Abstract Brey, Marybeth Kathryn

ABSTRACT

BREY, MARYBETH KATHRYN. Quantifying the Trophic Interactions of Introduced Fish on a Reservoir Food Web. (Under the direction of Dr. James A. Rice and Dr. D. Derek Aday).

As introductions to aquatic ecosystems become more commonplace, potential rises for trophic interactions to occur between multiple invaders. Reservoirs are particularly susceptible to invasion and often contain several introduced fish species, making them optimal systems in which to quantify the changes to community and trophic dynamics. Lake

Norman, an impoundment of the Catawba River in North Carolina, is stocked annually with striped bass Morone saxatilis (an example of an intentional sport fish introduction) and has been subjected to multiple species introductions (flathead catfish Pylodictis olivaris, white perch Morone americana, spotted bass Micropterus punctulatus, and alewife Alosa pseudoharengus) since its impoundment in 1963. With the goal of understanding trophic relationships between introduced species and the rest of the food web, I examined the specific relationships between stocked striped bass fingerlings and potential predators and competitors, developed an ecosystem trophic model of the reservoir, and simulated invasive species removal and management scenarios. Using analyses of diet and catch-per-unit-effort data, I found no evidence of high predation mortality on stocked fingerlings following stocking, and only moderate and short-lived diet overlap with striped bass and co-occurring nearshore fishes. To evaluate trophic interactions throughout the entire food web I used an

Ecopath with Ecosim trophic modeling approach. I parameterized an Ecopath model with 24 functional groups using empirically derived fish biomass and diet data collected on Lake

Norman from 2007-2009. Analysis of the balanced Ecopath model revealed that introductions increased the overall trophic level of fish in the reservoir. Knowing synergies among multiple invaders may produce effects greater than the sum of their parts, I ran simulations using Ecosim to investigate the effects of each introduced species individually, and combinations of species simultaneously, on trophic interactions and community structure. My simulations indicated that the effects of the four introductions acted in a largely nonadditive, antagonistic manner on the established fish community, resulting in combined effects that were typically less than the sum of their individual impacts. I also determined that some invaders, such as alewife and spotted bass, could ameliorate the negative consequences of previous invaders, such as white perch, by decreasing predation pressure on some species and reducing competition for others. Finally, I simulated the potential impacts of several system alterations and management strategies in the reservoir.

Management practices that supplemented fish biomass (e.g., additional striped bass stocking or additional threadfin shad Dorosoma petenense stocking) were unsuccessful in enhancing sport fish populations. It may also be difficult to control invasive white perch or enhance sport fish biomass with increased harvest of the white perch population because current harvest would have to be increased at least ten-fold to cause even modest changes. This investigation is the first study to quantify the ways in which multiple introduced species impact an established reservoir ecosystem. It provides insight into whether multiple invaders function independently or synergistically in food webs, and provides a template for future modeling of potential mitigation or management strategies for these frequently invaded systems.

by Marybeth Kathryn Brey

A dissertation submitted to the Graduate Faculty of North Carolina State University in partial fulfillment of the requirements for the degree of Doctor of Philosophy

Zoology

Raleigh, North Carolina

2012

APPROVED BY:

______James A. Rice D. Derek Aday Committee Chair Committee Co-chair

______Dr. James Gilliam Dr. Kevin Gross

DEDICATION

For my parents, Keith and Cheryl, sister Melissa who have supported me through every crazy

thought, random idea, and late night meltdown. You remind me that the best things in life

aren’t things.

For my grandfathers, Ed Brey and Floyd Van Wagner, whose unconditional love and support

got me through more than he will ever know. The memories of you play through my mind

daily. This is for you.

And for anyone who has dealt with mental illness:

"One ceases to recognize the significance of mountain peaks if they are not viewed

occasionally from the deepest valleys."--- Dr. Al Lorin

BIOGRAPHY

Born in the frozen tundra of the Upper Peninsula of Michigan, I grew up on the southern shore of Lake Superior (Eagle River) with my parents, sister, two dogs and a horse.

Contrary to popular belief, I am not Canadian, although my career choice and hobbies may indicate otherwise. I spent summers hiking, camping, horseback riding and leading copper mine tours, and winters snowboarding, playing volleyball, and figure skating. I graduated from Calumet High School in 1999 and made the journey south to Central Michigan

University. Although I started out with a pre-veterinary major, I eventually found my niche after taking a field ecology course with Dr. Beth Leuck at CMU’s biological station on

Beaver Island in Lake Michigan, and found my field while taking Tracy Galarowicz’s

Fisheries Biology course. After a study abroad stay at Exeter University in England, where I developed my love of traveling, I graduated from CMU with my B.S. in 2003. I spent the next year as a technician for the Michigan Department of Natural Resources at the Great

Lakes Fisheries Research Station in Charlevoix, MI and as a volunteer at the Jordan River

National Fish Hatchery. There I learned what it meant to be a fisheries biologist. I developed an interest in food web dynamics and invasive species research that has not diminished. This interest developed into my Master’s project at Eastern Illinois University under the direction of Dr. Bud Fisher. I graduated with my M.S. in Biology from E.I.U. in

2006 and quickly moved to Raleigh to start my Ph.D.

I rarely let my ―schooling interfere with my education (Mark Twain),‖ and have taken every opportunity to travel, learn a new craft, and meet new people, occasionally to the

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dismay of my advisors. Throughout my life, I have also dealt with sometimes-extreme bouts of anxiety and depression, but am proof that one can work hard and make it to the light at the end of the tunnel with support, love, and perseverance!

ACKNOWLEDGMENTS

This project would not have been possible without the collaborative effort from an insane number of individuals. First and foremost, words cannot express my gratitude to my advisors: Jim Rice and Derek Aday. They continued to believe in me even when I didn’t believe in myself. Their patience, emotional and intellectual support, advice, and even humor allowed me to push through the tough times and enjoy the good ones. I could not have asked for better mentors or a better lab. Other members of the FEAS lab provided equal amounts of technical assistance, brute labor, sarcasm, and advice, including Zach Feiner,

Steve Midway, Dan Brown, Bethany Galster, Kelsey Lincoln, Sally Petre, and Carrie

Russell. The work of a large number of undergraduate students, technicians, and volunteers including Caroline Steiner-Andrews, Miranda Wood, Shane Sills, Leslie Freeman, Leah

Snyder, Jeremy Remington, Ben Noffsinger, and Lindsey Garner contributed to the completion of this dissertation. Special thank you to Lindsay Campbell and Dana Sackett, whose constant support both in and out of the office kept me going throughout my tenure at

NC State.

Members of the NC Wildlife Resources Commission, especially Christian Waters and

Brian McRae, provided both field help and intellectual support during the entire project period. I learned a great deal from them. Additional members of the NCWRC helped immensely with field and laboratory work including: Nick Jeffers, Heather Dendy, Bryan

Richardson, Aaron Bunch, Corey Oakley, and Nick Shafer. Duke Energy biologists Dave

Coughlan and Mike Abney gave me a good deal of advice, data, patience, and friendship.

My thanks to all the members of the Duke Energy fisheries team: Kim Baker, Bob Doby,

Brian Kalb, Dave Coughlan and Mike Abney, who spent a good deal of time putting up with me to catch the ―young’uns‖ and the ―big’uns,‖ and always watched out for us when we were on the water.

This dissertation would never have happened without the love and support from my family and friends. My mom, dad, sister, Melissa Collard, and brother-in-law, Gregg

Collard, were there for every crazy adventure, long-winded story, and threat to quit, along with every small and large victory. I cannot thank them enough. My niece, Mackenzie, and nephew, Anders, have brought tremendous light and happiness to my life during the last few years and reminded me that there is so much more to life than school! I am grateful to the

NC State Counseling Center for their professionalism and resources. Without them I would never have met Meredith Adams, Janine Haugh, Angel Johnson, Felysha Jenkins, and many others, who have helped me, both personally and professionally, along the way. Finally, I was lucky enough to be a part of an amazing fisheries and wildlife community while at NC

State. I am grateful to my additional committee members, Jim Gilliam and Kevin Gross, who provided valuable insight in the completion of this dissertation. Joe Hightower, Nick

Haddad, Rob Dunn, Tom Kwak, Wilson Laney, Ken Pollock, Patrick and Julie Cooney,

Martha and Michael Fisk, Josh and Meredith Raabe, Jake and Erin Hughes, Lindsay and Bob

Campbell, Dana, Doug and Gavin Sackett, Dan Brown and Michelle Lisa, and Arielle and

Cameron Parsons, and many other graduate students all provided support in various ways along this journey. Thank you will never be enough!

Funding for the project was provided by the North Carolina Wildlife Resources

Commission through Federal Aid in Sport Fish Restoration grant F-68). Additional resources came from a Sigma Xi Grants in Aid of Research grant and a Robert M. Jenkins

Memorial Reservoir Research Scholarship from the Southern Division of the American

Fisheries Society.

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TABLE OF CONTENTS LIST OF TABLES ...... x LIST OF FIGURES ...... xiii CHAPTER 1 ...... 1 References ...... 4 CHAPTER 2 ...... 6 2.1. Introduction ...... 6 2.2. Study Area ...... 9 2.3. Methods...... 10 2.3.1. Field collections ...... 10 2.3.2. Analyses ...... 14 2.4. Results ...... 17 2.4.1. Newly stocked fish ...... 17 2.4.2. Predation ...... 18 2.4.3. Diet overlap ...... 19 2.4.4. Striped bass growth ...... 20 2.5. Discussion ...... 21 References ...... 27 CHAPTER 3 ...... 40 3.1. Introduction ...... 40 3.2. Materials and Methods ...... 42 3.2.1. Study Area ...... 42 3.2.2. Ecopath modeling ...... 43 3.2.3. Model construction ...... 45 3.2.4. Functional groups...... 46 3.2.4.1 Detritus ...... 46 3.2.4.2 Phytoplankton and Primary Production ...... 47 3.2.4.3. Zooplankton ...... 47 3.2.4.4 Benthic invertebrates and other invertebrates ...... 48 3.2.4.6 Fish ...... 48 Biomass ...... 48 Production/biomass (P/B) ratio ...... 49 Relative consumption (Q/B) ...... 49 Catch ...... 50 Diet composition ...... 50 3.2.5. Model analysis and adjustments ...... 52 3.2.6. Summary Statistics...... 52 3.2.7. Trophic levels and trophic flow ...... 54 3.2.8. Mixed Trophic Impacts ...... 54

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3.2.9. Niche overlap ...... 56 3.3. Results and Discussion ...... 56 3.3.1. Summary statistics ...... 57 3.3.2. Trophic flows ...... 60 3.3.3. Mixed Trophic Impacts ...... 61 3.3.4. Niche overlap ...... 62 3.4. Conclusion ...... 64 References ...... 65 CHAPTER 4 ...... 81 4.1. Introduction ...... 81 4.2. Study System ...... 85 4.3. Methods...... 86 4.3.1. Ecopath and post-introduction model ...... 86 4.3.2. Ecosim and pre-introduction baseline ...... 90 4.4.3. Simulation 3: Individual introductions ...... 95 4.4.5. Model 4: Individual removal of invaders...... 97 4.4.6. Simulation 5: Predator and prey models ...... 98 4.5. Discussion ...... 99 References ...... 106 CHAPTER 5 ...... 124 5.1. Introduction ...... 124 5.2. Study site ...... 127 5.3. Methods...... 128 5.3.1. Model parameterization ...... 128 5.3.2. Simulations ...... 132 5.4. Results ...... 135 5.5. Discussion ...... 137 References ...... 142 CHAPTER 6 ...... 157 References ...... 160 APPENDIX ...... 161

LIST OF TABLES

Table 2.1. Diet categories for newly stocked striped bass and potential competitors based on size and position in the water column...... 33

Table 2.2. Number and mean length of striped bass (STB) stocked, number of potential STB predators collected, number and mean length (mm TL) of predators that consumed STB, mean number of striped bass per successful predator stomach and the total number of STB in collected predator stomachs from two stocking locations, Mountain Creek and Stumpy Creek, in Lake Norman, North Carolina. Percentages are the percent of striped bass consumed (by collected predators) out of the total stocked for each year and the percent of predators containing newly stocked striped bass out of the total potential predators collected in each year...... 34

Table 2.3. Average fish length, sample sizes, number of stomachs with food, number and percent of empty stomachs, and average number of diet items per stomach that contained items for all abundant nearshore fish collected immediately following stocking. Fish from the Mountain Creek and Stumpy Creek stocking locations were combined for diet analysis...... 35

Table 2.4. Average lengths (± SD), total number of stomachs analyzed, number of stomachs containing food, number of empty stomachs, and average number of items per stomach with diet items for age 0 and age 1 striped bass, white perch and black bass collected one and two months post-stocking in 2009 and 2010...... 36

Table 3.1. Functional groups included in the Lake Norman Ecopath model. Numbers indicate ranking of each functional group based on fractional trophic levels, from highest to lowest (see text)...... 71

Table 3.2. Ecopath parameter input values (non-bold), estimated output parameters (bold), and key indices (model output) for the Lake Norman ecosystem model: TL is trophic level, B is biomass (t·km-2year-1), P/B is the production/biomass ratio (yr-1), Q/B is the consumption/biomass ration (yr-1), EE is the ecotrophic efficiency, P/Q is the production/consumption ratio (yr-1), NE is the net efficiency, FtD is the flow to detritus (t·km−2·yr−1), and OI is the omnivory index...... 72

Table 3.3. Diet matrix showing the proportion of each prey consumed by each predator for all functional groups in Lake Norman. All diets were calculated from stomachs sampled for this study except for gizzard shad, detritivores, other invertebrates, zooplankton, and benthic invertebrates...... 73

Table 3.4. System summary statistics calculated within the Ecopath model for Lake Norman, North Carolina...... 74

Table 4.2. Percent change in biomass of each functional group relative to pre-introduction biomass with each species introduced independently, the sum of the four individual introductions, and the original (post-introduction) model with all four species included. Dashes represent changes in biomass less than one percent. Additive effects (<1% difference between the sum of individual introductions and the post-introduction model), are shown in bold, antagonistic interactions (where the sum of individual introductions is greater than the post-introduction model effects) are italicized. For these comparisons multi-stanza functional groups (e.g., juvenile and adult white perch) were combined...... 117

Table 4.3. The percent change in biomass with each sequential introduction of each species relative to the pre-introduction baseline and relative to the biomass after the last introduction. The model was allowed to reach equilibrium after each species was added. Multi-stanza functional group (e.g., juvenile white perch and adult white perch) biomasses were grouped for simplicity...... 118

Table 4.4. The amount the biomass of each functional group would change, relative to current biomass estimates (steady state, post-introduction model), without the addition of each invader. Dashes represent changes in biomass less than 1% from the baseline (post-introduction model)...... 119

Table 5.2.—Relative change in biomass of each functional group due to changes in primary production, white perch fishing harvest, striped bass stocking, and supplemental threadfin shad stocking. Blanks indicate changes less than 0.1%...... 150

Table A.1. Summary table for fish analyzed for the Ecopath model including the total number analyzed for diet analysis (Diet; N), the length-weight (L-W) relationship, the R2 value for the L-W relationship, the number of fish used to discern the L-W relationship (N), and the von Bertalanffy growth parameter (K)...... 162

Table A.2. Matrix of predator-overlap values calculated by Pianka’s overlap index (O) .... 163

Table A.3. Matrix of prey-overlap values, calculated by the Pianka overlap index (O). .... 164

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LIST OF FIGURES

Figure 2.1. Lake Norman map indicating sampling locations and net configurations for sampling of striped bass, potential competitors and potential predators. A star () designates a stocking location, dark circles (●) mark electrofishing transects, and open triangles (∆) signify areas where age 0 and age 1 striped bass were collected in July and August of 2009 and 2010...... 37

Figure 2.2. Diet composition of newly stocked striped bass, black bass, bluegill, and redbreast sunfish (<100 mm TL) as percent of the diet by number of diet items consumed...... 38

Figure 2.3. Diet composition of age-0 (top panel) and age-1 (bottom panel) striped bass, white perch, and black bass in 2009 and 2010. Fish were collected in July and August following striped bass stocking...... 39

Figure 3.1. Map of Lake Norman, North Carolina, USA with purse seine, electrofishing, gill net, and plankton sampling sites...... 76

Figure 3.2. Trophic flow diagram for functional groups in Lake Norman, North Carolina. Circles are proportional to the biomass of each group in the system. Lines connecting nodes represent flow between groups by proportion of diet (by weight) of the consumer. Horizontal lines represent fractional trophic levels 1-4. Numbers in circles identify functional groups (Table 3.1): 1 = Flathead catfish, 2 = Largemouth bass-adult, 3 = Crappie, 4 = Striped bass-juvenile, 5 = Striped bass-adult, 6 = Spotted bass-adult, 7 = Littoral omnivores, 8=White perch-juvenile, 9 = White perch-adult, 10 = Bluegill/redear sunfish, 11 = Channel catfish, 12 = Alewife, 13=Largemouth bass-juvenile, 14 = Spotted bass-juvenile, 15 = Blue catfish, 16 = Benthic invertebrates, 17 = Threadfin shad, 18 = Other invertebrates, 19 = Detritivores, 20 = Gizzard shad, 21 = Zooplankton, 23 = Phytoplankton, 24 = Detritus...... 77

Figure 3.3. Lindeman spline representing the flow (t·km-2·yr-1) through each discrete trophic level (1-5+, where 5+ represents discrete trophic level 5-9) in Lake Norman. The legend shows the discrete trophic level (the box; TL), the total biomass at each TL (bottom left corner, t·km-2), the percent of the total system flow (TST%) that goes from -2 -1 TLi to TLi+1 , the amount of flow lost to respiration (bottom line; t·km ·yr ), consumption (arrow into a box; t·km-2·yr-1) and predation (arrow out of a box; t·km-2·yr- 1) by each TL, the percent transfer efficiency (TE; percent of flow that is transferred from TLi to TLi+1. The guide to interpreting these metrics is described in the methods, and the contribution of each functional group to the flow at each trophic level is in Table 3.5...... 78

Figure 3.4. Mixed Trophic Impact (MTI) plot for all functional groups and recreational

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fisheries in Lake Norman. The plot shows the direct and indirect impact that a one percent change in the biomass of each functional group (impacting group) has on all other functional groups (including itself and fishing fleets). Open bars extending upwards indicate positive impacts, while closed bars extending downwards show negative impacts. The bars should not be interpreted in an absolute sense: the impacts are relative, but comparable between groups...... 79

Figure 3.5. Niche overlap plot (as calculated from a modified Pianka overlap index) of predator and prey overlap values for all pairs of functional groups. Darker shaded circles represent higher overlap values; i.e., white circles have very low overlap values and black circles have very high overlap values). Points above the dotted line (at 0.6 prey overlap) represent significant overlap for prey resources. Numbers near circles identify functional groups (see Table 3.1): 1 = Flathead catfish, 2 = Largemouth bass- adult, 3 = Crappie, 4 = Striped bass-juvenile, 5 = Striped bass-adult, 6 = Spotted bass- adult, 7 = Littoral omnivores, 8 = White perch-juvenile, 9 = White perch-adult, 10 = Bluegill/redear sunfish, 11=Channel catfish, 12 = Alewife, 13=Largemouth bass- juvenile, 14=Spotted bass-juvenile, 15=Blue catfish, 16 = Benthic invertebrates, 17 = Threadfin shad, 18 = Other invertebrates, 19 = Detritivores, 20 = Gizzard shad, 21 = Zooplankton, 23 = Phytoplankton, 24 = Detritus. Numbers for pairs of species with low niche overlap were removed for clarity. Introduced species are shown in bold...... 80

Figure 4.1. Lake Norman, North Carolina...... 120

Figure 4.2. Ecosim simulation of introductions of flathead catfish (time step 0), white perch (time step 27), alewife (time step 28) and spotted bass (time step 29) into Lake Norman. Each line represents the biomass (t·km-2) of a functional group in the reservoir. Biomass at year-0 represents the pre-introduction biomass and year-50 biomass represents the post-introduction biomass of each functional group. Multi-stanza functional group (e.g., juvenile white perch and adult white perch) biomasses were grouped for simplicity...... 121

Figure 4.3. The percent change of each functional group from with the sequential addition of introduced species. The dashed line represents no net change in biomass. Impacted groups were assembled into four categories: a) groups that changed less than 12% (relative to the pre-introduction model) with the addition of each invader, b) groups that were affected positively by each introduction (*except detritivores who were affected negatively by flathead catfish), c) groups that were affected negatively by all species except enhanced by the introduction of spotted bass, and d) groups that were negatively affected by all introductions, but enhanced by the introduction of alewife...... 122

Figure 4.4. The percent change in each functional group, relative to the post-introduction

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baseline model with flathead catfish and alewife in the model (prey model) and flathead catfish, white perch, and spotted bass in the model (predator model)...... 123

Figure 5.1. Lake Norman, North Carolina showing electrofishing, gill net and purse seine sample sites...... 151

Figure 5.2. Absolute biomass (t·km-2) over time for each functional group in response to a moderate (10%) increase in reservoir primary production initiated at year one. Biomass of multi-stanza functional groups was combined...... 152

Figure 5.3. Percent change in biomass, relative to the original Ecopath model base biomass, for each functional groups in response to a 10% increase in primary production, initiated at year one. Biomass of multi-stanza functional groups was combined...... 153

Figure 5.4. Percent change in biomass, relative to the original Ecopath model base biomass, of each functional group with a ten-fold increase of fishing effort for white perch. For clarity, non-sport fish groups that changed less than 2% are not shown. Biomass of multi-stanza functional groups were combined for clarity...... 154

Figure 5.5. Percent change in biomass, relative to the original Ecopath model base biomass, of each functional group in response to doubling annual striped bass stocking. For clarity, groups that changed less than 2% are not shown. Biomass of multi-stanza functional groups were combined for clarity...... 155

Figure 5.6. Percent change in biomass, relative to the original Ecopath model base biomass, for each functional group in response to annual supplemental stocking of 40,000 adult threadfin shad. For clarity, groups that changed less than 0.5% are not shown. Biomass of multi-stanza functional groups was combined for clarity...... 156

CHAPTER 1

INTRODUCTION

Understanding food webs is one of the most complex problems facing ecological researchers today. Food web theory seeks to understand the functioning of an ecosystem by studying the trophic relations among its species (Cohen et al. 1990). With the addition of invasive species, these complex webs become particularly challenging to untangle due, in part, to unpredictable changes in trophic linkages. However, disentangling trophic interactions between native and invasive species lends insight into how communities are structured (Bruno and Cardinale 2008) and how energy is transferred though ecosystems

(Rooney et al. 2006). Ecologists such as Darwin (1859), MacArthur (1955), Paine (1966;

1980), and others have recognized the importance of disentangling multifaceted food webs to measure changes in biodiversity, community stability and ecosystem productivity. In aquatic ecosystems, food web interactions can result in trophic cascades (Pace et al. 1999; Stein et al.

1995; Carpenter et al. 1985), resource competition (Mills et al. 2003; Douglas et al. 1994;

DeBach 1966), and extinctions (Bodini et al. 2009).

Reservoirs are choice model systems for examining trophic interactions of existing populations and introduced species because they represent novel habitats that are subjected to colonization by fishes from pre-existing river basins as well as introductions of species by humans. A variety of factors such as heated effluent, dynamic hydrology, multiple user groups, and heavy fishing pressure make reservoirs as much as 300 times more susceptible to biological invasions than natural systems (Johnson et al. 2008). A problem somewhat unique

to reservoirs, humans have intentionally introduced species for creation of recreational fisheries, often with little regard for conservation of native fauna (Cambray 2003).

This dissertation focuses on trophic interactions and the effects of recent introductions into Lake Norman, a large, oligotrophic reservoir located north of Charlotte,

North Carolina. Components of this dissertation were developed from questions raised by the North Carolina Wildlife Resources Commission (NCWRC) in reply to growing concerns about the status of Lake Norman’s sport fisheries relative to recently introduced species, including flathead catfish Pylodictis olivaris, spotted bass Micropterus punctulatus, alewife

Alosa pseudoharengus and white perch Morone americana.

The goal of this research was to use multiple approaches to determine the effects of introduced species on the food web structure of Lake Norman. I focused on early interactions of stocked fish, developing an ecosystem model for the reservoir, community level effects of multiple introductions, and predicting outcomes of potential management strategies. Because stocked fish are a specific case of intentional introductions (Eby et al.

2006), Chapter 2 focused on quantifying predatory and competitive interactions of newly stocked striped bass Morone saxatilis fingerlings with other members of the community based on empirical analyses of gut contents. In Chapter 3, I developed an ecosystem trophic model using Ecopath with Ecosim (EwE), a suite of ecosystem modeling software used to explain trophic interactions and evaluate environmental and policy changes (Christensen and

Pauly 1992), to assess the contribution of introduced species to the reservoir community. I then used this model in a novel manner (Chapter 4) to investigate the individual and combined

effects of multiple introduced species on the community structure of Lake Norman. Finally, in Chapter 5, I use the model to simulate the outcomes of four ecosystem alterations, including nutrient loading, fish stocking, and increased harvest of an invasive species. Taken together, these studies provide valuable contributions to our understanding of reservoir food web structure, multi-species invasion dynamics, and ecosystem-based fisheries management in reservoirs.

References

Bodini, A., M. Bellingeri, S. Allesini, and C. Bondavalli. 2009. Using food web dominator trees to catch secondary extinctions in action. Philosophical Transactions of the Royal Society B 364(1524): 1725-1731.

