 600,000 rainbow trout were stocked every year. However, the
fish stocking program has been adjusted nearly every year since 2005 in response to the re-appearance
of Utah chub in gill nets (Hart and Birdsey 2011). Tiger trout (Salmo trutta, female × Salvelinus
fontinalis, male) and Bear Lake-strain Bonneville cutthroat trout O. clarkii utah have been stocked in the
fishery as a potential biological control for the Utah chub as well as alternative sport fishes. Non-game
species in the current assemblage include the Utah chub, redside shiner Richardsonius balteatus and
mountain sucker Catostomus platyrhynchus.
Methods
We conducted hydroacoustic surveys to estimate fish density and abundance information for the
dominant fish species > 100 mm TL. We conducted surveys during the new moon event when fish are
most likely dispersed and to reduce the likelihood of fish associating with the lake bottom, where they
could not be detected by the acoustic transducer.
We conducted night-time, cross-reservoir transects on Scofield Reservoir covering a representative area
of the reservoir (Figure A1.2). In August 2011, twelve acoustic transect distances ranged from 436 –
2,279 m with mean depths ranging from 4.0 – 10.5 m). In June 2013, due to lower water levels, eleven
acoustic transect distances ranged from 417 – 2,250 m with mean depths ranging from 4.4 – 7.6 m.
USU Budy. Project Completion Report. Scofield Reservoir
89
We collected data using a Biosonics Model DE6000 scientific echosounder with 420 kHz dual-beam
transducer (6 X 15o) and towed the transducer on a fin at 1-m depth while recording data using
Biosonics Visual Acquisition processing software. We sampled at a rate of two pings per second
traveling at a boat speed of 1 – 2 m/s (4 – 5 kph). Pulse width of the signal was 0.4 ms. We processed
acoustic target and density data using Biosonics Visual Analyzer software.
We used single fish targets with dual-beam target strengths ranging from -48 to -32 decibels (dB),
representing fish 100 mm and larger (Dahm et al. 1985). We chose this minimum size due to the high
productivity of the reservoir. We selected only echoes that met the single-target shape criteria used by
the analysis software to calculate target strengths and densities. Transects were treated as replicates in
the analysis to produce mean fish per cubic meter with 1 standard error. We extrapolated density of fish
(fish/m3) into lake-wide abundance using lake volume (Figure A1.1).
To verify targets, we used gill-net catch information collected during the summer combined with gill
netting during the evening after acoustic surveys. Trawling was not possible in this shallow reservoir.
We summarized gill-net catch by species and size classes (100 – 150 mm, 151 – 250 mm, 251 – 350 mm,
and fish larger than 350 mm) and determined percentage of species by size class to delineate our
acoustic-derived abundance estimates by species by size class.
Results and Discussion
Since 2008 in Scofield Reservoir, Utah chub have represented over 95% of the gill net catch (Figure
A1.3). In 2011 and 2013, the gill-net catch was at least 96% Utah chub. From our gill netting surveys in
July 2011 and near the night of sampling, we apportioned acoustic targets according to average gill-net
catch as 66% Utah chub, 18% tiger trout, 14% cutthroat trout, and 3% rainbow trout. From UDWR gillnet survey data in May 2013, we apportioned acoustic targets according to average gill-net catch as 69%
chub, 11% tiger trout, 20% cutthroat trout, and 2% rainbow trout. Although we did not catch redside
shiner and mountain sucker in our gill-netting efforts, those species, particularly redside shiners have
been captured in UDWR gill netting from 1997 – 2013, although catch rates in 2010 reached an all-time
low (Hart and Birdsey 2011). Our surveys and therefore density estimates did not include (1) near shore
zones, (2) depths less than 4 m, and (3) the southern portion of the lake, due to high densities of
macrophytes.
Across the 12 transects in August 2011, fish densities (all species combined) ranged from 0.006 to 0.065
fish/m3 (mean ± 1 standard error = 0.04 ± 0.02 fish/m3) or an overall abundance of 3,158,000 fish (± 1
SE = 1,553,000). Percentage of acoustic targets by length class was 33% in the 100 – 150 mm class, 41%
USU Budy. Project Completion Report. Scofield Reservoir
90
in the 151 – 250 mm class, 14% in the 251 – 350 mm class and 12% in the > 350 mm class. Across the 11
transects in June 2013, fish densities (all species combined) ranged from 0.04 to 0.37 fish/m3 (mean ± 1
standard error = 0.14 ± 0.04 fish/m3) or an overall abundance of 6,030,000 fish (± 1 SE = 1,834,000).
Percentage of acoustic targets by length class was 39% in the 100 – 150 mm class, 38% in the 151 – 250
mm class, 15% in the 251 – 350 mm class and 8% in the > 350 mm class.
In August 2011, considering only targets greater than -48 dB or fish greater than 100 mm, Utah chub
dominated (84% of acoustic targets overall) the fish community at densities of 2,327 fish/ha (Table
A1.1). The three salmonid sport fish (> 251 mm) combined to reach densities of 445 fish/ha. In June
2013, considering only targets greater than -48 dB or fish greater than 100 mm, Utah chub dominated
(88% of acoustic targets overall) the fish community at densities of 4,664 fish/ha (Table A1.2). The three
salmonid sport fish (> 251 mm) combined to reach densities of 630 fish/ha.
Although partitioning acoustic targets for species that may not be segregated in space and time (i.e., are
mixed homogenously in the lake) may provoke concern, we partitioned acoustic targets simply in terms
of proportion of gill-net catch. This simplified approach would affect fish particularly in the 251 – 350
mm size class. In August 2011, we apportioned these targets as 71% chub, 11% tiger trout, 9% cutthroat
trout, and 9% rainbow trout, representing 442,000 fish. In June 2013, we apportioned these targets as
74% chub, 17% tiger trout, 9% cutthroat trout, and 0% rainbow trout, representing 904,000 fish. Again,
we did not designate any acoustic targets as shiners or suckers due to the primarily “pelagic” nature of
our acoustic surveys; however, it is likely our estimates include a small proportion of both fishes.
Minimally, due to our confidence that acoustic targets between -48 to 40 db (100 – 250 mm) are very
unlikely to be stocked trout, we designated 100% of acoustic targets in that range as Utah chub,
representing a lake-wide abundance of at least 2.3 million Utah chub on 31 August 2011 and 4.6 million
chub on 6 June 2013 (Figure A1.4). Further, acoustic targets greater than -36 db (350 mm) were
expected to be 100% trout as these sizes are unlikely to be Utah chub; therefore, there were at least
379,000 large trout in Scofield Reservoir on 31 August 2011 and at least 482,000 large trout on 6 June
2013 (Figure A1.4).
USU Budy. Project Completion Report. Scofield Reservoir
91
Table A1.1. Acoustic-based abundance estimates for four fish species by size class in Scofield Reservoir,
Utah, August 2011. Density estimates (fish/ha) are also shown for each species, all sizes combined.
