An ecological approach to management of an important reservoir fishery
DISSERTATION
Presented in Partial Fulfillment of the Requirements for the Degree Doctor of
Philosophy in the Graduate School of The Ohio State University
By
Jahn Lee Kallis
Graduate Program in Evolution, Ecology, and Organismal Biology
The Ohio State University
2013
Dissertation Committee:
Professor Elizabeth A. Marschall, Adviser
Professor Stuart A. Ludsin
Professor Roy A. Stein
Copyright by
Jahn Lee Kallis
2013
Abstract
The research described herein was an attempt to determine the
mechanisms underlying variation in success of saugeye (female Sander vitreus X
male S. canadensis) stocked into Ohio reservoirs. In addition, we sought to
identify the mechanisms that can be affected by management practices and
provide a model framework for experimental assessments of fish stocking
alternatives. We accomplished our goals using laboratory experiments and field
assessments conducted at the individual and population levels. In a
manipulative field study, we evaluated two fish management alternatives,
stocking saugeye fry (approximately 6 mm total length (TL)) and stocking
saugeye fingerlings (approximately 30 mm TL). We based our evaluation on a
comprehensive analysis that included biological responses (i.e. saugeye growth
and survival), economic criteria (i.e., saugeye production costs), and multiple
fishery objectives. We also correlated saugeye growth and survival with
environmental variables to help inform future stocking decisions. Although
predation and the timing and abundance of larval gizzard shad prey have been
implicated in the success of stocked saugeye cohorts, results from our field
manipulative study did not strictly follow predictions from previous research.
ii
Thus, we combined saugeye historical data with data from our research to test
earlier assumptions about saugeye predation mortality and the influence of
gizzard shad on stocked saugeye cohorts. In separate study, we sought to link
growth rate while fish were in the hatchery with survival in the reservoirs
hypothesizing that knowing which traits were associated with high survival would
give insights into the major sources of mortality. Finally, to determine if the first
winter of life is an important recruitment bottleneck, we used laboratory and field
studies to quantify first-winter effects, including indirect effects on growth and
survival of stocked saugeye cohorts.
The work described here addresses the limitations to recruitment and
management in fish populations in general, but does so in the context of a
reservoir-stocked piscivore whose survival is highly variable and poorly
understood. We addressed basic questions about fish growth and survival
during the first weeks of life, first growing season, and first winter. From an
applied perspective we experimentally evaluated management alternatives and
used our ecological findings to provide management recommendations.
iii
Acknowledgments
I thank my adviser, Libby Marschall, for her continued support, counsel,
and guidance through my doctoral program. Libby was instrumental in helping
me find direction especially during the early parts of my research. She
encouraged independence but went to great effort to make herself accessible.
I also thank my committee members, Roy Stein and Stuart Ludsin, who
provided valuable insights throughout this project and feedback on early drafts of
this dissertation.
The research described herein would not have been possible without the
many people who assisted me in the field and laboratory. I thank former
technicians Erich Williams, Brittany Gunther, and Michael Bahler and
undergraduate researcher, Beth Dickey, who spent countless hours in the field
and laboratory. I was fortunate to have such a team. I also thank the large
number of people who assisted with fish tagging, a number that is far too long to
list here.
The Aquatic Ecology Lab has been a wonderful place to grow as a
researcher. This dissertation and my own personal development was influenced
by conversations with fellow graduate students, including Ruth Briland, Emily
iv
Burbacher, Paris Collingsworth, Thomas Evans, Troy Farmer, Brian Kinter,
Cassandra May, Jeramy Pinkerton, Joseph Smith, Adam Thompson, and Jason
Van Tassell. I am grateful to have overlapped with such a wonderful group of
scientists let alone good people. Two Aquatic Ecology Lab post-docs, Joe
Conroy and Kevin Pangle, were also influential. I especially thank Joe Conroy,
who mentored me during the first year of this project. Barb Fate, Melissa
Marburger, and Margarita Talavera helped me navigate technical issues and
provided council on any number of subjects.
This research was made possible by funding from the Ohio Department of
Natural Resources, Division of Wildlife: Federal Aid in Sport Fish Restoration
Project F-69-P, Fish Management in Ohio. This research also benefited through
support from and interactions with personnel from the Ohio Division of Wildlife. I
especially thank Scott Hale, Jon Sieber-Denlinger, and Rich Zweifel for their
support and guidance. I also thank the large number of biologists who provided
assistance in the field.
Finally, I thank my family for their endless support and inspiration. Most of
all, I thank my wife, Erica, who was always a source of love and encouragement.
v
Vita
January 1978 .......................................... Born - Yakima, Washington
2000 ....................................................... B.S. Biology, Whitworth College
2005 ...................................................... M.S. Environmental Science, Western
Washington University
2007 to present ...................................... Graduate Research Associate,
Department of Evolution, Ecology, and
Organismal Biology, The Ohio State
University
Publications
Kallis, J.L., L. Bodensteiner, and A. Gabriel. Hydrological controls and
freshening in meromictic Soap Lake, Washington, 1939-2001. Journal of the
American Water Resources Association. 46:744-756
Fields of study
Major Field: Evolution, Ecology, and Organismal Biology
vi
Table of Contents
Abstract .................................................................................................................ii
Acknowledgments ................................................................................................iv
Vita .......................................................................................................................vi
List of Tables ...................................................................................................... viii
List of Figures .......................................................................................................xi
1. Introduction ............................................................................................. 1
2. How body size and food availability influences first-winter growth
and survival of a stocked piscivore ......................................................... 7
Introduction ..................................................................................... 7
Methods ........................................................................................ 10
Results .......................................................................................... 16
Discussion ..................................................................................... 19
3. How size at stocking, reservoir conditions, and fishery objectives
influence success of a reservoir-stocked piscivore ............................... 32
Introduction ................................................................................... 32
Methods ........................................................................................ 35
Results .......................................................................................... 42
Discussion ..................................................................................... 47
4. Explaining variable survival of a reservoir-stocked piscivore using
retrospective analyses .......................................................................... 69
Introduction ................................................................................... 69
Methods ........................................................................................ 72
Results .......................................................................................... 81
Discussion ..................................................................................... 84
5. Does growth during the first weeks of life explain survival of a
reservoir-stocked piscivore? ................................................................. 99
Introduction ................................................................................... 99
Methods ...................................................................................... 101
Results ........................................................................................ 103
Discussion ................................................................................... 103
Literature Cited ................................................................................................. 112
vii
List of Tables
Table 1. Characteristics of Ohio reservoirs from which age-0 saugeye
were collected for energy density estimation during fall and
spring. The mean chlorophyll-a concentration was the grand
mean of outflow sites collected once per month (April - October)
during 2007 – 2011. .............................................................................. 26
Table 2. Characteristics of Ohio reservoirs stocked with fry (N = 4) and
fingerlings (N = 4) saugeye during spring 2008 – 2010 and
sampled during fall (22 September – 29 October) including
historical mean predator abundances (i.e., black bass and adult
saugeye, 1995 – 2010, N = 4 – 8 reservoir years per reservoir,
DOW unpublished data) and historical saugeye stocking success
(i.e., Age-0 saugeye abundance, 1995 – 2007, N = 7 – 23
reservoir years per reservoir, DOW unpublished data). Historical
age-0 saugeye data represents results of fingerling stocking. The
mean chlorophyll a (chl a) concentration was the grand mean of
outflow (i.e., near the dam) sites sampled once per month (April October) during 2007 – 2011 (DOW unpublished data). The
numbers of reservoir years used in analysis of saugeye fall
density and length are provided (fall density – fall length). Fall
length was not estimated for failed year classes in Clendening
and Delaware Lakes. ............................................................................ 58
Table 3. Stocking metrics from eight Ohio reservoirs stocked with fry and
fingerling saugeye during 2008 – 2010, including stocking rate,
stocking date, mean stocking total length, and time in reservoir
(i.e., difference in days between stocking date and 1 October).
Fry were stocked 3 – 5 d post-hatch or about 6 mm TL. SE
received two stockings separated by 5 d in 2009, thus the median
stocking date is reported. Data for DC and TA in 2010 were used
in comparison of stocking decisions under different management
objectives, but not in analysis of underlying ecological
mechanisms (see methods). For reservoir ID’s, see Table 2. .............. 59
viii
Table 4. Statistics of top models (AICc < 2) that explain variation in fall
density (log10 CPUE) and length (mm) of fry- and fingerlingstocked saugeye (N = 8 Ohio reservoirs sampled via
electrofishing during 2008 – 2010). Results of null models
(intercept-only models) were included for comparison. Models
were fixed-effect-only multiple regression models. Variables
included in the top models were mean zooplankton density (ZP),
mean temperature (Temp), adult panfish abundance (Pan),
relative time of stocking (RTS), adult saugeye abundance (SAE),
age-0 saugeye abundance (age-0 SAE), and time in reservoir
(TIR). Parenthetical + or – indicates the sign of the parameter.
The table includes number of parameters (K), AICc, difference in
AICc between each model and the model with the minimum AICc
(ΔAICc), model weight (W), proportion of variance explained by
the model (R2), and probability that the model results are due to
random processes (P). .......................................................................... 61
Table 5. Organizational table associating objectives (Obj.), analyses,
datasets, and the fixed-effects variables and the number of
reservoir years (N) included in each analysis. Continuous fixedeffects variables included peak gizzard shad density (GS), mean
saugeye stocking length (SL), and black bass (Bass), panfish
(Pan), crappie (Crappie), and saugeye (SAE) adult abundances,
panfish mean length (PanL), and age-1 saugeye abundance
(Age-1 SAE). Variables with subscripts represent abundances
(i.e., LMB396, SAE357, Pan150, Crappie180) of fish above the length
threshold indicated by the subscript. Fixed effects variables that
were modelled as factors/categorical variables included
abundance of panfish (fPan; high or low abundance assigned
using the threshold value from 2DKS test) relative time of stocking
(fRTS; before or after peak gizzard shad density), and reservoirs
(fRes). Random effects variables included reservoir and year,
except for analysis of strong saugeye year classes (reservoir
modelled as fixed effects variable). ....................................................... 92
Table 6. Statistics of the top candidate models, and for comparison, the
null models (i.e., random effects models) explaining variation in
age-0 saugeye oversummer instantaneous mortality in five Ohio
reservoirs, 2006 – 2010 (N = 25 reservoir years). Results are
from two separate analyses: A) analysis of the balanced dataset
with densities of adults and B) analysis of the balanced dataset
with densities of large adults only. Data include number of
parameters (K), AICc, difference between each model and the
ix
model with the minimum AICc (ΔAICc), and model weight (W).
For variable ID’s, see Table 5. .............................................................. 93
Table 7. Summary information from two Ohio reservoirs stocked with
saugeye during 2009 and 2010 including mean total length (TL)
and wet mass at stocking and fish/otolith collection dates and
sample sizes (i.e., unique fish). All collections were conducted
during the same year fish were stocked, except for the spring
collection which was conducted during the subsequent year. *Due
to a processing error mean size was not recorded. Mean sizes
here are from Ohio Division of Wildlife stocking records. .................... 108
x
List of Figures
Figure 1. Weekly mean temperature regime during fall through spring in
Hoover Reservoir (black solid line; 1 October 2008 – 15 April
2009), Deer Creek Lake (dashed line; 1 October 2009 – 15 April
2010) and outdoor pools (gray solid line; 9 November 2010 – 10
June 2011; N = 24 pools). Solid circles associated with lines
represent approximate timing of fall tagging and spring recapture
efforts in reservoirs (Hoover Reservoir and Deer Creek Lake) and
fall implementation and spring termination of overwinter pool
experiment. ........................................................................................... 27
Figure 2. Normalized length frequency distributions representing saugeye
captured via electrofishing from Hoover Reservoir, Deer Creek
Lake, and Deer Creek Lake tailwater (TW) during fall (Hoover
Reservoir: 6 – 23 October 2008; Deer Creek Lake: 5 – 21 October
2009) and spring (Hoover Reservoir: 11 – 21 May 2009; Deer
Creek Lake: 5 – 8 April 2010; Deer Creek Lake TW: 16 and 30
March 2010). No pit-tagged saugeye were captured in Hoover
Reservoir TW or Big Walnut Creek below Hoover Dam. Arrows
represent distribution means. Sample sizes are indicated in
parentheses. ......................................................................................... 28
Figure 3. Proportional change in saugeye wet weight as a function of fall
total length (mm) of saugeye PIT tagged in two Ohio reservoirs
during fall (Hoover Reservoir: 6 – 23 October 2008; Deer Creek
Lake: 5 – 21 October 2009) and subsequently recaptured during
spring (Deer Creek Lake: 5 – 8 April 2010; Deer Creek Lake
tailwater: 16 and 30 March 2010) and saugeye from the
overwinter pool experiment (8 November 2010 – 10 June 2011).
Sample sizes are indicated in parentheses. .......................................... 29
Figure 4. Energy density (kJ/g wet weight) as a function of TL of saugeye
captured via electrofishing during fall (open circles; 23 October
2008, Hoover Reservoir; 21 October 2009, Deer Creek Lake; 1
and 8 November 2010, Alum Creek Lake/overwinter pool
xi
experiment) and spring (solid circles; 11 – 18 May 2009, Hoover
Reservoir; 4 – 8 April 2010, Deer Creek Lake) from reservoirs and
fed (solid triangles) and unfed (open triangles) saugeye
originating from Alum Creek Lake and sampled from pools during
spring (10 June 2010). Sample sizes are indicated in
parentheses. ......................................................................................... 30
Figure 5. Mean consumption rates (proportion of Cmax; +/- 1SD) of fed and
unfed saugeye after the overwinter pool experiment when allowed
to forage on fathead minnows during spring foraging experiment
(15 June 2011). ..................................................................................... 31
Figure 6. Fall density of fry-stocked (N = 11 reservoir years) and
fingerling-stocked (N = 12 reservoir years) saugeye (DL stocked
during 2008 and 2009 only) and, for comparison, historical mean
fall density (solid circles; 1995 – 2007, N = 4 – 12 reservoir years
per reservoir except for SE, no historical data; DOW unpublished
results). Historical data represents results of fingerling stocking.
Bars for each reservoir are in chronological order, 2008 to 2010.
Dashed horizontal lines represent benchmarks denoting strong
(fall saugeye density > 60 CPUE) and failed (< 5 CPUE) year
classes. ND indicates no data available. Data for DC and TA in
2010 were used in comparison of stocking decisions under
different management objectives, but not in analysis of underlying
ecological mechanisms (see methods). Note y-axis break. For
reservoir ID’s, see Table 2. ................................................................... 62
Figure 7. Mean fall length of fry-stocked (N = 8 reservoir years) and
fingerling-stocked (N = 12 reservoir years) saugeye (DL stocked
during 2008 and 2009 only). Data for each reservoir are in
chronological order, 2008 to 2010. Asterisks represent missing
values due to small sample sizes/failed year classes; arrows
indicate strong year classes (fall saugeye density > 60 CPUE),
plus signs indicate cohorts stocked after peak gizzard shad
density, and ND indicates no data available. Data for DC 2010
and TA 2010 are for comparison only and were not used in our
statistical analyses of underlying ecological mechanisms. For
reservoir ID’s, see Table 2. ................................................................... 63
Figure 8. Akaike importance weights for variables used in the construction
of fall density models for fry-stocked (N = 11 reservoir years) and
fingerling-stocked (N = 10 reservoir years) saugeye in Ohio
reservoir during 2008 – 2010. Only the five most important
variables are presented including mean zooplankton density (ZP),
xii
mean temperature (Temp), mean secchi depth (Secchi), relative
time of stocking (RTS), stocking rate (STRT),and adult, panfish
(Pan), black bass (BASS), and saugeye (SAE) abundances. ............... 64
Figure 9. Fall density of fry-stocked (N = 11 reservoir years, solid circles)
and fingerling-stocked (N = 10 reservoir years, open circles)
saugeye as a function of (A) mean crustacean zooplankton
density, (B) mean temperature, (C) adult panfish abundance, (D)
relative time of stocking, and (E) adult saugeye abundance.
Arrow indicates potentially influential sample that was evaluated
during statistical analyses of the data. Negative values of relative
time of stocking represent the number of days saugeye were
stocked before peak gizzard shad density; positive values
represent the number of days saugeye were stocked after peak
gizzard shad density. Horizontal dashed lines represents
benchmark denoting strong year classes (fall saugeye density >
60 CPUE). ............................................................................................. 65
Figure 10. Mean fall length (1 October) of fry-stocked (N = 8 reservoir
years, solid circles) and fingerling-stocked (N = 10 reservoir
years, open circles) saugeye as a function of (A) relative time of
stocking, (B) age-0 saugeye fall density, (C) time in reservoir (i.e.,
1 October – stock date), (D) and panfish abundance. Negative
values of relative time of stocking represent the number of days
saugeye were stocked before peak gizzard shad density. .................... 67
Figure 11. Management recommendations based on two fishery
objectives, (A) maximize the ratio of fall density to production
costs and (B) maximize the ratio of proportion of strong year
classes to production costs. Points represent the production
costs (adjusted for inflation to 2010 US$ using the US Department
of labor Consumer Price Index) of the fry and fingerlings taken
from the literature (Santucci and Wahl 1993; Gunterson et al.
1996; Lucchesi 2002). The dividing lines separate the two
management recommendations (i.e., stocking fry versus stocking
fingerlings). The slopes of the dividing lines were calculated
based on observed saugeye stocking densities and fall densities
and the different fishery objectives. Combinations of fry and
fingerling production costs that are above the dividing line indicate
that stocking fry was more cost effective than stocking fingerlings,
whereas combinations that are below the dividing line indicate
that stocking fingerlings was more cost effective than stocking fry. ...... 68
xiii
Figure 12. Regressions showing the relationship between age-0 saugeye
oversummer instantaneous mortality and peak gizzard shad
density for saugeye stocked before (solid line and datapoints, N =
24) and after (dashed line and open datapoints, N = 12) peak
gizzard shad density in 11 Ohio reservoirs, 1991 – 2010 (N = 36).
Regression parameters were estimated using mixed models.