Bruno, J. F. and B. J. Cardinale. 2008. Cascading effects of predator richness. Frontiers In Ecology and the Environment 6(10): 539–546.

Cambray, J. A. 2003. Impact on indigenous species biodiversity caused by the globalization of alien recreational freshwater fisheries. Hydrobiologia 500: 217-230.

Carpenter, S. R., J. F. Kitchell, and J. R. Hodgson. 1985. Cascading trophic interactions and lake productivity. BioScience 35(10): 634-639.

Christensen, V. and D. Pauly. 1992. Ecopath II—a software for balancing steady-state ecosystem models and calculating network characteristics. Ecological Modeling 61(3- 4): 169-185.

Cohen, J. E., F. Briand, and C. M. Newman. 1990. Community Food Webs: Data and Theory. Springer; Berlin.

Darwin, C. 1859. The origin of species by means of natural selection, or the preservation of favoured races in the struggle for life. John Murray, London.

Douglas, M. E., P. C. Marsh and W. L. Minckley. 1994. Indigenous fishes of western North America and the hypothesis of competitive displacement: Meda fulgida (Cyrinidae) as a case study. Copeia 1994(1): 9-19.

Eby, L. A., W. J. Roach, L. B. Crowder and J. A. Stanford. 2006. Effects of stocking-up freshwater food webs. Trends in Ecology and Evolution 21(10): 576-584.

Johnson, P. T. J., J. D. Olden, and M. J. Vander Zanden. 2008. Dam invaders: impoundments facilitate biological invasions into freshwaters. Frontiers in Ecology and Evolution 6(7): 357-363.

MacArthur R. 1955. Fluctuations of animal populations and a measure of community stability. Ecology 36: 533–536.

Mills, E. L., J. M. Casselman, R. Dermott, J. D. Fitzimons, G. Gal, K. T. Holek, J. A. Hoyle, O. E. Johannsson, B. F. Lantry, J. C. Makarewics, E. S. Millard, I. F. Munawar, M.

Munawar, R. O’Gorman, R. W. Owens, L. G. Rudstam, T. Schaner, and T. J. Stewart. 2003. Lake Ontario: food web dynamics in a changing ecosystem (1970-2000). Canadian Journal of Fisheries and Aquatic Science 60: 471-490.

Pace, M. L., J. J. Cole, S. R. Carpenter, J. F. Kitchell. 1999. Trophic cascades revealed in diverse ecosystems. Trends in Ecology and Evolution 14(12); 483-488.

Paine, R. T. 1966. Food web complexity and species diversity. American Naturalist, 100: 65- 75.

Rooney, N., K. McCann, G. Gellner, and J. C. Moore. 2006. Structural asymmetry and the stability of diverse food webs. Nature 442: 265-269.

Stein, R.A., D. R. DeVries, and J. M. Dettmers. 1995. Food-web regulation by a planktivore: exploring the generality of the trophic cascade hypothesis. Canadian Journal of Fisheries and Aquatic Science 52(11); 2518-2526.

CHAPTER 2

PREDATION MORTALITY AND TROPHIC INTERACTIONS OF NEWLY STOCKED

STRIPED BASS FINGERLINGS

Marybeth K. Brey, James A. Rice, D. Derek Aday, and Brian J. McRae

In Review at: Transactions of the American Fisheries Society

2.1. Introduction

Stocking programs have long been used to establish and augment sport fisheries and to achieve specific management goals; however, these programs may overlook the broader effects that interactions with the existing fish community may have on the fate and survival of newly stocked fish (Cowx 1994; Eby et al. 2006). For many species, stocking success (or lack thereof) is a function of factors such as size at stocking (Sutton and Ney 2001; Hoffman and Bettoli 2005), predation pressure (Wahl et al. 1995), season of stocking (Neal and Noble

2002), food availability (Donovan et al. 1997) and interactions with established fish (Fayram et al. 2005). In particular, mortality associated with predation on newly stocked fish (Stein et al. 1981; Buckmeier and Betsill 2002; Jackson 2002; Kolar and Lodge 2002; Schlechte et al.

2005) and competition for food resources with existing juveniles (Davies et al. 1982; Stein et al. 1995) tends to drive stocking success. Additionally, when fingerlings are stocked during summer months, competition for available food resources may be high due to the increased density of juvenile littoral fishes and perhaps limited nearshore food resources in oligotrophic reservoirs. During this time other nearshore species such as black bass Micropterus spp. and bluegill Lepomis macrochirus, would be of similar length and age to many species of stocked

fingerlings and would consume similar sized prey items (Polis and Holt 1992). For stocking programs to be successful it will be necessary to identify sources of predation and competition and address them with changes in stocking procedures.

Fish predators have been shown to consume newly stocked fingerlings of many species (Marsh and Brooks 1989; Baldwin et al. 2003). In Texas, predation was the primary factor driving poststocking survival of fingerling Florida largemouth bass Micropterus salmoides (Buckmeier et al. 2005), and high mortality of stocked tiger muskellunge Esox masquinongy x E. lucius in Ohio reservoirs was attributed to extreme predation by resident largemouth bass (Stein et al. 1981). In Maine lakes, stocked Atlantic salmon Salmo salar were heavily preyed upon by native chain pickerel Esox niger soon after stocking (Warner

1972). Many stocked fish are less than 100 mm total length (TL) when stocked, their size and naïveté make them easy targets for these predators (Wahl et al. 1995). Because significant predation on newly stocked fish can result in decreased stocking success it is an important factor to consider during stocking events.

In addition to predation, competition for food and habitat, especially in the first year of life (Donovan et al. 1997), can structure aquatic communities and affect fish growth and survival (Davies et al. 1982; Stein et al. 1995). Osenberg et al. (1992) discovered that bluegill in Michigan lakes limited the availability of food (snails) for juvenile pumpkinseed

Lepomis gibbosus, thereby decreasing the recruitment of pumpkinseed to the adult stage.

This competition with bluegill ultimately led to altered growth, morphology, and diet composition of pumpkinseed. In Ontario lakes, competition with resident fish species

appeared to strongly influence success of stocked juvenile lake trout Salvelinus namaycush; in lakes where native coregonid populations were high, growth and condition of lake trout tended to be poor, suggesting competition for food resources (Gunn et al. 1987). When resources are limiting and strong resource overlap exists, competition may occur and have direct effects on fish populations (Zaret and Rand 1971).

In the Midwest and Southeast United States, stocking of striped bass Morone saxatilis and their hybrids with M. americana into freshwater reservoirs has become common practice.

Striped bass can survive and even attain large sizes by taking advantage of underutilized pelagic prey fish such as gizzard shad Dorosoma cepedianum and threadfin shad Dorosoma petenense (Scruggs Jr. 1957; Axon and Whitehurst 1985). Although studies involving young-of-the-year striped bass are lacking, adult striped bass interactions in reservoirs are well described. Previous studies have measured growth (e.g., Cox and Coutant 1981), survival (e.g., Hightower et al. 2001; Thompson et al. 2007), habitat preference (e.g., Coutant

1985; 1990; Thompson et al. 2010) and trophic interactions (e.g., Matthews et al. 1988) of adult striped bass in reservoirs. However, the fate and trophic interactions of juvenile striped bass over their first year of growth have been studied in only two systems that we are aware of – Lake Texoma, Oklahoma-Texas (Matthews 1992) and Smith Mountain Lake, Virginia

(Sutton and Ney 2001; Sutton and Ney 2002) – and neither competition nor predation have been assessed in the week immediately following stocking.

In Lake Norman, North Carolina, stocked striped bass are rarely observed in the period between stocking and recruitment to the fishery. By identifying habitat of fingerlings

and juvenile striped bass and quantifying predatory and competitive interactions during this early life period, we hope to better understand the influence of these important mechanisms on survival. With that information we should be able to assess the likelihood of success of current stocking procedures and, if necessary, recommend alternatives. Our specific objectives were to determine the extent to which striped bass were consumed by predators immediately (i.e., in the first four days) following stocking and to assess the amount of diet overlap between newly stocked striped bass and potential littoral competitors. Incidentally, fishery managers and anglers have expressed concern that invasive white perch may be negatively impacting recreational fisheries in the lake; as a result, we also assessed the extent to which white perch interacted with striped bass as a potential competitor.

2.2. Study Area

Lake Norman, impounded in 1963, is a 12,634-ha reservoir on the Catawba River in

North Carolina (Figure 2.1). The reservoir has been classified as oligotrophic since the late

1970s, with chlorophyll a concentrations typically averaging 5-9 g·L-1 and Secchi depths of 1.8-

2.6 m (NCDENR 2008). It currently serves as a cooling reservoir for two power stations on the lake, the Marshall Steam Station (coal burning), and the William B. McGuire Nuclear

Station, both operated by Duke Energy. At full pool capacity Lake Norman has 837 km of highly dendritic shoreline, a mean depth of 10.2 m, and a maximum depth of 36.6 m.

The North Carolina Wildlife Resources Commission (NCWRC) manages Lake

Norman for sport fish including largemouth bass, spotted bass Micropterus punctulatus, striped bass, black crappie Pomoxis nigromaculatus, channel catfish Ictalurus punctatus, and

blue catfish Ictalurus furcatus (Waters and McRae 2008). Striped bass are the only species maintained by stocking. In recent years, Lake Norman has also been subjected to several fish introductions, including alewife Alosa pseudoharengus, blueback herring Alosa aestivalis, white perch, flathead catfish Pylodictis olivaris, and spotted bass, making the reservoir more complex to manage and increasing the number of potential trophic interactions among fish species.

2.3. Methods

Stocking.— The NCWRC stocked Lake Norman with striped bass from the Watha

State Fish Hatchery in Watha, North Carolina on 12 June 2007, 3 June 2008, 2 June 2009 and

1 June 2010. Managers aim to stock fingerlings at 40 mm TL from shore in two approximately equal groups at two sites, Mountain Creek boat ramp and Stumpy Creek boat ramp (Figure 2.1), at the rate of 12.4 fingerlings per hectare, or about 163,000 striped bass fingerlings annually (Waters and McRae 2008).

2.3.1. Field collections

Newly stocked striped bass.—We collected newly stocked striped bass along with all co-occurring fish for three days following stocking in 2007, 2008 and 2009. Daytime, pulsed, DC boat electrofishing was used to collect newly stocked striped bass, littoral predators (initially categorized as all piscivores ≥75 mm TL), and potential competitors

(initially categorized as all co-occurring nearshore species <150 mm TL) within 1,500 shoreline meters north and south of the Stumpy Creek stocking location in contiguous 300-m transects. It was possible that fish could be initially categorized as both a potential predator

and potential competitor. Because the Mountain Creek stocking site is located in a small cove, we only electrofished two separate 300 m transects (A and B; Figure 2.1) originating from the Mountain Creek stocking location.

Because we collected no striped bass fingerlings in 2008, and none were collected outside of 1,200 m (shoreline distance) in 2007, we changed our sampling protocol to nighttime electrofishing in 2009 and limited the shoreline shocking distance to 1,200 shoreline meters. At the Mountain Creek location we were limited to two days of electrofishing, 24 and 48 hours post-stocking, due to inclement weather. Due to gear constraints and the small size of striped bass, we were unable to collect any stocked striped bass that may have moved offshore immediately following stocking.

Potential competitors at stocking time.— Following fish collections, potential competitors of newly stocked striped bass were ultimately defined as the most abundant nearshore fish species based on CPUE (number of fish of each species per 300-m transect, in this study as well as in shoreline electrofishing surveys from Lake Norman lake-wide surveys conducted from 2007-2009) that had similar gape, TL (<150 mm), and diet as newly stocked striped bass. Potential competitors included juvenile black bass, bluegill, and redbreast sunfish Lepomis auritus. During the initial electrofishing events all nearshore fish species less than150 mm were collected. We then subsampled electrofishing collections by retaining a maximum of ten fish from two narrower size classes (≤ 100 mm and >100 mm TL) for all species defined as competitors, when present, from each 300 m transect. All fish were counted, measured, weighed, placed on dry ice and returned to the laboratory for diet

analysis.

Potential predators at stocking time.—Potential predators were defined as all piscivorous or omnivorous fish whose gape size allowed for capture of a newly stocked striped bass (e.g., we did not include small bluegill (<150 mm) because their gape was smaller than that required to consume a striped bass). During previously described electrofishing, all Lepomis spp. 150 mm TL or longer, all black bass 75 mm TL or longer, all white perch, and catfish greater than 250 mm TL from each 300-m transect were collected, placed on dry ice and returned to the laboratory for processing. In general, all large predators

(≥250 mm TL) were weighed and measured in the field, and gastric lavage (Foster 1977;

Hartleb and Moring 1995) was used to evacuate stomach contents. In addition to electrofishing, gill nets were set approximately one hour prior to striped bass stocking to collect potential predators in offshore areas near the stocking sites in all three years of the study. At the Stumpy Creak stocking site, four nearshore experimental gillnets (1.83 m deep,

38.1 m long x 12.7, 19.1, 25.4, 31.8, and 50.8 mm bar mesh) were set each year in ―L‖ shapes on either side of the stocking site to capture predators moving onshore and offshore to feed (Figure 2.1). Two 100 x 2.4 m (25.4 and 31.8 mm bar mesh) gillnets were bottom set between 7.6 and 9.1 m to capture potential offshore predators (Figure 2.1). Gillnets were set at the Mountain Creek stocking site only in 2009, when two 100 x 2.44 m (31.8 mm bar mesh) nets were set offshore (~30 m), on the bottom (depth = 8-11 m), in a ―V‖ shape originating from the stocking location (Figure 2.1). We did not use gillnets in other years at the Mountain Creek location.

Striped bass one and two months post-stocking.— To assess diet overlap of striped bass with potential competitors as they grew, we returned approximately two months after stocking in 2009 (4 August) and both one and two months after stocking in 2010 (13 July and

30 August) to locate and collect age 0 (fish from the current stocking year), and juvenile (age

1-3, stocked in previous years) striped bass and co-occurring species of similar length and with similar gape size (potential competitors). Using boat electrofishing, we targeted shallow

(< 1 m), sandy points and coves with little to no cover near the stocking locations, areas we believed to be preferred habitat for juvenile striped bass based on reports from other studies

(Matthews et al. 1988; Matthews et al. 1989) and our observations on Lake Norman. All collected fish were counted, measured, weighed, placed on dry ice and returned to the laboratory for diet analysis. Most striped bass (N=55) were collected on three sandy nearshore flats at Stumpy Creek, two directly across the channel from the stocking location and one100 shoreline meters north of the original stocking location. Sixteen striped bass were collected in similar habitat at the Mountain Creek location approximately 100 meters across the channel from the stocking location (Figure 2.1, triangles).

For all age 0 and age 1 striped bass collected in July and August of 2009 and 2010, average specific growth rates (SGR, mm·mm-1·d-1) were calculated as:

SGR = [(loge Lt – loge L0)/t] × 100, where L0 is the average length of the striped bass stocked for a given year and Lt is the average length of striped bass collected at time t (in days).

Potential competitors one and two months post-stocking—When we returned to

collect age 0 and juvenile striped bass via boat electrofishing one and two months after stocking, we also collected any fish species that were in the same habitat. Fish were considered potential competitors of age 0 and juvenile striped bass if they were collected repeatedly with striped bass in electrofishing transects, had similar gape and size (TL) and were known to occupy similar habitat as that sampled for age 0 and juvenile striped bass.

Based on those criteria, we deemed white perch and juvenile black bass to be potential competitors of age 0 and juvenile striped bass. These fish were collected during boat electrofishing at the same time striped bass were collected in July and August of 2009 and

2010. We retained a maximum of 10 fish of each species from each transect for diet analysis. Fish were placed on ice and returned to the lab following sampling.

Ages of striped bass collected in July and August were confirmed using length-at-age relationships developed by Thompson (2006) for Lake Norman striped bass. If age was questionable, sagittal otoliths were removed, sectioned, and aged by two independent readers. White perch collected in July and August were aged using length at age relationships developed for Lake Norman white perch (Z. Feiner, unpublished data). Again, for any ages that were in question, sagittal otoliths were removed, sectioned, and aged under a dissecting microscope by two independent readers. All fish ranged in age from two months

(known age due to stocking) to just over one year.

2.3.2. Analyses

Predator diet analysis.—Stomachs of all potential predators were evaluated for the presence of striped bass fingerlings. Identification of striped bass was validated by counting

the number of vertebrae in each vertebral column of fish found in predator stomachs (striped bass have 25 vertebrae whereas other potential prey species in Lake Norman have 29-58 vertebrae). We counted the number of fingerling striped bass in each predator stomach then averaged the number of stocked striped bass consumed per predator species for each year and stocking location.

Competitor diet analysis.— We analyzed stomachs of all striped bass collected in all years, and five stomachs from each potential competitor species per size class (when present) from each transect. Stomach contents were identified to the lowest possible taxon (usually family or genus), weighed to the nearest 0.01g (wet weight), and counted. We restricted analyses of diet overlap to species with high diet overlap potential based on their size (<100 mm TL) and mouth gape. Then, because all sampled fish were less than 100 mm TL and diet items had relatively little variation in size (they were all relatively small), we analyzed the number of each diet item in each stomach to determine diet overlap. Composition of each diet category (Table 2.1) was calculated as a percent using the sum of each diet item in the stomach for striped bass, black bass, bluegill and redbreast sunfish (Hyslop 1980).

Diet overlap.— Diet overlap between newly stocked striped bass and potential competitors was assessed for 2007, the only year in which enough striped bass were available for analysis. We examined the gut contents of 67 newly stocked striped bass from Stumpy

Creek and 27 from Mountain Creek. A large percentage of striped bass stomachs collected from newly stocked fish were empty (50.7% at Stumpy Creek and 29.6% at Mountain

Creek); these were excluded from diet overlap comparisons. We collected six species of

nearshore fish that represented potential competitors with newly stocked striped bass: bluegill, redbreast sunfish, green sunfish Lepomis cyanellus, redear sunfish Lepomis microlophus, young-of-the-year black bass, and whitefin shiner Cyprinella nivea. Of those, bluegill (27%), black bass (25%), and redbreast sunfish (12%) were the most abundant, collectively representing 55% of the nearshore fish community. Therefore, we focused our diet overlap analysis on these three species. We also calculated the diet overlap for age 0 and one-year-old striped bass with potential competitors (white perch and black bass) from samples collected in the months following stocking in 2009 and 2010.

We used a simplified Morisita’s index, often called the Morisita-Horn index (CMH;

Morisita 1959; Horn 1966; see also Krebs 1999) to calculate the overlap in diets of newly stocked striped bass with potential competitors (bluegill, redbreast sunfish and juvenile black bass). The Morisita-Horn index is calculated as follows from counts of diet items:

s s s   22  CPPPPMH2 ij ik   ij  ik  i1   i  1 i  1 

CMH = Morisita-Horn index of niche overlap between fish species j and k

Pij and Pik = the proportion of prey species or categories i in the diet of fish species j and k

S = the total number of prey species or categories in all diets.

The index ranges from zero to slightly over one (Morisita 1959; Horn 1966), is nearly independent of sample size, and is recommended for count data (Wallace 1981; Krebs 1999).

We considered overlap values to be biologically significant when greater than 0.60 (Zarat and Rand 1971; Keast 1978).

2.4. Results

2.4.1. Newly stocked fish

Prior to stocking, a subset of 100 fish (in 2007 and 2008) or 50 fish (2009) per stocking location was measured (mm TL) to determine the average length of stocked fish for each year and site. The size at which striped bass were stocked varied significantly between all years and sites (ANOVA: F = 365.4; df = 2, 524; P < 0.0001). For a given year fish stocked at the Mountain Creek location were, on average, 10 mm smaller than fish stocked at the Stumpy Creek location. Average stocked striped bass length decreased from 2007 as well (Mountain Creek site: mean = 49.2; SD = 5.3 mm TL) to 2009 (Mountain Creek: mean

= 32.8 mm TL; SD = 5.7 mm TL; Table 2.2).

In sum, we collected 207 newly stocked striped bass from both stocking locations in

2007 in nearshore electrofishing transects. At the Stumpy Creek stocking site six hours post- stocking, 74 fish were collected between 300 and 600 shoreline meters north of the boat ramp and one fish was collected 600-900 m north of the boat ramp. The day after stocking five fish were collected 600-900 m south of the boat ramp. By day two we only collected one fish in the 0-300m south transect, and on day three one fish was collected in the 600-900 m north transect. At the Mountain Creek stocking site we collected 93 fish at transect A and 26 at transect B the day after stocking. Two days post-stocking we collected three striped bass at transect B. Despite similar sampling effort in 2008 and 2009, very few striped bass were collected. In 2008 we did not collect any striped bass at either stocking location, and in 2009 only five striped bass fingerlings were captured along the shoreline at Stumpy Creek 24

hours post-stocking. Three fish were collected 900-1200 m north, one fish 300-600 m north, and one fish 900-1200 shoreline meters south (Figure 2.1).

2.4.2. Predation

Across years and stocking locations we collected 1,514 potential predators and found, in sum, 173 newly stocked striped bass in their stomachs (Table 2.2), representing 0.04% of the total number stocked. We never observed more than 0.1% of the total striped bass stocked for a given year in collected predator diets. Predation was detected at all sites and in all years except at the Mountain Creek stocking site in 2008. Predators of newly stocked striped bass included channel catfish, flathead catfish, largemouth bass, spotted bass, white perch, and warmouth Lepomis gulosus. Of those predators, more spotted bass (N=31) consumed newly stocked striped bass than any other species. White perch consumed more newly stocked striped bass on average (푥 = 11.8) than any other species. Predator species ranged in length from 75 mm TL (a spotted bass) to 606 mm TL (a flathead catfish). The mean total length of all predators was 177 mm. The proportion of successful predators that were caught in offshore gillnets (4 of 116, 4.3%) and nearshore electrofishing (50 of 1,285,

3.9%) was only slightly greater than the proportion of successful predators captured in nearshore gillnets (1 of 113, 0.8%). A higher proportion of fish per predators stomach was found for predators collected in offshore gillnets (20 per predator) compared to nearshore nets (one per predator) and littoral (electrofishing) predators (1.9 per predator), but this was driven by three white perch from Mountain Creek in 2009 that each consumed between 17 and 38 individual striped bass. As expected, increased gillnet sampling effort in 2009

increased our predator catch. More striped bass were consumed per predator when they were stocked at a smaller size at the Mountain Creek Stocking site. In 2009, striped bass were stocked at 24 mm (shortest TL of any year) and an average of 16.2 striped bass was consumed per successful predator (Table 2.2).

2.4.3. Diet overlap

Newly stocked fish.— There was no significant diet overlap between newly stocked striped bass and all three potential competitors (bluegill, redbreast sunfish and black bass), as all diet overlap values were less than 0.6. Overlap between newly stocked striped bass and bluegill was the highest (CMH = 0.48), followed by diet overlap with black bass (CMH = 0.45) and redbreast sunfish (CMH = 0.40). The diets of newly stocked striped bass consisted primarily of cladocerans, copepods, and small benthic invertebrates (Figure 2.2). Smaller proportions of larval fish (threadfin shad), surface oriented organisms, and large benthic invertebrates were also found in their diets. Possibly due to their unfamiliarity with prey items, short time in the reservoir, and stress of being stocked, newly stocked striped bass had a much higher frequency of empty stomachs than their competitors, and those with food in their stomachs contained only 12-23% as many diet items as their competitors (Table 1.3).

Black bass consumed primarily cladocerans, whereas bluegill and redbreast sunfish diets were both typified by high proportions of small benthic invertebrates. In addition, redbreast sunfish consumed more mollusks (Corbicula spp.) than other nearshore species (Figure 2.2).

Age 0 and age 1 fish.—Diet analysis of age 0 striped bass collected 1-2 months after stocking indicated consumption of fish prey by fish that were 75 mm TL or bigger. By the

time age 0 striped bass were 120 mm TL their diets were comprised entirely of fish. Overall, stomach content analysis revealed that 25% of the diets of age 0 striped bass consisted of fish

(mostly threadfin shad), whereas black bass and white perch were consuming little or no fish

(Figure 2.3). There was significant diet overlap of age 0 striped bass with age 0 white perch

(CMH = 0.68) and age 0 black bass (CMH = 0.76) in 2009, but not in 2010 (white perch CMH =

0.41 and black bass CMH = 0.59). Incidentally, there was significant diet overlap between age

0 black bass and white perch in both 2009 and 2010 (CMH, 2009 = 0.64 and CMH, 2010 = 0.81).

Age 0 fish in both years had was highly variable numbers of diet items (per stomach containing food) and the number of empty stomachs was relatively low for all species and years (Table 2.4). By age 1, diet composition of striped bass and black bass was almost exclusively fish (Figure 2.3), and overlap values were near 1.0 (CMH, 2009 = 0.97, CMH, 2010 =

0.98). Conversely, diet overlap between age 1striped bass and white perch was near zero in both years (CMH, 2009 = 0.01, CMH, 2010 = 0.10) as white perch consumed primarily benthic invertebrates (Figure 2.3). Incidentally, diet overlap between black bass and white perch was also extremely low in both years (CMH < 0.01 in 2009 and 2010). By their first year the number of diet items per nonempty stomach was consistently at or near one item for striped bass and black bass, but the number of diet items was still highly variable for white perch.

2.4.4. Striped bass growth

The average growth rate of age 0 striped bass was 1.26 mm·mm-1·d-1 in 2009 (N= 18,

62 days post-stocking) and 1.31 mm·mm-1·d-1 in 2010 (N = 12, 42 days post-stocking).