Size class (mm)
Species
100 - 150
151 – 250
251 – 400
> 400
TOTAL
Fish/ha
Utah chub
1,042,215
1,294,874
313,928
0
2,651,017
2,327
Tiger trout
0
0
48,637
189,494
238,131
209
Cutthroat trout
0
0
39,794
174,334
214,128
188
Rainbow trout
0
0
39,794
15,159
54,953
48
1,042,215
1,294,874
442,153
378,987
3,158,229
2,772
TOTAL
Table A1.2. Acoustic-based abundance estimates for four fish species by size class in Scofield Reservoir,
Utah, 6 June 2013. Density estimates (fish/ha) are also shown for each species, all sizes combined.
Size class (mm)
Species
100 - 150
151 – 250
251 – 400
> 400
TOTAL
Fish/ha
Utah chub
2,351,669
2,291,370
669,321
0
5,312,360
4,664
Tiger trout
0
0
153,763
135,070
288,833
254
Cutthroat trout
0
0
81,404
337,676
419,079
368
Rainbow trout
0
0
0
9,648
9,648
8
2,291,370
904,488
482,394
6,029,920
5,294
TOTAL 2,351,669
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Table A1.3. Lake-wide biomass estimates based on expanded acoustic-abundance estimates for four
species of fish (> 100 mm TL) in Scofield Reservoir, August 2011 and June 2013. Total biomass is simply
based on an expansion of total fish of each species multiplied by the average weight of an individual fish
of that species captured in gill nets in the reservoir.
Mass (g) of an
average individual
August 2011 total
biomass (kg)
June 2013 total
biomass (kg)
Utah chub
141
373,793
749,043
Tiger trout
885
210,745
255,617
Cutthroat trout
609
130,404
255,219
Rainbow trout
420
23,080
4,052
738,022
1,263,931
Species
TOTAL
Summary of findings from other systems
1) In Flaming Gorge Reservoir, Utah, Utah chub dominated gill-net catch (86%) in water warmer (>
17oC); chub density in 0 – 13 m depths was 0.347 chub/1000 m3 or an abundance of 431,000 chub,
in the 13 – 30 m depth strata, chub density was 0.186 chub/1000 m3 or an estimated abundance of
89,000 chub, and the total abundance was estimated to be 520,000 chub (biomass = 83,000 kg) in
August 1990 (Yule 1992). Also, kokanee salmon (Oncorhynchus nerka) dominated gill-net catch
(76%) in 13- 30 m depth strata, and acoustic kokanee densities were 0.347 kokanee/1000 m3 or an
abundance of 27,400 kokanee in the 0 – 13 m strata and 0.386 kokanee/1000 m3 or an abundance
of 365,000 kokanee in the 13 – 30 m strata, with a total kokanee abundance estimate of 402,000
(209,000 kg) in August 1990 (Yule 1992). Further, Yule (1992) estimated that lake trout consumed
79,000 kg of Utah chub and 196,000 kg of kokanee.
2) Tuescher (2001) estimated abundance of Utah chub in two Idaho reservoirs. In Pallisades Reservoir,
Utah chubs represented 37% of gill-net catch; and using hydroacoustics, fish abundance was
estimated to be 274,000 fish (72.3 fish/ha including cutthroat trout, brown trout Salmo trutta, Utah
chub, and sucker Catastomus spp.) or 101,380 chub. In Ririe Reservoir, chubs were 55% of the gillnet catch, and acoustic fish abundance was estimated to be 256,000 fish (468.4 fish/ha including
Utah chub, kokanee salmon [36% of catch], and cutthroat trout, rainbow trout, sucker, and yellow
perch Perca flavescens) or 140,800 chub.
3) In Diamond Lake (surface area approximately 1210 ha), Oregon, based on trout to chub ratios in trap
net catches, researchers estimated tui chub (Gila bicolor) abundance to be between 7.6 million (174
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93
tonnes) and 23.4 million (540 tonnes) chub (Jackson et al. 2003), and researcher reported a
hydroacoustic estimate of greater than 10 million chub (cited in Jackson et al. 2003). This compares
with a post-rotenone treatment estimate of 250 tonnes in the lake (Eilers et al. 2011). Further, this
compares the estimate to the estimate of a total mass of 363 tonnes of chub removed in the 1954
rotenone treatment of Diamond Lake (cited in Eilers et al. 2011).
Table A1.4. Comparison of acoustic-derived chub densities in western water bodies.
Species
Mean
density
(chub/ha)
Source
1,139
Utah chub
2,327
This study
Scofield Reservoir, Utah (June 2013)
1,139
Utah chub
4,664
This study
Diamond Lake, Oregon
1,226
Tui chub
6,500
Eilers et al. 2011
Pyramid Lake, Nevada
45,325
Tui chub
958
Unpublished data
Ririe Reservoir, Idaho
547
Utah chub
192
Tuescher 2001
Strawberry Reservoir, Utah
6,946
Utah chub
100
Unpublished data
Flaming Gorge Reservoir, Utah
17,000
Utah chub
31
Yule 1992
Pallisades Reservoir, Idaho
3,790
Utah chub
27
Tuescher 2001
Surface
area (ha)
Scofield Reservoir, Utah (August 2011)
Lake
References
BOR (Bureau of Reclamation). 2013. Upper Colorado Region Reservoir Operations, Scofield Reservoir.
Website: http://www.usbr.gov/uc/crsp/GetDateInfo?d0=1729&d1=1800&d2=1880&d3=1936&id
Count=4&l=SCOFIELD+RESERVOIR. Visited 14 August 2013.
Dahm, E., J. Hartmann, T. Lindem, and H. Loffler. 1985. ELFAC experiments on pelagic fish stock
assessment by acoustic methods in Lake Constance. European Inland Fisheries Advisory Commission
Occasional Paper number 15. FAO, Rome, Italy. 14 pages.
USU Budy. Project Completion Report. Scofield Reservoir
94
Eilers, J.M., H.A. Truemper, L.S. Jackson, B.J. Eilers, and D.W. Loomis. 2011. Eradication of an invasive
cyprinid (Gila bicolor) to achieve water quality goals in Diamond Lake, Oregon (USA). Lake and
Reservoir Management 27: 194-204.
Hart, J.M., and P. Birdsey. 2011. Spring and fall gill-net survey of fish populations at Scofield Reservoir in
2010 compared with previous years. U. S. Fish and Wildlife Service, Sport Fish Restoration
Program, Project F-44-R. 24 pages. Salt Lake City, Utah.
Jackson, L.S., D.H. Jackson, and D.W. Loomis. 2003. Analysis of tui chub data collected at Diamond Lake
by the Oregon Department of Fish and Wildlife, 1992 – 2003. Oregon Department of Fish and
Wildlife. 30 pages.
Tuescher, D. 2001. Job Performance Report. Project 5 – Lake and Reservoir Research. Grant F-73-R-23.
IDFG Report Number 01 – 40. Idaho Department of Fish and Game, Boise Idaho. 105 pages.
Yule, D. 1992. Investigations of forage fish and lake trout Salvelinus namaycush interactions in Flaming
Gorge Reservoir, Wyoming – Utah. MS thesis, Utah State University, Logan, Utah. 120 pages.