Results from the top model are presented. ........................................... 94
Figure 13. Akaike importance weights for variables used in candidate
models that predict age-0 saugeye oversummer instantaneous
mortality in five Ohio reservoirs, 2006 – 2010 (N = 25 reservoir
years). Results are from two separate analyses, analysis of the
balanced dataset with densities of adults and analysis of the
balanced dataset with densities of only large adults. For variable
ID’s, see Table 5. .................................................................................. 95
Figure 14. Age-0 saugeye oversummer instantaneous mortality as a
function of black bass and panfish densities (A and B, sampled
via spring electrofishing) and saugeye densities (C, sampled via
fall gillnetting) in five Ohio reservoirs stocked during 2006 – 2010
(solid circles, N = 25). Also included are all panfish records from
historical data (open circles, 13 additional Ohio reservoirs stocked
during 2003 – 2010, N = 28 additional reservoir years). ....................... 96
Figure 15. Age-0 saugeye oversummer instantaneous mortality as a
function of large panfish density (A, sampled via spring
electrofishing) and large crappie density (B, sampled via fall
trapnetting) in five Ohio reservoirs stocked during 2006 – 2010 (N
= 25). ..................................................................................................... 97
Figure 16. Oversummer age-0 saugeye instantaneous mortality at year
t+1 as a function of fall density of age-0 saugeye stocked at year t
in three Ohio reservoirs stocked during 1993 – 2010 (N = 43). ............. 98
Figure 17. Wet mass of individual saugeye on the day (22 May 2010) they
were stocked into Atwood Lake, Ohio as function of early growth
increment (i.e., distance from the hatch mark to the twentieth daily
ring). .................................................................................................... 109
Figure 18. Wet mass of individual saugeye as a function of early growth
increment (i.e., distance from the hatch mark to the twentieth daily
ring) sampled on 15 and 16 July 2009 from Deer Creek Lake (top)
and on 21 and 22 July from Atwood Lake, Ohio (bottom). .................. 110
xiv
Figure 19. Box plots of early growth rate distributions of age-0 saugeye
from the hatchery and survivors from Deer Creek Lake (top),
stocked during 2009 and Atwood Lake (bottom), stocked during
2010 and sampled via electrofishing during summer, fall, and
spring. The horizontal line denotes the median value; the box, the
inter-quartile range; the vertical dashed line, 1.5 times the interquartile range; points outside the vertical dashed lines indicate
outliers. Letters above each boxplot denote statistical differences
detected using Tukey’s HSD. .............................................................. 111
xv
CHAPTER 1
Introduction
Understanding the factors that govern population dynamics is crucial to
fisheries management. Most fish species exhibit dramatic fluctuations in
population size through time. The quest to understand population fluctuations
has inspired a great deal of fisheries research including early studies by Hjort
(1914), who identified variation in reproductive success (i.e., recruitment), as the
major cause of fluctuations. Although recruitment variability is central to
population fluctuations, the recruitment process itself has been shown to be far
more complex than originally believed. It is now universally accepted that
recruitment is influenced by a suite biotic and abiotic conditions experienced
during early life stages.
Although many of the mortality sources that drive recruitment variability in
fish populations have been identified, managing them remains a daunting task.
For many fishes, a substantial amount of variability in recruitment can be
explained by abiotic factors such as water temperature (e.g., Francis 1993),
which can have direct or indirect effects on fish mortality, or hydrodynamics,
which determines whether young fish are transported to favorable nursery
grounds (e.g., Iles and Sinclair 1982). Biotic factors, including the availability of
1
appropriate prey types (Rilling and Houde 1999), predation (Bailey and Houde
1989), and traits of individual fish, such as growth rate or hatch date, also
influence recruitment variability (Rice et al. 1987). Despite our strong
understanding of recruitment, the life history of fish is full of complexities,
including ontogenetic diet and habitat shifts in sometimes heterogeneous and
stochastic systems. This makes management of fish populations inherently
difficult.
The work described here addresses general ecological and managementrelated research questions in the context of a reservoir-stocked piscivore.
Specifically, we report results from laboratory and field studies of saugeye
(female Sander vitreum X male S. Canadensis) stocked into Ohio reservoirs.
Saugeye support economically and recreationally important fisheries in many
parts of the United Sates. In Ohio, saugeye are usually stocked as fingerlings
(approximately 30 mm total length (TL)). Stocked in large numbers, saugeye
also exhibit variable success among reservoirs as well as among years within
reservoirs (Hale et al. 2008). Saugeye are vulnerable to predation and starvation
during early life stages (Stahl et al. 1996), and rely on the availability of
appropriate prey sizes and types (Donovan et al. 1997; Qin et al. 1994).
Previous research suggests that predation mortality, which is strongly influenced
by the magnitude and timing of peak gizzard shad (Dorosoma cepedianum)
density relative to timing of stocking, strongly influences saugeye survival
(Donovan et al. 1997). Analogous to fisheries research as a whole, my research
2
was inspired by practical management questions, previous findings using
saugeye, and new findings revealed at different stages of the project.
Determining at what life stage recruitment is set is crucial for managing
fish populations. In some systems, mortality during the first winter of life can
strongly affect cohort strength (Post and Evans 1989; Cargnelli and Gross 1996).
In Chapter 2, we sought to determine if the first winter of life represented an
important recruitment bottleneck for saugeye stocked into Ohio reservoirs. In
reservoirs, we used individually tagged fish to distinguish between growth and
size-dependent mortality. To determine if accumulated energy reserves are
sufficient to allow saugeye to survive unusually low winter prey availability, we
held two groups (fed and unfed) of reservoir-captured saugeye during November
through mid-June in outdoor pools. Finally, to quantify indirect effects of winter,
we used survivors from pools in spring foraging trials. In reservoirs, we found no
evidence of size-dependent mortality; saugeye of all sizes increased in length. In
pools, overwinter mortality was zero and spring foraging trials revealed that even
starved fish can resume feeding once prey become available. Hence, first winter
survival and growth do not influence recruitment of saugeye in Ohio reservoirs.
Fish recruitment is a complex process characterized by interactions
among many biotic and abiotic factors, most of which are uncontrollable.
Managers of stocked fish populations, however, can guide many aspects of the
stocking process, including when, where, and at what size fish are stocked. Fish
management decisions will be most successful when they are based on
3
experimental assessments of alternatives. The assessments must be based on
explicit management objectives and must consider the costs and constraints
associated with implementing a given management decision. In Chapter 3, we
evaluated two fish management alternatives implemented in Ohio reservoirs,
stocking saugeye fry (6 mm TL) and stocking saugeye fingerlings. We
discovered that the optimal stocking size depended on fishery objectives and
production costs of the different stocking sizes. To maximize success of future
stockings, we sampled the environmental conditions that saugeye were stocked
into, and correlated those factors with fall density and length of the stocked
saugeye. Based on our results, fry-stocked saugeye will be most successful in
reservoirs that support high densities of crustacean zooplankton. Fall densities
of fingerling-stocked saugeye will be highest when fingerlings are stocked after
peak gizzard shad density. Fall density of both fry- and fingerling-stocked
saugeye was negatively associated with abundance of panfish, but we found no
evidence that numbers of adult saugeye or black bass (Micropterus spp.)
adversely affected saugeye survival. Our findings suggest that fishery managers
should use different stocking sizes to meet different fishery objectives and we
provide a framework for matching fry- and fingerling-stocked saugeye with
reservoir conditions to enhance the likelihood of stocking success.
Previous research using saugeye suggests that stocking success is
limited by predation mortality which is strongly influenced by the magnitude of
peak gizzard shad density and its timing relative to stocking. Results from
4
Chapter 3 revealed that survival of age-0 saugeye was unrelated to predator
abundance, but was negatively associated with numbers of panfish (Lepomis
spp.) Furthermore, contrary to previous findings, some of the cohorts stocked
before peak gizzard shad density, which were expected to suffer high mortality
rates, produced strong year classes. In Chapter 4, we sought to quantify
mortality of age-0 stocked saugeye using Ohio reservoir sportfish data, including
panfish collected under the Ohio Division of Wildlife standardized monitoring
program and gizzard shad abundance and timing data. Our findings indicate that
managers can minimize mortality of saugeye stocked before peak gizzard shad
density by stocking into prey-rich systems that support large numbers of larval
gizzard shad, whereas mortality of saugeye stocked after peak gizzard shad
density may be minimized by stocking into systems where growth rates of larval
gizzard shad are limited. Similar to results from Chapter 3, we found that
saugeye mortality was negatively correlated with abundance of panfish, however,
we were unable to determine why. Analyses of predator data revealed that
relationships between age-0 saugeye mortality and densities of predators are
highly variable, potentially because our observational dataset did not allow us to
account for interactions among predator abundance and density and timing of
gizzard shad.
Identifying traits of individual fish that are associated with high survival can
provide insights into the major sources of mortality. In Chapter 5, we tested
whether growth rates of individual saugeye while fish were in hatchery ponds
5
affect survival, and whether those effects last throughout the first year of life
(e.g., overwinter) or just during early life stages. We used otoliths to characterize
the early growth rate distribution of same-age saugeye coming out of the
hatchery and compared it to the growth rate distributions of survivors collected
from reservoirs. We detected growth-dependent survival within the first 3 months
after stocking, when saugeye are most vulnerable to gape-limited predators.
During this time, stocked saugeye underwent selective mortality with the
preferential loss of individuals that grew slowly in hatchery ponds. As a result,
year classes of stocked saugeye were probably dominated by individuals that
exhibited high growth in the hatchery. Although early growth rate was linked to
survival during the first 3 months after stocking, we found no evidence that early
growth rate affected survival later during the first year of life. Our results suggest
high growth rate in the hatchery provided saugeye with an initial size advantage
that minimized vulnerability to mortality sources such as predation.
The work described here addresses the limitations to recruitment and
management in fish populations in general, but does so in the context of a
reservoir-stocked piscivore whose survival is highly variable and poorly
understood. We addressed basic questions about fish growth and survival
during the first weeks of life (Chapter 5), first growing season (Chapter 3 and 4),
and first winter (Chapter 2). From an applied perspective, we experimentally
evaluated management alternatives (Chapter 3) and used our ecological findings
to provide management recommendations.
6
CHAPTER 2
How body size and food availability influences first-winter growth and survival of
a stocked piscivore
Introduction
For many temperate freshwater fishes, survival through the first winter of life
is considered the final mortality bottleneck before recruitment. Winter, a period of
low temperatures and limited resource availability, is associated with poor growth
(Conover 1992), energy depletion (Hook and Pothoven 2009), and predation risk
(Miranda and Hubbard 1994b; Garvey et al. 1998b). Energy depletion in fish
may lead to increased predation risk (Biro and Booth 2009) and reduced foraging
ability (Adams and DeAngelis 1987). Thus, despite available prey and warm
temperatures during early spring, survivors emerging from their first winter in
poor energetic condition (Pratt and Fox 2002; Hook and Pothoven 2009) may
continue to suffer survival and growth consequences via indirect effects such as
reduced foraging success or risky foraging behavior.
Large body size offers a host of benefits that potentially minimizes direct
negative overwinter effects (e.g., starvation, predation, thermal stress) and
potential indirect effects of winter that could manifest during early spring. For
example, body size is negatively related to predation risk via a positive relation
7
between body size and swimming ability (Fisher et al. 2000) as well as gape
limits of predators (Werner and Gilliam 1984). Compared to small fish, large fish
have lower starvation risk through higher energy stores (Schultz and Conover
1997; Sutton and Ney 2001; McCollum et al. 2003), lower mass-specific
metabolic rates (Sutton and Ney 2001), and greater breadth of prey sizes and
types available to them (Werner and Mittelbach 1981).
Even if the energetic consequences of the first winter are non-lethal, poor
energetic condition in spring may have consequences for growth and survival
(Jonas and Wahl 1998; Ostrand et al. 2005). During periods of resource
limitation, organisms often face a trade-off between growth and survival.
Foraging provides energy for growth and reproduction, but also may require
activity that increases vulnerability to predation; minimizing predation risk may
reduce growth potential. First-winter survivors may begin spring in poor condition
and, if relatively small, potentially vulnerable to predation. Starvation and
predation risk may be especially high at the end of a low-prey winter, when
spring-warming increases metabolic demands but prey availability remains low.
While size-biased patterns in overwinter survival and growth have been
documented in fish (Forney 1976; Oliver et al. 1979; Michaletz 2010), few studies
have examined indirect effects of winter and their potential to influence
recruitment and subsequent growth (but see Jonas and Wahl 1998; Ostrand et
al. 2005).
8
Investigators have documented size-dependent overwinter mortality in many
fish species including, yellow perch (Post and Evans 1989), walleye (Chevalier
1973; Johnson et al. 1992), largemouth bass (Ludsin and DeVries 1997; Garvey
et al. 1998b), and bluegill l(Toneys and Coble 1979; Cargnelli and Gross 1996).
Mean length of age-0 saugeye, a popular sportfish stocked into Ohio reservoirs
has been shown to increase overwinter, though the underlying mechanisms are
not understood (Donovan et al. 1997). Length increases overwinter could be
caused by growth or size-dependent mortality from starvation or predation.
Alternatively, emigration out of the reservoir could be size-dependent.
The goal of this study was to determine what leads to over winter shifts in
saugeye length distributions. Multiple mechanisms can drive this phenomenon.
Thus, we used three research objectives to distinguish among them. First, we
used individual fish data to distinguish among growth, size-dependent
emigration, and size-dependent mortality (e.g., predation, starvation) using a
reservoir-based PIT (passive integrated transponder) tagging study. Second, we
quantified one potential source of size-dependent mortality; starvation, by
comparing growth and survival of fed and unfed saugeye held over winter (> 200
d) in outdoor pools. Finally, we investigated whether saugeye emerging from
their first winter in poor energetic condition (owing to limited winter food
availability) exhibit reduced foraging efficiency that could lead to mortality.
Specifically, we quantified indirect effects of winter food availability on spring
foraging success of fed and unfed survivors from outdoor pools.
9
Methods
Reservoir Tagging Study
We tagged saugeye in two Ohio reservoirs. Hoover Reservoir
(40.099463˚ N -82.88129˚ W) is a 1,143-ha impoundment of the Upper Big
Walnut Creek and is the main source of drinking water for the City of Columbus,
Ohio. It is eutrophic and has a mean depth of 7.3 m. Deer Creek Lake
(39.605662˚ N -83.244914˚ W) is a 527-ha impoundment of Deer Creek and is
managed by the U.S. Army Corps of Engineers for flood control. Deer Creek
Lake is eutrophic and has a mean depth of 4 m.
Saugeye fingerlings were stocked during spring (Hoover Reservoir: 22 –
23 May 2008, 474/hectare, mean total length (TL) = 29 mm; Deer Creek Lake: 28
May 2009, 551/hectare, mean TL = 30 mm) and initially captured during fall
(Hoover Reservoir: 6 – 23 October 2008; Deer Creek Lake: 5 – 21 October 2009)
by night shoreline electrofishing. Upon capture, we recorded fish length and
weight, implanted a PIT tag (Biomark, Inc. Seattle WA) in the peritoneal cavity
using a 12-gauge hypodermic needle (Prentice et al. 1990), and released fish in
a previously sampled area of the reservoir. A proportion of tagged fish then were
recaptured during spring night shoreline electrofishing (Hoover Reservoir: 11 –
21 May 2009; Deer Creek Lake: 5 – 8 April 2010).
Because we sought to distinguish between size-dependent emigration and
size-dependent mortality, we also recaptured fish from sites below the dam at
each reservoir (Hoover tailwater & Big Walnut Creek: 11 and 13 May 2009; Deer
10
Creek Lake tailwater: 16 and 30 March 2010). Deer Creek Lake’s tailwater is a
well-defined 50 m wide by 420 m long channelized stretch immediately below the
dam. High angler catch rates and previous research reveal that saugeye moving
downstream of the dam generally remain in the tailwater area rather than moving
farther downstream (Spoelstra et al. 2008). Consequently, we focused our
below-dam efforts to this area by electrofishing the entire Deer Creek Lake
tailwater. In contrast, the tailwater area below Hoover Dam is broad, shallow,
and offers very little habitat for saugeye. Thus, we electrofished sites along Big
Walnut Creek between Hoover Dam and 14 km upstream of the confluence of
the Scioto River, including accessible sites in the tailwaters. Six sites (mean
length = 1.2 km) were selected based on accessibility, and each site generally
consisted of several pool-riffle complexes.
To determine whether age-0 saugeye deplete energy reserves during
winter, we characterized pre- and post-winter energy densities of saugeye from
Hoover, Deer Creek, and Alum Creek reservoirs (Table 1). These reservoirs
spanned gradients of productivity, which could influence food availability and prewinter energy density. Saugeye were collected during fall (October – early
November) and spring (mid-April – mid May) via night shoreline electrofishing
(Table 1). Upon capture, fish were euthanized in a solution of MS-222 and
stored on wet ice during transport to the lab where they were frozen in water for
later estimation of energy density. From each reservoir collection (i.e., fall,
spring), we estimated energy density (kJ/g wet mass) of homogenized whole fish
11
(5 fish per 10-mm length-class or all fish if fewer were available) using a Parr
isoperibol calorimeter and procedures described in McCollum et al. (2003).
Temperature can influence starvation risk through a positive relationship
with metabolic rate. If saugeye rely on their energy reserves during winter, then
starvation may increase with water temperature. Thus, we compared daily water
temperatures from an in situ logger located near Hoover Dam (Roderick Dunn,
City of Columbus, unpublished data) and temperatures from Deer Creek Lake
outflow (Vincent Marchese, USACOE, unpublished data). Because water
temperatures were not recorded over winter in Deer Creek Lake, we derived a
relationship between lake and outflow temperatures. Using data from in situ
temperature loggers situated near Deer Creek Lake dam and limiting the data to
those periods when the reservoir was thermally mixed, we found Lake
temperature = 0.98 (outflow temperature) + 1.02 (R2 = 0.98, P < 0.0001, N = 75
d, DOW unpublished data).
Overwinter Experiment
To determine how fall energy reserves, body size, and winter food
availability influence overwinter survival and growth of age-0 saugeye, we
conducted an overwinter experiment using 2,650-L flow-through outdoor pools
located at The Ohio State University’s Aquatic Ecology Laboratory outdoor pool
facility. Water temperature and oxygen, which were allowed to follow natural
fluctuations through the winter, were recorded daily. Age-0 saugeye were
captured via electrofishing during late September 2010 from Alum Creek Lake
12
(40.184263˚ N -82.963793˚ W), transported to the lab in a 600-L hauling tank,
PIT tagged, and placed into pools (24 pools; 4 – 7 saugeye/pool), each holding a
different length-class of saugeye (six 10-mm length classes). Fish were fed
fathead minnows Pimephales promelas ad libitum for about 5 d, or until the
experiment began. When the experiment began (9 November 2010) and weekly
thereafter, saugeye in 12 of the pools received no ration and saugeye in the
remaining 12 pools were fed weekly using fathead minnows as prey. Fed fish
never exhausted their food supply. On 10 June 2011, we terminated the
experiment by measuring length and weight of all saugeye. We also measured
energy density in 25 of the fed and 22 of the unfed saugeye.