Average growth of age 1 fish from 2008 to 2009 was 0.28 mm·mm-1·d-1 (N = 8, 427 days

post-stocking), and was nearly the same from 2009 to 2010 (0.26 mm·d-1, N = 6, 426 days post-stocking). Growth rates of striped bass were not calculated for other years (2007 and

2008) because age 0 and age 1 fish were not collected in July or August of those years.

2.5. Discussion

We aimed to determine the extent to which striped bass fingerlings were consumed by predators in the days following stocking, and to assess the amount of diet overlap between newly stocked striped bass and potential littoral competitors of similar size during their first year. We observed rapid dispersal of newly stocked striped bass fingerlings and found little evidence to suggest mass consumption of stocked striped bass in the week immediately following stocking. The rapid dispersal of striped bass from the stocking locations over the first 24 hours most likely reduced the potential for intraspecific competition and helped minimize the attraction of predators (Stein et al. 1981).

Overall, a limited number of predators consumed very few striped bass fingerlings after stocking. Based on the large number of predator diets we analyzed over three years, we observed very low predation on stocked striped bass fingerlings in all years. Documented predation mortality constituted less than 0.1% of all stocked fish, even with our increased sampling effort to capture potential predators in 2009. Even if we were to extrapolate predation to the rest of the lake, the number of striped bass predators was very low. Of these predators, spotted bass and white perch, two relatively abundant and invasive fish (D.

Coughlan, Duke Energy, personal communication), were observed to have the greatest predation on stocked striped bass. Our results are similar to those observed in Smith

Mountain Lake, VA, where the estimated loss of stocked striped bass to largemouth bass in coves where they were stocked was 0.1-3.0% in the month following stocking (Michaelson et al. 2001). In our study, captured predators never consumed more than 0.1% of the total number of striped bass stocked at a site and the average over all years and sites was 0.02%.

Even in 2007, when we observed large aggregations of striped bass near the stocking location for several hours following stocking, predators collected near that site had not consumed more striped bass than in any other area. Interestingly, our predation estimates contrast greatly with observations of predation on stocked fish of other species. For example, losses of stocked largemouth bass to predation following stocking in a Texas Reservoir were estimated to be as high as 27.5% (Buckmeier et al. 2005), and Stein et al. (1981) showed that tiger muskellunge Esox masquinongy x E. lucius stocked in Ohio reservoirs suffered predation mortality as high as 45% from largemouth bass up to three days after stocking.

A significantly smaller stocking size appeared to increase predation risk of stocked striped bass in 2009. Although a small number of stocked striped bass were actually consumed, striped bass that were consumed occurred in larger quantities per predator stomach in 2009. The average total length of striped bass stocked at the Mountain Creek location in 2009 was markedly smaller (24 mm) than for those stocked at either site in all other years. The total number of striped bass consumed and the number of striped bass per successful predator at Mountain Creek in 2009 was also much greater in that year. Although we only collected five successful predators that year, smaller stocking size has led to increased predation in a number of other species, including channel catfish (Storck and

Newman 1988), walleye (Hoxmeier et al. 2006) muskellunge (McKeown et al. 1999), and largemouth bass (Diana and Wahl 2009, although weak). These results concur with recommendations (e.g., Wahl and Stein 1989, Szendrey and Wahl 1996, Sutton and Ney

2001) to stock fish at a larger size in order to reduce vulnerability to predators and increase survival rates.

Young-of-the-year black bass and assorted sunfish are abundant near shore during

June when striped bass are stocked in Lake Norman, yet we found only moderate diet overlap, suggesting limited competition for food among newly stocked striped bass and abundant nearshore fishes. This was most likely due to the variety of food items consumed by striped bass in contrast to the relative specialization of black bass on cladocerans, and of sunfish on small benthic invertebrates (Figure 2.2). The relatively quick ontogenetic diet shift we observed by striped bass from invertebrates to fish is consistent with reports from other reservoirs (Van Den Avyle 1983, Sutton and Ney 2002, Shepherd and Maceina 2009) and may allow striped bass to survive and even flourish in the reservoir setting. In Smith

Mountain Lake, Virginia, fish made up 90% of the diet of striped bass by approximately 125 mm TL, similar to our results, and individuals had completely shifted to fish prey around 150 mm TL (Sutton and Ney 2002).

Competition for food resources may be occurring in nearshore areas preferred by age

0 and age 1 striped bass (fish collected 1-2 months following stocking). During July and

August striped bass were collected in nearshore areas with spotted bass, a non-native sport fish that became established in Lake Norman in the late 1990s (D. Coughlan, Duke Energy,

personal communication). The high diet overlap we observed between young striped bass and black bass (we grouped all black bass together for this study) was similar to values calculated for striped bass and largemouth bass collected in June in Lewis Smith Lake,

Alabama (Shepherd and Maceina 2009). In Lewis Smith Lake, diet overlap between young striped bass and spotted bass was 0.71 during June, but that overlap was short-lived and overlap decreased into the fall. In Lake Norman, diet overlap between striped bass and black bass continued to be high for age 0 fish two months after stocking and was still high into age

1 when both species consumed primarily threadfin shad and sunfish. However, when overlap values are high between striped bass and black bass, microhabitat preferences during this time may limit competitive interactions. Striped bass seemed to prefer areas with no cover, whereas black bass were collected in areas with rip rap or woody debris, as reported in other studies (Shepherd and Maceina 2009, Sutton and Ney 2002). In sum, competitive interactions seemed to have little effect on young striped bass; growth of age 0 fish during the first two months post-stocking was rapid and growth over the first year was comparable to other systems (Dey 1981, Schaffler 2005).

We were particularly interested in the amount of resource overlap striped bass shared with white perch, a highly invasive fish detected in Lake Norman around 2000. White perch were collected in nearshore areas with age 0 and age 1 striped bass in July and August, and aside from the contribution of fish to the diet of striped bass, white perch shared similar diet items to age 0 striped bass. In Richibucto Estuary, New Brunswick (native range for striped bass and white perch), young-of-the-year (<50 mm TL) striped bass also had very similar

diets to young-of-the-year white perch, although exact diet items were different (St-Hilaire et al. 2002); both species fed almost exclusively on copepod zooplankton in low salinity areas of the estuary. However, by the time fish reached age 1 in Lake Norman there was no longer significant diet overlap between striped bass and white perch (whose diets changed little from age 0). Although white perch are known to be highly opportunistic and will continue to consume large amounts of zooplankton and benthic invertebrates throughout their juvenile stage, it is not until they reach approximately 250 mm TL that piscivory dominates (Sierszen et al. 1996). The ability of striped bass to incorporate multiple prey items into their diet when young and switch to fish early may be the reason they are able to occupy the same habitat as white perch without observable negative effects to the young population (Holt

1977, Hodgson et al. 1997).

In conclusion, we found no evidence that striped bass are subject to strong competitive pressures or predation immediately following stocking; very few newly stocked bass were found in predator stomachs, and diet overlap with nearshore fishes was not significant. However, competition for food resources may be occurring in nearshore areas preferred by age 0 and age 1 striped bass (fish collected 1-2 months following stocking).

Because competition with abundant littoral fishes may become an issue when resources are more limiting, we recommend stocking fish in areas of the reservoir with higher productivity to limit the potential for competition to occur. Striped bass are continuing to recruit to the fishery, indicating that the current stocking strategy is successful. Based on the number of individuals consumed in 2009 when fingerlings were unusually small compared to other

years, we do suggest that stocking fish at a larger size may potentially increase initial survival. In our study, larger individuals tended to appear in predator diets less than smaller individuals. Finally, competition with introduced white perch does not seem to be a problem for juvenile striped bass because striped bass are able to take advantage of the prey fish community before juvenile white perch. Ongoing research in this and similar systems will provide insight into the role that additional introduced species may play in influencing striped bass populations specifically and reservoir communities more generally.

References

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Table 2.1. Diet categories for newly stocked striped bass and potential competitors based on size and position in the water column.

Benthic Invertebrates Surface Zooplankton Fish Other Large Small Oriented Cladocerans Copepods Alewife Corbicula spp.1 Amphipoda Arachnida2 Daphnia spp. Calanoid Plant seeds Black bass Crayfish Ceratopogonid larvae Coleoptera Sididae Cyclopoid Fish scales Larval fish Ephemeroptera pupae Chironomid larvae Diptera adults Bosminidae Harpactacoid Plant pieces Threadfin shad Gastropoda1 Fish eggs Diptera pupae Chydoridae Sediment Megaloptera Hydrachnidia Hemiptera3 Unknown Odonata Nematodes Hymenoptera invertebrates Terrestrial Tricoptera Ostracods Isopoda 1 Corbicula spp. and Gastropoda were their own “mollusca” category for newly stocked fish 2 spiders 3 water striders and water boatmen

Stocked striped bass Potential predators Successful STB predators Mean Total STB Number Mean mm Number collected 1 Percent of Mean TL STB per consumed (% stocked TL ± SD (number successful) Site and year collected (range, mm) predator of stocked) EF GN Stumpy Creek EF GN a 2007 60,000 49.2 ± 5.3 352 (5) --- 1.4% 184 (106 -304) 1.6 --- 16 (0.03%) b 2008 80,000 42.2 ± 2.4 165 (5) 100 (0) 1.9% 208 (75-303) 1.2 --- 9 (0.01%) 2009 85,280 32.8 ± 5.7c 341(28) 83 (2) 7.1% 143 (99-494) 1.7 1.5 48 (0.06%)

Mountain Creek d 2007 100,000 39.7 ± 3.5 115 (11) --- 9.6% 330 (122 -606) 2.3 -- - 20 (0.02%) e 2008 82,500 31.5 ± 2.3 203 (0) ------2009 77,235 24.1 ± 3.9f 109 (1) 46 (4) 3.3% 200 (113-238) 1.7 20.0 81 (0.10%) Both sites, all years Total 485,015 1,285 (50) 229 (6) 3.6% 174 214.2 76.3 Average 80,836 38.2 ± 8.4 (8.3) (2) 9 (3.56%) 177 (75-606) 1.7 10.8 16 (0.02%) 1Letters designate statistically different mean le ngths by Tukey-Kramer HSD test.

Mean TL T ot al stomachs Stomachs with Empty stomachs Mean items per Species ± SD (mm) analyzed diet items (% of total) stomach with food Striped bass 41.6 ± 5.3 101 52 49 (48.5%) 7.4

Black bass 34.5 ± 6.3 89 81 8 (9.0%) 59.4

Bluegill 78.3 ± 15.2 68 63 5 (7.4%) 39.5

Redbreast sunfish 82.0 ± 14.9 79 74 5 (6.3%) 32.3

Stomachs Empty Mean diet items Age and Mean TL ± Stomachs species Year SD (mm) analyzed with diet stomachs (% per stomach items (N) of analyzed) with food Age 0 Striped Bass 2009 76.0 ± 7.70 19 13 6 (31.6%) 1.9 2010 74.9 ± 14.6 33 28 5 (15.1%) 23 White Perch 2009 62.5 ± 4.60 9 8 1 (10%) 9.9 2010 72.9 ± 16.1 32 31 1 (3.1%) 11.5 Black Bass 2009 57.0 ± 9.50 11 8 3 (27.3%) 1.9 2010 72.0 ± 9.30 6 4 2 (33.3%) 2.7 Age 1 Striped Bass 2009 138.0 ± 12.4 9 9 0 (0%) 1.1 2010 154.0 ± 26.0 8 7 1 (12.5%) 1 White Perch 2009 120.0 ± 8.50 32 29 3 (9.4%) 10.7 2010 135.7 ± 13.0 10 7 3 (30%) 3.7 Black Bass 2009 148.4 ± 12.8 5 5 0 (0%) 1 2010 148.0 ± 12.7 6 5 1 (20%) 1

North Stumpy Creek 900-1200 m Gillnet configuration Carolina

600-900 m Lake Norman 300-600 m

0-300 m

300-600 m

600-900 m

900-1200 m

Mountain Creek

B A

100%

Fish

75% Surface oriented organisms Copepods 50% Cladocerans

Small benthic 25% invertebrates Large benthic

Percent of diet by numberbydiet of Percent invertebrates Molluscs 0% Striped bass Black bass Bluegill Redbreast sunfish

Figure 2.2. Diet composition of newly stocked striped bass, black bass, bluegill, and redbreast sunfish (<100 mm TL) as percent of the diet by number of diet items consumed.

Age-0 100%

75%100% 100% Other 50% Surface 75% 75% 25% Sm. Benthic Invertebrates 50% 50% Lg. Benthic 0% Invertebrates Age-1 Copepods

100%diet of Percent Percent of diet of Percent

25% 25% Cladocerans (by number of diet of items)number (by

(by number of diet items)diet of number (by 75% Fish 0% 0% 50% 2009200920102010 2009200920102010 2009200920102010 StripedStriped bass White perch Black bassbass 25%

0% 2009 2010 2009 2010 2009 2010 Striped bass White perch Black bass

CHAPTER 3

MODELING THE TROPHIC CONNECTIONS OF INTRODUCED FISHES IN A

RESERVOIR FOOD WEB

3.1. Introduction

The impoundment of rivers has led to the formation of over half a million reservoirs covering 260,000 km2 throughout the world (Dowing et al. 2006) and has created arguably one of the most important freshwater resources today. In the United States alone over 75,000 dams impound one million kilometers of river (National Registry of Reservoirs 2007), with a large proportion of those in southern areas. These multi-use ecosystems provide flood control, irrigation, power generation, water supply, and recreational angling opportunities

(Allen et al. 2008). Because they are so heavily used, these ecosystems also tend to be frequently disrupted by changes in the surrounding watershed, constant fishing pressure

(Friedl 2002, Jansson 2002), and frequent stocking of high trophic level fish species (Eby et al. 2006). In addition, their connectivity to river systems (Williamson et al. 2009) and high level of recreational use make them extremely susceptible to species invasions (Havel et al.

2005, Johnson et al. 2008, Johnson et al. 2009).

Recent research suggests that most reservoirs contain not one, but multiple introduced species, obscuring already complex community interactions (Zavaleta et al. 2001, Dunne et al. 2002). New species can alter trophic linkages, change trophic structure, or increase demand on prey and productivity, ultimately causing trophic cascades (Zavaleta et al. 2001,

Cucherousset and Olden 2011). Although reservoirs are heavily used and frequently invaded, we do not have a good understanding of these trophic relationships or the role of introduced fishes within them.

To properly manage reservoir ecosystems, especially those that have been subjected to multiple introductions, it is necessary to understand how introduced species are connected to the rest of the community and how they alter other connections. Ecosystem trophic modeling is a useful way to evaluate changes to the community structure of reservoirs and provide ecosystem-based management recommendations. Moving from single-species to ecosystem-based fisheries management has been recommended for a variety of marine

(Latour et al. 2003, Hall and Mainprize 2004; Pikitch et al. 2004) and large freshwater ecosystems (e.g., Lake Superior; Kitchell et al. 2000). Although it has not been done, it seems only logical that management of reservoir systems should also follow this recommendation.

Ecopath with Ecosim (EwE) is a common approach for modeling trophic relationships in aquatic systems (Christensen and Pauly 1992, Walters et al. 1997, Walters et al. 1999, Pauly et al. 1990, 2000, Christensen and Walters 2004, Christensen and Pauly

2005). In addition to their prevalent use in marine systems, EwE models are well documented for a variety of freshwater lakes and reservoirs including Kelavarapalli reservoir,

India (Khan and Panikkar 2009), Bagre reservoir in Burkina Faso, (Villanueva et al. 2006),

Lake Awassa in Ethiopia (Fetahi and Mengistou 2007), Lake Qiandaohu in China (Liu et al.

2007) and a variety of neotropical lakes in Brazil (Gubiani et al. 2011). Despite the impacts

of introduced species and climate change on reservoir ecosystems in the United States, reservoir trophic models for this area are rare (e.g., Kinter et al. 2011; Harvey and Kariva

2005). Changes to U.S. reservoirs can directly affect recreational angling opportunities and cause large impacts to both ecological functioning and economic development.

Ecopath with Ecosim models have great potential as a tool to explore the impacts of introduced species and management options in reservoirs. We apply this EwE approach to

Lake Norman, North Carolina, an oligotrophic reservoir subjected to multiple fish introductions and perceived declines in sport fish populations. Before we can explore the ramifications of changes to the reservoir community or changes to management, the first step is to assemble and evaluate a trophic model for this system. Thus, our objectives were to 1) create a baseline trophic model (using Ecopath with Ecosim) and describe trophic flows within the Lake Norman food web, 2) use this model to evaluate the contribution of multiple introduced species to the trophic structure of the reservoir community, and 3) compare this model to other published reservoir EwE models in terms of reservoir maturity and trophic flow. This model will provide a detailed description of the food web with which we may ask additional questions regarding management scenarios and assess the impacts of current and future invaders.

3.2. Materials and Methods

3.2.1. Study Area

Lake Norman (latitude: 35° 26' 08'' N, longitude: 80° 57' 27'' W), impounded in 1963, is a 12,634-ha reservoir on the Catawba River in North Carolina, USA (Figure 3.1). The reservoir

has been classified as oligotrophic since the late 1970s, with chlorophyll a concentrations typically averaging 5-9 g·L-1 and Secchi depths of 1.8-2.6 m (NCDENR 2008). It currently serves as a cooling reservoir for two electrical power generation facilities on the lake, the

Marshall Steam Station, a coal-fired plant, and the William B. McGuire Nuclear Station, both currently operated by Duke Energy. At full pool capacity, Lake Norman has 837 km of highly dendritic shoreline, a mean depth of 10.2 m, and a maximum depth of 36.6 m (Waters and McRae 2008).

The North Carolina Wildlife Resources Commission (NCWRC) manages Lake

Norman for sport fish including largemouth bass Micropterus salmoides, spotted bass

Micropterus punctulatus, striped bass Morone saxatilis, black crappie Pomoxis nigromaculatus, channel catfish Ictalurus punctatus, and blue catfish Ictalurus furcatus

(Waters and McRae 2008). Striped bass are the only species maintained by stocking. Since its impoundment, Lake Norman has also been subjected to several fish introductions including alewife Alosa pseudoharengus, blueback herring Alosa aestivalis, white perch

Morone americana, flathead catfish Pylodictis olivaris, and spotted bass, making the reservoir more complex to manage, and increasing the number of potential trophic interactions among fish species.

3.2.2. Ecopath modeling

The ecosystem modeling package Ecopath with Ecosim (EwE; version 6.2; www.ecopath.org) is comprised of three components: Ecopath, Ecosim, and Ecospace

(Christensen et al. 2000). For this analysis we used Ecopath (the portion of the program used

to model a snapshot of trophic relationships in an ecosystem; Christensen and Pauly 1992;

Christensen and Walters 2000). Ecopath is comprised of a set of simultaneous linear equations, one for each functional group (species or groups of species with similar diet and habitat use), where group production equals the sum of all predatory and non-predatory losses, and export (Christensen et al. 2005). The model can be parameterized with data from any time series, but is mass balanced for one year. Initialization of the model requires specific inputs (empirical or estimated) for each functional group, including biomass (B), production to biomass ratio (P/B), consumption to biomass ratio (Q/B), diet composition information, and biomass harvested (Y). Specifically,

Bi · (P/B)i · EEi = Yi + Σ(Bj) · DCij · (Q/B)i + EXi. where Bi is the biomass of functional group i; P/Bi is the production/biomass ratio of group i;

EEi is the ecotrophic efficiency of group i; Yi is the catch (yield) of group i; Bj is the biomass of functional group j; DCij is the proportion of group i in the diet of group j; Q/Bj is the food consumption per unit biomass of group j; and EXi is the export of group i. The diet composition (DC) of each functional group must be entered into the model and each functional group requires input values for at least three of the four parameters: B, P/B, Q/B, and EE. One of these parameters can remain unknown for each functional group and be estimated by the model (typically EE because it is difficult to measure). Input data were standardized, B as wet weight (t·km−2) and P/B and Q/B as annual rates. Ecotrophic efficiency is a unitless parameter that represents the proportion of production that is actually used in the system. A more detailed description of Ecopath and its parameters can be found

in multiple sources (e.g., Christensen et al. 1992; Walters et al. 1997; Christensen and

Walters 2004; Christensen et al. 2005).

3.2.3. Model construction

Extensive fish and invertebrate data were collected from Lake Norman May 2007-

December 2009. Forty 300-m transects distributed throughout the lake were used for fish and invertebrate sampling (Figure 3.1). In 2007 all 40 transects were sampled every three weeks from May to September. In 2008 and 2009, three of ten randomly selected 300-m transects in each of four lake regions were sampled during the day (2007 and 2008) and at night (2009) every three to six weeks from May to December. During each sample period at each site we electrofished 300 m of shoreline habitat, collecting all fish. One graded mesh gill net (1.83 m deep, consisting of five-7.62 m panels of 12.7, 19.1, 25.4, 31.8, and 50.8 mm bar mesh) was set at each site in 1.9 m to 9 m of water at a 45-75 degree angle to shore.

Additionally, four open-water gill nets (2.4 m deep, consisting of two-50 m panels of 25.4 and 31.8 mm bar mesh and one 200 m long x 2.4 m net deep net consisting of 50.8 mm bar mesh) were set near each site (water depth = 12-22.8 m) to collect fish located off shore.

Nets were set on the bottom from approximately March-May, when the water column was not yet stratified. When temperatures increased and the water column became stratified, nets were suspended just above the oxycline where dissolved oxygen was at least 2.0 mg·L-1.

Purse seine samples were collected four times per year (once per season) at two sites (Figure

3.1) to assess the pelagic prey fish community. Because biomass has been shown to be directly proportional to catch-per-unit-effort (CPUE) estimates from gillnets and

electrofishing transects, fish biomass was extrapolated from CPUE calculations (Leslie and

Davis 1939; Ricker 1975; Serns 1982; Buynak and Mitchell 1993; Tate et al. 2003). Catch data and diet data were augmented with information from Duke Energy reports (2008; 2009) and other published works specific to Lake Norman (Grist 2002; Thompson 2006; Godbout

2009; Feiner 2011; Feiner et al. 2012; Table A.1).

3.2.4. Functional groups

The Lake Norman model contained 24 functional groups (Table 3.1; Table 3.2) including 19 fish groups, two invertebrate groups, two plankton groups (zooplankton and phytoplankton) and one detritus group. Some groups consisted of multiple species that had similar diets and used comparable habitat (e.g., littoral omnivores). Striped bass, spotted bass, largemouth bass, and white perch were divided into multi-stanza groups to account for ontogenetic differences in both diet and energetic parameters within a species. The exports of fish and invertebrates by birds and other animals were assumed to be small when compared with those by fisheries and therefore were not included in the model. Bacteria were included in the detritus functional group.

3.2.4.1 Detritus

Detritus biomass (D) was calculated with the relationship suggested by Christensen et al. (2005):

Log퐷 = 0.954· log푃푃 + 0.863 ·log퐸 – 2.41, where D = detritus biomass (kg·km–2); PP = primary production (70,080 kg C·km–2·year–1;

Duke Energy 2008), and E = mean euphotic depth (0.0042 km, calculated from average

secchi depth; Strickland 1958).

3.2.4.2 Phytoplankton and Primary Production

Phytoplankton biomass and primary production varied both seasonally and spatially in Lake Norman, but our estimates were within the twenty-year average for the lake (Duke

Energy 2008). Chlorophyll-a concentrations ranged from a 1.31 μg·L-1 in the south part of the lake in May to 12.51 μg·L-1 in the upper region of the lake in November. Annual mean chlorophyll-a concentration was 4.766 mg·m-3 (Duke Energy 2008) for the area considered.

Chlorophyll-a concentration was converted to a fresh biomass of 8.19 t·km-2 and used as input to the model. Cryptophytes (primarily, Rhodomonas minuta) were the dominant group in winter and spring, whereas green algae (primarily Cosmarium asphearosporum) were the dominant group during the summer (Duke Energy 2008). Because P/B for phytoplankton was not available for Lake Norman, we used an annual estimate of 365 from oligotrophic

Lake Hokkaido, Japan (Hossain et al. 2010).

3.2.4.3. Zooplankton

Forty-eight taxa of zooplankton were identified in Lake Norman during 2008 (Duke

Energy 2009). The zooplankton community is dominated by copepods (primarily

Tropocyclops), comprising over two-thirds of all samples in 2007 and 2008 (Duke Energy

2008). Cladocerans were present in smaller quantities with Bosmina dominating the adult form. Zooplankton biomass was estimated from four replicate whole water column samples taken in February, May, August and November of 2008, and averaged 1.268 t·km-2, and an annual mean P/B of 69.1 was used (Han et al. 2011).

3.2.4.4 Benthic invertebrates and other invertebrates

Limited data were available for these groups. Benthic invertebrates were comprised of chironomids and mollusks. Other invertebrates consisted of crayfish and terrestrial insects

(e.g., termites, ants, etc.). Using EE, P/B, and Q/B estimates from other systems (Rogers and

Allen 2011; Momot et al. 1995), biomass was estimated within the Ecopath model.

3.2.4.6 Fish

Biomass

Biomass estimates for all littoral fish groups (littoral omnivores, spotted bass, largemouth bass, white perch, crappie) were calculated from catches in lake-wide surveys conducted every 3-6 weeks in April-December from 2008-2009 using shoreline boat electrofishing and nearshore gillnet surveys at 40 randomly selected 300-m transects throughout the lake (10 sites in each of four regions; Figure 3.1). Catch per unit effort

(CPUE; biomass of fish per second of shocking or hours of gillnet soak time) were calculated for each fish species and averaged for all 40 transects. Average number of fish per meter of shoreline was extrapolated to the entire lake margin for littoral species (by multiplying the average catch per meter shoreline by the total meters of shoreline in Lake Norman) and then standardized to t·km-2 for each group. Striped bass are stocked each year at a rate of 12.4 fingerlings (~19.2 g each) per hectare, or about 163,000 striped bass fingerlings annually

(Waters and McRae 2008), therefore, we applied a stocking forcing function in Ecopath so that striped bass were stocked at an annual rate of 0.0243 t·km-2·yr-1, as striped bass do not naturally reproduce in the reservoir.