USU Budy. Project Completion Report. Scofield Reservoir
95
90
3
Volume (X million m )
80
70
60
50
40
30
20
10
0
Jul
201
0
2
Nov
010
2
Mar
011
Jul
201
1
2
Nov
011
2
Mar
012
Jul
201
2
2
Nov
012
2
Mar
013
Jul
201
3
Date
Figure A1.1. Volume of Scofield Reservoir from July 2010 to July 2013 according to Bureau of
Reclamation gauge (BOR 2013). Circles on line indicate dates when we conducted hydroacoustic
surveys.
USU Budy. Project Completion Report. Scofield Reservoir
96
Dam
Figure A1.2. Map of Scofield Reservoir, Utah, displaying the 11 to 12 hydroacoustic transects (thick, dashed lines) surveyed during August 2011
and June 2013. The southern part of the reservoir was too shallow to survey, and was full of macrophytes. The bay of the dam site is partially
cut off. The Price River flows east below the dam.
USU Budy. 2011 Interim Progress Report, Scofield Reservoir
97
1.0
Rainbow trout
0.8
Proportion of total catch (based on CPUE)
0.6
spring
summer
autumn
0.4
0.2
0.0
1.0
Cutthroat trout
0.8
0.6
0.4
0.2
0.0
1.0
Tiger trout
0.8
0.6
0.4
0.2
0.0
1.0
0.8
Utah chub
0.6
0.4
0.2
0.0
2004
2005
2006
2007
2008
2009
2010
2011
2012
Year
Figure A1.3. A relative index of catch based on proportion of catch by year (2004 – 2012) and season
(for 2010 to 2012) for three species of trout and Utah chub in Scofield Reservoir, Utah.
USU Budy. 2011 Interim Progress Report, Scofield Reservoir
98
2500
June 2013
2000
1500
Thousands of fish
1000
500
0
2500
2000
August 2011
Utah chub
Tiger trout
Cutthroat trout
Rainbow trout
1500
1000
500
0
100 - 150
151 - 250
251 - 350
> 350
Size class (mm)
Figure A1.4. Abundance estimates by size class of Utah chub and three trout species derived from
hydroacoustic surveys in Scofield Reservoir on 6 June 2013 (spring, top panel) and 31 August 2011
(summer, bottom panel).
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99
APPENDIX 2
Agonistic behavior between rainbow trout, cutthroat trout, and tiger trout in a
novel Utah reservoir community
INTRODUCTION
Agonistic behavior in fishes is a means to obtain resources, defend resources, and establish dominance
over other individuals in a community, thus increasing the fitness of aggressive individuals or species
(Grant 1990; Stamps 2007; Vollestad et al. 2003). Many laboratory experiments have associated
increased growth rates and feeding opportunities with aggressive behavior in multiple salmonid species
(Abbott and Dill 1989; Hojesjo et al. 2002; Li and Brocksen 1977). Grant (1990) documented similar
results in the field and provided evidence that aggressive individuals are able to obtain better territories
and have more feeding opportunities than subordinate and non-aggressive individuals. Furthermore,
when species occur sympatrically, differences in exhibition of agonistic behavior may allow one species
to gain dominance and growth advantage over another (Gunckel et al. 2002), or limit access of less
aggressive species to the best feeding habitat (Grant 1990). An individual’s size is also important in
determining its level of aggression, with larger individuals generally being more aggressive (Newman
1956; Abbot et al. 1985; Berejikian 1995). In addition, the presence of food has been shown to increase
agonistic behavior between individuals (Newman 1956; and Symonds 1968), as well as the density of
prey (Slaney and Northcote 1974).
Differences in agonistic behavior between species have important implications for fishery management
(Newman 1956). While aggressiveness may benefit and individual’s performance in the hatchery,
agonistic behavior may be detrimental in natural environments. Species that select for aggressive
behavior and high growth rates in hatcheries often experience lower survival in nature because of
greater energy expenditure (Nilsson and Northcote 1981; Mesa 1991) and higher predation rates
(Berejikian 1995; Biro 2004). However, in communities comprising multiple salmonid species, one
species may gain growth or competitive advantage over another by establishing dominance through
increased aggression (Newman 1956; Gunckel et al 2002), supporting the role of agonistic interactions
between salmonid species in fitness and species survival (Li 1977; Grant 1990; Deverill et al. 1999).
In Utah, novel communities of predatory salmonids have been created by managers to simultaneously
hedge the potentially uncontrollable expansion of nongame fish in certain reservoirs and provide angling
opportunities. For example, exponential growth of the Utah chub (Gila atraria) population in Scofield
Reservoir prompted managers to shift the stocking program from exclusively rainbow trout
(Oncorhynchus mykiss) to include tiger trout (Salmo trutta, female X Salvelinus fontinalis, male) and Bear
Lake-strain Bonneville cutthroat trout (O. clarkii utah) after 2005, as potential biological controls.
Winters (2014) demonstrated substantial niche overlap between rainbow trout and Utah chub at lower
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tropic levels, and tiger trout and cutthroat trout at top trophic positions, indicating possible direct
competition for food and space and raising concern that the effect of niche overlap may be an overall
decrease in trout condition. However, little is known about inter-specific interactions among this
unusual complex of predatory species.
We determined the extent of agonistic behavior between tiger trout, cutthroat trout, and rainbow trout,
and explored the possible drivers of such behavior, as part of a larger study focusing on the feeding
ecology and the potential for biological control of this novel species assemblage (Winters 2014)
Understanding agonistic behavior between top predators in this assemblage will aid in decisions of
which fish to stock and in management of other novel communities, where direct competition from one
species could reduce the survival or fitness of another.
METHODS
We obtained tiger trout (n = 15), cutthroat trout (n = 17), and rainbow trout (n = 18) from Fountain
Green State Fish Hatchery. Upon arrival at our research center, we weighed (nearest 0.1 g) and
measured (TL, nearest mm) each fish, and marked fish with a plastic anchor tag. Tags were uniquely
colored for each species and uniquely numbered for each individual. Tiger trout averaged 246.7 mm (1
S.E. = 4.0 mm), and 135.7 g (S.E. = 5.5 g), cutthroat trout 248.2 mm (1 S.E. = 3.1 mm), and 145.3 g (1 S.E.
= 3.1 g), and rainbow trout 243.1 mm (S.E. = 6.8 mm), and 169.7 g (1 S.E. = 14.4 g). We separated fish
into three separate holding tanks by species. Once daily, automatic feeders dispensed commercial trout
feed to the fish. We fed fish approximately 2% of their body weight daily. Prior to behavior experiments
fish we did not feed for at least 24 hours.
To examine behavior between species we randomly selected one tiger trout, cutthroat trout, and
rainbow trout to interact in an observation tank 122-cm diameter filled to 35-cm depth with fresh well
water. We video recorded all behavior trials in high definition (1080 pixel) to minimize effects of human
presence on fish behavior (Wagner et al. 2006). We placed the video camera above the tank and began
recording after all fish were in the tank; each trial was recorded for one hour. The first 40 minutes of
each trial was allotted as an acclimation period, and behavior was not analyzed (Newman 1956; Wagner
et al. 2006). We designated a 40 minute acclimation period because we wanted to observe the subject
fish before one achieved dominance over the others; once dominance is established agonistic behavior
declines (Wagner et al. 2006).