Spring Foraging Experiment
How indirect effects of winter prey availability influence foraging success
of saugeye emerging from their first winter was assessed using survivors from
overwinter experiments. Saugeye (3/pool) and treatments (fed or unfed) were
randomly assigned to 12 (N = 6 replicates per treatment) 1,500-L outdoor pools
(experimental unit). Because we wanted to test for the effect of long-term
energetic condition rather than the effect of hunger itself, we next standardized
for hunger by allowing saugeye, including unfed fish, to feed on fathead minnows
daily for 3 d. Saugeye were then starved for 48 h, thus emptying their stomachs.
Next, we implemented our experiment by adding six fathead minnows of
vulnerable sizes to each pool. Minnows were replaced after 1, 2, 4, and 8 h after
13
the initial minnow stocking (about 1600 h). We terminated the experiment after
16 h by euthanizing saugeye and immediately counting prey in diets.
Statistical analysis
TL distributions from the reservoir tagging study were compared using 2sample Kolmogorov–Smirnov (KS) tests. To determine if length changed
overwinter, fall TL distributions from reservoir-caught saugeye were compared to
spring TL distributions of reservoir- and tailwater-caught saugeye. Because PIT
tags permit individual identification of each recaptured fish, we know the original
fall length of each fish recaptured in the spring. Thus, to determine if overwinter
mortality was size-dependent, fall TL distributions of spring recaptures were
compared to fall TL distributions of all saugeye captured during fall using KS
tests.
We used PIT tag data to also estimate growth, expressed as proportional
change in wet weight of individuals from reservoirs and individuals from the
overwinter pool experiment. Differences in proportional change in wet weight
were evaluated using ANCOVA. Fall TL was set as a covariate. The categorical
variable included five levels and represented the different sources of spring fish.
Specifically, (1) recaptures from Hoover Reservoir, (2) Deer Creek Lake and (3)
Deer Creek Lake tailwater and (4) fed and (5) unfed fish from the outdoor pool
experiment. When significant differences were detected, we used Tukey’s
Honestly Significant Difference (HSD) post-hoc test to determine where
differences occurred.
14
To determine whether small saugeye had lower mass-specific energy
density than large saugeye we pooled data across all reservoirs and regressed
saugeye fall energy density against fall TL. Next we used ANCOVA to test
whether saugeye depleted energy reserves over winter. Individual tests (N = 3)
were run on reservoir-specific data and data from the overwinter pool
experiment. For reservoir data, saugeye energy density was modeled as a
function of season (fall, spring), fall TL, and their interaction. Overwinter
experiment data was analyzed similarly, except that the categorical variable
consisted of 3 levels including (1) fall energy density and spring energy density of
(2) fed and (3) unfed fish. When significant differences were detected, we used
Tukey’s HSD post-hoc test to determine where differences occurred.
Differences in foraging success (i.e. consumption) were analyzed using
ANCOVA. To account for the general relationship between body size and
feeding rate, we expressed consumption as a proportion of Cmax (Zweifel et al.
2010). Winter feeding treatment (fed or unfed) was set as the categorical
variable. Since the relationship between body size and feeding rate could differ
for fed and unfed fish, fall TL was included as a covariate. All statistical tests
were implemented in R 2.12.2 (2011) using α = 0.05.
15
Results
Temperature
In all pools, dissolved oxygen concentrations ranged 6.2 – 18.9 mg/L
during the experiment. Fall through spring water temperature regimes in Hoover
Reservoir, Deer Creek Lake, and outdoor pools were strikingly similar (2009 –
2011; Figure 1). Fall cooling and spring warming were well synchronized and
occurred at comparable rates. December through March water temperatures
were about 3˚ C warmer in pools than in reservoirs, where temperatures
generally were 1 – 2˚ C (Figure 1). Because temperatures in pools were warmer
than in reservoirs, we assumed that fish basal metabolic costs were higher in
pools than reservoirs.
Size-dependent mortality and emigration
During 6 – 23 October 2008 (Hoover Reservoir) and 5 – 21 October 2009
(Deer Creek Lake), we tagged 1,023 and 1,768 individual saugeye. Saugeye
were periodically resampled during the following spring within reservoirs and in
areas below the dams. Spring electrofishing yielded 21, 45, and 38 recaptures
from Hoover Reservoir, Deer Creek Lake, and Deer Creek Lake tailwater. No
tagged age-0 saugeye were recaptured below Hoover Dam.
Saugeye TL distributions shifted over winter toward larger sizes at all
three sites (2-sample KS test: Hoover Reservoir, D = 0.66, P < 0.001; Deer
Creek Lake, D = 0.08, P = 0.04; Deer Creek Lake tailwater, D = 0.42, P < 0.001),
16
though the shift in mean TL was only 2 mm in Deer Creek Lake. Only one
untagged age-0 saugeye was captured below Hoover Dam.
Reservoir-specific pairwise comparisons (2-sample KS tests) of fall TL
distributions of spring recaptures and fall TL distributions of all saugeye captured
during fall revealed no evidence of size-dependent mortality within Hoover
Reservoir (D = 0.12, P = 0.93; Figure 2) or Deer Creek Lake (D = 0.07, P = 0.97;
Figure 2). However, based on tailwater recapture data, we did find evidence of
size-dependent downstream movement from Deer Creek Lake to the tailwater
below the dam (D = 0.33, P = 0.01; Figure 2); spring tailwater recaptures were on
average larger during fall than the average of all tagged fish in the fall.
Reservoir data were supported by overwinter pool experiments; no
evidence of size-dependent starvation mortality. Despite being held at winter
temperatures for more than 200 days, neither fed nor unfed saugeye of any size
died during the experiment.
Overwinter growth
Except for unfed fish in the pool experiment, experimental and fieldrecaptured saugeye of all sizes fed and grew overwinter. Proportional change in
wet weight differed among reservoirs and pools (ANCOVA; F = 21.22, df = 1, P <
0.001). Mean proportional change in wet weight of spring recaptures was higher
in Hoover Reservoir, where all fish increased in wet weight, than in Deer Creek
Lake and Deer Creek Lake tailwater, where a small proportion of fish (6 and 15%
respectively) lost wet weight over winter (Tukey-Kramer HSD; Figure 3). Even
17
though fed saugeye in experimental pools never exhausted their food supply,
their proportional change in wet weight was lower than reservoir saugeye, and a
large percent (39%) lost wet weight over winter (Figure 3). Unfed saugeye
consistently lost wet weight over winter (Figure 3).
Energy density
Saugeye fall energy density (kJ/g wet weight) increased with fish TL
(linear regression; data pooled across three reservoirs; F = 171.91, df = 1, P <
0.0001; Figure 4). To determine whether saugeye depleted energy reserves
over winter, we modeled saugeye energy density as a function of season (fall,
spring), TL, and their interaction. Reservoir-specific comparisons revealed no
evidence that saugeye depleted energy reserves over winter (Figure 4). Mean
energy density of fall- and spring-caught saugeye from Hoover Reservoir were
similar (ANCOVA; season, F = 0.23, df = 1, P = 0.64). However, for saugeye
from Deer Creek Lake, energy density of fish recaptured in spring was higher
than when this population was sampled in fall (ANCOVA; season, F = 13.32, df =
1, P < 0.001). In addition, the relationship between energy density and TL
differed between fall and spring captures (ANCOVA; season X TL, F = 6.96, df =
1, P = 0.01), with energy density increasing with fish TL during fall, but not spring.
Large and small fish from Deer Creek Lake emerged from their first winter in
similar energetic condition. Data from experimental fish revealed that the
overwinter feeding treatment strongly influenced spring energy density
(ANCOVA; season, F = 129.97, df = 1, P < 0.0001). Energy density of unfed
18
saugeye declined over winter (Tukey-Kramer HSD; P < 0.001), whereas energy
density of fed saugeye did not (Tukey-Kramer HSD; P = 0.55). Spring energy
density of fed saugeye was greater than spring energy density of starved
saugeye (Tukey-Kramer HSD; P < 0.001).
Spring foraging success
In spring feeding experiments, unfed saugeye consumed fathead minnow
prey at over 3X the rate (measured as proportion of Cmax to remove effect of body
size on consumption rate) of fed saugeye (ANCOVA; F = 27.90, df = 1, P <
0.001; Figure 5). Neither saugeye TL nor the interaction between saugeye mean
TL and winter feeding treatment influenced consumption rates (ANCOVA; TL, F =
1.02, df = 1, P = 0.34; TL X winter feeding treatment, F = 1.28, df = 1 P = 0.28;
Figure 5).
Discussion
We found no evidence that saugeye in Ohio reservoirs suffer direct effects
of energy depletion (i.e., mortality) or its indirect effects (i.e., reductions in spring
foraging success) over winter. In overwinter pool experiments, saugeye that
were starved for over 200 d under energetically challenging conditions depleted
their energy reserves but experienced no mortality and emerged from winter
capable of capturing and eating live prey. Saugeye from reservoirs maintained
energy reserves over winter and in one system, increased energy reserves over
19
winter. Finally, data from individually tagged fish revealed that saugeye grew
overwinter and that survival was unrelated to fall body size.
Our findings add to the growing body of evidence that few temperate coolwater fish, including saugeye in Ohio reservoirs, suffer size-dependent
overwinter mortality from starvation. In overwinter laboratory and pond
experiments, mortality of several temperate cool-water species (walleye,
Kershner 1998; crappie, McCollum et al. 2003; yellow perch, Fitzgerald et al.
2006) could not be attributed to starvation or body size. Rather, the authors
suggested that other factors, including predation and osmoregulatory failure,
explained survival rates. In our experiment, winter duration (i.e., the period
during which fish went unfed) was far longer and food availability for unfed fish
much lower than would be expected in reservoirs. Further, because pools were
warmer than reservoirs, basal metabolic costs were also higher. The combination
of withholding food and high metabolic costs due to warm temperatures should
have maximized starvation risk; however, even small fish did not die due to
starvation. Fed fish in our experiment grew overwinter indicating that saugeye
are also capable of feeding at low temperatures.
While some studies show overwinter fish mortality to be due to starvation
(e.g., Post and Evans 1989; Miranda and Hubbard 1994a), studies of latent
effects of overwinter declines in energetic condition are much rarer. For larval
and juvenile fish, even short periods of starvation can indirectly affect foraging
ability in adverse ways (Jonas and Wahl 1998). Ostrand et al (2005) found that
20
multiple sizes of largemouth bass had uniformly high overwinter survival, but
swimming ability during spring depended on food quality over winter. Because
we withheld food from saugeye for an unusually long period of time (>200 d)
under energetically challenging conditions, we expected that unfed saugeye may
be physiologically unable to resume feeding during spring. Nevertheless, in our
study, unfed saugeye consumed prey at over 3X the rate of fed ones. Our
findings are similar to those of Jonas and Wahl (1998), who found that fed and
unfed walleye held overwinter for 150 days exhibited similar prey capture
efficiencies and handling times during spring. Because all unfed saugeye
demonstrated a strong ability to capture and eat live prey, low winter prey
availability or poor energetic condition should not limit spring growth potential or
survival of saugeye in Ohio reservoirs.
Despite the fact that cool-water fish in temperate systems do not exhibit
size-biased starvation mortality, we frequently see positive shifts in size
distributions of cohorts over their first winter. These shifts potentially are
explained by overwinter growth, size-dependent predation, or size-dependent
emigration. The relative risk of overwinter predation may depend on the
abundance of predators and alternative prey. Large numbers of alternative prey
benefit age-0 piscivores by, among other things, providing a predation buffer
(Fitzgerald et al. 2006). For a cohort of age-0 fish, declines in alternative prey
during winter may increase predation risk, especially for smaller individuals that
may still be vulnerable to a large proportion of adult predators. Smaller
21
individuals may also be at a greater disadvantage due to their higher massspecific metabolic rates and lower energy reserves than large fish, which may
force them to forage more frequently or take greater foraging risks (Biro and
Booth 2009). For most temperate cool-water fish, however, size-dependent
overwinter predation does not appear to be an important mortality source or
explain overwinter shifts in size distributions (but see Chevalier 1973; Fitzgerald
et al. 2006). Not surprisingly then, our PIT-tag data revealed no evidence that
saugeye overwinter survival is related to fall length. Downstream movement of
saugeye, however, was indeed size-dependent. To explain the observed
overwinter shifts towards larger body sizes, small fish would have had to have
been more likely to move downstream than large ones. In our study however, we
observed the opposite; large fish more likely to move downstream than small
fish. Moreover, PIT-tag data revealed that all sizes of saugeye grew over winter.
Without individually tagging fish, it is nearly impossible to distinguish between
growth and size-dependent mortality or movement as explanations for overwinter
shifts in size distributions. By individually tagging fish in reservoirs we confirmed
that growth, not size-dependent overwinter mortality, drives shifts in reservoir
length distributions.
In addition to growing in length and mass over the winter, some saugeye
in our study increased their energy density over the winter. In Hoover Reservoir,
energy density remained unchanged. In Deer Creek Lake, where the saugeye
tended to be considerably smaller than in Hoover Reservoir, energy density of
22
the smallest fish increased during winter whereas that of the relatively larger fish
remained the same. Jonas and Wahl (1998) attributed a similar size-dependent
pattern in walleye in Illinois ponds to differences in prey between small
(invertebrate and fish prey) and large (fish prey only) walleye. Similarly,
overwinter change in energy density decreased with fish size in walleye held over
winter in Ontario ponds (Pratt and Fox 2002). The authors argued that, under
harsh energetic conditions, the lower energy maintenance demands of small fish
leaves them with more energy to put towards growth and storage than large fish.
The patterns we observed in saugeye, with only the smallest fish allocating their
surplus energy to storage rather than maximizing growth in length, can be
expected when the risk of starvation decreases with body size (Ludsin and
DeVries 1997; Biro et al. 2005). For small saugeye in Ohio reservoirs, such as
the smallest in Deer Creek Lake, growing energy reserves during the fall may
reap greater survival benefits than growing larger in size. This size-dependent
allocation pattern may by partly responsible for the lack of a size-dependent
starvation pattern in this system.
In this research, we used laboratory experiments to assess the direct and
indirect effects of low winter prey availability and subsequently applied our
findings to interpret results from field studies. By combining field and laboratory
studies, we ruled out factors that are likely to be affecting first-winter dynamics of
saugeye in reservoirs. In laboratory experiments, we found no evidence that low
food availability limits first-winter survival or adversely effects spring foraging
23
success of saugeye stocked into Ohio reservoirs. Field studies demonstrated
that saugeye become larger over winter and yielded no evidence that mortality is
size-dependent. Relative to most studies, which use population data to identify
size-dependent processes, our tagging data from reservoirs is particularly
compelling because it allowed us to quantify individual fish growth and identify
fish characteristics associated with survival.
Our study indicates that cool-water piscivores such as saugeye are well
adapted to winter conditions characteristic of productive mid-latitudinal
reservoirs. By feeding heavily on abundant prey during the first growing season,
saugeye reach large body size and store considerable energy reserves that allow
them to deal with potentially long periods of low prey availability. Large energy
reserves allowed saugeye of all sizes to survive winter with no food and also
allowed them to resume foraging during spring when prey became available. For
saugeye in reservoirs, winter was a period of substantial growth. Overwinter
growth may be especially high when cool-water predators such as saugeye
overlap with warm-water prey such as gizzard shad Dorosoma cepedianum.
During fall and winter, saugeye experience temperatures closer to those required
for optimal growth, whereas for gizzard shad, cool winter temperatures may lead
to reduced predator avoidance and increased vulnerability to saugeye predation.
Finally, starvation risk in laboratory experiments and survival in reservoirs was
unrelated to body size. Consequently, while stocking saugeye late in the year to
maximize oversummer survival results in a tradeoff with length (i.e., lower
24
oversummer growth; Donovan et al. 1997), our findings indicate that smaller fall
sizes should not reduce overwinter survival nor should it affect spring foraging
success and hence growth or survival of saugeye emerging from first winter.
25
Reservoir
Mean chl a
Latitude
(µg/L)
Energy density collection dates
Fall
Spring
Hoover Reservoir
40.0995˚
17.1
26 Nov 2008
17 May 2009
Deer Creek Lake
39.6057˚
38.1
20 Oct 2009
8 Apr 2010
Pre - experimental fish
(Alum Creek Lake)
40.1843˚
9.7 1 & 11 Nov 2010
Post-experimental fish
15 Jun 2011
Table 1. Characteristics of Ohio reservoirs from which age-0 saugeye were
collected for energy density estimation during fall and spring. The mean
chlorophyll-a concentration was the grand mean of outflow sites collected once
per month (April - October) during 2007 – 2011.
26
Figure 1. Weekly mean temperature regime during fall through spring in Hoover
Reservoir (black solid line; 1 October 2008 – 15 April 2009), Deer Creek Lake
(dashed line; 1 October 2009 – 15 April 2010) and outdoor pools (gray solid line;
9 November 2010 – 10 June 2011; N = 24 pools). Solid circles associated with
lines represent approximate timing of fall tagging and spring recapture efforts in
reservoirs (Hoover Reservoir and Deer Creek Lake) and fall implementation and
spring termination of overwinter pool experiment.
27
Figure 2. Normalized length frequency distributions representing saugeye
captured via electrofishing from Hoover Reservoir, Deer Creek Lake, and Deer
Creek Lake tailwater (TW) during fall (Hoover Reservoir: 6 – 23 October 2008;
Deer Creek Lake: 5 – 21 October 2009) and spring (Hoover Reservoir: 11 – 21
May 2009; Deer Creek Lake: 5 – 8 April 2010; Deer Creek Lake TW: 16 and 30
March 2010). No pit-tagged saugeye were captured in Hoover Reservoir TW or
Big Walnut Creek below Hoover Dam. Arrows represent distribution means.
Sample sizes are indicated in parentheses.