Production/biomass (P/B) ratio

Because P/B ratio is equivalent to the instantaneous rate of total mortality (Z) used in fisheries biology (Allen, 1971), and M = F for unexploited populations, we were able to use the estimate of M for the P/B ratio. Production/biomass ratios were estimated several ways for fish functional groups. When data were available from this study, we used the empirical equation:

0.65 0.463 M = K · L∞ - 0.279 · TC , developed by Pauly (1980), where M is the natural mortality (yr-1), K is the growth rate from the von Bertalanffy growth curve, L∞ is the asymptotic length and Tc is the average annual temperature (20.3 ± 1.4°C). For species where only lengths were available, a length- converted catch curve method (Pauly 1983) was employed in FiSAT II (Gayalino et al. 2002) to calculate L∞ and K. For exploited fish groups we determined P/B by summing natural and fishing mortality (i.e., Z = F + M). When local data were not available species-specific P/B estimates were taken from FishBase (www.fishbase.org).

Relative consumption (Q/B)

Consumption/biomass ratios were calculated during this study using the empirical relationship developed by Palomares and Pauly (1989, 1998):

Log(Q/B) = 7.964 – 0.204 · logWinf - 1.965 · T + 0.083 · A + 0.532 · h + 0.398 · d, where, Winf is the asymptotic weight (g), T is the mean annual temperature of Lake Norman

(T = 10000/K, where K is the average annual temperature of Lake Norman in degrees Kelvin

(285.8 degrees Kelvin; Duke Energy, unpublished data), A is the aspect ratio of the fish’s

caudal fin, and h and d is are dummy variables indicating food type (1 or 0). The aspect ratio of the caudal ﬁn, which is closely related to the average level of activity, is calculated as A = l · 2/s, where l is the height of the caudal ﬁn and s is the surface area. For herbivores h is equal to 1 and for detritivores d is equal to 1; otherwise h and d are equal to zero. In addition, we assumed asymptotic weight (W∞) was equal to the weight at L∞, and estimated it

b as W∞ = a·L∞ , where a and b are the parameters of the allometric length-weight relationship obtained from nonlinear ﬁtting to length and weight data from Lake Norman (Table A.1).

For functional groups consisting of multiple species, we used the mean Q/B from all species included in the group (Pine et al. 2007).

Catch

Nine fishing ―fleets‖ were defined in the Ecopath model, one for each introduced species (white perch, flathead catfish, spotted bass, and alewife) and for all other species that are targeted by recreational anglers. Landings for introduced fish with no fishing pressure were set to 0.001 (t·km-2·yr-1). Landings for exploited fish groups were calculated from a

Lake Norman creel survey conducted from 2007-2008 (B. McRae, NCWRC, unpublished data): largemouth bass, 0.016 t·km-2·yr-1; spotted bass, 0.016 t·km-2·yr-1; striped bass, 0.017 t·km-2·yr-1; channel catfish, 0.028 t·km-2·yr-1; flathead catfish, 0.028 t·km·-2·yr-1; blue catfish,

0.056 t·km-2·yr-1; crappie, 0.002 t·km-2·yr-1; Lepomis spp., 0.0004 t·km-2·yr-1; and white perch, 0.030 t·km-2·yr-1.

Diet composition

Because relatively few extensive diet studies have been completed for southeastern

U.S. reservoir fish communities, fish collected during this study were used directly to compile our predator-prey diet matrix. Diet composition of all functional groups was then arranged in a predator-prey diet matrix that shows the proportion of each prey consumed by each predator (Table 3.3). All fish diets were obtained from samples collected during this study except for gizzard shad (Denlinger et al. 2006) and detritivores (common carp

Cyprinus carpio; García-Berthou 2001). Five-percent of zooplankton were assumed to be predatory (i.e., consisting of other zooplankton), so zooplankton diets were 95% phytoplankton an 5% zooplankton. Diets of crayfish were used for the diet composition of other invertebrates (Momot 1995). Fish diet composition estimates from this study were based on a minimum of 20 fish of each species collected from each of three periods (spring, summer, fall-winter) in one or more years (until at least 30 non-empty stomachs were analyzed) from 2007-2009. In general, stomachs were removed from all fish under 100 mm

TL and preserved in 70% ethanol.

For analysis, stomachs were removed from ethanol, blotted with paper towel and weighed to the nearest 0.01 g. Contents were removed and placed in a petri dish under a dissecting microscope for identification. All invertebrates in the diet were counted and assigned a wet weight calculated from the average live wet weight of 100 individuals. Diet contents were analyzed and summarized according to proportion of the diet (wet weight).

Fish diet contents were reconstructed based on length and weight of the prey using vertebral length to total length and total length to wet weight regression equations. We used regression equations specific to this study (Lepomis spp., white perch, crappie, and juvenile striped

bass) or Lake Norman fishes (clupeids; Thompson 2006) and utilized relationships developed by others (www.fishbase.org) where necessary. Blue catfish diets were supplemented with

Lake Norman-specific data from Grist (2002). Diets of striped bass less than six months old were omitted, and assumed to be ―import only,‖ in terms of the Ecopath model (Christensen et al. 2000).

3.2.5. Model analysis and adjustments

Model parameters were adjusted to obtain ecotrophic efficiency (EE; the proportion of production that is consumed by predators or exported) values less than one for all groups

(Christensen et al. 2005) and gross efficiency values (P/Q) between 0.1 and 0.3 (Christensen et al., 2002) for all fish groups. Parameters were adjusted by small increments (0.05 units or less) until the model balanced (Table 3.2).

3.2.6. Summary Statistics

We used various summary statistics to characterize the trophic structure of the Lake

Norman food web and the role of each functional group in the food web. First, we examined the overall quality of the model using the pedigree index routine, and measure of fit (t*), where an index of one equals a high quality model with data fully rooted in local data and zero equals a low quality model that relies on external sources or ―guestimates‖ (Christensen et al. 2005). Next, we used the omnivory index (OI, Pauly et al. 1993) to determine the specialization (generalist or specialist) of each functional group on prey. The omnivory index is calculated for each functional group in the Ecopath model as the variance of the trophic level of a consumer's prey groups:

Where TLj is the trophic level of prey j, TLi is the trophic level of the predator i, and, DCij is the proportion prey j contributes to the diet of predator i. When the value of the omnivory index is close to zero, the consumer is more specialized on one trophic level, as opposed to a large value (i.e., > 0) that suggests a consumer feeds at different levels.

The net food conversion efficiency (NE) was calculated for each functional group as its production divided by the assimilated part of food for that group, or:

NE = P/B / (Q/B · (1 - GS)), where P/B is the production/biomass ratio, Q/B is the consumption/biomass ratio, and GS is the proportion of unassimilated food (typically 0.2 for most groups). The flow to detritus

(FtD) was calculated as the sum of the egested products (non-assimilated food) and ―other mortality‖ (i.e., members that senesce or die of disease; FtD = 1 – EE) in t·km-2·yr-1

(Christensen and Walters 2004, Christensen et al. 2005). In addition to the routines available in EwE, we also calculated the relative weighted trophic level of introduced versus established species in the reservoir and the contribution of biomass by introduced fishes.

Odum (1969) proposed 24 metrics of ecosystem maturity. Some of these factors can be calculated within the EwE framework, including gross production/respiration, gross production/biomass, total organic matter, nutrient conservation, ecosystem stability, and the role of detritus (Christensen and Pauley 1998). We examined a number of these statistics for the Lake Norman model, and report on eight of them here. System level summary statistics

included indices related to the maturity of the reservoir ecosystem.

3.2.7. Trophic levels and trophic flow

There are two routines for calculating trophic levels in Ecopath. We used the first method (Odum and Heald 1975) to assign functional groups to fractional trophic levels.

Producers and detritus were assigned trophic levels of 1; trophic levels of all other groups

(consumers) were calculated as 1 + [the weighted average of their preys’ trophic levels]

(Christensen et al. 2008). Recreational fisheries were assigned a trophic level equal to the average trophic level of their catch. The second method was used to characterize the contribution of each functional group to flow (t·km-2·yr-1) through discrete trophic levels

(Lindeman 1942; Ulanowicz 1995), creating a representation of the biomass contribution of each functional group to each discrete trophic level (represented by a Lindeman spline and flow pyramid; Lindeman 1942) in Lake Norman. This calculation is essentially the inverse of the procedure above for estimating fractional trophic levels of each group. The trophic flow (F) between discrete trophic levels is described in terms of absolute flow (t·km-2·yr-1) and in terms of transfer efficiency. The transfer efficiency between consecutive discrete trophic levels was calculated as:

(Sum of Exports from TLi + Flow from TLi to TLi +1) / (Throughput of TLi), where TLi is discrete trophic level i, and the throughput is equal to the sum of consumption, export, flow to detritus, and respiration at each discrete trophic level.

3.2.8. Mixed Trophic Impacts

Hannon (1973) and Joiris (1989) introduced the Leontief matrix (developed to assess

the direct and indirect interactions in the U.S. economy) to ecology to assess the impact that changing the biomass of one functional group would have on the biomass of another. A similar approach to determining direct and indirect impacts in a food web was then developed by Ulanowicz and Puccia (1990), and included in Ecopath. The mixed trophic impacts (MTI) for functional groups (including fishing fleets, i.e., recreational fisheries) is calculated by constructing an i x j matrix, where the i,jth element representing the interaction between the impacting group i and the impacted group j is

MTIi,j = DCij - FCji , where DCij is the diet composition term expressing the proportion j contributes to the diet of i, and FCji is a host composition term giving the proportion of the predation on j that is due to i as a predator. For detritus groups the DCij terms are set to 0. For each fishing fleet a

―diet composition‖ is calculated representing how much each functional group contributes to the catch of each fleet, while the host composition term as mentioned above includes both predation and catches. One was added to the diagonal elements of the MTI matrix, i.e., 1

+ MTIij , and the matrix was then inversed using a standard matrix inversion routine

(Christensen and Pauley 1992).

The MTI routine was used as a tool to show possible impacts of direct and indirect interactions (including competition) in this steady-state model and to identify groups that had either an unexpected impact or caused large changes in other functional groups. This routine was not used as an instrument for making predictions of what will happen in the future if certain interaction terms are changed. The resultant matrix values provide a relative measure

of the direct and indirect impacts that a very small increase in biomass of each functional group (impacting group) has on all other functional groups (including itself and fishing fleets).

3.2.9. Niche overlap

We were particularly interested in the extent of niche overlap between introduced species and other members of the community. Within the EwE module, a modified

(Christensen et al. 2011) Pianka’s overlap index (O; Pianka 1973) was used to calculate resource overlap for both shared predators (on a prey) and shared prey (by a predator) for each combination of functional groups. The overlap in resource use between functional groups j and k is defined as:

where Pji and Pki are the proportions of the resource i used by functional groups j and k, respectively. The index ranges from 0 to 1, with numbers closer to one indicating greater resource overlap. Typically, index values greater than 0.60 are considered as ecologically significant (Zaret and Rand 1971).

3.3. Results and Discussion

Based on our high confidence in the input parameters (especially our diet matrix), the

Pedigree Index value of 0.71 (t* = 4.71; Christensen et al. 2005), and resemblance of output to that of other models (e.g., Liu et al. 2007; Khan and Panikkar 2009), we were confident accepting the Ecopath model created for Lake Norman. The Pedigree Index (PI)

demonstrates that our model is rooted strongly in local or regional data, which is ideal for this type of model. Our model fared much better and was composed of more localized data than for many other models including lagoons, lakes and other reservoirs such as Lake Toya,

Japan (PI = 0.413; Hossain et al. 2010) and Lake Annecy, France (PI = 0.422; Janjua and

Gerdeaux 2009). The next closest pedigree index for a reservoir model was for Bagré

Reservoir, Burkina Faso (PI = 0.68; Villanueva et al. 2006).

3.3.1. Summary statistics

The summary statistics for the Lake Norman ecosystem (Table 3.4) were indicative of an unproductive, oligotrophic reservoir dominated by high- to mid-trophic level predators.

Total biomass in the reservoir (excluding detritus) was low (17.83 t·km-2; Table 3.4) compared to other reservoirs of similar size (>10,000 ha) with introduced fish (3.925 t·km-2) making up one-third of the total fish biomass (11.48 t·km-2).

In general, ecotrophic efficiency for upper trophic level groups that experienced little predation tended to be low (<0.10), as would be expected for a stable ecosystem (Dickie

1972). However, the EE of some prey groups were approaching one (e.g., EE = 0.98 for threadfin shad, 0.92 for alewife, and 0.91 for benthic invertebrates; Table 3.2) indicating very high predation pressure on these groups. Ecotrophic efficiency of the detritus group (defined as the ratio between flow to and from the detritus group) was low, suggesting that there is a large annual contribution to the detritus compartment.

Odum (1969) initially proposed 24 indicators of ecosystem maturity. Many of these factors were calculated within the EwE framework, and provided context with which to

categorize the maturity of the reservoir. We report seven ecosystem indicators here, including: net system production (lower in mature systems), the gross primary production/respiration (PP:R; tends toward 1:1 in mature systems), gross primary production/biomass, biomass/total system throughput (B/TST; higher in mature systems), nutrient flow, system omnivory index, and the role of detritus (Christensen and Pauly 1993,

1998).

Based on Odum’s indicators of system maturity (Odum 1969), we would categorize

Lake Norman as an intermediate to mature system. Although not close to zero, which would indicate a very mature system (Odum 1969), net system production was low at 157.2 t·km-2

·yr-1 in the Lake Norman model (Table 3.4) compared to other lakes and reservoirs, such as

Lake Qiandaonu, (a reservoir; 3,083.1 t·km-2·yr-1; Liu et al. 2007), Lake Awassa, Ethiopia

(6,974.1 t·km-2·yr-1; Fetahi and Mengistou 2007), and Kelavarappalli reservoir, India

(10,655.1 t·km-2·yr-1; Khan and Panikkar 2009).

Odum (1969) also hypothesized that system gross primary production/respiration would trend towards one when a system was fully mature; however, in a review of 41 ecosystem models that included lakes, rivers, reservoirs, coastal systems, and ocean models,

Christensen and Pauly (1993) showed that production exceeds respiration in most systems, and was between 0.8 and 3.2 for the majority. The PP:R ratio was 1.96 in the Lake Norman model, well within the typical range for most aquatic systems. Interestingly, the value is lower than PP:R ratios reported for other reservoirs (7.78 for Kelavarappalli reservoir, India;

Khan and Panikkar 2009, and 3.24 for Lake Annecy, France; Janjua and Gerdeaux 2009) and

lakes (e.g., 5.834 for Lake Awassa, Ethiopia; Fetahi and Mengistou 2007), indicating that

Lake Norman tends to be more ―mature‖ or possibly just less disturbed or polluted than these other lakes and reservoirs.

In addition, total system throughput (TST), a measure of system complexity (Szyrmer and Ulanowicz 1987) that is directly comparable among systems, was 926 t·km-2·yr-1 (Table

3.4), much lower than in many freshwater and marine systems. For example, Kelavarapalli reservoir in India has a TST of 10,655 t·km-2·yr-1 (Khan and Panikkar 2009), Lake Kivu,

Africa has a TST of 6,686 t·km-2·yr-1 (Villanueva et al. 2008), and the Bay of Bengal was reported to have a TST of 2,628 t·km-2·yr-1 (Uhhah et al. 2012). Likely, because TST can increase when there are more species or more system activity (e.g., phytoplankton blooms;

Scharler 2008), Lake Norman more closely resembles the TST calculated for Lake Toya

(72.0 t·km-2·yr-1), an oligotrophic lake in Japan (Hossain et al. 2010).

Higher values of biomass/total system throughput (B/TST) generally represent more mature systems (Odum 1969). The B/TST for Lake Norman was 0.02. This value was slightly higher than (Lake Kivu, B/TST = 0.008; Villanueva et al. 2008 and Kelavarapalli reservoir, B/TST = 0.006; Khan and Panikkar 2009) or comparable to (Lake Annecy (B/TST

= 0.016; Janjua and Gerdeaux 2009) other reservoirs, but much lower than most estuarine and ocean models (e.g., B/TST = 1.244, Caete´ Estuary, Brazil; Wolff et al. 2000) indicting that the system was likely mature for a reservoir, but less ecologically mature than estuaries or bays.

The system omnivory index, a dimensionless index ranging from 0.0 (highly

specialized) to 1.0 (feeding on many trophic levels), was low for Lake Norman (0.193; Table

3.4; Pauly et al. 1993; Monaco and Ulanowicz 1997). This implies that, in general, species in Lake Norman tend to be specialized on particular prey. This is indicative of a more mature system with differentiated niches. Individual omnivory indices (Table 3.2) indicate that adult striped bass (OI = 0.084), adult spotted bass (OI = 0.079), and bluegill-redear sunfish (OI = 0.055) are the most specialized functional groups in Lake Norman. Striped bass and spotted bass consume primarily threadfin shad, whereas bluegill-redear sunfish are heavily specialized on benthic invertebrates in Lake Norman.

3.3.2. Trophic flows

Fractional trophic levels of functional groups (Odum and Heald 1975) ranged from

1.0 for detritus and primary producers to 3.639 for flathead catfish (i.e., top predators in Lake

Norman), although the majority of fish were within trophic level 3. The trophic structure of

Lake Norman (Figure 3.2) has a large amount of biomass in the middle to upper trophic levels, indicating a ―top-heavy‖ ecosystem. The average trophic level, weighted by biomass, of the four introduced species was 3.385, indicating that the introduction of these species has likely increased the average trophic level of the reservoir fish community (Figure 3.2), thus increasing the demand for lower trophic level prey.

Separation of flow into discrete trophic levels allowed us to construct a Lindeman spline (Lindeman 1942; Figure 3.3) depicting the flow (t·km-2·yr-1) through each trophic level. The flows by trophic level and functional group (Table 3.5) are similar to other

reservoirs such as Lake Toya, Japan (Hossain et al. 2010) and Ria Formosa water reservoir, south Portugal (Garnito and Erzini 2005). Phytoplankton exclusively comprised the producers level (trophic level 1), whereas zooplankton, benthic invertebrates, other invertebrates, and gizzard shad contribute most to trophic flow on the herbivore-detritivore level (trophic level 2). Several fish groups (bluegill-redear sunfish, littoral omnivores, threadfin shad, and alewife), benthic invertebrates, and other invertebrates dominated the trophic flow from first order carnivores (trophic level 3) The higher trophic levels consist primarily of piscivorous fish groups. The trophic transfer efficiencies, or percent of energy transferred from one level to the next, ranged from 34.5% (trophic levels 1 to 2) to 5.5%

(trophic levels 5 to 6+; Table 3.5). The connectance index, or the ratio of the total number of actual links to the total number of possible links (Gardner and Ashby 1970; Christensen and

Walters 2007), was 0.26, indicating generally low connectivity of functional groups in this food web. Connectivity commonly decreases as the number of functional groups in the model or the number of species in the ecosystem increase (Christensen and Pauly 1993).

3.3.3. Mixed Trophic Impacts

In our model, most groups have a negative impact on themselves, most likely reflecting within-group competition for resources. Exceptions do exist, however. For example, the impact of both spotted bass and largemouth bass on themselves is slightly positive because they cannibalize themselves and thus reduce intraspecific competition. The mixed trophic impact routine can also be regarded as a form of 'ordinary' sensitivity analysis

(Majkowski 1982) in that it implies what the sensitivity of each functional group is to all

others. Based on the MTI routine for the Lake Norman community, we concluded that there was negligible impact of channel catfish, blue catfish, juvenile spotted and largemouth bass, juvenile white perch, crappie, and detritivores on any other functional groups (Figure 3.4).

Either they are too scarce to have a quantitative impact (e.g., crappie and channel catfish) or they consume prey in quantities that do not impact the flux of energy from one group to another. These results suggest that one need not allocate much effort to refining the parameter estimates for these groups; it may be better to concentrate on other groups.

3.3.4. Niche overlap

We were interested in two components of the niche overlap analysis using the modified Pianka’s index (P; Pianka 1973; Christensen et al. 2011). First, we identified species that had both very high (>0.80) predator overlap (Ppred; Table A.2) and prey overlap

(Pprey; Table A.3). Groups that overlap heavily in both prey and predators could possibly be combined into one functional group; however, we had only three groups that met these criteria. Littoral omnivores had high niche overlap with white perch juveniles (Ppred = 0.729,

Pprey = 0.994; Figure 3.5), benthic invertebrates with other invertebrates (Ppred = 0.918, Pprey =

0.909), and littoral omnivores with bluegill-redear sunfish, (Ppred = 0.985, Pprey = 0.911).

Although we considered grouping bluegill-redear sunfish and littoral omnivores into one functional group, we believe that the other parameters in the EwE model were too different to justify this grouping. The other two pairs occupied different habitat in the reservoir, therefore we felt justified in our grouping of species and left our functional groups as originally described.

Second, high diet (or prey) overlap can suggest that species are competing for prey

(Wallace 1981) if resources are limiting. We identified all groups with significant prey overlap (>0.60; Zaret and Rand 1971, Keast 1978; Figure 3.5; Table A3). Of the greatest interest were those groups that overlapped with introduced species. White perch adults had high prey overlap with striped bass juveniles, crappie, largemouth bass adults, flathead catfish, and spotted bass adults (Figure 3.5; Table A.2; Table A.3). White perch juveniles had high prey overlap with crappie, bluegill-redear sunfish, littoral omnivores, and blue catfish. Flathead catfish had high prey overlap with channel catfish, adult largemouth bass, adult white perch, and crappie. Spotted bass adults and juveniles overlapped with largemouth bass adults, striped bass juveniles, and adults; however, juvenile spotted bass overlapped significantly with juvenile largemouth bass, and alewife. Finally, alewife had significant prey overlap with juvenile largemouth bass, juvenile spotted bass, and benthic invertebrates (Figure 3.5; Appendix A.3). The high prey overlap values observed between multiple groups of introduced fish and both crappie and largemouth bass support reports from Lake Norman anglers and managers that populations of these popular sport fish are declining. Annual spring electrofishing surveys conduced over the last 10 years clearly show a constant decline in largemouth bass CPUE with a constant increase in spotted bass CPUE

(M. Abney, Duke Energy, unpublished data). Given that prey overlap is high between many introduced fish and established fish groups, competition between introduced fish and established fish for prey resources seems likely. White perch adults and juveniles had the greatest number of overlap values in the significant range, suggesting that they may have the

greatest impact on other functional groups in the reservoir.

3.4. Conclusion

We were able to use the Ecopath with Ecosim framework to successfully model the trophic relationships in Lake Norman. Although reservoirs may be thought of as ecologically immature due to their short time since impoundment compared to natural lakes, Lake

Norman is one of the longer established reservoirs. We categorized it as a mature system based on summary statistics for our model (sensu Odum1969). Fish biomass in the reservoir was low, but within range of other reservoirs, especially oligotrophic ones. We conclude that introduced species have had, and may continue to have, large impacts on the trophic structure of Lake Norman, as they comprise over one-third of the biomass in the lake. White perch and spotted bass showed the greatest impacts on other functional groups and the most diet overlap with established fish species. Because Lake Norman is a mid-trophic level to upper- trophic level heavy system with low productivity, it is improbable that the reservoir could successfully support additional predators without negative consequences for other fish.

Further possibilities now exist for simulating changes to the Lake Norman ecosystem. The model presented here is a useful tool that can now be used to explore scenarios and develop testable hypotheses regarding the effects of additional invaders and other system alterations.

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Table 3.1. Functional groups included in the Lake Norman Ecopath model. Numbers indicate ranking of each functional group based on fractional trophic levels, from highest to lowest (see text).