During the remaining 20 minutes we recorded ‘chases’ and ‘bites/contacts’ initiated by each species and
the recipient species of the aggressive behavior. A ‘chase’ was defined as one fish advancing within one
body length of another and provoking a response, and a ‘bite/contact’ was defined as any contact
between two fish. Ten minutes (halfway) into the 20 minute observation period an automatic feeder
dispensed approximately 4 g of commercial trout feed into the observation tank. We separated
aggressive initiations by each species into pre, and post feeding categories. The same observer recorded
all behavioral interactions to eliminate observer bias.
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We conducted trials with all three species present in the observation tank, and pairwise trials with
combinations of two species in the observation tank (Table A2.1). To assess the effects of density and as
well as inter- and intraspecific interactions, we altered the number of fish present in the observation
tank between two (density = 4.9 fish/m3), three (7.3 fish/m3), and four (9.8 fish/m3). To reach an
adequate sample size we used fish in multiple trials; after a fish participated in a trial it was given at
least 24 hours of rest before participation in another trial.
Data Analysis
We tested for statistical differences among species-specific aggression and also evaluated the drivers
influencing agonistic behavior using the MASS package (Venables and Ripley 2002) for the negative
binomial distribution in program R (R Development Core Team 2008; Table A2.2). We analyzed
differences in encounter type (e.g., chase or bite/contact) and encounters before and after feeding with
a t-test. During model construction we considered species, length, weight, and density as factors
contributing to aggressive behavior. To capture in-trial length differences, we used the metric of
relative length, calculated as length of a given fish divided by the length of the longest fish in the trial
(Saunders and Fausch 2012). Therefore, the largest fish in each trial has a relative length of one, and the
other fish a proportionally smaller value.
We conducted trials with all three species present in the observation tank, and pairwise trials with
combinations of two species in the observation tank (Table A2.2). To assess the effects of density and as
well as inter and intraspecific competition we altered the number of fish present in the observation tank
between two, three, and four. To reach an adequate sample size we used fish in multiple trials. After a
fish participated in a trial it was given at least 24 hours of rest before participation in another trial.
Table A2.1. Sampling design for each combination of species and density tested during experimental
trials. Number of replications (n) for each combination is given. TRT = tiger trout, CTT = cutthroat trout,
and RWT = rainbow trout.
Number of
each species
Total fish
in tank
Relative
density
n
TRT, CTT, RWT
1
3
Medium
30
TRT, CTT
2
4
High
6
TRT, CTT
1
2
Low
6
TRT, RWT
2
4
High
6
TRT, RWT
1
2
Low
6
RWT, CTT
2
4
High
6
RWT, CTT
1
2
Low
6
Participating species
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We created models to determine the drivers influencing agonistic behavior using the MASS package for
the negative binomial distribution in R (Table A2.2). We analyzed differences in encounter type (chase
or bite/contact) and encounters before and after feeding with a t-test. We considered species, length,
weight, and density as factors contributing to aggressive behavior in the models. To capture in-trial
length differences we used the metric of relative length (Saunders and Fausch 2012). The relative length
for a given fish is its length divided by the length of the longest fish in the trial (Saunders and Fausch
2012). Therefore, the largest fish in each trial has a relative length of one, and the other fish a
proportionally smaller value.
Table A2.2. Top ranking models describing aggressive initiations. The number of parameters (K) in each
model is given.
K
Log
Likelihood
∆AICc
Model
weight
Species * Relative Length + Density
4
-814.5
0
0.46
Species * Relative Length
3
-819.4
0.9
0.30
(Species * Density) + (Species * Relative Length)
5
-808.1
1.54
0.22
Species * Density + Relative Length
4
-817.7
7.16
0.01
Species + Density + Relative Length
4
-825.9
7.38
0.01
Model
RESULTS
Our top model included species, relative length, and density to be the most important drivers of
interspecific agonistic behavior between tiger trout, cutthroat trout, and rainbow trout (Table A2.2).
Aggressive behavior differed significantly by species (Figure 2.1). Rainbow trout instigated significantly
more aggressive bouts than tiger trout or cutthroat trout (p < 0.01). On average rainbow trout initiated
10.2 times more aggressive interactions than tiger trout and 24.8 times more cutthroat trout per trial.
Initiations by tiger trout and cutthroat trout did not differ significantly. Rainbow trout did not attack
either cutthroat trout or tiger trout more frequently than the other (p = 0.65). Chases were the primary
expression of aggressive behavior (p < 0.000). Chases and bites/contacts per trial followed the same
general trend for each species, with rainbow trout being most aggressive in each instance (Figure A2.1).
No difference was observed between pre and post feeding aggression overall (t = 0.3688, p = 0.71), or in
any species combination (Figure A2.2). However, we observed that when trout feed was introduced into
the observation tank, only rainbow trout commenced feeding. Tiger trout and cutthroat trout appeared
to be uninfluenced by the introduction of food. The same observation was made outside of observed
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trials where rainbow trout fed voraciously while tiger trout and cutthroat trout seemed to take some
time to acclimate to the commercial feed.
Although no significant differences in aggressive initiations were observed across the three tested
densities (p = 0.31), density was determined to be a significant covariate in four of the five top models
(Table A2.2). Across all species mean initiations per trial at high, medium, and low density were 9.21,
7.84, and 4.24, respectively. Also, there was a trend (though not significant) indicating increased
agonistic behavior at higher fish densities. In general, for each species more aggressive initiations were
observed at higher densities than at lower densities (Figure A2.3).
In our top model overall, greater relative length was positively associated with agonistic behavior (p =
0.02). Overall the larger fish in a given trial initiated more aggression than smaller fish. Relationships
between aggressive initiations and relative length were not significant (p = 0.89) for any species.
However, tiger trout and cutthroat trout demonstrated a trend toward increased aggression at higher
relative lengths, while rainbow trout tended to be equally aggressive regardless of relative length.
Aggressive behavior differed significantly by species (Figure A2.1). Rainbow trout instigated significantly
more aggressive bouts than tiger trout or cutthroat trout (p < 0.01). Initiations by tiger trout and
cutthroat trout did not differ significantly from each other. Rainbow trout did not attack either
cutthroat trout or tiger trout more frequently than the other (p = 0.65). Chases were the primary
expression of aggressive behavior (p < 0.000). Chases and bites/contacts per trial followed the same
general trend for each species, with rainbow trout being most aggressive in each instance (Figure A2.1).
No difference was observed between pre and post feeding aggression overall (t = 0.3688, p = 0.71), or in
any species (Figure A2.2). However, we observed that when trout feed was introduced into the
observation tank, only rainbow trout commenced feeding. Tiger trout and cutthroat trout appeared
uninfluenced by the introduction of food. The same observation was made outside of observed trials
where rainbow trout fed voraciously while tiger trout and cutthroat trout seemed to take some time to
acclimate to the commercial feed.
Although no significant differences in aggressive initiations were observed across the three tested
densities (p = 0.31), density was determined to be a significant covariate in four of the five top models
(Table A2.2). Also, there is a trend (though not significant) indicating increased agonistic behavior at
higher fish densities. In general, for each species more aggressive initiations were observed at higher
densities than at lower densities (Figure A2.3).