28
Figure 3. Proportional change in saugeye wet weight as a function of fall total
length (mm) of saugeye PIT tagged in two Ohio reservoirs during fall (Hoover
Reservoir: 6 – 23 October 2008; Deer Creek Lake: 5 – 21 October 2009) and
subsequently recaptured during spring (Deer Creek Lake: 5 – 8 April 2010; Deer
Creek Lake tailwater: 16 and 30 March 2010) and saugeye from the overwinter
pool experiment (8 November 2010 – 10 June 2011). Sample sizes are indicated
in parentheses.
29
Figure 4. Energy density (kJ/g wet weight) as a function of TL of saugeye
captured via electrofishing during fall (open circles; 23 October 2008, Hoover
Reservoir; 21 October 2009, Deer Creek Lake; 1 and 8 November 2010, Alum
Creek Lake/overwinter pool experiment) and spring (solid circles; 11 – 18 May
2009, Hoover Reservoir; 4 – 8 April 2010, Deer Creek Lake) from reservoirs and
fed (solid triangles) and unfed (open triangles) saugeye originating from Alum
Creek Lake and sampled from pools during spring (10 June 2010). Sample sizes
are indicated in parentheses.
30
Figure 5. Mean consumption rates (proportion of Cmax; +/- 1SD) of fed and unfed
saugeye after the overwinter pool experiment when allowed to forage on fathead
minnows during spring foraging experiment (15 June 2011).
31
CHAPTER 3
How size at stocking, reservoir conditions, and fishery objectives influence
success of a reservoir-stocked piscivore
Introduction
Fish management decisions will be most successful when they are based
on experimental assessments of alternatives (FAO 1995; Beddington et al.
2007). These assessments must be based on explicit management objectives
and consider the costs and constraints associated with their implementation.
Assessments of these sorts gain value when they include an understanding of
the ecology underlying the success or failure of different options (Walters and
Holling 1990). For example, understanding ecological mechanisms allows
researchers to recommend situation-specific management, such as applying
different management strategies to functionally different fisheries (Tonn et al.
1983; Dolman 1990). Herein, we apply these principles to a fish stocking
question to (1) use experimental results to recommend management solutions
under explicitly different sets of fishery objectives and (2) use an understanding
of the underlying ecological processes to inform management of systems beyond
the experimental ones.
32
Decisions about when and where to stock fish remain relevant, even for
modern stocking programs (Brooks et al. 2002; Michaletz et al. 2008). Stocking
decisions must consider the limitations and costs of hatchery-produced fish as
well as consider our knowledge of biotic/abiotic characteristics of recipient
systems. Further, the decision of what stage (i.e., age or size) to stock is critical.
A tradeoff exists between stocking many small fish, vulnerable to predation or
starvation, with stocking fewer large fish, which cost more to produce and may be
available in limited numbers (Heidinger 1999). In this study, we evaluated this
tradeoff using saugeye (female walleye Sander vitreus X male sauger S.
canadensis) stocked into Ohio reservoirs. Specifically, we evaluated growth and
survival of fry (6 mm total length (TL)) and fingerlings (30 mm TL) stocked into
individual reservoirs and assessed how biotic/abiotic factors and fishery
objectives influenced stocking success.
Previous studies have identified many of the factors that contribute to the
success of stocked saugeye. Larval walleye and, presumably, saugeye are
susceptible to fluctuations in water temperature, with smaller fish being more
vulnerable than larger fish (Santucci and Wahl 1993; Clapp et al. 1997). After
exogenous feeding commences, saugeye feed in sequence on zooplankton, then
macroinvertebrates, and then fish (Mathias and Li 1982; Galarowicz and Wahl
2005). Larval gizzard shad Dorosoma cepedianum hatch in large numbers and
are the most abundant and preferred prey of age-0 saugeye and their predators
(Noble 1981; Humphreys et al. 1987; Michaletz 1997; Denlinger et al. 2006).
33
Further, the timing and magnitude of peak gizzard shad density strongly
influences saugeye survival and growth. Saugeye survival can be enhanced
when fingerlings are stocked after peak gizzard shad density, presumably by
reducing predation intensity via predatory buffering (Donovan et al. 1997).
However, because age-0 gizzard shad grow rapidly and may experience several
days of growth before saugeye are stocked, large proportions of the gizzard shad
population may become invulnerable to all but the largest age-0 saugeye, leading
to low saugeye growth potential (Donovan et al. 1997). In contrast, saugeye
stocked before peak gizzard shad density reach large fall size but exhibit low
survival, presumably due to differences in gizzard shad vulnerability and
predation buffering (Donovan et al. 1997).
Our goal was to evaluate the success of two sizes of saugeye stocked into
Ohio reservoirs during 2008 – 2010. We assessed the relationship between our
experimental results (Fall CPUE of fry- and fingerling-stocked saugeye) and
management recommendations under four different fishery objectives, including
(1) maximize frequency of strong year classes, defined as the proportion of year
classes with fall CPUE > 60 saugeye/h (Hale et al. 2011), (2) minimize frequency
of failed year classes, which we defined as the proportion of year classes with fall
CPUE< 5 saugeye/h, (3) maximize the ratio of fall density to production costs,
and (4) maximize the ratio of strong year classes to production costs. We also
assessed how biotic/abiotic factors influenced survival and growth of stocked
saugeye and provided management recommendations based on those findings.
34
Methods
Stocking experiment
We stocked 11 fry (N = 4 reservoirs) and 10 fingerling (N = 4 reservoirs)
cohorts into Ohio reservoirs during 2008 – 2010 (N = 5 – 8 cohorts per year). We
selected study reservoirs that span gradients in size, productivity, predator
abundance, and historical saugeye stocking success (Table 2). Fish community
composition among reservoirs was similar, including centrachids, clupeids, and
percids. Alum Creek Lake also supported a stocked esocid fishery. Fry and
fingerlings were stocked into different systems because of concerns that fry and
fingerlings stocked into a single system could interact via predation or
competition and consequently confound the comparison. Indeed, consistent with
our concerns, fry and fingerlings concurrently stocked into Seneca Lake, Ohio
during 2005 yielded a bimodal fall length distribution (DOW, unpublished data).
Fry (3 – 5 d post-hatch, 6-mm total length (TL)) obtained from Senecaville
State Fish Hatchery were stocked during early spring (11 – 20 April); fingerlings
(30-mm +/- 2.3 mm TL) obtained from Senecaville, St. Mary’s, and Hebron State
Fish hatcheries were stocked into separate systems 4 – 7 weeks later (19 May –
2 June; Table 3). To estimate mortality caused by stress solely related to
stocking, we estimated 24-h and 48-h survival at Alum Creek Lake (fry-stocked)
and Hoover Reservoir (fingerling-stocked) during the first 2 years of our study.
For fry, 50 individuals were placed into each of 16 19-L sealed enclosures
containing reservoir water, which were submerged just below the surface in a
35
sheltered area of the reservoir. At 24 and 48 h after stocking, we assessed
mortality by filtering the contents of eight enclosures through 0.5-mm mesh net
and counting survivors. For fingerlings, one individual was placed into each of
150 350-mL flow-through enclosures. 25 enclosures were placed into each of 6
holding apparatuses which were submerged just below the surface in a sheltered
area of the reservoir. At 24 and 48 h after stocking, we assessed mortality by
counting survivors from 75 enclosures.
To capture spatial and temporal heterogeneity in ambient reservoir
conditions, we recorded weekly temperature and dissolved oxygen (DO) every 1
m from surface to bottom, and sampled zooplankton and ichthyoplankton at fixed
inflow (near stream inflow) and outflow (near the dam) sites. We began weekly
sampling when saugeye were initially stocked and stopped when larval fish were
no longer found in our ichthyoplankton samples (24 June – 14 July). Two
replicate zooplankton samples were collected at each site using vertical tows,
from 1 m above the bottom to the surface, using a 63-micron mesh net. Water
volume sampled was estimated using a flow meter. Samples were immediately
preserved using sugar formalin. Zooplankton were identified to genus for
clodocerans; copepods were classified as calanoid, cyclopoid, or nauplii, and
rotifers were quantified, but not distinguished. To estimate zooplankton density,
each sample was diluted to a known volume, and individual taxa from at least
two 5-ml sub-samples were counted until 200 individuals of the most common
36
taxa (excluding copepod nauplii and rotifers) were recorded (Partridge and
DeVries 1999). We then extrapolated to derive numbers in the total sample.
Two replicate ichthyoplankton samples were collected at each site by
towing a neuston net (1 X 2-m wide mouth, 0.5-mm mesh) at 3 – 5 m/s for 2.5 –
5 minutes. A flow meter was used to estimate water volume sampled. Samples
were immediately preserved in sugar formalin and returned to the lab for
processing. Because larvae larger than 15 mm could evade our gear (Bremigan
and Stein 2001), we estimated density only of larvae smaller than 15 mm by
counting the number of individuals in a 10% subsample and then extrapolated to
the total sample.
The abundance of predators and potential competitors was assessed
using standardized surveys (Burt and Sieber Denlinger 2008). To capture spatial
variation within each reservoir, sample sites were stratified by basin
(distinguished by bathymetry or causeways); sample sites within basins were
randomly selected. Black bass (primarily largemouth bass Micropterus
salmoides and smallmouth bass M. dolomieu) and panfish (Lepomis spp.; e.g.,
bluegill L. macrochirus, green sunfish L. cyanellus, pumpkinseed L. gibbosus)
were sampled via electrofishing 18 or 24 transects during May. Sampling effort
was 15 min per transect for black bass and 5 min per transect for panfish. Only
panfish larger than 80 mm TL were counted. Adult saugeye abundance was
assessed via gillnets during mid-October – November. Gillnets were 54.9-m long
X 1.8-m deep and consisted of six 9.15-m panels of different mesh sizes (19, 25,
37
38, 51, 64, and 76 mm). Gillnets (N = 8 or 12) were set 2 hours before sunset,
perpendicular to the nearest shoreline, with the smallest mesh most inshore, and
were fished for 4 h. Black crappie Pomoxis nigromaculatus and white crappie P.
annularis (crappie) were sampled using Missouri-style trap nets (13-mm mesh,
with two 0.9-m X 1.8-m square frames, four 0.8-m diameter hoops, and a 21-m
lead) during October through mid-November. On 3 or 4 consecutive days, 10
nets were set in water 2 – 5 m in depth. Nets were fished daily. Due to its
smaller size, Pleasant Hill Lake was sampled at the lower efforts listed above,
whereas all other study reservoirs were sampled at the higher efforts. Missing
values for adult saugeye (N = 1 reservoir year) and panfish (N = 3 reservoir
years) abundances were replaced with historical means. Data used to calculate
historical means were collected using the methods described above during 2003
– 2010. At least 3 reservoir years of data were used to calculate each mean.
To compare saugeye survival and growth among cohorts, we used fall
density (CPUE, number/h) and TL (mm) data from night shoreline electrofishing
surveys conducted during fall (22 September – 29 October) by sampling 16
(Pleasant Hill Lake) or 24 (all other lakes) 15-min shoreline transects. To capture
spatial variation, sample sizes were stratified by basin; sample sites within basins
were randomly selected. Because saugeye continue to grow during fall in Ohio
reservoirs and because fall sampling did not always occur on the same dates
each year, we adjusted fall saugeye mean length estimates to estimate length on
38
1 October (Donovan et al. 1997) using a fall growth rate of 0.91 mm/d (J.L. Kallis,
unpublished data).
Analysis of underlying ecological mechanisms
In our statistical analyses, we used weekly secchi depth and temperature
measurements (averaged across all depths) to calculate means for the entire
sampling period. For ichthyoplankton, we used peak density (hereafter peak
gizzard shad density) within a reservoir because (1) peak ichthyoplankton
densities in Ohio reservoirs are comprised of about 80 – 99% gizzard shad
(Figure B.4, Arend 2002), (2) gizzard shad are preferred prey for age-0 saugeye
(Humphreys et al. 1987; Denlinger et al. 2006) and their predators (Noble 1981;
Michaletz 1997), and (3) peak gizzard shad density in Ohio reservoirs is strongly
correlated with the sum of the daily estimates (Donovan et al. 1997). For
zooplankton, we selected periods during which we expected they would be most
important to each size of stocked saugeye. In analyses of fingerling-stocked
saugeye, we calculated the mean crustacean zooplankton density using data
from the 2 weeks after stocking. Fingerlings do not immediately begin feeding on
fish after being stocked; rather, they rely on zooplankton and benthic
invertebrates, but typically transition to piscivory within the first 2 weeks after
stocking (Stahl et al. 1996). Because fingerlings spend about 4 weeks in
hatchery ponds before being stocked, the total amount of time that fingerlingstocked saugeye rely on zooplankton is about 6 weeks. For fry-stocked
39
saugeye, which are stocked about 3 – 5 d after hatching; we calculated mean
zooplankton density using data from the first 6 weeks after stocking.
We modeled saugeye fall density and TL separately for fry-stocked and
fingerling-stocked saugeye. Predictor variables used in our analysis included
reservoir, year, mean water temperature and secchi depth, peak gizzard shad
density, timing of stocking relative to peak gizzard shad density (stocking date
minus date of peak gizzard shad density), time in reservoir (stocking date minus
1 October), and mean crustacean zooplankton density. Abundances of adult
crappie, saugeye, panfish, and black basses were included in fall density models,
and abundance of age-0 saugeye was included in models of fall length. Due to
variation in stocking rates, we also included stocking rate of fingerlings as a
variable in our analyses. Saugeye fall density was log10 transformed to meet the
assumption of normality. Peak gizzard shad density and crappie abundance
were log10 transformed to linearize relationships with saugeye fall density and TL.
We developed main-effects-only multiple regression models by
considering all combinations of our predictors and then identified the best models
(i.e., those with ΔAICc less than 2, Burnham and Anderson 2002). Null models
(intercept-only model) were also considered. We assessed the relative
importance of individual predictors by calculating Akaike importance weights for
these variables and estimated parameter values using model averaging over all
possible combinations of models (Calcagno and de Mazancourt 2010). We
subsequently focused our discussion on important variables (i.e., variables that
40
were included in the top models or those with the high Akaike importance
weights).
Comparisons of stocking decisions under different fishery objectives
The relationship between our experimental results (Fall CPUE of fry- and
fingerling-stocked saugeye) and management recommendations were assessed
under the four previously mentioned fishery objectives. Sample size was
increased by including two reservoir years that were not part of our original
stocking experiment. This was possible because Deer Creek Lake and Tappan
Lake received fingerlings during spring 2010 and were sampled via electrofishing
during fall, which provided the data necessary for our comparisons.
Production costs for saugeye fry and fingerlings have not been estimated
by state fish hatcheries in Ohio. Thus, the combination of costs per fry and costs
per fingerling that would result in equivalent benefits/costs for the two stocking
sizes were calculated based on the observed stocking densities and fall catch
rates summed over all reservoir years. For comparison, the equivalent
benefits/costs for the two stocking sizes were plotted overlaid with costs
estimates for walleye production reported in the literature. Estimates for walleye
fry and fingerlings varied widely, and it was unclear which were most comparable
to actual production costs of saugeye in Ohio hatcheries. Because our analysis
may be sensitive to the production costs of the fry and fingerlings, we considered
estimates from several sources. Production costs per individual fry included
$0.001 (Santucci and Wahl 1993), $0.003 (Lucchesi 2002), and $0.008
41
(Gunterson et al. 1996) US$. Production costs per individual fingerling included
$0.016, $0.050 (Santucci and Wahl 1993), and $0.030 (Lucchesi 2002) US$.
Production costs were adjusted to 2010 US$ using the US Department of Labor
Consumer Price Index.
Results
Stocking experiment
We consistently stocked fry at about 2,300 per hectare. Due to poor
survival in hatchery ponds during 2009 and 2010, stocking rates of fingerlings
varied dramatically; two-fold among cohorts included in our analysis of underlying
ecological mechanisms and six-fold among cohorts included in our comparison of
stocking decisions under different fishery objectives (Table 3). Mean stocking
lengths of fingerlings were similar across reservoirs and years and were between
26 and 33 mm TL. Fry were stocked at 3 – 5 d post-hatch, at about 6 mm TL.
48-h survival of both sizes of stocked saugeye was high (mean, SD; fry, 97%, 4;
fingerlings, 92%, 3), suggesting factors other than stocking mortality were
responsible for the variation we observed in saugeye fall density.
Fall density of fry-stocked saugeye was relatively low in all reservoirs
during 2008 (0 – 7 CPUE). Densities during 2009 and 2010 were between 0 and
117 CPUE (Figure 6). 2 of 4 fry-stocked reservoirs generated failed classes
during at least 1 year of our study (Figure 6). Historical fall densities from
fingerling stockings in Delaware Lake and Clendening Lake were consistently
42
poor, whereas historical fall densities in Alum Creek Lake have at times
exceeded 60 CPUE (Figure 6). Results of fry stockings in our study were similar
to historical results of fingerlings stockings in these same reservoirs. Fry
stockings failed or nearly failed in Delaware and Clendening Lakes and were
successful during 1 of 3 years in Alum Creek Lake (Figure 6). Seneca Lake
generated two strong year classes during our study; however, there is no
historical data for comparison (Figure 6).
Fall density was 5 – 117 CPUE in 9 out of 10 reservoir years of fingerling
stockings and was exceptionally high (316 CPUE) during 1 reservoir year (Figure
6). Historical stockings reflected our data. Deer Creek and Pleasant Hill Lakes,
two reservoirs where historical fall densities have at times exceeded 60 CPUE,
generated strong year classes. Stocking success in Tappan Lake, where fall
densities have rarely exceeded 60 CPUE, yielded the weakest year classes
among fingerling-stocked reservoirs; nevertheless no fingerlings stockings
resulted in year class failure during our study. The only failed year class from
fingerling stockings (i.e., Tappan Lake during 2010) came from a cohort of
saugeye that was not part of our original stocking experiment. In comparison to
Deer Creek Lake and Pleasant Hill Lake, fall density in Hoover Reservoir was
lower and relatively stable (24 – 39 CPUE).
Because fall length was not estimated for failed year classes, 8 and 10
reservoir years of fry and fingerling data were available for analysis. Mean fall
length of fry-stocked saugeye (199 – 232 mm TL) was similar to mean fall length
43
of fingerling-stocked saugeye (196 – 263 mm TL; Figure 7). Mean fall lengths of
fry- and fingerling-stocked saugeye from strong year classes were no larger than
mean fall lengths from cohorts with lower year class strength (Figure 7). The
smallest fall lengths occurred in the only two year classes stocked after peak
gizzard shad density (Figure 7).