No. Functional group Species or family 1 Flathead Catfish Flathead catfish Pylodictus olivarus 2 Largemouth bass (A) Adult largemouth bass (≥ 1 year) Micropterus salmoides 3 Crappie Black crappie Poxomis nigromaculata White crappie P. annularis 4 Striped bass (A) Adult striped bass (≥ 2 years) Morone saxatilis 5 Striped bass (J) Juvenile striped bass (6 mo.- 2 years) Morone saxatilis 6 Spotted bass (A) Spotted bass ≥ 1 year Micropterus punctulatus 7 Littoral omnivores Green sunfish Lepomis cyanellus Redbreast sunfish Lepomis auritus Warmouth Lepomis gulosus 8 White perch (J) White perch < 1 year Morone americana 9 White perch (A) White perch ≥ 1 year Morone americana 10 Bluegill/redear sunfish Bluegill Lepomis macrochirus Redear sunfish Lepomis microlophus 11 Channel Catfish Channel catfish Ictalurus punctatus 12 Alewife Alewife Alosa pseudoharangus 13 Largemouth bass (J) Juvenile largemouth bass Micropterus salmoides 14 Spotted bass (J) Spotted bass ≤ 1 year Micropterus punctulatus 15 Blue Catfish Blue catfish Ictalurus furcatus 16 Benthic invertebrates Annelids, Amphipods, Chironomid larvae, Odonata, Corbicula, Ceratopogonid larvae, Gastropoda, Hydrachnidia, Megaloptera larvae, Nematodes, Odonate larvae, Tricoptera larvae 17 Threadfin Shad Threadfin shad Dorosoma petenense 18 Other invertebrates Crayfish, terrestrial insects 19 Detritovores Common carp Cyprinus carpio Quillback Carpoides cyprinus Shorthead redhorse Moxostoma macrolepidotum V-lip redhorse Moxostoma pappillosum 20 Gizzard Shad Gizzard shad Dorosoma cepedianum 21 Zooplankton All zooplankton 22 Striped bass (<6mo) Stocked striped bass (< 6 months) 23 Phytoplankton All phytoplankton 24 Detritus Detritus

Functional Group TL B P/B Q/B EE P/Q NE FtD OI 1 Flathead Catfish 3.639 0.36 1.10 3.90 0.068 0.282 0.652 0.353 0.347 2 Largemouth bass (A) 3.606 0.51 0.91 6.20 0.035 0.147 1.080 0.183 0.271 3 Crappie 3.570 0.10 2.00 8.00 0.103 0.250 0.339 0.313 0.233 4 Striped bass (J) 3.542 0.23 1.00 6.57 0.004 0.152 0.523 0.190 0.123 5 Striped bass (A) 3.534 0.31 0.92 4.24 0.058 0.217 0.532 0.271 0.084 6 Spotted bass (A) 3.500 0.51 0.90 4.40 0.035 0.205 0.892 0.256 0.079 7 Littoral omnivores 3.480 0.63 2.10 7.80 0.820 0.269 1.219 0.337 0.117 8 White perch (J) 3.434 0.07 1.50 12.95 0.043 0.116 0.292 0.145 0.137 9 White perch (A) 3.417 2.10 0.78 4.00 0.047 0.195 3.243 0.244 0.145 10 Bluegill/redear sunfish 3.401 1.30 1.41 18.00 0.683 0.078 5.260 0.098 0.055 11 Channel Catfish 3.268 0.11 1.85 6.50 0.000 0.285 0.418 0.407 0.546 12 Alewife 3.139 0.86 2.02 8.92 0.920 0.226 1.673 0.283 0.037 13 Largemouth bass (J) 3.095 0.06 4.16 23.23 0.001 0.179 0.518 0.224 0.017 14 Spotted bass (J) 3.095 0.02 1.20 13.25 0.128 0.091 0.080 0.113 0.017 15 Blue Catfish 2.856 1.14 0.25 4.60 0.195 0.054 1.803 0.078 0.633 16 Benthic invertebrates 2.526 2.60 9.60 22.00 0.901 0.436 13.903 0.545 0.277 17 Threadfin Shad 2.408 1.13 7.49 43.40 0.980 0.173 9.978 0.216 0.280 18 Other invertebrates 2.316 1.60 11.20 24.00 0.911 0.467 9.279 0.583 0.233 19 Detritivores 2.258 0.04 2.00 10.00 0.216 0.200 0.143 0.250 0.277 20 Gizzard Shad 2.211 2.00 0.90 19.90 0.215 0.045 9.374 0.057 0.177 21 Zooplankton 2.053 1.27 69.10 110.00 0.936 0.628 33.518 0.785 0.05 22 Striped bass (<6mo) 1.000 0.00 0.01 17.86 0.000 0.001 0.02 0.001 0.00 23 Phytoplankton 1.000 0.88 365.00 0.00 0.618 122.58 0.00 24 Detritus 1.000 5.500 0.255 0.000 0.000 0.623

Prey Predator 1a 2a 3a 4a,b 5j 6a,c 7a,c 8a,c 9a 10a 11 a 12a 13a 14a,d 15e 16a 17f 18g 19h 20i 21e 22e 1 Flathead Catfish 2 Channel Catfish 3 Striped bass (A) 4 Striped bass (J) 0.0001 0.0001 5 Striped bass (<6mo) 6 Blue Catfish 7 Largemouth bass (A) 8 Largemouth bass (J) 0.0001 9 Spotted bass (A) 10 Spotted bass (J) 0.001 11 White perch (A) 0.032 0.001 12 White perch (J) 0.001 0.001 13 Crappie 0.001 0.005 14 Bluegill/redear sunfish 0.137 0.082 0.025 0.025 0.005 0.110 0.021 0.026 0.020 0.101 0.040 15 Littoral omnivores 0.137 0.082 0.025 0.005 0.110 0.021 0.026 0.020 0.101 0.003 16 Threadfin Shad 0.123 0.086 0.825 0.650 0.033 0.395 0.010 0.810 0.010 0.308 0.037 0.005 0.003 17 Alewife 0.140 0.150 0.050 0.027 0.075 0.048 0.022 0.008 0.050 18 Gizzard Shad 0.037 0.022 0.005 0.010 0.017 0.017 0.005 19 Detritovores 0.012 20 Other invertebrates 0.419 0.470 0.010 0.100 0.040 0.222 0.020 0.061 0.020 0.196 0.230 0.307 0.321 0.226 0.020 0.319 21 Benthic invertebrates 0.066 0.085 0.387 0.069 0.070 0.001 0.070 0.209 0.551 0.320 0.581 0.584 0.014 0.035 0.100 22 Zooplankton 0.001 0.001 0.050 0.007 0.032 0.900 0.900 0.151 0.087 0.138 0.074 0.073 0.342 0.633 0.200 0.100 0.300 0.5 0.05 23 Phytoplankton 0.050 0.278 0.023 0.015 0.032 0.007 0.013 0.015 0.532 0.013 0.300 0.200 0.300 0.25 0.95 24 Detritus 0.033 0.145 0.173 0.005 0.000 0.003 0.004 0.092 0.500 0.600 0.400 0.250 a This study d Grist (2002) g García-Berthou 2001 j Diet was assumed to be "import only" b Thompson (2006) e Duke Energy (2008, 2009) h Mundahll & Wissing (1987) c Feiner (2011) f Momot (1995) i Assumed 5% predatory zooplankton

Table 3.4. System summary statistics calculated within the Ecopath model for Lake Norman, North Carolina.

Parameter Value Units Sum of all consumption 387.75 t·km-2·yr-1 Sum of all exports 159.25 t·km-2·yr-1 Sum of respiratory flows 164.00 t·km-2·yr-1 Sum of all flows to detritus 214.60 t·km-2·yr-1 Sum of all production 471.48 t·km-2·yr-1 Total system throughput (TST) 925.61 t·km-2·yr-1 Total net primary production (calculated) 321.20 t·km-2·yr-1 Net system production 157.19 t·km-2·yr-1 Total biomass (excluding detritus) 17.83 t·km-2 Total primary production/respiration 1.96 Total primary production/total biomass 18.01 Total biomass/total throughput 0.02 Total catches 0.17 t·km-2·yr-1 Mean trophic level of catch 3.31 Connectance Index 0.26 System Omnivory Index 0.19 Finn's mean path length 3.55 Finn's cycling index 4.06 % of TST Ecopath Pedigree Index 0.71 Measure of fit, t* 4.62

Table 3.5. The absolute trophic transfer in the Lake Norman ecosystem showing the distribution of flows (t·km-2·yr-1) through discrete trophic levels (1-6+, where 6+ represents trophic levels 6-9) for each functional group. Reading down each column gives the contribution of each functional group (t·km-2·yr-1) to the flow at that discrete trophic level. Transfer efficiency is the percent biomass transferred between successive discrete trophic levels. Functional group Trophic level 1 2 3 4 5 6+ Flathead Catfish 0 0.054 0.727 0.608 0.214 0.018 Channel Catfish 0 0.175 0.385 0.265 0.068 0.005 Striped bass (A) 0 0 0.788 0.633 0.079 0.004 Striped bass (J) 0 0 0.674 0.525 0.077 0.005 Striped bass (<6mo) 0 0 0 0 0 0 Blue Catfish 0 2.927 1.738 1.682 0.141 0 Largemouth bass (A) 0 0.082 1.708 1.385 0.392 0.305 Largemouth bass (J) 0 0 1.457 0.136 0.007 0 Spotted bass (A) 0 0 1.424 0.988 0.131 0.008 Spotted bass (J) 0 0 0.305 0.029 0.001 0 White perch (A) 0 0.197 5.685 3.274 0.42 0.027 White perch (J) 0 0.035 0.599 0.424 0.04 0.003 Crappie 0 0.007 0.491 0.346 0.087 0.007 Bluegill/redear sunfish 0 0.447 16.41 10.43 0.055 0.03 Littoral omnivores 0 0.103 3.039 2.384 0.227 0.012 Threadfin Shad 0 36.42 20.21 1.662 0.083 0.004 Alewife 0 0.115 7.362 1.201 0.061 0.003 Gizzard Shad 0 37.88 8.998 0.045 0.022 0.001 Detritovores 0 0.378 0.069 0.025 0.001 0 Other invertebrates 0 31.94 13 0.65 0.033 0.002 Benthic invertebrates 0 33.84 32.15 1.607 0 0.004 Zooplankton 0 154.4 7.921 0.396 0.198 0.001 Phytoplankton 321.6 0 0 0 0 0 Detritus 85.52 0 0 0 0 0 Transfer efficiency (%) 34.5% 19.7% 8.2% 5.5%

North Carolina

Purse seine sites

Electrofishing, gill net and plankton sampling sites

Lake Norman

Figure 3.1. Map of Lake Norman, North Carolina, USA with purse seine, electrofishing, gill net, and plankton sampling sites.

Bluegill/redear sunfish Flathead Catfish Largemouth bass (A) Spotted bass (A) White perch (A) Crappie Alewife Littoral omnivores Striped bass (A) Spotted bass (J) Striped bass (J) Largemouth bass (J) Diet White perch (J) Benthic invertebrates < 0.190 Channel Catfish < 0.380 < 0.570 < 0.760 < 0.950 4 4

4 3 1 2 5 6 8 7 11 9 10 13 14

3 12 Bluegill/redear sunfish

3 l Largemouth bass (A) e Flathead Catfish Spotted bass (A) v 15

e Alewife

l White perch (A)

Crappie c i 16 Spotted bass (J) Littoral omnivores h 17 p 19 Strip18e d bass (A) o r 20 T 2 Striped bass (J21) Largemouth bass (J) 2 Diet White perch (J) Proportion of diet Benthic invertebrates < 0.19 Channel Catfish 0.191-< 0 0.380.1 90

0.381-< 0 0.570.3 80 1 0.571-< 0 0.760.5 70 1 23 24 > <0.761 0 .760 < 0.950 Figure 3.2. Trophic flow diagram for functional groups in LakeD eNorman,tritovores NorthOth eCarolina.r invertebrat e Circless are proportionalZoop tolan ktheton biomass Striped bass (<6mo) Phytoplankton of each groupB luine C theatfis hsystem. Lines connecting nodes represent flow between groups by proportionDetr itofus diet (by weight) of the Threadfin Shad consumer. Horizontal lines represent fractionalGizzard trophic Shad levels 1-4. Numbers in circles identify functional groups (Table 3.1): 1 = Flathead catfish, 2 = Largemouth4 bass-adult, 3 = Crappie, 4 = Striped bass-juvenile, 5 = Striped bass-adult, 6 = Spotted bass-adult, 7 = Littoral omnivores, 8=White perch-juvenile, 9 = White perch-adult, 10 = Bluegill/redear sunfish, 11 = Channel catfish, 12 = Alewife, 13=Largemouth bass-juvenile, 14 = Spotted bass-juvenile, 15 = Blue catfish, 16 = Benthic invertebrates, 17 = Threadfin shad, 18 = Other invertebrates, 19 = Detritivores, 20 = Gizzard shad, 21 = Zooplankton, 23 = Phytoplankton, 24 = Detritus.

Detritovores Other invertebrates Zooplankton Striped bass (<6mo) Phytoplankton Blue Catfish Detritus Threadfin Shad Gizzard Shad

exports and catches

TST (%) consumption predation TL TE biomass flow to respiration detritus

0.0272 0.0272 0.0668 0.0511 0.009 35.13 35.13 35.13 3.355 0.315 197.8 105.2 24.6 2.322 1 2 34.5% 3 19.7% 4 8.18% 5 0.890 6.565 7.001 7.001 7.001 137.1 74.05 19.72 1.816 122.6

55.34 62.33 26.48 7.004 0.079 9.814 D 5.50 219.2 Figure 3.3. Lindeman spline representing the flow (t·km-2·yr-1) through each discrete trophic level (1-5+, where 5+ represents discrete trophic level 5-9) in Lake Norman. The legend shows the discrete trophic level (the box; TL), the total biomass at each -2 TL (bottom left corner, t·km ), the percent of the total system flow (TST%) that goes from TLi to TLi+1 , the amount of flow lost to respiration (bottom line; t·km-2·yr-1), consumption (arrow into a box; t·km-2·yr-1) and predation (arrow out of a box; t·km-2·yr-1) by each TL, the percent transfer efficiency (TE; percent of flow that is transferred from TLi to TLi+1. The guide to interpreting these metrics is described in the methods, and the contribution of each functional group to the flow at each trophic level is in Table 3.5.

Figure 3.4. Mixed Trophic Impact (MTI) plot for all functional groups and recreational fisheries in Lake Norman. The plot shows the direct and indirect impact that a 10% change in the biomass of each functional group (impacting group) has on all other functional groups (including itself and fishing fleets). Open bars extending upwards indicate positive impacts, while closed bars extending downwards show negative impacts. The bars should not be interpreted in an absolute sense: the impacts are relative, but comparable between groups.

Figure 3.5. Niche overlap plot (as calculated from a modified Pianka overlap index) of predator and prey overlap values for all pairs of functional groups. Darker shaded circles represent higher overlap values; i.e., white circles have very low overlap values and black circles have very high overlap values). Points above the dotted line (at 0.6 prey overlap) represent significant overlap for prey resources. Numbers near circles identify functional groups (see Table 3.1): 1 = Flathead catfish, 2 = Largemouth bass-adult, 3 = Crappie, 4 = Striped bass-juvenile, 5 = Striped bass-adult, 6 = Spotted bass-adult, 7 = Littoral omnivores, 8 = White perch-juvenile, 9 = White perch-adult, 10 = Bluegill/redear sunfish, 11=Channel catfish, 12 = Alewife, 13=Largemouth bass-juvenile, 14=Spotted bass-juvenile, 15=Blue catfish, 16 = Benthic invertebrates, 17 = Threadfin shad, 18 = Other invertebrates, 19 = Detritivores, 20 = Gizzard shad, 21 = Zooplankton, 23 = Phytoplankton, 24 = Detritus. Numbers for pairs of species with low niche overlap were removed for clarity. Introduced species are shown in bold.

CHAPTER 4 MODELING THE INDIVIDUAL AND INTERACTIVE EFFECTS OF RESERVOIR FISH

INTRODUCTIONS

4.1. Introduction

Invasive species have been widely identified as a significant threat to aquatic diversity and a primary cause of declining native populations (Mills et al. 1993; Pitcher and

Hart 1995; Kolar and Lodge 2002; Wilcove et al. 1998). As the number of introductions continues to grow, so too does the probability of ecosystems containing multiple invaders, as well as the potential for interactive effects among those invaders (Johnson et al. 2009;

Zavaleta et al. 2001). From the impacts of Nile Perch Lates niloticus in Lake Victoria

(Ogutu-Ohwayo 1990, Goldschmidt et al. 1993) to sea lamprey Petromyzon marinus, zebra mussels Dreissena polymorpha, and alewife Alosa pseudoharengus in the Great Lakes

(Smith and Tibbles 1980, Vanderploeg et al. 2002, Mills et al. 1993), and nonnative brook trout Salvelinus fontinalis in the Western United States (Peterson et al. 2004), scientific literature is replete with the impacts of individual introductions on trophic dynamics

(Herbold and Moyle 1986, Mills et al. 1993, Strayer et al. 1999, Mills et al. 2003, Holeck et al. 2004). Yet, untangling the web of trophic interactions is more complex and often more unpredictable when multiple invaders are involved (Moyle and Light 1996), and extrapolating the impacts of invaders to the community or ecosystem level is rarely achieved

(Parker et al. 1999). In a recent literature review on the impacts of nonnative freshwater fish,

Cucherousset and Olden (2011) identified the need for additional scientific inquiry in five

core areas of nonnative species research, including further emphasis on the impacts of introductions on ecosystems and how species interact with environmental change. We believe that this area of focus can be expanded to include interactions with other introduced species, a special category of ecosystem change.

To supplement the known effects of individual introductions, studies highlighting the importance of interactive effects of multiple introductions have recently emerged (Simberloff and Von Holle 1999, Nystrom et al. 2001, Rodriguez 2006, Schmitz 2007, Ricciardi and

Kipp 2008). Although often assumed to be undesirable (debated in Brown and Sax 2004), introduced species can actually influence ecosystems in a variety of ways. For example, the combined effects of multiple invaders may be greater than the sum of their parts, acting synergistically to competitively exclude a species (Kiesecker and Blaustein 1998) or to enhance survival of co-occurring groups (e.g., Asian horn snail Batillaria attramentaria;

Wonham et al. 2005). Conversely, multiple invaders may act in an antagonistic fashion when interactions between or among invaders lessen their impacts (Darling and Côté 2008).

Finally, invasive species may act in a linear (additive) manner in which their combined impacts are equal to the sum of the individual impacts, as would be expected when multiple invaders have no interactive effect on each other or when the positive effects of one negate the negative effects of another (Cope & Winterbourn 2004, Ricciardi and Kipp 2008). When interactions between invaders or potential invaders are not considered, we may be missing the bigger picture (e.g., Johnson et al. 2009). However, outside of plant communities, very few studies have attempted to quantify the effects of multiple introductions on community or

ecosystem structure (Mooney and Cleland 2001).

Reservoirs are optimal systems for evaluating the effects of introduced species on trophic interactions because they are particularly susceptible to species invasions (Power

1996; Havel et al. 2005; Johnson et al. 2008) and often contain many introduced species

(Leprieur et al. 2009). Sport fish introductions are often seen as desirable regardless of the implications (Eby et al. 2006). Because of sampling constraints and lack of historical data, however, we often have a fairly poor understanding of how newly introduced species have changed the ecological landscape of reservoir ecosystems. That may be changing with new approaches to ecosystem modeling that allow for improved quantification of trophic interactions. A common approach (used in over 400 publications) for modeling trophic flow and ecosystem processes in aquatic systems is Ecopath with Ecosim (EwE; Christensen and

Pauly 1992; Christensen and Pauly 1993; Christensen et al. 2004). The program consists of two components, the initial model (Ecopath), which is based on a mass balance trophic modeling system using biomass and food consumption of species or groups of species, and the dynamic simulation portion (Ecosim; Walters et al. 1997, Walters et al. 2000), which uses the output from the original Ecopath and a series of differential equations to explore user- specified changes in the system through time. These models have been used, for example, to explore past and future impacts of ecological disturbances and varying fishing pressures

(Christensen and Pauly 1993; Pauly et al. 2000; Christensen and Walters 2004). Compared to estuarine and marine systems, relatively few studies have been published using EwE to model freshwater ecosystems, but the application to these systems is growing. Ecopath with

Ecosim has been used to successfully model the effects of single-species invasions in a variety of aquatic systems (Villanueva et al. 2008; Janjua and Gerdeaux 2009), including flathead catfish Pylodictis olivaris in a North Carolina river (Pine et al. 2007), Mozambique tilapia Oreochromis mossambicus and African catfish Clarias gariepinus interactions in

Indian reservoirs (Khan and Panikkar 2009), and possible effects of non-indigenous species removal on endangered salmon Oncorhynchus spp. in a Colombia River reservoir (Harvey and Kareiva 2005). Because pre-invasion data are not usually available, use of community or ecosystem models is a unique approach to understanding the interactions among and between invaders and the impact these interactions can have on the rest of the food web.

Reservoirs in the southeastern United States have been inundated by numerous intentional and unintentional introductions (Havel et al. 2005, Johnson et al. 2008). Lake

Norman, North Carolina, one such reservoir, has received its share of fish introductions.

Negative impacts of four introduced species (spotted bass Micropterus punctulatus, white perch Morone americana, alewife, and flathead catfish) on native populations have been inferred from declines in some sport fish populations (e.g., black crappie Pomoxis nigromaculatus and white bass Morone chrysops; B. J. McRae, N.C. Wildlife Resources

Commission, personal communication), but not confirmed. These four introduced fish, in particular, are known to have negative impacts in other systems (Thomas 1995, Pine et al.

2005, Crowder 1980, Madenjian et al. 2000, Brandt et al. 1987, Madenjian et al. 2008,

Pflieger 1997) and the concern over their potential impacts (by anglers and managers) in the southeast United States is growing.

Because so little is known about potential community-level effects of multiple invaders, especially in reservoirs, our objective was to compare the individual and combined effects of four introduced species (flathead catfish, white perch, alewife, and spotted bass) on existing populations in Lake Norman using an Ecopath with Ecosim modeling approach.

This allowed us to determine whether combined effects of species introductions were additive, nonadditive, or neutral.

4.2. Study System

Lake Norman, impounded in 1963, is a 12,634-ha reservoir on the Catawba River in

North Carolina (Figure 4.1). The reservoir has been classified as oligotrophic since the late

1970s, with chlorophyll a concentrations typically averaging 5-9 g·L-1 and Secchi depths of 1.8-

2.6 m (NCDENR 2008). It currently serves as a cooling reservoir for two power stations on the lake, the Marshall Steam Station, a coal-burning station, and the William B. McGuire

Nuclear Station, both currently operated by Duke Energy. At full pool capacity, Lake

Norman has 837 km of highly dendritic shoreline, a mean depth of 10.2 m, and a maximum depth of 36.6 m (Waters and McRae 2008).

Of our four species of interest, flathead catfish were the first to be introduced into

Lake Norman in 1965; the impacts of this introduction have never been studied. White perch were first detected in 1998 (B. J. McRae, NCWRC, personal communication); soon after,

Lake Norman anglers reported catches of very few white bass (B. K. Baker, Duke Energy, personal communication), yet no direct connection has been documented. Alewives, first detected in 1999, were added to Lake Norman by anglers intending to enhance striped bass

Morone saxatilis growth in the reservoir. At present, neither negative nor positive effects have been attributed to alewife in Lake Norman. Spotted bass, the most recent introduction, were first detected in Lake Norman in 2000 (D. Coughlan, Duke Energy, unpublished data).

Since then, the proportion of largemouth bass Micropterus salmoides collected by Duke

Energy (2009) in annual spring electrofishing surveys has declined to the lowest level since surveys began in 1993.

4.3. Methods

4.3.1. Ecopath and post-introduction model

We used an Ecopath with Ecosim (EwE. Version 6.1; www.ecopath.org) modeling approach to quantify the effects of introductions. Specifically, we initially parameterized an

Ecopath model with twenty-four functional groups (species or groups of species with diet;

Table 4.1) using data collected at Lake Norman from 2007-2009. The EwE model and sampling procedures used to parameterize the model are described in detail in Chapter 2.

Briefly, forty 300-m transects distributed throughout the lake were used for fish and invertebrate sampling (see Chapter 3; Figure 3.1). In 2007, all 40 transects were sampled every three weeks from May to September. In 2008 and 2009, three of ten randomly selected

300-m transects in each of four lake regions were sampled during the day (2007 and 2008) and at night (2009) every three to six weeks from May to December. All fish were identified, counted, and an aggregate weight for each species was collected. Catch per unit effort (CPUE, fish per hour of gill-net soak time, per 300-m electrofishing transect, or per purse seine haul) was calculated for each transect on each sample date. Subsamples of fish

(minimum of 20 fish per species, per site, when available) were returned to the laboratory for further analysis. Stomachs were collected and diets were analyzed (see below) for a minimum of 10 fish per region per sample date in 2007 and 2008 (Table A.1).

In general, an Ecopath model requires input of three of the following four parameters: biomass (B), production/biomass ratio (P/B; this is equal to total mortality (Z)), consumption/biomass ratio (Q/B), and ecotrophic efficiency (EE; proportion of production used in the system) in addition to the diet (in proportion of wet weight) for each functional group in a model. Ecopath uses a series of linear equations to solve for unknown values while simultaneously establishing mass-balance (Christensen and Walters 2004). Biomass estimates for all littoral fish groups (littoral omnivores, spotted bass, largemouth bass, white perch, crappie) were calculated from the above described boat electrofishing and nearshore gill net surveys throughout the lake. Biomass for other functional groups was estimated from

CPUE calculations from gill net, purse seine, and plankton net assessments (see Chapter 3), and augmented with Duke Energy reports (Duke Energy 2008) and supplemental literature.

Striped bass are stocked each year at the rate of 12.4 fingerlings per hectare, or about

163,000 striped bass fingerlings annually (Waters and McRae 2008). To provide this input, a stocking forcing function was employed in Ecopath so that striped bass were stocked at an annual rate of 0.0243 t·km-2·yr-1, as striped bass do not naturally reproduce in the reservoir.

Production/biomass (P/B) was calculated using the empirical relationship developed by Pauly (1980). Production/biomass was calculated using length at age data (Brey et al.

2012; Duke Energy 2009); P/B is equivalent to the instantaneous rate of total mortality (Z)

used in fisheries biology (Allen 1971). For species in which age data were not available, a length converted catch curve method (Pauly 1990) was employed in FiSAT (Gayalino et al.