Increased relative length positively affected agonistic behavior (p = 0.02). Overall the larger fish in a
given trial initiated more aggression than smaller fish. While relationships between aggressive initiations
and relative length any species were not significant (p = 0.89) tiger trout and cutthroat trout showed
increased aggression at higher relative lengths, while rainbow trout appeared equally aggressive
regardless of relative length.
USU Budy. Project Completion Report. Scofield Reservoir
104
Figure A2.1. Number of aggressive initiations by type for three trout species used in the experimental
trials. Error bars are one standard error from the mean. TRT = tiger trout, CTT = cutthroat trout, and
RWT = rainbow trout.
Figure A2.2. Number of aggressive initiations before (pre-) and after (post-) feeding for each species
during the experimental trials. Error bars indicate one standard error from the mean. TRT = tiger trout,
CTT = cutthroat trout, and RWT = rainbow trout.
USU Budy. Project Completion Report. Scofield Reservoir
105
Figure A2.3. Number of aggressive initiations at each tested density (high, medium, low) by three trout
species in experimental trials. Error bars indicate one standard error from the mean.
DISCUSSION
Our study clearly demonstrates that in an experimental setting rainbow trout are more aggressive than
cutthroat and tiger trout. Our results are further supported by others who documented a competitive
advantage of rainbow trout over cutthroat trout (Nilsson and Northcote 1981; Kreuger and May 1991).
However, the low aggression of tiger trout was unexpected. Newman (1956) observed high aggression
in brook trout; and brown trout are widely recognized as competitively superior to many species
(Townsend 1996), particularly cutthroat trout (McHugh and Budy 2005), but agonistic behavior by tiger
trout, which are claimed to be more aggressive than their parent species (McCale 1974; Sheerer et al.
1987), did not differ from cutthroat trout which displayed the least aggression. This pattern (or
observation) may be explained by differences in hatchery strategies of each species based on food
availability and growth rate. For example, higher aggression was observed in hatchery raised brown
trout than wild trout of the same species (Deverill et al. 1999). Yet, Vollestad et al. (2003) surmise that
while aggressive individuals have better competitive ability hatcheries often select for decreased
aggression, because food is not limiting and cannot be monopolized. Perhaps, in this instance, rainbow
trout exhibited a higher intrinsic growth rate than cutthroat trout or tiger trout, such that they have a
higher metabolic requirement, thus stimulating more aggressive behavior, relative to the other species.
We also identified relative length and density as important factors contributing to agonistic behavior.
We considered both total length and relative length in our analysis; however, only relative length was a
USU Budy. Project Completion Report. Scofield Reservoir
106
significant covariate in our models (Table A2.2). This pattern suggests a fish becomes more aggressive
as it becomes larger than those around it, not just as it gets bigger, a premise supported by multiple
other studies(Symons 1968; Abbott et al. 1985; Vollestad et al. 2003). As more fish were added to the
observation tank agonistic behavior increased. Newman (1956) tested density by maintaining the same
number of individuals in an observation but decreasing the size of the observation tank, and recorded
similar results.
In contrast to other studies however, in our study, the introduction of food did not prompt changes in
agonistic behavior (Newman 1956; Symonds 1968). Although rainbow trout fed more vigorously than
cutthroat trout and tiger trout (both species rarely fed during observations) when food was introduced
into the observation tank, the degree of aggression expressed during and after feeding did not increase.
Similar behavior was noted outside of behavioral observations; rainbow trout fed voraciously when
commercial trout feed was added to their tank, but cutthroat trout and tiger trout did not begin feeding
immediately and frequently failed to completely consume their rations.
Although our results are clear and consistent, perhaps the largest limitation of our study is small sample
size. We effectively recorded differences in aggression between our three study species and identified
relative length and density as contributors to agonistic behavior, but were unable to statistically
separate species-specific differences and trends with relation to density and relative length. Hatchery
conditions may play more to the strengths of rainbow trout than cutthroat trout or tiger trout, which did
not readily recognize commercial trout feed as an exceptional food source. Introduction of a more
natural food source, readily recognized by cutthroat trout and tiger trout could have important
influences on their feeding behavior and interactions with other species. Also, it is likely aggression
rates observed in the laboratory are higher than those found in nature, as subordinate fish may not be
able to escape aggressive displays while enclosed in a tank (Chiszar and Drake 1975). Finally, a study
examining these three species in natural environments would yield different results. Trout used in our
experiments were slightly smaller overall than the average-sized trout found in reservoirs. Despite
limitations, we demonstrate the usefulness of controlled lab experiments to identify species
interactions, as species combinations can be tightly manipulated and behavior can be closely observed.
We also highlight some important management implications for novel reservoir fish communities.
Although we determined rainbow trout to be the most aggressive species in this study, their survival is
lowest relative to tiger trout and cutthroat trout when stocked sympatrically in a high elevation Utah
reservoir (Winters 2014). Thus, it is possible that excessive energy expenditure during aggressive
behavior could be inhibiting rainbow trout growth and survival in the reservoir (Nilsson and Northcote
1981; Deverill et al. 1999). The bold, aggressive behavior that allows high hatchery growth rates in
hatchery rainbow trout may render them more susceptible to predators when introduced into reservoir
communities (Berejikian 1995; Biro et al. 2004; Stamps 2007). Agonistic behavior is most important
immediately following the stocking of fish into a reservoir. Aggression may be important in obtaining a
defending a territory, but could greatly increase the risk of naïve fish to predation. With larger
predaceous fish already established in a reservoir agonistic behavior could easily be more of a hindrance
USU Budy. Project Completion Report. Scofield Reservoir
107
than an advantage (Berejikian 1995; Deverill et al 1999; Biro et al. 2004). These are important
considerations in designing and maintaining a stocking program.
LITERATURE CITED
Abbott, J.C., and L.M. Dill. 1989. The Relative Growth of Dominant and Subordinate Juvenile Steelhead Trout
(Salmo gairdneri) Fed Equal Rations. Behavior 108: 104-113.
Abbott, J.C., R.L. Dunbrack, and C.D. Orr. 1985. The Interaction of Size and Experience in Dominance Relationships
of Juvenile Steelhead Trout (Salmo gairdneri). Behavior 92: 241-253.
Berejikian, B.A. 1995. The effects of hatchery and wild ancestry and experience on the relative ability of steelhead
trout fry (Oncorhynchus mykiss) to avoid a benthic predator. Canadian Journal of Fisheries and Aquatic
Sciences 52: 2476-2482.
Biro, P.A., M.V. Abrams, J.R. Post, and E.A. Parkinson. 2004. Predators select against high growth rates and risktaking behavior in domestic trout populations. Proceedings of the Royal Society of London 271:2233-2237.
Budy, P., L. Winters, and G.P. Thiede. 2012. Scofield Reservoir predator-prey interactions: investigating the roles of
interspecific interactions and forage availability on the performance of three predatory fishes. 2011 Progress
Report to the Utah Division of Wildlife Resources. UTCFWRU 2012(3):1-42.
Deverill, J.I., C.E. Adams, and C.W. Bean. 1999. Prior residence, aggression and territory acquisition in hatcheryreared and wild brown trout. Journal of fish Biology 55: 868-875.
Chiszar, D., and R.W. Drake. 1975. Aggressive Behavior in Rainbow Trout (Salmo gairdneri Richardson) of Two Ages.