Analysis of underlying ecological mechanisms
Analysis of fall density of fry-stocked saugeye yielded two models having
ΔAICc less than 2 (i.e., top models; Table 4). Little support existed for the null
model, which had ΔAICc greater than 10 (Table 4). The top models included
zooplankton density, temperature, and panfish abundance and explained a
substantial amount of the variance in fall density (Table 4). Akaike’s importance
weights revealed that zooplankton density, which was included in both of the top
models, was the most important parameter (Figure 8). Temperature, which was
included in both of the top models, and panfish abundance, which was included
in one of the top models, also had high importance weights (Table 4, Figure 8).
Model-averaged parameter estimates for these variables revealed that fall
densities of fry-stocked saugeye increased with zooplankton density (Figure 9A)
and temperature (Figure 9B) and declined with abundance of panfish (Figure
9C). Adult black bass, saugeye, or crappie did not influence survival of frystocked saugeye.
Analysis of fall density of fingerling-stocked saugeye revealed that time in
reservoir and year were strongly correlated. Thus, we dropped the nominal
44
variable from further consideration. Subsequent analysis yielded one model with
ΔAICc less than 2 (Table 4). Little support existed for the null model, which had
ΔAICc greater than 10 (Table 4). The top model included adult panfish and
saugeye densities and relative time of stocking (i.e., stocking date minus date of
peak gizzard shad density) and explained a substantial amount of the variance in
fall density (Table 4). All 3 variables had high Akaike importance weights (Figure
8). Model averaged parameter estimates revealed that fall density of fingerling
stocked saugeye was negatively associated with panfish abundance (Figure 9C)
and positively associated with relative of stocking (Figure 9D). Predator
abundances did not reduce survival of fingerling-stocked saugeye; black bass
and crappie densities were insufficiently important to be included in the top
model. Adult saugeye abundance, included in the top model, was positively
associated with fall density of fingerling-stocked saugeye (Figure 9E). Akaike
importance weights revealed that reservoirs were among the least important
parameters in our analysis suggesting that reservoir effects were not responsible
for the positive relationship (Figure 8). Neither was an extreme value in the
dataset, which when removed, did not change the overall findings of our analysis
(Figure 9E).
Analysis of fall length data of fry-stocked saugeye yielded 2 univariate
models with ΔAIC less than 2. One of the models included age-0 saugeye fall
density, whereas the other model included relative time of stocking (Table 4).
Age-0 saugeye fall density and relative time of stocking were correlated (linear
45
regression, P = 0.01). Akaike importance weights revealed that relative time of
stocking was about twice as important as saugeye fall density (0.65 versus 0.35).
Model averaged parameter estimates revealed that fall length of fry-stocked
saugeye was negatively associated with both relative time of stocking and
saugeye fall density (Figures 10A, 10B).
Analysis of fall length data of fingering-stocked saugeye yielded 1 model
with ΔAIC less than 2 (Table 4). Substantial support existed for the null model,
which had ΔAIC less than 4 (Table 4). The best model included time in reservoir
and panfish abundance. Akaike importance weights revealed that time in
reservoir was nearly twice as important as panfish abundance (0.72 versus 0.38).
Both variables were positively associated with fall length of fingerling-stocked
saugeye (Figures 10C, 10D).
Comparisons of stocking decisions under different fishery objectives
For both fry- and fingerling-stocked saugeye, about one third of the
stockings generated a strong year class (0.27 versus 0.30), suggesting that if the
fishery goal is to maximize the frequency of strong year classes, then no clear
advantage exists of stocking one size over the other. The frequency of failed
year classes was much higher for fry-stocked saugeye (0.36), than for fingerlingstocked saugeye, which generated zero failed year classes. Thus, if the fishery
objective is to minimize the frequency of failed year classes, then stocking
fingerlings is best.
46
Incorporating production costs revealed that management decisions
derive from both fishery objectives and production costs for fry and fingerlings.
Thus, we calculated the combination of costs per fry and costs per fingerling that
would lead to equivalence of benefits/costs for the two stocking decisions (Figure
11). When we overlaid the actual costs of fry and fingerling production as
reported in the literature, neither strategy (i.e., stocking fry or fingerlings) was
consistently better than the other (Figure 11). Indeed, the best strategy was
quite sensitive to the actual costs of production. If fisheries managers seek to
maximize the probability of strong year classes relative to production costs,
stocking fry will be recommended over a larger range of production costs than
when the goal is to maximize absolute fall density relative to production costs.
Only by knowing both the fishery objective and its production costs can
managers make informed stocking decisions.
Discussion
Using a multi-year, multi-reservoir field study, and consideration of
multiple fishery objectives, we demonstrated that stocking saugeye as fry or as
fingerlings are both feasible. The most suitable stocking size depended on
fishery objectives and production costs. Hence, managers must explicitly define
program objectives and depending on costs and benefits, make decisions. We
found no evidence that survival in reservoirs with historically poor recruitment of
fingering-stocked saugeye was enhanced by stocking fry. Those ecological
mechanisms that drive success of fry- and fingerling-stocked saugeye were
47
identified and, in some cases, found to be similar. These results should help
fishery managers match reservoirs receiving saugeye with the most suitable
stocking size. Finally, gaps in our understanding about how survival of newly
stocked saugeye is influenced by panfish, adult saugeye, and black bass were
revealed.
Stocking decisions under different fishery objectives
How survival relates to stocking size has been evaluated for many fishes
(e.g., channel catfish, Storck and Newman 1988; walleye, Brooks et al. 2002;
largemouth bass, Diana and Wahl 2009). For walleye, multiple sizes were often
concurrently stocked into the same reservoir, revealing that fingerlings survive
better than fry through their first summer (Fielder 1992; Koppelman et al. 1992;
e.g., Brooks et al. 2002). Just because fingerlings survive better than fry does
not necessarily mean that they contribute more age-0 fish to the fall population or
survive at rates that make stocking them more cost effective. For example,
walleye fry stocked into South Dakota lakes consistently produced stronger year
classes than fingerling stockings (Lucchesi 2002). Analysis of walleye fry and
fingerlings stocked into Illinois reservoirs revealed that fingerlings consistently
survived better than fry but were not always more cost effective (Brooks et al.
2002). In our study, frequencies of strong year classes for fry- and fingerlingstocked saugeye were similar, though fry-stocked saugeye, were more prone to
year-class failure.
48
Accounting for production costs across sizes often leads to recommending
intermediate sizes, which allows for moderate per capita survival while limiting
production costs. For example, stocking small walleye fingerlings (37 mm TL)
into Missouri reservoirs was more cost effective than stocking fry or large
fingerlings (102 mm TL, Koppelman et al. 1992). Similarly, stocking small
walleye fingerlings (50 mm TL) into Illinois reservoirs was on average more cost
effective than stocking fry or large fingerlings (100 mm TL, Brooks et al. 2002).
In our analysis, the most cost-effective stocking size was sensitive to production
costs and fishery objectives. Thus, both the fishery objective and the production
costs must be known to make an informed stocking recommendation.
Analysis of underlying ecological mechanisms
Few studies have been designed to elucidate underlying ecological
mechanisms governing success or failure of different stocking sizes (but see
Hoxmeier et al. 2006). In this research we sought to generalize our results
beyond our study reservoirs by understanding what drove patterns in growth and
survival of fry- and fingerling-stocked saugeye. Below, we discuss ecological
mechanisms that may help explain patterns in our data and provide insights into
the factors contributing to the success of fry- and fingerling- stocked saugeye.
Predators and panfish – Predation can dramatically influence stocking
success (Wahl and Stein 1993; Henderson and Letcher 2003), typically by
varying with stocking size (Santucci and Wahl 1993), characteristics of the
predator population (e.g., sizes, numbers, species composition; Rice et al. 1993;
49
Wahl 1995), and the timing and abundance of alternative prey (Forney 1974;
Donovan et al. 1997). We concluded that predator abundance did not adversely
affect fall densities of stocked saugeye. Fall density of fingerling-stocked
saugeye, though, was positively associated with adult saugeye abundance. We
postulated an extreme sample unduly influenced our results; indeed removing
this sample did not change our results. We believe this relationship is caused by
reservoir effects (i.e., adult abundance is higher in reservoirs with high stocking
success), despite Akaike importance weights which revealed that reservoirs were
among the least important parameters. Quantifying predation mortality may
require detailed information about resident piscivores such as diet and population
size structure; predator abundance alone can be a poor indicator of predatory
pressure (Abrams 1993). Understanding (1) how variation in the number and
quality of available refuge sites (Gibson 1994), (2) availability and timing of
alternative prey (Lyons and Magnuson 1987; Donovan et al. 1997) such as
gizzard shad or zooplankton, or (3) variation in growth rates, which may prolong
or minimize stages of intense predation mortality (Karakiri et al. 1989), may also
be important.
Abundance of adult panfish was negatively associated with fall densities of
fry- and fingerling-stocked saugeye. Panfish species are found in all Ohio
reservoirs, commonly reach high adult densities (> 300 catch per hour
electrofishing), feed on a wide variety of prey, and thus could adversely affect
stocked age-0 saugeye via direct (e.g., predation or competition) or indirect (e.g.,
50
food web interactions with zooplankton) processes. Recruitment of age-0
walleye in a small Michigan lake declined when densities of bluegill reached 50
kg/ha, presumably due to predation and competition for food during early life
stages (Schneider 1997). Fall length of fingerling-stocked saugeye and panfish
abundance were positively associated, opposite of what one would expect if
saugeye and panfish were competing for prey. Alternatively, panfish may
influence saugeye fall length by preferentially feeding on smaller or slower
growing individuals, which could skew length distributions toward larger sizes.
Field observations reveal that fish contribute a small proportion of the diets of
bluegill, pumpkinseed, and green sunfish, and green sunfish hybrids (Etnier
1971). However, when large numbers of age-0 fish are stocked, this may cause
a temporary diet shift toward increased fish consumed. Given that (1) we were
unable to link densities of top predators to saugeye survival and (2) panfish reach
high densities in Ohio reservoirs and may opportunistically feed on stocked
saugeye, we need to understand how panfish influence saugeye success.
Prey timing and abundance – Similar to Donovan et al. (1997), fall
densities of fingerling-stocked saugeye were typically higher for year classes
stocked after peak gizzard shad density than for year classes stocked before it,
presumably because gizzard shad buffered predation rates of newly stocked
saugeye. Inconsistent with Donovan et al. (1997), survival of fingerlings stocked
greater than 7 d before peak gizzard shad density was not uniformly low. In fact,
a cohort of fingerlings stocked 21 d before peak gizzard shad density was the
51
strongest year class, suggesting that mitigating factors can dramatically enhance
fingerling survival rates. For example, highest fall density of fingerling-stocked
saugeye corresponded to highest peak gizzard shad density, which was greater
than 2.5X higher than any peak we observed and 4.5X higher than any observed
by Donovan et al. (1997). Perhaps saugeye suffered high predation pressure
before peak gizzard shad density, but grew rapidly once high numbers of gizzard
shad become available, thus increasing survival during the period after the peak
leading to more fish in the fall than would have been possible under lower
gizzard shad densities. Alternatively, perhaps gizzard shad or non-gizzard shad
prey buffered predation mortality during the time between stocking and peak
gizzard shad density.
Size-dependent predator-prey interactions can influence growth of stocked
fish. Saugeye fingerlings stocked before peak gizzard shad density in Ohio
reservoirs were able to consume fast-growing gizzard shad through summer
leading to larger fall size than saugeye stocked after peak gizzard shad density,
which were unable to exploit large gizzard shad (Donovan et al. 1997). Fall
length of fry-stocked saugeye was influenced relative time of stocking. Though
only 2 cohorts of fingerlings were stocked after peak gizzard shad density they
had the smallest fall mean lengths. Fall length of fry-stocked saugeye was
negatively associated with relative time of stocking. Fry stocked less than 45 d
before peak gizzard shad density were relatively small, potentially because they
were unable to exploit large gizzard shad through summer. However, this
52
conclusion is confounded by multicollinearity. Fall length of fry-stocked saugeye
was associated with 2 strongly correlated variables (i.e., relative time of stocking
and age-0 saugeye fall density). Competition for limited prey resources (i.e.,
density-dependent growth) could also explain patterns in our data. However,
given that Ohio reservoirs are prey-rich systems (Denlinger et al. 2006) growth of
fry-stocked saugeye was probably limited by prey vulnerability mediated by
relative time of stocking rather than competition for limited prey reseources.
Save for the one extreme case described above, the magnitude of peak
gizzard shad density did not influence fall densities or lengths of fry- or fingerlingstocked saugeye. Forage fish abundance was positively associated with survival
of small walleye fingerlings (46 mm TL) stocked into Illinois reservoirs but was
unrelated to their growth or to the growth and survival of walleye fry (Hoxmeier et
al. 2006). Donovan et al. (1997) documented that survival of saugeye fingerlings
stocked into Ohio reservoirs increased with peak gizzard shad density, but only
during years when gizzard shad peaked relatively early in the season.
Conversely, when peaks happened late in the season, peak gizzard shad density
was unrelated to saugeye survival (Donovan et al. 1997).
Zooplankton availability influences survival and growth during early life
stages of fish. For walleye fry, lab experiments reveal that 50 zooplankton/L
permits good survival and growth (Hoxmeier et al. 2004). Indeed, success of
walleye fry in reservoirs has been linked to densities of zooplankton during the
first few weeks after stocking (Jennings and Philipp 1992). In a large field study,
53
survival and growth of walleye stocked into Illinois reservoirs as fry and two sizes
of fingerlings was unrelated to zooplankton abundance (Hoxmeier et al. 2006).
In our study, fall density and length of fingerling-stocked saugeye were
unaffected by zooplankton density. Fall density, but not fall length of fry-stocked
saugeye, was positively associated with zooplankton density. Further, strong
year classes of fry-stocked saugeye occurred only when mean zooplankton
densities were above 50/L, whereas zooplankton densities below 50/L always led
to poorer year classes of fry-stocked saugeye. Because fry require zooplankton
before transitioning to other prey, high zooplankton densities may enhance
survival of fry-stocked saugeye, but not fingerling-stocked saugeye which are
capable of feeding on larger prey items (e.g., invertebrates, fish). Even if
zooplankton did affect early growth of fry- or fingerling-stocked saugeye, we may
not have been able to detect it given that over 122 d transpired between stocking
and fall sampling, and for most of this period, stocked saugeye were feeding on
other prey items such as age-0 gizzard shad.
Abiotic factors – Temperature can have direct and indirect effects on fish
growth and survival. For example, temperature differences between the hatchery
and recipient system that fish are stocked into can directly affect mortality rates
(Clapp et al. 1997). Stocked saugeye did not suffer high mortality from thermal
stress at the time of stocking; survival of newly stocked saugeye over 48 h was
high for both fry and fingerlings. Temperature can also influence survival via
interactions with growth rate with growth rates of larval fish being more sensitive
54
to temperature changes than growth rates of juvenile fish (Otterlei et al. 1999).
Perhaps warm temperatures enhanced saugeye growth and survival during the
larval stage. Indeed, fall density of fry-stocked saugeye, but not fingerlingstocked saugeye was correlated with temperature. If warm temperatures
enhanced growth of fry-stocked saugeye during the larval stage, the effects did
not influence fall length; fall length was unrelated to temperature. Alternatively,
warm temperatures may advance the timing of peak gizzard shad density, which
would increase survival by reducing the amount of time that saugeye survived
with limited gizzard shad buffering (Donovan et al. 1997).
Similar to Donovan et al. (1997), the longer fingerling-stocked saugeye
spent in the reservoir, the larger they became. A largely unlimited food supply
may explain why time in reservoirs predicts fall length of fingerling-stocked
saugeye. Saugeye in Ohio reservoirs are probably not prey limited given that
gizzard shad abundance is consistently high relative to predator demand
(Denlinger et al. 2006), and gizzard shad remain vulnerable to gape-limited
saugeye throughout the first growing season, at least to those saugeye stocked
before peak gizzard shad density (80% of our fingerling stockings; Donovan et al.
1997).
Management recommendations for the future
To identify the optimal stocking size for a given fishery objective, actual
production costs of saugeye fry and fingerlings must be estimated. Based on our
findings, reservoirs that previously have not had successful fingerling stocking
55
will not be enhanced by fry stockings. Zooplankton-rich reservoirs can be
successfully managed using fry stockings. In turn, managers should expect low
survival of fry- and fingerling-stocked saugeye in systems with large numbers of
panfish. It was previously thought that, in general, by manipulating stock date
relative to peak gizzard shad density, managers could increase either survival or
growth of fingerling-stocked saugeye, but not both (Donovan et al. 1997). We
found evidence that saugeye stocked before peak gizzard shad density can have
both high survival and high growth. The mechanisms that led to high growth are
well understood (i.e., high gizzard shad vulnerability; Donovan et al. 1997), but
the factors that contributed to high survival are not, a knowledge gap that should
receive further investigation.
The choice between stocking saugeye fry or stocking saugeye fingerlings
depends not only on their average success, but also on specific fishery
objectives, production costs, and reservoir conditions. Stocking reservoirs using
fry rather than fingerlings may be preferred because it frees up hatchery space
for other culture activities, a potential added benefit that was not included in our
analysis. Identifying reservoirs that could be effectively managed using frystocked saugeye should not only reduce costs but also lead to more efficient use
of hatchery resources. Given the benefits of this multifaceted approach, we
recommend that management alternatives, such as stocking fry versus stocking
fingerlings be evaluated by combining a broad range of relevant fishery
56
objectives with a comprehensive understanding of the underlying ecological
mechanisms that drive experimental results.
57
Reservoir
Fry
Seneca
Alum Creek
Clendening
Delaware
Fingerlings
Deer Creek
Hoover
Tappan
Pleasant Hill
ID
SE
AL
CL
DL
Surface Mean Black
Adult
area
chl a
bass saugeye
(ha)
(µg/L) (CPUE) (CPUE)
Age-0
saugeye Reservoir
(CPUE)
years
1,420
1,323
663
412
23.3
9.0
22.0
32.6
62
39
65
51
2.9
0.8
1.9
0.2
NA
46
7
5
3-3
3-3
3-2
2-0
DC
521
HV 1,140
TA
863
PH
318
41.6
18.9
27.2
32.4
50
52
83
121
2.0
1.8
2.2
3.7
145
24
21
85
2-2
3-3
2-2
3-3
Table 2. Characteristics of Ohio reservoirs stocked with fry (N = 4) and
fingerlings (N = 4) saugeye during spring 2008 – 2010 and sampled during fall
(22 September – 29 October) including historical mean predator abundances
(i.e., black bass and adult saugeye, 1995 – 2010, N = 4 – 8 reservoir years per
reservoir, DOW unpublished data) and historical saugeye stocking success (i.e.,
Age-0 saugeye abundance, 1995 – 2007, N = 7 – 23 reservoir years per
reservoir, DOW unpublished data). Historical age-0 saugeye data represents
results of fingerling stocking. The mean chlorophyll a (chl a) concentration was
the grand mean of outflow (i.e., near the dam) sites sampled once per month
(April - October) during 2007 – 2011 (DOW unpublished data). The numbers of
reservoir years used in analysis of saugeye fall density and length are provided
(fall density – fall length). Fall length was not estimated for failed year classes in
Clendening and Delaware Lakes.