1996) to calculate Z. The P/B ratios for groups with data were estimated using the empirical

(regression) models incorporated in the Ecopath with Ecosim (EwE) software

(www.ecopath.org) and from FishBase (www.fishbase.org). Relative consumption ratios

(Q/B) were calculated for this study using the empirical relationship developed by Palomares and Pauly (1989, 1998). Fishing mortalities (F) were calculated from a Lake Norman creel survey conducted from 2007-2008 (B. J. McRae, NCWRC, personal communication). Nine fishing ―fleets‖ were defined in the Ecopath model, one for each introduced species (white perch, flathead catfish, spotted bass, and alewife) and for all other species that are targeted by recreational anglers. Landings for introduced fish with no fishing pressure were set to 0.001

(t·km-2·yr-1). Landings for exploited fish groups were as follows: largemouth bass (0.016 t·km-2yr-1), spotted bass (0.016 t·km-2·yr-1), striped bass (0.017 t·km-2·yr-1), channel catfish

Ictalurus punctatus (0.028 t·km-2·yr-1), flathead catfish (0.028 t·km-2·yr-1), blue catfish

Ictalurus furcatus (0.056 t·km-2·yr-1), crappie (0.002 t·km-2·yr-1), Lepomis spp. (0.0004 t·km-

2·yr-1), and white perch (0.030 t·km-2·yr-1). Finally, we allowed the model to estimate EE

(Appendix).

Diet analysis was completed for all species during this study and entered into a diet matrix as percent of each functional group’s diet by mass (Table A.1). For all species, stomach contents were identified to the lowest possible taxon (usually family or genus for invertebrates and species for fish prey), weighed to the nearest 0.01g (wet weight), and

counted. For functional groups that consumed only invertebrate prey (sans phytoplankton), diet items were counted and assigned an average mass (based on a subset of 50 to 100 individuals of that prey group sampled from Lake Norman) and the sum of each diet category was recorded. Composition of each diet category was then calculated as a percent using the sum of each diet item’s mass in the stomach (Hyslop 1980). For piscivorous functional groups, each prey fish was identified to species (when possible). Identification of prey fish species was validated by counting the number of vertebrae in each vertebral column of fish found in predator stomachs (e.g., striped bass have 25 vertebrae whereas other potential prey species in Lake Norman have 29-58 vertebrae). If the prey were not digested, or only slightly digested, a total length (TL) and weight was obtained. When digestion did not allow for an individual mass and TL to be obtained, a vertebral length (VL) was measured and TL and weight were calculated based on species specific TL to VL and length-weight curves calculated for Lake Norman fishes (Thompson 2006; Table A.1).

The model was successfully balanced when EE was between 0 and 1 and P/Q was less than or equal to 0.3 for all fish groups (Christensen and Walters 2004). Ecopath was then used to estimate key indices for each group, including fractional trophic level, net efficiency, flow to detritus, and omnivory index (see Chapter 3; Table 3.2). For a detailed description of the ecosystem model and further methods see Chapter 3. This established our

“post-introduction” model, or what the system resembles at present.

4.3.2. Ecosim and pre-introduction baseline

To evaluate the effects of introduced species on the Lake Norman community we needed to determine what the fish community might have looked like prior to the four introductions. For this process we used the Ecosim component of EwE, the dynamic, simulation portion of EwE (Walters et al. 1997, Walters et al. 2000). Because data were not available from the pre-introduction time period, we started with the original balanced ―post introduction‖ model parameterized with current biomass estimates and removed the four introduced species to estimate pre-introduction biomass for each of the other functional groups. We removed all introduced species from the model by applying high fishing pressure (fishing effort function in Ecosim), fishing all species to 0.001 t·km-2 (fishing effort: flathead catfish 17 times original catch (C), white perch 59 times C, alewife 15,000 times C, and spotted bass 35 times C), and allowed the model to reach equilibrium. This simulation yielded our ―pre-introduction‖ baseline, which includes a biomass estimate (t·km-2) for each functional group in the reservoir (biomass at time-0 in Figure 4.2), and demonstrates our best estimate of what the ecosystem would resemble without flathead catfish, white perch, alewife, and spotted bass.

4.3.3. Simulation 1: Invasion process

Once we had our ―pre-introduction‖ baseline we were able to mimic the invasion process within Ecosim, following the example of Pine et al. (2007). We sequentially removed the fishing pressure from each species in the order they were detected in the reservoir: flathead catfish (time step 23, year 1963), spotted bass (time step 54, year 1998),

alewife (time step 56, year 2000), and white perch (time step 57, year 2001). The percent change in each functional group due to the introduction of all four species was subsequently determined by calculating its change in biomass from the pre-introduction baseline to the post-introduction model.

4.3.4. Simulation 2: Step-wise introduction of species

With the baseline model in hand we were able to simulate specific scenarios and compare the changes in biomass to the pre- and post-introduction models. Because flathead catfish were introduced long before any other invader of interest, we were able to assess their individual impacts on the Lake Norman trophic structure from responses to their introduction in Simulation 1 prior to the other introductions. However, the introduction of white perch, alewife, and spotted bass occurred in such a short time period (three years) that we were unable differentiate among their individual effects and the cumulative effects of all three.

Therefore, we used our original pre-introduction model and added each invader, one at a time, and allowed the model to reach equilibrium after each introduction to determine the sequential impacts of each invader (i.e., the difference between Simulation 1 and the present scenario is that this simulation was allowed to reach equilibrium following each introduction; in Simulation 1, equilibrium was reached only after all four invaders were added). We then calculated the percent change in biomass caused by each sequential introduction relative to the ―pre-introduction‖ model and relative to the biomass after the last introduction (e.g., change in biomass of striped bass from the introduction of flathead catfish + white perch + alewife to the introduction of flathead catfish + white perch + alewife + spotted bass).

4.3.5. Simulation 3: Individual introductions

To address questions about the individual versus combined effects of invaders, we needed to determine the individual impact of each invader independent of the other three, and compare the sum of those introductions to the effect of all four invaders combined in the

―post-introduction‖ model. We started with the pre-introduction model and added one species at a time, independent of other invaders, and allowed the model ecosystem to reach equilibrium. The resulting biomass of each functional group was compared to the pre- introduction baseline, resulting in a percent change in biomass for each functional group.

4.3.5.6. Simulation 4: Individual removal of invaders

It is rare that the complete removal of an invasive species can be done in nature, but we can easily simulate this in our model. Though it is valid to compare the effects of these four introduced species to the ―pre-introduction‖ community (simulations 1-3), it is of equal or greater interest to compare the impacts to the current community structure. We wondered, had one of our four species not invaded, how would the food web look compared to what we observe now (post-introduction model)? Therefore, we removed each introduced species from the model one at a time, leaving the other three species to ―invade‖ as in our ―post- introduction‖ model and allowed the model to run to equilibrium. Then we compared the resulting biomass of each functional group to our ―post-introduction‖ biomass values to determine what the Lake Norman community may have looked like today if one of the species had not invaded.

4.3.5.7. Simulation 5: Predator and prey models

Clearly, many other permutations of these invaders into the ecosystem are possible and may have yielded different results; yet, running all possible combinations of invaders would not be practical. However, after observing the results of prior simulations we decided to consider one additional scenario that further explored the effects of invaders at two different trophic levels. Interestingly, only one of our invaders (alewife) is a heavily preyed upon planktivore, as well as a known angler introduction, whereas the others are top trophic level predators. So, knowing that flathead catfish had little impact on the community and that their introduction happened long ago we kept flathead catfish in both models for this comparison, but then asked two questions: 1) what would be different if alewife were introduced without the other two predators (white perch and spotted bass? and 2) How would the system look if only top predators were introduced (no alewife)? Therefore, we developed two additional models: a ―prey model,‖ in which only flathead catfish and alewife were introduced, and a ―predator model‖ with flathead catfish, white perch, and spotted bass introduced. We then compared the change in biomass relative to the post-introduction baseline.

4.4. Results

4.4.1. Simulation 1: The invasion process

The results of this simulation suggest how the biomass of each functional group changed over time in response to the four invasions, and how the current reservoir structure

(end of the simulation) differs relative to what the system would have looked like had the introductions not occurred (start of simulation biomass in Figure 4.2). Substantial negative

impacts of the four invaders on total biomass were observed for multiple functional groups, including channel catfish (-40%), threadfin shad (-43%), striped bass (-67%), and largemouth bass (-42%; Figure 4.2, Table 4.2). Conversely, benthic invertebrates, zooplankton and blue catfish all increased in biomass over the 50-year simulation period (Figure 4.2, Table 4.2).

Because three of the four introductions occurred within a three-year time period, we were unable to determine which species caused specific declines or increases in biomass following these three introductions (Figure 4.2). This is the post-introduction model.

4.4.2. Simulation 2: Step-wise introduction of species

By allowing the biomass of each functional group to reach equilibrium after the introduction of each invader we were able to identify specific interactions that were not obvious from the post-introduction model. Four general groups were identified based on their response to each sequential introduction. There were three species (gizzard shad

Dorosoma cepedianum, other invertebrates, and zooplankton) that showed little to no change

(within ±12%) with the addition of each invader relative to the pre-introduction baseline

(Figure 4.3, panel a; Table 4.3). Blue catfish, detritivores, and benthic invertebrates generally increased in biomass with each subsequent introduction, with the impact of flathead catfish on detritivores being the one exception (Figure 4.3, panel b; Table 4.3).

Flathead catfish initially caused a change of -30% in detritivores, but subsequent introductions each increased biomass of detritivores. There were also groups that experienced negative impacts from three of the invaders, but were positively influenced by the introduction of a fourth (either alewife or spotted bass). For example, four groups

(striped bass, threadfin shad, largemouth bass, and littoral omnivores) experienced negative effects due to the introduction of flathead catfish, white perch and spotted bass (excepting a slight increase in littoral omnivores with the introduction of spotted bass), but those effects were somewhat mitigated by the alewife introduction (Figure 4.3, panel c; Table 4.3). Three additional groups (crappie, bluegill/redear, and channel catfish) responded negatively to the introductions of flathead catfish, white perch and alewife, but those negative (or ~neutral) effects were lessened by the introduction of spotted bass, thus producing a higher final biomass than would have existed had spotted bass not invaded (Figure 4.3, panel d; Table

4.3).

4.4.3. Simulation 3: Individual introductions

Knowing the impacts of each species in combination with the others as well as their effects when sequentially added to the model, we wanted to identify the impacts of each introduced species independent of the others. When introduced in the absence of other invaders, flathead catfish increased to approximately 0.6 t·km-2, and had the smallest impact on other functional groups, causing declines of detritivores (-27%) and littoral omnivores (-

13%) and enhancing blue catfish biomass by 5% (Table 4.2). In contrast, when white perch were introduced in the absence of other invaders their population biomass increased rapidly to 2.58 t·km-2 and had large negative effects on threadfin shad (-45%), striped bass (-64%), and largemouth bass (-37%; Table 4.2). Alewife and spotted bass had intermediate impacts on many functional groups. Without other invaders in the system, alewife increased to a biomass of 0.98 t·km-2, three times their biomass (0.31 t·km-2) in the post-introduction

model. Alewife had positive impacts on many functional groups, including striped bass

(+12%), blue catfish (+20%) and threadfin shad (+10%), although negative effects were also observed on other groups such as channel catfish, crappie, bluegill-redear (each -15%).

Spotted bass increased to 0.98 t·km-2 in the absence of other invaders (compared to 0.29 t·km-2 in the post-introduction model) and had small positive impacts on all groups except channel catfish (-8%), striped bass (-44%), largemouth bass (-16%) and threadfin shad (-

24%; Table 4.2).

4.4.4. Comparing individual and combined effects.

(+12%), blue catfish (+20%) and threadfin shad (+10%), although negative effects were also

observed on other groups such as channel catfish, crappie, bluegill-redear (each -15%).

24%; Table 4.2).

Comparing individual and combined effects.—Because we were interested in knowing whether the trophic impacts of invaders were additive, we compared the sum of the effects of the four individual introductions (Simulation 3) to the results from the ―post- introduction‖ model, which represented the combined effects of all four introductions (Table

4.2). The sum of the individual impacts was not equal to the combined effects for all functional groups; however, the effects of introductions on certain functional groups, including blue catfish, gizzard shad, littoral omnivores, bluegill/redear, zooplankton, and other invertebrates were additive or nearly so (within three percent of the post-introduction model). When the combined effects of introductions were non-additive (i.e., different than the sum of the individual introductions), the sum of the individual effects were generally greater than the combined effects for all other functional groups (―post-introduction‖ model;

Table 4.2). In no case was the magnitude of the introduced species’ combined effects greater

(whether positive or negative) than the sum of the individual effects.

4.4.5. Model 4: Individual removal of invaders

Because we were interested in how the Lake Norman food web might differ now if

one of the invaders was not present, we removed each introduced species individually, but not sequentially, from the ―post-introduction‖ model. As in the other simulations, flathead catfish had little impact on any functional group other than detritivores, which increased 20% with the removal of flathead catfish. The removal of white perch from the model had positive impacts on important sport and forage species like striped bass (+45%), largemouth bass (+21%) and threadfin shad (+25%), but also on two invaders: flathead catfish (+18%) and spotted bass (+47%; Table 4.4). The same mixed impacts were observed with the removal of the spotted bass; striped bass (+33%) and threadfin shad (+19%) increased in biomass, but there was also an increase in introduced white perch (+14%) and a decline in a popular sport fish, crappie (-10%). Multiple species declined in biomass with the removal of alewife, including striped bass (-11%), blue catfish (-11%), and spotted bass (-15%), and groups such as channel catfish (+2%), bluegill/redear (+12%), crappie (14%), and flathead catfish (+19%) increased in biomass when alewife were removed from the model (Table 4.4).

4.4.6. Simulation 5: Predator and prey models

Our simulation in which alewife, a prey species, was introduced without white perch or spotted bass (the prey model) had very distinct effects on reservoir populations than when white perch and spotted bass were introduced but alewife was not (the predator model). In general, flathead catfish, channel catfish and largemouth bass were the only species that responded in the same (positive) direction, relative to current biomass (post-introduction model), to both the predator and prey models. The prey model produced much larger relative changes in biomass, while the predator model resulted in relative changes in biomass less

than 25% in either direction (Figure 4.4). Striped bass, threadfin shad, largemouth bass, and flathead catfish were affected the most by the removal of predators (prey model) showing increases in biomass (relative to ―post-introduction‖ model) from 23% (flathead catfish) to

223% (striped bass). All lower trophic levels (zooplankton, benthic invertebrates, other invertebrates, and detritivores) were negatively affected in the ―predator‖ model, showing declines from -12% (other invertebrates) to -19% (zooplankton; Figure 4.4).

4.5. Discussion

Large community- and population-level impacts were observed in our invasion simulations involving all four introduced species, and in our simulations emulating individual introductions of white perch, alewife, and spotted bass. Our simulations indicated strong but variable effects of multiple invaders on established reservoir populations, and that interactions among invaders were mostly nonadditive and antagonistic. Each individual invader influenced specific species and groups of species in different ways: some were largely unaffected (e.g., gizzard shad, invertebrates, and zooplankton), others increased (e.g., blue catfish and benthic invertebrates) and important sport and forage species generally decreased. Similar to previous investigations indicating strong effects of introduced white perch in reservoir systems (Hurley and Christie 1977; Parrish and Margraf 1990; Wong et al.

1999; Madenjian et al. 2000; Feiner 2011), white perch appeared to cause the largest decreases in sport fish populations of all four invaders. Additionally, the removal of an introduced prey fish (alewife) produced more substantial changes in functional groups than did the removal of introduced predators (i.e., spotted bass and white perch). As in other

reservoir ecosystems where demand for clupeid prey is high (Cyterski et al. 2002), species that specialize on or may have similar diets to clupeids showed the greatest changes in biomass with the removal of alewife.

In other systems where multiple introductions have occurred, particularly the Great

Lakes (Mills et al. 1994), cumulative effects of multiple invaders caused declines in top predators. The cumulative effects of multiple invaders also caused declines in top predator populations in our model. In Lake Norman, high trophic level predators, such as largemouth bass and striped bass, declined, whereas declines in lake trout Salvelinus namaycush populations were observed in the Great Lakes (Christie 1974; Eshenroder 1992; Coble et al.

1990). Observed changes in functional group biomasses in both cases were likely due to the combination of predatory and competitive interactions associated with multiple introductions

(Soluk 1993; Sih et al. 1998). Similar to the increased salmonid stocking in Lake Ontario that created increased demand for pelagic prey fishes (Jones et al. 1993; Raborn et al. 2002;

Raborn et al 2007), additional predators (spotted bass and white perch) introduced into Lake

Norman created increased demand for threadfin shad, the primary prey of largemouth bass and striped bass. However, the supply of threadfin shad is generally limited by low primary production (Kimmel and Groeger 1984) and winterkills (Van Horn 1999) in the reservoir.

Even the subsequent invasion by additional prey species, alewife, was unable to supplement forage enough to increase predator biomass, because competition with threadfin shad and predation from blue catfish and striped bass increased the complexity of reservoir trophic interactions. The declines in high trophic level predators with the introduction of the four

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invaders correlates with complaints received from anglers in the last 10 years that the striped bass population has been declining (Waters and McRae 2008). The introduction of exotic predators such as white perch and spotted bass may have caused deleterious effects on established predator populations (Madenjian et al. 2000; Eby et al. 2006). In addition, introduced predators appeared to increase predator richness in our model, albeit decreasing population biomass of some predators (e.g., striped bass and largemouth bass), and creating a

―top-heavy‖ trophic structure (Chapter 3).

Interactions among introduced and established groups may result in synergistic, antagonistic, additive, or neutral effects (Folt et al. 1999; Cope and Winterbourn 2004;

Ricciardi and Kipp 2008). Our results suggest that, in general the effects of the four invaders combined were nonadditive and antagonistic, generally having smaller impacts on other components of the food web than the sum of the invaders’ individual effects. While the impacts of multiple introduced species are not always clear, others have shown similar effects in different systems. For example, Crowder et al. (1997) found that the combined impact of predatory birds and southern flounder Paralichthys lethostigma on juvenile spot

Leiostomus xanthurus survival was less than the sum of their effects acting alone. In a survey of marine studies, Crain et al. (2008) found that interactions among stressors

(including, but not limited to, invasive species) at the community level were antagonistic, but the effects became increasingly synergistic as more stressors were added.

Interestingly, we also found evidence that facilitative interactions may exist in Lake

Norman. Both alewife and spotted bass have the potential to mitigate the impacts of other

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invaders in Lake Norman, moderating the reduction in biomass of established fishes. In other systems, the negative effects of alewife (e.g., Smith 1980; Crowder 1980; Kohler and

Ney 1980) and spotted bass (e.g., Clady and Luker 1992; Godbout 2009) introductions on other species, individually, are well documented. For example, the establishment of alewife in Lake McConaughy, Nebraska, coincided with a significant decline in the biomass of white bass (Porath et al. 2003). In Lake Norman, however, the addition of alewife reduced the negative impact of flathead catfish, white perch and spotted bass on some species (striped bass, threadfin shad, largemouth bass, and littoral omnivores), and the spotted bass introduction lessened the negative impacts of flathead catfish, white perch, and alewife on crappie, bluegill-redear sunfish, and channel catfish. The original intention of the anglers that introduced alewife into the reservoir was to increase prey for striped bass (Waters and

McRae 2008). Our results indicate that the introduction of alewife did seem to increase stb biomass by approximately 11-12 %, but this increase was outweighed by the large negative impacts of spotted bass and white perch in the post introduction model (with all invaders).

This highlights the importance of considering interactions among introduced species. Recent research has also revealed the influence of facilitative interactions of invasive species in other systems. In certain instances the invader may benefit other invasive species

(Simberloff and Van Holle 1999; Johnson et al. 2009) and in some cases may affect established or native species (Rodriguez 2006; Riley et al. 2008). According to Rodriguez

(2006), invaders can elicit facilitative effects by both competitive and predatory release, both of which are plausible in Lake Norman.

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Contrary to observations from river studies (Pine et al. 2007; Guier et al. 1984), flathead catfish had minimal effects on all functional groups except detritivores, which responded with a large reduction in biomass. These findings differ from results observed in the Neuse River, North Carolina, where the introduction of flathead catfish increased the detritivore biomass (Pine et al. 2007) and in the Cape Fear River, where flathead catfish appeared to become the dominant predator in the drainage basin (Guier et al. 1984).

Differences between simulation results for Lake Norman and these river systems are most likely due to differences in diet parameters. In our system, flathead catfish were the only group found to consume juvenile detritivores (confirmed by diet studies of flathead catfish in

Lake Norman), whereas Pine et al. (2007) did not include predation of detritivores by flathead catfish in their model. Declines of detritivores in our model appeared to be a direct result of increased predation by flathead catfish.

Removal simulations provided insight into the food web structure of Lake Norman in the absence of recent species introductions. Possibly due to their high connectivity and large population biomass (Chapter 3), the removal of white perch from the ecosystem caused the greatest system-wide increases in biomass of other groups. However, physical or chemical removal of introduced white perch from reservoirs has proven difficult and often unsuccessful (Meronek et al. 2006; Gosch 2008; Chizinski 2010). Furthermore, the removal of introduced fish can sometimes increase the biomass of other introduced species (Mills et al. 2004). For example, our simulations suggested that the removal of white perch could actually increase spotted bass biomass by nearly 50%, highlighting the high connectivity of

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invasive species in food webs and the potential for secondary impacts of ecosystem change

(Zavaleta et al. 2001).

Differences in the outcome of invasive prey (alewife) and predator (spotted bass and white perch) removals showed that invasions at different trophic levels contrast in their ecosystem impacts. The effects of invasive predators have been widely studied, with general indications of top-down trophic cascades (decreases in planktivorous fish, increases in herbaceous zooplankton, decreases in phytoplankton) when they are introduced and a resurgence of native fauna when they are removed or reduced (e.g., Drenner and Hambright

2002; Maezona et al. 2005; Eby et al. 2006; Eastwood et al. 2007; Baum and Worm 2009).

Introduced predators can also have strong negative effects on native predators. In Ontario lakes, the introduction of smallmouth bass coincided with a reduction in native minnow

(small bodied) fish abundances, causing native lake trout (Salvelinus namaycush, a piscivorous species) to switch to invertebrates (a less energetic prey source) in the absence of minnows and pelagic prey (Vander Zanden et al. 2004). Although we expected a strong top- down effect from the predator model in Lake Norman, no predictable trend was observed.

This may have been a result of an interaction between the two introduced species, spotted bass and white perch.

The effects of introducing prey species can be difficult to predict because they can increase predator biomass by providing an alternate prey source (thereby positively affecting native predators), but they can also change the size and composition of zooplankton and phytoplankton communities, altering food web structure in unpredictable ways (e.g.,

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Hutchinson 1971; Evans and Jude 1986; Harman et al. 2002). In our prey model for Lake

Norman, assuming little effect of the introduction of flathead catfish on the food web, the sole introduction of alewife had strong effects on the entire food web. In this scenario, in which no introduced predators (barring flathead catfish) were involved, it seems likely that predation pressure was released from threadfin shad and ―middle out‖ effects occurred (Stein et al. 1995). The result was increased biomass at the top of the food web and decreased plankton biomass at the bottom (Carpenter et al. 1985; Carpenter 2001).

Multiple introduced species create complex interactions in reservoir food webs.

Using an ecosystem modeling approach we quantified the ways in which multiple invaders impacted species and groups of species. The combined effects of flathead catfish, white perch, spotted bass and alewife were not the same as the sum of their individual effects.

Furthermore, removal of one or more of these species altered the effects of the remaining ones. These complexities should be considered when developing reservoir management strategies for invasive species, as unexpected outcomes may occur. Our model also suggested that while white perch had the greatest negative impact on other reservoir groups, the overall Lake Norman food web structure we see today was a result of the combinations of all introductions. To our knowledge, this is the first use of a multi-species model to quantify the effects of numerous fish invaders at the ecosystem level.