Behavioral Biology 13: 425-431.
Grant, J.W.A. 1990. Aggressiveness and the Foraging Behaviour of Young-of-the-Year brook charr (Salvelinus
fontinalis). Canadian Journal of Fisheries and Aquatic Sciences 47: 915-920.
Gunckel, S.L., A.R. Hemmingsen, and J.L. Li. 2002. Effect of bull trout and brook trout interactions on foraging
habitat, feeding behavior, and growth. Transactions of the American Fisheries Society 131: 1119-1130.
Hösejö, J., J.I Johnsson, and T. Bohlin. 2002. Can laboratory studies on dominance predict fitness of young brown
trout in the wild? Behavioral Ecology and Sociobiology 52: 102-108.
Krueger, C.C., and B. May. 1991. Ecological and Genetic Effects of Salmonid Introductions in North America.
Canadian Journal of Fisheries and Aquatic Sciences 48: 66-77.
Li, H. W., and R. W. Brocksen. 1977. Approaches to the analysis of energetic costs of interspecific competition for
space by rainbow trout. Journal of Fish Biology 11: 329-341.
McClane, A.J. 1974. McClane’s New Standard Fishing Encyclopedia. Holt, Rinehart and Winston Inc., New York.
McHugh, P., and P. Budy. 2005. An experimental evaluation of competitive and thermal effects on brown trout
(Salmo trutta) and Bonneville cutthroat trout (Oncorhynchus clarkii utah) performance along an altitudinal
gradient. Canadian Journal of Fisheries and Aquatic Sciences 62:2784-2795.
Mesa, G. 1991. Variation in feeding, aggression, and position choice between hatchery and wild cutthroat trout in
an artificial stream. Transactions of the American Fisheries Society 113: 737-743.
USU Budy. Project Completion Report. Scofield Reservoir
108
Newman, M.A. 1956. Social behavior and interspecific competition in two trout species. Physiological Zoology 29:
64-81.
Nilsson, N.-A., and T.G. Northcote. 1981. Rainbow trout (Salmo gairdneri) and cutthroat trout (S. clarki)
Interactions in Coastal British Columbia Lakes. Canadian Journal of Fisheries and Aquatic Sciences 38: 12281246.
R Development Core Team. 2008. R: A language and environment for statistical computing. R Foundation for
Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, URL http://www.R-project.org.
Saunders, W.C., and K.D. Fausch. 2012. Grazing management influences the subsidy of terrestrial prey to trout in
Rocky Mountain streams (USA). Freshwater Biology 57: 1512-1529.
Scheerer, P.D., G.H. Thorgaard, and J.E. Seeb. 1987. Performance and development stability of triploid tiger trout
(brown trout, female X brook trout, male). Transactions of the American Fisheries Society 116: 92-97.
Slaney, P.A., and T.G. Northcote. 1974. Effects of prey abundance on density and territorial behavior of young
rainbow trout (Salmo gairdneri) in laboratory stream channels. Journal Fisheries Research Board of Canada 31:
1201-1209.
Stamps, J.A. 2007. Growth-mortality tradeoffs and ‘personality traits’ in animals. Ecology Letters 10: 355-363.
Symons, P.E.K. 1968. Increase in aggression and in strength of the social hierarchy among juvenile Atlantic salmon
deprived of food. Journal Fisheries Research Board of Canada 25: 2387-2401.
Townsend. C.R. 1996. Invasion biology and ecological impacts of brown trout Salmo trutta in New Zeland.
Biological Conservation 78: 13-22.
Venables, W.N., and B.D. Ripley. 2002. Modern Applied Statistics with S. Fourth Edition. Springer, New York. ISBN
0-387-95457-0.
Vøllestad, L.A. and T.P. Quinn. 2003. Trade-off between growth rate and aggression in juvenile Coho salmon,
Oncorhynchus kisutch. Animal Behaviour. 66: 561-568.
Wagner, E.J., R.E. Arndt, M.D. Routledge, D. Latremouille, and R.F. Mellenthin. 2006. Comparison of hatchery
performance, agonistic behavior, and post stocking survival between diploid and triploid rainbow trout of
three different Utah strains. North American Journal of Aquaculture 68: 63-73.
USU Budy. Project Completion Report. Scofield Reservoir
109
APPENDIX 3
Evaluation of trout performance under various fish assemblages
in experimental ponds
Introduction and Methods
To determine the performance of three trout species commonly stocked into Scofield Reservoir in a
controlled environment, on 10 May 2012, we stocked six ponds at the Millville Aquatic Research Facility
with various assemblages of trout, 50 total trout per pond. We obtained trout from the Fountain Green
State Fish Hatchery, the same source of trout that are stocked into Scofield Reservoir. Rainbow trout
stocked into ponds ranged from 179 – 256 mm TL, cutthroat trout ranged from 148 – 234 mm TL, and
tiger trout ranged from 185 – 255 mm TL (Table A3.1).
The ponds have a surface area of 500 m2, approximate volume of 1050 m3, and average depth of 2.2 m.
We filled ponds in early spring with 10 oC well water, a macroinvertebrate community was developed,
and we subsequently inoculated each pond with zooplankton. We deployed temperature loggers in all
ponds at approximately 1-m deep. Over the summer, when necessary to maintain water depth, we
added 10 oC well water to ponds to maintain water levels.
To mimic the reservoir food web, in late May 2012, we stocked 600 Utah chub (Gila atraria) into ponds,
100 into each pond. We obtained the chub from Kesko Ranch, Ephraim, Utah (Certificate of Registration
number 5001–57). New juvenile Utah chub were seen in the ponds during June.
Midway through the experiment, 11 July 2012, fish were seined, measured, weighed, and returned to
their respective ponds. At the end of the evaluation (22 – 24 July 2012), trout and chub were removed
from ponds, measured, weighed, and euthanized. Stomach contents and fin clips were taken from a
subsample of trout. All fish were euthanized and ponds were drained and dried.
Results and Discussion
At the start of the evaluation, average temperature across ponds was 16.1 oC and mean dissolved
oxygen (DO) level was 12.0 mg/L (Table A3.2; Figure A3.1). Water temperatures rose during the
summer, out of optimal range for all three trout species by mid-June, and reaching 23.1 oC at the end of
the evaluation (Table A3.2, Figure A3.1); however, average DO levels were very favorable for all trout,
11.2 mg/L (Table A3.2; Figure A3.1).
On 9 May 2012, zooplankton in ponds were scarce; across pond average zooplankton density was 0.4
daphnids/L, 0.2/calanoids/L, and 1.3 cyclopoids/L. By 30 May 2012, mean zooplankton abundance
increased substantially: 47 daphnids /L, 10/calanoids/L, and 24 cyclopoids/L. By mid-summer, 25 June
2012, zooplankton were abundant: 105 daphnids /L, 35/calanoids/L, and 10 cyclopoids/L. Bosmina spp,
ostracods, and Alona spp. were also present in low densities.
