An Ecological Approach to Management of an Important Reservoir Fishery

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

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

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.

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.

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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

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.

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

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

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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

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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

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

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

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

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

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

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

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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

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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 inter- quartile range; points outside the vertical dashed lines indicate outliers. Letters above each boxplot denote statistical differences detected using Tukey’s HSD...... 111

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 management- related 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

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.

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).

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.

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 pre- winter 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

(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 2- sample 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.

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 field- recaptured 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 cool- water 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 mass- specific 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.

Mean chl a Energy density collection dates Reservoir Latitude (µg/L) 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 40.1843˚ 9.7 1 & 11 Nov 2010 (Alum Creek Lake)

Post-experimental fish 15 Jun 2011

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.

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.

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).

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.

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,

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

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 fingerling- stocked 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 fry- stocked 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 fingerling- stocked 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.

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 fingerling- stocked saugeye were similar, though fry-stocked saugeye, were more prone to year-class failure.

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 fingerling- stocked 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 fingerling- stocked 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 fry- stocked 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.

Surface Mean Black Adult Age-0 area chl a bass saugeye saugeye Reservoir Reservoir ID (ha) (µg/L) (CPUE) (CPUE) (CPUE) years Fry Seneca SE 1,420 23.3 62 2.9 NA 3-3 Alum Creek AL 1,323 9.0 39 0.8 46 3-3 Clendening CL 663 22.0 65 1.9 7 3-2 Delaware DL 412 32.6 51 0.2 5 2-0 Fingerlings Deer Creek DC 521 41.6 50 2.0 145 2-2 Hoover HV 1,140 18.9 52 1.8 24 3-3 Tappan TA 863 27.2 83 2.2 21 2-2 Pleasant Hill PH 318 32.4 121 3.7 85 3-3

Table 3.

Reservoir Stocking density Stocking Mean length Time in reservoir ID (Number/ha) date (mm) (d) Fry 2008 AL 2371 18 April 166 CL 2390 19 April 165 DL 2040 18 April 166 SE 2464 19 April 167 2009 AL 2368 15 April 166 CL 2383 20 April 165 DL 2094 17 April 168 SE 2422 19 April 165 2010 AL 2368 12 April 173 CL 2382 11 April 174 SE 2417 12 April 173 Fingerlings 2008 DC 603 21 May 31 133 HO 474 22 May 29 132 PH 502 27 May 26 127 TA 348 27 May 30 127 2009 DC 551 28 May 30 127 HO 545 27 May 31 130 PH 248 2 June 33 122 TA 233 2 June 33 122 2010 DC 248 18 May 29 136 HO 243 19 May 26 136 PH 250 22 May 29 133 TA 92 22 May 29 132

2 K AICc ΔAICc W R P Saugeye fall density Fry ZP(+), Temp(+) 3 18.11 0.0 0.20 0.85 < 0.001 ZP(+), Temp(+), Pan(-) 4 18.96 0.8 0.13 0.92 < 0.001 Null 1 29.96 11.9 0.00 Fingerlings Pan(-), RTS(+), SAE(+) 4 1.27 0.0 0.86 0.98 < 0.001 Null 1 20.12 18.8 0.00 Saugeye fall length Fry RTS(-) 2 59.29 0.00 0.59 0.80 0.002 Age-0 SAE(-) 2 60.60 1.31 0.31 0.77 0.003 Null 1 67.73 8.44 0.01 Fingerlings TIR(+), Pan(+) 3 88.83 0.00 0.33 0.67 0.008 Null 1 92.26 3.43 0.06

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).

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.

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.

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.

Figure 9

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 age-

1 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 reservoir- years 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

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 late- fall (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.

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).

-Zt We calculated instantaneous mortality (Z) from Nt = N0e , 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-effects- only models. Variables included in the top models (i.e., those with ΔAICc 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.

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.

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 age-

0 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.

Obj. Analysis Dataset Fixed-effects variables N 1 Gizzard shad timing Gizzard shad fRTS, GS, SL 36 and abundance dataset Densities of adult predators Balanced 2,3 Bass, SAE, Pan, Crappie 25 and panfish dataset Densities of large adult Balanced 2,3 Bass , SAE , Pan , Crappie , SL 25 predators and panfish dataset 396 357 150 180 3 Effects of panfish Panfish records fPan, PanL, SL 53 4 Strong saugeye year Saugeye Age-1 SAE, fRes 43 classes time-series

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).

Model K AICc ΔAICc W A) Densities of adults LMB(+), SAE(+) 6 -195.2 0.0 0.25 LMB(+), Pan(-) 6 -194.6 0.6 0.18 LMB(+), Pan(-), SAE(+) 7 -194.1 1.1 0.15 Null 4 -191.8 3.3 0.05 B) Densities of large adults Null 4 -191.8 0.0 0.20

Pan150(-) 5 -191.9 0.1 0.20

Pan150(-), Crappie180(+) 4 -191.3 -0.6 0.16

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 growth- dependent 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 early- hatched 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 Mean sizes (1 SD) Collection dates (Sample size) (Year) TL (mm) Mass (g) Hatchery Summer Fall Spring Deer Creek 28 May 15-16 Jul 20 Oct 8 Apr 29.4* 0.21* Lake (2009) (64) (71) (51) (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

109

Deer Creek Lake R2 < 0.01 P = 0.56 N = 71

Atwood Lake R2 < 0.01 P = 0.28 N = 51

110

111

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