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Table 4.1. List of species or groups of species that make up each functional group in the EwE model. Groups are listed in order of discrete trophic position (Chapter 2) from highest (flathead catfish) to lowest (detritus). Functional group Species or family Flathead Catfish Flathead catfish Pylodictus olivarus Largemouth bass (A) Adult largemouth bass (≥ 1 year) Micropterus salmoides Crappie Black crappie Poxomis nigromaculatu White crappie P. annularis Striped bass (A) Adult striped bass (≥ 2 years) Morone saxatilis Striped bass (J) Juvenile striped bass (6 mo.- 2 years) Morone saxatilis Spotted bass (A) Spotted bass ≥ 1 year Micropterus punctulatus Littoral omnivores Green sunfish Lepomis cyanellus Redbreast sunfish Lepomis auritus Warmouth Lepomis gulosus White perch (J) White perch < 1 year Morone americana White perch (A) White perch ≥ 1 year Morone americana Bluegill/redear sunfish Bluegill Lepomis macrochirus Redear sunfish Lepomis microlophus Channel Catfish Channel catfish Ictalurus punctatus Alewife Alewife Alosa pseudoharangus Largemouth bass (J) Juvenile largemouth bass Micropterus salmoides Spotted bass (J) Spotted bass ≤ 1 year Micropterus punctulatus Blue Catfish Blue catfish Ictalurus furcatus Benthic invertebrates Odonata, Chironomidae, Ephemeroptera pupae, Tricoptera, Ceratopogonidae, Hydrachnidia, Ostracods Threadfin Shad Threadfin shad Dorosoma petenense Other invertebrates Crayfish, terrestrial insects Detritovores Common carp Cyprinus carpio Quillback Carpoides cyprinus Shorthead redhorse Moxostoma macrolepidotum V-lip redhorse Moxostoma pappillosum Gizzard Shad Gizzard shad Dorosoma cepedianum Zooplankton All zooplankton Striped bass (<6mo) Stocked striped bass (< 6 months) Phytoplankton All phytoplankton Detritus Detritus

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Individual introductions Sum of individual Post-introduction

Impacted group Flathead catfish White perch Alewife Spotted bass introductions model

Channel catfish -5.3% -21.7% -15.0% -8.2% -50% -40%

Striped bass -2.1% -53.4% 12.0% -43.6% -87% -67%

Blue catfish 8.8% -1.1% 20.0% -- 28% 23%

Largemouth bass -7.6% -26.3% -3.0% -16.5% -53% -42%

Crappie -9.4% -6.7% -15.0% 8.8% -22% -15%

Bluegill/Redear -3.2% -4.5% -15.0% 7.7% -15% -13%

Littoral omnivores -12.8% -16.0% 4.0% 3.5% -21% -21%

Threadfin shad -7.0% -29.6% 10.0% -24.2% -51% -43%

Gizzard shad -4.3% -1.9% 2.0% 2.7% -2% -2%

Detritivores -26.5% 7.0% 3.0% 4.2% -12% -9%

Other invertebrates --- 4.0% -17.0% 5.6% -7% -6%

Benthic invertebrates 7.8% 13.9% -- 10.6% 32% 29%

Zooplankton -2.0% 11.1% -5.0% 7.1% 11% 12%

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+Flathead catfish + White perch + Alewife + Spotted bass

Impacted group baseline baseline last baseline last baseline last

Flathead catfish -- -- -26% -- -18% -- -5%

Channel catfish -5% -25% -20% -40% -20% -40% -1%

Striped bass -2% -56% -55% -51% 10% -67% -33%

Blue catfish 9% 7% -2% 20% 13% 23% 2%

Largemouth bass -8% -30% -24% -34% -5% -42% -12%

Spotted bass ------

White perch ------1% -- -14%

Crappie -7% -9% -3% -23% -16% -15% 10%

Bluegill/Redear -2% -7% -5% -19% -13% -13% 8%

Littoral omnivores -11% -26% -17% -23% 3% -21% 4%

Threadfin shad -2% -31% -29% -29% 3% -43% -19%

Alewife ------1%

Gizzard shad -4% -5% -2% -4% 1% -2% 2%

Detritivores -27% -18% 13% -13% 5% -9% 5%

Other invertebrates 0% 4% 4% -11% -15% -6% 5%

Benthic invertebrates 8% 22% 13% 21% -1% 29% 7%

Zooplankton -5% 10% 16% 5% -5% 12% 7%

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Species removed from model Flathead White Spotted Alewife Impacted group catfish perch bass Flathead catfish -- 18% 19% 5% Channel catfish 4% 13% 20% 1% Striped bass 3% 45% -11% 33% Blue catfish -7% 2% -11% -2% Largemouth bass 5% 21% 6% 12% Spotted bass 4% 49% -15% -- White perch 2% -- 1% 14% Crappie 3% 2% 14% -10% Bluegill/Redear 3% 3% 12% -8% Littoral omnivores 10% 16% -3% -4% Threadfin shad 2% 25% -2% 19% Alewife -2% 12% -- 1% Gizzard shad 3% 2% -1% -2% Detritovores 20% -9% -5% -5% Other invertebrates -- -5% 14% -5% Benthic invertebrates -6% -9% 1% -7% Zooplankton 1% -11% 4% -7%

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North Carolina Lake Norman

Figure 4.1. Lake Norman, North Carolina.

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2.5 Flathead catfish Striped bass 2

Blue catfish

) Largemouth bass 2 - m 1.5 Spotted bass

t (

s White perch s

m 1 Bluegill/Redear

o i

B Threadfin shad

Alewife 0.5 Gizzard shad

Detritovores 0 Crappie 0 10 20 30 40 50 Time steps (years)

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a. b. 30%

10%

-10%

* -30% Gizzard shad Blue catfish Other invertebrates Detritovores -50% Zooplankton Benthic invertebrates

c. d.

10% Change in biomass in Change -10%

-30%

Striped bass -50% Threadfin shad Crappie Largemouth bass Bluegill/Redear Littoral omnivores Channel catfish -70%

Flathead White Spotted Flathead White Spotted + + Alewife + + + Alewife + catfish perch bass catfish perch bass

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Flathead catfish Channel catfish Striped bass Blue catfish Largemouth bass Spotted bass White perch Crappie Bluegill/Redear Littoral omnivores Alewife Threadfin shad Gizzard shad Detritovores Other invertebrates Prey model Benthic invertebrates Predator model Zooplankton

-20% 0% 20% 40% 60% 80% 100%

Figure 4.4. The percent change in each functional group, relative to the post-introduction baseline model with flathead catfish and alewife in the model (prey model) and flathead catfish, white perch, and spotted bass in the model (predator model).

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CHAPTER 5

ASSESSING THE TROPHIC EFFECTS OF NUTRIENT LOADING AND

MANAGEMENT MANIPULATIONS ON A RESERVOIR FOOD WEB

5.1. Introduction

Reservoir fisheries management has become more difficult in recent years due to increases in nutrient loading, shoreline development and alteration, invasive species, and demands from anglers for larger and greater numbers of sport fish (Van Horn et al. 1999;

Van Horn 2001; Allen et al. 2008; Waters and McRae 2008). Historically, reservoir forage fish and predator species have been intentionally stocked with little regard for ecosystem- level interactions, instead focusing solely on predator and prey relationships (Noble 1981;

Noble 1986; Jenkins and Morais 1976). However, managing reservoirs from a single-species management perspective (e.g., Boxrucker 1986; Bulak et al. 1995; Hesthagen et al. 2010;

Quist et al. 2010) is becoming less effective as interactions between species increase the complexity of trophic linkages. For example, stocking high trophic level predators can impact the prey they consume and trigger trophic cascades that cause impacts beyond the intended interactions (Seda et al. 2000; Eby et al. 2006) and increase the costs of stocking and management (Johnson et al. 2000).

Carpenter et al. (2011) identified multiple drivers of change to freshwater systems worldwide. Major drivers included changes in morphology or hydrology, addition of nutrients or toxic substances, loss of native species, expansion of invasive species, and changes in fisheries harvest. For example, changes in land use practices and the addition of

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wastewater treatment plants has led to increased nutrient loading and the eutrophication of lakes and reservoirs (Bennett et al. 2001, Knoll et al. 2003). Furthermore, nutrient loading can cause such things as trophic cascades and ecosystem level effects (Cooke et al. 2005;

Schindler 2006; Attayde and Ripa 2008), with implications that may go undetected for years.

In reservoir fisheries management, stocking fish is one of the more common management techniques used today. In reservoirs where forage is depleted or winterkills occur annually, supplemental stocking of forage fish to enhance year class strength has been used to reestablish populations (Johnson 1970; Devries et al. 1991; Keith 1996). Additionally, predator stocking is used to enhance the numbers of piscivorous sport fish, often as a result of angler requests (Waters and McRae 2008). Finally, eradication or reduction of invasive fish has been attempted to reduce competition for prey resources with desirable sport fish.

Strategies for management and reduction of invasive species have included poisons (e.g., rotenone), physical removal by managers (e.g., annual or monthly electrofishing), and establishment (or enhancement) of a fishery for the undesirable species (Wydoski 1999;

McClay 2000; Gozlan et al. 2010).

Although some of these changes may seem beneficial in that they supplementally enhance sport fish populations and reduce biomass of invasive species in reservoirs, the holistic effects are seldom known prior to ecosystem change. In marine systems and the

Laurentian Great Lakes, comparatively more emphasis has been placed on ecosystem-based fishery management (EBFM), a more holistic approach to managing aquatic ecosystems, than has been for reservoirs (Larkin 1996; Slocombe 1998; Brodziak and Link 2002; Hall

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and Mainprize 2004; Pikitch et al. 2004). Following the stream of successes in using ecosystem-based approaches in marine systems (e.g., Fletcher et al. 2010), it seems only logical that a more holistic approach to reservoir assessment and management may be beneficial.

Ecosystem modeling is an increasingly common tool for directing EBFM decisions in marine systems (Latour et al. 2003). In particular, modeling programs such as Ecopath with

Ecosim (Christensen et al. 2005) and Atlantis (Fulton et al. 2011) are frequently used and generally considered very user-friendly approaches to quantifying ecosystem-level interactions and dynamics. Applications of this approach include determining the risk of ecosystem damage prior to stocking, finding alternative approaches to maintaining long-term socioeconomic benefits without compromising the ecosystem, and generating knowledge of ecosystem processes sufficient to understand potential consequences of human actions

(Pikitch et al. 2004). These programs allow users to parameterize models with information often already available from reservoir assessments, such as fish and zooplankton biomass, primary productivity, and diet data, and to model the linkages among all groups in the model.

Furthermore, once a model is parameterized, users can simulate potential ecosystem transformations due to management strategies, such as manipulation of stocking rates or angling pressure, to predict how they may impact an ecosystem.

In Lake Norman, an oligotrophic reservoir in the piedmont region of North Carolina, the North Carolina Wildlife Resources Commission (NCWRC) must contend with conflicting angler desires in the management of a large, unproductive system (Waters and

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McRae 2008). Unsatisfied with management plans and actions, some angling groups have introduced additional forage species with the objective of increasing the size of reservoir striped bass Morone saxatilis (stocked annually by the NCWRC), despite evidence from previous studies that limited primary productivity is the reason for low fish production and growth in the reservoir (Van Horn et al. 1999; Thompson 2006). Other nonnative fish species have also established sizable populations in the reservoir, including white perch

Morone americana, flathead catfish Pylodictis olivaris, and spotted bass Micropterus punctulatus, another angler introduction.

Because little information is available to evaluate the response of a reservoir ecosystem to changes in management practices or other ecosystem transformation, we developed an ecosystem model (using Ecopath with Ecosim) to explore the implications of four ecosystem alterations that may enhance sport fish populations in Lake Norman including

1) altering the number of striped bass stocked annually, 2) supplemental stocking of threadfin shad Dorosoma petenense, 3) increasing primary production, and 4) increasing fishing pressure on white perch Morone americana (an invasive fish species).

5.2. Study site

Lake Norman, a 13,156-ha cooling reservoir for two power stations (McGuire

Nuclear Station and Marshall Steam Station), is an impoundment of the Catawba River just north of Charlotte, North Carolina (Figure 5.1). Impounded in 1963, Lake Norman has been classified as oligotrophic, although the upper reaches are occasionally elevated to mesotrophic classification, with average chlorophyll a concentrations between 5-9 g·L-1 and

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Secchi depths of 1.8-2.6 m (NCDENR 2008), a mean depth of 10.2 m, and a hydraulic retention time of 239 days (Waters and McRae 2008). The reservoir is managed by the

North Carolina Wildlife Resources Commission (NCWRC) for multiple sport fish populations including blue catfish Ictalurus furcatus, crappie Pomoxis spp., largemouth bass

Micropterus salmoides, spotted bass, and striped bass. Striped bass have been stocked annually into the reservoir at an average rate of 12.4 fingerlings·ha-1 since 1995 (Waters and

McRae 2008).

5.3.Methods

5.3.1. Model parameterization

Using the Ecopath with Ecosim framework (EwE; www.ecopath.org; Christensen and

Walters 2000), we initially parameterized a mass-balance ecosystem model (Ecopath) for

Lake Norman using primarily empirical data collected from 2007-2010 (Chapter 3). The model included twenty-four functional groups (species or groups of species with similar diets and habitat use; Table 5.1, Chapter 3). Lake Norman sampling and the Lake Norman EwE model are described in detail in Chapter 3, and specifics of the EwE modeling approach can be found in detail in numerous sources (e.g., Christensen et al. 2000; Pauly et al. 2000;

Christensen and Walters 2004). Briefly, sampling was conducted seven times per year from

2007-2009. Forty 300-m transects distributed throughout four regions of the lake were used for fish and invertebrate sampling (Figure 5.1). Three randomly selected transects in each of four regions of Lake Norman were selected each collection period and sampled using electrofishing, gill nets, and purse seines. During each sample period at each site we

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electrofished 300 m of shoreline habitat, collecting all fish. Three nearshore (bottom set, one near each transect) and four offshore gill nets (bottom set until the water column stratified, then set above the oxycline in at least 2.0 mg·L-1 dissolved oxygen) were set overnight in each of 4 regions of the lake (Figure 5.1), near each transect (see Chapter 3 for details).

Purse seine samples were collected four times per year (once per season) at two sites (Figure

5.1) to assess the pelagic prey fish community. We counted, aggregate weighed, then calculated catch-per-unit-effort (CPUE; fish·m-1 for electrofishing, fish·hr-1 for gillnetting, and kg·haul-1 for purse seines) by species for each for each transect and gear type on each sample date. Subsamples of fish (minimum of 20 fish per species, per site, when available) were returned to the laboratory for further analysis. Stomachs were collected and diets were analyzed (see below) for a minimum of 10 fish per region per sample date in 2007 and 2008

(Table A.1).

Because fish biomass has been shown to be directly proportional to CPUE estimates

(Leslie and Davis 1939; Ricker 1975), log(CPUE) was plotted against cumulative effort (K) to estimate fish biomass per transect and extrapolated to whole lake estimates (t·km-2). Catch data and diet data were augmented with information from Duke Energy reports (2008; 2009) and other published works specific to Lake Norman (Grist 2002; Thompson 2006; Godbout

2009; Feiner 2011; Feiner et al. 2012; Table A1).

Balance of an Ecopath model generally requires input of three of the following four parameters for each functional group: biomass (B), production/biomass ratio (P/B; this is equal to total mortality, Z), consumption/biomass ratio (Q/B), and ecotrophic efficiency (EE;

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proportion of production used in the system) in addition to diet composition (in proportion of wet weight). Through a series of linear equations, Ecopath solves for unknown values while simultaneously establishing mass-balance in the system (Christensen and Walters 2004).

Biomass estimates for all littoral fish groups (littoral omnivores, spotted bass, largemouth bass, white perch, crappie) were calculated from lake-wide surveys (Chapter 3). Biomass for other functional groups was estimated from catch-per-unit-effort (CPUE) calculations from gill net, purse seine, and plankton net assessments, and augmented with Duke Energy reports

(Duke Energy 2008) and supplemental literature (see Chapter 3).

Production/biomass (P/B) was calculated using the empirical relationship developed by Pauly (1980). Production/biomass was calculated using length-at-age data (Feiner 2011;

Duke Energy 2009); P/B is equivalent to the instantaneous rate of total mortality (Z) used in fisheries biology (Allen 1971). For species for which age data were not available, a length- converted catch curve method (Pauly 1990) was employed in FiSATII (Gayalino et al. 1996) to calculate Z. The P/B ratios for groups with data were estimated using the empirical

(regression) models incorporated in the Ecopath with Ecosim (EwE) software

(www.ecopath.org) and from FishBase (www.fishbase.org). Relative consumption ratios

(Q/B) were calculated for this study using the empirical relationship developed by Palomares and Pauly (1989; 1998).

To link functional groups in the food web, the EwE model requires input of a diet matrix for all functional groups. To that end, diet composition (percent by mass) was derived from extensive empirical sampling of Lake Norman (Chapter 3) for all species (except

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gizzard shad, detritivores and invertebrates, which came from the literature; Chapters 3 and

4). Briefly, stomach contents for all species were removed and identified to the lowest possible taxon (family or genus for invertebrates and species for fish prey), weighed to the nearest 0.01g (wet weight), and counted (Table 3.3 in Chapter 3; Hyslop 1980).

Angler harvest was calculated using Lake Norman creel survey data collected from

2007-2008 (B. J. McRae, NCWRC, personal communication) to assign an annual harvest rate for each species. In Ecopath, each designated fishery (comprised of one or multiple functional groups) is considered a ―fleet‖ (amount harvested by any group in t·km-2·yr-1).

We modified the fishing ―fleets‖ from the original Ecopath model (Chapter 2) to include three fleets. One ―angler group,‖ for recreational angling, harvested largemouth bass (0.016 t·km-2·yr-1), spotted bass (0.016 t·km-2·yr-1), striped bass (0.017 t·km-2·yr-1), channel catfish

Ictalurus punctatus (0.028 t·km-2·yr-1), flathead catfish (0.028 t·km-2·yr-1), blue catfish

Ictalurus furcatus (0.056 t·km-2·yr-1), crappie (0.002 t·km-2·yr-1), and Lepomis spp. (0.0004 t·km-2·yr-1). One ―white perch‖ group (0.0296 t·km-2·yr-1) was developed to simulate modifications to harvest by the current white perch fishery. A final, artificial fleet was developed for the purpose of manipulating the threadfin shad biomass in the reservoir.

To balance the Ecopath model, we modified those input parameters that had the greatest uncertainty first (i.e., those we did not empirically calculate) and changed them systematically by no more than 0.01 units until the model balanced. The model was successfully balanced when EE was between 0 and 1 and P/Q was ≤ 0.3 for all fish groups

(Christensen and Walters 2004). This established our ―original‖ model, or what the system

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presently resembles.

5.3.2. Simulations

To simulate changes in the ecosystem we used Ecosim, the simulation portion of EwE

(Walters et al. 1997; Walters et al. 2000) that allows users to manipulate parameters over time and evaluate system responses. Ecosim works within the framework of ecosystem mass balance, assuming that declines in one group result in increases in one or more others, and vice versa. Starting with the original balanced Ecopath model we considered four simulations in Ecosim that were meant to mimic three specific management scenarios and one possible ecosystem alteration (nutrient enrichment) with potential implications for fisheries management. Multi-stanza groups (e.g., juvenile white perch and adult white perch) were parameterized separately within the model (to account for differences in diet and consumption among life stages), but the resultant biomass for each species was combined for presentation and analysis.

Simulation 1: Nutrient enrichment.— Lake Norman is largely an oligotrophic system, with only small sections of the upper reservoir achieving productivity closer to mesotrophic status. Although point-source discharge is a concern, non-point source discharge is the major source of phosphorus in freshwater systems, thus it is reasonable to assume changes in land use practices around Lake Norman could modify primary production in the reservoir

(Bennett et al. 2001). Currently, mean total phosphorous (TP) concentration in Lake Norman averages 15 μg·L-1 (Grist 2002), but total phosphorous concentrations are generally higher in the upper reaches (Siler et. al 1986). Based on ranges for increasing primary production and

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TP from nutrient loading documented in the literature (Goldman 1988; Downing et al. 1990;

Bennett 2001; Knoll et al. 2001), we assumed non-point source phosphorus loading could likely increase PP between 1% (low) and 20% (high). Therefore, we ran three simulations within that range, including a 1%, 10%, and 20% increase in PP using the primary production forcing function in Ecosim. We then calculated the relative change in the biomass of each functional group from our original model.

Simulation 2: Increasing harvest for an introduced predator.— According to results from prior modeling exercises (Chapter 4), white perch is the introduced species that may have the greatest impact on other functional groups in Lake Norman. Therefore, we were interested in the effect that enhancing harvest, by manipulating the white perch fishery, could have on the Lake Norman fish community. Using Ecosim we systematically increased the angling effort for white perch. Because we were interested in how much effort could affect ecosystem dynamics, we used three different increases in harvest: small (a likely increase, similar to the harvest of catfish biomass in Lake Norman, ~2x the current harvest; 0.0592 t·km-2), large (10x; 0.296 tkm-2, comparable to a major striped bass fishery; Axon and

Whitehurst 1985), and very large (20x; 0.520 tkm-2, comparable to fishing white perch to half their current biomass).

Simulation 3: Modifying striped bass stocking.— When popular sport fisheries (e.g., striped bass) are dependent on stocking, anglers often request increases in stocking rates. But managers have few tools for evaluating the potential efficacy or consequences of changes in stocking. Therefore, we were interested in how much changing the striped bass stocking rate

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would influence community dynamics as well as striped bass biomass. We doubled the current stocking rate of 12.4 fingerlings·ha-1. Doubling the number of fingerlings stocked into Lake Norman is likely the largest feasible increase due to hatchery and transportation limitations, therefore we used only one scenario for this simulation (high end of the possible stocking range) (B. J. McRae, NCWRC, personal communication). This simulation represented a stocking rate of 0.0486 t·km-2·yr-1, or about 326,000 striped bass fingerlings at

~19.2 g each. We applied a stocking forcing function in Ecosim so that striped bass stocking was doubled annually.

Simulation 4: Supplemental stocking of threadfin shad.— Although Lake Norman has a viable, naturally reproducing threadfin shad population sustained during the winter by warm power station effluent, a moderate portion of the population likely undergoes winterkill in cold years (Van Horn 1999). In addition to winterkills, there is limited prey availability for threadfin shad and heavy predation on threadfin shad year-round (Chapter 3), and the viable spawning population declines annually. To simulate supplemental stocking of threadfin shad to boost forage fish production as a potential management manipulation by the

NCWRC (see simulations below), we first added an additional 0.00107 t·km-2·yr-1 to our

Ecopath model, then added that much harvest to cancel it out during balancing (40,000 threadfin shad would likely be in the high range for threadfin shad stocking into Lake

Norman; B. J. McRae, NCWRC, personal communication). Removal of this ―fishery‖ was then simulated in Ecosim to emulate threadfin shad supplemental stocking (40,000 threadfin shad; mean length 76.2 mm and 3.4 g each). We then evaluated the resulting change in

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biomass of other functional groups over time.

5.4. Results

Simulation 1: Nutrient enrichment.— In our simulations of nutrient enrichment the ecosystem reached equilibrium after approximately 12 annual time steps. A very small (1%) increase in primary production, something that may be observed during a one-time input of primary production (Goldman 1988), caused very little change (<3%) in biomass of any of the functional groups (Table 5.2). In addition, predator-prey oscillations occurred in the first

10 years of the simulation, but once equilibrium was reached the small net changes in all groups were positive. A moderate (10%) increase in primary productivity, however, caused biomass of all functional groups to increase by between 11.9% and 25.0% (Table 5.2; Figure

5.2; Figure 5.3). Overall reservoir biomass (excluding detritus) increased 3.305 t·km-2

(+18%) in just 12 years. Finally, a substantial (20%) increase in primary production had an even greater effect, as flathead catfish, channel catfish, blue catfish, largemouth bass, white perch, and crappie increased by more than 40% (Table 5.2; Figure 5.3). Regardless of the level of increase (1%, 10%, or 20%) predator-prey oscillations occurred in the first eight to

11 years of the simulation, but once equilibrium was reached all groups increased in biomass between 14% (phytoplankton) and 37.5% (gizzard shad; Table 5.2).

Simulation 2: Increased fishing effort for white perch.— Doubling the angler effort for white perch had little effect on other groups (Table 5.2). Alewife was the only group to change biomass by more than 2% (-3.5%), and even white perch only declined 1.8%. A10x increase in angler harvest only decreased white perch biomass 14.3%, but did result in

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modest increases in both striped bass (5.2%) and spotted bass (10.1%) biomass. All other groups had smaller relative changes in biomass and fluctuated less than 5% (Table 5.2;

Figure 5.4). The greatest changes in relative biomass were observed with a 20-fold increase in angler effort, causing white perch to decline by 32.1%. As in the previous simulation spotted bass (+25.6%) and striped bass (+16.1%) increased the most, but largemouth bass

(5.8%) and threadfin shad (10.9%) also increased noticeably. Only groups near the bottom of the food web decreased in relative biomass, and even then by less than 2%, except for zooplankton where a 6.2% decrease in biomass was predicted (Table 5.2).

Simulation 3: Double striped bass stocking.— According to our simulation, doubling the striped bass stocking rate in Lake Norman would lead to an overall increase in striped bass biomass of only 7% (Table 5.2; Figure 5.5), and the majority of that biomass (75%) would be juveniles. It took the system about 10 annual time steps to reach equilibrium after the stocking rate was increased, and noticeable declines in alewife (-6.7%) and spotted bass

(-6.2%) were evident after six years. Very little change was observed in the biomass of any other group (Table 5.2; Figure 5.5).

Simulation 4: Supplemental stocking of threadfin shad.—Stocking 40,000 additional adult threadfin shad into the reservoir each year resulted in very little change in any functional group (Table 5.2; Figure 5.6). Nearly equal proportions of groups declined in biomass as increased in biomass. Groups that decreased did so by less than 3%, whereas groups that increased in biomass (flathead catfish, blue catfish, striped bass, channel catfish, littoral omnivores, detritivores, and phytoplankton) did so by less than 1.3 % (Table 5.2).

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Further exploration indicated that an increased stocking of 120,000 threadfin shad would be required to observe even a small increase in sport fish (e.g., striped bass) biomass in the reservoir, which would come at the expense of alewife and juvenile sport fish populations.

5.5. Discussion

Our results suggest ways in which alterations to reservoirs, such as increases in nutrient loading or changes in fishery management strategies (e.g., stocking or harvest rates), can influence food web structure. Lake Norman’s nutrient limitation appears to restrict the range of successful management strategies available to managers. Reservoir modifications that removed fish biomass (i.e., increasing white perch fishing effort) and substantially increased primary productivity were much more influential than alterations that introduced more biomass into the reservoir (i.e., increasing stocking rates).

Of the four ecosystem alterations we considered, increasing primary production had the greatest potential to enhance sport fish production; however, it was also the one manipulation that is out of managers’ control. Increasing primary production caused bottom- up effects, ultimately increasing the overall biomass in the reservoir and having substantial benefits on sport fish populations. Although multiple studies have confirmed positive relationships between phosphorus levels, primary production, and fish biomass in freshwater systems (Hecky et al. 1981; Carpenter et al. 1985; Yurk and Ney 1989), few have been able to predict the total ecosystem response without experimentation. Predicting alterations in reservoir dynamics due to shifts in nutrient input is becoming increasingly valuable because cultural eutrophication and bottom-up effects, like the ones we modeled here, are predicted

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for other reservoir systems (Folke et al. 2004; Rahel et al. 2008). Our results indicate that even moderately increasing primary production (placing the reservoir in the mesotrophic category) would elicit bottom-up trophic effects in Lake Norman.