USU Budy. Project Completion Report. Scofield Reservoir
110
Table A3.1. Demographic information of stocked trout (rainbow, cutthroat, and tiger) at start and end of evaluation in six ponds at the USU
Millville Aquatic Research Facility, Millville, Utah. Twenty five of each species were stocked into multi-species ponds and 50 trout were stocked
into single-species ponds on 10 May 2012. End sampling occurred on 22 – 24 July 2012. Survival was simply estimated based on number of fish
stocked then subsequently the number recovered from ponds at the end of the evaluation.
Assemblage
Rainbow
start TL
(mm) and
mass (g)
Rainbow
end TL
(mm) and
mass (g)
Rainbow
survival
(%)
Cutthroat
start TL
(mm) and
mass (g)
Cutthroat
end TL
(mm) and
mass (g)
P3
Rainbow + Cutthroat
204 (85)
245 (147)
56
212 (83)
233 (91)
36
P4
Rainbow + Tiger
237 (133)
286 (249)
68
–
–
P7
Rainbow only
224 (114)
256 (166)
52
–
P5
Cutthroat only
–
–
–
P8
Cutthroat + Tiger
–
–
–
P6
Tiger only
–
–
–
Pond
Tiger end
TL (mm)
and mass
(g)
Tiger
survival
(%)
–
–
–
–
237 (136)
260 (152)
36
–
–
–
–
–
186 (58)
–
10
–
–
–
206 (77)
233 (102)
24
216 (103)
249 (136)
32
–
–
205 (107)
243 (128)
26
USU Budy. 2011 Interim Progress Report, Scofield Reservoir
Tiger
Cutthroat start TL
survival (mm) and
(%)
mass (g)
111
Rainbow trout performed well in these pond environments. Over the 46-day evaluation period, survival,
estimated based on number of fish stocked then subsequently recovered from ponds at the end of the
evaluation, was highest for rainbow trout (52 – 68%) and much lower for tiger trout (26 – 36%) and
cutthroat trout (10 – 36%; Table A3.1).
Table A3.2. Physical conditions across the summer in six experimental ponds used to evaluate trout
performance, USU Millville Aquatic Research Facility, Utah.
Temperature,
o
C (1 SE)
Dissolved
oxygen, mg/L
(1 SE)
Secchi depth,
m (1 SE)
9 May 2012
16.1 (0.2)
12.0 (0.2)
1.75 (0.1)
30 May 2012
17.8 (0.2)
12.6 (0.3)
1.95 (0.1)
25 June 2012
20.3 (0.1)
10.3 (0.2)
2.2 (0.1)
25 July 2012
23.1 (0.2)
11.2 (0.2)
2.2 (0.2)
Date
Overall, rainbow trout performed best of all trout species in these experimental ponds, while cutthroat
trout and tiger trout performed best in ponds when not in the presence of other species. In the
cutthroat trout only pond, cutthroat trout grew 56 mm (90 g), yet performed poorest in the pond with
rainbow trout growing only 17 mm (4 g; Figure A3.2). Cutthroat trout grew well in the cutthroat trout
only pond even though temperatures were never within the optimal range for cutthroat trout growth
(Figure A3.1). Similarly, tiger trout performed best in the tiger-trout-only pond, growing 42 mm (40 g),
and performed poorest when mixed with rainbow trout, growing only 23 mm (16 g; Figure A3.2).
Conversely, rainbow trout performed best in interspecies ponds growing 49 mm (115 g) when mixed
with tiger trout and growing 43 mm (68 g) when mixed with cutthroat trout (Figure A3.2). In contrast,
rainbow trout only grew 33 mm (56 g) when raised with conspecifics. In sum, growth for all trout
species was poorest when co-occurring with rainbow trout.
Rainbow trout were found to be the most aggressive of these three trout species in laboratory-tank
behavioral evaluations under various assemblages and densities (see Appendix 2). Unsurprisingly, due
to their aggressive nature, these rainbow trout performed better when grouped with other species in
ponds, and demonstrated poorer performance when with conspecifics.
Table A3.3. Lethal temperatures and optimum temperature ranges for growth and survival for rainbow
trout (Raleigh et al. 1984), cutthroat trout (Hickman and Raleigh 1982), brown trout (Raleigh et al. 1986),
and brook trout (Raleigh 1982). Consumption thermal optimum (CTO) for each species is also given
(Hartman and Hayward 2007).
Temperature level
Lethal
Overall optimal range
CTO
Rainbow
trout
Cutthroat
trout
Brown
trout
Brook
trout
25
26
27.2
24
12 – 18
12 – 15
12 – 19
11 – 16
20
14
17.5
At the end of the evaluations, we removed and measured a subsample of at least two distinct size
classes of Utah chub: a small size class 20 – 53 mm TL (0.1 – 1.8 grams) likely representing age-0 and
age-1 chub, and a large size class 117 – 151 mm TL (18.6 – 41.4 g), likely representing age-2 and older
chub. Pond P8 (cutthroat trout + tiger trout) and Pond P5 (cutthroat trout only) had the highest
numbers of chub, while Pond P7 (rainbow trout only) had the lowest numbers of chub based on equaleffort seining of all ponds.
We analyzed the diets of a subsample of trout from the six ponds. Rainbow trout primarily consumed
molluscans and terrestrial invertebrates, cutthroat trout primarily consumed dipterans and terrestrial
invertebrates, and tiger trout consumed primarily molluscans, odonates, and terrestrial invertebrates
(Table A3.4). In late September, months after the experimental trials were finished and just prior to
pond draining, several relict trout were taken from ponds, and we found chub in the diets of a few trout.
Isotopic analysis of tissue samples taken at the end of the evaluation (25 July 2012) will help determine
if piscivory occurred during the evaluation period.
USU Budy. Project Completion Report. Scofield Reservoir
113
Table A3.4. Diets contents (percent by wet weight) of rainbow trout, cutthroat trout, and tiger trout
across ponds in late July 2012, USU Millville Aquatic Research Facility, Utah. Rainbow trout diets from
ponds 3, 4, and 7. Cutthroat trout diets from ponds 3, 5, and 8. Tiger trout diets from ponds 6 and 4.
Sample size (n), number of empty stomachs, and size range of trout with full stomachs (full) is given.
Rainbow trout
Cutthroat trout
Tiger trout
Prey item
(n = 27)
(n = 13)
(n = 3)
Mollusca
49
0
33
Terrestrial invertebrates
20
34
31
Diptera
9
38
0
Hemiptera
15
9
1
Ephemeroptera
3
8
2
Odonata
3
0
33
Other aquatic invertebrates
1
11
0
Size range (mm) of full trout
230 – 305
205 – 254
255 – 276
Number of empty stomachs
1
1
0
References
Hartman, K. J., and R. S. Hayward. 2007. Bioenergetics. Pages 515–560 in C. S. Guy and M. L. Brown,
editors. Analysis and interpretation of freshwater fisheries data. American Fisheries Society,
Bethesda, Maryland.
Hickman, T., and R.F. Raleigh. 1982. Habitat suitability index models: cutthroat trout. USDA, US Fish and
Wildlife Service. FWS/OBS-82/10.5. 38 pages
Raleigh, R.F. 1982. Habitat suitability index models: brook trout. US Department of Interior, US Fish and
Wildlife Service. FWS/OBS-82/10.24. 42 pages.