58
Table 3. Stocking metrics from eight Ohio reservoirs stocked with fry and
fingerling saugeye during 2008 – 2010, including stocking rate, stocking date,
mean stocking total length, and time in reservoir (i.e., difference in days between
stocking date and 1 October). Fry were stocked 3 – 5 d post-hatch or about 6
mm TL. SE received two stockings separated by 5 d in 2009, thus the median
stocking date is reported. Data for DC and TA in 2010 were used in comparison
of stocking decisions under different management objectives, but not in analysis
of underlying ecological mechanisms (see methods). For reservoir ID’s, see
Table 2.
59
Table 3.
Reservoir Stocking density
ID
(Number/ha)
Fry
2008
AL
2371
CL
2390
DL
2040
SE
2464
2009
AL
2368
CL
2383
DL
2094
SE
2422
2010
AL
2368
CL
2382
SE
2417
Fingerlings
2008
DC
603
HO
474
PH
502
TA
348
2009
DC
551
HO
545
PH
248
TA
233
2010
DC
248
HO
243
PH
250
TA
92
Stocking
date
Mean length
(mm)
Time in reservoir
(d)
18 April
19 April
18 April
19 April
166
165
166
167
15 April
20 April
17 April
19 April
166
165
168
165
12 April
11 April
12 April
173
174
173
21 May
22 May
27 May
27 May
31
29
26
30
133
132
127
127
28 May
27 May
2 June
2 June
30
31
33
33
127
130
122
122
18 May
19 May
22 May
22 May
29
26
29
29
136
136
133
132
60
Saugeye fall density
Fry
ZP(+), Temp(+)
ZP(+), Temp(+), Pan(-)
Null
Fingerlings
Pan(-), RTS(+), SAE(+)
Null
Saugeye fall length
Fry
RTS(-)
Age-0 SAE(-)
Null
Fingerlings
TIR(+), Pan(+)
Null
K
AICc
3
4
1
18.11
18.96
29.96
4
1
ΔAICc
2
W
R
P
0.0
0.8
11.9
0.20
0.13
0.00
0.85
0.92
< 0.001
< 0.001
1.27
20.12
0.0
18.8
0.86
0.00
0.98
< 0.001
2
2
1
59.29
60.60
67.73
0.00
1.31
8.44
0.59
0.31
0.01
0.80
0.77
0.002
0.003
3
1
88.83
92.26
0.00
3.43
0.33
0.06
0.67
0.008
Table 4. Statistics of top models (AICc < 2) that explain variation in fall density
(log10 CPUE) and length (mm) of fry- and fingerling-stocked saugeye (N = 8 Ohio
reservoirs sampled via electrofishing during 2008 – 2010). Results of null
models (intercept-only models) were included for comparison. Models were
fixed-effect-only multiple regression models. Variables included in the top
models were mean zooplankton density (ZP), mean temperature (Temp), adult
panfish abundance (Pan), relative time of stocking (RTS), adult saugeye
abundance (SAE), age-0 saugeye abundance (age-0 SAE), and time in reservoir
(TIR). Parenthetical + or – indicates the sign of the parameter. The table
includes number of parameters (K), AICc, difference in AICc between each model
and the model with the minimum AICc (ΔAICc), model weight (W), proportion of
variance explained by the model (R2), and probability that the model results are
due to random processes (P).
61
Figure 6. Fall density of fry-stocked (N = 11 reservoir years) and fingerlingstocked (N = 12 reservoir years) saugeye (DL stocked during 2008 and 2009
only) and, for comparison, historical mean fall density (solid circles; 1995 – 2007,
N = 4 – 12 reservoir years per reservoir except for SE, no historical data; DOW
unpublished results). Historical data represents results of fingerling stocking.
Bars for each reservoir are in chronological order, 2008 to 2010. Dashed
horizontal lines represent benchmarks denoting strong (fall saugeye density > 60
CPUE) and failed (< 5 CPUE) year classes. ND indicates no data available.
Data for DC and TA in 2010 were used in comparison of stocking decisions
under different management objectives, but not in analysis of underlying
ecological mechanisms (see methods). Note y-axis break. For reservoir ID’s,
see Table 2.
62
Figure 7. Mean fall length of fry-stocked (N = 8 reservoir years) and fingerlingstocked (N = 12 reservoir years) saugeye (DL stocked during 2008 and 2009
only). Data for each reservoir are in chronological order, 2008 to 2010.
Asterisks represent missing values due to small sample sizes/failed year classes;
arrows indicate strong year classes (fall saugeye density > 60 CPUE), plus signs
indicate cohorts stocked after peak gizzard shad density, and ND indicates no
data available. Data for DC 2010 and TA 2010 are for comparison only and were
not used in our statistical analyses of underlying ecological mechanisms. For
reservoir ID’s, see Table 2.
63
Figure 8. Akaike importance weights for variables used in the construction of fall
density models for fry-stocked (N = 11 reservoir years) and fingerling-stocked (N
= 10 reservoir years) saugeye in Ohio reservoir during 2008 – 2010. Only the
five most important variables are presented including mean zooplankton density
(ZP), mean temperature (Temp), mean secchi depth (Secchi), relative time of
stocking (RTS), stocking rate (STRT),and adult, panfish (Pan), black bass
(BASS), and saugeye (SAE) abundances.
64
Figure 9. Fall density of fry-stocked (N = 11 reservoir years, solid circles) and
fingerling-stocked (N = 10 reservoir years, open circles) saugeye as a function of
(A) mean crustacean zooplankton density, (B) mean temperature, (C) adult
panfish abundance, (D) relative time of stocking, and (E) adult saugeye
abundance. Arrow indicates potentially influential sample that was evaluated
during statistical analyses of the data. Negative values of relative time of
stocking represent the number of days saugeye were stocked before peak
gizzard shad density; positive values represent the number of days saugeye
were stocked after peak gizzard shad density. Horizontal dashed lines
represents benchmark denoting strong year classes (fall saugeye density > 60
CPUE).
65
Figure 9
66
Figure 10. Mean fall length (1 October) of fry-stocked (N = 8 reservoir years,
solid circles) and fingerling-stocked (N = 10 reservoir years, open circles)
saugeye as a function of (A) relative time of stocking, (B) age-0 saugeye fall
density, (C) time in reservoir (i.e., 1 October – stock date), (D) and panfish
abundance. Negative values of relative time of stocking represent the number of
days saugeye were stocked before peak gizzard shad density.
67
Figure 11. Management recommendations based on two fishery objectives, (A)
maximize the ratio of fall density to production costs and (B) maximize the ratio of
proportion of strong year classes to production costs. Points represent the
production costs (adjusted for inflation to 2010 US$ using the US Department of
labor Consumer Price Index) of the fry and fingerlings taken from the literature
(Santucci and Wahl 1993; Gunterson et al. 1996; Lucchesi 2002). The dividing
lines separate the two management recommendations (i.e., stocking fry versus
stocking fingerlings). The slopes of the dividing lines were calculated based on
observed saugeye stocking densities and fall densities and the different fishery
objectives. Combinations of fry and fingerling production costs that are above
the dividing line indicate that stocking fry was more cost effective than stocking
fingerlings, whereas combinations that are below the dividing line indicate that
stocking fingerlings was more cost effective than stocking fry.
68
CHAPTER 4
Explaining variable survival of a reservoir-stocked piscivore using retrospective
analyses
Introduction
Stocking is an important management tool used to maintain fisheries and
augment those with poor or declining natural reproduction. Despite
improvements in stocking techniques, success of stocked fish cohorts can vary
dramatically among recipient systems as well as among years within systems
(Stahl et al. 1996; McEachron et al. 1998; Wahl 1999). The substantial costs
associated with producing fish for stocking combined with variable stocking
success has inspired specific research studies and monitoring programs
designed to improve fisheries management practices. Data compiled from
different research studies and monitoring programs represent opportunities for
retrospective analyses (Lindenmayer and Likens 2009). Among other things,
these studies may be used to address new research questions (Nichols and
Williams 2006) or revisit conclusions from a previous study using a larger
updated dataset (e.g., Maceina 2003). Herein, we describe retrospective
analyses of oversummer survival of a reservoir-stocked piscivore using an
69
updated dataset compiled from individual research projects and data from a
standardized fish monitoring program (Ohio Division of Wildlife (DOW),
unpublished data)
Many factors can influence the survival of young fish, including availability
of appropriate prey types and sizes (Crowder et al. 1987), predator abundance
and size structure (Santucci and Wahl 1993), and abiotic conditions such as
temperature (Clapp et al. 1997) and reservoir hydrology (Maceina 2003). North
American mid-latitudinal reservoirs are often dominated by gizzard shad
Dorosoma cepedianum, an important species which can regulate food web
structure from an intermediate trophic level (Stein et al. 1995). Age-0 gizzard
shad are the preferred prey of many age-0 piscivores and can buffer predation
rates of newly stocked fish (Donovan et al. 1997). Cohorts stocked after peak
gizzard shad density begin benefitting from gizzard shad buffering immediately
after stocking and consequently have higher survival than cohorts of fish stocked
before peak gizzard shad density, which must survive and grow for days to
weeks with little or no gizzard shad buffering (Donovan et al. 1997). The
abundance of buffering prey such as gizzard shad is also important and is
positively associated with survival of other young fish (Forney 1974; Donovan et
al. 1997).
Saugeye (female Sander vitreum X male S. canadensis) are an
economically important and popular sportfish commonly stocked into Ohio
reservoirs (Hale et al. 2008). Stocked as fingerlings (30 mm total length (TL)) in
70
large numbers, saugeye are vulnerable to predation during early life stages
(Stahl et al. 1996; Aman 2007), and rely on the availability of appropriate prey
sizes and types (Qin et al. 1994; Donovan et al. 1997). Previous research
suggests that predation mortality, which is strongly influenced by the magnitude
and timing of peak gizzard shad density relative to timing of stocking, strongly
influences saugeye survival (Donovan et al. 1997). Results from recent saugeye
stockings (10 reservoir years; 2008 – 2010) revealed that oversummer saugeye
survival was unrelated to abundance of black bass (largemouth Micropterus
salmoides and smallmouth bass M. dolomieu) or adult saugeye (Chapter 2
herein). Instead, survival was strongly associated with numbers of panfish
(common Lepomis spp. including bluegill L. macrochirus, green L. cyanellus,
etc.). Cohorts stocked before peak gizzard shad density, which were expected to
suffer high oversummer mortality, sometimes experienced high survival (Chapter
2 herein). Finally, a relatively complete time-series of age-0 saugeye fall catch
rates from monitoring data suggested that strong year classes of age-0 saugeye
may limit stocking success of the next cohort of stocked saugeye (DOW,
unpublished data).
We pursued four distinct objectives using retrospective analyses of DOW
reservoir sportfish monitoring data and data from previous research projects that
sampled larval gizzard shad. For our first objective, we updated the original
dataset of Donovan et al. (1997) with 17 more reservoir years of data to reassess
how first-year mortality of age-0 saugeye is influenced by the magnitude and
71
timing of peak gizzard shad density. Second, we quantified predation mortality of
stocked saugeye by considering both densities and sizes of predators, including
adult black bass, saugeye, crappie (white crappie Pomoxis annularis and black
crappie P. nigromaculatus), and panfish (Lepomis spp.; e.g., bluegill L.
macrochirus, green sunfish L. cyanellus, pumpkinseed L. gibbosus). Third, using
more reservoir years of monitoring data, we sought to validate the relationship
between oversummer mortality and panfish abundance, and determine if our
dataset offered insights into how panfish adversely affect saugeye survival.
Fourth, we evaluated whether strong saugeye year classes (i.e., numbers of age1 fish) limit oversummer survival of stocked age-0 saugeye.
In general, we expected that oversummer mortality would (1) increase as
densities of black bass, saugeye, age-1 saugeye, and panfish increased and (2)
decline as peak gizzard shad density increased. We also hypothesized that, if
panfish abundance was correlated with saugeye mortality, then variation in
saugeye stocking length or panfish mean length would provide insights into how
panfish adversely affect saugeye survival (e.g., predation versus competition).
Methods
We used historical fish monitoring data from the DOW and data compiled
from previous studies that collected larval gizzard shad data from Ohio. Ideally,
the factors considered in this analysis would be analyzed using a single dataset
and analysis; however, because our data are observational, very few reservoir
years have observations for all factors. Because building a comprehensive
72
dataset without missing cells dramatically limits our sample size, we instead used
individual data subsets for each of the 4 objectives included in our study (Table
5).
Predator abundance (CPUE) and size data (length distributions) come
from annual population assessments conducted using standardized surveys
described in Burt and Sieber-Denlinger (2008; black bass and panfish
electrofishing, crappie trap net, and Sander spp. gillnetting and electrofishing
surveys). Peak gizzard shad density (number/m3) and timing data were taken
from four studies (Donovan et al. 1997; Bunnell et al. 2003; Aman 2007; Chapter
3 herein) that used similar methods to sample ichthyoplankton (i.e.,
ichthyoplankton tows averaged across upstream and downstream ends of the
reservoir). Numbers and sizes of saugeye stocked into Ohio reservoirs were
taken from DOW historical records.
Prey timing and abundance
To quantify the effect of peak gizzard shad density and timing on saugeye
oversummer mortality we updated Donovan et al.’s (1997) original dataset (19
cohorts stocked during 1991 – 1994) with results from more recent stockings (17
cohorts stocked during 1998 – 2010). We hypothesized that saugeye
oversummer mortality would decline as prey abundance increased. We also
hypothesized that the timing of stocking may be less important at extremely high
prey densities. Thus, oversummer mortality rates of cohorts stocked before peak
prey abundance could be as low as mortality rates of cohorts stocked after peak
73
gizzard shad density. We assessed the influence of peak gizzard shad timing
and density on saugeye oversummer mortality by building predictive models
using mean stocking length, peak gizzard density, and relative time of stocking
(i.e., stock date relative to date of peak gizzard shad density). Growth and
survival conditions differ substantially for saugeye stocked before or after peak
gizzard shad density (Donovan et al. 1997). We therefore summarized relative
time of stocking as a categorical variable (i.e., before or after peak gizzard shad
density). We then combined available peak gizzard shad density and timing
estimates with saugeye oversummer mortality data, which offered 36 reservoiryears for analysis (Table 5). Next, we modeled saugeye oversummer mortality
using all possible combinations of our predictor variables (i.e., peak gizzard shad
density, relative time of stocking, mean stocking length) as well as all 2-way
interactions. This provided a test of our expectation that survival of stocked
saugeye would be enhanced by greater peak larval gizzard shad densities and
would be higher for saugeye stocked after peak gizzard shad density than for
saugeye stocked before the peak. Reservoir and year were included in all
candidate models as random-effect variables including the null model which was
a random-effects-only model.
Densities of predators and panfish
We expected that saugeye oversummer mortality would be positively
associated with densities of predators, particularly densities of black bass and
adult saugeye which are the primary predators of age-0 saugeye (Stahl et al.
74
1996). In a preliminary assessment of the data, we found that the relationship
between saugeye oversummer mortality and densities of predators was highly
variable likely due to, among other things, year or reservoir effects.
Consequently, we set out to quantify how predators influence saugeye survival
by compiling a balanced dataset that could account for year and reservoir effects
while still offering a suitable number of reservoir years for analysis. Our
balanced dataset consisted of saugeye oversummer mortality, saugeye mean
stocking length, and adult black bass, saugeye, crappie, and panfish abundance
and TL from five Ohio reservoirs stocked during 2006 – 2010 (N = 25 reservoir
years). Even this modestly sized dataset included missing values. Missing
values (i.e., reservoir-years of density and TL data; adult saugeye N = 1, adult
panfish N = 4) were replaced using reservoir-specific means calculated using our
balanced dataset.
We fit predictive models of saugeye oversummer mortality by considering
all combinations of saugeye mean stocking length and densities of the four target
species recorded in our fish community data. Reservoir and year were included
in all of the candidate models including the null model as random-effect
variables. We expected that saugeye oversummer mortality would be positively
associated with numbers of predators, specifically black bass and adult saugeye;
with this approach, we also could assess the relationship between saugeye
mortality and panfish abundance. Because stocked fish may remain susceptible
to predation longer when predator populations are large-bodied (Santucci and
75
Wahl 1993), we ran a separate analysis using densities of predators above
certain size thresholds estimated using length distributions that came from of the
monitoring data. In the case of adult saugeye and black bass, we used mean
prey to predator size ratios for largemouth bass feeding on bluegill in laboratory
experiments (0.27, Hoyle and Keast 1987) and pikeperch Sander lucioperca
feeding on natural prey in inland lakes (0.3, Keskinen and Marjomaki 2004) to
define TL thresholds (black bass: 386, saugeye: 357 mm TL) that we could then
use to estimate the abundance of adults capable of consuming an averaged
sized age-0 saugeye during mid-summer (30 June – 22 July, mean TL = 107, SD
= 25 mm, N = 228, estimated from three Ohio reservoirs sampled via
electrofishing during 2008 – 2010, J.L. Kallis unpublished data), a time by which
most predation mortality should have already occurred. Because black and white
crappies larger than 180 mm TL consume fish we used densities of crappie that
were this size and larger (Keast 1968; O'Brien et al. 1984). In a similar fashion,
we used densities of panfish that were larger than 150 mm TL because piscivory
in green sunfish larger than this size is well documented (Lemly 1985; Lohr and
Fausch 1996).