Eradication or even reduction of invasive fish populations has proved to be costly and difficult (Leung et al. 2002; Chizinski et al. 2010; Vander Zanden et al. 2010). Consistent with those results, our findings suggest that achieving declines in invasive white perch biomass in Lake Norman with increased fishing effort is not likely, as an increase of at least ten times the current harvest would most likely be required to observe measurable results.

The strong influence of white perch on ecosystem structure highlights their importance and high connectivity in the food web. Multiple studies have documented similar white perch trophic interactions (Parrish and Margraf 1990; Madenjian et al. 2000; Harris 2006; Feiner

2011). Biological control measures for white perch, specifically in Midwest reservoirs, have had only limited success (Gosch 2008; Gosch and Pope 2011). Furthermore, the system- wide effects resulting from the 20-fold increase in white perch harvest we simulated in this study still were only about one third the magnitudes of those observed in a simulation completely eliminating white perch from Lake Norman (Chapter 3). Because of their high ecosystem connectivity and the fact that impacts of white perch introductions are likely to be substantial and irreversible, it’s especially important to prevent their introduction where possible.

Decreased competition for threadfin shad as white perch were removed seemed to be the key mechanism associated with increases in piscivorous sport fish, especially striped bass

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and spotted bass. In addition, because white perch are highly omnivorous and trophic generalists (Harris 2006; Gosch 2008; Feiner 2011), reduction in white perch biomass also likely reduced competitive interactions throughout the food web. Strong trophic interactions with introduced white perch have been implicated in declines of white crappie Pomoxis annularis (Wong et al. 1999), walleye Sander vitreus (Hurley and Christie 1977), white bass

(Madenjian et al. 2000), and yellow perch Perca flavescens (Parrish and Margraf 1994) across a variety of systems. Likewise, measures of diet-overlap suggest high levels of competition for prey may exist with other fish populations (Wong 2002; Feiner 2011; Rettig and Mittelbach 2002; Hergenrader and Bliss 1971; Harris 2006).

Although stocking novel species can have dramatic effects on fish communities and can create exceptional sport fisheries (e.g., Evans and Loftus 1987; Flecker and Townsend

1994; Bronte et al. 2003), supplemental stocking of a species already present in the system may cause less dramatic changes. Our simulations suggest that increasing striped bass stocking likely would not significantly increase striped bass biomass. The limited effects of stocking probably reflect a population that suffers from both inter- and intra-specific competition for limited prey, observed in the decline of threadfin shad and the relatively small change in adult biomass of striped bass. These results are congruent with empirical declines in adult striped bass condition that coincided with a period of increased stocking starting in 1988 (Van Horn 1999). Furthermore, resource limitation has been suggested for the poor condition of adult striped bass in Lake Norman (Thompson 2006; Van Horn et al.

1999), and our results from prior modeling (Chapter 2; Mixed Trophic Impacts routine)

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bolster these conclusions. A slight increase (<10%) in striped bass biomass resulted in a negative impact on total striped bass biomass in the reservoir, suggesting intraspecific competition was occurring.

Similar to the effects of increased striped bass stocking, supplemental stocking of threadfin shad had little effect on reservoir trophic dynamics. Although stocking of threadfin shad has been successful in some southeastern reservoirs (e.g., DeVries and Stein 1990), other similar stocking efforts have not been effective. In Kentucky reservoirs, for example, where pre-spawn threadfin shad were supplementally stocked to increase threadfin shad production and thus enhance the white crappie Pomoxis annularis population, no observed benefit due to stocking was observed. Furthermore, data suggested that management success

(defined as increased white crappie growth) would only be possible when stocking rates were very high (at least 125 fish/ha; Hale 1996). Due to the minimal change in any functional group biomass, our results suggest that supplemental stocking of threadfin shad, at least at a level feasible for the NCWRC (40,000 fish/year) is not a viable management strategy for either enhancing sport fish populations or increasing threadfin shad standing stock in Lake

Norman.

While changes in freshwater systems that result from ecosystem alterations and biomanipulations can make formulating management decisions challenging, we have provided an initial framework from which to initiate further investigations. We believe early predictions about how reservoirs react to stressors or other changes, in an ecosystem context, will alleviate some angst over the unknown involved in reservoir management. Based on our

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results, we do not recommend additional stocking of fish into Lake Norman as prey resources are too limiting, and programs that may increase angler effort for white perch are encouraged, as this may relieve some competitive interaction with other sport fish populations. Finally, if Lake Norman were to experience an increase in phosphorus input, the response of the system could actually be beneficial to sport fish populations as primary production currently limits available prey.

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Table 5.1. Functional groups and the taxa included within each group for the Lake Norman Ecopath and Ecosim models. Multi-stanza groups (e.g., juvenile (J) and adult (A) largemouth bass) were modeled separately then grouped for analysis and interpretation purposes. Functional group Species or family Flathead Catfish Flathead catfish Pylodictus olivarus Largemouth bass (A) Adult largemouth bass (≥ 1 year) Micropterus salmoides Crappie Black crappie Poxomis nigromaculatu White crappie P. annularis Striped bass (A) Adult striped bass (≥ 2 years) Morone saxatilis Striped bass (J) Juvenile striped bass (6 mo.- 2 years) Morone saxatilis Spotted bass (A) Spotted bass ≥ 1 year Micropterus punctulatus Littoral omnivores Green sunfish Lepomis cyanellus Redbreast sunfish Lepomis auritus Warmouth Lepomis gulosus White perch (J) White perch < 1 year Morone americana White perch (A) White perch ≥ 1 year Morone americana Bluegill/redear sunfish Bluegill Lepomis macrochirus Redear sunfish Lepomis microlophus Channel Catfish Channel catfish Ictalurus punctatus Alewife Alewife Alosa pseudoharangus Largemouth bass (J) Juvenile largemouth bass Micropterus salmoides Spotted bass (J) Spotted bass ≤ 1 year Micropterus punctulatus Blue Catfish Blue catfish Ictalurus furcatus Benthic invertebrates Odonata, Chironomidae, Ephemeroptera pupae, Tricoptera, Ceratopogonidae, Hydrachnidia, Ostracods Threadfin Shad Threadfin shad Dorosoma petenense Other invertebrates Crayfish, terrestrial insects Detritovores Common carp Cyprinus carpio Quillback Carpoides cyprinus Shorthead redhorse Moxostoma macrolepidotum V-lip redhorse Moxostoma pappillosum Gizzard Shad Gizzard shad Dorosoma cepedianum Zooplankton All zooplankton Striped bass (<6mo) Stocked striped bass (< 6 months) Phytoplankton All phytoplankton Detritus Detritus

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Increase primary production Harvest of white perch Striped bass stocking Threadfin shad stocking

Functional group 1% 10% 20% × 2 × 10 × 20 × 2 + 40,000 annually

Flathead catfish 2.0% 25.0% 41.9% 1.7% 4.5% 0.1% 0.2%

Channel catfish 1.9% 24.7% 40.0% 1.7% 4.0% 0.7% 0.2%

Striped bass 1.5% 12.6% 24.1% 1.2% 5.2% 16.0% 6.9% -1.6%

Blue catfish 2.2% 25.0% 44.3% 0.4% 0.5% 0.5% 0.4%

Largemouth bass 2.0% 22.3% 41.5% -1.2% 1.3% 5.8% -3.5% -1.9%

Spotted bass 1.5% 18.2% 34.6% -0.4% 10.1% 25.6% -8.2% -0.5%

White perch 2.1% 22.4% 41.3% -1.8% -14.3% -32.1% -2.7% -0.8%

Crappie 2.1% 24.3% 42.3% -0.6% -0.5% -0.5% 1.2% -0.5%

Bluegill-redear sunfish 1.5% 17.9% 31.4% 0.5% 1.0% 1.4%

Littoral omnivores 1.2% 14.9% 26.9% 0.4% 2.6% 5.8% 0.3% 0.2%

Threadfin shad 1.2% 14.7% 27.2% 0.7% 4.7% 10.9% -3.4% 0.2%

Alewife 1.6% 16.8% 32.3% -3.5% -1.8% 0.6% -6.7% -3.7%

Gizzard shad 1.8% 23.1% 37.5% 0.2% 0.6% 0.4%

Detritivores 1.7% 23.0% 36.0% -0.8% -2.3% 0.8% 0.3%

Other invertebrates 1.4% 17.5% 29.8% -0.6% -1.2% -2.2% 0.6% -0.5%

Benthic invertebrates 1.6% 17.5% 32.9% 0.9% -1.9% -3.6% 0.4% -0.7%

Zooplankton 1.3% 13.4% 26.2% 0.8% -4.3% -6.2% -1.9% -2.9%

Phytoplankton 0.6% 11.9% 14.0% 1.2% 1.1% -1.0% 1.2% 1.2%

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North Carolina

North Purse seine sites Carolina Electrofishing, gill net and plankton sampling sites

Lake Norman

Figure 5.1. Lake Norman, North Carolina showing electrofishing, gill net and purse seine sample sites.

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3.5 Flathead catfish

Channel catfish 3 Striped bass Blue catfish

Largemouth bass

) 2.5

2 - Spotted bass

2 White perch Crappie

1.5 Bluegill/Redear Littoral omnivores

Threadfin shad Absolutebiomass (t·km 1 Alewife

Gizzard shad 0.5 Other invertebrates

Benthic invertebrates 0 0 2 4 6 8 10 12 14 16 Zooplankton

Time (years) Phytoplankton

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40.0% Flathead catfish 35.0% Channel catfish

Striped bass

30.0% s

s Largemouth bass

m 25.0%

o Spotted bass

i b

n White perch i 20.0%

e Crappie g

n a

h 15.0% Bluegill/Redear

e Littoral omnivores

i t

a 10.0%

l Threadfin shad e R Alewife 5.0% Gizzard shad 0.0% Detritovores Benthic invertebrates -5.0% Zooplankton

0 2 4 6 8 10 12 14 Phytoplankton Time (years)

153

10.0%

s Striped bass s

m 5.0% Spotted bass o

i b White perch

e 0.0%

g Crappie

n a

h Littoral omnivores c

e -5.0%

v Threadfin shad

t a

e Alewife

R -10.0% Benthic invertebrates

-15.0% Zooplankton 0 2 4 6 8

Time (years)

Figure 5.4. Percent change in biomass, relative to the original Ecopath model base biomass, of each functional group with a ten- fold increase of fishing effort for white perch. For clarity, non-sport fish groups that changed less than 2% are not shown. Biomass of multi-stanza functional groups were combined for clarity.

154

10.0% Striped bass

s s

a Largemouth bass

m o i Spotted bass

b 5.0%

i White perch

n Crappie

a 0.0% h

Bluegill/Redear

v i

t Littoral omnivores a l -5.0% e Threadfin shad R

Alewife -10.0% Benthic invertebrates 0 2 4 6 8 10 Zooplankton Time (years)

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2.0%

s a

m 1.0% Flathead catfish o

b Striped bass

Largemouth bass

e g

n 0.0% Spotted bass a

c Bluegill/Redear

i Littoral omnivores

t a

l -1.0%

e Threadfin shad

R Alewife Benthic invertebrates -2.0%

0 2 4 6 8 10 12 Time (years)

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CHAPTER 6

CONCLUSIONS

A better understanding of trophic relationships in aquatic systems is becoming increasingly important as freshwater systems are rapidly changing due to anthropogenic and ecological effects. Reservoir systems are particularly vulnerable to ecological change because they are heavily managed, multiple use resources. Furthermore, reservoirs are particularly susceptible to species invasions and often contain several introduced fish species, making them optimal systems in which to quantify the ways that invaders alter community structure and trophic dynamics. Our research suggests that taking a more ecosystem-based approach in studying trophic relationships in reservoir systems, rather than the traditional single-species perspective, can be a powerful tool for providing insight into key areas of invasion ecology and fisheries management. Our findings from studies conducted on Lake

Norman, North Carolina provide valuable contributions to both these fields.

Despite the extensive research in invasion ecology, few studies have focused on the interactive effects of multiple invaders (Johnson et al. 2009; Hudina et al. 2011). By using a combination of empirical and modeling approaches we were able to provide insight into the effects of multiple invaders on a reservoir food web. Our simulations indicated that the effects of four fish introductions (flathead catfish Pylodictis olivaris, white perch Morone americana, spotted bass Micropterus punctulatus, and alewife Alosa pseudoharengus) acted in a largely nonadditive, antagonistic manner on the established fish community, resulting in combined effects that were typically less than the sum of their individual impacts. We also

157

determined that some invaders, such as alewife and spotted bass, could ameliorate the negative consequences of previous invaders, such as white perch, by decreasing predation pressure on some species and reducing competition for others. We showed that four fish introductions to Lake Norman increased the average trophic level of reservoir fish and created a middle- to top-heavy trophic structure with high prey demand. Furthermore, our results suggest that introduced species can lessen the negative impacts of prior invaders for some species or groups of species, something rarely reported in the literature (Johnson et al.

2009; Atwater et al. 2011) and never specifically described for fish. Alewife, for example, lessened the negative effects of prior invaders flathead catfish and spotted bass on threadfin shad Dorosoma petenense and largemouth bass Micropterus salmoides in Lake Norman.

Likewise, this research contributes to the field of fisheries management. We know that introduced species can alter food web dynamics and these alterations may have unforeseen consequences. What conventional fisheries management is lacking, however, is the ability to predict how these alterations due to ecosystem changes would modify the effectiveness of management practices. Using our ecosystem modeling approach, we were able to successfully predict the potential outcomes of a variety of management practices or ecosystem alterations to the Lake Norman ecosystem. Our results showed that fish production was constrained by low biomass of primary production in the reservoir, limiting the range of successful management strategies available to managers. The effectiveness of further fish stocking (e.g., striped bass or threadfin shad) to the reservoir would likely prove costly and unsuccessful. However, nutrient loading that increased primary production in the

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reservoir could likely increase the ability of the reservoir to sustain additional biomass.

Additional findings showed that it would be difficult to control invasive white perch or enhance sport fish biomass with increased harvest of the white perch population because current harvest would have to be increased at least ten-fold to cause even modest changes.

Finally, we have provided a useful new tool for reservoir management and a predictive tool for consequences of reservoir invasions. This model can be further modified for other reservoirs and applied to a variety of management and ecological concerns.

Comparison of our results to findings from applications of this approach to additional systems will help elucidate whether the kinds of food web impacts caused by multiple invaders that we observed in Lake Norman represent a general pattern of response or are system- or species-specific.

159

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Hudina S., N. Galic, I. Roessink, and K. Hock. 2011. Competitive interactions between co- occurring invaders: identifying asymmetries between two invasive crayfish species. Biological Invasions 13(8): 1791-1803.

Johnson P., Olden J., Solomon C., Vander Zanden M. J. 2009. Interactions among invaders: community and ecosystem effects of multiple invasive species in an experimental aquatic system. Oecologia 159(1): 161-170.

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APPENDIX

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APPENDIX Table A.1. Summary table for fish analyzed for the Ecopath model including the total number analyzed for diet analysis (Diet; N), the length-weight (L-W) relationship, the R2 value for the L-W relationship, the number of fish used to discern the L-W relationship (N), and the von Bertalanffy growth parameter (K).

Functional Group Diet (N) Length (cm)-Weight (g) R² N K 1 Flathead Catfish 148 W = 0.0067·L3.0869 0.97 67 0.67 2 Largemouth bass (A) 198 W = 0.0083·L3.0908 0.99 271 0.30 3 Crappie 40 W = 0.0084·L3.1567 0.98 92 0.20 4 Striped bass (J) 52 W = 0.0096·L3.0791 0.99 43 -- 5 Striped bass (A) 102 W = 0.0236·L2.7746 0.96 249 0.69 6 Spotted bass (A) 129 W = 0.0096·L3.0165 0.98 583 0.30 7 Littoral omnivores 647 ------1.11 8 White perch (J) 93 W = 0.0393*L 2.5632 0.79 164 -- 9 White perch (A) 132 W = 0.0099*L 3.0934 0.92 965 0.30 10 Bluegill 507 W = 0.0117*L3.1234 0.97 5400 0.38 11 Channel Catfish 14 W = 0.0084*L 2.9551 0.92 108 0.86 12 Alewife 59 W = 0.0075*L 2.972 0.98 450 1.14 13 Largemouth bass (J) 20 W = 0.0093*L 3.038 0.98 76 -- 14 Spotted bass (J) 74 W = 0.0138*L 2.8429 0.96 312 -- 15 Blue Catfish 93 W = .0129*L 2.8952 0.85 528 1.12 16 Benthic invertebrates ------17 Threadfin Shad 40 W = .0089*L 2.9342 0.95 1100 1.11 18 Other invertebrates ------19 Detritovores ------1.00 20 Gizzard Shad --- W = 0.0105*L 2.968 0.96 278 0.50 21 Zooplankton ------22 Striped bass (<6mo) ------23 Phytoplankton ------24 Detritus ------

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Table A.2. Matrix of predator-overlap values calculated by Pianka’s overlap index (O)

Functional group 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 1 Flathead Catfish ------2 Channel Catfish ------3 Striped bass (A) ------4 Striped bass (J) ------1.00 ------0.17 0.07 0.01 0.37 0.42 0.58 0.49 0.65 0.17 0.18 0.12 0.03 0.00 5 Striped bass (<6mo) ------6 Blue Catfish ------7 Largemouth bass (A) ------8 Largemouth bass (J) ------1.00 -- -- 0.05 -- -- 0.07 0.07 0.38 0.19 0.09 -- 0.01 0.00 -- -- 9 Spotted bass (A) ------10 Spotted bass (J) ------1.00 -- 0.87 0.99 0.48 0.54 0.26 0.10 0.07 -- 0.07 0.01 0.00 0.00 11 White perch (A) ------0.17 ------0.05 -- -- 1.00 0.41 0.09 0.28 0.32 0.06 0.01 0.22 1.00 0.06 0.01 0.00 -- 12 White perch (J) ------0.07 ------0.87 0.41 1.00 0.92 0.66 0.73 0.29 0.10 0.18 0.40 0.10 0.02 0.00 0.00 13 Crappie ------0.01 ------0.99 0.09 0.92 1.00 0.53 0.60 0.27 0.10 0.09 0.09 0.08 0.01 0.00 0.00 14 Bluegill/redear sunfish ------0.37 ------0.07 -- 0.48 0.28 0.66 0.53 1.00 0.91 0.63 0.60 0.56 0.27 0.23 0.14 0.02 0.00 15 Littoral omnivores ------0.42 ------0.07 -- 0.54 0.32 0.73 0.60 0.91 1.00 0.66 0.46 0.55 0.30 0.31 0.19 0.02 0.00 16 Threadfin Shad ------0.58 ------0.38 -- 0.26 0.06 0.29 0.27 0.63 0.66 1.00 0.79 0.64 0.04 0.22 0.13 0.03 0.00 17 Alewife ------0.49 ------0.19 -- 0.10 0.01 0.10 0.10 0.60 0.46 0.79 1.00 0.80 -- 0.21 0.20 0.03 0.00 18 Gizzard Shad ------0.65 ------0.09 -- 0.07 0.22 0.18 0.09 0.56 0.55 0.64 0.80 1.00 0.21 0.21 0.20 0.03 0.01 19 Detritovores ------0.17 ------1.00 0.40 0.09 0.27 0.30 0.04 -- 0.21 1.00 0.06 0.01 0.00 -- 20 Other invertebrates ------0.18 ------0.01 -- 0.07 0.06 0.10 0.08 0.23 0.31 0.22 0.21 0.21 0.06 1.00 0.92 0.14 0.02 21 Benthic invertebrates ------0.12 ------0.00 -- 0.01 0.01 0.02 0.01 0.14 0.19 0.13 0.20 0.20 0.01 0.92 1.00 0.07 0.01 22 Zooplankton ------0.03 ------0.00 0.00 0.00 0.00 0.02 0.02 0.03 0.03 0.03 0.00 0.14 0.07 1.00 0.36 23 Phytoplankton ------0.00 ------0.00 -- 0.00 0.00 0.00 0.00 0.00 0.00 0.01 -- 0.02 0.01 0.36 1.00

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Table A.3. Matrix of prey-overlap values, calculated by the Pianka overlap index (O).

Functional group 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 1 Flathead Catfish 1.00 0.94 0.23 0.37 -- 0.22 0.75 0.03 0.29 0.03 0.64 0.47 0.75 0.51 0.44 0.04 0.37 0.05 0.08 0.05 0.03 0.00 -- 2 Channel Catfish 0.94 1.00 0.16 0.29 -- 0.35 0.64 0.03 0.22 0.03 0.60 0.51 0.74 0.56 0.48 0.15 0.40 0.27 0.31 0.24 0.15 0.08 -- 3 Striped bass (A) 0.23 0.16 1.00 0.96 -- 0.07 0.71 0.01 0.99 0.01 0.58 0.07 0.01 0.01 0.02 0.00 0.01 ------4 Striped bass (J) 0.37 0.29 0.96 1.00 -- 0.09 0.83 0.08 0.97 0.08 0.71 0.13 0.13 0.09 0.09 0.04 0.13 0.02 0.01 0.04 0.06 0.00 -- 5 Striped bass (<6mo) ------6 Blue Catfish 0.22 0.35 0.07 0.09 -- 1.00 0.23 0.06 0.07 0.06 0.46 0.74 0.56 0.68 0.73 0.51 0.09 0.53 0.58 0.51 0.36 0.45 -- 7 Largemouth bass (A) 0.75 0.64 0.71 0.83 -- 0.23 1.00 0.08 0.75 0.08 0.87 0.37 0.50 0.34 0.31 0.09 0.25 0.04 0.04 0.06 0.07 0.04 -- 8 Largemouth bass (J) 0.03 0.03 0.01 0.08 -- 0.06 0.08 1.00 0.01 1.00 0.31 0.21 0.29 0.18 0.18 0.51 0.88 0.30 0.16 0.47 0.76 0.05 -- 9 Spotted bass (A) 0.29 0.22 0.99 0.97 -- 0.07 0.75 0.01 1.00 0.01 0.61 0.09 0.05 0.04 0.04 0.00 0.03 -- 0.00 ------10 Spotted bass (J) 0.03 0.03 0.01 0.08 -- 0.06 0.08 1.00 0.01 1.00 0.31 0.21 0.29 0.18 0.18 0.51 0.88 0.30 0.16 0.47 0.76 0.05 -- 11 White perch (A) 0.64 0.60 0.58 0.71 -- 0.46 0.87 0.31 0.61 0.31 1.00 0.66 0.70 0.61 0.60 0.22 0.47 0.13 0.14 0.19 0.28 0.04 -- 12 White perch (J) 0.47 0.51 0.07 0.13 -- 0.74 0.37 0.21 0.09 0.21 0.66 1.00 0.87 0.99 0.99 0.15 0.34 0.07 0.18 0.10 0.14 0.05 -- 13 Crappie 0.75 0.74 0.01 0.13 -- 0.56 0.50 0.29 0.05 0.29 0.70 0.87 1.00 0.86 0.85 0.19 0.53 0.10 0.14 0.15 0.23 0.02 -- 14 Bluegill/redear sunfish 0.51 0.56 0.01 0.09 -- 0.68 0.34 0.18 0.04 0.18 0.61 0.99 0.86 1.00 0.98 0.11 0.36 0.05 0.16 0.07 0.10 0.02 -- 15 Littoral omnivores 0.44 0.48 0.02 0.09 -- 0.73 0.31 0.18 0.04 0.18 0.60 0.99 0.85 0.98 1.00 0.11 0.31 0.05 0.17 0.07 0.11 0.03 -- 16 Threadfin Shad 0.04 0.15 0.00 0.04 -- 0.51 0.09 0.51 0.00 0.51 0.22 0.15 0.19 0.11 0.11 1.00 0.51 0.69 0.48 0.80 0.83 0.79 -- 17 Alewife 0.37 0.40 0.01 0.13 -- 0.09 0.25 0.88 0.03 0.88 0.47 0.34 0.53 0.36 0.31 0.51 1.00 0.30 0.15 0.46 0.73 0.06 -- 18 Gizzard Shad 0.05 0.27 -- 0.02 -- 0.53 0.04 0.30 -- 0.30 0.13 0.07 0.10 0.05 0.05 0.69 0.30 1.00 0.95 0.97 0.79 0.46 -- 19 Detritovores 0.08 0.31 -- 0.01 -- 0.58 0.04 0.16 0.00 0.16 0.14 0.18 0.14 0.16 0.17 0.48 0.15 0.95 1.00 0.87 0.63 0.29 -- 20 Other invertebrates 0.05 0.24 -- 0.04 -- 0.51 0.06 0.47 -- 0.47 0.19 0.10 0.15 0.07 0.07 0.80 0.46 0.97 0.87 1.00 0.91 0.48 -- 21 Benthic invertebrates 0.03 0.15 -- 0.06 -- 0.36 0.07 0.76 -- 0.76 0.28 0.14 0.23 0.10 0.11 0.83 0.73 0.79 0.63 0.91 1.00 0.41 -- 22 Zooplankton 0.00 0.08 -- 0.00 -- 0.45 0.04 0.05 -- 0.05 0.04 0.05 0.02 0.02 0.03 0.79 0.06 0.46 0.29 0.48 0.41 1.00 -- 23 Phytoplankton ------

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