Raleigh, R.F., T. Hickman, R.C. Solomon, and P.C. Nelson. 1984. Habitat suitability information: rainbow
trout. US Fish and Wildlife Service. FWS/OBS-82/10.60. 64 pages.
Raleigh, R.F., L.D. Zuckerman, and P.C. Nelson. 1986. Habitat suitability index models and instream flow
suitability curves: brown trout, revised. US Fish and Wildlife Service, Biol. Rep. 82(10.124). 65 pages.
[First printed as: FWS/OBS-82/10.71, September 1984].
USU Budy. Project Completion Report. Scofield Reservoir
114
26
o
Temperature ( C)
24
22
20
RBT + CUT
RBT + TIG
CUT only
TIG only
RBT only
CUT + TIG
18
16
14
Dissolved oxygen (mg/L)
17
16
15
14
13
12
11
10
9
8
ay
7M
21
y
Ma
un
4J
e
J
18
un
e
uly
2J
16
Ju
ly
30
Ju
ly
Date
Figure A3.1. Temperature (mean ± 2 SE) and dissolved oxygen (mean ± 2 SE) in six experimental trout
ponds at the USU Millville Aquatic Research Facility, Utah, summer 2012. RBT = rainbow trout, CUT =
cutthroat trout, and TIG = tiger trout. Consumption thermal optimum (CTO) is 14 °C for cutthroat trout,
17.5 °C for tiger trout (assumed similar to brown trout), and 20 °C for rainbow trout.
USU Budy. Project Completion Report. Scofield Reservoir
115
60
50
Cutthroat trout
90 g
40
30
25 g
20
4g
10
Growth (TL, mm)
0
60
Tiger trout
50
40
40 g
30
31 g
20
16 g
10
0
60
Rainbow trout
50
40
114 g
68 g
30
56 g
20
10
0
with Cutthroat
with Rainbow
with Tiger
Species assemblage in pond
Figure A3.2. Average growth (total length, TL, mm) of the three stocked trout species raised in
experimental ponds, 10 May 2012 (start) to 24 July 2012 (end). Average growth in mass (g) is given on
bars.
USU Budy. Project Completion Report. Scofield Reservoir
116
APPENDIX 4
Demographics and diet of Utah chub in Scofield Reservoir, Utah
In this appendix, we compiled literature and current information on Utah chub in Scofield
Reservoir and other systems.
Table A4.1. Length-at-age for Utah chub based on Olson (1959) and Graham (1961).
Scofield Reservoir (Olsen 1959)
Hebgen Lake (Graham 1961)
Mean standard
length (mm)
Mean total
length (mm)
Mean total
length (inches)
Mean total
length (mm)
1
80
97
2.8
71
2
114
138
4.5
114
3
150
183
6.2
157
4
184
223
7.6
193
5
204
247
9.5
241
6
215
262
10.75
273
7
292
355
12.15
309
8
–
–
13.6
345
Age
(yr)
USU Budy. Project Completion Report. Scofield Reservoir
117
Table A4.2. Fecundity of female Utah chub from Scofield Reservoir based on collections and
data from Olson (1959).
Age
(yrs)
3
2
3
3
3
3
4
4
5
4
5
5
4
5
5
5
6
6
6
6
7
7
Standard
length
(mm)
108
119
127
145
151
163
177
185
188
189
203
208
211
214
221
225
238
241
269
271
306
309
Total
length
(mm)
131
144
154
176
183
198
215
225
229
230
247
253
257
260
269
274
289
293
327
330
372
376
Eggs-perfemale
21,543
12,292
24,076
17,782
28,965
10,470
24,794
26,411
27,398
24,902
24,255
24,684
24,876
25,578
27,083
20,790
27,105
29,325
29,975
29,580
38,123
36,216
Graham, R.J. 1961. Biology of the Utah chub in Hebgen Lake, Montana. Transactions of the American
Fisheries Society 90: 269-276.
Olson, H.F. 1959. Biology of the Utah Chub, gila atraria, of Scofield Reservoir, Utah. MS Thesis. Utah
State University, Logan, Utah.
USU Budy. Project Completion Report. Scofield Reservoir
118
15
Summer 2011
12
n = 100
9
6
3
0
October 2011
12
n = 35
9
6
Utah chub catch (%)
3
0
Spring 2012
9
n = 115
6
3
0
Summer 2012
9
n = 45
6
3
0
15
October 2012
n = 51
12
9
6
3
0
Spring 2013
12
n = 62
9
6
3
0
0
50
10
0
15
0
20
0
25
0
30
0
35
0
40
0
Total length bins (mm)
Figure A4.1. Length-frequency (%) distributions of Utah chub captured in gill nets during
seasonal sampling events from July 2011 – May 2013 in Scofield Reservoir. Number (n)
captured in nets is given.
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12
in lake
10
8
6
4
Percentage of Utah chub
2
0
14
in cutthroat trout diets
12
10
8
6
4
2
0
14
in tiger trout diets
12
10
8
6
4
2
0
0
50
100
150
200
250
300
350
400
Total length bins (mm)
Figure A4.2. Length-frequency (%) distributions of Utah chub captured in gill nets (top panel)
and found in cutthroat trout and tiger trout diets, 2011 –2013 in Scofield Reservoir.
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Frequency of chub in ponds
60
50
40
30
20
10
0
0
20
40
60
80
10
0
12
0
14
0
16
0
18
0
20
0
Total length bins (mm)
Figure A4.3. Length-frequency (%) distribution of Utah chub taken from six experimental
ponds, USU Millville Aquatic Research Facility, Utah, July 2012. Source of Utah chub was Kesko
Ranch, Ephraim, Utah. Few < 60 mm, age-0 chub were captured in gill nets in Scofield
Reservoir, therefore these measurements can provide insight into size and growth of young-ofyear (age-0) chub in Utah.
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600
Utah chub weight (g)
-6
3.126
WT = 6.64 X 10 (TL)
R2 = 0.97, n = 2202, p < 0.0001
500
400
300
200
100
0
50
100
150
200
250
300
350
Utah chub total length (mm)
Figure A4.4. Length-to-weight (body mass) relationship for Utah chub in Scofield Reservoir,
Utah, 2011 - 2012. Equation is given with R2 value and sample size.
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Diet (%) by wet mass
100
2012
80
60
40
chironomid
diptera
mollusca
unid inv parts
crayfish
zooplankton
terr inv
organics
20
0
< 250 mm
> 250 mm
Size class of Utah chub
Figure A4.5. Diets (percentage by wet mass) across seasons of small (< 250 mm) and large (>
250 mm) Utah chub taken from Scofield Reservoir in 2012. Size range of small chub (n = 25,
36% empty) was 143 – 241 mm (mean = 187 mm, 1 SE = 6.4; 89.7 g, 1 SE = 9.9) and large chub
(n = 17, 35% empty) was 251 – 348 mm (mean = 292 mm, 1 SE = 7.6; 322.5 g, 1 SE = 19.3). Diet
items included chironomids, dipterans, molluscans, unidentified aquatic invertebrate parts,
crayfish (decapods), zooplankton, terrestrial invertebrates, and organic matter (e.g., plant
material and detritus). See Chapter 1 for information on diet overlap between chub and trout.
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