Densities and sizes of panfish
We considered panfish in a separate analysis because panfish (1) can
compete with age-0 saugeye for limited resources, (2) can potentially prey upon
newly stocked saugeye, and (3) have been negatively associated with fall catch
rates of fry- and fingerling-stocked saugeye (Chapter 3 herein). We further
76
sought to validate the relationship between panfish abundance and saugeye
oversummer mortality by using the largest dataset possible (i.e., Ohio panfish
records for which saugeye survival also was available) while at the same time
providing insights into the underlying mechanisms driving the association. Our
panfish dataset included panfish abundance, their TL, and saugeye stocking
length. Because predation is influenced by predator to prey size ratios, we
hypothesized that if panfish affect saugeye via predation, then saugeye
oversummer mortality would be negatively associated with saugeye stocking
length and positively associated with panfish mean length. To test our
hypothesis, we compared the null model and all combinations of saugeye
stocking length, panfish mean length, and panfish abundance. Reservoir and
year were included in all of the candidate models as random-effect variables.
Strong saugeye year classes
Annual fall catch rates of age-0 saugeye in one Ohio reservoir (Pleasant
Hill Lake) revealed that saugeye oversummer mortality was typically high during
the year after a strong age-0 year class, suggesting that strong age-1 year
classes may limit success of newly stocked saugeye. As previously described,
we used densities and sizes of adult saugeye to explain variation in saugeye
oversummer mortality. However, we also used densities of age-1 saugeye
estimated using their densities when they were age-0 to predict saugeye
mortality. We included this second approach because we were concerned that
adult (fish older than age-0) saugeye abundance, which was estimated during fall
77
gillnet surveys, may not be representative of the densities during the previous
spring, when the next cohort of age-0 saugeye would be stocked. Large
numbers of age-1 saugeye, which can contribute more individuals to the
population than all older ages combined (DOW, unpublished data) and thus have
a disproportionate effect, may possibly emigrate or be harvested sometime after
age-0 saugeye are stocked, but before fall predator surveys. Consequently,
using age-0 fall density of a cohort as an index of the size of the cohort the
following summer may better represent actual predation pressure experienced by
newly stocked saugeye. To assess the potential relationship between mortality
of stocked saugeye and densities of age-1 saugeye, we used time-series of fall
population data from the three Ohio reservoirs (Deer Creek, Tappan, and
Pleasant Hill Lakes) the most complete time-series of saugeye survival among
Ohio reservoirs.
Despite high densities of age-0 saugeye during fall, subsequent losses via
emigration or overwinter mortality may dramatically reduce cohort size. We
expected that mortality of age-0 saugeye would be correlated with densities of
age-1 saugeye, as represented by their density when they were age-0 during the
previous fall, especially in systems that retain large numbers of individuals from
each saugeye cohort. Because first-winter mortality is negligible (Donovan et al.
1997; Chapter 2 herein) and because age-0 saugeye often support strong
tailwater fisheries by moving downstream of reservoir impoundments during latefall (Silk 2001), emigration is likely to be the main mechanism driving variation
78
between numbers of age-0 saugeye in the fall and numbers of age-1 saugeye
after their first winter. Consequently, size structure data should reflect the
relative emigration rates of saugeye in each of our three study systems, thus
setting expectations for how age-0 saugeye mortality should correlate with age-1
densities across reservoirs. Proportional size distribution (PSD, formally
proportional stock density; Guy et al. 2007) is used to numerically describe
length-frequency data. Populations with high PSD have greater percentages of
large fish. In Deer Creek Lake, annual PSD from fall gillnet surveys is relatively
low (mean = 38; SD = 17; 2006 – 2010, DOW, unpublished data), indicating that
saugeye stocked into Deer Creek Lake readily leave the system in large
numbers. Relative to Deer Creek Lake, PSD in Tappan Lake (mean = 80; SD =
17; 2003 – 2012) and Pleasant Hill Lake (mean = 63; SD = 21; 2003 – 2012) are
high. Consequently, we expected that the relationship between saugeye
oversummer mortality and fall densities of age-0 saugeye from the previous year
would be strongest in Tappan Lake and Pleasant Hill Lake and weakest in Deer
Creek Lake. To assess potential interannual correlations among saugeye
mortality and densities of age-1 saugeye, we modeled saugeye mortality as a
function of age-1 abundance using their densities when they were age-0.
Because we had specific expectations for how this relationship would vary across
reservoirs, reservoir was modeled as a fixed-effect variable, whereas year was
modeled as a random-effect variable.
79
Response variable
To assess the influence of biotic factors on saugeye stocking success, we
used oversummer instantaneous mortality as the response variable in all of our
analyses. Saugeye instantaneous mortality was estimated from the time of
stocking through fall population assessments (mid-May to early October). By
using instantaneous mortality rather than absolute density in the fall, we sought
to account for inconsistent stocking dates, fall sampling dates, and stocking
densities. Instantaneous mortality was calculated using data from stocking
records (date and number of fish stocked) and number of fall survivors estimated
from a regression model: number per hectare = 0.9763 (log10[catch per hour]) –
0.424 (r2 = 0.74, P < 0.0001, N = 30 reservoir years; DOW, unpublished data).
We calculated instantaneous mortality (Z) from Nt = N0e-Zt, where Nt is the
population size in the fall, N0 is the population size at time of stocking, and t is
the number of days between the day of stocking and fall sampling.
Statistical analysis
Environmental variables and their interactions were modeled as fixed
effects, whereas the factors, year and reservoir (except where noted) were
modeled as random effects (Table 5). We assessed the importance of reservoir
effects in our mixed-model analyses by comparing the full model (i.e., all
predictor variables) with a nested model that did not include reservoir effects
using least likelihood ratio tests (Zuur et al. 2009). Next, we identified the top
models using the second-order Akaike information criterion (AICc; Burnham and
80
Anderson 2002). All candidate models in our mixed-model analysis included the
random-effect variable(s) including the null models which were random-effectsonly models. Variables included in the top models (i.e., those with ΔAIC c less
than 2) were used for inference. Parameter estimates were calculated via
averaging, weighted by AICc model weight, over all models. Peak gizzard shad
density and fall age-0 saugeye density data were log10 transformed to linearize
relationships with saugeye mortality. When noted, we used two-dimensional
Kolmogorov-Smirnov (2DKS) tests to detect potential threshold relationships in
our data (Garvey et al. 1998a). All statistical tests were implemented in R 2.12.2
(2011) and the packages lme4 (Bates et al. 2011) and MuMIn (Barton 2011),
except for 2DKS tests, which were implemented using ez2DKS (Garvey et al.
1998a).
Results
Prey timing and abundance
Mixed model analysis of gizzard shad timing and abundance data
revealed that reservoir effects were not important (least likelihood ratio test, df =
1, P = 0.99). Model selection, the second step of our analysis, yielded one model
with ΔAICc less than 2. The best model included the main effects of relative time
of stocking and peak gizzard shad density and their interaction. The null model
(i.e., random-effects-only model) received substantially less support than the best
model (i.e., ΔAICc greater than 4). Saugeye stocking length was unrelated to
81
saugeye oversummer mortality. Model-averaged parameter estimates revealed
that oversummer mortality of saugeye stocked after peak gizzard shad density
was lower than mortality of saugeye stocked before peak gizzard shad density
(Figure 12). Mortality of saugeye stocked before peak gizzard shad density
declined as peak gizzard shad density increased, whereas mortality of saugeye
stocked after peak gizzard shad density weakly increased as peak gizzard shad
density increased (Figure 12).
Densities of predators and panfish
Analysis of our balanced dataset with densities of adult predators and
panfish revealed that reservoir effects were important (least likelihood ratio test,
df = 1, P = 0.02). Model selection yielded three models having ΔAIC less than 2
(Table 6). The top models included densities of adult black bass, saugeye, and
panfish. The null model, which had ΔAIC less than 4 also received considerable
support (Table 6). Akaike importance weights revealed that adult black bass was
the most important model parameter. Importance weights for adult panfish and
saugeye densities also were high, whereas importance weights for crappie
abundance and saugeye stocking length were low (Figure 13). Model-averaged
parameter estimates indicate that saugeye oversummer mortality decreased as
abundances of adult black bass and saugeye increased and increased as
abundance of adult panfish increased (Table 6, Figures 14A, 14B, 14C).
Reservoir effects were not important when we analyzed the balanced
dataset with densities of large predators and panfish (least likelihood ratio test, df
82
= 1, P = 0.21). Model selection yielded three models, including the null model
with ΔAICc less than 2 (Table 6). The top models included densities of large
panfish and crappie. Akaike importance weights revealed densities of large
panfish and crappie were the most important model parameters, whereas
densities of large saugeye and black bass, which were very important in our
previous analysis, were among the least important model parameters (Figure
13). Consistent with our previous analysis, saugeye stocking length had the
lowest importance weight among all of the predictor variables. Relationships
between saugeye oversummer mortality and abundances of large panfish (Figure
15A) and large crappie (Figure 15B) were highly variable.
Densities and sizes of panfish
We used density of adult panfish, including those smaller than 150 mm TL
to validate the relationship between numbers of panfish and saugeye
oversummer mortality. Analysis of all panfish records indicated that saugeye
mortality varied greatly at low panfish abundance, whereas at high panfish
abundance, saugeye mortality was always high (2DKS test, D = 0.099, P <
0.015, N = 53 reservoir years; Figure 14B). Thus, we used the threshold value
estimated from the 2DKS test (Figure 14B) to create a new categorical variable
with two levels, high and low panfish abundance. We then modeled saugeye
mortality using the categorical variable for large panfish abundance, saugeye
stocking length, and panfish mean length.
83
Mixed model analysis of our panfish dataset revealed that reservoir effects
were important (least likelihood ratio test, df = 1, P < 0.001). Model selection
using AICc yielded two models with ΔAICc less than 2; the model that included
panfish abundance and the null model. Parameter estimates indicated that
saugeye oversummer mortality was higher at high panfish abundance than at low
panfish abundance. We found no evidence that saugeye stocking length or
panfish mean length influenced saugeye mortality.
Strong saugeye year classes
Mortality rates appeared to decline with densities of age-1 saugeye
(Figure 16). Our statistical analysis, however, revealed that this pattern was due
to reservoir effects. Model selection yielded one model having ΔAICc less than 2,
the model with reservoir. The null model received virtually no support and had
ΔAICc greater than 10. Parameter estimates from the best model indicated that
mortality was highest in Tappan Lake, intermediate in Pleasant Hill Lake, and
lowest in Deer Creek Lake (Figure 16).
Discussion
Increasing densities of adult saugeye, black bass, and crappie did not
adversely affect saugeye survival. Saugeye mortality was not influenced by
saugeye stocking length or strong saugeye year classes. Analysis of our
balanced dataset revealed that saugeye mortality increased linearly with panfish
abundance, though the association was best described by a threshold
84
relationship when all panfish records were included. Saugeye mortality was
associated with relative time of stocking and the magnitude of peak gizzard shad
density. Finally, reservoir effects were important in many of our analyses.
Reservoir effects
Results from analyses using the saugeye time-series dataset and the
balanced dataset with densities of adult predators and panfish suggest that
stocking success in Ohio reservoirs is related to some characteristic(s) of the
reservoirs themselves. However, reservoir effects were not important in the
analysis of gizzard shad timing and abundance data or the analysis of large
predators and panfish data, suggesting that reservoir-specific differences in
gizzard shad dynamics and size structure of predator populations may contribute
to reservoir effects detected in some of our other analyses. For example,
survival will be highest in reservoirs that support large numbers of age-0 gizzard
shad and when saugeye stocking occurs after peak gizzard shad density
(Donovan et al. 1997). If the characteristics (e.g., productivity, spring warming
rate) of a given reservoir increase the likelihood that these two criteria will be
met, then overall survival in that reservoir will be higher than in reservoirs where
the probability is relatively low. This will lead to strong reservoir effects in
statistical analyses that do not explicitly include gizzard shad data. In addition to
the size structure of predator populations, other reservoir-specific characteristics
such as reservoir hydrology, which could affect the foraging environment of
85
saugeye and their predators (e.g., turbidity) as well as delivery of nutrients, also
may contribute to differences in saugeye mortality rates across reservoirs.
Prey timing and abundance
Availability of alternative prey can strongly influence predation (Forney
1974), as it does with age-0 stocked saugeye (Donovan et al. 1997). Predators
that are eating age-0 saugeye are also eating other prey types. Further, they
consume relatively few saugeye when alternative prey such as gizzard shad are
abundant (Donovan et al. 1997). Consequently, mortality of saugeye stocked
before peak gizzard shad density was higher than mortality of saugeye stocked
after peak gizzard shad density. Donovan et al. (1997) also observed that
survival of saugeye stocked into Ohio reservoirs was positively associated with
the magnitude of peak gizzard shad density. In our study, mortality of saugeye
stocked before peak gizzard shad density declined as peak density increased.
Peak gizzard shad density is positively correlated with reservoir productivity
(Bremigan and Stein 2001), and with higher productivity comes greater amounts
of prey. Thus, we suggest that instead of saugeye, predators consumed
alternative prey that was available before peak gizzard shad density, which
enhanced saugeye oversummer survival. These prey types may have included
non-gizzard shad prey as well as gizzard shad that hatched before peak density.
Mortality of saugeye stocked after peak gizzard shad density increased as
the magnitude of the peak increased. This result is surprising given that
predation buffering enhances survival of saugeye stocked after peak gizzard
86
shad density. Saugeye stocked after peak gizzard shad density, however, also
experience reduced growth potential (Donovan et al. 1997), which may
negatively affect saugeye survival by prolonging the period of vulnerability to
gape-limited predators. For example, age-0 walleye in Oneida Lake that grow
quickly minimized the period of vulnerability to predation, positively influencing
year-class strength (Forney 1976). If high gizzard shad growth rates increased
saugeye mortality via indirect effects on saugeye growth potential, and if peak
gizzard shad density was positively correlated with gizzard shad growth rates,
then saugeye mortality and peak gizzard shad density could be positively
correlated. Consistent with this hypothesis even at high densities, age-0 gizzard
shad grow rapidly in hypereutrophic Ohio reservoirs (Bremigan and Stein 1999).
Thus, saugeye stocked after peak gizzard shad density survived better as
numbers of gizzard shad increased, but these same saugeye were adversely
affected by high gizzard shad growth rates reducing their vulnerability to age-0
saugeye. Consequently, saugeye stocked after peak gizzard shad density will
survive better in systems that support low gizzard shad growth rates and survive
less well in systems that support high gizzard shad growth rates.
Predators and the importance of panfish
Year-class strength of age-0 percids in other systems has been correlated
with predator densities. For example, fall catch rates of age-0 walleye were
negatively correlated with densities of largemouth bass in Lake Oneida (Brooking
et al. 2001). In contrast, largemouth bass had only negligible effects on walleye
87
stocking success in Illinois reservoirs (Freedman et al. 2012). Research using
saugeye stocked into Ohio reservoirs suggests that predation mortality is highly
variable. Predation mortality was 0 – 75% of the stocked population across four
Ohio reservoirs (Stahl et al. 1996) and was linked to complete cohort failure in
two years of stockings in one other (Aman 2007). In our analysis, we did not
detect an effect of saugeye and black bass densities on age-0 saugeye
oversummer mortality. Because our predator analysis used the balanced but
limited dataset, we also could not test for the effect of gizzard shad timing and
abundance, which we know to be important in explaining success of stocked
saugeye. Thus, our analysis may not have been able to detect a predator effect
if it interacted with timing and abundance of gizzard shad. Future attempts to link
predator densities with saugeye mortality must consider additional environmental
factors such as gizzard shad dynamics (i.e., abundance, growth, timing).
We considered how (1) total abundance of panfish and (2) abundance of
large panfish influenced saugeye mortality using a balanced dataset and (3)
using a dataset that included all available panfish records. This corresponded to
three separate analyses. Analysis of the balanced dataset revealed that
saugeye mortality increased linearly with total panfish abundance, whereas
analysis of the larger panfish dataset revealed that saugeye mortality and panfish
abundance were related via a threshold relationship. Saugeye mortality was high
at high panfish abundance and low at low panfish abundance. Saugeye mortality
was unrelated to abundance of large panfish.
88
In a separate study, panfish abundance in Ohio reservoirs had a strong
negative correlation with fall catch rates of fry- and fingerling-stocked saugeye
(Chapter 2 herein). Schneider (1997) observed that recruitment of age-0 walleye
in a Michigan lake declined once bluegill densities reached 50 kg/ha, but was
unable to determine the underlying mechanism. In addition to densities, species
composition of panfish populations also may influence saugeye mortality rates.
Etnier (1971) observed that green sunfish and their hybrids, which are present in
Ohio reservoirs, consumed more fish prey than either bluegill or pumpkinseed in
three Minnesota lakes. We also sought to determine if our panfish dataset
offered insights into how panfish possibly affect age-0 saugeye. Because the
period of vulnerability increases with predator-to-prey size ratio, either a positive
relationship between saugeye mortality and adult panfish size, or a negative
relationship between saugeye mortality and saugeye stocking length would have
suggested that panfish consume saugeye, presumably immediately after
stocking until saugeye become invulnerable to gape-limited panfish. Mean
panfish length and mean saugeye stocking length, however, were unrelated to
saugeye mortality rates.
Strong saugeye year classes
Strong saugeye year classes result in high numbers of age-1 saugeye the
following year, which could then compete with or cannibalize newly stocked age0 saugeye. Forney (1976) suggested that strong year classes of age-0 walleye
in Oneida Lake would increase mortality of subsequent cohorts through
89
cannibalism. In a modeling study, Lantry and Stewart (2000) showed that
cannibalism by age-1 and older rainbow smelt drove large fluctuations in
recruitment rates in Lakes Ontario and Erie. In our study, densities of age-1
saugeye, as estimated using their densities when they were age-0, was
unrelated to saugeye mortality. In fact, saugeye mortality rates appeared to
decline with age-1 abundance; however, this relationship was best explained by
reservoir-specific patterns in mortality (i.e., reservoir effects). Our findings
indicate that reservoir effects, such as reservoir-specific patterns in gizzard shad
abundance and timing, are better predictors of saugeye survival than densities of
age-1 saugeye.
Management recommendations
Our research yielded new insights into how relative time of stocking and
peak gizzard shad density influenced saugeye stocking success. We found that
the magnitude and timing of peak gizzard shad availability influences saugeye
oversummer mortality, presumably via interactions with predator densities. The
limitations of our dataset prevented us from considering the interaction between
densities of predators and gizzard shad timing and abundance, possibly
explaining why we were unable to link predator densities with saugeye mortality.
Despite the substantial effort required to collect prey data, future attempts to
understand the effects of predators on first-year saugeye survival must, at a
minimum, account for gizzard shad timing, abundance, and growth. Data on
panfish abundance, which was correlated with saugeye mortality, turbidity, which
90
may influence quantity and quality of predator refugia (Gibson 1994), and
temperature, which may strongly influence predator consumptive demand (Aman
2007) and saugeye growth rates (Zweifel et al. 2010), should also be collected
alongside prey data to provide a comprehensive dataset for future analyses.
Previous research indicated that managers can maximize growth by
stocking saugeye before peak gizzard shad density or maximize survival by
stocking saugeye after peak prey production, but not both (Donovan et al. 1997).
Conceivably, some reservoirs may support high amounts of alternative prey that
is present before peak gizzard shad density; in these systems, managers may be
able to stock before the peak thus achieving high survival and growth. Managers
may be able to minimize mortality of saugeye stocked after peak gizzard shad
density by stocking into systems with lower gizzard shad growth rates.
Our research supports the idea that gizzard shad are an important
component of reservoir ecosystems (Vanni et al. 2005). Explaining annual
variation in the timing and magnitude of peak gizzard shad density is difficult.
However, given that gizzard shad peak abundance and timing is critical to
saugeye survival as well as to survival of other important species such as black
bass additional effort may be justified.
91
Obj.
Analysis
1 Gizzard shad timing
and abundance
Dataset
Gizzard shad
dataset
Fixed-effects variables
fRTS, GS, SL
N
36
25
2,3
Densities of adult predators Balanced
and panfish
dataset
Bass, SAE, Pan, Crappie
2,3
Densities of large adult
predators and panfish
Balanced
dataset
Bass396, SAE357, Pan150, Crappie180, SL 25
3
Effects of panfish
Panfish records fPan, PanL, SL
53
4
Strong saugeye year
classes
Saugeye
time-series
43
Age-1 SAE, fRes
Table 5. Organizational table associating objectives (Obj.), analyses, datasets,
and the fixed-effects variables and the number of reservoir years (N) included in
each analysis. Continuous fixed-effects variables included peak gizzard shad
density (GS), mean saugeye stocking length (SL), and black bass (Bass), panfish
(Pan), crappie (Crappie), and saugeye (SAE) adult abundances, panfish mean
length (PanL), and age-1 saugeye abundance (Age-1 SAE). Variables with
subscripts represent abundances (i.e., LMB396, SAE357, Pan150, Crappie180) of
fish above the length threshold indicated by the subscript. Fixed effects variables
that were modelled as factors/categorical variables included abundance of
panfish (fPan; high or low abundance assigned using the threshold value from
2DKS test) relative time of stocking (fRTS; before or after peak gizzard shad
density), and reservoirs (fRes). Random effects variables included reservoir and
year, except for analysis of strong saugeye year classes (reservoir modelled as
fixed effects variable).
92
Model
A) Densities of adults
LMB(+), SAE(+)
LMB(+), Pan(-)
LMB(+), Pan(-), SAE(+)
Null
B) Densities of large adults
Null
Pan150(-)
Pan150(-), Crappie180(+)
K
AICc
ΔAICc
W
6
6
7
4
-195.2
-194.6
-194.1
-191.8
0.0
0.6
1.1
3.3
0.25
0.18
0.15
0.05
4
5
4
-191.8
-191.9
-191.3
0.0
0.1
-0.6
0.20
0.20
0.16
Table 6. Statistics of the top candidate models, and for comparison, the null
models (i.e., random effects models) explaining variation in age-0 saugeye
oversummer instantaneous mortality in five Ohio reservoirs, 2006 – 2010 (N = 25
reservoir years). Results are from two separate analyses: A) analysis of the
balanced dataset with densities of adults and B) analysis of the balanced dataset
with densities of large adults only. Data include number of parameters (K), AICc,
difference between each model and the model with the minimum AICc (ΔAICc),
and model weight (W). For variable ID’s, see Table 5.
93
Figure 12. Regressions showing the relationship between age-0 saugeye
oversummer instantaneous mortality and peak gizzard shad density for saugeye
stocked before (solid line and datapoints, N = 24) and after (dashed line and
open datapoints, N = 12) peak gizzard shad density in 11 Ohio reservoirs, 1991 –
2010 (N = 36). Regression parameters were estimated using mixed models.
Results from the top model are presented.
94
Figure 13. Akaike importance weights for variables used in candidate models
that predict age-0 saugeye oversummer instantaneous mortality in five Ohio
reservoirs, 2006 – 2010 (N = 25 reservoir years). Results are from two separate
analyses, analysis of the balanced dataset with densities of adults and analysis
of the balanced dataset with densities of only large adults. For variable ID’s, see
Table 5.
95
Figure 14. Age-0 saugeye oversummer instantaneous mortality as a function of
black bass and panfish densities (A and B, sampled via spring electrofishing) and
saugeye densities (C, sampled via fall gillnetting) in five Ohio reservoirs stocked
during 2006 – 2010 (solid circles, N = 25). Also included are all panfish records
from historical data (open circles, 13 additional Ohio reservoirs stocked during
2003 – 2010, N = 28 additional reservoir years).
96
Figure 15. Age-0 saugeye oversummer instantaneous mortality as a function of
large panfish density (A, sampled via spring electrofishing) and large crappie
density (B, sampled via fall trapnetting) in five Ohio reservoirs stocked during
2006 – 2010 (N = 25).
97
Figure 16. Oversummer age-0 saugeye instantaneous mortality at year t+1 as a
function of fall density of age-0 saugeye stocked at year t in three Ohio reservoirs
stocked during 1993 – 2010 (N = 43).
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CHAPTER 5
Does growth during the first weeks of life explain survival of a reservoir-stocked
piscivore?
Introduction
Recruitment variability in fish populations has received considerable
investigation in fisheries research. Early findings highlighted the importance of
starvation (Hjort 1914) during early life stages. Recent studies have explored
how traits of individual fish, such as growth rates or hatch dates, influence
mortality outcomes during the larval and juvenile stages (e.g., Ludsin and
DeVries 1997; Pine and Allen 2001; Weber et al. 2011). Because mortality is
often mediated by body size, one of the most consistent factors implicated in fish
recruitment is growth during early life stages. Among other benefits, growing
rapidly minimizes the period of vulnerability to gape-limited predators (Meekan
and Fortier 1996; Vigliola et al. 2007), increases the breadth of available prey
types and sizes (Crowder et al. 1987), and enhances resistance to environmental
extremes (Luecke et al. 1990).
Identifying traits of individual fish associated with high survival can provide
insights into the major sources of mortality. For example, evidence of size- or
growth-dependent survival during early life stages may be indicative of predation,
99
which can be strongly size-dependent (Rice et al. 1993). To assess growthdependent survival in fish populations, many researchers compare the survival of
fast- versus slow-growing individuals hatched at different times (e.g., early or late
in the season). However, given that factors such as predation intensity can vary
through the hatch period, survival of fish hatched at different times may not be
comparable. Removing the confounding effects of different hatch times by using
same-age fish would provide a robust test of the growth rate - survival link. Fish
from hatchery ponds are, therefore, ideal for exploring the relationship between
growth and survival as they are of common age and experience similar growth
and survival conditions in hatchery ponds.
Saugeye (male Sander canadensis X female S. vitreus) is a popular
sportfish commonly stocked into Ohio reservoirs (Hale et al. 2008). Raised in
hatcheries to the fingerling stage (30 mm total length (TL)), saugeye are stocked
during early spring when prey availability for resident piscivores can be low
(Donovan et al. 1997). They suffer high predation rates until they grow
sufficiently large to become invulnerable to gape-limited predators or until
predation rates are buffered by alternative prey, such as age-0 gizzard shad
(Donovan et al. 1997). Gizzard shad dominate total prey biomass and are the
primary prey resource for age-0 saugeye (Denlinger et al. 2006). However,
gizzard shad are deep-bodied, grow quickly, and thus rapidly become
invulnerable to gape-limited predators (Donovan et al. 1997). Interactions among
age-0 saugeye and their predators and prey suggest that high growth rate during
100
early life stages can both increase the likelihood that gizzard shad are available
as prey and shorten the period of vulnerability of saugeye to predators.
As part of a larger study to determine mechanisms driving saugeye
stocking success, we tested whether hatchery growth of individual saugeye
influences post-stocking survival, and perhaps initial in-reservoir growth. We
used otoliths to characterize the early growth rate distribution of same-age
saugeye from hatchery ponds. We then compared this distribution to the growth
rate distributions of survivors collected after three possible mortality bottlenecks:
(1) summer, to assess potential starvation and predation mortality during the first
few weeks after stocking, (2) fall, to assess oversummer predation mortality, and
(3) spring, to assess the effects of early growth on overwinter mortality. We
hypothesized that saugeye that grew quickly in hatchery ponds would have
survive better than saugeye that grew slowly. In addition, because peak mortality
occurs during the first few weeks after stocking, when saugeye are most
vulnerable to gape-limited predators, we hypothesized that selection pressure
would be strong within the first growing season, but not during overwinter.
Methods
Two cohorts of saugeye, one stocked into Deer Creek Lake during 2009
and one stocked into Atwood Lake during 2010 were sampled. Deer Creek Lake
is a eutrophic reservoir located in southwest Ohio, whereas Atwood Lake, also
eutrophic, is located in northeast Ohio. Both reservoirs received a single
stocking of fingerlings from St. Mary’s State Fish Hatchery (Table 7). Just prior
101
to stocking, saugeye were sampled from the transport vehicle. Saugeye were
sampled in reservoirs during summer, fall, and spring via night shoreline
electrofishing (Table 7). To increase sample sizes, initial catches were
augmented with fish from targeted sampling sites that yielded greater numbers of
fish in Atwood Lake. In the laboratory, we measured fish TL (nearest 0.1 mm)
and wet weight (nearest 0.001 g) and then extracted sagittal otoliths. Due to a
processing error, length and weight were not measured for hatchery fish that
were to be stocked into Deer Creek Lake. Ultimately, we extracted otoliths from
358 individuals (N = 7 – 71 per collection per reservoir), collected on the day of
stocking, as well as survivors, collected from reservoirs, on each of three dates
(Table 7).
In the laboratory, sagittal otoliths were removed, dried, and embedded on
glass microscope slides using cyanoacetate. Using progressively finer lapping
film (1, 3, and 9 µm), otoliths were polished on a glass surface until the origin,
hatch mark, and daily growth rings were discernible. Next, we measured early
growth increment (EGI), defined as the distance from the hatch mark to the
twentieth daily ring, using digital analysis software and 500 X magnification. We
defined EGI using the twentieth daily ring as it was the maximum daily growth
ring that we could consistently and reliably discern.
We used linear regressions to test whether our index of growth (i.e., EGI)
was correlated with fish mass across our three sampling dates. A one-way
ANOVA was used to test if mean daily EGI’s differed across dates (i.e., hatchery,
102
summer, etc.). Tukey’s Honestly Significant Difference (HSD) was used to make
pairwise comparisons across dates. All statistical analyses were implemented in
R 2.12.2 (2011).
Results
Mass of individual saugeye that were to be stocked into Atwood Lake
increased as EGI increased (Figure 17; linear regression, P < 0.001). Summer
mass of individual fish was unrelated to EGI in saugeye captured from Deer
Creek Lake (Figure 18A; P = 0.28) and Atwood Lake (Figure 18B; linear
regression, P = 0.56).
Reservoir-specific analyses revealed that mean EGI varied significantly
across sample collections (ANOVA, Deer Creek Lake and Atwood Lake, P <
0.001). Within each reservoir, mean EGI of fish from the hatchery was lower
than mean EGI of survivors collected from the reservoir (Tukey’s HSD, P <
0.003), except for mean EGI of saugeye captured from Atwood Lake during
spring (Figure 19, Tukey’s HSD, P = 0.09). The spring collection from Atwood
Lake, however, was relatively small (Table 7). Mean EGI did not vary among
summer, fall, and spring collections from Deer Creek Lake or Atwood Lake
(Figure 19, Tukey’s HSD, P > 0.05).
Discussion
Early growth increment of saugeye that were to be stocked into Atwood
Lake was positively associated with wet mass at time of stocking. Thus,
103
individuals with high EGI were generally larger at time of stocking than those with
low EGI. Early growth increment was unrelated to summer saugeye wet mass in
both study reservoirs. Thus, the initial size differences that were reflected in EGI
at time of stocking were not maintained after fish were stocked, suggesting that
fast-growing individuals in the hatchery were not necessarily the same individuals
that grew fast in the reservoir.
Saugeye stocked into Deer Creek Lake and Atwood Lake exhibited
growth-dependent survival. The shift in mean EGI observed between time of
stocking and summer coincides with when saugeye were most vulnerable to
gape-limited predators. During this time, stocked saugeye underwent selective
mortality with the preferential loss of individuals that grew slowly during the first
weeks of life. As a result, year classes of stocked saugeye were probably
dominated by individuals that grew quickly in the hatchery. Early growth in the
hatchery explained patterns in early survival (i.e., between stocking and
summer), but not for the remainder of the first year of life.
In many fish populations, mortality is non-uniform across hatch dates.
Individuals associated with successful hatch dates are often those that exhibit
high growth rate during early life stages. For example, late-hatched larvae of
bloater in Lake Michigan and black crappie in a Florida lake exhibited higher
growth rates and contributed more individuals to their year classes than earlyhatched larvae (Rice et al. 1987; Pine and Allen 2001). However, it is not clear
whether these differences were due to factors related to hatch timing (e.g., timing
104
and duration of predator–prey interactions) or to growth- or size-dependent
processes, such as predation mortality. Hatch date and location can strongly
influence survival during early life stages. For example, while growth of larval
yellow perch from two different locations in Lake Erie was similar, their survival
rates differed substantially (Reichert et al. 2010).
Fast-growing saugeye exhibited higher survival to summer than slow
growing ones. By reaching large size, individuals increase the number of prey
types and sizes they can eat (Crowder et al. 1987; Bremigan and Stein 1994;
Graeb et al. 2004) and simultaneously reduce the number of predators capable
of eating them, as well as reduce their vulnerability to abiotic conditions (Werner
and Gilliam 1984; Luecke et al. 1990). Although each of these mechanisms
could explain patterns in our data, reduced predation risk stands out as the most
likely one. Ohio reservoirs support large populations of predators including
largemouth bass and adult saugeye (Stahl et al. 1996). Survival of stocked
saugeye cohorts has also been linked to panfish (Lepomis spp.) abundance;
however, the underlying mechanisms have not been identified (Chapters 3 and 4
herein). If panfish, which have relatively small gapes, consume newly stocked
saugeye, then the small advantages in body size exhibited by hatchery fish could
have had large survival rewards in the reservoir.
In some fish populations, relationships among early life stages are
strongly linked. For example, Ludsin and DeVries (1997) found that growth and
survival at a given life stage was correlated with growth and survival in
105
subsequent life stages of largemouth bass in experimental Alabama ponds.
Herein, growth in the hatchery explained patterns in early survival (i.e., between
stocking and summer), but not during the remainder of the first year of life. This
may have been because the relative size differences among fish being stocked,
which were reflected in fish EGI, were not maintained in the reservoir. Although
EGI was correlated with saugeye wet mass at time of stocking, EGI was
unrelated to saugeye summer wet mass. If selection pressure favored larger
body sizes during the post-summer periods, then this analysis would not have
detected it given that there was no relationship between fish EGI and summer
wet mass.
Results from this study suggest that high growth rates in the hatchery
provide saugeye with an initial size advantage that minimizes the number of
predators capable of eating them as well their period of vulnerability. To test this
hypothesis, future studies could compare growth-dependent survival across a
gradient of predator densities. Panfish densities should be considered, given
that they have relatively small gapes and have been negatively correlated with
saugeye survival (see Chapters 2 and 3). The relationship between growth rate
in the hatchery and year class strength must be quantified. This study found
that smaller, slowly growing individuals suffered higher mortality than larger fast
growing individuals; however, it is unclear how this affected population size. If
growth in the hatchery correlates with year-class strength, then the critical period
of growth (e.g., 1st 20 d) and the factors (e.g., temperature, maternal effects) that
106
influence it should be identified. The identification of recruitment mechanisms
that can be manipulated via management practices advances our ability to
manage fish populations. Given that growth rate in the hatchery can be
influenced by fish culture techniques, the relationship between growth and year
class strength should receive further investigation.
107
Reservoir
(Year)
Deer Creek
Lake (2009)
Mean sizes (1 SD)
TL (mm)
Mass (g)
29.4*
Collection dates (Sample size)
Hatchery Summer
Fall
Spring
28 May
(64)
0.21*
15-16 Jul
(71)
20 Oct
(51)
8 Apr
(46)
Atwood Lake
28.7
0.165
22 May 21-22 Jul 27 Sep 9 May
(2010)
(2.22)
(0.026)
(45)
(51)
(23)
(7)
Table 7. Summary information from two Ohio reservoirs stocked with saugeye
during 2009 and 2010 including mean total length (TL) and wet mass at stocking
and fish/otolith collection dates and sample sizes (i.e., unique fish). All
collections were conducted during the same year fish were stocked, except for
the spring collection which was conducted during the subsequent year. *Due to a
processing error mean size was not recorded. Mean sizes here are from Ohio
Division of Wildlife stocking records (N = 30 saugeye).
108
Atwood Lake
R2 = 0.32
P < 0.001
N = 64
Figure 17. Wet mass of individual saugeye on the day (22 May 2010) they were
stocked into Atwood Lake, Ohio as function of early growth increment (i.e.,
distance from the hatch mark to the twentieth daily ring).
109
Deer Creek Lake
R2 < 0.01
P = 0.56
N = 71
Atwood Lake
R2 < 0.01
P = 0.28
N = 51
Figure 18. Wet mass of individual saugeye as a function of early growth
increment (i.e., distance from the hatch mark to the twentieth daily ring) sampled
on 15 and 16 July 2009 from Deer Creek Lake (top) and on 21 and 22 July from
Atwood Lake, Ohio (bottom).
110
Figure 19. Box plots of early growth rate distributions of age-0 saugeye from the
hatchery and survivors from Deer Creek Lake (top), stocked during 2009 and
Atwood Lake (bottom), stocked during 2010 and sampled via electrofishing
during summer, fall, and spring. The horizontal line denotes the median value;
the box, the inter-quartile range; the vertical dashed line, 1.5 times the interquartile range; points outside the vertical dashed lines indicate outliers. Letters
above each boxplot denote statistical differences detected using Tukey’s HSD.
111
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